Enrichment of Clinically Relevant Organisms in Spontaneous Preterm-Delivered Placentas and Reagent Contamination across All Clinical Groups in a Large Pregnancy Cohort in the United Kingdom.

In this study, differences in the placental microbiota from term and preterm deliveries in a large pregnancy cohort in the United Kingdom were studied by using 16S-targeted amplicon sequencing. The impacts of contamination from DNA extraction, PCR reagents, and the delivery itself were also examined. A total of 400 placental samples from 256 singleton pregnancies were analyzed, and differences between spontaneous preterm-, nonspontaneous preterm-, and term-delivered placentas were investigated. DNA from recently delivered placentas was extracted, and screening for bacterial DNA was carried out by using targeted sequencing of the 16S rRNA gene on the Illumina MiSeq platform. Sequenced reads were analyzed for the presence of contaminating operational taxonomic units (OTUs) identified via sequencing of negative extraction and PCR-blank samples. Differential abundances and between-sample (beta) diversity metrics were then compared. A large proportion of the reads sequenced from the extracted placental samples mapped to OTUs that were also found for negative extractions. Striking differences in the compositions of samples were also observed, according to whether the placenta was delivered abdominally or vaginally, providing strong circumstantial evidence for delivery contamination as an important contributor to observed microbial profiles. When OTU- and genus-level abundances were compared between the groups of interest, a number of organisms were enriched in the spontaneous preterm-delivery cohort, including organisms that have been associated previously with adverse pregnancy outcomes, specifically Mycoplasma spp. and Ureaplasma spp. However, analyses of the overall community structure did not reveal convincing evidence for the existence of a reproducible "preterm placental microbiome."IMPORTANCE Preterm birth is associated with both psychological and physical disabilities and is the leading cause of infant morbidity and mortality worldwide. Infection is known to be an important cause of spontaneous preterm birth, and recent research has implicated variation in the "placental microbiome" in the risk of preterm birth. Consistent with data from previous studies, the abundances of certain clinically relevant species differed between spontaneous preterm- and nonspontaneous preterm- or term-delivered placentas. These results support the view that a proportion of spontaneous preterm births have an intrauterine-infection component. However, an additional observation from this study was that a substantial proportion of sequenced reads were contaminating reads rather than DNA from endogenous, clinically relevant species. This observation warrants caution in the interpretation of sequencing outputs from low-biomass samples such as the placenta

Genetic diversity of next generation antimalarial targets: A baseline for drug resistance surveillance programmes.

Drug resistance is a recurrent problem in the fight against malaria. Genetic and epidemiological surveillance of antimalarial resistant parasite alleles is crucial to guide drug therapies and clinical management. New antimalarial compounds are currently at various stages of clinical trials and regulatory evaluation. Using ?2000 Plasmodium falciparum genome sequences, we investigated the genetic diversity of eleven gene-targets of promising antimalarial compounds and assessed their potential efficiency across malaria endemic regions. We determined if the loci are under selection prior to the introduction of new drugs and established a baseline of genetic variance, including potential resistant alleles, for future surveillance programmes

Genetic diversity of the Plasmodium falciparum GTP-cyclohydrolase 1, dihydrofolate reductase and dihydropteroate synthetase genes reveals new insights into sulfadoxine-pyrimethamine antimalarial drug resistance.

Plasmodium falciparum parasites resistant to antimalarial treatments have hindered malaria disease control. Sulfadoxine-pyrimethamine (SP) was used globally as a first-line treatment for malaria after wide-spread resistance to chloroquine emerged and, although replaced by artemisinin combinations, is currently used as intermittent preventive treatment of malaria in pregnancy and in young children as part of seasonal malaria chemoprophylaxis in sub-Saharan Africa. The emergence of SP-resistant parasites has been predominantly driven by cumulative build-up of mutations in the dihydrofolate reductase (pfdhfr) and dihydropteroate synthetase (pfdhps) genes, but additional amplifications in the folate pathway rate-limiting pfgch1 gene and promoter, have recently been described. However, the genetic make-up and prevalence of those amplifications is not fully understood. We analyse the whole genome sequence data of 4,134 P. falciparum isolates across 29 malaria endemic countries, and reveal that the pfgch1 gene and promoter amplifications have at least ten different forms, occurring collectively in 23% and 34% in Southeast Asian and African isolates, respectively. Amplifications are more likely to be present in isolates with a greater accumulation of pfdhfr and pfdhps substitutions (median of 1 additional mutations; P<0.00001), and there was evidence that the frequency of pfgch1 variants may be increasing in some African populations, presumably under the pressure of SP for chemoprophylaxis and anti-folate containing antibiotics used for the treatment of bacterial infections. The selection of P. falciparum with pfgch1 amplifications may enhance the fitness of parasites with pfdhfr and pfdhps substitutions, potentially threatening the efficacy of this regimen for prevention of malaria in vulnerable groups. Our work describes new pfgch1 amplifications that can be used to inform the surveillance of SP drug resistance, its prophylactic use, and future experimental work to understand functional mechanisms

Using deep learning to identify recent positive selection in malaria parasite sequence data.

BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. METHODS: A deep learning-based approach (called "DeepSweep") was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep . RESULTS: Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60-75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). CONCLUSION: The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making

An analysis of large structural variation in global Plasmodium falciparum isolates identifies a novel duplication of the chloroquine resistance associated gene.

The evolution of genetic mechanisms for host immune evasion and anti-malarial resistance has enabled the Plasmodium falciparum malaria parasite to inflict high morbidity and mortality on human populations. Most studies of P. falciparum genetic diversity have focused on single-nucleotide polymorphisms (SNPs), assisting the identification of drug resistance-associated loci such as the chloroquine related crt and sulfadoxine-pyrimethamine related dhfr. Whilst larger structural variants are known to impact adaptation, for example, mdr1 duplications with anti-malarial resistance, no large-scale, genome-wide study on clinical isolates has been undertaken using whole genome sequencing data. By applying a structural variant detection pipeline across whole genome sequence data from 2,855 clinical isolates in 21 malaria-endemic countries, we identified >70,000 specific deletions and >600 duplications. Most structural variants are rare (48.5% of deletions and 94.7% of duplications are found in single isolates) with 2.4% of deletions and 0.2% of duplications found in >5% of global isolates. A subset of variants was present at high frequency in drug-resistance related genes including mdr1, the gch1 promoter region, and a putative novel duplication of crt. Regional-specific variants were identified, and a companion visualisation tool has been developed to assist web-based investigation of these polymorphisms by the wider scientific community

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artifcial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expertlevel performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identifed eight loci associated with delta age (p ≤ 5 × 10−8), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine

Artemether-lumefantrine treatment failure of uncomplicated Plasmodium falciparum malaria in travellers coming from Angola and Mozambique

Funding Information: We are grateful to the two patients who participated in the study. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This work was supported by Funda??o para a Ci?ncia e Tecnologia (FCT) through GHTM (UID/04413/2020); Medical Research Council UK (Grant no. MR/M01360X/1, MR/N010469/1, MR/R025576/1, and MR/R020973/1); BBSRC (Grant no. BB/R013063/1). Study sponsors had no involvement in the study design; in the collection, analysis and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication. Both patients signed a written informed consent for the analysis of parasite genes, under the IHMT Ethics Committee Approval n?11.18 and CHLO Ethics Committee Approval RNEC:20170700050. Funding Information: This work was supported by Fundação para a Ciência e Tecnologia (FCT) through GHTM (UID/04413/2020); Medical Research Council UK (Grant no. MR/M01360X/1, MR/N010469/1, MR/R025576/1, and MR/R020973/1); BBSRC (Grant no. BB/R013063/1). Study sponsors had no involvement in the study design; in the collection, analysis and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication. Publisher Copyright: © 2021 The AuthorsThe failure of artemisinin combination therapy (ACT) in malaria patients returning from endemic regions may be driven by parasite resistance to this treatment. ACT is used globally as the first-line treatment for Plasmodium falciparum malaria. However, artemisinin-resistant strains of P. falciparum have emerged and spread across Southeast Asia, with the risk of reaching high malaria burden regions in Africa and elsewhere. Here, we report on two malaria imported cases from Africa with possible parasite resistance to the ACT artemether-lumefantrine (AL). Case presentation: Two middle-aged males returning from Angola and Mozambique developed malaria symptoms in Portugal, where they were diagnosed and received treatment with AL as hospital inpatients. After apparent cure and discharge from hospital, these individuals returned to hospital showing signs of late clinical failure. Molecular analysis was performed across a number of drug resistance associated genes. No evidence of pfk13-mediated artemisinin resistance was found. Both subjects had complete parasite clearance after treatment with a non-ACT antimalarials. Conclusion: Our case-studies highlight the need for close monitoring of signs of unsatisfactory antimalarial efficacy among AL treated patients and the possible implication of other genes or mutations in the parasite response to ACTs.publishersversionpublishe

De Novo Assembly of Plasmodium knowlesi Genomes From Clinical Samples Explains the Counterintuitive Intrachromosomal Organization of Variant SICAvar and kir Multiple Gene Family Members.

Plasmodium knowlesi, a malaria parasite of Old World macaque monkeys, is used extensively to model Plasmodium biology. Recently, P. knowlesi was found in the human population of Southeast Asia, particularly Malaysia. P. knowlesi causes uncomplicated to severe and fatal malaria in the human host with features in common with the more prevalent and virulent malaria caused by Plasmodium falciparum. As such, P. knowlesi presents a unique opportunity to develop experimental translational model systems for malaria pathophysiology informed by clinical data from same-species human infections. Experimental lines of P. knowlesi represent well-characterized genetically stable parasites, and to maximize their utility as a backdrop for understanding malaria pathophysiology, genetically diverse contemporary clinical isolates, essentially wild-type, require comparable characterization. The Oxford Nanopore PCR-free long-read sequencing platform was used to sequence and de novo assemble P. knowlesi genomes from frozen clinical samples. The sequencing platform and assembly pipelines were designed to facilitate capturing data and describing, for the first time, P. knowlesi schizont-infected cell agglutination (SICA) var and Knowlesi-Interspersed Repeats (kir) multiple gene families in parasites acquired from nature. The SICAvar gene family members code for antigenically variant proteins analogous to the virulence-associated P. falciparum erythrocyte membrane protein (PfEMP1) multiple var gene family. Evidence presented here suggests that the SICAvar family members have arisen through a process of gene duplication, selection pressure, and variation. Highly evolving genes including PfEMP1family members tend to be restricted to relatively unstable sub-telomeric regions that drive change with core genes protected in genetically stable intrachromosomal locations. The comparable SICAvar and kir gene family members are counter-intuitively located across chromosomes. Here, we demonstrate that, in contrast to conserved core genes, SICAvar and kir genes occupy otherwise gene-sparse chromosomal locations that accommodate rapid evolution and change. The novel methods presented here offer the malaria research community not only new tools to generate comprehensive genome sequence data from small clinical samples but also new insight into the complexity of clinically important real-world parasites

Studying accelerated cardiovascular ageing in Russian adults through a novel deep-learning ECG biomarker

Background: A non-invasive, easy-to-access marker of accelerated cardiac ageing would provide novel insights into the mechanisms and aetiology of cardiovascular disease (CVD) as well as contribute to risk stratification of those who have not had a heart or circulatory event. Our hypothesis is that differences between an ECG-predicted and chronologic age of participants (δage) would reflect accelerated or decelerated cardiovascular ageing Methods: A convolutional neural network model trained on over 700,000 ECGs from the Mayo Clinic in the U.S.A was used to predict the age of 4,542 participants in the Know Your Heart study conducted in two cities in Russia (2015-2018). Thereafter, δage was used in linear regression models to assess associations with known CVD risk factors and markers of cardiac abnormalities. Results: The biomarker δage (mean: +5.32 years) was strongly and positively associated with established risk factors for CVD: blood pressure, body mass index (BMI), total cholesterol and smoking. Additionally, δage had strong independent positive associations with markers of structural cardiac abnormalities: N-terminal pro b-type natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT) and pulse wave velocity, a valid marker of vascular ageing. Conclusion: The difference between the ECG-age obtained from a convolutional neural network and chronologic age (δage) contains information about the level of exposure of an individual to established CVD risk factors and to markers of cardiac damage in a way that is consistent with it being a biomarker of accelerated cardiovascular (vascular) ageing. Further research is needed to explore whether these associations are seen in populations with different risks of CVD events, and to better understand the underlying mechanisms involved

A molecular barcode to inform the geographical origin and transmission dynamics of Plasmodium vivax malaria.

Although Plasmodium vivax parasites are the predominant cause of malaria outside of sub-Saharan Africa, they not always prioritised by elimination programmes. P. vivax is resilient and poses challenges through its ability to re-emerge from dormancy in the human liver. With observed growing drug-resistance and the increasing reports of life-threatening infections, new tools to inform elimination efforts are needed. In order to halt transmission, we need to better understand the dynamics of transmission, the movement of parasites, and the reservoirs of infection in order to design targeted interventions. The use of molecular genetics and epidemiology for tracking and studying malaria parasite populations has been applied successfully in P. falciparum species and here we sought to develop a molecular genetic tool for P. vivax. By assembling the largest set of P. vivax whole genome sequences (n = 433) spanning 17 countries, and applying a machine learning approach, we created a 71 SNP barcode with high predictive ability to identify geographic origin (91.4%). Further, due to the inclusion of markers for within population variability, the barcode may also distinguish local transmission networks. By using P. vivax data from a low-transmission setting in Malaysia, we demonstrate the potential ability to infer outbreak events. By characterising the barcoding SNP genotypes in P. vivax DNA sourced from UK travellers (n = 132) to ten malaria endemic countries predominantly not used in the barcode construction, we correctly predicted the geographic region of infection origin. Overall, the 71 SNP barcode outperforms previously published genotyping methods and when rolled-out within new portable platforms, is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria