A recombinant Aspergillus oryzae fungus transmitted from larvae to adults of Anopheles stephensi mosquitoes inhibits malaria parasite oocyst development.

The control of malaria parasite transmission from mosquitoes to humans is hampered by decreasing efficacies of insecticides, development of drug resistance against the last-resort antimalarials, and the absence of effective vaccines. Herein, the anti-plasmodial transmission blocking activity of a recombinant Aspergillus oryzae (A. oryzae-R) fungus strain, which is used in human food industry, was investigated in laboratory-reared Anopheles stephensi mosquitoes. The recombinant fungus strain was genetically modified to secrete two anti-plasmodial effector peptides, MP2 (midgut peptide 2) and EPIP (enolase-plasminogen interaction peptide) peptides. The transstadial transmission of the fungus from larvae to adult mosquitoes was confirmed following inoculation of A. oryzae-R in the water trays used for larval rearing. Secretion of the anti-plasmodial effector peptides inside the mosquito midguts inhibited oocyst formation of P. berghei parasites. These results indicate that A. oryzae can be used as a paratransgenesis model carrying effector proteins to inhibit malaria parasite development in An. stephensi. Further studies are needed to determine if this recombinant fungus can be adapted under natural conditions, with a minimal or no impact on the environment, to target mosquito-borne infectious disease agents inside their vectors.

Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears.

Diagnosis of malaria in endemic areas is hampered by the lack of a rapid, stain-free and sensitive method to directly identify parasites in peripheral blood. Herein, we report the use of Fourier ptychography to generate wide-field high-resolution quantitative phase images of erythrocytes infected with malaria parasites, from a whole blood sample. We are able to image thousands of erythrocytes (red blood cells) in a single field of view and make a determination of infection status of the quantitative phase image of each segmented cell based on machine learning (random forest) and deep learning (VGG16) models. Our random forest model makes use of morphology and texture based features of the quantitative phase images. In order to label the quantitative images of the cells as either infected or uninfected before training the models, we make use of a Plasmodium berghei strain expressing GFP (green fluorescent protein) in all life cycle stages. By overlaying the fluorescence image with the quantitative phase image we could identify the infected subpopulation of erythrocytes for labelling purposes. Our machine learning model (random forest) achieved 91% specificity and 72% sensitivity while our deep learning model (VGG16) achieved 98% specificity and 57% sensitivity. These results highlight the potential for quantitative phase imaging coupled with artificial intelligence to develop an easy to use platform for the rapid and sensitive diagnosis of malaria.