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Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches.

ABSTRACT: The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the environmental influence on knowlesi malaria transmission in Peninsular Malaysia. Therefore, our study aimed to investigate the ecological distribution of human P. knowlesi malaria in relation to environmental factors in Peninsular Malaysia. A total of 2,873 records of human P. knowlesi infections in Peninsular Malaysia from 1st January 2011 to 31st December 2019 were collated from the Ministry of Health Malaysia and geolocated. Three machine learning-based models, maximum entropy (MaxEnt), extreme gradient boosting (XGBoost), and ensemble modeling approach, were applied to predict the spatial variation of P. knowlesi disease risk. Multiple environmental parameters including climate factors, landscape characteristics, and anthropogenic factors were included as predictors in both predictive models. Subsequently, an ensemble model was developed based on the output of both MaxEnt and XGBoost. Comparison between models indicated that the XGBoost has higher performance as compared to MaxEnt and ensemble model, with AUC_ROC values of 0.933 ± 0.002 and 0.854 ± 0.007 for train and test datasets, respectively. Key environmental covariates affecting human P. knowlesi occurrence were distance to the coastline, elevation, tree cover, annual precipitation, tree loss, and distance to the forest. Our models indicated that the disease risk areas were mainly distributed in low elevation (75-345 m above mean sea level) areas along the Titiwangsa mountain range and inland central-northern region of Peninsular Malaysia. The high-resolution risk map of human knowlesi malaria constructed in this study can be further utilized for multi-pronged interventions targeting community at-risk, macaque populations, and mosquito vectors.

SUBMITTER: Phang WK

PROVIDER: S-EPMC9977793 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches.

Phang Wei Kit WK Hamid Mohd Hafizi Bin Abdul MHBA Jelip Jenarun J Mudin Rose Nani Binti RNB Chuang Ting-Wu TW Lau Yee Ling YL Fong Mun Yik MY

Frontiers in microbiology 20230216

The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the ...[more]

PMID: 36876062

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Project description:BackgroundThe zoonotic malaria parasite Plasmodium knowlesi has become an emerging threat to South East Asian countries particular in Malaysia. A recent study from Sarawak (Malaysian Borneo) discovered two distinct normocyte binding protein xa (Pknbpxa) types of P. knowlesi. In the present study, the Pknbpxa of clinical isolates from Peninsular Malaysia and Sabah (Malaysian Borneo) were investigated for the presence of Pknbpxa types and natural selection force acting on the gene.MethodBlood samples were collected from 47 clinical samples from Peninsular Malaysia (n = 35) and Sabah (Malaysian Borneo, n = 12) were used in the study. The Pknbpxa gene was successfully amplified and directly sequenced from 38 of the samples (n = 31, Peninsular Malaysia and n = 7, Sabah, Malaysian Borneo). The Pknbpxa sequences of P. knowlesi isolates from Sarawak (Malaysian Borneo) were retrieved from GenBank and included in the analysis. Polymorphism, genetic diversity and natural selection of Pknbpxa sequences were analysed using DNAsp v 5.10, MEGA5. Phylogentics of Pknbpxa sequences was analysed using MrBayes v3.2 and Splits Tree v4.13.1. The pairwise F ST indices were used to determine the genetic differentiation between the Pknbpxa types and was calculated using Arlequin 3.5.1.3.ResultsAnalyses of the sequences revealed Pknbpxa dimorphism throughout Malaysia indicating co-existence of the two types (Type-1 and Type-2) of Pknbpxa. More importantly, a third type (Type 3) closely related to Type 2 Pknbpxa was also detected. This third type was found only in the isolates originating from Peninsular Malaysia. Negative natural selection was observed, suggesting functional constrains within the Pknbpxa types.ConclusionsThis study revealed the existence of three Pknbpxa types in Malaysia. Types 1 and 2 were found not only in Malaysian Borneo (Sarawak and Sabah) but also in Peninsular Malaysia. A third type which was specific only to samples originating from Peninsular Malaysia was discovered. Further genetic studies with a larger sample size will be necessary to determine whether natural selection is driving this genetic differentiation and geographical separation.

Project description:BackgroundThe simian malaria parasite Plasmodium knowlesi has been reported to cause significant numbers of human infection in South East Asia. Its merozoite surface protein-3 (MSP3) is a protein that belongs to a multi-gene family of proteins first found in Plasmodium falciparum. Several studies have evaluated the potential of P. falciparum MSP3 as a potential vaccine candidate. However, to date no detailed studies have been carried out on P. knowlesi MSP3 gene (pkmsp3). The present study investigates the genetic diversity, and haplotypes groups of pkmsp3 in P. knowlesi clinical samples from Peninsular Malaysia.MethodsBlood samples were collected from P. knowlesi malaria patients within a period of 4 years (2008-2012). The pkmsp3 gene of the isolates was amplified via PCR, and subsequently cloned and sequenced. The full length pkmsp3 sequence was divided into Domain A and Domain B. Natural selection, genetic diversity, and haplotypes of pkmsp3 were analysed using MEGA6 and DnaSP ver. 5.10.00 programmes.ResultsFrom 23 samples, 48 pkmsp3 sequences were successfully obtained. At the nucleotide level, 101 synonymous and 238 non-synonymous mutations were observed. Tests of neutrality were not significant for the full length, Domain A or Domain B sequences. However, the dN/dS ratio of Domain B indicates purifying selection for this domain. Analysis of the deduced amino acid sequences revealed 42 different haplotypes. Neighbour Joining phylogenetic tree and haplotype network analyses revealed that the haplotypes clustered into two distinct groups.ConclusionsA moderate level of genetic diversity was observed in the pkmsp3 and only the C-terminal region (Domain B) appeared to be under purifying selection. The separation of the pkmsp3 into two haplotype groups provides further evidence of the existence of two distinct P. knowlesi types or lineages. Future studies should investigate the diversity of pkmsp3 among P. knowlesi isolates in North Borneo, where large numbers of human knowlesi malaria infection still occur.

Project description:BackgroundPlasmodium knowlesi is one of the monkey malaria parasites that can cause human malaria. The Duffy binding protein of P. knowlesi (PkDBPαII) is essential for the parasite's invasion into human and monkey erythrocytes. A previous study on P. knowlesi clinical isolates from Peninsular Malaysia reported high level of genetic diversity in the PkDBPαII. Furthermore, 36 amino acid haplotypes were identified and these haplotypes could be separated into allele group I and allele group II. In the present study, the PkDBPαII of clinical isolates from the Malaysian states of Sarawak and Sabah in North Borneo was investigated, and compared with the PkDBPαII of Peninsular Malaysia isolates.MethodsBlood samples from 28 knowlesi malaria patients were used. These samples were collected between 2011 and 2013 from hospitals in North Borneo. The PkDBPαII region of the isolates was amplified by PCR, cloned into Escherichia coli, and sequenced. The genetic diversity, natural selection and phylogenetics of PkDBPαII haplotypes were analysed using MEGA5 and DnaSP ver. 5.10.00 programmes.ResultsForty-nine PkDBPαII sequences were obtained. Comparison at the nucleotide level against P. knowlesi strain H as reference sequence revealed 58 synonymous and 102 non-synonymous mutations. Analysis on these mutations showed that PkDBPαII was under purifying (negative) selection. At the amino acid level, 38 different PkDBPαII haplotypes were identified. Twelve of the 28 blood samples had mixed haplotype infections. Phylogenetic analysis revealed that all the haplotypes were in allele group I, but they formed a sub-group that was distinct from those of Peninsular Malaysia. Wright's FST fixation index indicated high genetic differentiation between the North Borneo and Peninsular Malaysia haplotypes.ConclusionsThis study is the first to report the genetic diversity and natural selection of PkDBPαII of P. knowlesi from Borneo Island. The PkDBPαII haplotypes found in this study were distinct from those from Peninsular Malaysia. This difference may not be attributed to geographical separation because other genetic markers studied thus far such as the P. knowlesi circumsporozoite protein gene and small subunit ribosomal RNA do not display such differentiation. Immune evasion may possibly be the reason for the differentiation.

Project description:BACKGROUND: The monkey malaria parasite Plasmodium knowlesi is now recognized as the fifth species of Plasmodium that can cause human malaria. Like the region II of the Duffy binding protein of P. vivax (PvDBPII), the region II of the P. knowlesi Duffy binding protein (PkDBP?II) plays an essential role in the parasite's invasion into the host's erythrocyte. Numerous polymorphism studies have been carried out on PvDBPII, but none has been reported on PkDBP?II. In this study, the genetic diversity, haplotyes and allele groups of PkDBP?II of P. knowlesi clinical isolates from Peninsular Malaysia were investigated. METHODS: Blood samples from 20 knowlesi malaria patients and 2 wild monkeys (Macaca fascicularis) were used. These samples were collected between 2010 and 2012. The PkDBP?II region of the isolates was amplified by PCR, cloned into Escherichia coli, and sequenced. The genetic diversity, natural selection and haplotypes of PkDBP?II were analysed using MEGA5 and DnaSP ver. 5.10.00 programmes. RESULTS: Fifty-three PkDBP?II sequences from human infections and 6 from monkeys were obtained. Comparison at the nucleotide level against P. knowlesi strain H as reference sequence showed 52 synonymous and 76 nonsynonymous mutations. Analysis on the rate of these mutations indicated that PkDBP?II was under purifying (negative) selection. At the amino acid level, 36 different PkDBP?II haplotypes were identified. Twelve of the 20 human and 1 monkey blood samples had mixed haplotype infections. These haplotypes were clustered into 2 distinct allele groups. The majority of the haplotypes clustered into the large dominant group. CONCLUSIONS: Our present study is the first to report the genetic diversity and natural selection of PkDBP?II. Hence, the haplotypes described in this report can be considered as novel. Although a high level of genetic diversity was observed, the PkDBP?II appeared to be under purifying selection. The distribution of the haplotypes was skewed, with one dominant major and one minor group. Future study should investigate PkDBP?II of P. knowlesi from Borneo, which hitherto has recorded the highest number of human knowlesi malaria.

Dataset Information

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches.

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Predicting <i>Plasmodium knowlesi</i> transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches.

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