METHODS: Plasmodium berghei infection in male ICR mice was used as the rodent model of choice. The time course of IL-35 expression in the systemic circulation and tissues of P. berghei infected mice as well as their healthy control counterparts was assessed by enzyme linked immunosorbent assay and immunohistochemistry respectively. The effect of modulating IL-35 by recombinant IL-35 protein or neutralizing anti-Epstein-Barr virus-induced gene 3 antibody on the cytokine environment during P. berghei infection was assessed by flow cytometry. Furthermore, the influence of modulating IL-35 on histopathological hallmarks of malaria and disease progression was evaluated.
RESULTS: Interleukin-35 was significantly up regulated in serum and tissues of P. berghei infected mice and correlated with parasitaemia. Neutralization of IL-35 significantly enhanced the release of IFN-γ, decreased the expression of IL-6 and decreased parasitaemia patency. Neutralization of IL-35 was also associated with a tendency towards increased survival as well as the absence of pathological features associated with malaria infection unlike recombinant IL-35 protein administration which sustained a normal course of infection and unfavourable malaria associated histological outcomes in P. berghei infected mice.
CONCLUSION: These results indicate the involvement of IL-35 in P. berghei induced malaria infection. IL-35 neutralization strategies may represent viable therapeutic modalities beneficial for the resolution of malaria infection.
RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.