Whole-genome sequencing of Chromobacterium subtsugae strains exhibiting toxicity to Drosophila melanogaster.

Chromobacterium subtsugae exhibits toxicity to Drosophila melanogaster, providing a new infection model to study host homeostasis. Previous studies using pathogen models have proven to be a useful tool to understand host physiology. Here, we report on the whole-genome sequences of these microbes obtained from short and long reads.

Microbiome-derived acidity protects against microbial invasion in Drosophila.

Microbial invasions underlie host-microbe interactions resulting in pathogenesis and probiotic colonization. In this study, we explore the effects of the microbiome on microbial invasion in Drosophila melanogaster. We demonstrate that gut microbes Lactiplantibacillus plantarum and Acetobacter tropicalis improve survival and lead to a reduction in microbial burden during infection. Using a microbial interaction assay, we report that L. plantarum inhibits the growth of invasive bacteria, while A. tropicalis reduces this inhibition. We further show that inhibition by L. plantarum is linked to its ability to acidify its environment via lactic acid production by lactate dehydrogenase, while A. tropicalis diminishes the inhibition by quenching acids. We propose that acid from the microbiome is a gatekeeper to microbial invasions, as only microbes capable of tolerating acidic environments can colonize the host. The methods and findings described herein will add to the growing breadth of tools to study microbe-microbe interactions in broad contexts.

Analysis of justification for and gender bias in author order among those contributing equally.

The practice of designating two or more authors as equal contributors (EC) on a scientific publication is increasingly common as a form of sharing credit. However, EC authors are often unclearly attributed on CVs or citation engines, and it is unclear how research teams determine author order within an EC listing. In response to studies showing that male authors were more likely to be placed first in an EC listing, the American Society of Microbiology (ASM) required that authors explain the reasons for author order beginning in 2020. In this study we analyze data from over 2500 ASM publications to see how this policy affected gender bias and how research teams are making decisions on author order. Data on publications from 2018-2021 show that gender bias was largely nonsignificant both before and after authors were asked by ASM to provide an EC statement. The most likely reasons for EC order included alphabetical order, seniority, and chance, although there were differences for publications from different geographic regions. However, many research teams used unique methods in order selection, highlighting the importance of EC statements to provide clarity for readers, funding agencies, and tenure committees.

Analysis of justification for author order and gender bias in author order among those contributing equally.

The practice of designating two or more authors as equal contributors (ECs) on a scientific publication is increasingly common as a form of sharing credit. However, EC authors are often unclearly attributed on curriculum vitae (CVs) or citation engines, and it is unclear how research teams determine author order within an EC listing. In response to studies showing that male authors were more likely to be placed first in an EC listing, the American Society for Microbiology (ASM) required that authors explain the reasons for author order beginning in 2020. In this study, we analyze data from over 2,500 ASM publications to see how this policy affected gender bias and how research teams are making decisions on author order. Data on publications from 2018 to 2021 show that gender bias was largely nonsignificant both before and after authors were asked by ASM to provide an EC statement. The most likely reasons for EC order included alphabetical order, seniority, and chance, although there were differences for publications from different geographic regions. However, many research teams used unique methods in order selection, highlighting the importance of EC statements to provide clarity for readers, funding agencies, and tenure committees. IMPORTANCE: First-author publications are important for early career scientists to secure funding and educational opportunities. However, an analysis published in eLife in 2019 noted that female authors are more likely to be placed second even when both authors report they have contributed equally. American Society for Microbiology announced in response that they would require submissions to include a written justification of author order. In this paper, we analyze the resultant data and show that laboratories are most likely to use some combination of alphabetical order, seniority, and chance to determine author order. However, the prevalence of these methods varies based on the research team's geographic location. These findings highlight the importance of equal contributor statements to provide clarity for readers, funding agencies, and tenure committees. Furthermore, this work is critically important for understanding how these decisions are made and provides a glimpse of the sociology of science.

Massively parallel mutant selection identifies genetic determinants of Pseudomonas aeruginosa colonization of Drosophila melanogaster.

Pseudomonas aeruginosa is recognized for its ability to colonize diverse habitats and cause disease in a variety of hosts, including plants, invertebrates, and mammals. Understanding how this bacterium is able to occupy wide-ranging niches is important for deciphering its ecology. We used transposon sequencing [Tn-Seq, also known as insertion sequencing (INSeq)] to identify genes in P. aeruginosa that contribute to fitness during the colonization of Drosophila melanogaster. Our results reveal a suite of critical factors, including those that contribute to polysaccharide production, DNA repair, metabolism, and respiration. Comparison of candidate genes with fitness determinants discovered in previous studies on P. aeruginosa identified several genes required for colonization and virulence determinants that are conserved across hosts and tissues. This analysis provides evidence for both the conservation of function of several genes across systems, as well as host-specific functions. These findings, which represent the first use of transposon sequencing of a gut pathogen in Drosophila, demonstrate the power of Tn-Seq in the fly model system and advance the existing knowledge of intestinal pathogenesis by D. melanogaster, revealing bacterial colonization determinants that contribute to a comprehensive portrait of P. aeruginosa lifestyles across habitats.IMPORTANCEDrosophila melanogaster is a powerful model for understanding host-pathogen interactions. Research with this system has yielded notable insights into mechanisms of host immunity and defense, many of which emerged from the analysis of bacterial mutants defective for well-characterized virulence factors. These foundational studies-and advances in high-throughput sequencing of transposon mutants-support unbiased screens of bacterial mutants in the fly. To investigate mechanisms of host-pathogen interplay and exploit the tractability of this model host, we used a high-throughput, genome-wide mutant analysis to find genes that enable the pathogen P. aeruginosa to colonize the fly. Our analysis reveals critical mediators of P. aeruginosa establishment in its host, some of which are required across fly and mouse systems. These findings demonstrate the utility of massively parallel mutant analysis and provide a platform for aligning the fly toolkit with comprehensive bacterial genomics.

Preliminary evidence for chaotic signatures in host-microbe interactions.

Host-microbe interactions constitute dynamical systems that can be represented by mathematical formulations that determine their dynamic nature and are categorized as deterministic, stochastic, or chaotic. Knowing the type of dynamical interaction is essential for understanding the system under study. Very little experimental work has been done to determine the dynamical characteristics of host-microbe interactions, and its study poses significant challenges. The most straightforward experimental outcome involves an observation of time to death upon infection. However, in measuring this outcome, the internal parameters and the dynamics of each particular host-microbe interaction in a population of interactions are hidden from the experimentalist. To investigate whether a time-to-death (time-to-event) data set provides adequate information for searching for chaotic signatures, we first determined our ability to detect chaos in simulated data sets of time-to-event measurements and successfully distinguished the time-to-event distribution of a chaotic process from a comparable stochastic one. To do so, we introduced an inversion measure to test for a chaotic signature in time-to-event distributions. Next, we searched for chaos in the time-to-death of Caenorhabditis elegans and Drosophila melanogaster infected with Pseudomonas aeruginosa or Pseudomonas entomophila, respectively. We found suggestions of chaotic signatures in both systems but caution that our results are preliminary and highlight the need for more fine-grained and larger data sets in determining dynamical characteristics. If validated, chaos in host-microbe interactions would have important implications for the occurrence and outcome of infectious diseases, the reproducibility of experiments in the field of microbial pathogenesis, and the prediction of microbial threats.IMPORTANCEIs microbial pathogenesis a predictable scientific field? At a time when we are dealing with coronavirus disease 2019, there is intense interest in knowing about the epidemic potential of other microbial threats and new emerging infectious diseases. To know whether microbial pathogenesis will ever be a predictable scientific field requires knowing whether a host-microbe interaction follows deterministic, stochastic, or chaotic dynamics. If randomness and chaos are absent from virulence, there is hope for prediction in the future regarding the outcome of microbe-host interactions. Chaotic systems are inherently unpredictable, although it is possible to generate short-term probabilistic models, as is done in applications of stochastic processes and machine learning to weather forecasting. Information on the dynamics of a system is also essential for understanding the reproducibility of experiments, a topic of great concern in the biological sciences. Our study finds preliminary evidence for chaotic dynamics in infectious diseases.