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Article

Inference of Essential Genes of the Parasite Haemonchus contortus via Machine Learning

by
Túlio L. Campos
1,2,
Pasi K. Korhonen
1,
Neil D. Young
1,
Tao Wang
1,
Jiangning Song
3,4,5,
Richard Marhoefer
6,
Bill C. H. Chang
1,
Paul M. Selzer
6 and
Robin B. Gasser
1,*
1
Department of Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC 3010, Australia
2
Bioinformatics Core Facility, Aggeu Magalhães Institute (Fiocruz), Recife 50740-465, PE, Brazil
3
Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, VIC 3800, Australia
4
Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
5
Monash Data Futures Institute, Monash University, Clayton, VIC 3800, Australia
6
Boehringer Ingelheim Animal Health, Binger Strasse 173, 55216 Ingelheim am Rhein, Germany
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(13), 7015; https://doi.org/10.3390/ijms25137015
Submission received: 28 May 2024 / Revised: 19 June 2024 / Accepted: 21 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Parasite Biology and Host-Parasite Interactions: 2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Over the years, comprehensive explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic–phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial for life, called ‘essential genes’. Recently, we investigated the feasibility of inferring essential genes from such data sets using advanced bioinformatics and showed that a machine learning (ML)-based workflow could be used to extract or engineer features from DNA, RNA, protein, and/or cellular data/information to underpin the reliable prediction of essential genes both within and between C. elegans and D. melanogaster. As these are two distantly related species within the Ecdysozoa, we proposed that this ML approach would be particularly well suited for species that are within the same phylum or evolutionary clade. In the present study, we cross-predicted essential genes within the phylum Nematoda (evolutionary clade V)—between C. elegans and the pathogenic parasitic nematode H. contortus—and then ranked and prioritised H. contortus proteins encoded by these genes as intervention (e.g., drug) target candidates. Using strong, validated predictors, we inferred essential genes of H. contortus that are involved predominantly in crucial biological processes/pathways including ribosome biogenesis, translation, RNA binding/processing, and signalling and which are highly transcribed in the germline, somatic gonad precursors, sex myoblasts, vulva cell precursors, various nerve cells, glia, or hypodermis. The findings indicate that this in silico workflow provides a promising avenue to identify and prioritise panels/groups of drug target candidates in parasitic nematodes for experimental validation in vitro and/or in vivo.

1. Introduction

Parasitic worms (helminths) cause substantial mortality and morbidity in animals and substantial losses for agriculture and food production globally. Roundworms (nematodes) cause particularly destructive diseases in livestock animals, affecting hundreds of millions of animals (e.g., sheep, goats, cattle, and pigs) worldwide, resulting in substantial economic losses (billions of dollars) per annum globally [1,2,3]. Despite substantial efforts to control gastrointestinal worms, highly effective commercial vaccines are lacking, and treatment relies heavily on only a small number of anthelmintics, such as monepantel, albendazole, and/or ivermectin [4]. Because anthelmintic resistance to the majority of these compounds is now widespread [5,6,7], there is substantial demand for new anthelmintics with mechanisms/modes of action that are distinct from those presently available on the market and to which resistance has developed.
The discovery of novel anthelmintic targets using conventional methods is time-consuming and challenging [8,9], and we have been promoting and evaluating the use of in silico methods for the prediction and prioritisation of ‘essential genes’ for subsequent validation as drug target candidates. Our initial exploratory studies [10,11] have been focused on assessing and employing machine learning (ML)-based approaches for the prediction of such genes in the most intensively studied multicellular model organisms—Caenorhabditis elegans (free-living nematode) and Drosophila melanogaster (vinegar fly). We have taken this focus because these organisms can be maintained in cultures in the laboratory and, importantly, because chromosome-continuous genomes and extensive functional genomic, transcriptomic, proteomic, biochemical, physiological, biological, morphological, developmental, and reproductive data sets and information are publicly available via well-curated databases including WormBase and FlyBase [12,13,14,15]. This wealth of resources has enabled deep and meaningful investigations of essential genes [16,17] for these two Ecdysozoan species. Our ML-based studies have shown that informative features can be extracted/engineered from such data sets, allowing the confident (statistically valid) prediction and prioritisation of known essential genes both within and between C. elegans and D. melanogaster (see [10,11,16,17]).
As the complete life cycles of species of gastrointestinal nematodes (order Strongylida, or strongylid nematodes) cannot be maintained in vitro, and laboratory culture conditions vary from those in nature (in the environment and in the host animal), establishing functional genomic assays for the different developmental stages and sexes of these parasitic nematodes has been a major obstacle [18,19] to evaluating the essentiality of genes in order to infer or prioritise intervention target candidates. Now that we have demonstrated the feasibility of an ML-based bioinformatic approach for the reliable prediction and prioritisation of essential genes in C. elegans and D. melanogaster, and in both of these species [16,17], we are confident that this approach can predict essential genes across relatively closely related species of nematodes or arthropods (Ecdysozoa) provided that suitable data sets are available for analyses. For us, the next logical step is to infer such genes in economically important strongylid nematodes, which are in the same evolutionary clade (V) as C. elegans, so that the large-scale inference of essential genes across related species becomes feasible and efficient.
Here, we selected Haemonchus contortus (barber’s pole worm) as a key representative of the order Stronglida because it is one of the most pathogenic nematodes of livestock animals worldwide and because it is now one of the best-studied parasitic nematodes at the molecular level, thus being elevated to model organism status [20,21,22]. Importantly, a chromosome-contiguous genome is available for H. contortus, and extensive genomic, genetic, transcriptomic, variomic, and proteomic data sets are readily accessible publicly (via WormBase ParaSite; cf. [20,21,23,24]) for comprehensive in silico explorations.
In this study, we harnessed these data sets for H. contortus to predict and prioritise essential genes in this species through ML (cf. [17]), employing data predominantly for C. elegans and D. melanogaster for algorithm training purposes and comparative analyses, and we explore the relationship between gene essentiality and transcription in H. contortus.

2. Results

2.1. Identification of Strong Predictors of Essential Genes in H. contortus

In total, we extracted or engineered 9588 features for 19,450 protein-coding genes encoded in the nuclear genome of H. contortus. Of these features, we selected 25 that had been identified previously as strong predictors of gene essentiality within C. elegans and D. melanogaster (see [10,11]) and between these two species (see [16]); we also selected two more features: “num_cells_expressed”, the number of nuclei in which individual genes are transcribed in H. contortus (eggs), and “evolutionary conservation” among 16 divergent eukaryotic species including H. contortus. The descriptions of the 27 features used for the prediction of essential genes are given in the Table in the Materials and Methods section.
Prior to the prediction of essential genes, we evaluated the predictive power of this set of 27 features for C. elegans and for D. melanogaster using ML approaches and a subsampling strategy for training, testing, and evaluation (ROC-AUC and PR-AUC metrics). For C. elegans (Figure 1), the ROC-AUC value was ≥0.9 for all six ML models assessed (i.e., GBM, GLM, NN, RF, SVM, and XGB). The PR-AUC value was >0.6, achieving close to 0.8 for the best performers (GBM and XGB) using 90% of the data to train the models. For D. melanogaster (Figure 1), the ROC-AUC value was ≥0.8, achieving ~0.9 for the best performers (GBM, GLM, RF, and XGB). The PR-AUC value was variable, depending on the ML model used, and ranged from ~0.25 to ~0.5 for the best performers (GBM, RF, and XGB) using 90% of the data in the training set. Of the 27 features employed, the most important predictors of essential genes in both C. elegans and D. melanogaster were num_cells_expressed, OrthoFinder_species, and exon numbers (exons), followed by two inferred subcellular localisations (nucleus and cytoplasm). Following the prediction of essential genes in H. contortus using the best-performing models (GBM and XGB, trained with C. elegans and D. melanogaster features), 499 genes had a probability of >0.7 of being essential and 17,587 had a probability of <0.3. In total, >95% of the top 1000 essential genes predicted have orthologs in C. elegans, D. melanogaster, and/or Ovis aries (sheep host), and ≥90% are single-copy genes.

2.2. Clear Association between Essential Genes and Their Transcription Profiles

Since the feature “num_cells_expressed” was a key predictor of essentiality, we explored whether essential and non-essential genes have distinct transcription profiles. Interestingly, H. contortus genes ranked according to their essentiality probabilities, defined by the ML models, correlated with their rankings based on mean levels of transcription, variances, and numbers of nuclei in which these genes were transcribed (values between ~0.3 and ~0.6; Figure 2). The strongest correlation was between the ML-based ranking and transcription in the egg stage (snRNA-seq), followed by transcription in the adult female and then adult male of H. contortus. This order was somewhat expected since the egg snRNA-seq data were used to train the ML models and adult females contain eggs. However, the strong pairwise correlations between the different gene rankings using snRNA-seq data were unexpected. We determined a final score for each gene, taking into consideration both the ML predictions and transcription levels; the final scores for all genes correlated well (>0.7) with each ML or snRNA-seq ranking (ML_RankTpm_Final) (Figure 2). Using these final scores, we created the priority list of essential genes, the top 1000 of which are listed in Supplementary Table S1.
To further explore the relationship between gene essentiality and transcription in H. contortus, we identified UMAP gene clusters based on their transcription (RNA-seq) in 99 distinct samples. After filtering out genes that were not transcribed in all of these samples, 6173 genes (31.7%) remained (Figure 3A). Of these genes, 980 of 1000 (98.0%) of the high-priority essential genes were present, and most of them clustered together. On the other hand, following the filtering step, only 905 of 10,000 (9.1%) of the most likely non-essential genes remained; most of these 905 genes clustered to the exclusion of the essential genes (Figure 3B). A similar observation was made following the UMAP clustering of genes linked to female snRNA-seq data; here, we used 3000 genes with the highest average transcription levels (Figure 3C,D). In this analysis, 847 of 1000 (84.7%) essential genes remained and mostly clustered together, compared with only 118 of 10,000 (1.1%) non-essential genes that remained. To confirm the grouping of essential genes based on transcription profiles, we independently evaluated data for the related species C. elegans using its genes and respective essentiality annotations [10]. Clusters were defined using RNA-seq data from 295 samples; following the filtering step, 833 of 1000 (83.3%) essential genes were retained and mostly clustered together whereas only 1021 of 10,000 (10.2%) of the non-essential genes remained and clustered to the exclusion of essential genes.

2.3. Essential Genes of H. contortus Are Inferred to Be Involved Predominantly in Ribosome Biogenesis, Translation, RNA Binding/Processing, and Signalling

GO enrichment analysis for the prioritised list of the top 1000 essential genes of H. contortus inferred ribosome structure, translation, and peptide and RNA binding/processing (p < 10−8) for ‘molecular function’; peptide/amide biosynthesis and translation/gene expression for ‘biological process’ (p < 10−14); and ribosomes/ribonucleoproteins (intracellular non-membrane-bound organelles) for ‘cellular component’ (p < 10−11). Pathway enrichment analysis revealed that orthologs of many of these genes were significantly (p < 10−16) linked to functions including the following: (i) the assembly of the ribosome (e.g., GTP hydrolysis, the joining of the 60S ribosomal subunit, and the formation of free 40S subunits); (ii) translation initiation (e.g., eukaryotic and cap-dependent); and (iii) signalling and regulatory roles (e.g., L13a-mediated translation silencing of ceruloplasmin expression, SRP-dependent co-translational protein targeting to membranes, and nonsense-mediated decay).

2.4. Linking Essential Genes to Genome Locations and Their Transcription to Cell Type or Tissue

First, we first plotted the ML-based gene essentiality probabilities along individual H. contortus chromosomes (Figure 4). Most (85%) of the 499 high-priority essential genes predicted (probability of >0.7) were linked to autosomal chromosomes chr1–chr4 without a distinct clustering. A small percentage (15%) of these genes were on chr5 and the sex chromosome (chrX), and were located mainly on chromosome arms.
Second, we studied the density distribution of the top 1000 essential and 10,000 non-essential genes on the chromosomes (Figure 5). Essential genes were located in “hotspots” that were relatively evenly distributed on chr1–chr5, with high densities detected on the arms of chrX vis-à-vis the centre of this chromosome; there were no apparent location preferences for non-essential genes. Third, using information available for C. elegans (see [25]), we inferred cell types or tissues in which essential genes were highly transcribed. To do this, we mapped the transcription of C. elegans orthologs of the top 1000 genes predicted/prioritised as essential in H. contortus to known cell and tissue types in C. elegans. For cell types, we observed that 341 essential genes were highly transcribed in the germline, 308 in somatic gonad precursors, 209 in sex myoblasts, and 193 in vulva cell precursors. Considering only nerve cells, 193 essential genes were highly transcribed in canal-associated neurons (CANs), 174 in amphid neurons with finger-like ciliated endings (AFD), and 121 in amphid wing “A” neurons (AWA). For tissues, we identified 711 essential genes abundantly transcribed in the gonad, and 396 in glia, followed by 387 in the hypodermis.

3. Discussion

Extending previous work on the gene essentiality, particularly in the model Ecdysozoans C. elegans and D. melanogaster (see [17]), this study has provided the first comprehensive, large-scale prediction of essential genes in the parasitic nematode H. contortus using ML and included relevant complementary analyses. We have provided evidence of a relationship between essential genes and transcription and defined a feature set for H. contortus that will likely be a useful resource for identifying essential genes in related strongylid nematodes.
To predict gene essentiality in H. contortus, we used 27 features that had been shown to be strong predictors of essential genes within and between the model organisms C. elegans and D. melanogaster (see [16]), and we defined two additional features linked to sequence conservation and transcription level, which allowed reliable predictions in each of these model species. This work yielded some genomic sequence, gene conservation, and transcription profile characteristics that are key for gene essentiality predictions, corroborating some previous studies [16,17,26,27,28,29]. These types of features can be readily obtained from genomic and transcriptomic data sets, and we also inferred some highly ranked “essential genes” using features that appeared to be exclusive to H. contortus and that could be applied to related strongylid nematodes. Nonetheless, these predictions will need to be validated experimentally using gene knockout and/or knockdown methods (cf. [30,31]). Select features of essential genes in C. elegans and D. melanogaster, such as histone modification markers (e.g., H3K4me3 and H3K27me3; [10,11]), which have been found to be important predictors of gene essentiality, could not be assessed herein, as comparable data are not presently available for H. contortus.
We identified a strong relationship between gene essentiality and transcription profile. Using snRNA-seq data, we showed that the number of nuclei for which a gene is transcribed and the level and variance of transcription correlated relatively well with ML-based predictions of gene essentiality, particularly for H. contortus eggs. We also showed that selections of essential genes clustered according to transcription profiles in RNA-seq and snRNA-seq data, and that “essential genes” usually grouped together to the exclusion of “non-essential” genes of H. contortus (Figure 3). This latter finding was supported by analyses of C. elegans data in that most (83.3%) “essential genes” were usually transcribed in all samples (295 bulk RNA-seq data sets), and those genes were amongst the most highly transcribed in nuclei (three snRNA-seq data sets), whereas most “non-essential genes” were lowly transcribed and, thus, were removed upon filtering. Moreover, most essential genes predicted were not present in lists of genes known to be differentially transcribed between developmental stages or sexes (cf. [21]).
Through comparative analysis of scRNA-seq data for C. elegans, we inferred that a high proportion (>30–70%) of essential gene orthologs in H. contortus was more likely to be transcribed in tissues and cells of the reproductive tract (germline and associated tissues and cells) than other organ systems. Considering only neuronal cells, essential genes were more likely to be found in CANs than in other cell types. Interestingly, CANs are critical for the larval development and survival of C. elegans and are governed by regulatory mechanisms that are currently unknown [32]. These findings will pave the way for future studies of the functions, structures, and/or interactions of essential proteins encoded in these reproductive and neuronal cell types as a starting point for anthelmitic target validation.
Essential genes were much more likely to be found on autosomes than on the sex chromosome in H. contortus, in accordance with findings for C. elegans (see [10]), although the relatively even distribution of such genes on the autosomes was distinct from that seen in C. elegans (in or near the centre of chromosomes; [10]) or D. melanogaster (away from the centre/centromeres; [11]). The distinction between H. contortus and C. elegans might relate to the different genome and/or centromere organisations and/or gene regulatory mechanisms (genetic vs. epigenetic) [33]. Despite the conservation of one-to-one orthologs between H. contortus and C. elegans, the order and location of such genes on chromosomes are distinctly different between these species [21]. Using GO and pathway analyses, we inferred that many essential genes of H. contortus are involved in transcriptional regulation and particularly in RNA binding, ribosome formation, and/or translation initiation functions, which supports previous findings for C. elegans and D. melanogaster (see [16,17]). Ribosome formation and translation initiation are biologically crucial and very energy-demanding (e.g., [34,35,36]), which suggests that the chemical or functional genomic disruption of these processes and associated pathways in H. contortus would lead to serious detrimental effects on this species.

4. Materials and Methods

We obtained published genomic and transcriptomic data sets for H. contortus from multiple sources and employed a workflow (Figure 6) to predict essential genes and explore the transcriptional and functional characteristics of prioritised gene candidates.

4.1. RNA Sequence Data Sets

RNA-seq data sets from 99 H. contortus and 295 C. elegans samples (whole worms) representing different developmental stages, strains, and both sexes were obtained from WormBase Parasite [13,37]. Also, single-nuclei (sn)RNA-seq data for eggs, adult females, or adult males of H. contortus (Haecon5 strain) were publicly available [38]. For individual nuclei, reads that mapped to individual genes were enumerated. All data sets were normalised using transcripts per million (TPM) and loaded into data frames in R (https://www.r-project.org; accessed on 1 May 2024) for subsequent use and analyses. Genes without evidence of transcription (i.e., mapped read counts = 0) in a sample were removed.

4.2. Feature Extraction/Engineering for Subsequent ML

For each gene of H. contortus (PRJEB506.WBPS14; WormBaseParaSite), we extracted 9588 features that were derived from RNA-seq and/or protein sequence data sets, subcellular localisation (inferred using DeepLoc 1.0; [39]), and snRNA-seq data (feature: “num_cells_expressed”—representing the number of cells/nuclei in which a particular gene is transcribed for a particular sample) using an established method [10,11]. Then, we selected 27 features for H. contortus (Table 1) for the prediction and evaluation of gene essentiality through ML [10]. Of these 27 features, 25 represented predictors of essential genes in both C. elegans and D. melanogaster (cf. [16]). Feature 26 was “num_cells_expressed” from H. contortus egg snRNA-seq data, which is a strong essential gene predictor using single-cell RNA-seq (scRNA-seq) data for early developmental stages of C. elegans (larval—L1; [10]) and D. melanogaster (embryo; [11]). Feature 27 (OrthoFinder_species) relates to protein sequence conservation among species. In brief, predicted proteomes (FASTA files) representing 16 eukaryotic species (including H. contortus) from divergent branches in the Tree of Life [40] were obtained from Ensembl [41] and WormBase ParaSite [13,37]; orthologous groups were identified in these proteomes using the tool OrthoFinder [42], employing default parameters. Then, we identified the number of species represented within individual orthologous protein groups, which represented feature 27 (for C. elegans, D. melanogaster, and H. contortus).

4.3. Predicting Gene Essentiality through ML

We assessed the individual and collective powers of the 27 features selected to predict essential genes in C. elegans and in D. melanogaster, employing six distinct machine learning (ML) models (Gradient Boosting Machine—GBM, Generalised Linear Model—GLM, Neural Network—NN, Random Forest—RF, Support Vector Machine—SVM, and Extreme Gradient Boosting Machine—XGB; [10,11]). The best-performing ML models for C. elegans and D. melanogaster, based on ROC-AUC and PR-AUC metrics, were used to predict essential genes in H. contortus. Then, corresponding orthologs in C. elegans, D. melanogaster, and Ovis aries (sheep host) were identified for all H. contortus genes using g:Profiler [43] or OrthoFinder [42].

4.4. Establishing the List of Genes, Ranked According to the Probability of Being Essential

We ranked all H. contortus genes based on their probability (descending) of being essential, defined using the best-performing ML model. Moreover, using the snRNA-seq data, three rankings per sample were established for H. contortus genes according to the following criteria: (i) the number of nuclei in which a particular gene was transcribed, (i) the mean transcription, and (iii) variance of transcription among all nuclei. A final ranking was established by averaging all other rankings, defined by the snRNA and ML data. To evaluate the pairwise correlations among the defined rankings, a correlation plot (“corrplot” package for R) was produced.

4.5. Gene Clustering

We clustered genes according to transcription in 99 RNA-seq samples for H. contortus and in 295 samples representing C. elegans. For this analysis, we used unsupervised clustering, employing uniform manifold approximation and projection (UMAP; “umap” package for R), with random initialisation. Only genes with evidence of transcription in all RNA-seq samples were included in the analysis. Following assignment, gene clusters were displayed using “ggplot2” for R. For H. contortus, a similar clustering analysis was performed using snRNA-seq, representing the adult female stage, but only a selection of 3000 genes with the highest mean transcription levels among all nuclei was used.

4.6. Methods Used to Infer Genome Locations and Transcription Profiles for Essential Genes in H. contortus

We located the top 1000 most likely essential and 10,000 most likely non-essential genes predicted for H. contortus in the genome of this species using the General Feature Format (GFF) annotation file obtained from WormBase ParaSite [13,37]. The density of genes on H. contortus chromosomes and their probability of being essential (defined through ML) were displayed using “ggplot2” and “chromoMap” for R, respectively. We also inferred cell types and tissues in which the top 1000 “essential gene orthologs” of H. contortus predicted were abundantly transcribed in C. elegans. For these analyses, we used existing scRNA-seq data that were available (Cao_et_al_2017_vignette.RData file; cf. [25]). Genes were subjected to gene ontology (GO) and pathway enrichment analyses using g:Profiler [43] and the Reactome Knowledgebase [44], respectively.

5. Conclusions

In conclusion, this study provided the first genome-wide ML-based prediction and prioritisation of essential genes in H. contortus based on key features identified in the Ecdysozoans C. elegans and D. melanogaster. We found that the highly ranked genes in H. contortus were involved in fundamental molecular processes, such as transcription and translation, and exhibited transcription profiles that were distinct compared with genes that had a low ranking. The genomic locations of essential genes were quite different among the three invertebrate species, suggesting specific genomic architectures and regulation mechanisms. These findings could inform functional investigations to determine a complete set of genes that sustain life in H. contortus. The lack of species-specific functional genomic and histone methylation data sets for H. contortus was a limitation of the present study. Once available, such data would likely assist in improving ML-based predictions. Given the challenges associated with the treatment and control of H. contortus, it is of paramount importance to prioritise essential genes for future validation studies as drug targets by harnessing computational methods and the abundance of omic data sets available for this species. To this end, ML approaches should contribute to accelerating fundamental and applied investigations of essential genes and their suitability as drug targets, enabling the development of novel anthelmintics. The approach employed here can be extended to explore essential genes in other parasitic worms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms25137015/s1.

Author Contributions

Conceptualisation, T.L.C. and R.B.G.; methodology, T.L.C.; software, T.L.C.; validation, P.K.K. and N.D.Y.; formal analysis, T.L.C.; investigation, T.L.C.; resources, R.B.G.; data curation, P.K.K. and N.D.Y.; writing—original draft preparation, T.L.C.; writing—review and editing, R.B.G., P.K.K., N.D.Y., J.S., T.W., B.C.H.C., R.M., and P.M.S.; visualisation, T.L.C.; supervision, R.B.G.; project administration, R.B.G.; funding acquisition, R.B.G., J.S., B.C.H.C., R.M., and P.M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the Australian Research Council (ARC), namely Linkage project grant LP220200614 (R.B.G., J.S. and B.C.H.C.), together with Oz Omics, and LP180101085 (R.B.Z. and B.C.H.C.), together with Boehringer Ingelheim Vetmedica GmbH (R.M. and P.M.S.).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data and code used are publicly available and have been referenced in the Materials and Methods section.

Conflicts of Interest

R.M. and P.M.S. are employed by Boehringer Ingelheim.

References

  1. Charlier, J.; van der Voort, M.; Keyon, F.; Skuce, P.; Vercruysse, J. Chasing helminths and their economic impact on farmed ruminants. Trends Parasitol. 2014, 30, 361–367. [Google Scholar] [CrossRef] [PubMed]
  2. Emery, D.L.; Hunt, P.W.; Jambre, L.F.L. Haemonchus contortus: The then and now, and where to from here? Int. J. Parasitol. 2016, 46, 755–769. [Google Scholar] [CrossRef] [PubMed]
  3. Selzer, P.; Epe, C. Antiparasitics in animal health—Quoad vadis? Trends Parasitol. 2020, 37, 77–89. [Google Scholar] [CrossRef] [PubMed]
  4. Jiao, Y.; Preston, S.; Hofmann, A.; Taki, A.; Baell, J.; Chang, B.C.H.; Jabbar, A.; Gasser, R.B. A perspective on the discovery of selected compounds with anthelmintic activity against the barber’s pole worm—Where to from here? Adv. Parasitol. 2020, 108, 1–45. [Google Scholar] [PubMed]
  5. Kaminsky, R. Drug resistance in nematodes: A paper tiger or a real problem? Curr. Opin. Infect. Dis. 2003, 16, 559–564. [Google Scholar] [CrossRef] [PubMed]
  6. Shalaby, H.A. Anthelmintics resistance; how to overcome it? Iran J. Parasitol. 2013, 8, 18–32. [Google Scholar] [PubMed]
  7. Kotze, A.C.; Prichard, R.K. Anthelmintic resistance in Haemonchus contortus: History, mechanisms and diagnosis. Adv. Parasitol. 2016, 93, 397–428. [Google Scholar] [PubMed]
  8. Geary, T.G.; Sakanari, J.A.; Caffrey, C.R. Anthelmintic drug discovery: Into the future. J. Parasitol. 2015, 101, 125–133. [Google Scholar] [CrossRef]
  9. Sepúlveda-Crespo, D.; Reguera, R.M.; Rojo-Vásquez, F.; Balaña-Fouce, R.; Martínez-Valladares, M. Drug discovery technologies: Caenorhabditis elegans as a model for anthelmintic therapeutics. Med. Res. Rev. 2020, 40, 1715–1753. [Google Scholar] [CrossRef] [PubMed]
  10. Campos, T.L.; Korhonen, P.K.; Sternberg, P.W.; Gasser, R.B.; Young, N.D. Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning. Comput. Struct. Biotechnol. 2020, 15, 1093–1102. [Google Scholar] [CrossRef]
  11. Campos, T.L.; Korhonen, P.K.; Hofmann, A.; Gasser, R.B.; Young, N.D. Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster. NAR Genom. Bioinform. 2020, 22, lqaa051. [Google Scholar] [CrossRef] [PubMed]
  12. Marygold, S.J.; Crosby, M.A.; Goodman, J.L. FlyBase Consortium. Using FlyBase, a database of Drosophila genes & genomes. In Drosophila: Methods in Molecular Biology; Dahmann, C., Ed.; Springer: Berlin/Heidelberg, Germany,, 2016; Volume 1478, pp. 1–31. [Google Scholar]
  13. Howe, K.L.; Bolt, B.J.; Shafie, M.; Kersey, P.; Berriman, M. WormBase ParaSite—A comprehensive resource for helminth genomics. Mol. Biochem. Parasitol. 2017, 215, 2–10. [Google Scholar] [CrossRef] [PubMed]
  14. Harris, T.W.; Arnaboldi, V.; Cain, S.; Chan, J.; Chen, W.J.; Cho, J.; Davis, P.; Gao, S.; Grove, C.A.; Kishore, R.; et al. WormBase: A modern Model Organism Information Resource. Nucleic Acids Res. 2020, 48, D762–D767. [Google Scholar] [CrossRef] [PubMed]
  15. Kimble, J.; Nüsslein-Volhard, C. The great small organisms of developmental genetics: Caenorhabditis elegans and Drosophila melanogaster. Dev. Biol. 2022, 485, 93–122. [Google Scholar] [CrossRef] [PubMed]
  16. Campos, T.L.; Korhonen, P.K.; Young, N.D. Cross-predicting essential genes between two model eukaryotic species using machine learning. Int. J. Mol. Sci. 2022, 22, 5056. [Google Scholar] [CrossRef] [PubMed]
  17. Campos, T.L.; Korhonen, P.K.; Hofmann, A.; Gasser, R.B.; Young, N.D. Harnessing model organism genomics to underpin the machine-learning-based prediction of essential genes in eukaryotes—Biotechnological implications. Biotechnol. Adv. 2021, 54, 107822. [Google Scholar] [CrossRef] [PubMed]
  18. Britton, C.; Roberts, B.; Marks, N.D. Functional genomics tools for Haemonchus contortus and lessons from other helminths. Adv. Parasitol. 2016, 93, 599–623. [Google Scholar]
  19. Castelletto, M.L.; Gang, S.S.; Hallem, E.A. Recent advances in functional genomics for parasitic nematodes of mammals. J. Exp. Biol. 2020, 223, jeb206482. [Google Scholar] [CrossRef]
  20. Gasser, R.B.; Schwarz, E.M.; Korhonen, P.K.; Young, N.D. Understanding Haemonchus contortus better through genomics and transcriptomics. Adv. Parasitol. 2016, 93, 16–67. [Google Scholar]
  21. Doyle, S.R.; Tracey, A.; Laing, R.; Holroyd, N.; Bartley, D.; Bazant, W.; Beasley, H.; Beech, R.; Britton, C.; Brooks, K.; et al. Genomic and transcriptomic variation defines the chromosome-scale assembly of Haemonchus contortus, a model gastrointestinal worm. Commun. Biol. 2020, 9, 656. [Google Scholar] [CrossRef]
  22. Doyle, S.R. Improving helminth genome resources in the post-genomic era. Trends Parasitol. 2022, 38, 831–840. [Google Scholar] [CrossRef]
  23. Schwarz, E.M.; Korhonen, P.K.; Campbell, B.E.; Young, N.D.; Jex, A.R.; Jabbar, A.; Hall, R.S.; Mondal, A.; Howe, A.C.; Pell, J.; et al. The genome and developmental transcriptome of the strongylid nematode Haemonchus contortus. Genome Biol. 2013, 14, R89. [Google Scholar] [CrossRef] [PubMed]
  24. Wang, T.; Gasser, R.B. Prospects of using high-throughput proteomics to underpin the discovery of animal host-nematode interactions. Pathogens 2021, 10, 825. [Google Scholar] [CrossRef] [PubMed]
  25. Cao, J.; Packer, J.S.; Ramani, V.; Cusanovich, D.A.; Huynh, C.; Daza, R.; Waterson, R.H.; Trapnell, C.; Shendure, J. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 2017, 357, 661–667. [Google Scholar] [CrossRef] [PubMed]
  26. Aromolaran, O.; Aromolaran, D.; Isewon, I.; Oyelade, J. Machine learning approach to gene essentiality prediction: A review. Brief. Bioinform. 2021, 22, bbab128. [Google Scholar] [CrossRef] [PubMed]
  27. Aromolaran, O.; Beder, T.; Oswald, M.; Oyelade, J.; Adebiyi, E.; Koening, R. Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features. Comput. Struct. Biotechnol. J. 2020, 10, 612–621. [Google Scholar] [CrossRef] [PubMed]
  28. Beder, T.; Aromolaran, O.; Dönitz, J.; Tapanelli, S.; Adedeji, E.O.; Adebiyi, E.; Bucher, G.; Koenig, R. Identifying essential genes across eukaryotes by machine learning. NAR Genom. Bioinform. 2021, 3, lqab110. [Google Scholar] [CrossRef] [PubMed]
  29. Marques de Castro, G.; Hastenreiter, Z.; Silva Monteiro, T.A.; Martins da Silva, T.T.; Pereira Lobo, F. Cross-species prediction of essential genes in insects. Bioinformatics 2022, 6, btac009. [Google Scholar] [CrossRef] [PubMed]
  30. Boettcher, M.; McManus, M. Choosing the right tool for the job: RNAi, TALEN, CRISPR. Mol. Cell 2015, 58, 575–585. [Google Scholar] [CrossRef] [PubMed]
  31. Quinzo, M.J.; Perteguer, M.J.; Brindley, P.J.; Loukas, A.; Sotillo, J. Transgenesis in parasitic helminths: A brief history and prospects for the future. Parasit. Vectors 2022, 15, 110. [Google Scholar] [CrossRef] [PubMed]
  32. Chien, J.; Wolf, F.W.; Grosche, S.; Yosef, N.; Garriga, G.; Mörk, C. The enigmatic Canal-Associated Neurons regulate Caenorhabditis elegans larval development through a cAMP signalling pathway. Genetics 2019, 213, 1465–1478. [Google Scholar] [CrossRef] [PubMed]
  33. Carlton, P.M.; Davis, R.E.; Ahmed, S. Nematode chromosomes. Genetics 2022, 221, iyac014. [Google Scholar] [CrossRef] [PubMed]
  34. Mayer, C.; Grummt, I. Ribosome biogenesis and cell growth: mTOR coordinates transcription by all three classes of nuclear RNA polymerases. Oncogene 2006, 25, 6384–6391. [Google Scholar] [CrossRef] [PubMed]
  35. Kressler, D.; Hurt, E.; Bassler, J. Driving ribosome assembly. Biochim. Biophys. Acta 2010, 1803, 673–683. [Google Scholar] [CrossRef] [PubMed]
  36. Zhou, X.; Liao, W.J.; Liao, J.M.; Liao, P.; Lu, H. Ribosomal proteins: Functions beyond the ribosome. J. Mol. Cell Biol. 2015, 7, 92–104. [Google Scholar] [CrossRef] [PubMed]
  37. Bolt, B.J.; Rodgers, F.H.; Shafie, M.; Kersey, P.J.; Berriman, M.; Howe, K.L. Using WormBase ParaSite: An integrated platform for exploring helminth genomic data. Methods Mol. Biol. 2018, 1757, 471–491. [Google Scholar] [PubMed]
  38. Korhonen, P.K.; Wang, T.; Young, N.D.; Byrne, J.J.; Campos, T.L.; Taki, A.C.; Gasser, R.B. Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates. Comput. Struct. Biotechnol. J. 2024, 23, 1026–1035. [Google Scholar] [CrossRef] [PubMed]
  39. Armenteros, J.J.A.; Sønderby, C.K.; Sønderby, S.K.; Nielsen, H.; Winther, O. DeepLoc: Prediction of protein subcellular localization using deep learning. Bioinformatics 2017, 33, 3387–3395. [Google Scholar] [CrossRef]
  40. Hug, L.A.; Baker, B.J.; Anantharaman, K.; Brown, C.T.; Probst, A.J.; Castelle, C.J.; Butterfield, C.N.; Hernsdorf, A.W.; Amano, Y.; Ise, K.; et al. A new view of the tree of life. Nat. Microbiol. 2016, 1, 16048. [Google Scholar] [CrossRef] [PubMed]
  41. Howe, K.L.; Achuthan, P.; Allen, J.; Allen, J.; Alvarez-Jarreta, J.; Amode, M.R.; Armean, I.M.; Azov, A.G.; Bennett, R.; Bhai, J.; et al. Ensembl 2021. Nucleic Acids Res. 2021, 49, D884–D891. [Google Scholar] [CrossRef] [PubMed]
  42. Emms, D.M.; Kelly, S. OrthoFinder: Phylogenetic ortholog inference for comparative genomics. Genome Biol. 2019, 20, 238. [Google Scholar] [CrossRef] [PubMed]
  43. Raudvere, U.; Kolberg, L.; Kuzmin, I.; Arak, T.; Adler, P.; Peterson, H.; Vilo, J. g:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019, 47, W191–W198. [Google Scholar] [CrossRef] [PubMed]
  44. Gillespie, M.; Jassal, B.; Stephan, R.; Milacic, M.; Rothfels, K.; Senff-Ribeiro, A.; Griss, J.; Sevilla, C.; Matthews, L.; Gong, C.; et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022, 50, D687–D692. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Machine learning (ML) performance metrics (ROC-AUC and PR-AUC) for the prediction of essential genes in Caenorhabditis elegans (top) or D. melanogaster (bottom) using features available for Haemonchus contortus. ML methods used: Gradient Boosting Machines (GBMs), Generalised Linear models (GLMs), Neural Networks (NNs), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Gradient Boosting Machines (XGBs).
Figure 1. Machine learning (ML) performance metrics (ROC-AUC and PR-AUC) for the prediction of essential genes in Caenorhabditis elegans (top) or D. melanogaster (bottom) using features available for Haemonchus contortus. ML methods used: Gradient Boosting Machines (GBMs), Generalised Linear models (GLMs), Neural Networks (NNs), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Gradient Boosting Machines (XGBs).
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Figure 2. Pairwise correlations between ranked lists of genes predicted to be essential in Haemonchus contortus through ML and levels of transcription (snRNA-seq, number of nuclei (cells) in which a gene is transcribed, mean/average transcription level, and variance of transcription). A final score was defined by considering both ML and snRNA-seq data (ML_RankTpm_Final). Circle size represents correlation strength.
Figure 2. Pairwise correlations between ranked lists of genes predicted to be essential in Haemonchus contortus through ML and levels of transcription (snRNA-seq, number of nuclei (cells) in which a gene is transcribed, mean/average transcription level, and variance of transcription). A final score was defined by considering both ML and snRNA-seq data (ML_RankTpm_Final). Circle size represents correlation strength.
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Figure 3. Establishing the relationship between transcription profiles and essential genes. (A) A selection of 6173 Haemonchus contortus genes was clustered using uniform manifold approximation and projection (UMAP) based on the level of transcription in 99 samples (RNA-seq; with genes transcribed in all samples included). (B) The same plot with 980/1000 H. contortus essential (black) and 905/10,000 non-essential (light orange) genes overlaid. (C,D) The clustering analysis using a H. contortus snRNA-seq sample (female—32,426 cells); 3000 genes with the highest mean transcription are represented. In total, 847/1000 essential (black) and 118/10,000 non-essential (light orange) genes were included.
Figure 3. Establishing the relationship between transcription profiles and essential genes. (A) A selection of 6173 Haemonchus contortus genes was clustered using uniform manifold approximation and projection (UMAP) based on the level of transcription in 99 samples (RNA-seq; with genes transcribed in all samples included). (B) The same plot with 980/1000 H. contortus essential (black) and 905/10,000 non-essential (light orange) genes overlaid. (C,D) The clustering analysis using a H. contortus snRNA-seq sample (female—32,426 cells); 3000 genes with the highest mean transcription are represented. In total, 847/1000 essential (black) and 118/10,000 non-essential (light orange) genes were included.
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Figure 4. The probabilities of genes (Y-axis) on the autosomal (chr1-5) and sex chromosomes (chrX) of H. contortus being essential, as defined via machine learning (ML). The probabilities of individual genes were mapped to their respective genomic coordinates (X-axis).
Figure 4. The probabilities of genes (Y-axis) on the autosomal (chr1-5) and sex chromosomes (chrX) of H. contortus being essential, as defined via machine learning (ML). The probabilities of individual genes were mapped to their respective genomic coordinates (X-axis).
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Figure 5. The distribution densities of both the ‘top’ 1000 genes inferred to be essential (red) as well as 10,000 non-essential (blue) genes on the autosomal (chr1-5) and sex chromosomes (chrX) of Haemonchus contortus as defined via machine learning (ML).
Figure 5. The distribution densities of both the ‘top’ 1000 genes inferred to be essential (red) as well as 10,000 non-essential (blue) genes on the autosomal (chr1-5) and sex chromosomes (chrX) of Haemonchus contortus as defined via machine learning (ML).
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Figure 6. The workflow used for the prediction of essential genes in Haemonchus contortus using machine learning (ML) and complementary analyses. A range of features (see Materials and Methods) were extracted for H. contortus genes, and selected features were used to train ML models and predict essential genes. The relationship between essentiality and transcription was investigated through clustering and correlation analyses; other complementary analyses included gene ontology (GO)/pathway enrichments and genomic locations.
Figure 6. The workflow used for the prediction of essential genes in Haemonchus contortus using machine learning (ML) and complementary analyses. A range of features (see Materials and Methods) were extracted for H. contortus genes, and selected features were used to train ML models and predict essential genes. The relationship between essentiality and transcription was investigated through clustering and correlation analyses; other complementary analyses included gene ontology (GO)/pathway enrichments and genomic locations.
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Table 1. Features (n = 27) that were used to predict essential genes in Haemonchus contortus; these features are predictive of essential genes within and between Caenorhabditis elegans and Drosophila melanogaster.
Table 1. Features (n = 27) that were used to predict essential genes in Haemonchus contortus; these features are predictive of essential genes within and between Caenorhabditis elegans and Drosophila melanogaster.
FeatureDescriptionSource
OrthoFinder_speciesOrthologs in other speciesOrthoFinder analysis
num_cells_expressedNumber of cells/nuclei where a
gene is transcribed		snRNA-seq data
exonsNumber of exonsBioMart (WomBase ParaSite)
exons_total_lengthTotal length of exonsBioMart (WomBase ParaSite)
CytoplasmSubcellular localisationDeepLoc analysis
MitochondrionSubcellular localisationDeepLoc analysis
NucleusSubcellular localisationDeepLoc analysis
AAC_SProtein sequence featureExtracted using protR *
APAAC_Pc2.Hydrophobicity.2Protein sequence featureExtracted using protR *
CTDC_secondarystruct.Group1Protein sequence featureExtracted using protR *
CTDD_prop4.G2.residue0Protein sequence featureExtracted using protR *
CTDD_prop4.G2.residue25Protein sequence featureExtracted using protR *
CTriad_VS153Protein sequence featureExtracted using protR *
CTriad_VS431Protein sequence featureExtracted using protR *
CTriad_VS613Protein sequence featureExtracted using protR *
DC_HAProtein sequence featureExtracted using protR *
DC_MPProtein sequence featureExtracted using protR *
DC_MSProtein sequence featureExtracted using protR *
DC_VFProtein sequence featureExtracted using protR *
Geary_CHOC760101.lag7Protein sequence featureExtracted using protR *
Moran_CHAM820102.lag7Protein sequence featureExtracted using protR *
GCDNA sequence featureBioMart (WormBase ParaSite)
kmer_3_GCTDNA sequence featureExtracted using rDNAse *
PseKNC_3_Xc1.CCCDNA sequence featureExtracted using rDNAse *
PseKNC_5_Xc1.CGTDNA sequence featureExtracted using rDNAse *
PseKNC_5_Xc1.GCTDNA sequence featureExtracted using rDNAse *
TACC_Nucleosome.lag2DNA Sequence featureExtracted using rDNAse *
* For further information about those sequence features, refer to the documentations of the R packages protR (https://cran.r-project.org/web/packages/protr/vignettes/protr.html, accessed on 1 May 2024) and rDNAse (https://cran.r-project.org/web/packages/rDNAse/vignettes/rDNAse.pdf, accessed on 1 May 2024).
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Campos, T.L.; Korhonen, P.K.; Young, N.D.; Wang, T.; Song, J.; Marhoefer, R.; Chang, B.C.H.; Selzer, P.M.; Gasser, R.B. Inference of Essential Genes of the Parasite Haemonchus contortus via Machine Learning. Int. J. Mol. Sci. 2024, 25, 7015. https://doi.org/10.3390/ijms25137015

AMA Style

Campos TL, Korhonen PK, Young ND, Wang T, Song J, Marhoefer R, Chang BCH, Selzer PM, Gasser RB. Inference of Essential Genes of the Parasite Haemonchus contortus via Machine Learning. International Journal of Molecular Sciences. 2024; 25(13):7015. https://doi.org/10.3390/ijms25137015

Chicago/Turabian Style

Campos, Túlio L., Pasi K. Korhonen, Neil D. Young, Tao Wang, Jiangning Song, Richard Marhoefer, Bill C. H. Chang, Paul M. Selzer, and Robin B. Gasser. 2024. "Inference of Essential Genes of the Parasite Haemonchus contortus via Machine Learning" International Journal of Molecular Sciences 25, no. 13: 7015. https://doi.org/10.3390/ijms25137015

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