TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data.

The identification and characterization of essential genes are central to our understanding of the core biological functions in eukaryotic organisms, and has important implications for the treatment of diseases caused by, for example, cancers and pathogens. Given the major constraints in testing the functions of genes of many organisms in the laboratory, due to the absence of in vitro cultures and/or gene perturbation assays for most metazoan species, there has been a need to develop in silico tools for the accurate prediction or inference of essential genes to underpin systems biological investigations. Major advances in machine learning approaches provide unprecedented opportunities to overcome these limitations and accelerate the discovery of essential genes on a genome-wide scale. Here, we developed and evaluated a large language model- and graph neural network (LLM-GNN)-based approach, called 'Bingo', to predict essential protein-coding genes in the metazoan model organisms Caenorhabditis elegans and Drosophila melanogaster as well as in Mus musculus and Homo sapiens (a HepG2 cell line) by integrating LLM and GNNs with adversarial training. Bingo predicts essential genes under two 'zero-shot' scenarios with transfer learning, showing promise to compensate for a lack of high-quality genomic and proteomic data for non-model organisms. In addition, the attention mechanisms and GNNExplainer were employed to manifest the functional sites and structural domain with most contribution to essentiality. In conclusion, Bingo provides the prospect of being able to accurately infer the essential genes of little- or under-studied organisms of interest, and provides a biological explanation for gene essentiality.

Haem transporter HRG-1 is essential in the barber's pole worm and an intervention target candidate.

Parasitic roundworms (nematodes) have lost genes involved in the de novo biosynthesis of haem, but have evolved the capacity to acquire and utilise exogenous haem from host animals. However, very little is known about the processes or mechanisms underlying haem acquisition and utilisation in parasites. Here, we reveal that HRG-1 is a conserved and unique haem transporter in a broad range of parasitic nematodes of socioeconomic importance, which enables haem uptake via intestinal cells, facilitates cellular haem utilisation through the endo-lysosomal system, and exhibits a conspicuous distribution at the basal laminae covering the alimentary tract, muscles and gonads. The broader tissue expression pattern of HRG-1 in Haemonchus contortus (barber's pole worm) compared with its orthologues in the free-living nematode Caenorhabditis elegans indicates critical involvement of this unique haem transporter in haem homeostasis in tissues and organs of the parasitic nematode. RNAi-mediated gene knockdown of hrg-1 resulted in sick and lethal phenotypes of infective larvae of H. contortus, which could only be rescued by supplementation of exogenous haem in the early developmental stage. Notably, the RNAi-treated infective larvae could not establish infection or survive in the mammalian host, suggesting an indispensable role of this haem transporter in the survival of this parasite. This study provides new insights into the haem biology of a parasitic nematode, demonstrates that haem acquisition by HRG-1 is essential for H. contortus survival and infection, and suggests that HRG-1 could be an intervention target candidate in a range of parasitic nematodes.

Exosomes, and the potential for exosome-based interventions against COVID-19.

Since late 2019, the world has been devastated by the coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with more than 760 million people affected and ∼seven million deaths reported. Although effective treatments for COVID-19 are currently limited, there has been a strong focus on developing new therapeutic approaches to address the morbidity and mortality linked to this disease. An approach that is currently being investigated is the use of exosome-based therapies. Exosomes are small, extracellular vesicles that play a role in many clinical diseases, including viral infections, infected cells release exosomes that can transmit viral components, such as miRNAs and proteins, and can also include receptors for viruses that facilitate viral entry into recipient cells. SARS-CoV-2 has the ability to impact the formation, secretion, and release of exosomes, thereby potentially facilitating or intensifying the transmission of the virus among cells, tissues and individuals. Therefore, designing synthetic exosomes that carry immunomodulatory cargo and antiviral compounds are proposed to be a promising strategy for the treatment of COVID-19 and other viral diseases. Moreover, exosomes generated from mesenchymal stem cells (MSC) might be employed as cell-free therapeutic agents, as MSC-derived exosomes can diminish the cytokine storm and reverse the suppression of host anti-viral defences associated with COVID-19, and boost the repair of lung damage linked to mitochondrial activity. The present article discusses the significance and roles of exosomes in COVID-19, and explores potential future applications of exosomes in combating this disease. Despite the challenges posed by COVID-19, exosome-based therapies could represent a promising avenue for improving patient outcomes and reducing the impact of this disease.

Ocular Dirofilariasis in Migrant from Sri Lanka, Australia.

We describe a case of imported ocular dirofilariasis in Australia, linked to the Hong Kong genotype of Dirofilaria sp., in a migrant from Sri Lanka. Surgical extraction and mitochondrial sequences analyses confirmed this filarioid nematode as the causative agent and a Dirofilaria sp. not previously reported in Australia.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. RESULTS: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chromosome-level genome of Schistosoma haematobium underpins genome-wide explorations of molecular variation.

Urogenital schistosomiasis is caused by the blood fluke Schistosoma haematobium and is one of the most neglected tropical diseases worldwide, afflicting > 100 million people. It is characterised by granulomata, fibrosis and calcification in urogenital tissues, and can lead to increased susceptibility to HIV/AIDS and squamous cell carcinoma of the bladder. To complement available treatment programs and break the transmission of disease, sound knowledge and understanding of the biology and ecology of S. haematobium is required. Hybridisation/introgression events and molecular variation among members of the S. haematobium-group might effect important biological and/or disease traits as well as the morbidity of disease and the effectiveness of control programs including mass drug administration. Here we report the first chromosome-contiguous genome for a well-defined laboratory line of this blood fluke. An exploration of this genome using transcriptomic data for all key developmental stages allowed us to refine gene models (including non-coding elements) and annotations, discover 'new' genes and transcription profiles for these stages, likely linked to development and/or pathogenesis. Molecular variation within S. haematobium among some geographical locations in Africa revealed unique genomic 'signatures' that matched species other than S. haematobium, indicating the occurrence of introgression events. The present reference genome (designated Shae.V3) and the findings from this study solidly underpin future functional genomic and molecular investigations of S. haematobium and accelerate systematic, large-scale population genomics investigations, with a focus on improved and sustained control of urogenital schistosomiasis.

Strongyloides coinfection in COVID-19 patients treated with corticosteroids: A systematic review.

The COVID-19 pandemic linked to the virus SARS-CoV-2, which began in China, affected ∼765 million people as of 30 April 2023. The widespread use of corticosteroids for the symptomatic treatment of COVID-19 could lead to the reactivation of infections of opportunistic pathogens, including Strongyloides. We sought to determine the clinical symptoms and demographic characteristics of SARS-CoV-2-Strongyloides co-infection, particularly in patients with severe disease and being treated with immunosuppressive drugs. To do this, we undertook a systematic review of the literature, and searched public accessible scientific databases-the Web of Science, Scopus, PubMed/Medline and Embase -for eligible studies (1 December 2019 to 30 August 2022). The review protocol is registered in PROSPERO (CRD42022377062). Descriptive statistical analyses were used to present the clinical and laboratory parameters of the co-infection; for this, we calculated prevalence using the following formula: positive cases/total number of cases × 100. Of a total of 593 studies identified, 17 studies reporting 26 co-infected patients met the criteria for inclusion in this review. The median age of these patients was 55.14 years. Most of cases (53.8%) were treated with dexamethasone, followed by methylprednisolone (26.9%). Eighteen of 26 patients were immigrants living in European countries or the USA; most of these immigrants originated from Latin America (58%) and South-East Asia (11%). The commonest symptoms of co-infection were abdominal pain (50%), fever (46.1%), dyspnoea (30.7%) and cough (30.7%), and frequently reported laboratory findings were high absolute eosinophil count (38.4%), high white blood cell count (30.7%), high C-reactive protein (23.0%) and high neutrophil count (19.2%). Two of the 26 patients (7.7%) had fatal outcomes. Most of the SARS-CoV-2-Strongyloides coinfected cases were immigrants living in developed countries, emphasising the need for clinicians in these countries to be aware of clinical and laboratory parameters associated with such co-infections, as well as the key importance of rapid and accurate diagnostic tests for timely and effective diagnosis and patient management.

Structure activity relationship and target prediction for ABX464 analogues in Caenorhabditis elegans.

Global challenges with treatment failures and/or widespread resistance in parasitic worms against commercially available anthelmintics lend impetus to the development of new anthelmintics with novel mechanism(s) of action. The free-living nematode Caenorhabditis elegans is an important model organism used for drug discovery, including the screening and structure-activity investigation of new compounds, and target deconvolution. Previously, we conducted a whole-organism phenotypic screen of the 'Pandemic Response Box' (from Medicines for Malaria Venture, MMV) and identified a hit compound, called ABX464, with activity against C. elegans and a related, parasitic nematode, Haemonchus contortus. Here, we tested a series of 44 synthesized analogues to explore the pharmacophore of activity on C. elegans and revealed five compounds whose potency was similar or greater than that of ABX464, but which were not toxic to human hepatoma (HepG2) cells. Subsequently, we employed thermal proteome profiling (TPP), protein structure prediction and an in silico-docking algorithm to predict ABX464-target candidates. Taken together, the findings from this study contribute significantly to the early-stage drug discovery of a new nematocide based on ABX464. Future work is aimed at validating the ABX464-protein interactions identified here, and at assessing ABX464 and associated analogues against a panel of parasitic nematodes, towards developing a new anthelmintic with a mechanism of action that is distinct from any of the compounds currently-available commercially.

iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.

Human Neural Larva Migrans Caused by Ophidascaris robertsi Ascarid.

We describe a case in Australia of human neural larva migrans caused by the ascarid Ophidascaris robertsi, for which Australian carpet pythons are definitive hosts. We made the diagnosis after a live nematode was removed from the brain of a 64-year-old woman who was immunosuppressed for a hypereosinophilic syndrome diagnosed 12 months earlier.

DEMoS: a deep learning-based ensemble approach for predicting the molecular subtypes of gastric adenocarcinomas from histopathological images.

MOTIVATION: The molecular subtyping of gastric cancer (adenocarcinoma) into four main subtypes based on integrated multiomics profiles, as proposed by The Cancer Genome Atlas (TCGA) initiative, represents an effective strategy for patient stratification. However, this approach requires the use of multiple technological platforms, and is quite expensive and time-consuming to perform. A computational approach that uses histopathological image data to infer molecular subtypes could be a practical, cost- and time-efficient complementary tool for prognostic and clinical management purposes. RESULTS: Here, we propose a deep learning ensemble approach (called DEMoS) capable of predicting the four recognized molecular subtypes of gastric cancer directly from histopathological images. DEMoS achieved tile-level area under the receiver-operating characteristic curve (AUROC) values of 0.785, 0.668, 0.762 and 0.811 for the prediction of these four subtypes of gastric cancer [i.e. (i) Epstein-Barr (EBV)-infected, (ii) microsatellite instability (MSI), (iii) genomically stable (GS) and (iv) chromosomally unstable tumors (CIN)] using an independent test dataset, respectively. At the patient-level, it achieved AUROC values of 0.897, 0.764, 0.890 and 0.898, respectively. Thus, these four subtypes are well-predicted by DEMoS. Benchmarking experiments further suggest that DEMoS is able to achieve an improved classification performance for image-based subtyping and prevent model overfitting. This study highlights the feasibility of using a deep learning ensemble-based method to rapidly and reliably subtype gastric cancer (adenocarcinoma) solely using features from histopathological images. AVAILABILITY AND IMPLEMENTATION: All whole slide images used in this study was collected from the TCGA database. This study builds upon our previously published HEAL framework, with related documentation and tutorials available at http://heal.erc.monash.edu.au. The source code and related models are freely accessible at https://github.com/Docurdt/DEMoS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The redlegged earth mite draft genome provides new insights into pesticide resistance evolution and demography in its invasive Australian range.

Genomic data provide valuable insights into pest management issues such as resistance evolution, historical patterns of pest invasions and ongoing population dynamics. We assembled the first reference genome for the redlegged earth mite, Halotydeus destructor (Tucker, 1925), to investigate adaptation to pesticide pressures and demography in its invasive Australian range using whole-genome pool-seq data from regionally distributed populations. Our reference genome comprises 132 autosomal contigs, with a total length of 48.90 Mb. We observed a large complex of ace genes, which has presumably evolved from a long history of organophosphate selection in H. destructor and may contribute towards organophosphate resistance through copy number variation, target-site mutations and structural variants. In the putative ancestral H. destructor ace gene, we identified three target-site mutations (G119S, A201S and F331Y) segregating in organophosphate-resistant populations. Additionally, we identified two new para sodium channel gene mutations (L925I and F1020Y) that may contribute to pyrethroid resistance. Regional structuring observed in population genomic analyses indicates that gene flow in H. destructor does not homogenize populations across large geographic distances. However, our demographic analyses were equivocal on the magnitude of gene flow; the short invasion history of H. destructor makes it difficult to distinguish scenarios of complete isolation vs. ongoing migration. Nonetheless, we identified clear signatures of reduced genetic diversity and smaller inferred effective population sizes in eastern vs. western populations, which is consistent with the stepping-stone invasion pathway of this pest in Australia. These new insights will inform development of diagnostic genetic markers of resistance, further investigation into the multifaceted organophosphate resistance mechanism and predictive modelling of resistance evolution and spread.

Zoonotic helminths of dogs and risk factors associated with polyparasitism in Grenada, West Indies.

Canine soil-transmitted helminths (STHs) cause important zoonoses in the tropics, with varying degrees of intensity of infection in humans and dogs. This study aimed to investigate the prevalence and associated risk factors for STHs in community dogs residing in Grenada, West Indies. In May 2021, 232 canine fecal samples were examined for zoonotic helminths by microscopy (following flotation), and genomic DNA from a subset of 211 of these samples were subjected to multiplex qPCR for the detection and specific identification of hookworms, Toxocara spp. and Strongyloides. Microscopic examination revealed that 46.5% (108/232, 95% CI 40­52.9), 9% (21/232, 95% CI 5.35­12.7) and 5.2% (12/232, 95% CI 2.3­8) of the samples contained eggs of Ancylostoma spp., Toxocara spp. and Trichuris vulpis, respectively. Multiplex qPCR revealed that, 42.2% (89/211, 95% CI 35.5­48.8) were positive for at least 1 zoonotic parasite. Of these, 40.8% (86/211, 95% CI 34.1­47.3) of samples tested positive for Ancylostoma spp., 36% (76/211, 95% CI 29.5­42.9) were positive for A. caninum, 13.3% (28/211, 95% CI 9­18.6) for A. ceylanicum, 5.7% for T. canis (12/211, 95% CI 2.97­8.81) and 1% (2/211, 95% CI 0­2.26) for Strongyloides spp. (identified as S. stercoralis and S. papillosus by conventional PCR-based Sanger sequencing). Using a multiple logistic regression model, a low body score and free-roaming behaviour were significant predictors of test-positivity for these parasitic nematodes in dogs (P < 0.05). Further studies of zoonotic STHs in humans should help elucidate the public health relevance of these parasites in Grenada.

Using a Bioactive Eremophila-Derived Serrulatane Scaffold to Generate a Unique Carbamate Library for Anti-infective Evaluations.

The known Eremophila microtheca-derived diterpenoid 3,7,8-trihydroxyserrulat-14-en-19-oic acid (1) was targeted for large-scale purification, as this bioactive plant compound has proven to be an attractive scaffold for semisynthetic studies and subsequent library generation. Compound 1 was converted to a selectively protected trimethyl derivative, 3-hydroxy-7,8-dimethoxyserrulat-14-en-19-oic acid methyl ester (2), using simple and rapid methylation conditions. The resulting scaffold 2 was reacted with a diverse series of commercially available isocyanates to generate an 11-membered carbamate-based library. The chemical structures of the 11 new semisynthetic analogues were fully characterized by spectroscopic and spectrometric analysis. All natural products and semisynthetic compounds were evaluated for their anthelmintic, antimalarial, and anti-HIV activities. Compound 3 was shown to elicit the greatest antiplasmodial activity of all compounds tested, with IC50 values of 4.6 and 11.6 µM against Plasmodium falciparum 3D7 and Dd2, respectively. Compound 11 showed the greatest inhibition of development to fourth-stage Haemonchus contortus larvae (L4) and induction of a skinny (Ski) phenotype (67.5% of nematodes) at 50 µM. Compound 7, which inhibited 59.0% of HIV production at 100 µg/mL, was the carbamate analogue that displayed the best antiviral activity.

OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines.

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

Genome-Wide Analysis of Haemonchus contortus Proteases and Protease Inhibitors Using Advanced Informatics Provides Insights into Parasite Biology and Host-Parasite Interactions.

Biodiversity within the animal kingdom is associated with extensive molecular diversity. The expansion of genomic, transcriptomic and proteomic data sets for invertebrate groups and species with unique biological traits necessitates reliable in silico tools for the accurate identification and annotation of molecules and molecular groups. However, conventional tools are inadequate for lesser-known organismal groups, such as eukaryotic pathogens (parasites), so that improved approaches are urgently needed. Here, we established a combined sequence- and structure-based workflow system to harness well-curated publicly available data sets and resources to identify, classify and annotate proteases and protease inhibitors of a highly pathogenic parasitic roundworm (nematode) of global relevance, called Haemonchus contortus (barber's pole worm). This workflow performed markedly better than conventional, sequence-based classification and annotation alone and allowed the first genome-wide characterisation of protease and protease inhibitor genes and gene products in this worm. In total, we identified 790 genes encoding 860 proteases and protease inhibitors representing 83 gene families. The proteins inferred included 280 metallo-, 145 cysteine, 142 serine, 121 aspartic and 81 "mixed" proteases as well as 91 protease inhibitors, all of which had marked physicochemical diversity and inferred involvements in >400 biological processes or pathways. A detailed investigation revealed a remarkable expansion of some protease or inhibitor gene families, which are likely linked to parasitism (e.g., host-parasite interactions, immunomodulation and blood-feeding) and exhibit stage- or sex-specific transcription profiles. This investigation provides a solid foundation for detailed explorations of the structures and functions of proteases and protease inhibitors of H. contortus and related nematodes, and it could assist in the discovery of new drug or vaccine targets against infections or diseases.

The Proteome and Lipidome of Extracellular Vesicles from Haemonchus contortus to Underpin Explorations of Host-Parasite Cross-Talk.

Many parasitic worms have a major adverse impact on human and animal populations worldwide due to the chronicity of their infections. There is a growing body of evidence indicating that extracellular vesicles (EVs) are intimately involved in modulating (suppressing) inflammatory/immune host responses and parasitism. As one of the most pathogenic nematodes of livestock animals, Haemonchus contortus is an ideal model system for EV exploration. Here, employing a multi-step enrichment process (in vitro culture, followed by ultracentrifugation, size exclusion and filtration), we enriched EVs from H. contortus and undertook the first comprehensive (qualitative and quantitative) multi-omic investigation of EV proteins and lipids using advanced liquid chromatography-mass spectrometry and informatics methods. We identified and quantified 561 proteins and 446 lipids in EVs and compared these molecules with those of adult worms. We identified unique molecules in EVs, such as proteins linked to lipid transportation and lipid species (i.e., sphingolipids) associated with signalling, indicating the involvement of these molecules in parasite-host cross-talk. This work provides a solid starting point to explore the functional roles of EV-specific proteins and lipids in modulating parasite-host cross-talk, and the prospect of finding ways of disrupting or interrupting this relationship to suppress or eliminate parasite infection.

Ancylostoma ceylanicum Hookworms in Dogs, Grenada, West Indies.

Ancylostoma ceylanicum hookworms are recognized agents of human infection in the Asia-Pacific region. We investigated prevalence of zoonotic hookworm infections in dogs in Grenada in 2021; 40.8% were infected by hookworms, including Ancylostoma ceylanicum. Surveillance of this parasite in dogs and humans is needed in tropical/subtropical countries in the Americas.

PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations.

An essential step in engineering proteins and understanding disease-causing missense mutations is to accurately model protein stability changes when such mutations occur. Here, we developed a new sequence-based predictor for the protein stability (PROST) change (Gibb's free energy change, ΔΔG) upon a single-point missense mutation. PROST extracts multiple descriptors from the most promising sequence-based predictors, such as BoostDDG, SAAFEC-SEQ, and DDGun. RPOST also extracts descriptors from iFeature and AlphaFold2. The extracted descriptors include sequence-based features, physicochemical properties, evolutionary information, evolutionary-based physicochemical properties, and predicted structural features. The PROST predictor is a weighted average ensemble model based on extreme gradient boosting (XGBoost) decision trees and an extra-trees regressor; PROST is trained on both direct and hypothetical reverse mutations using the S5294 (S2647 direct mutations + S2647 inverse mutations). The parameters for the PROST model are optimized using grid searching with 5-fold cross-validation, and feature importance analysis unveils the most relevant features. The performance of PROST is evaluated in a blinded manner, employing nine distinct data sets and existing state-of-the-art sequence-based and structure-based predictors. This method consistently performs well on frataxin, S217, S349, Ssym, S669, Myoglobin, and CAGI5 data sets in blind tests and similarly to the state-of-the-art predictors for p53 and S276 data sets. When the performance of PROST is compared with the latest predictors such as BoostDDG, SAAFEC-SEQ, ACDC-NN-seq, and DDGun, PROST dominates these predictors. A case study of mutation scanning of the frataxin protein for nine wild-type residues demonstrates the utility of PROST. Taken together, these findings indicate that PROST is a well-suited predictor when no protein structural information is available. The source code of PROST, data sets, examples, and pretrained models along with how to use PROST are available at https://github.com/ShahidIqb/PROST and https://prost.erc.monash.edu/seq.