The Extracellular Milieu of Toxoplasma's Lytic Cycle Drives Lab Adaptation, Primarily by Transcriptional Reprogramming

Evolve and resequencing (E&R) was applied to lab adaptation of Toxoplasma gondii for over 1,500 generations with the goal of mapping host-independent in vitro virulence traits. Phenotypic assessments of steps across the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Nonsynonymous single-nucleotide polymorphisms (SNPs) in only one gene, a P4 flippase, fixated across two different evolving populations, whereas dramatic changes in the transcriptional signature of extracellular parasites were identified. Newly developed computational tools correlated phenotypes evolving at different rates with specific transcriptomic changes. A set of 300 phenotype-associated genes was mapped, of which nearly 50% is annotated as hypothetical. Validation of a select number of genes by knockouts confirmed their role in lab adaptation and highlights novel mechanisms underlying in vitro virulence traits. Further analyses of differentially expressed genes revealed the development of a “pro-tachyzoite” profile as well as the upregulation of the fatty acid biosynthesis (FASII) pathway. The latter aligned with the P4 flippase SNP and aligned with a low abundance of medium-chain fatty acids at low passage, indicating this is a limiting factor in extracellular parasites. In addition, partial overlap with the bradyzoite differentiation transcriptome in extracellular parasites indicated that stress pathways are involved in both situations. This was reflected in the partial overlap between the assembled ApiAP2 and Myb transcription factor network underlying the adapting extracellular state with the bradyzoite differentiation program. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii.

IMPORTANCE It has been well established that prolonged in vitro cultivation of Toxoplasma gondii augments progression of the lytic cycle. This lab adaptation results in increased capacities to divide, migrate, and survive outside a host cell, all of which are considered host-independent virulence factors. However, the mechanistic basis underlying these enhanced virulence features is unknown. Here, E&R was utilized to empirically characterize the phenotypic, genomic, and transcriptomic changes in the non-lab-adapted strain, GT1, during 2.5 years of lab adaptation. This identified the shutdown of stage differentiation and upregulation of lipid biosynthetic pathways as the key processes being modulated. Furthermore, lab adaptation was primarily driven by transcriptional reprogramming, which rejected the starting hypothesis that genetic mutations would drive lab adaptation. Overall, the work empirically shows that lab adaptation augments T. gondii’s in vitro virulence by transcriptional reprogramming and that E&R is a powerful new tool to map multigenic traits.

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.

Simplified geometric representations of protein structures identify complementary interaction interfaces

Protein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a reduced representation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and evolutionary rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 185 overall known protein complexes, we observe predictions comparable to other structure-based tools at correctly identifying protein interaction surfaces. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å Cα-RMSD. Thus, a shape reduction of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.