Macronutrient intake and simulated infection threat independently affect life history traits of male decorated crickets.

Nutritional geometry has advanced our understanding of how macronutrients (e.g., proteins and carbohydrates) influence the expression of life history traits and their corresponding trade-offs. For example, recent work has revealed that reproduction and immune function in male decorated crickets are optimized at very different protein:carbohydrate (P:C) dietary ratios. However, it is unclear how an individual's macronutrient intake interacts with its perceived infection status to determine investment in reproduction or other key life history traits. Here, we employed a fully factorial design in which calling effort and immune function were quantified for male crickets fed either diets previously demonstrated to maximize calling effort (P:C = 1:8) or immune function (P:C = 5:1), and then administered a treatment from a spectrum of increasing infection cue intensity using heat-killed bacteria. Both diet and a simulated infection threat independently influenced the survival, immunity, and reproductive effort of males. If they called, males increased calling effort at the low infection cue dose, consistent with the terminal investment hypothesis, but interpretation of responses at the higher threat levels was hampered by the differential mortality of males across infection cue and diet treatments. A high protein, low carbohydrate diet severely reduced the health, survival, and overall fitness of male crickets. There was, however, no evidence of an interaction between diet and infection cue dose on calling effort, suggesting that the threshold for terminal investment was not contingent on diet as investigated here

Genetic covariance in immune measures and pathogen resistance in decorated crickets is sex and pathogen specific

Insects are important models for studying immunity in an ecological and evolutionary context. Yet, most empirical work on the insect immune system has come from phenotypic studies meaning we have a limited understanding of the genetic architecture of immune function in the sexes. We use nine highly inbred lines to thoroughly examine the genetic relationships between a suite of commonly used immune assays (haemocyte count, implant encapsulation, total phenoloxidase activity, antibacterial zone of inhibition and pathogen clearance) and resistance to infection by three generalist insect pathogens (the gram-negative bacterium Serratia marcescens, the gram-positive bacterium Bacillus cereus and the fungus Metarhizium robertsii) in male and female Gryllodes sigillatus. There were consistent positive genetic correlations between haemocyte count, antibacterial and phenoloxidase activity and resistance to S. marcescens in both sexes, but these relationships were less consistent for resistance to B. cereus and M. robertsii. In addition, the clearance of S. marcescens was genetically correlated with the resistance to all three pathogens in both sexes. Genetic correlations between resistances to the different pathogen species were inconsistent, indicating that resistance to one pathogen does not necessarily mean resistance to another. Finally, while there is ample genetic (co)variance in immune assays and pathogen resistance, these genetic estimates differed across the sexes and many of these measures were not genetically correlated across the sexes, suggesting that these measures could evolve independently in the sexes. Our finding that the genetic architecture of immune function is sex and pathogen specific suggests that the evolution of immune function in male and female G. sigillatus is likely to be complex. Similar quantitative genetic studies that measure a large number of assays and resistance to multiple pathogens in both sexes are needed to ascertain if this complexity extends to other species

Active and covert infections of cricket Iridovirus and Acheta domesticus Densovirus in reared Gryllodes sigillatus crickets

Interest in developing food, feed, and other useful products from farmed insects has gained remarkable momentum in the past decade. Crickets are an especially popular group of farmed insects due to their nutritional quality, ease of rearing, and utility. However, production of crickets as an emerging commodity has been severely impacted by entomopathogenic infections, about which we know little. Here, we identified and characterized an unknown entomopathogen causing mass mortality in a lab-reared population of Gryllodes sigillatus crickets, a species used as an alternative to the popular Acheta domesticus due to its claimed tolerance to prevalent entomopathogenic viruses. Microdissection of sick and healthy crickets coupled with metagenomics-based identification and real-time qPCR viral quantification indicated high levels of cricket iridovirus (CrIV) in a symptomatic population, and evidence of covert CrIV infections in a healthy population. Our study also identified covert infections of Acheta domesticus densovirus (AdDNV) in both populations of G. sigillatus. These results add to the foundational research needed to better understand the pathology of mass-reared insects and ultimately develop the prevention, mitigation, and intervention strategies needed for economical production of insects as a commodity

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

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

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