Immunogens Obtained From Plasmodium Yoelii Using Quantitative Sequence-linkage Group Selection Method

IMMUNOGENS OBTAINED FROM PLASMODIUM YOELII USING QUANTITATIVE SEQUENCE-LINKAGE GROUP SELECTION METHOD

BACKGROUND

[0001] Malaria parasite strains are genotypically polymorphic, leading to a diversity of phenotypic characteristics that impact on disease severity. Discovering the genetic basis for such phenotypic traits can inform the design of new drugs and vaccines. Both association mapping and linkage analyses approaches have been adopted to understand the genetic mechanisms behind various phenotypes of malaria parasites and with the application of whole genome sequencing (WGS), the resolution of these methodologies has been dramatically improved, allowing the discovery of selective sweeps as they arise in the field. However, both approaches suffer from drawbacks when working with malaria parasites: linkage mapping requires the cloning of individual recombinant offspring, a process that is both laborious and time-consuming, and association studies require the collection of a large number of individual parasites (usually in the thousands) from diverse geographical origins and over periods of several months or years to produce enough resolution for the detection of selective sweeps.

[0002] Linkage Group Selection (LGS), like linkage mapping, relies on the generation of genetic crosses, but bypasses the need for extracting and phenotyping individual recombinant clones. Instead, it relies on quantitative molecular markers to measure allele frequencies in the recombinant progeny and identify loci under selection. This approach bears similarity to Bulked Segregant Analysis (BSA), a technique developed to study disease resistance in plants. In BSA, individuals from a population are segregated based upon their phenotype (e.g. disease resistance), following which the frequencies of genetic markers in each population are analysed, identifying loci at which different alleles are found for the differently phenotypes populations. Segregating individuals by phenotype, while relatively straight forward for large organisms such as plants, is not feasible for unicellular pathogens such as malaria parasites. Instead, in LGS, the segregating population is grown both in the presence or absence of a selection pressure (e.g. drug treatment, immune pressure, etc.). Selection removes susceptible individuals in the selected "pool", while leaving both susceptible and resistant individuals in the unselected "pool". In its original implementation, LGS was successfully applied in studying strain- specific immunity (SSI), drug resistance and growth rate in malaria and SSI in Eimeria tenella. LGS is essentially identical to the extreme QTL approach (xQTL) that was independently developed by yeast researchers based on BSA.

[0003] In both the original implementations of BSA and LGS a limiting factor is the availability of molecular markers differentiating the two populations. One step in increasing the number of molecular markers was through the use of array hybridisation that allowed the identification of thousands of SNPs as molecular markers in Arabidopsis thaliana. BSA (still using pre-selected pools) was also combined with tiling microarray hybridisation and used probe intensities to detect a gene underlying xylose utilisation in yeast. The xQTL method increased the power and rapidity of the approach by making use of available yeast microarray data as well as Next Generation Sequencing (NGS) of DNA hybridised to microarray probes to identify a large number of markers across the genome, this time comparing selected and unselected populations, rather then generating pools based on phenotype. In the absence of microarray databases, an alternative approach was to use NGS short reads to identify genome-wide SNPs between two parents and then use these SNPs as molecular markers to identify target genes in the selected progeny population compared against the unselected population, as done to study chloroquine resistance in malaria.

[0004] Identifying the genetic determinants of phenotypes that impact disease severity is of fundamental importance for the design of new interventions against malaria. Here we use a novel approach to study two important properties of the parasite; the rate at which parasites grow within a single host, and the means by which parasites are affected by the host immune system.

BRIEF DESCRIPTION OF THE FIGURES

[0005] Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale.

[0006] Figure 1 shows a schematic representation of the multi-crossing LGS approach. The process starts with the identification of distinct selectable phenotypes in cloned strains of the pathogen population (in this case malaria parasites) and their sequencing, usually from the vertebrate blood stage. A genetic cross between two cloned strains is subsequently produced, in this case inside the mosquito vector. The cross progeny is then grown with and without selection pressure(s). Selection pressure will remove those progeny individuals carrying allele(s) associated with sensitivity to the selection pressure(s), while allowing progeny individuals with the resistant allele(s) to survive. DNA is then extracted from the whole, uncloned progeny for sequencing. SNPs distinguishing both parents are used to measure allele frequencies in the selected and unselected progenies. A mathematical model is then applied to identify and define loci under selection. Regions in these loci are then analyzed in detail to identify potential target polymorphisms underlying the phenotype(s) under investigation. Targeted capillary sequencing can be employed to verify or further characterize polymorphisms. Finally, where applicable, allele replacement experiments can be carried out to confirm the effect of target polymorphisms.

[0007] Figure 2 shows pure strain growth rates. (A) Growth rate of Plasmodium yoelii strains 17X1. Ipp and CU in CBA mice inoculated with 1 x 106 iRBCs on Day 0. Error bars indicate the standard error of the mean for three mice per group. (B) The relative proportions of CU and 17X1. Ipp were measured by Q-RT-PCR targeting the polymorphic mspl locus at Day 4 post-inoculation with a mixed inoculum containing approximately equal proportions of both strains in naive mice and mice immunized with one of the two strains. Error bars show the standard error of the mean of five mice per group. *p<.05, Wilcoxon rank sum test, W=25, p=0.0119, n=5; ** p<0.01, Wilcoxon rank sum test, W=25, p=0.0075, n=5.

[0008] Figure 3A and 3B shows genome-wide sequencing data. Figure 3A shows genome-widePlasmodium yoelii CU allele frequency of two independent genetic crosses grown in (a,b) naive mice, (c,d) 17X1. Ipp immunized mice and (e,f) CU-immunized mice. Light gray dots represent observed allele frequencies. Dark gray dots represent allele frequencies retained after filtering. Dark blue lines represent a smoothed approximation of the underlying allele frequency; a region of uncertainty in this frequency, of size three standard deviations, is shown in light blue. A conservative confidence interval describing the position of an allele evolving under selection is shown via a red bar. Allele frequencies are shown in log scale. Figure 3B shows evolutionary models fitted to allele frequency data. Filtered allele frequencies are shown as gray dots, while the model fit is shown as a red line. Dark blue and light blue vertical bars show combined and conservative confidence intervals for the location of the selected allele as reported in Figure 9.

Numbers in parentheses equate figures with locations in Figure 3A. A black vertical line shows the position of a gene of interest.

[0009] Figure 4A, 4B, and 4C shows EBL Amino acid sequence alignment of various malaria species and Plasmodium yoelii strains, and predicted protein structure

consequences of the C351Y polymorphism. Figure 4A shows EBL orthologous and paralogous sequences from a variety of malaria species and P. yoelii strains were aligned using ClustalW. Only the amino acids surrounding position 351 are shown. The cysteine in positon 351 in P. yoelii is highly conserved across strains and species, with only strain 17X1. Ipp bearing a C to Y substitution. PchAS: Plasmodium chabaudi AS strain;

PbANKA: Plasmodium berghei ANKA strain; Pyl7X/17Xl . lpp/CU/YM: P. yoelii 17X,17Xl. lpp,CU,YM strains; Pk-DBLa/β/γ: Plasmodium knowlesi Duffy Binding Ligand alpha/beta/gamma (H strain); PvDBP: Plasmodium vivax Duffy Binding Protein (Sal-I strain) ;PcynB_DBP 1/2: Plasmodium cynomolgi Duffy Binding Proteins 1/2 (B strain); Pf3D7_EBA140/175/181: Plasmodium falciparum Erythrocyte Binding Antigens 140/175/181 (3D7 strain). Figure 4B shows energy minimized homology model of the wild type P. yoelii (Pyl7XWT) Erythrocyte Binding Ligand (EBL). Inset depicts the disulfide bond between C351 and C420. (The protein is represented in cyan and the disulfide bonds are in yellow). Figure 4C shows energy minimized homology model of the mutant (C351Y) P. yoelii (Pyl7Xl. lpp) Erythrocyte Binding Ligand (EBL). Inset depicts the lack of a disulfide bond between Y351 (substituted C351) and C420. (The protein is represented in cyan and the disulfide bonds are in yellow and Tyr351 [mutated] is represented in magenta).

[0010] Figure 5 shows localization of EBL. The C351Y polymorphism does not affect EBL subcellular localization in Plasmodium yoelii. (A) P. yoelii schizonts of wild type and transgenic parasite lines were incubated with fluorescent mouse anti-EBL serum, fluorescent rabbit anti-AMAl serum, and DAPI nuclear staining. Colors indicate the localization of the Pyebl (green) and AMA-1 (red) proteins, as well as nuclear DNA (blue). 17XL: fast growing 17X clone previously shown to traffic EBL to the dense granules, not the micronemes, 17X1. Ipp: 17x1. Ipp strain, CU: CU strain, 17X1.1- 351 Y>C: 17X1. Ipp strain transfected with the CU allele for Pyebl, CU-35 lOY: CU strain transfected with the 17X1. Ipp allele of Pyebl. (B) The distance of EBL from AMAl measured for five parasite strains and for 5-9 schizonts per strain; stars indicate p < 0.01 using a Mann- Whitney U test. This indicates a shift in the location of Pyebl occurring in 17XL, but not in any other parasite lines.

[0011] Figure 6 shows site directed mutagenesis of pyebl AA position 351 reverses the phenotypes of parasites with slow and intermediate growth rates. (A) Growth rate of P. yoelii strains 17X1. Ipp, CU and of the CU-strains transfected with either CU (CU-EBL- 35 IOC) or 17X1.1 (CU-EBL-3510Y) Pyebl gene in CBA mice inoculated with 1x106 iRBCs on Day 0. (B) Growth rate of P. yoelii strains 17X1. Ipp, CU and of the 17X1. lpp- strains transfected with either 17X1.1 (17Xl. lpp-EBL-351Y>Y) or CU (17Xl. lpp-EBL- 351Y>C) Pyebl gene alleles in CBA mice inoculated with 1x106 iRBCs on Day 0.

Transfection with the 17X1. Ipp (EBL-351Y) allele produces a significantly increased growth rate in the CU strain (CU-EBL-3510C vs CU-EBL-3510 Y: p < 0.01, Two-way ANOVA with Tukey post-test correction) that is not significantly different from 17X1. Ipp growth rate following transfection with its native allele (17Xl. lpp-EBL-351Y> Y vs. CU- EBL-351C>Y: p > 0.05, Two-way ANOVA with Tukey post-test correction). Conversely, transfection with the CU (EBA-351C) allele significantly reduces growth (17X1. lpp- EBL-351Y>Y vs 17Xl. lpp-EBL-351Y> C: p < 0.01, Two-way ANOVA with Tukey post-test correction) and produces a phenotype that is not significantly different from CU transfected with its own allele (CU EBL-3510 C vs 17Xl. lpp-EBL-351Y> C: p > 0.05, Two-way ANOVA with Tukey post-test correction).

[0012] Figure 7 shows sudden changes in allele frequency identified using a jump- diffusion model. Details are given for loci at which a sudden jump in frequency was inferred with probability at least 1%. The latter value is the inferred probability that the change in allele frequency at a given locus arose from a jump to a random position between 0 and 1, as opposed to arising from a small change to the frequency at the previous locus. Data are shown for the naive and 17-X immunized experiments; no jumps of this significance were inferred for the CU-immunized experiment.

[0013] Figure 8 shows identification of candidate regions by non-neutrality score and SD model selected allele location. The non-neutrality score for region in replica r is denoted Sr. The optimal driver location in the same region is given by i* r. Where a chromosome is divided into parts, by potential jump alleles, the resulting genomic regions are denoted by their chromosome number, a subscript indicating which part of the genome was under consideration. Identified candidate regions were defined as those at which selection was identified at positions within 200kb in both replicates, and are here highlighted in bold type.

[0014] Figure 9 shows confidence intervals for driver locations as determined by mathematical modeling.

[0015] Figure 10 shows parasitaemias after immune challenges. (A) The course of infection of 1: 1 mixtures of blood stage Plasmodium yoelii yoelii 17x1.1 and CU parasites in mock- immunised (red line), 17x1.1 (green line) and CU (purple line) immunised mice through time. Error bars indicate standard errors of the mean of 6 mice per group. (B) The course of infection of uncloned recombinant progeny of a cross between Plasmodium yoelii yoelii 17x1.1 and CU parasites in mock- immunised (red line), 17x1.1 (green line) and CU (purple line) immunised mice through time. (C-E) The course of infection of 1: 1 mixtures of blood stage Plasmodium yoelii yoelii 17x1.1 and CU parasites in mock- immunised (blue lines), 17x1.1 (red lines) and CU (green lines) immunised mice through time in BALB/c (C), CBA/n (D) and C57/BL6 (E) mice. Error bars indicate standard errors of the mean of 3 mice per group.

[0016] Figure 11 shows intracellular localization of EBL in parasite strains CU, 17XL, 17X1. Ipp and in transfected parasites CU(CY) and 17Xl. lpp(YC). (A) Antibody- mediated staining of EBL (green), AMAl (red) and DAPI staining of DNA (blue) inside the parasite cell in strain 17XL.(B) Intensity of fluorescent staining related to location in strain 17XL, Y-axis indicates fluorescence intensity, X-axis indicates distance along the merozoite starting from the posterior terminal end. (C) Comparisons of the distances of EBL from DNA and AMAl from DNA in the 5 parasite strains. The distance of EBL or AMAl from DNA measured across 5 parasite strains and between 5-9 merozoites for each strain; stars indicate p<0.05 using a Wilcoxon signed-rank test.

[0017] Figure 12 shows expression of Pyebl alleles in both wild type (WT) and transfected strains. mRNA from the parental WT strains CU and 17X1. Ipp, as well the CU strain transfected with the 17X1. Ipp allele (CU C351Y) and the 17XNL strain (which also carries a C at position 351) was sequenced by strand- specific RNA sequencing. Reads were visualized on the genome using the Artemis software. (A) Each strain displays the expected allele at position 351 (highlighted in red) of the Pyebl gene. (B) The pyebl gene is expressed in all samples, including the transfected CU strain (CU C351Y).

[0018] Figure 13 shows selected alleles identified by the SDR model. The identified alleles are substantially closer than those identified with the more basic SD model (†indicates that the identified selected alleles were under selection for alleles from different parents).

[0019] Figure 14 shows Bayesian Information Criterion (BIC) values for varying models for candidate regions of the genome, within each replica, calculated under different models. BIC scores are given for the maximum likelihood candidate allele, i* found within each region, in each replica. Optimal BIC scores for each genomic region within each replica, are given in bold text. In the first part of chromosome VIII, and the second part of chromosome XIII, a candidate allele could only be identified in only one of the two replicas. [0020] Figure 15 shows inferred recombination rates from driver models.

Recombination rates were inferred close to selected loci within each cross population. A step-wise model of recombination was applied. Recombination rates are described as number of events per base per generation.

[0021] Figure 16 shows list of genes contained within the mathematically defined Confidence Intervals (725,528-813,866 bp) of the locus under selection on Chromosome 7. The figure shows gene ID and location for P. yoelii, protein description, number of Transmembrane domains, presence of a signal peptide, P. falciparum orthologous gene and non-synonymous to synonymous SNP ratio in P. falciparum.

[0022] Figure 17 shows list of genes contained within the mathematically defined Confidence Intervals (1,229,582 -1,363,920 bp) of the locus under selection on

Chromosome 8. The figure shows gene ID and location for P. yoelii, protein description, number of Transmembrane domains, presence of a signal peptide, P. falciparum orthologous gene and non-synonymous to synonymous SNP ratio in P. falciparum.

[0023] Figure 18 shows list of genes contained within the mathematically defined Confidence Intervals (1,436,717-1,528,275 bp) of the locus under selection on

Chromosome 13. The figure shows gene ID and location for P. yoelii, protein description, number of Transmembrane domains, presence of a signal peptide, P. falciparum orthologous gene and non-synonymous to synonymous SNP ratio in P. falciparum.

[0024] Figure 19 shows PCR primers used to generate constructs for transfection experiments.

DEFINITIONS

[0025] The terms "protein," "polypeptide," and "peptide," used interchangeably herein, refer to polymeric forms of amino acids of any length, including coded and non- coded amino acids and chemically or biochemically modified or derivatized amino acids. The terms include polymers that have been modified, such as polypeptides having modified peptide backbones.

[0026] Proteins are said to have an "N-terminus" and a "C-terminus." The term "N- terminus" relates to the start of a protein or polypeptide, terminated by an amino acid with a free amine group (-NH2). The term "C-terminus" relates to the end of an amino acid chain (protein or polypeptide), terminated by a free carboxyl group (-COOH).

[0027] The terms "nucleic acid" and "polynucleotide," used interchangeably herein, refer to polymeric forms of nucleotides of any length, including ribonucleotides, deoxyribonucleotides, or analogs or modified versions thereof. They include single-, double-, and multi- stranded DNA or RNA, genomic DNA, cDNA, DNA-RNA hybrids, and polymers comprising purine bases, pyrimidine bases, or other natural, chemically modified, biochemically modified, non-natural, or derivatized nucleotide bases.

[0028] Nucleic acids are said to have "5' ends" and "3' ends" because

mononucleotides are reacted to make oligonucleotides in a manner such that the 5' phosphate of one mononucleotide pentose ring is attached to the 3' oxygen of its neighbor in one direction via a phosphodiester linkage. An end of an oligonucleotide is referred to as the "5' end" if its 5' phosphate is not linked to the 3' oxygen of a mononucleotide pentose ring. An end of an oligonucleotide is referred to as the "3' end" if its 3' oxygen is not linked to a 5' phosphate of another mononucleotide pentose ring. A nucleic acid sequence, even if internal to a larger oligonucleotide, also may be said to have 5' and 3' ends. In either a linear or circular DNA molecule, discrete elements are referred to as being "upstream" or 5' of the "downstream" or 3' elements.

[0029] "Codon optimization" refers to a process of modifying a nucleic acid sequence for enhanced expression in particular host cells by replacing at least one codon of the native sequence with a codon that is more frequently or most frequently used in the genes of the host cell while maintaining the native amino acid sequence. For example, a polynucleotide encoding a fusion polypeptide can be modified to substitute codons having a higher frequency of usage in a given host cell as compared to the naturally occurring nucleic acid sequence. Codon usage tables are readily available, for example, at the "Codon Usage Database." The optimal codons utilized by L. monocytogenes for each amino acid are shown US 2007/0207170, herein incorporated by reference in its entirety for all purposes. These tables can be adapted in a number of ways. See Nakamura et al. (2000) Nucleic Acids Research 28:292, herein incorporated by reference in its entirety for all purposes. Computer algorithms for codon optimization of a particular sequence for expression in a particular host are also available (see, e.g., Gene Forge).

[0030] "Sequence identity" or "identity" in the context of two polynucleotides or polypeptide sequences makes reference to the residues in the two sequences that are the same when aligned for maximum correspondence over a specified comparison window. When percentage of sequence identity is used in reference to proteins it is recognized that residue positions which are not identical often differ by conservative amino acid substitutions, where amino acid residues are substituted for other amino acid residues with similar chemical properties (e.g., charge or hydrophobicity) and therefore do not change the functional properties of the molecule. When sequences differ in conservative substitutions, the percent sequence identity may be adjusted upwards to correct for the conservative nature of the substitution. Sequences that differ by such conservative substitutions are said to have "sequence similarity" or "similarity." Means for making this adjustment are well known to those of skill in the art. Typically, this involves scoring a conservative substitution as a partial rather than a full mismatch, thereby increasing the percentage sequence identity. Thus, for example, where an identical amino acid is given a score of 1 and a non-conservative substitution is given a score of zero, a conservative substitution is given a score between zero and 1. The scoring of conservative substitutions is calculated, e.g., as implemented in the program PC/GENE (Intelligenetics, Mountain View, California).

[0031] "Percentage of sequence identity" refers to the value determined by comparing two optimally aligned sequences (greatest number of perfectly matched residues) over a comparison window, wherein the portion of the polynucleotide sequence in the

comparison window may comprise additions or deletions (i.e., gaps) as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. The percentage is calculated by determining the number of positions at which the identical nucleic acid base or amino acid residue occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison, and multiplying the result by 100 to yield the percentage of sequence identity. Unless otherwise specified (e.g., the shorter sequence includes a linked heterologous sequence), the comparison window is the full length of the shorter of the two sequences being compared.

[0032] Unless otherwise stated, sequence identity/similarity values refer to the value obtained using GAP Version 10 using the following parameters: % identity and % similarity for a nucleotide sequence using GAP Weight of 50 and Length Weight of 3, and the nwsgapdna.cmp scoring matrix; % identity and % similarity for an amino acid sequence using GAP Weight of 8 and Length Weight of 2, and the BLOSUM62 scoring matrix; or any equivalent program thereof. "Equivalent program" includes any sequence comparison program that, for any two sequences in question, generates an alignment having identical nucleotide or amino acid residue matches and an identical percent sequence identity when compared to the corresponding alignment generated by GAP Version 10. [0033] The term "conservative amino acid substitution" refers to the substitution of an amino acid that is normally present in the sequence with a different amino acid of similar size, charge, or polarity. Examples of conservative substitutions include the substitution of a non-polar (hydrophobic) residue such as isoleucine, valine, or leucine for another non- polar residue. Likewise, examples of conservative substitutions include the substitution of one polar (hydrophilic) residue for another such as between arginine and lysine, between glutamine and asparagine, or between glycine and serine. Additionally, the substitution of a basic residue such as lysine, arginine, or histidine for another, or the substitution of one acidic residue such as aspartic acid or glutamic acid for another acidic residue are additional examples of conservative substitutions. Examples of non-conservative substitutions include the substitution of a non-polar (hydrophobic) amino acid residue such as isoleucine, valine, leucine, alanine, or methionine for a polar (hydrophilic) residue such as cysteine, glutamine, glutamic acid or lysine and/or a polar residue for a non-polar residue. Typical amino acid categorizations are summarized below.

[0034] A "homologous" sequence (e.g., nucleic acid sequence) refers to a sequence that is either identical or substantially similar to a known reference sequence, such that it is, for example, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% identical to the known reference sequence.

[0035] The term "fragment" when referring to a protein means a protein that is shorter or has fewer amino acids than the full length protein. The term "fragment" when referring to a nucleic acid means a nucleic acid that is shorter or has fewer nucleotides than the full length nucleic acid. A fragment can be, for example, an N-terminal fragment (i.e., removal of a portion of the C-terminal end of the protein), a C-terminal fragment (i.e., removal of a portion of the N-terminal end of the protein), or an internal fragment. A fragment can also be, for example, a functional fragment or an immunogenic fragment.

[0036] The terms "immunogenicity" or "immunogenic" refer to the innate ability of a molecule (e.g., a protein, a nucleic acid, an antigen, or an organism) to elicit an immune response in a subject when administered to the subject. Immunogenicity can be measured, for example, by a greater number of antibodies to the molecule, a greater diversity of antibodies to the molecule, a greater number of T-cells specific for the molecule, a greater cytotoxic or helper T-cell response to the molecule, and the like.

[0037] The term "antigen" is used herein to refer to a substance that, when placed in contact with a subject or organism (e.g., when present in or when detected by the subject or organism), results in a detectable immune response from the subject or organism. An antigen may be, for example, a lipid, a protein, a carbohydrate, a nucleic acid, or combinations and variations thereof. For example, an "antigenic peptide" refers to a peptide that leads to the mounting of an immune response in a subject or organism when present in or detected by the subject or organism. For example, such an "antigenic peptide" may encompass proteins that are loaded onto and presented on MHC class I and/or class II molecules on a host cell's surface and can be recognized or detected by an immune cell of the host, thereby leading to the mounting of an immune response against the protein. Such an immune response may also extend to other cells within the host, such as diseased cells (e.g., tumor or cancer cells) that express the same protein.

[0038] The term "in vitro" refers to artificial environments and to processes or reactions that occur within an artificial environment (e.g., a test tube).

[0039] The term "in vivo" refers to natural environments (e.g., a cell or organism or body) and to processes or reactions that occur within a natural environment.

[0040] Compositions or methods "comprising" or "including" one or more recited elements may include other elements not specifically recited. For example, a composition that "comprises" or "includes" a protein may contain the protein alone or in combination with other ingredients.

[0041] Designation of a range of values includes all integers within or defining the range, and all subranges defined by integers within the range.

[0042] Unless otherwise apparent from the context, the term "about" encompasses values within a standard margin of error of measurement (e.g., SEM) of a stated value or variations + 0.5%, 1%, 5%, or 10% from a specified value.

[0043] The singular forms of the articles "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "an antigen" or "at least one antigen" can include a plurality of antigens, including mixtures thereof.

[0044] Statistically significant means p <0.05.

DETAILED DESCRIPTION

[0045] Various embodiments of the inventions now will be described more fully hereinafter with reference to the attached Appendices A-C, in which some, but not all embodiments of the inventions are shown. Indeed, these inventions may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. The term "or" is used herein in both the alternative and conjunctive sense, unless otherwise indicated. The terms "illustrative" and "exemplary" are used to be examples with no indication of quality level.

I. Exemplary Embodiments

[0046] Details regarding various embodiments are described in connection with the attached Appendices A-C, which are herein incorporated by reference. By way of background, identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Presented herein is a novel, rapid, genome-wide approach (termed quantitative- seq Linkage Group Selection, qSeq-LGS) capable of identifying multiple genetic drivers of medically relevant phenotypes within malaria parasites via a single experiment at single gene or allele resolution. In a proof of principle study disclosed herein, a previously undescribed single nucleotide polymorphism in the binding domain of the erythrocyte binding like protein (EBL) conferred a dramatic change in red blood cell invasion in mutant rodent malaria parasites Plasmodium yoelii. In the same experiment, merozoite surface protein 1 (MSP1) and other polymorphic antigen genes were implicated as major drivers of strain- specific immunity. Using allelic replacement, functional validation of the mutation in the EBL gene controlling the growth rate in the blood stages of the parasites was provided. Using this new approach which combines genetics, genomics and mathematical modelling, the inventors identified several new genes as malaria vaccine candidates. In some embodiments, the presently disclosed subject matter provides new potential vaccine candidates for human malaria parasites.

[0047] Also provided are immunogenic compositions, pharmaceutical compositions, or vaccines comprising an immunogenic polypeptide as disclosed herein, a nucleic acid encoding an immunogenic polypeptide as disclosed herein. In one embodiment, immunogenic polypeptide is encoded by a nucleic acid sequence with at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98% or 99% sequence identity to SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof. In one embodiment, immunogenic polypeptide is encoded by a nucleic acid sequence with at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98% or 99% sequence identity to SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof. In one embodiment, immunogenic polypeptide is encoded by a nucleic acid sequence with at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98% or 99% sequence identity to SEQ ID NOs: 31, 32, 33, 34, 35, 36, or a fragment thereof. In one embodiment, immunogenic polypeptide is encoded by a nucleic acid sequence with at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98% or 99% sequence identity to SEQ ID NOs: 43, 44, 45, 46, 47, 48, or a fragment thereof.

[0048] The term "immunogenic composition" refers to any composition containing an antigen that elicits an immune response against the antigen in a subject upon exposure to the composition. The immune response elicited by an immunogenic composition can be to a particular antigen or to a particular epitope on the antigen.

[0049] An immunogenic composition can additionally comprise an adjuvant (e.g., two or more adjuvants), a cytokine, a chemokine, or combination thereof. Optionally, an immunogenic composition can additionally comprises antigen presenting cells (APCs), which can be autologous or can be allogeneic to the subject.

[0050] The term adjuvant includes compounds or mixtures that enhance the immune response to an antigen. For example, an adjuvant can be a non-specific stimulator of an immune response or substances that allow generation of a depot in a subject which when combined with an immunogenic composition disclosed herein provides for an even more enhanced and/or prolonged immune response. An adjuvant can favor, for example, a predominantly Thl-mediated immune response, a Thl-type immune response, or a Thl- mediated immune response. Likewise, an adjuvant can favor a cell-mediated immune response over an antibody-mediated response. Alternatively, an adjuvant can favor an antibody-mediated response. Some adjuvants can enhance the immune response by slowly releasing the antigen, while other adjuvants can mediate their effects by any of the following mechanisms: increasing cellular infiltration, inflammation, and trafficking to the injection site, particularly for antigen-presenting cells (APC); promoting the activation state of APCs by upregulating costimulatory signals or major histocompatibility complex (MHC) expression; enhancing antigen presentation; or inducing cytokine release for indirect effect.

[0051] Examples of adjuvants include saponin QS21, CpG oligonucleotides, unmethylated CpG-containing oligonucleotides, MPL, TLR agonists, TLR4 agonists, TLR9 agonists, Resiquimod®, imiquimod, cytokines or nucleic acids encoding the same, chemokines or nucleic acids encoding same, IL- 12 or a nucleic acid encoding the same, IL-6 or a nucleic acid encoding the same, and lipopolysaccharides. Another example of a suitable adjuvant is Montanide ISA 51. Montanide ISA 51 contains a natural

metabolizable oil and a refined emulsifier. Other examples of a suitable adjuvant include granulocyte/macrophage colony- stimulating factor (GM-CSF) or a nucleic acid encoding the same and keyhole limpet hemocyanin (KLH) proteins or nucleic acids encoding the same. The GM-CSF can be, for example, a human protein grown in a yeast (S. cerevisiae) vector. GM-CSF promotes clonal expansion and differentiation of hematopoietic progenitor cells, antigen presenting cells (APCs), dendritic cells, and T cells.

[0052] Yet other examples of adjuvants include growth factors or nucleic acids encoding the same, cell populations, Freund' s incomplete adjuvant, aluminum phosphate, aluminum hydroxide, BCG (bacille Calmette-Guerin), alum, interleukins or nucleic acids encoding the same, quill glycosides, monophosphoryl lipid A, liposomes, bacterial mitogens, bacterial toxins, or any other type of known adjuvant (see, e.g., Fundamental Immunology, 5th ed. (August 2003): William E. Paul (Editor); Lippincott Williams & Wilkins Publishers; Chapter 43: Vaccines, GJV Nossal, which is herein incorporated by reference in its entirety for all purposes).

[0053] An immunogenic composition can further comprise one or more

immunomodulatory molecules. Examples include interferon gamma, a cytokine, a chemokine, and a T cell stimulant. [0054] An immunogenic composition can be in the form of a vaccine or

pharmaceutical composition. The terms "vaccine" and "pharmaceutical composition" are interchangeable and refer to an immunogenic composition in a pharmaceutically acceptable carrier for in vivo administration to a subject. A vaccine may be, for example, a peptide vaccine (e.g., comprising a recombinant fusion polypeptide as disclosed herein), a DNA vaccine (e.g., comprising a nucleic acid encoding a recombinant fusion polypeptide as disclosed herein), or a vaccine contained within and delivered by a cell (e.g., a attenuated bacterial cell). A vaccine may prevent a subject from contracting or developing a disease or condition and/or a vaccine may be therapeutic to a subject having a disease or condition. Methods for preparing peptide vaccines are well known and are described, for example, in EP 1408048, US 2007/0154953, and Ogasawara et al. (1992) Proc. Natl Acad Sci USA 89:8995-8999, each of which is herein incorporated by reference in its entirety for all purposes. Optionally, peptide evolution techniques can be used to create an antigen with higher immunogenicity. Techniques for peptide evolution are well known and are described, for example, in US 6,773,900, herein incorporated by reference in its entirety for all purposes.

[0055] A "pharmaceutically acceptable carrier" refers to a vehicle for containing an immunogenic composition that can be introduced into a subject without significant adverse effects and without having deleterious effects on the immunogenic composition. That is, "pharmaceutically acceptable" refers to any formulation which is safe, and provides the appropriate delivery for the desired route of administration of an effective amount of at least one immunogenic composition for use in the methods disclosed herein. Pharmaceutically acceptable carriers or vehicles or excipients are well known.

Descriptions of suitable pharmaceutically acceptable carriers, and factors involved in their selection, are found in a variety of readily available sources such as, for example,

Remington 's Pharmaceutical Sciences, 18th ed., 1990, herein incorporated by reference in its entirety for all purposes. Such carriers can be suitable for any route of administration (e.g., parenteral, enteral (e.g., oral), or topical application). Such pharmaceutical compositions can be buffered, for example, wherein the pH is maintained at a particular desired value, ranging from pH 4.0 to pH 9.0, in accordance with the stability of the immunogenic compositions and route of administration.

[0056] Suitable pharmaceutically acceptable carriers include, for example, sterile water, salt solutions such as saline, glucose, buffered solutions such as phosphate buffered solutions or bicarbonate buffered solutions, alcohols, gum arabic, vegetable oils, benzyl alcohols, polyethylene glycols, gelatine, carbohydrates (e.g., lactose, amylose or starch), magnesium stearate, talc, silicic acid, viscous paraffin, white paraffin, glycerol, alginates, hyaluronic acid, collagen, perfume oil, fatty acid monoglycerides and diglycerides, pentaerythritol fatty acid esters, hydroxy methylcellulose, polyvinyl pyrrolidone, and the like. Pharmaceutical compositions or vaccines may also include auxiliary agents including, for example, diluents, stabilizers (e.g., sugars and amino acids), preservatives, wetting agents, emulsifiers, pH buffering agents, viscosity enhancing additives, lubricants, salts for influencing osmotic pressure, buffers, vitamins, coloring, flavoring, aromatic substances, and the like which do not deleteriously react with the immunogenic composition.

[0057] For liquid formulations, for example, pharmaceutically acceptable carriers may be aqueous or non-aqueous solutions, suspensions, emulsions, or oils. Non-aqueous solvents include, for example, propylene glycol, polyethylene glycol, and injectable organic esters such as ethyl oleate. Aqueous carriers include, for example, water, alcoholic/aqueous solutions, emulsions or suspensions, including saline and buffered media. Examples of oils include those of petroleum, animal, vegetable, or synthetic origin, such as peanut oil, soybean oil, mineral oil, olive oil, sunflower oil, and fish- liver oil. Solid carriers/diluents include, for example, a gum, a starch (e.g., corn starch, pregeletanized starch), a sugar (e.g., lactose, mannitol, sucrose, or dextrose), a cellulosic material (e.g., microcrystalline cellulose), an acrylate (e.g., polymethylacrylate), calcium carbonate, magnesium oxide, talc, or mixtures thereof.

[0058] Optionally, sustained or directed release pharmaceutical compositions or vaccines can be formulated. This can be accomplished, for example, through use of liposomes or compositions wherein the active compound is protected with differentially degradable coatings (e.g., by microencapsulation, multiple coatings, and so forth). Such compositions may be formulated for immediate or slow release. It is also possible to freeze-dry the compositions and use the lyophilisates obtained (e.g., for the preparation of products for injection). II. Listing of Embodiments

[0059] The subject matter disclosed herein includes, but is not limited to, the following embodiments.

1. An immunogenic composition against Plasmodium comprising all or part of the nucleotide sequence PY17X_0721800 found in genomic location Pyl7X-07- v2: 799,281-800,081 (+) on chromosome 7 of Plasmodium yoelii or an ortholog thereof in Plasmodium falciparum or a polypeptide encoded by all or part of the nucleotide sequence PY17X_0721800 or an ortholog thereof in Plasmodium falciparum.

2. An immunogenic composition against Plasmodium comprising an immunogenic polypeptide, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 75% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof, optionally wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof.

3. The immunogenic composition of embodiment 2, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof. 4. An immunogenic composition against Plasmodium comprising all or part of the nucleotide sequence PY17X_0720100 found in genomic location Pyl7X-07- v2: 727,812-742,672 (+) on chromosome 7 of Plasmodium yoelii or an ortholog thereof in Plasmodium falciparum or a polypeptide encoded by all or part of the nucleotide sequence PY17X_0720100 or an ortholog thereof in Plasmodium falciparum. 5. An immunogenic composition against Plasmodium comprising an immunogenic polypeptide, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 75% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof, optionally wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof.

6. The immunogenic composition of embodiment 5, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof. 7. An immunogenic composition against Plasmodium comprising all or part of the nucleotide sequence PY17X_0721500 found in genomic location Pyl7X-07- v2: 784,994-791,991 (+) on chromosome 7 of Plasmodium yoelii or an ortholog thereof in Plasmodium falciparum or a polypeptide encoded by all or part of the nucleotide sequence PY17X_0721500 or an ortholog thereof in Plasmodium falciparum.

8. An immunogenic composition against Plasmodium comprising an immunogenic polypeptide, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 75% sequence identity to a sequence selected from the group consisting of: SEQ ID Nos: 31, 32, 33, 34, 35, 36, or a fragment thereof, optionally wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID Nos:

31, 32, 33, 34, 35, 36, or a fragment thereof.

9. The immunogenic composition of embodiment 8, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID Nos: 31,

32, 33, 34, 35, 36, or a fragment thereof.

10. The immunogenic composition of any one of embodiments 1 to 9, wherein the immunogenic composition comprises an adjuvant, optionally wherein the adjuvant comprises a granulocyte/macrophage colony- stimulating factor (GM-CSF) protein, a nucleotide molecule encoding a GM-CSF protein, saponin QS21,

monophosphoryl lipid A, or an unmethylated CpG-containing oligonucleotide.

11. The immunogenic composition of any one of embodiments 1 to 10, wherein the immunogenic composition is against Plasmodium falciparum.

12. An immunogenic composition for use in a method of immunizing a subject against Plasmodium, the method comprising the step of administering to the subject an immunogenic amount of the immunogenic composition of any one of embodiments 1 to 11, optionally wherein the Plasmodium is Plasmodium falciparum.

13. An immunogenic composition for use in a method of eliciting an immune response in a subject against Plasmodium, the method comprising the step of administering to the subject an immunogenic amount of the immunogenic composition of any one of embodiments 1 to 11, optionally wherein the Plasmodium is Plasmodium falciparum.

14. A method of identifying parasite genes driving medically important selectable phenotypes, comprising performing a quantitative- seq linkage group selection (qSeq-LGS) method as described herein.

15. A kit, comprising a container, wherein the container comprises at least one dose of an immunogenic composition against Plasmodium comprising an immunogenic polypeptide encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, 19, 20, 21, 22, 23, 24, 31, 32, 33, 34, 35, 36, or a fragment thereof.

III. Examples

Materials and Methods

[0060] Parasites, mice and mosquitoes

[0061] Plasmodium yoelii CU (with slow growth rate phenotype) and 17X1. lpp (with intermediate growth rate phenotype) strains were maintained in CBA mice (SLC Inc., Shizuoka, Japan) housed at 23°C and fed on maintenance diet with 0.05% para- aminobenzoic acid (PABA)-supplemented water to assist with parasite growth. Anopheles stephensi mosquitoes were housed in a temperature and humidity controlled insectary at 24°C and 70% humidity, adult flies were maintained on 10% glucose solution

supplemented with 0.05% PABA.

[0062] Testing parasite strains for growth rate and SSI

[0063] Plasmodium yoelii parasite strains were typed for growth rate in groups of mice following the intravenous inoculation of 1 x 106 iRBCs of either CU, 17X1. lpp or transfected clones per mouse and measuring parasitaemia over 8-9 days. In order to verify the existence of SSI between the CU and 17X1. lpp strains, groups of five mice were inoculated intravenously with 1 x 106 iRBCs of either CU or 17X1. lpp parasite strains. After four days, mice were treated with mefloquine (20mg/kg/per day, orally) for four days to remove infections. Three weeks post immunization, mice were then challenged intravenously with 1 x 106 iRBCs of a mixed infection of 17X1. lpp and CU parasites. A group of five naive control mice was simultaneously infected with the same material. After four days of growth 10 μΐ of blood were sampled from each mouse and DNA extracted. [0064] Strain proportions were then measured by Quantitative Real Time PCR using primers designed to amplify the mspl gene. All measurements were plotted and standard errors calculated using the Graphpad Prism software (v6.01)

(http://www.graphpad.com/scientific-software/prism/). Wilcoxon rank sum tests with continuity corrections were used to measure the SSI effect, and were performed in R.

Linear mixed model analyses and likelihood ratio tests to test parasite strain differences in growth rate were performed on log-transformed parasitaemia by choosing parasitaemia and strain as fixed factors and mouse nested in strain as a random factor, as described previously. Pair-wise comparisons of samples for the transfection experiments were performed using multiple 2-way ANOVA tests and corrected with a Tukey's post-test in Graphpad Prism software (v6.01).

[0065] Preparation of genetic cross

[0066] Plasmodium yoelii CU and 17X1. lpp parasite clones were initially grown separately in donor mice. These parasite clones were then harvested from the donors, accurately mixed to produce an inoculum in a proportion of 1: 1 and inoculated

intravenously at 1 x 106 infected red blood cells (iRBCs) per mouse into a group of CBA mice. Three days after inoculation, the presence of gametocytes of both sexes was confirmed microscopically and mice were anesthetized and placed on a mosquito cage containing -400 female A. stephensi mosquitoes six to eight days post emergence.

Mosquitoes were then allowed to feed on the mice without interruption. Seven days after the blood meal, 10 female mosquitoes from this cage were dissected to examine for the presence of oocysts in mosquito midguts. Seventeen days after the initial blood meal, the mosquitoes were dissected, and the salivary glands (containing sporozoites) were removed. The glands were placed in 0.2-0.4 mL volumes of 1: 1 foetal bovine

serum/Ringer's solution (2.7 mM potassium chloride, 1.8 mM calcium chloride, 154 mM sodium chloride) and gently disrupted to release sporozoites. The suspensions were injected intravenously into groups of CBA mice in 0.1 mL aliquots to obtain blood stage P. yoelii CU17Xl. lpp cross progeny. Three days after inoculation with sporozoites, blood stage P. yoelii CU17Xl. lpp cross progeny parasitized-RBC (pRBC) were harvested.

[0067] Two independent genetic crosses between CU and 17X1. lpp were produced. In the first cross, 150 mosquitoes were allowed to feed on mice inoculated 3 days previously with a 50:50 mixture of the two parental strains. Seven days later, a sub sample of mosquitoes (n=25) were dissected for oocyst detection. In this case, 90% of mosquitoes were infected, with an average burden of 87 oocysts per mosquito. Given that 50% of the oocysts are expected to be the products of selfing (i.e. CU male gametes fertilizing CU female gametes, and 17X1. lpp male gametes fertilizing 17x1. lpp female gametes), and that the remaining 50% of oocysts resulting from cross-strain fertilization would each produce four recombinant progeny types, we estimate that this cross resulted in the inoculation of 15,660 recombinant progeny types to recipient mice on day 21 post- mosquito feed, when 100 mosquitoes were dissected and the sporozoites removed from the salivary glands for inoculation. For the second cross, which followed the same protocol, 60% of mosquitoes were infected with an average oocyst burden of 77 oocysts per mosquito, leading to an estimated 9240 recombinants in the cross inoculation.

[0068] Selection of uncloned cross progeny for linkage group selection analysis.

[0069] For immune selection, mice immunized with blood stage parasites of either P. yoelii CU or 17X1. lpp through exposure and drug cure (as above) were inoculated intravenously with 1 x 106 parasitized-RBC (pRBC) of the uncloned cross progeny, as described above. The resulting infections were followed by microscopic examination of thin blood smears stained with Giemsa's solution.

[0070] DNA and RNA isolation

[0071] Parental strains and growth rate- or immune- selected recombinant parasites were grown in naive mice. Parasite-infected blood was passed through a single CF11 cellulose column to deplete host leukocytes, and the genomic DNA (gDNA) was isolated from the saponin- lysed parasite pellet using DNAzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. For RNA isolation, a schizont- enriched fraction was collected on a 50% Nycodenz solution (Sigma Aldrich) and total RNA was then isolated using TRIzol (Invitrogen).

[0072] Whole genome re-sequencing and mapping

[0073] Plasmodium yoelii genomic DNA was sequenced using paired end Illumina reads (100 bp), which are available at the European Nucleotide Archive (ENA:

PRJEB 15102).

[0074] The paired-end Illumina data were first quality-trimmed using Trimmomatic. Illumina sequencing adaptors were then removed from the sequences. Following that, trailing bases from both the 5' and 3' ends with less than Q20 were trimmed. Lastly, reads with an average base quality of less than Q20 within a window size of four bases were discarded. Only read pairs where both reads were retained after trimming were used for mapping with BWA version 0.6.1 using standard options onto the publicly available genome of P. yoelii 17X strain (May 2013 release; ftp://ftp.sanger.ac.uk/pub /pathogens/Plasmodium/yoeliil7X /version_2/May_2013/). The SAM alignment files were converted to BAM using Samtools. Duplicated reads were marked and removed using Picard (http://picard.sourceforge.net).

[0075] SNP calling

[0076] The Python script used to determine SNP functions as a wrapper for SAMtools mpileup and SNP calls based on mapping quality and Phred base quality scores. In this experiment the values were set at 30 for mapping quality and 20 for base quality. Also, since the P. yoelii genome is haploid and the parental strains are clonal, only SNPs where the proportion of the major non-reference allele was more than 80% were retained, to exclude possible sequencing errors or genuine but uninformative SNPs. The script produces a tab-delimited, human readable table that shows the total number of reads for each of the four possible nucleotides at each SNP. SNPS were called on both parental strains. CU SNPs were then filtered against the 17X1. lpp SNPs to remove any shared SNP calls. The remaining CU SNPs were then used as reference positions to measure the number of reads for each nucleotide in the genetic crosses produced in this study through another Python script. This script produced a final table consisting of read counts for each nucleotide of the original CU SNPs in every sample.

[0077] Mathematical Methods for the Identification of loci under Growth Rate and Immune Selection

[0078] SNP frequencies were processed to filter potential misalignment events. We note that, during the cross, a set of individual recombinant genomes are generated.

Considering the individual genome g, we define the function ag(i) as being equal to 1 if the genome has the CU allele at locus i, and equal to 0 if the genome has the 17X1. lpp allele at this locus. In any subsequent population of N individuals, the allele frequency q(i) at locus i can then be expressed as

[0079] To filter the allele frequencies, we note that each function ag(i) changes only at recombination points in the genome g. As such, q(i) should change relatively smoothly with respect to i. Using an adapted version of code developed for the inference of subclones in populations, we therefore modeled the reported frequencies q(i) as being (beta-binomially distributed) emissions from an underlying diffusion process (denoted by x(i)) along each chromosome, plus uniformly distributed errors, using a hidden Markov model to infer the variance of the diffusion process, the emission parameters, and an error rate. A likelihood ratio test was then applied to identify reported frequencies that were inconsistent with having been emitted from the inferred frequency x(i) at locus i relative to having been emitted from an inferred global frequency distribution fitted using the

Mathematica package via Gaussian kernel estimation to the complete set of values x(i); this test filters out reported frequencies potentially arising from elsewhere in the genome.

[0080] Next, the above logic was extended to filter out clonal growth. In the event that a specific genome g is highly beneficial, this genome may grow rapidly in the population, such that ng becomes large. Under such circumstances the allele frequency q(i) gains a step-like quality, mirroring the pattern of ag(i). Such steps may potentially mimic selection valleys, confounding any analysis. As such, a jump-diffusion variant of the above hidden Markov model was applied, in which the allele frequency can change either through a diffusion process or via sudden jumps in allele frequency, modeled as random emissions from a uniform distribution on the interval [0, 1] . For each interval + 1) the probability that a jump in allele frequency had occurred was estimated. Where potential jumps were identified, the allele frequency data were split, such that analyses of the allele frequencies did not span sets of alleles containing such jumps. The resulting segments of genome were then analyzed under the assumption that they were free of allele frequency change due to clonal behavior.

[0081] Inference of the presence of selected alleles was performed using a series of methods. In the absence of selection in a chromosome, the allele frequency is likely to remain relatively constant across each chromosome. A 'non-neutrality' likelihood ratio test was applied to each contiguous section of genome, calculating the likelihood difference between a model of constant frequency x( i) and the variable frequency function x( i) inferred using the jump-diffusion model. Next, an inference was made of the position of the allele potentially under selection in each region. Under the assumptions that selection acts for an allele at locus i, and that the rate of recombination is constant within a region of the genome, previous work on the evolution of cross populations can be extended to show that the allele frequencies within that region of the genome at the time of sequencing are given by

for each locus j not equal to i, where X is the CU allele frequency at the time of the cross, p is the local recombination rate, is the distance between the loci i and j, x is an allele

frequency, and Ax describes the effect of selection acting upon alleles in other regions of the genome. A likelihood-based inference was used to identify the locus at which selection was most likely to act. In regions for which the 'non-neutrality' test produced a positive result for data from both replica crosses, and for which both the inferred locus under selection, and the direction of selection acting at that locus were consistent between replicas, an inference of selection was made.

[0082] For regions in which an inference of selection was made, an extended version of the above model was applied, in which the assumption of locally constant

recombination rate was relaxed. Successive models, including an increasing number of step-wise changes in the recombination rate, were applied, using the Bayesian Information Criterion for model selection. A model of selection at two loci within a region of the genome was also examined. Given an inference of selection, a likelihood-based model was used to derive confidence intervals for the position of the locus under selection.

[0083] Filtering of allele frequency data: diffusion model

[0084] Allele frequency data were filtered using a likelihood ratio in an effort to remove sites where alleles had been mapped to the wrong genomic location. Given the structure of the genetic cross, the allele frequency is expected to change incrementally with small changes in genetic location. We therefore generated a smoothed representation of the underlying allele frequencies. For each genetic locus i, with read depth Ni, we denote the read count of CU alleles by m, and the true underlying CU allele frequency by x We then suppose that, with some probability 1 - r, m was drawn from a beta-binomial distribution Beta(7V,, a, β), where a = cxi, and β = c(l - x_i), for some unknown parameter c, while with probability r, m resulted from a mapping error, being drawn from the uniform distribution U 0, N). We further supposed that changes in the true allele frequencies between nearby loci are small, being represented by a diffusion process:

in which the difference between subsequent allele frequencies is normally distributed with zero mean and standard deviation proportional to s times the square root of the distance between the segregating sites (reflecting boundaries were used to keep within the

interval [0,1]). Given this model, a forward-backward algorithm was used to identify maximum likelihood values for r, c, and s. Our algorithm gave a posterior distribution for each of the xi; we calculated the mean of this distribution to obtain approximations , for each locus.

[0085] A likelihood ratio test was then applied to exclude frequencies of alleles that were likely to have been mapped to the wrong location in the genome. Expressed in terms of the above parameters, the likelihood L\ that an allele frequency belonged to the genomic region with which it had been associated was estimated as

where B(a,b) is the beta function. In contrast to this, a mismapped read could arise from anywhere in the genome. Using the Mathematica software package, a smooth kernel distribution was fitted to the set {¾·}, of all observed frequencies genome-wide, obtaining the probability density function P for this distribution. The likelihood L2 was then calculated as

[0086] Data from loci for which the log ratio log in at least one of the

replicates were excluded from further analysis in all datasets.

[0087] Particular care was taken with alleles mapped to regions at the ends of chromosomes. Firstly, small sets of isolated allele frequencies, occurring at the ends of chromosomes, were excluded from the analysis. Loci within each chromosome were partitioned into subsets, separated by gaps of at least 20kb in which no SNPs were observed. Subsets of fewer than 10 isolated loci at the ends of chromosomes were removed from the data. [0088] Jump diffusion analysis

[0089] From visual inspection of the data, occasional apparent discontinuities were seen, at which the observed allele frequency changed substantially between adjacent SNPs. These jumps could occur either from the growth of a clone, or clones, with near- identical genomes, in the experimental population, or alternatively through some gross misalignment of data, whereby regions some distance apart in the genome were placed together.

[0090] The location of significant jumps in the allele frequency was inferred by modeling the observed data as being generated by a jump-diffusion process, fitting a set of frequencies xi to the observations which change either smoothly, according to a diffusion model as described above, or through sudden changes to different, arbitrary frequencies. Specifically, xi was modeled as changing via the equations

with probability with probability ,

where the value p represents the probability per base of a jump in allele frequency.

Parameters were inferred as above, with the addition of the value p. The beta-binomial coefficient c was fixed as the value inferred for each dataset from the previous calculation.

Due to the earlier filtering steps, applied above, the inferred error rate r was less than 10"10 for each set of allele frequencies, so was removed from the model. For each locus i the posterior probability pi that a jump occurred at i was calculated.

[0091] Loci with posterior jump probabilities greater than 1% are listed in Figure 7. Three of these loci, towards the ends of chromosomes, were conserved between replicates, being seen in both of the 17X-immunised datasets, a jump in chromosome XIV being observed in both naive replicates as well. Such consistency in the location of jumps between replica experiments is highly improbable if they occur independently; we supposed these jumps to result from misalignment errors, or errors in the genome reference sequence. Alleles further towards the end of each chromosome than these jumps were removed from consideration in all datasets.

[0092] Other loci at which jumps were inferred were only seen in the first replicate experiment, primarily in the 17X-immunised data, but also in the naive dataset. This result is consistent with the existence of clonal growth in the first replica experiment, some of it occurring before the separation of parasite populations into naive and selected groups. The reduced number of jumps in the naive and CU-immunised cases may be explained by a difficulty in inference; due to pervading selection for 17X alleles, the mean allele frequency in these two populations is generally close to 0, reducing the magnitude of observed jumps in frequency.

[0093] In order to fit models of continuous allele frequency change to the observed frequency data, chromosomes were subdivided into smaller regions at the location of potential jumps, such that the frequencies within each region under analysis changed in a continuous manner.

[0094] Likelihood models

[0095] Regions of the genome containing alleles under selection were identified using a likelihood-based modeling framework. Given a model M describing allele frequencies after selection, the model parameters were optimised to identify the maximum likelihood fit between the model, and the observed frequencies in a genomic region, using the noise model learnt in the diffusion model above:

[0096] In order to distinguish between likelihoods generated from models with differing numbers of parameters, the Bayesian Information Criterion (BIC) was used. For a given model fit to the data, the BIC value is given by

where k is the number of model parameters, and n is the number of loci to which the model was fitted. In any comparison between models, the model giving the lowest BIC value was selected.

[0097] A variety of models were applied, modeling changes in the allele frequency over time between the beginning of the experiment and the time of sequencing. A neutral model assumed that no alleles were under selection. A single driver model (SD) assumed that a single allele, or "driver" within the region was under selection. These standard models assumed a locally-constant recombination rate; extensions of the single-driver model allowed for one (SDR), two (SD2R), or three (SD3R) changes in recombination rate within the local region. Further comparison was made to the jump-diffusion (J-D) model described above, in which a smooth line was fitted directly to the allele frequencies; the jump-diffusion model is by its definition a very good fit to the data.

[0098] Identification of non-neutral regions of the genome

[0099] Non-neutral regions of the genome were identified according to two

characteristics. Firstly, we note that, if no alleles in a given region of the genome are under selection, the allele frequencies in this region may still change during the

experiment, due to selection acting upon pure genotypes during the cross, but will do so in a uniform way, plus noise. However, if a single allele is under selection, this will result in local variation in the observed allele frequencies, according to the pattern of a selective sweep. As such, regions of the genome were tested for deviation from neutrality;

comparing the log likelihoods generated by the neutral and J-D models. The ^non- neutrality score" S for a region of the genome g taken from replica r, was defined as

where division of the likelihood difference by ng, the number of loci in the region g, normalises the score per locus.

[0100] In order to identify candidate alleles under selection, the sum of the non-neutrality scores from both replicas was calculated for each region of the genome,

ranking the results by this score, and retaining regions for which both were

greater than 0.1 (Figure 8). Next, the SD model was fitted to the allele frequency data, identifying a putative locus under selection. Regions for which the driver alleles identified within both replicas were within 200kb, and for which the direction of selection was consistent between the two replicas, were retained for further investigation. On this basis, six regions of the genome were retained.

[0101] Retained regions were analysed using successively more complex models of recombination, allowing for increasing numbers of changes in the recombination rate, and performing model selection using BIC. Under this approach, the distance between candidate alleles in the two replicas narrowed, from a mean of 87 kb to just over 17 kb (Figure 13). The candidate region in chromosome IV, however, was identified as a false positive of the previous method, the SDR model suggesting selection for alleles from different parents in the two replica datasets; this region was excluded from further analysis. Increasingly complex models of recombination change were fitted to the data using BIC for model selection. Calculated BIC values are shown in Figure 14, with local inferences of recombination rate given in Figure 15.

[0102] Confidence intervals for allele locations

[0103] Confidence intervals for the location of each inferred selected were found by calculating likelihoods for models in which the location of the selected allele was fixed. Regions of the genome for which the calculated model likelihood was consistently within 3 log likelihood units of the maximum log likelihood were derived, corresponding roughly to a 99% confidence interval.

[0104] A first confidence interval was generated in this manner by forcing the location of the selected allele to be consistent between the two replicates, and calculating the sum of the model log likelihoods for the two replicates. Allowing for the potential effects of biological noise in the data, a second, more conservative interval was also generated, representing the span of alleles for which the likelihood calculated in either replicate was within 3 log likelihood units of the maximum; this second interval becomes large when data in either one of the two experiments is ambiguous about the allele location.

Confidence intervals are illustrated in Figure 3 of the main text.

[0105] Mathematical models of allele frequency change

[0106] For convenience, we denote the 17X allele at any locus as 1, and the CU allele as

0. Thus, at a given locus i we denote the frequency of the 17X allele, as and the

frequency of the CU allele as x . Given a set of two loci, i and j, we denote the frequency ab

of individuals with allele a at locus i and allele b at locus /' as x .., where a and b are either O or 1.

[0107] We assume that, before the cross occurs, changes in the frequency of the CU and 17X malaria types may occur due to selection upon one type or another. At the time of the cross, we assume that the frequency of 17X types is equal to some value, X, where 0 < X <

1. Following the cross, the population comprises a fraction X2 of pure 17X individuals, (1- X)2 pure CU individuals, and 2X(l-X) individuals which have undergone crossing.

Subsequent selection can change both the fraction of pure types in the population, and the composition of the crossed individuals.

[0108] Neutral model

[0109] The neutral model assumes that a given region of the genome does not contain an allele under selection. Under this model, over the course of time, allele frequencies in the region can change, but only due to selection upon pure types acting at alleles elsewhere in the genome. In consequence, the allele frequencies are expected to remain uniform across the region. We describe the allele frequencies as

learning the value of the frequency parameter x.

[0110] Single driver model

[0111] Given a region of the genome, we suppose that the allele 1 at locus i is under selection, with strength σ (which may be positive or negative).

[0112] We denote the time of the cross as tc. Following the cross, the selected allele is modeled as changing frequency deterministically according to the equation

[0113] We denote the frequency of this allele at the time of observation as

[0114] Between tc and t0, the frequency of an allele j≠ i, while not itself under selection, will change via linkage disequilibrium with the allele at i, as described by the equation

[0115] To calculate the haplotype frequencies we consider separately

the pure and crossed genotypes. The pure genotypes contribute a frequency X2 towards the frequency but make no contribution to the frequency Considering allele

frequencies among the crossed fraction of the population, we denote the frequency

of the allele 1 at the locus i within the crossed individuals alone. Following the cross, we have that

where p is the rate of recombination per site per generation, z/y is the sequence length between the loci i and j, and D is the linkage disequilibrium between alleles at i and j before the cross. Assuming that no selection took place during the crossing procedure, we have

[0116] Furthermore, the mating process involves equal numbers of pure types, so that D'y = 0.25. We thus have the result

and, combining the cross and pure types, [0117] In a similar manner, we obtain the result

so that

[0118] Combining these terms, and remembering tha

derive the equation

[0119] We add to this one other term, e, denoting the effect of selection acting upon loci in other chromosomes upon the frequencies of the pure genotypes, obtaining the final model

where x is equivalent to in the model above. To specify the model, it is sufficient to

learn the parameters i, X, p and e, where i denotes a locus in the given genomic region, 0

[0120] Single-driver with variable recombination rate

[0121] The models above assume that the rate of recombination during the cross is constant within each chromosome. However, where the rate of recombination is variable, such an assumption can lead to incorrect placement of the locus under selection. We therefore developed a hierarchy of SD models, allowing for variable recombination rate. In the k^ such model, we learnt k recombination rates pi, ... , pk ,and k - 1 loci, ipi, ... , ipk-i, such that, where ipo and ipk are defined as the first and last loci in the genomic region, the recombination rate between locus iPj and iPj+i was equal to ip+i. Mathematically, such a model is identical to the SD model described above, except that the term pAij, describing the breakage in linkage disequilibrium between loci i and j, is replaced by the sum

where pr± is the recombination rate between the alleles We

denote the SD model with one change of recombination rate as the SDR model, the SD model with two changes of recombination rate as the SD2R model, and so forth.

[0122] Information on genes in identified loci under selection

[0123] For each combined conservative interval of relevant loci under selection, genes were listed based on the annotation available in version 6.2 of PlasmoDB and verified against the current annotation (release 26). For each gene, information on predicted transmembrane domains, signal peptides and P. falciparum orthologues. For the P.

falciparum orthologues, the NS/S SNP ratios were obtained from PlasmoDB, based on the count of synonymous and non-synonymous SNPs found in 202 individual strains collected from 6 data sets stored on the website. More details on the data sets can be found at the following link: https://goo.gl/lUwKnl .

[0124] Plasmid construction to modify P. yoelii ebl gene locus

[0125] All primer sequences are given in Figurel9. Plasmids were constructed using MultiSite Gateway cloning system (Invitrogen). [0126] PCR amplification and sequencing of the Pyebl gene

[0127] The Pyebl gene was PCR-amplified from gDNA using KOD Plus Neo DNA polymerase (Toyobo, Japan) with specific primers designed based on the ebl sequence in PlasmoDB (PY17X_1337400). Pyebl sequences of CU and 17X1. lpp strains were determined by direct sequencing using an ABI PRISM 310 genetic analyzer (Applied

Biosystems) from PCR-amplified products. Sequences were aligned using online sequence alignment software Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) provided by EMBL-EBI.

[0128] Plasmid construction to modify the Pyebl locus

[0129] attB-flanked ebl gene products, attB 12-PyCU-EBL.ORF and attB 12-Pyl7Xl. lpp- EBL.ORF, were generated by PCR-amplifying both P. yoelii CU and P. yoelii 17X1. lpp ebl gene with yEBL-ORF.B lF and yEBL-ORF.B2R primers. attB-flanked ebl-3V (attB41- PyCU-EBL-3U and attB41-Pyl7Xl. lpp-EBL-3U) was similarly generated by PCR- amplifying P. yoelii gDNA with yEBL-3U.B4F and yEBL-3U.B lR primers. attB 12- PyCU-EBL.ORF and attB 12-Pyl7Xl . lpp-EBL.ORF were then subjected to a separate BP recombination with pDONR 221 (Invitrogen) to yield entry plasmids, pENT12-PyCU- EBL.ORF and pENT12-Pyl7Xl. lpp-EBL.ORF, respectively. attB41-PyCU-EBL-3U and attB41-Pyl7Xl. lpp-EBL-3U fragments were also subjected to independent BP

recombination with pDONR P4-P1R (Invitrogen) to generate pENT41-PyCU-EBL-3U and pENT41-Pyl7Xl. lpp-EBL-3U, respectively.

[0130] All BP reactions were performed using the BP Clonase II enzyme mix (Invitrogen) according to the manufacturer's instructions. To change P. yoelii CU ebl gene nucleotide 1052G to 1052A (351Cys to 351Tyr), pENT12-PyCU-EBL.ORF entry clone was modified using KOD-Plus-Mutagenesis Kit (TOYOBO) with primers Pl.F and Pl.R to yield pENT12-PyCU-EBL.ORF-C351Y. pENT12-Pyl7Xl. lpp-EBL.ORF was also modified from 1052A to 1052G (351Tyr to 351Cys) using primers P2.F and Pl.R to yield pENT12-Pyl7Xl. lpp-EBL.ORF- Y351C. pHDEFl-mh that contains a pyrimethamine resistant gene selection cassette (a gift from Hernando del Portillo) was digested with Smal and Apal to remove PfHRP2 3' UTR DNA fragment, cohesive end was blunted, and a DNA fragment containing ccdB-R43 cassette and P. berghei DHFR-TS 3' UTR that was amplified from pCHD43(II) with primers M13R.F3F and PbDT3U.F3R was ligated to generate pDST43-HDEF-F3. pENT12-PyCU-EBL.ORF-C351Y and pENT12- Pyl7Xl. lpp-EBL.ORF- Y351C entry plasmids were each separately subjected to LR recombination reaction (Invitrogen) with a destination vector pDST43-HDEF-F3, pENT41-PyCU-EBL-3U or pENT41-Pyl7Xl. lpp-EBL-3U and a linker pENT23-3Tyl vector to yield replacement constructs pREP-PyCU-EBL-C351Y and pREP-Pyl7Xl. lpp- EBL-Y351C, respectively. Control constructs pREP-PyCU-EBL-C351C and pREP- Pyl7Xl. lpp-EBL-Y351Y were also prepared in a similar manner. These LR reactions were performed using the LR Clonase II Plus enzyme mix (Invitrogen) according to the manufacturer's instructions.

[0131] Phenotype analysis

[0132] To assess the course of infection of wild type and transgenic parasite lines, 1 x 106 pRBCs were injected intravenously into five 8-week old female CBA mice for each parasite line. Since the 17X1. lp and CU-recipient strains were transfected on separate occasions, the transgenic lines were tested separately. Thin blood smears were made daily, stained with Giemsa's solution, and parasitaemias were examined microscopically.

[0133] RNA-seq

[0134] Whole blood from mice infected with P. yoelii on day 5 post- infection were host WBC depleted and saponin lysed to obtain the parasite pellet. Total RNA was extracted using TRIzol reagent. Strand- specific RNA sequencing was performed from total RNA using TruSeq Stranded mRNA Sample Prep Kit LT according to manufacturer's instructions. Libraries were sequenced on an Illumina HiSeq 2000 with paired-end lOObp read chemistry and are publicly available at the European Nucleotide Archive (ENA: PRJEB 15102). RNA-seq reads were mapped onto P. yoelii 17X version 2 from GeneDB (http://www.genedb.org) using TopHat 2.0.13 and visualized using Artemis genome visualization tool.

[0135] Indirect immunofluorescence assay

[0136] Schizont-rich whole blood was obtained from P. yoelii infected mouse tail and prepared air-dried thin smears on glass slides. The smears were fixed in 4%

paraformaldehyde containing 0.0075% glutaraldehyde (Nacalai Tesque) in PBS at room temperature (RT) for 15 min, rinsed with 50 mM glycine (Wako) in PBS. Samples were permeabilized with 0.1% Triton X-100 (Calbiochem) in PBS for 10 min, then blocked with 3% BSA (Sigma) in PBS at RT for 30 min. Next, samples were immunostained with primary antibodies using mouse anti-PyEBL (final 1:500) and Rabbit anti-PyAMAl (a gift from Takafumi Tsuboi, final concentration 1:500) at 37°C for 1 h. This was followed by 3 washes with PBS then incubation with Alexa Fluor 488 goat anti-mouse and Alexa Fluor 594 goat anti-rabbit antibodies (Invitrogen; final 1: 1000) in 3% BSA in PBS at 37°C for 30 min. Parasite nuclei were stained with 4', 6-diamidino-2-phenylindole (DAPI; Invitrogen, final 0.2 /g/mL). Stained parasites were mounted with Prolong Gold antifade reagent (Invitrogen). Slides were visualized using a fluorescence microscope (Axio imager Z2; Carl Zeiss) with lOOx oil objective lens (NA 1.4, Carl Zeiss). Images were captured using a CCD camera (AxioCam MRm; Carl Zeiss) and imaged using Axio Vision software (Carl Zeiss). Mann- Whitney U tests were performed using Graphpad Prism software (v6.01).

[0137] Structural modeling of PyEBL protein in wild-type and mutant parasites

[0138] Since the atomic structures of EBL protein of P. yoelii Wild Type: (Pyl7X-WT) and its mutant P. yoelii (C351Y): (Pyl7Xl. lpp) are not known, homology models were generated. The homology models were generated using P. vivax Duffy Binding Protein (PvDBP) atomic structure (PDB ID: 3RRC), with the Swiss-Model server

(https://swissmodel.expasy.org). The homology models showed maximum amino acid sequence homology of 32% with Pyl7X-WT EBL, compared to another homologous protein P. falciparum Erythrocyte Binding Antigen 140 (PfEB A- 140/B AEB L) (PDB ID: 4GF2) that had 26% sequence homology. These models were then subsequently stabilized by minimizing their energies for at least 10 times each, to attain reasonably well equilibrated structures using the YASARA server (www.yasara.org).

[0139] The prediction of disulfide bonds in our homology models were performed using DISULFIND (http://disulfind.dsi.unifi.it). Our analysis showed high probability of disulfide bond formation by this Cys351 residue. Confirming that C351 is a potential residue for forming a disulfide bond, the energy minimized stable homology models were subjected to Disulfide bond visualization to check whether the Cys351 is involved in any disulfide bond formation with any other Cys and what is the effect of the C351Y substitution.

[0140] The homology models along with their disulfide bonds were visualized

(Fig 4B and Fig 4C) and the images were obtained using the "Disulfide by Design 2.0" server (http : //cpt web . cp t . w ay ne . edu ) .

Example 1: Identifying Candidate Genes

[0141] The development of LGS has facilitated functional genomic analysis of malaria parasites over the past decade. In particular, it has simplified and accelerated the detection of loci underlying selectable phenotypes such as drug resistance, SSI and growth rate. Here we present a radically modified LGS approach that utilizes deep, quantitative WGS of parasite progenies and the respective parental populations, multiple crossing and mathematical modeling to identify loci under selection at ultra-high resolution. This enables the accurate definition of loci under selection and the identification of multiple genes driving selectable phenotypes within a very short space of time. This modified approach allows the simultaneous detection of genes or alleles underlying multiple phenotypes, including those with a multigenic basis.

[0142] Applying this modified LGS approach to study SSI and growth rate in P. yoelii, we identified three loci under selection that contained three strong candidate genes controlling both phenotypes. Two loci were implicated in SSI; the first time LGS has identified multigenic drivers of phenotypic differences in malaria parasites in a single experimental set-up. The strong locus under selection in Chr VIII, associated with the gene encoding MSP1, is consistent with existing knowledge of malaria immunity. The Chr VII locus, which includes the orthologue of Pf34 as well as other potential unannotated antigens, underscores the power for hypothesis generation and gene detection of the LGS approach using multiple crosses.

[0143] Our approach also provided a genetic rationale for the difference in growth rate of the parental clones CU and 17X1. Ipp. Phenotypically, this occurs due to the ability of 17X1. Ipp to invade both reticulocytes and normocytes, while CU is restricted to reticulocytes. Previously, differences in growth rates between strains of P. yoelii have been linked to a polymorphism in Region 6 of the Pyebl gene that alters its trafficking so that the protein locates in the dense granules rather than the micro nemes. In the case of 17x1. Ipp however, direct sequencing of the Pyebl gene revealed a previously unknown SNP in region 2, the predicted receptor-binding region of the protein, with no

polymorphism in region 6. Consistent with this, the EBL protein of 17X1. Ipp was shown to be located in the micronemes, indicating that protein trafficking was unaffected by the region 2 substitution. Allelic replacement of the parasite strains with the alternative allele resulted in a switching of the growth rate to that of the other clone, thus confirming the role of the substitution.

[0144] Region 2 of the Pyebl orthologues of P. falciparum and Plasmodium vivax are known to interact with receptors on the red blood cell (RBC) surface. Furthermore, the substitution falls within the central portion of the region, which has been previously described as being the principal site of receptor recognition in P. vivax. Wild-type strains of P. yoelii (such as CU) preferentially invade reticulocytes but not mature RBCs, whereas highly virulent strains are known to invade a broader repertoire of RBCs. Further structural and functional studies are required to elucidate how the polymorphism described here enables mutant parasites to invade a larger repertoire of erythrocytes than wild type parasites. We show that the cysteine residue at position 351 in EBL forms a disulphide bond with a cysteine at position 420, and that this is abolished following the C351Y substitution, altering the tertiary structure of the binding region. This leads to the possibility that such an alteration of the shape of the binding domain may enable the ligand to bind to a larger repertoire of receptors.

Characterization of strain specific immunity and growth rate phenotypic differences between CU and 17X1. lpp

[0145] The difference in blood-stage parasite growth rate between the two clones was followed in vivo for nine days in CBA mice. A likelihood ratio test using general linear mixed models indicated a more pronounced growth rate for 17X1. lpp compared to CU clone by time interaction term, L = 88.60, df = 21, p<0.0001, Fig 2A). To verify that the two malaria clones could also be used to generate protective SSI, groups of mice were immunized with 17X1. lpp, CU or mock immunized, prior to challenge with a mixture of the two clones (Figure 10). The relative proportions of the two clones were measured on day four of the infection by real time quantitative PCR (Q-RT-PCR) targeting the polymorphic mspl locus. A strong, statistically significant SSI was induced by both parasite strains in CBA mice (Fig 2B).

Identification of high-confidence SNPs

[0146] Two kinds of selection pressure were applied in this study: growth rate driven selection and SSI. Two independent genetic crosses between 17X1. lpp and CU were produced, and both these crosses were subjected to immune selection (in which the progeny were grown in mice made immune to either of the two parental clones), and grown in non- immune mice. Progeny were harvested from mice four days after challenge, at which point strain- specific immune selection in the immunized mice, and selection of faster growing parasites in the non-immune mice had occurred. Using deep sequencing by Illumina technology, a total of 29,053 high confidence genome-wide SNPs that distinguish the parental strains were produced by read mapping with custom-made Python scripts. SNP frequencies from these loci from each population were filtered using a likelihood ratio test to remove sites where alleles had been erroneously mapped to the wrong genome location. Identification of clonality within the data

[0147] A hidden Markov model was applied to the data to identify allele frequency changes (Figure 7) that were likely to have arisen from the clonal growth of individuals within the cross population or possible incorrect assembly of the reference genome, as discussed above). In a genetic cross population, an especially high fitness clone generated by random recombination events can grow to substantial frequency, this being manifested as sudden jumps in allele frequency occurring at the recombination points in this individual. Jumps of this type were primarily identified in the 17X-immunized population, where the increased virulence of the 17X strain had less of an effect in driving alleles to high frequency, and in the first replica experiment; the data in the first experiment seemed to have been more affected by clonal growth in the population. The consistency of identified jumps between treatment conditions reflects the common origin of the differently treated populations; the jump at the end of chromosome XIV inferred in both replicas may be artefactual.

Identification of loci under selection

[0148] Based upon an analytical evolutionary model describing patterns of allele frequencies following selection, a maximum likelihood approach was used to define confidence intervals for the positions of alleles under selection in each of the genetic cross populations. In the absence of selection acting for a variant in a region of the genome, the allele frequencies in that region are expected to be locally constant. In common with a previous approach to identifying selected alleles, a search was therefore made for regions of the genome in which allele frequencies varied substantially according to their position in the genome. Next, wherever deviations of this form were consistently identified in both replica experiments a model of selection was applied to the data, inferring for each set of replica data the position in that region of the genome that was most likely to be under selection; this model was based upon expected changes in allele frequency under a constant local rate of recombination and is described further in the Methods section. Regions of the genome in which this inference of selection produced consistent results across replica datasets were then identified (Figure 8). Of a total of 11 genomic regions suggesting evidence of non-neutrality, six showed sufficient evidence of consistent selection.

[0149] For each of these regions of the genome, a more sophisticated evolutionary model, accounting for variation in the local recombination rate, was then applied to the data, refining the position of the putatively selected allele. At this point, a putative selected allele in chromosome IV was removed from consideration, leaving five cases of potential alleles under selection in three regions of the genomes; confidence intervals for the positions of the selected loci are given in Figure 9. Optimal positions of variant loci derived from each replicate are detailed in Figure 13; results of the variable recombination rate model are shown in Figure 14, with inferred recombination rates in Figure 15.

[0150] Of the final three putative loci, two were detected under multiple experimental conditions (Figure 3). When considering the combined largest intervals, a selective sweep was inferred at position 1,436-1,529 kb on Chromosome (Chr) XIII in replicate crosses grown in both non- immunized mice and 17Xl. lpp-immunized mice, resulting from selection against CU-specific alleles at the target locus. A second sweep was inferred at position 1,229-1,364 kb on Chr VIII, detected in the parasite crosses grown in both CU and 17X1. lpp immunized mice, though not in the non- immunized mice. Here, selection pressure acted against different alleles according to the strain against which mice were immunized. The third sweep was detected at a locus between positions 725-814 kb on Chr VII. This event was only detected in mice replicates immunized with the 17X1. lpp strain, albeit that a consistent change in allele frequencies was also observed between replicas grown under these conditions (Fig 3B). The remaining loci (on Chrs VIII and XIII) were not consistently detected between replicates (Figure 13) and were thus considered to be non-significant.

Potential target genes within the three main loci under selection

[0151] All the genes in the combined conservative intervals of the three main loci under selection are listed in Figures 16-18, along with annotation pertaining to function, structure, orthology with P. falciparum genes and Non-synonymous/Synonomous SNP (NS/S) ratio in the P. falciparum orthologue, which is calculated by the PlasmoDB website (6.2) based on SNP data from 202 individual strains. These include both laboratory strains and field isolates obtained from six collections (see Methods for more details). The locus associated with SSI on Chr VIII contains 41 genes. We considered the presence of either transmembrane (TM) domains or a signal peptides as necessary features of potential antigen-encoding genes. Only 16 genes met these criteria. Functional annotation indicated 10 likely candidates among these; eight genes described as

"conserved Plasmodium proteins", and two encoding RhopH2 and merozoite surface protein 1 (MSP1). Of these genes, the P. falciparum orthologue of mspl had the highest NS/S SNP ratio (8.43). MSP1 is a well characterized major antigen of malaria parasites that has formed the basis of several vaccine studies and has been previously linked to SSI in Plasmodium chabaudi.

[0152] The locus under selection on Chr VII consists of 21 genes. Only seven contained TM domains and/or a signal peptide motif. Based on functional annotation, four of these could be potential targets for SSI. One of these genes, PY17X_0721800, encodes an apical membrane protein orthologous to Pf34 in P. falciparum. This protein has recently been described as a surface antigen that can elicit an immune response. Three conserved proteins of unknown function (PY17X_0720100, PY17X_0721500 and PY17X_0721600) also displayed potential signatures as target antigens. PY17X_0721800,

PY17X_0720100, PY17X_0721500 were selected as candidate genes based on their predicted immunogenicity.

[0153] The growth rate associated selected locus on Chr XIII contains 29 genes. In this case, the presence of TM domains or signal peptide motifs were not considered

informative criteria. Only eight genes contained NS SNPs between the parental strains 17X1. lpp and CU according to the WGS data. Among these was a duffy binding protein, Pyebl. Pyebl, is a gene that has been previously implicated in growth rate differences between strains of P. yoelii. A single NS SNP was predicted from the WGS data in this gene. Due to the very high likelihood of its involvement based on previous work, this gene was considered for further analysis.

Characterization of EBL as the major driver of growth rate differences through allelic replacement

[0154] Examining the Pyebl gene, Sanger capillary sequencing re-confirmed the existence in 17X1. lpp of an amino acid substitution (Cys > Tyr) at position 351 within region 2 of the encoded protein. When aligned against other P. yoelii strains and other Plasmodium species, this cysteine residue is highly conserved, and the substitution observed in

17X1. lpp was novel (Fig 4A). Crucially, no other polymorphisms were detected in the coding sequence of the gene, including in region 6, the location of the SNP previously implicated in parasite virulence in other strains of P. yoelii. Structural modeling of the

EBL protein in both wild-type and 17x1. lpp (C351Y) mutants predicted the abolition of a a disulphide bond between C351 and C420 in the mutant parasites that alters the tertiary structure of the receptor binding region of the ligand in these parasites (Figures 4B and 4C). [0155] The functional role of this polymorphism was verified by experimental means. In order to study the functional consequences of the polymorphism, the Pyebl alleles of slow growing CU and faster growing 17X1. lpp clones were replaced with the alternative allele (i.e. CU-EBL-3510Y and 7xl. lpp-EBL-351Y > C), as well as with the homologous allele (i.e. CU-EBL-3510C and 17xl. lpp-EBL-351Y>Y). The latter served as a control for the actual allelic swap, as the insertion of the plasmid for allelic substitution could potentially affect parasite fitness independently of the allele being inserted. To establish whether the C351Y substitution affected EBL localization, as was shown for the previously described region 6 mutation, Immunoflurescence Analysis (IF A) was performed. This revealed that, unlike the known mutation in region 6, the EBL proteins of 17X1. lpp and CU were both found to be located in the micronemes (Figures 5 and 11).

[0156] Transgenic clones were grown in mice for 10 days alongside wild-type clones. Pair-wise comparisons between transgenic clones with the parental allele against transgenic clones with the alternative allele (that is CU-EBL-351C>C vs CU-EBL- 3510Y and 17xl. lpp-EBL-351Y>Y vs 17xl. lpp-EBL-351Y>C) showed that allele substitution could switch growth phenotypes in both strains (Figures 6A and 6B). This confirmed the role of the C351Y mutation as underlying the observed growth rate difference.

[0157] RNA-seq analysis revealed that transfected EBL gene alleles were expressed normally, (Figure 12), thus indicating a structural effect of the polymorphism on parasite fitness, rather than an alteration in protein expression.

[0158] LGS with multiple crosses offers a powerful and rapid methodology for identifying genes or non-coding regions controlling important phenotypes in malaria parasites and, potentially, in other apicomplexan parasites. Through bypassing the need to clone and type hundreds of individual progeny, and by harnessing the power of genetics, genomics and mathematical modeling, genes can be linked to phenotypes with high precision in a matter of a few months, rather than years. Here we have demonstrated the ability of LGS to identify multiple genetic polymorphisms underlying two independent phenotypic differences between a pair of malaria parasite strains; growth rate and SSI. This methodology has the potential power to identify the genetic components controlling a broad range of selectable phenotypes, and can be applied to studies of drug resistance, transmissibility, virulence, host preference, etc., in a range of apicomplexan parasites that are amenable to genetic crossing. [0159] The applicability of the approach to human malaria species has been recently demonstrated: the original LGS approach was successfully applied to study P. falciparum immune evasion in mosquitoes in vivo, while we recently tested its

applicability in vitro to detect loci under selection following antifolate drug treatment and in vitro growth rate competition. With the advent of humanized mice that are able to support the complete malaria life cycle, the generation of new genetic crosses between strains of human malaria has become more feasible, as recently demonstrated. With the ability to maintain these crosses without the need of simian hosts, application of a broader range of selection pressures (excluding, for now, selection mediated by the presence of a complete immune response) is now more feasible in vivo, thus extending the application of the LGS approach to medically relevant malaria species.

[0160] The qSEQ-LGS method described herein enables us to quickly and more precisely identify antigens or drug/vaccine targets within the malaria parasite's genome that would be effective drug or vaccine targets.

Example 2: In Vivo Experimentation with Candidate Genes

[0161] Healthy mice are administered an immunogenic composition comprising an immunogenic polypeptide encoded by the nucleic acid sequence PY17X_0721800, PY17X_0720100, PY17X_0721500, or a fragment thereof (treatment groups).

[0162] Both treatment group and control mice will then be exposed to a malaria parasite.

[0163] It is expected that mice in the treatment group will have protective immunity against the subsequent malaria parasite challenge while control mice who did not receive the immunization will have a higher rate of malaria parasite infection.

[0164] Treatment group mice will then be rechallenged at with the malaria parasite at several time points to test the lasting effects of the protective immunity.

BRIEF DESCRIPTION OF THE SEQUENCES

[0165] The nucleotide and amino acid sequences listed in the accompanying sequence listing are shown using standard letter abbreviations for nucleotide bases, and three-letter code for amino acids. The nucleotide sequences follow the standard convention of beginning at the 5' end of the sequence and proceeding forward (i.e., from left to right in each line) to the 3' end. Only one strand of each nucleotide sequence is shown, but the complementary strand is understood to be included by any reference to the displayed strand. The amino acid sequences follow the standard convention of beginning at the amino terminus of the sequence and proceeding forward (i.e., from left to right in each line) to the carboxy terminus.

tatattcattttgtttattttttttatgactagtgcatttatatttttattaataaaattttacaaaaattggaaatattacagaaattatgaaagttt caaagaatatgatgaaacaattagtctttttgatgatgatgatatataa (SEQ ID NO: 36)

III. Conclusion

[0166] Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which the inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. BIBLIOGRAPHY

Wellems TE, Panton LJ, Gluzman IY, do Rosario VE, Gwadz, RW, Walker- Jonah A, Krogstad DJ. Chloroquine resistance not linked to mdr-like genes in a Plasmodium falciparum cross. Nature. 1990 May 17;345(6272):253-255.

Vaidya AB, Muratova O, Guinet F, Keister D, Wellems TE, Kaslow DC. A genetic locus on Plasmodium falciparum chromosome 12 linked to a defect in mosquito-infectivity and male gametogenesis. Mol Biochem Parasitol. 1995 Jan;69(l):65— 71.

Su X, Kirkman LA, Fujioka H, Wellems TE. Complex polymorphisms in an

approximately 330 kDa protein are linked to chloroquine-resistant P. falciparum in Southeast Asia and Africa.Cell. 1997 Nov 28;91(5):593-603.

Carlton J, Mackinnon M, Walliker D. A chloroquine resistance locus in the rodent malaria parasite Plasmodium chabaudi. Mol Biochem Parasitol. 1998 May 15;93(1):57— 72.

Nair S, Williams JT, Brockman A, Paiphun L, Mayxay M, Newton PN, Guthmann JP, Smithuis FM, Hien TT, White NJ, Nosten F, Anderson TJ. A selective sweep driven by pyrimethamine treatment in southeast asian malaria parasites. Mol Biol Evol. 2003 Sep;20(9): 1526-1536.

Miotto O, Amato R, Ashley EA, Maclnnis B, Almagro-Garcia J, Amaratunga C, Lim P, Mead D, Oyola SO, Dhorda M, Imwong M, Woodrow C, Manske M, Stalker J, Drury E, Campino S, Amenga-Etego L, Thanh TN, Tran HT, Ringwald P, Bethell D, Nosten F, Phyo AP, Pukrittayakamee S, Chotivanich K, Chuor CM, Nguon C, Suon S, Sreng S,

Newton PN, Mayxay M, Khanthavong M, Hongvanthong B, Htut Y, Han KT, Kyaw MP, Faiz MA, Fanello CI, Onyamboko M, Mokuolu OA, Jacob CG, Takala-Harrison S, Plowe CV, Day NP, Dondorp AM, Spencer CC, McVean G, Fairhurst RM, White NJ,

Kwiatkowski DP. Genetic architecture of artemisinin-resistant Plasmodium falciparum. Nat Genet. 2015 Mar;47(3):226-234.

CuUeton R, Martinelli A, Hunt P, Carter R. Linkage group selection: rapid gene discovery in malaria parasites. Genome Res. 2005 Jan;15(l):92— 97.

Pattaradilokrat S, CuUeton RL, Cheesman SJ, Carter R. Gene encoding erythrocyte binding ligand linked to blood stage multiplication rate phenotype in Plasmodium yoelii yoelii. Proc Natl Acad Sci U S A. 2009 Apr 28;106(17):7161-7166.

Michelmore RW, Paran I, Kesseli RV. Identification of markers linked to disease- resistance genes by bulked segregant analysis: A rapid method to detect markers in specific genomic regions by using segregating populations. PN AS. 1991 Nov;88:9828— 9832.

Martinelli A, Cheesman S, Hunt P, CuUeton R, Raza A, Mackinnon M, Carter R. A genetic approach to the de novo identification of targets of strain- specific immunity in malaria parasites. Proc Natl Acad Sci U S A. 2005 Jan 18;102(3):814-819. Cheesman S, O'Mahony E, Pattaradilokrat S, Degnan K, Knott S, Carter R. A single parasite gene determines strain- specific protective immunity against malaria: the role of the merozoite surface protein I. Int J Parasitol. 2010 Jul;40(8):951— 961.

Hunt P, Martinelli A, Modrzynska K, Borges S, Creasey A, Rodrigues L, Beraldi D, Loewe L, Fawcett R, Kumar S, Thomson M, Trivedi U, Otto TD, Pain A, Blaxter M, Cravo P. Experimental evolution, genetic analysis and genome re-sequencing reveal the mutation conferring artemisinin resistance in an isogenic lineage of malaria parasites. BMC Genomics. 2010 Sep 16;11:499.

Blake DP, Billington KJ, Copestake SL, Oakes RD, Quail MA, Wan KL, Shirley MW, Smith AL. Genetic mapping identifies novel highly protective antigens for an

apicomplexan parasite. PLoS Pathog. 2011 Feb 10;7(2):el001279.

Ehrenreich IM, Torabi N, Jia Y, Kent J, Martis S, Shapiro JA, Gresham D, Caudy AA, Kruglyak L. Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature. 2010 Apr 15;464(7291): 1039-42.

Wolyn DJ, Borevitz JO, Loudet O, Schwartz C, Maloof J, Ecker JR, Berry CC,Chory J. Light-response quantitative trait loci identified with composite interval and eXtreme array mapping in Arabidopsis thaliana. Genetics. 2004 Jun;167(2):907-17.

Wenger JW, Schwartz K, Sherlock G. Bulk segregant analysis by high-throughput sequencing reveals a novel xylose utilization gene from Saccharomyces cerevisiae. PLoS Genet. 2010 May 13;6(5):el000942.

Ehrenreich IM, Bloom J, Torabi N, Wang X, Jia Y, Kruglyak L. Genetic architecture of highly complex chemical resistance traits across four yeast strains. PLoS Genet.

2012;8(3):el002570.

Modrzynska K, Creasey A, Loewe L, Cezard T, Trindade Borges S, Martinelli A, Rodrigues L, Cravo P, Blaxter M, Carter R, Hunt P. Quantitative genome re-sequencing defines multiple mutations conferring chloroquine resistance in rodent malaria. BMC Genomics. 2012 Mar 21;13: 106.

lUingworth CJ, Parts L, Schiffels S, Liti G, Mustonen V. Quantifying selection acting on a complex trait using allele frequency time series data. Mol Biol Evol. 2012

Apr;29(4): 1187-1197.

lUingworth CJ, Mustonen V. Quantifying selection in evolving populations using time- resolved genetic data. Journal of Statistical Mechanics: Theory and Experiment. 2013: P01004.

Edwards MD, Gifford DK. High-resolution genetic mapping with pooled sequencing. BMC Bioinformatics. 2012 Apr 19;13 Suppl 6:S8.

Abkallo HM, Tangena JA, Tang J, Kobayashi N, Inoue M, Zoungrana A, Colegrave N, Culleton R. Within-host competition does not select for virulence in malaria parasites; studies with Plasmodium yoelii. PLoS Pathog. 2015 Feb 6;l l(2):el004628. Vazquez-Garcia I, Salinas F, Li J, Fischer A, Barre B, Hallin J, Bergstrom A, Alonso- Perez E, Warringer J, Mustonen V, Liti G. Background-dependent effects of selection on subclonal heterogeneity. Preprint at bioRxiv, https://doi.org/10.1101/039859.

Holder, AA. The carboxy-terminus of merozoite surface protein 1: structure, specific antibodies and immunity to malaria. Parasitology. 2009 Oct;136(12): 1445— 1456.

Proellocks NI, Kovacevic S, Ferguson DJ, Kats LM, Morahan BJ, Black CG, Waller KL, Coppel RL. Plasmodium falciparum Pf34, a novel GPI-anchored rhoptry protein found in detergent-resistant microdomains. Int J Parasitol. 2007 Sep;37(l l): 1233— 1241.

Otsuki H, Kaneko O, Thongkukiatkul A, Tachibana M, Iriko H, Takeo S, Tsuboi T, Torii M. Single amino acid substitution in Plasmodium yoelii erythrocyte ligand determines its localization and controls parasite virulence. Proc Natl Acad Sci U S A. 2009 Apr

28;106(17):7167-7172.

Sim BK, Chitnis CE, Wasniowska K, Hadley TJ, Miller LH. Receptor and ligand domains for invasion of erythrocytes by Plasmodium falciparum. Science. 1994 Jun

24;264(5167): 1941-1944.

Mayer DCG, Kaneko O, Hudson-Taylor DE, Reid ME, Miller LH. Characterization of a Plasmodium falciparum erythrocyte binding protein paralogous to EBA-175. Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5222-5227.

Van Buskirk KM, Sevova E, Adams JH. Conserved residues in the Plasmodium vivax Duffy-binding protein ligand domain are critical for erythrocyte receptor recognition. Proc Natl Acad Sci U S A. 2004 Nov 2;101(44): 15754-15759.

CuUeton R, Kaneko O. Erythrocyte binding ligands in malaria parasites: intracellular trafficking and parasite virulence. Acta Trop. 2010 Jun;114(3): 131— 137.

Molina-Cruz A, Garver LS, Alabaster A, Bangiolo L, Haile A, Winikor J, Ortega C, van Schaijk BC, Sauerwein RW, Taylor-Salmon E, Barillas-Mury C. The human malaria parasite Pfs47 gene mediates evasion of the mosquito immune system. Science. 2013 May 24;340(6135):984-7.

Vaughan AM, Pinapati RS, Cheeseman IH, Camargo N, Fishbaugher M, Checkley LA, Nair S, Hutyra CA, Nosten FH, Anderson TJ, Ferdig MT, Kappe SH. Plasmodium falciparum genetic crosses in a humanized mouse model. Nat Methods. 2015

Jul;12(7):631-3.

Inoue M, Tang J, Miyakoda M, Kaneko O, Yui K, CuUeton R. The species specificity of immunity generated by live whole organism immunisation with erythrocytic and pre- erythrocytic stages of rodent malaria parasites and implications for vaccine development. Int J Parasitol. 2012 Aug;42(9):859-70.

Abkallo HM, Liu W, Hokama S, Ferreira PE, Nakazawa S, Maeno Y, Quang NT, Kobayashi N, Kaneko O, Huffman MA, Kawai S, Marchand RP, Carter R, Hahn BH, CuUeton R. DNA from pre-erythrocytic stage malaria parasites is detectable by PCR in the faeces and blood of hosts. Int J Parasitol. 2014 Jun;44(7):467-473. Team RDC. R: A language and environment for statistical computing. 2014:

http ://www . R-project . org/.

Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014 Aug 1;30(15):2114-2120.

Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009 Jul 15;25(14): 1754~1760.

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence

Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-2079. Fischer A, Vazquez-Garcia I, Illingworth CJ, Mustonen V. High-Definition

Reconstruction of Clonal Composition in Cancer. Cell Rep. 2014 Jun 12;7(5): 1740- 1752.

Cingolani P, Platts A, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain wl l l8; iso-2; iso-3. Fly. 2012 6(2):90-92.

Schwarz G Estimating the Dimension of a Model. The Annals of Statistics. 1978; 6: 461— 464.

Fernandez-Becerra C, de Azevedo MF, Yamamoto MM, del Portillo HA. Plasmodium falciparum: new vector with bi-directional promoter activity to stably express transgenes. Exp Parasitol. 2003 Jan-Feb;103(l-2):88-91.

Sakura Tl, Yahata K, Kaneko O. The upstream sequence segment of the C-terminal cysteine-rich domain is required for microneme trafficking of Plasmodium falciparum erythrocyte binding antigen 175. Parasitol Int. 2013 Apr;62(2): 157-164.

Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013 Apr 25;14(4):R36.

Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B.

Artemis: sequence visualization and annotation. Bioinformatics. 2000 Oct;16(10):944— 945.

Mutungi JK, Yahata K, Sakaguchi M, Kaneko O. Expression and localization of rhoptry neck protein 5 in merozoites and sporozoites of Plasmodium yoelii.. Parasitol Int. 2014 Dec;63(6):794-801.

Batchelor JD1, Zahm JA, Tolia NH. Dimerization of Plasmodium vivax DBP is induced upon receptor binding and drives recognition of DARC. Nat Struct Mol Biol. 2011 Jul 10;18(8):908-914.

Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Gallo Cassarino T, Bertoni M, Bordoli L, Schwede T. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W252-258.

Kiefer F, Arnold K, Kiinzli M, Bordoli L, Schwede T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res. 2009 Jan;37(Database issue):D3873-92. Arnold K, Bordoli L, Kopp J, Schwede T. The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling. Bio informatics. 2006 Jan

15 ;22(2): 195-201.

Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis. 2009 Jun;30 Suppl 1:S 162-5173.

Lin DH, Malpede BM, Batchelor JD, Tolia NH. Crystal and Solution Structures of Plasmodium falciparum Erythrocyte Binding Antigen 140 Reveal Determinants of Receptor Specificity during Erythrocyte Invasion. J Biol Chem. 2012 Oct

26;287(44):36830-36836.

Ceroni A, Passerini A, VuUo A, Frasconi P. DISULFIND: a Disulfide Bonding State and Cysteine Connectivity Prediction Server. Nucleic Acids Res. 2006 Jul l;34(Web Server issue):W177-181.

VuUo A, Frasconi P. Disulfide connectivity prediction using recursive neural networks and evolutionary information. Bio informatics. 2004 Mar 22;20(5):653-659.

Frasconi P, Passerini A, VuUo A. A two-stage SVM architecture for predicting the disulfide bonding state of cysteines. Proc. IEEE Workshop on Neural Networks for Signal Processing. 2002: 25-34.

Ceroni A, Passerini A, VuUo A. Predicting the disulfide bonding state of cysteines with combinations of kernel machines. Journal of VLSI Signal Processing. 2003; 35: 287—295. Craig DB, Dombkowski AA. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bio informatics. 2013 Dec 1;14:346.

THAT WHICH IS CLAIMED:

1. An immunogenic composition against Plasmodium comprising all or part of the nucleotide sequence PY17X_0721800 found in genomic location Pyl7X-07- v2: 799,281-800,081 (+) on chromosome 7 of Plasmodium yoelii or an ortholog thereof in Plasmodium falciparum or a polypeptide encoded by all or part of the nucleotide sequence PY17X_0721800 or an ortholog thereof in Plasmodium falciparum.

2. An immunogenic composition against Plasmodium comprising an immunogenic polypeptide, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 75% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof.

3. The immunogenic composition of claim 2, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof.

4. The immunogenic composition of claim 3, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, or a fragment thereof.

5. An immunogenic composition against Plasmodium comprising all or part of the nucleotide sequence PY17X_0720100 found in genomic location Pyl7X-07- v2: 727,812-742,672 (+) on chromosome 7 of Plasmodium yoelii or an ortholog thereof in Plasmodium falciparum or a polypeptide encoded by all or part of the nucleotide sequence PY17X_0720100 or an ortholog thereof in Plasmodium falciparum.

6. An immunogenic composition against Plasmodium comprising an immunogenic polypeptide, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 75% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof.

7. The immunogenic composition of claim 6, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof.

8. The immunogenic composition of claim 7, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 19, 20, 21, 22, 23, 24, or a fragment thereof.

9. An immunogenic composition against Plasmodium comprising all or part of the nucleotide sequence PY17X_0721500 found in genomic location Pyl7X-07- v2: 784,994-791,991 (+) on chromosome 7 of Plasmodium yoelii or an ortholog thereof in Plasmodium falciparum or a polypeptide encoded by all or part of the nucleotide sequence PY17X_0721500 or an ortholog thereof in Plasmodium falciparum.

10. An immunogenic composition against Plasmodium comprising an immunogenic polypeptide, wherein the immunogenic polypeptide is encoded by a nucleic acid sequence with at least 75% sequence identity to a sequence selected from the group consisting of: SEQ ID Nos: 31, 32, 33, 34, 35, 36, or a fragment thereof.

11. The immunogenic composition of claim 10, wherein the

immunogenic polypeptide is encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID Nos: 31, 32, 33, 34, 35, 36, or a fragment thereof. 12. The immunogenic composition of claim 11, wherein the

immunogenic polypeptide is encoded by a nucleic acid sequence with at least 90% sequence identity to a sequence selected from the group consisting of: SEQ ID Nos: 31, 32, 33, 34, 35, 36, or a fragment thereof.

13. The immunogenic composition of any one of claims 1 to 12, wherein the immunogenic composition comprises an adjuvant.

14. The immunogenic composition of claim 13, wherein the adjuvant comprises a granulocyte/macrophage colony- stimulating factor (GM-CSF) protein, a nucleotide molecule encoding a GM-CSF protein, saponin QS21, monophosphoryl lipid A, or an unmethylated CpG-containing oligonucleotide.

15. The immunogenic composition of any one of claims 1 to 14, wherein the immunogenic composition is against Plasmodium falciparum.

16. A method of immunizing a subject against Plasmodium, comprising administering an immunogenic amount of the immunogenic composition of any one of claims 1 to 15.

17. A method of eliciting an immune response in a subject against Plasmodium, comprising administering an immunogenic amount of the immunogenic composition of any one of claims 1 to 15.

18. The method of claims 16-17, wherein the Plasmodium is

Plasmodium falciparum.

19. A method of identifying parasite genes driving medically important selectable phenotypes, comprising performing a quantitative- seq linkage group selection (qSeq-LGS) method as described herein.

20. A kit, comprising a container, wherein the container comprises at least one dose of an immunogenic composition against Plasmodium comprising an immunogenic polypeptide encoded by a nucleic acid sequence with at least 80% sequence identity to a sequence selected from the group consisting of: SEQ ID NOs: 7, 8, 9, 10, 11, 12, 19, 20, 21, 22, 23, 24, 31, 32, 33, 34, 35, 36, or a fragment thereof.

Download Citation


Sign in to the Lens

Feedback