Mettl1-dependent m7G tRNA modification is essential for maintaining spermatogenesis and fertility in Drosophila melanogaster.

Modification of guanosine to N7-methylguanosine (m7G) in the variable loop region of tRNA is catalyzed by the METTL1/WDR4 heterodimer and stabilizes target tRNA. Here, we reveal essential functions of Mettl1 in Drosophila fertility. Knockout of Mettl1 (Mettl1-KO) causes no major effect on the development of non-gonadal tissues, but abolishes the production of elongated spermatids and mature sperm, which is fully rescued by expression of a Mettl1-transgene, but not a catalytic-dead Mettl1 transgene. This demonstrates that Mettl1-dependent m7G is required for spermatogenesis. Mettl1-KO results in a loss of m7G modification on a subset of tRNAs and decreased tRNA abundance. Ribosome profiling shows that Mettl1-KO led to ribosomes stalling at codons decoded by tRNAs that were reduced in abundance. Mettl1-KO also significantly reduces the translation efficiency of genes involved in elongated spermatid formation and sperm stability. Germ cell-specific expression of Mettl1 rescues disrupted m7G tRNA modification and tRNA abundance in Mettl1-KO testes but not in non-gonadal tissues. Ribosome stalling is much less detectable in non-gonadal tissues than in Mettl1-KO testes. These findings reveal a developmental role for m7G tRNA modification and indicate that m7G modification-dependent tRNA abundance differs among tissues.

Neural-circuit basis of song preference learning in fruit flies.

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its auditory behaviors according to prior sound experiences. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the brain, with these neurons playing a key role in mating behavior. The neural circuit basis of this GABAergic input, however, is not known. Here, we find that GABAergic neurons expressing the sex-determination gene doublesex are necessary for song preference learning. In the brain, only four doublesex-expressing GABAergic neurons exist per hemibrain, identified as pCd-2 neurons. pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between them. Moreover, GABAergic and dopaminergic inputs to doublesex-expressing GABAergic neurons are necessary for song preference learning. Together, this study provides a neural circuit model that underlies experience-dependent auditory plasticity at a single-cell resolution.

Collaborative hunting in artificial agents with deep reinforcement learning.

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

From wolves to ants, many animals are known to be able to hunt as a team. This strategy may yield several advantages: going after bigger preys together, for example, can often result in individuals spending less energy and accessing larger food portions than when hunting alone. However, it remains unclear whether this behavior relies on complex cognitive processes, such as the ability for an animal to represent and anticipate the actions of its teammates. It is often thought that 'collaborative hunting' may require such skills, as this form of group hunting involves animals taking on distinct, tightly coordinated roles ­ as opposed to simply engaging in the same actions simultaneously. To better understand whether high-level cognitive skills are required for collaborative hunting, Tsutsui et al. used a type of artificial intelligence known as deep reinforcement learning. This allowed them to develop a computational model in which a small number of 'agents' had the opportunity to 'learn' whether and how to work together to catch a 'prey' under various conditions. To do so, the agents were only equipped with the ability to link distinct stimuli together, such as an event and a reward; this is similar to associative learning, a cognitive process which is widespread amongst animal species. The model showed that the challenge of capturing the prey when hunting alone, and the reward of sharing food after a successful hunt drove the agents to learn how to work together, with previous experiences shaping decisions made during subsequent hunts. Importantly, the predators started to exhibit the ability to take on distinct, complementary roles reminiscent of those observed during collaborative hunting, such as one agent chasing the prey while another ambushes it. Overall, the work by Tsutsui et al. challenges the traditional view that only organisms equipped with high-level cognitive processes can show refined collaborative approaches to hunting, opening the possibility that these behaviors may be more widespread than originally thought ­ including between animals of different species.

Difference in the impact of coinfections and secondary infections on antibiotic use in patients hospitalized with COVID-19 between the Omicron-dominant period and the pre-Omicron period.

INTRODUCTION: This study evaluated the effect of coinfections and/or secondary infections on antibiotic use in patients hospitalized with coronavirus disease 2019 (COVID-19). METHOD: Days of therapy per 100 bed days (DOT) in a COVID-19 ward were compared between 2022 (Omicron period) and 2021 (pre-Omicron period). Antibiotics were categorized as antibiotics predominantly used for community-acquired infections (CAIs) and antibiotics predominantly used for health care-associated infections (HAIs). Bacterial and/or fungal infections which were proved or assumed on admission were defined as coinfections. Secondary infections were defined as infections that occurred following COVID-19. RESULTS: Patients with COVID-19 during the Omicron period were older and had more comorbidities. Coinfections were more common in the Omicron period than in the pre-Omicron period (44.4% [100/225] versus 0.8% [2/257], respectively, p < 0.001), and the mean DOT of antibiotics for CAIs was significantly increased in the Omicron period (from 3.60 to 17.84, p < 0.001). Secondary infection rate tended to be higher in the Omicron period (p = 0.097). Mean DOT of antibiotics for HAIs were appeared to be lower in the COVID-19 ward than in the general ward (pre-Omicron, 3.33 versus 6.37, respectively; Omicron, 3.84 versus 5.22, respectively). No multidrug-resistant gram-negative organisms were isolated in the COVID-19 ward. CONCLUSION: Antibiotic use for CAIs was limited in the pre-Omicron period but increased in the Omicron period because of a high coinfection rate on admission. With the antimicrobial stewardship, excessive use of antibiotics for HAIs was avoided in the COVID-19 ward during both periods.

Protocol to investigate the neural basis for copulation posture of Drosophila using a closed-loop real-time optogenetic system.

In internal fertilization animals, maintaining a copulation posture facilitates the process of transporting gametes from male to female. Here, we present a protocol to investigate the neural basis for copulation posture of fruit flies using a closed-loop real-time optogenetic system. We describe steps for using deep learning analysis to enable optogenetic manipulation of neural activity only during copulation with high efficiency. This system can be applied to various animal behaviors other than copulation. For complete details on the use and execution of this protocol, please refer to Yamanouchi et al. (2023).1.

Piezo-mediated mechanosensation contributes to stabilizing copulation posture and reproductive success in Drosophila males.

In internal fertilization animals, reproductive success depends on maintaining copulation until gametes are transported from male to female. In Drosophila melanogaster, mechanosensation in males likely contributes to copulation maintenance, but its molecular underpinning remains to be identified. Here we show that the mechanosensory gene piezo and its' expressing neurons are responsible for copulation maintenance. An RNA-seq database search and subsequent mutant analysis revealed the importance of piezo for maintaining male copulation posture. piezo-GAL4-positive signals were found in the sensory neurons of male genitalia bristles, and optogenetic inhibition of piezo-expressing neurons in the posterior side of the male body during copulation destabilized posture and terminated copulation. Our findings suggest that the mechanosensory system of male genitalia through Piezo channels plays a key role in copulation maintenance and indicate that Piezo may increase male fitness during copulation in flies.

Distinct decision-making properties underlying the species specificity of group formation of flies.

Many animal species form groups. Group characteristics differ between species, suggesting that the decision-making of individuals for grouping varies across species. However, the actual decision-making properties that lead to interspecific differences in group characteristics remain unclear. Here, we compared the group formation processes of two Drosophilinae fly species, Colocasiomyia alocasiae and Drosophila melanogaster, which form dense and sparse groups, respectively. A high-throughput tracking system revealed that C. alocasiae flies formed groups faster than D. melanogaster flies, and the probability of C. alocasiae remaining in groups was far higher than that of D. melanogaster. C. alocasiae flies joined groups even when the group size was small, whereas D. melanogaster flies joined groups only when the group size was sufficiently large. C. alocasiae flies attenuated their walking speed when the inter-individual distance between flies became small, whereas such behavioural properties were not clearly observed in D. melanogaster. Furthermore, depriving C. alocasiae flies of visual input affected grouping behaviours, resulting in a severe reduction in group formation. These findings show that C. alocasiae decision-making regarding grouping, which greatly depends on vision, is significantly different from D. melanogaster, leading to species-specific group formation properties.

Behavioral Evolution of Drosophila: Unraveling the Circuit Basis.

Behavior is a readout of neural function. Therefore, any difference in behavior among different species is, in theory, an outcome of interspecies diversification in the structure and/or function of the nervous system. However, the neural diversity underlying the species-specificity in behavioral traits and its genetic basis have been poorly understood. In this article, we discuss potential neural substrates for species differences in the courtship pulse song frequency and mating partner choice in the Drosophila melanogaster subgroup. We also discuss possible neurogenetic mechanisms whereby a novel behavioral repertoire emerges based on the study of nuptial gift transfer, a trait unique to D. subobscura in the genus Drosophila. We found that the conserved central circuit composed primarily of fruitless-expressing neurons (the fru-circuit) serves for the execution of courtship behavior, whereas the sensory pathways impinging onto the fru-circuit or the motor pathways downstream of the fru-circuit are susceptible to changes associated with behavioral species differences.

Balanced genetic diversity improves population fitness.

Although genetic diversity within a population is suggested to improve population-level fitness and productivity, the existence of these effects is controversial because empirical evidence for an ecological effect of genetic diversity and the underlying mechanisms is scarce and incomplete. Here, we show that the natural single-gene behavioural polymorphism (Rover and sitter) in Drosophila melanogaster has a positive effect on population fitness. Our simple numerical model predicted that the fitness of a polymorphic population would be higher than that expected with two monomorphic populations, but only under balancing selection. Moreover, this positive diversity effect of genetic polymorphism was attributable to a complementarity effect, rather than to a selection effect. Our empirical tests using the behavioural polymorphism in D. melanogaster clearly supported the model predictions. These results provide direct evidence for an ecological effect of genetic diversity on population fitness and its condition dependence.

Optogenetic Activation of the fruitless-Labeled Circuitry in Drosophila subobscura Males Induces Mating Motor Acts.

It remains an enigma how the nervous system of different animal species produces different behaviors. We studied the neural circuitry for mating behavior in Drosophila subobscura, a species that displays unique courtship actions not shared by other members of the genera including the genetic model D. melanogaster, in which the core courtship circuitry has been identified. We disrupted the D. subobscura fruitless (fru) gene, a master regulator for the courtship circuitry formation in D. melanogaster, resulting in complete loss of mating behavior. We also generated frusoChrimV , which expresses the optogenetic activator Chrimson fused with a fluorescent marker under the native fru promoter. The fru-labeled circuitry in D. subobscura visualized by frusoChrimV revealed differences between females and males, optogenetic activation of which in males induced mating behavior including attempted copulation. These findings provide a substrate for neurogenetic dissection and manipulation of behavior in non-model animals, and will help to elucidate the neural basis for behavioral diversification.SIGNIFICANCE STATEMENT How did behavioral specificity arise during evolution? Here we attempted to address this question by comparing the parallel genetically definable neural circuits controlling the courtship behavior of Drosophila melanogaster, a genetic model, and its relative, D. subobscura, which exhibits a courtship behavioral pattern unique to it, including nuptial gift transfer. We found that the subobscura fruitless circuit, which is required for male courtship behavior, was slightly but clearly different from its melanogaster counterpart, and that optogenetic activation of this circuit induced subobscura-specific behavior, i.e., regurgitating crop contents, a key element of transfer of nuptial gift. Our study will pave the way for determining how and which distinctive cellular elements within the fruitless circuit determine the species-specific differences in courtship behavior.

Development of a novel evaluation method for air particles using surface plasmon resonance spectroscopy analysis.

The aim of this study was to develop a novel evaluation method for air particles using surface plasmon resonance spectroscopy (SPR) analysis. An L1 sensor chip modified with immobilized liposome was used as a model of the membrane of epithelial cells in organs of respiration. A test suspension of dispersed air particles was flowed onto the sensor chip. The interaction between the surface of the sensor chip and particulates in the sample solution was detected by SPR. It is deduced that the SPR measurement provides information about the adsorption/desorption behavior of the particles on the membrane. Environmentally certified reference materials, diesel particulate matter, vehicle exhaust particulates, urban particulate matter, coal fly ash, and rocks, were used as air particulate samples. Filtrates of suspensions of these samples were analyzed by SPR. Each sample revealed characteristic SPR sensor-gram patterns. For example, diesel particulate matter strongly interacted with the lipid bilayer, and was hardly dissociated. On the other hand, coal fly ash and rock particles interacted poorly with the membrane. The presented method could be used to evaluate or characterize air particles.