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The increasing resistance to currently available insecticides in the malaria vector, Anopheles mosquitoes, hampers their use as an effective vector control strategy for the prevention of malaria transmission. Therefore, there is need for new insecticides and/or alternative vector control strategies, the development of which relies on the identification of possible targets in Anopheles. Some known and promising targets for the prevention or control of malaria transmission exist among Anopheles metabolic proteins. This review aims to elucidate the current and potential contribution of Anopheles metabolic proteins to malaria transmission and control. Highlighted are the roles of metabolic proteins as insecticide targets, in blood digestion and immune response as well as their contribution to insecticide resistance and Plasmodium parasite development. Furthermore, strategies by which these metabolic proteins can be utilized for vector control are described. Inhibitors of Anopheles metabolic proteins that are designed based on target specificity can yield insecticides with no significant toxicity to non-target species. These metabolic modulators combined with each other or with synergists, sterilants, and transmission-blocking agents in a single product, can yield potent malaria intervention strategies. These combinations can provide multiple means of controlling the vector. Also, they can help to slow down the development of insecticide resistance. Moreover, some metabolic proteins can be modulated for mosquito population replacement or suppression strategies, which will significantly help to curb malaria transmission.

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BackgroundMembers of the phylum Chlamydiae are obligate intracellular pathogens of humans and animals and have a serious impact on host health. They comprise several zoonotic species with varying disease outcomes and prevalence. To investigate differences in virulence, we focused on Chlamydia psittaci, C. abortus and Waddlia chondrophila. Most threatening is C. psittaci, which frequently infects humans and causes psittacosis associated with severe pneumonia. The closest relative of C. psittaci is C. abortus, which shares the vast majority of genes but less frequently infects humans, and causes stillbirth and sepsis. W. chondrophila is more distantly related, and occasional human infections are associated with respiratory diseases or miscarriage. One possible explanation for differences in virulence originate from species-specific genes as well as differentially expressed homologous virulence factors.ResultsRNA-sequencing (RNA-Seq) was applied to purified infectious elementary bodies (EBs) and non-infectious reticulate bodies (RBs) in order to elucidate the transcriptome of the infectious and replicative chlamydial states. The results showed that approximately half of all genes were differentially expressed. For a descriptive comparison, genes were categorised according to their function in the RAST database. This list was extended by the inclusion of inclusion membrane proteins, outer membrane proteins, polymorphic membrane proteins and type III secretion system effectors. In addition, the expression of fifty-six known and a variety of predicted virulence and immunogenic factors with homologs in C. psittaci, C. abortus and W. chondrophila was analysed. To confirm the RNA-Seq results, the expression of nine factors was validated using real-time quantitative polymerase chain reaction (RT-qPCR). Comparison of RNA-Seq and RT-qPCR results showed a high mean Pearson correlation coefficient of 0.95.ConclusionsIt was shown that both the replicative and infectious chlamydial state contained distinctive transcriptomes and the cellular processes emphasised in EBs and RBs differed substantially based on the chlamydial species. In addition, the very first interspecies transcriptome comparison is presented here, and the considerable differences in expression of homologous virulence factors might contribute to the differing infection rates and disease outcomes of the pathogens. The RNA-Seq results were confirmed by RT-qPCR and demonstrate the feasibility of interspecies transcriptome comparisons in chlamydia.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4961-x) contains supplementary material, which is available to authorized users.

Current classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). "High confidence predictions" were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained "unclassified". ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared pattenrs of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.

IntroductionThe malignant transformation leading to a maturation arrest in B-cell precursor acute lymphoblastic leukemia (BCP-ALL) occurs early in B-cell development, in a pro-B or pre-B cell, when somatic recombination of variable (V), diversity (D), and joining (J) segment immunoglobulin (IG) genes and the B-cell rescue mechanism of VH replacement might be ongoing or fully active, driving clonal evolution. In this study of newly diagnosed BCP-ALL, we sought to understand the mechanistic details of oligoclonal composition of the leukemia at diagnosis, clonal evolution during follow-up, and clonal distribution in different hematopoietic compartments.MethodsUtilizing high-throughput sequencing assays and bespoke bioinformatics we identified BCP-ALL-derived clonally-related IGH sequences by their shared ‘DNJ-stem’.ResultsWe introduce the concept of ‘marker DNJ-stem’ to cover the entirety of, even lowly abundant, clonally-related family members. In a cohort of 280 adult patients with BCP-ALL, IGH clonal evolution at diagnosis was identified in one-third of patients. The phenomenon was linked to contemporaneous recombinant and editing activity driven by aberrant ongoing DH/VH-DJH recombination and VH replacement, and we share insights and examples for both. Furthermore, in a subset of 167 patients with molecular subtype allocation, high prevalence and high degree of clonal evolution driven by ongoing DH/VH-DJH recombination were associated with the presence of KMT2A gene rearrangements, while VH replacements occurred more frequently in Ph-like and DUX4 BCP-ALL. Analysis of 46 matched diagnostic bone marrow and peripheral blood samples showed a comparable clonal and clonotypic distribution in both hematopoietic compartments, but the clonotypic composition markedly changed in longitudinal follow-up analysis in select cases. Thus, finally, we present cases where the specific dynamics of clonal evolution have implications for both the initial marker identification and the MRD monitoring in follow-up samples.DiscussionConsequently, we suggest to follow the marker DNJ-stem (capturing all family members) rather than specific clonotypes as the MRD target, as well as to follow both VDJH and DJH family members since their respective kinetics are not always parallel. Our study further highlights the intricacy, importance, and present and future challenges of IGH clonal evolution in BCP-ALL.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.