Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications.

With >1 million associated deaths in 2020, human tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis remains one of the deadliest infectious diseases. A plethora of genomic tools and bioinformatics pipelines have become available in recent years to assist the whole genome sequencing of M. tuberculosis. The Oxford Nanopore Technologies (ONT) portable sequencer is a promising platform for cost-effective application in clinics, including personalizing treatment through detection of drug resistance-associated mutations, or in the field, to assist epidemiological and transmission investigations. In this study, we performed a comparison of 10 clinical isolates with DNA sequenced on both long-read ONT and (gold standard) short-read Illumina HiSeq platforms. Our analysis demonstrates the robustness of the ONT variant calling for single nucleotide polymorphisms, despite the high error rate. Moreover, because of improved coverage in repetitive regions where short sequencing reads fail to align accurately, ONT data analysis can incorporate additional regions of the genome usually excluded (e.g. pe/ppe genes). The resulting extra resolution can improve the characterization of transmission clusters and dynamics based on inferring closely related isolates. High concordance in variants in loci associated with drug resistance supports its use for the rapid detection of resistant mutations. Overall, ONT sequencing is a promising tool for TB genomic investigations, particularly to inform clinical and surveillance decision-making to reduce the disease burden.

Detection of simple and complex de novo mutations with multiple reference sequences.

The characterization of de novo mutations in regions of high sequence and structural diversity from whole-genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, in which short reads do not capture the long-range context required for resolution, and mapping approaches, in which improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of genetic variant. We construct multisample, colored de Bruijn graphs from short-read data for all samples, align long-read-derived haplotypes and multiple reference data sources to restore graph connectivity information, and call variants using graph path-finding algorithms and a model for simultaneous alignment and recombination. We validate and evaluate the approach using extensive simulations and use it to characterize the rate and spectrum of de novo mutation events in 119 progeny from four Plasmodium falciparum experimental crosses, using long-read data on the parents to inform reconstructions of the progeny and to detect several known and novel nonallelic homologous recombination events.

Emergence of KPC-3- and OXA-181-producing ST13 and ST17 Klebsiella pneumoniae in Portugal: genomic insights on national and international dissemination.

BACKGROUND: Carbapenem-resistant Klebsiella pneumoniae (CRKP) strains are of particular concern, especially strains with mobilizable carbapenemase genes such as blaKPC, blaNDM or blaOXA-48, given that carbapenems are usually the last line drugs in the ß-lactam class and, resistance to this sub-class is associated with increased mortality and frequently co-occurs with resistance to other antimicrobial classes. OBJECTIVES: To characterize the genomic diversity and international dissemination of CRKP strains from tertiary care hospitals in Lisbon, Portugal. METHODS: Twenty CRKP isolates obtained from different patients were subjected to WGS for species confirmation, typing, drug resistance gene detection and phylogenetic reconstruction. Two additional genomic datasets were included for comparative purposes: 26 isolates (ST13, ST17 and ST231) from our collection and 64 internationally available genomic assemblies (ST13). RESULTS: By imposing a 21 SNP cut-off on pairwise comparisons we identified two genomic clusters (GCs): ST13/GC1 (n = 11), all bearing blaKPC-3, and ST17/GC2 (n = 4) harbouring blaOXA-181 and blaCTX-M-15 genes. The inclusion of the additional datasets allowed the expansion of GC1/ST13/KPC-3 to 23 isolates, all exclusively from Portugal, France and the Netherlands. The phylogenetic tree reinforced the importance of the GC1/KPC-3-producing clones along with their rapid emergence and expansion across these countries. The data obtained suggest that the ST13 branch emerged over a decade ago and only more recently did it underpin a stronger pulse of transmission in the studied population. CONCLUSIONS: This study identifies an emerging OXA-181/ST17-producing strain in Portugal and highlights the ongoing international dissemination of a KPC-3/ST13-producing clone from Portugal.

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.

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.

COVID-profiler: a webserver for the analysis of SARS-CoV-2 sequencing data.

BACKGROUND: SARS-CoV-2 virus sequencing has been applied to track the COVID-19 pandemic spread and assist the development of PCR-based diagnostics, serological assays, and vaccines. With sequencing becoming routine globally, bioinformatic tools are needed to assist in the robust processing of resulting genomic data. RESULTS: We developed a web-based bioinformatic pipeline ("COVID-Profiler") that inputs raw or assembled sequencing data, displays raw alignments for quality control, annotates mutations found and performs phylogenetic analysis. The pipeline software can be applied to other (re-) emerging pathogens. CONCLUSIONS: The webserver is available at http://genomics.lshtm.ac.uk/ . The source code is available at https://github.com/jodyphelan/covid-profiler .

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.

BACKGROUND: Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. RESULTS: We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). CONCLUSION: Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

The primate malaria parasites Plasmodium malariae, Plasmodium brasilianum and Plasmodium ovale spp.: genomic insights into distribution, dispersal and host transitions.

During the twentieth century, there was an explosion in understanding of the malaria parasites infecting humans and wild primates. This was built on three main data sources: from detailed descriptive morphology, from observational histories of induced infections in captive primates, syphilis patients, prison inmates and volunteers, and from clinical and epidemiological studies in the field. All three were wholly dependent on parasitological information from blood-film microscopy, and The Primate Malarias" by Coatney and colleagues (1971) provides an overview of this knowledge available at that time. Here, 50 years on, a perspective from the third decade of the twenty-first century is presented on two pairs of primate malaria parasite species. Included is a near-exhaustive summary of the recent and current geographical distribution for each of these four species, and of the underlying molecular and genomic evidence for each. The important role of host transitions in the radiation of Plasmodium spp. is discussed, as are any implications for the desired elimination of all malaria species in human populations. Two important questions are posed, requiring further work on these often ignored taxa. Is Plasmodium brasilianum, circulating among wild simian hosts in the Americas, a distinct species from Plasmodium malariae? Can new insights into the genomic differences between Plasmodium ovale curtisi and Plasmodium ovale wallikeri be linked to any important differences in parasite morphology, cell biology or clinical and epidemiological features?

How has mass drug administration with dihydroartemisinin-piperaquine impacted molecular markers of drug resistance? A systematic review.

The World Health Organization (WHO) recommends surveillance of molecular markers of resistance to anti-malarial drugs. This is particularly important in the case of mass drug administration (MDA), which is endorsed by the WHO in some settings to combat malaria. Dihydroartemisinin-piperaquine (DHA-PPQ) is an artemisinin-based combination therapy which has been used in MDA. This review analyses the impact of MDA with DHA-PPQ on the evolution of molecular markers of drug resistance. The review is split into two parts. Section I reviews the current evidence for different molecular markers of resistance to DHA-PPQ. This includes an overview of the prevalence of these molecular markers in Plasmodium falciparum Whole Genome Sequence data from the MalariaGEN Pf3k project. Section II is a systematic literature review of the impact that MDA with DHA-PPQ has had on the evolution of molecular markers of resistance. This systematic review followed PRISMA guidelines. This review found that despite being a recognised surveillance tool by the WHO, the surveillance of molecular markers of resistance following MDA with DHA-PPQ was not commonly performed. Of the total 96 papers screened for eligibility in this review, only 20 analysed molecular markers of drug resistance. The molecular markers published were also not standardized. Overall, this warrants greater reporting of molecular marker prevalence following MDA implementation. This should include putative pfcrt mutations which have been found to convey resistance to DHA-PPQ in vitro.

Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers.

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel 'cannibalistic' elimination algorithm ("Hungry, Hungry SNPos") that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n = 3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS.

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.

Drug resistance profile and clonality of Plasmodium falciparum parasites in Cape Verde: the 2017 malaria outbreak.

BACKGROUND: Cape Verde is an archipelago located off the West African coast and is in a pre-elimination phase of malaria control. Since 2010, fewer than 20 Plasmodium falciparum malaria cases have been reported annually, except in 2017, when an outbreak in Praia before the rainy season led to 423 autochthonous cases. It is important to understand the genetic diversity of circulating P. falciparum to inform on drug resistance, potential transmission networks and sources of infection, including parasite importation. METHODS: Enrolled subjects involved malaria patients admitted to Dr Agostinho Neto Hospital at Praia city, Santiago island, Cape Verde, between July and October 2017. Neighbours and family members of enrolled cases were assessed for the presence of anti-P. falciparum antibodies. Sanger sequencing and real-time PCR was used to identify SNPs in genes associated with drug resistance (e.g., pfdhfr, pfdhps, pfmdr1, pfk13, pfcrt), and whole genome sequencing data were generated to investigate the population structure of P. falciparum parasites. RESULTS: The study analysed 190 parasite samples, 187 indigenous and 3 from imported infections. Malaria cases were distributed throughout Praia city. There were no cases of severe malaria and all patients had an adequate clinical and parasitological response after treatment. Anti-P. falciparum antibodies were not detected in the 137 neighbours and family members tested. No mutations were detected in pfdhps. The triple mutation S108N/N51I/C59R in pfdhfr and the chloroquine-resistant CVIET haplotype in the pfcrt gene were detected in almost all samples. Variations in pfk13 were identified in only one sample (R645T, E668K). The haplotype NFD for pfmdr1 was detected in the majority of samples (89.7%). CONCLUSIONS: Polymorphisms in pfk13 associated with artemisinin-based combination therapy (ACT) tolerance in Southeast Asia were not detected, but the majority of the tested samples carried the pfmdr1 haplotype NFD and anti-malarial-associated mutations in the the pfcrt and pfdhfr genes. The first whole genome sequencing (WGS) was performed for Cape Verdean parasites that showed that the samples cluster together, have a very high level of similarity and are close to other parasites populations from West Africa.

Novel genetic polymorphisms associated with severe malaria and under selective pressure in North-eastern Tanzania.

Significant selection pressure has been exerted on the genomes of human populations exposed to Plasmodium falciparum infection, resulting in the acquisition of mechanisms of resistance against severe malarial disease. Many host genetic factors, including sickle cell trait, have been associated with reduced risk of developing severe malaria, but do not account for all of the observed phenotypic variation. Identification of novel inherited risk factors relies upon high-resolution genome-wide association studies (GWAS). We present findings of a GWAS of severe malaria performed in a Tanzanian population (n = 914, 15.2 million SNPs). Beyond the expected association with the sickle cell HbS variant, we identify protective associations within two interleukin receptors (IL-23R and IL-12RBR2) and the kelch-like protein KLHL3 (all P<10-6), as well as near significant effects for Major Histocompatibility Complex (MHC) haplotypes. Complementary analyses, based on detecting extended haplotype homozygosity, identified SYNJ2BP, GCLC and MHC as potential loci under recent positive selection. Through whole genome sequencing of an independent Tanzanian cohort (parent-child trios n = 247), we confirm the allele frequencies of common polymorphisms underlying associations and selection, as well as the presence of multiple structural variants that could be in linkage with these SNPs. Imputation of structural variants in a region encompassing the glycophorin genes on chromosome 4, led to the characterisation of more than 50 rare variants, and individually no strong evidence of associations with severe malaria in our primary dataset (P>0.3). Our approach demonstrates the potential of a joint genotyping-sequencing strategy to identify as-yet unknown susceptibility loci in an African population with well-characterised malaria phenotypes. The regions encompassing these loci are potential targets for the design of much needed interventions for preventing or treating malarial disease.

Whole genome sequencing of Mycobacterium tuberculosis isolates and clinical outcomes of patients treated for multidrug-resistant tuberculosis in Tanzania.

BACKGROUND: Tuberculosis (TB), particularly multi- and or extensive drug resistant TB, is still a global medical emergency. Whole genome sequencing (WGS) is a current alternative to the WHO-approved probe-based methods for TB diagnosis and detection of drug resistance, genetic diversity and transmission dynamics of Mycobacterium tuberculosis complex (MTBC). This study compared WGS and clinical data in participants with TB. RESULTS: This cohort study performed WGS on 87 from MTBC DNA isolates, 57 (66%) and 30 (34%) patients with drug resistant and susceptible TB, respectively. Drug resistance was determined by Xpert® MTB/RIF assay and phenotypic culture-based drug-susceptibility-testing (DST). WGS and bioinformatics data that predict phenotypic resistance to anti-TB drugs were compared with participant's clinical outcomes. They were 47 female participants (54%) and the median age was 35 years (IQR): 29-44). Twenty (23%) and 26 (30%) of participants had TB/HIV co-infection BMI < 18 kg/m2 respectively. MDR-TB participants had MTBC with multiple mutant genes, compared to those with mono or polyresistant TB, and the majority belonged to lineage 3 Central Asian Strain (CAS). Also, MDR-TB was associated with delayed culture-conversion (median: IQR (83: 60-180 vs. 51:30-66) days). WGS had high concordance with both culture-based DST and Xpert® MTB/RIF assay in detecting drug resistance (kappa = 1.00). CONCLUSION: This study offers comparison of mutations detected by Xpert and WGS with phenotypic DST of M. tuberculosis isolates in Tanzania. The high concordance between the different methods and further insights provided by WGS such as PZA-DST, which is not routinely performed in most resource-limited-settings, provides an avenue for inclusion of WGS into diagnostic matrix of TB including drug-resistant TB.

PrimedRPA: primer design for recombinase polymerase amplification assays.

SUMMARY: Recombinase polymerase amplification (RPA), an isothermal nucleic acid amplification method, is enhancing our ability to detect a diverse array of pathogens, thereby assisting the diagnosis of infectious diseases and the detection of microorganisms in food and water. However, new bioinformatics tools are needed to automate and improve the design of the primers and probes sets to be used in RPA, particularly to account for the high genetic diversity of circulating pathogens and cross detection of genetically similar organisms. PrimedRPA is a python-based package that automates the creation and filtering of RPA primers and probe sets. It aligns several sequences to identify conserved targets, and filters regions that cross react with possible background organisms. AVAILABILITY AND IMPLEMENTATION: PrimedRPA was implemented in Python 3 and supported on Linux and MacOS and is freely available from http://pathogenseq.lshtm.ac.uk/PrimedRPA.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A forward genetic screen reveals a primary role for Plasmodium falciparum Reticulocyte Binding Protein Homologue 2a and 2b in determining alternative erythrocyte invasion pathways.

Invasion of human erythrocytes is essential for Plasmodium falciparum parasite survival and pathogenesis, and is also a complex phenotype. While some later steps in invasion appear to be invariant and essential, the earlier steps of recognition are controlled by a series of redundant, and only partially understood, receptor-ligand interactions. Reverse genetic analysis of laboratory adapted strains has identified multiple genes that when deleted can alter invasion, but how the relative contributions of each gene translate to the phenotypes of clinical isolates is far from clear. We used a forward genetic approach to identify genes responsible for variable erythrocyte invasion by phenotyping the parents and progeny of previously generated experimental genetic crosses. Linkage analysis using whole genome sequencing data revealed a single major locus was responsible for the majority of phenotypic variation in two invasion pathways. This locus contained the PfRh2a and PfRh2b genes, members of one of the major invasion ligand gene families, but not widely thought to play such a prominent role in specifying invasion phenotypes. Variation in invasion pathways was linked to significant differences in PfRh2a and PfRh2b expression between parasite lines, and their role in specifying alternative invasion was confirmed by CRISPR-Cas9-mediated genome editing. Expansion of the analysis to a large set of clinical P. falciparum isolates revealed common deletions, suggesting that variation at this locus is a major cause of invasion phenotypic variation in the endemic setting. This work has implications for blood-stage vaccine development and will help inform the design and location of future large-scale studies of invasion in clinical isolates.

A transcriptional switch underlies commitment to sexual development in malaria parasites.

The life cycles of many parasites involve transitions between disparate host species, requiring these parasites to go through multiple developmental stages adapted to each of these specialized niches. Transmission of malaria parasites (Plasmodium spp.) from humans to the mosquito vector requires differentiation from asexual stages replicating within red blood cells into non-dividing male and female gametocytes. Although gametocytes were first described in 1880, our understanding of the molecular mechanisms involved in commitment to gametocyte formation is extremely limited, and disrupting this critical developmental transition remains a long-standing goal. Here we show that expression levels of the DNA-binding protein PfAP2-G correlate strongly with levels of gametocyte formation. Using independent forward and reverse genetics approaches, we demonstrate that PfAP2-G function is essential for parasite sexual differentiation. By combining genome-wide PfAP2-G cognate motif occurrence with global transcriptional changes resulting from PfAP2-G ablation, we identify early gametocyte genes as probable targets of PfAP2-G and show that their regulation by PfAP2-G is critical for their wild-type level expression. In the asexual blood-stage parasites pfap2-g appears to be among a set of epigenetically silenced loci prone to spontaneous activation. Stochastic activation presents a simple mechanism for a low baseline of gametocyte production. Overall, these findings identify PfAP2-G as a master regulator of sexual-stage development in malaria parasites and mark the first discovery of a transcriptional switch controlling a differentiation decision in protozoan parasites.

Analysis of nuclear and organellar genomes of Plasmodium knowlesi in humans reveals ancient population structure and recent recombination among host-specific subpopulations.

The macaque parasite Plasmodium knowlesi is a significant concern in Malaysia where cases of human infection are increasing. Parasites infecting humans originate from genetically distinct subpopulations associated with the long-tailed (Macaca fascicularis (Mf)) or pig-tailed macaques (Macaca nemestrina (Mn)). We used a new high-quality reference genome to re-evaluate previously described subpopulations among human and macaque isolates from Malaysian-Borneo and Peninsular-Malaysia. Nuclear genomes were dimorphic, as expected, but new evidence of chromosomal-segment exchanges between subpopulations was found. A large segment on chromosome 8 originating from the Mn subpopulation and containing genes encoding proteins expressed in mosquito-borne parasite stages, was found in Mf genotypes. By contrast, non-recombining organelle genomes partitioned into 3 deeply branched lineages, unlinked with nuclear genomic dimorphism. Subpopulations which diverged in isolation have re-connected, possibly due to deforestation and disruption of wild macaque habitats. The resulting genomic mosaics reveal traits selected by host-vector-parasite interactions in a setting of ecological transition.

SV-Pop: population-based structural variant analysis and visualization.

BACKGROUND: Genetic structural variation underpins a multitude of phenotypes, with significant implications for a range of biological outcomes. Despite their crucial role, structural variants (SVs) are often neglected and overshadowed by single nucleotide polymorphisms (SNPs), which are used in large-scale analysis such as genome-wide association and population genetic studies. RESULTS: To facilitate the high-throughput analysis of structural variation we have developed an analytical pipeline and visualisation tool, called SV-Pop. The utility of this pipeline was then demonstrated through application with a large, multi-population P. falciparum dataset. CONCLUSIONS: Designed to facilitate downstream analysis and visualisation post-discovery, SV-Pop allows for straightforward integration of multi-population analysis, method and sample-based concordance metrics, and signals of selection.

Indels, structural variation, and recombination drive genomic diversity in Plasmodium falciparum.

The malaria parasite Plasmodium falciparum has a great capacity for evolutionary adaptation to evade host immunity and develop drug resistance. Current understanding of parasite evolution is impeded by the fact that a large fraction of the genome is either highly repetitive or highly variable and thus difficult to analyze using short-read sequencing technologies. Here, we describe a resource of deep sequencing data on parents and progeny from genetic crosses, which has enabled us to perform the first genome-wide, integrated analysis of SNP, indel and complex polymorphisms, using Mendelian error rates as an indicator of genotypic accuracy. These data reveal that indels are exceptionally abundant, being more common than SNPs and thus the dominant mode of polymorphism within the core genome. We use the high density of SNP and indel markers to analyze patterns of meiotic recombination, confirming a high rate of crossover events and providing the first estimates for the rate of non-crossover events and the length of conversion tracts. We observe several instances of meiotic recombination within copy number variants associated with drug resistance, demonstrating a mechanism whereby fitness costs associated with resistance mutations could be compensated and greater phenotypic plasticity could be acquired.