Adaptive periodicity in the infectivity of malaria gametocytes to mosquitoes

Daily rhythms in behaviour, physiology, and molecular processes are expected to enable organisms to appropriately schedule activities according to consequences of the daily rotation of the Earth. For parasites, this includes capitalizing on periodicity in transmission opportunities and for hosts/vectors, this may select for rhythms in immune defence. We examine rhythms in the density and infectivity of transmission forms (gametocytes) of rodent malaria parasites in the host’s blood, parasite development inside mosquito vectors, and potential for onwards transmission. Furthermore, we simultaneously test whether mosquitoes exhibit rhythms in susceptibility. We reveal that at night, gametocytes are twice as infective, despite being less numerous in the blood. Enhanced infectiousness at night interacts with mosquito rhythms to increase sporozoite burdens four-fold when mosquitoes feed during their rest phase. Thus, changes in mosquito biting time (due to bed nets) may render gametocytes less infective, but this is compensated for by the greater mosquito susceptibility

Host circadian rhythms are disrupted during malaria infection in parasite genotype-specific manners

Infection can dramatically alter behavioural and physiological traits as hosts become sick and subsequently return to health. Such “sickness behaviours” include disrupted circadian rhythms in both locomotor activity and body temperature. Host sickness behaviours vary in pathogen species-specific manners but the influence of pathogen intraspecific variation is rarely studied. We examine how infection with the murine malaria parasite, Plasmodium chabaudi, shapes sickness in terms of parasite genotype-specific effects on host circadian rhythms. We reveal that circadian rhythms in host locomotor activity patterns and body temperature become differentially disrupted and in parasite genotype-specific manners. Locomotor activity and body temperature in combination provide more sensitive measures of health than commonly used virulence metrics for malaria (e.g. anaemia). Moreover, patterns of host disruption cannot be explained simply by variation in replication rate across parasite genotypes or the severity of anaemia each parasite genotype causes. It is well known that disruption to circadian rhythms is associated with non-infectious diseases, including cancer, type 2 diabetes, and obesity. Our results reveal that disruption of host circadian rhythms is a genetically variable virulence trait of pathogens with implications for host health and disease tolerance

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

ZT0 replicates

Raw proteomic results (n=5) from mosquito bodies collected at ZT0 (dawn)

Additional proteomics for identification

Additional raw proteomics files used for identificatio

ZT8 replicates

Raw proteomic results (n=5) from mosquito bodies collected at ZT

ZT4 replicates

Raw proteomic results (n=5) from mosquito bodies collected at ZT

ZT16 replicates

Raw proteomic results (n=5) from mosquito bodies collected at ZT16