Wolbachia infection-responsive immune genes suppress Plasmodium falciparum infection in Anopheles stephensi.

Wolbachia, a maternally transmitted symbiotic bacterium of insects, can suppress a variety of human pathogens in mosquitoes, including malaria-causing Plasmodium in the Anopheles vector. However, the mechanistic basis of Wolbachia-mediated Plasmodium suppression in mosquitoes is not well understood. In this study, we compared the midgut and carcass transcriptomes of stably infected Anopheles stephensi with Wolbachia wAlbB to uninfected mosquitoes in order to discover Wolbachia infection-responsive immune genes that may play a role in Wolbachia-mediated anti-Plasmodium activity. We show that wAlbB infection upregulates 10 putative immune genes and downregulates 14 in midguts, while it upregulates 31 putative immune genes and downregulates 15 in carcasses at 24 h after blood-fed feeding, the time at which the Plasmodium ookinetes are traversing the midgut tissue. Only a few of these regulated immune genes were also significantly differentially expressed between Wolbachia-infected and non-infected midguts and carcasses of sugar-fed mosquitoes. Silencing of the Wolbachia infection-responsive immune genes TEP 4, TEP 15, lysozyme C2, CLIPB2, CLIPB4, PGRP-LD and two novel genes (a peritrophin-44-like gene and a macro domain-encoding gene) resulted in a significantly greater permissiveness to P. falciparum infection. These results indicate that Wolbachia infection modulates mosquito immunity and other processes that are likely to decrease Anopheles permissiveness to Plasmodium infection.

Characterization of Plasmodium falciparum prohibitins as novel targets to block infection in humans by impairing the growth and transmission of the parasite.

Prohibitins (PHBs) are highly conserved pleiotropic proteins as they have been shown to mediate key cellular functions. Here, we characterize PHBs encoding putative genes ofPlasmodium falciparum by exploiting different orthologous models. We demonstrated that PfPHB1 (PF3D7_0829200) and PfPHB2 (PF3D7_1014700) are expressed in asexual and sexual blood stages of the parasite. Immunostaining indicated hese proteins as mitochondrial residents as they were found to be localized as branched structures. We further validated PfPHBs as organellar proteins residing in Plasmodium mitochondria, where they interact with each other. Functional characterization was done in Saccharomyces cerevisiae orthologous model by expressing PfPHB1 and PfPHB2 in cells harboring respective mutants. The PfPHBs functionally complemented the yeast PHB1 and PHB2 mutants, where the proteins were found to be involved in stabilizing the mitochondrial DNA, retaining mitochondrial integrity and rescuing yeast cell growth. Further, Rocaglamide (Roc-A), a known inhibitor of PHBs and anti-cancerous agent, was tested against PfPHBs and as an antimalarial. Roc-A treatment retarded the growth of PHB1, PHB2, and ethidium bromide petite yeast mutants. Moreover, Roc-A inhibited growth of yeast PHBs mutants that were functionally complemented with PfPHBs, validating P. falciparum PHBs as one of the molecular targets for Roc-A. Roc-A treatment led to growth inhibition of artemisinin-sensitive (3D7), artemisinin-resistant (R539T) and chloroquine-resistant (RKL-9) parasites in nanomolar ranges. The compound was able to retard gametocyte and oocyst growth with significant morphological aberrations. Based on our findings, we propose the presence of functional mitochondrial PfPHB1 and PfPHB2 in P. falciparum and their druggability to block parasite growth.

A comprehensive overview of the existing microbial symbionts in mosquito vectors: An important tool for impairing pathogen transmission.

The emergence of drug-resistant parasites and/or insecticide-resistant mosquito vectors necessitates developing alternative tools that either supplement or replace the conventional malaria control strategies. Trans-infecting the mosquito vector with symbionts that can either compete with a targeted pathogen or manipulate the host biology by reducing its vectorial capacity could be a promising and innovative biological approach for the control of infectious diseases This idea could be utilized to develop a novel and efficient vector control strategy; symbionts are dispersed into vector populations to reduce their ability to transmit human pathogens. Here, we reported the natural existence of Microsporidian (an obligate fungus) in the field-collected An. stephensi mosquito. However, laboratory-reared An. stephensi and An. culicifacies did not exhibit microsporidian infection. Similarly, 16s rRNA PCR identified ∼1kb amplicons in laboratory-reared An. stephensi and An. culicifacies, indicating the presence of naturally residing different bacterial species. DNA sequencing of these amplicons revealed the identities of different bacteria which are not well-characterized in terms of plasmodia-interaction activity in the Indian malaria vector. This article summarizes an overview of the previously studied microbial symbionts for their role in Plasmodium transmission along with a list of new or unexplored symbionts in the disease transmitting mosquito vectors. The summarized information could be utilized to explore such microbial symbionts for their role in Plasmodium-transmission biology in-depth and implementation in the malaria control interventions globally.

Cyclic constrained immunoreactive peptides from crucial P. falciparum proteins: potential implications in malaria diagnostics.

Malaria is still a global challenge with significant morbidity and mortality, especially in the African, South-East Asian, and Latin American regions. Malaria diagnosis is a crucial pillar in the control and elimination efforts, often accomplished by the administration of mass-scale Rapid diagnostic tests (RDTs). The inherent limitations of RDTs- insensitivity in scenarios of low transmission settings and deletion of one of the target proteins- Histidine rich protein 2/3 (HRP-2/3) are evident from multiple reports, thus necessitating the need to explore novel diagnostic tools/targets. The present study used peptide microarray to screen potential epitopes from 13 antigenic proteins (CSP, EXP1, LSA1, TRAP, AARP, AMA1, GLURP, MSP1, MSP2, MSP3, MSP4, P48/45, HAP2) of P. falciparum. Three cyclic constrained immunoreactive peptides- C6 (EXP1), A8 (MSP2), B7 (GLURP) were identified from 5458 cyclic constrained peptides (in duplicate) against P. falciparum-infected sera. Peptides (C6, A8, B7- cyclic constrained) and (G11, DSQ, NQN- corresponding linear peptides) were fairly immunoreactive towards P. falciparum-infected sera in dot-blot assay. Using direct ELISA, cyclic constrained peptides (C6 and B7) were found to be specific to P. falciparum-infected sera. A substantial number of samples were tested and the peptides successfully differentiated the P. falciparum positive and negative samples with high confidence. In conclusion, the study identified 3 cyclic constrained immunoreactive peptides (C6, B7, and A8) from P. falciparum secretory/surface proteins and further validated for diagnostic potential of 2 peptides (C6 and B7) with field-collected P. falciparum-infected sera samples.

Intelligent type 2 diabetes risk prediction from administrative claim data.

Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if there is an early warning. This study aims to apply supervised machine learning algorithms for developing predictive models for type 2 diabetes using administrative claim data. Following guidelines from the Elixhauser Comorbidity Index, 31 variables were considered. Five supervised machine learning algorithms were used for developing type 2 diabetes prediction models. Principal component analysis was applied to rank variables' importance in predictive models. Random forest (RF) showed the highest accuracy (85.06%) among the algorithms, closely followed by the k-nearest neighbor (84.48%). The analysis further revealed RF as a high performing algorithm irrespective of data imbalance. As revealed by the principal component analysis, patient age is the most important predictor for type 2 diabetes, followed by a comorbid condition (i.e., solid tumor without metastasis). This study's finding of RF as the best performing classifier is consistent with the promise of tree-based algorithms for public data in other works. Thus, the outcome can guide in designing automated surveillance of patients at risk of forming diabetes from administrative claim information and will be useful to health regulators and insurers.