Insect Behavioral Change and the Potential Contributions of Neuroinflammation—A Call for Future Research

Many organisms are able to elicit behavioral change in other organisms. Examples include different microbes (e.g., viruses and fungi), parasites (e.g., hairworms and trematodes), and parasitoid wasps. In most cases, the mechanisms underlying host behavioral change remain relatively unclear. There is a growing body of literature linking alterations in immune signaling with neuron health, communication, and function; however, there is a paucity of data detailing the effects of altered neuroimmune signaling on insect neuron function and how glial cells may contribute toward neuron dysregulation. It is important to consider the potential impacts of altered neuroimmune communication on host behavior and reflect on its potential role as an important tool in the “neuro-engineer” toolkit. In this review, we examine what is known about the relationships between the insect immune and nervous systems. We highlight organisms that are able to influence insect behavior and discuss possible mechanisms of behavioral manipulation, including potentially dysregulated neuroimmune communication. We close by identifying opportunities for integrating research in insect innate immunity, glial cell physiology, and neurobiology in the investigation of behavioral manipulation

Persistent Infection with Herpes Simplex Virus 1 and Alzheimer’s Disease—A Call to Study How Variability in Both Virus and Host may Impact Disease

Increasing attention has focused on the contributions of persistent microbial infections with the manifestation of disease later in life, including neurodegenerative conditions such as Alzheimer’s disease (AD). Current data has shown the presence of herpes simplex virus 1 (HSV-1) in regions of the brain that are impacted by AD in elderly individuals. Additionally, neuronal infection with HSV-1 triggers the accumulation of amyloid beta deposits and hyperphosphorylated tau, and results in oxidative stress and synaptic dysfunction. All of these factors are implicated in the development of AD. These data highlight the fact that persistent viral infection is likely a contributing factor, rather than a sole cause of disease. Details of the correlations between HSV-1 infection and AD development are still just beginning to emerge. Future research should investigate the relative impacts of virus strain- and host-specific factors on the induction of neurodegenerative processes over time, using models such as infected neurons in vitro, and animal models in vivo, to begin to understand their relationship with cognitive dysfunction

Viral infection of human neurons triggers strain-specific differences in host neuronal and viral transcriptomes.

Infection with herpes simplex virus 1 (HSV-1) occurs in over half the global population, causing recurrent orofacial and/or genital lesions. Individual strains of HSV-1 demonstrate differences in neurovirulence in vivo, suggesting that viral genetic differences may impact phenotype. Here differentiated SH-SY5Y human neuronal cells were infected with one of three HSV-1 strains known to differ in neurovirulence in vivo. Host and viral RNA were sequenced simultaneously, revealing strain-specific differences in both viral and host transcription in infected neurons. Neuronal morphology and immunofluorescence data highlight the pathological changes in neuronal cytoarchitecture induced by HSV-1 infection, which may reflect host transcriptional changes in pathways associated with adherens junctions, integrin signaling, and others. Comparison of viral protein levels in neurons and epithelial cells demonstrated that a number of differences were neuron-specific, suggesting that strain-to-strain variations in host and virus transcription are cell type-dependent. Together, these data demonstrate the importance of studying virus strain- and cell-type-specific factors that may contribute to neurovirulence in vivo, and highlight the specificity of HSV-1-host interactions

Sexually divergent induction of microglial-associated neuroinflammation with hippocampal aging

Abstract Background The necessity of including both males and females in molecular neuroscience research is now well understood. However, there is relatively limited basic biological data on brain sex differences across the lifespan despite the differences in age-related neurological dysfunction and disease between males and females. Methods Whole genome gene expression of young (3 months), adult (12 months), and old (24 months) male and female C57BL6 mice hippocampus was analyzed. Subsequent bioinformatic analyses and confirmations of age-related changes and sex differences in hippocampal gene and protein expression were performed. Results Males and females demonstrate both common expression changes with aging and marked sex differences in the nature and magnitude of the aging responses. Age-related hippocampal induction of neuroinflammatory gene expression was sexually divergent and enriched for microglia-specific genes such as complement pathway components. Sexually divergent C1q protein expression was confirmed by immunoblotting and immunohistochemistry. Similar patterns of cortical sexually divergent gene expression were also evident. Additionally, inter-animal gene expression variability increased with aging in males, but not females. Conclusions These findings demonstrate sexually divergent neuroinflammation with aging that may contribute to sex differences in age-related neurological diseases such as stroke and Alzheimer’s, specifically in the complement system. The increased expression variability in males suggests a loss of fidelity in gene expression regulation with aging. These findings reveal a central role of sex in the transcriptomic response of the hippocampus to aging that warrants further, in depth, investigations

Same data, different conclusions : radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed