Biology subjects

Johnson, E. C.

Publications and source records attributed to Johnson, E. C..

At least 19 recordsLinked to original sources

bioRxiv2025

Modeling Organoid Population Electrophysiology Dynamics

Improving models to investigate neurodegenerative disease, neurodevelopmental disease, neuro-toxicology and neuropharmacology is critical to improve our basic understanding of the human nervous system, as well as to accelerate discovery of interventions and drugs. Improved models of the human central nervous system could enable critical discoveries related to functional changes induced by sensory stimulation or toxic exposures. Neural organoids, complex three-dimensional cell cultures derived from adult human stem cells, have been grown with complex connectivity and neuroanatomy. Moreover, these cultures have been interfaced with bi-directional electrical stimulation and recording, as well as chemical stimulation. This effort sought to develop new computational techniques which could be applied to comparative studies using neural organoids. In particular, we adapted CEBRA, a state of the art model from the in vivo modeling literature, to generate 2D and 3D embeddings (projections into structured low-dimensional spaces) of high dimensional neural organoid electrophysiology data. This can be done in an unsupervised or semi-supervised manner. Results indicate these embeddings can be quickly and reliably generated and serve as a low-dimensional, interpretable embeddings for characterizing changes in neural organoid activity over time, as well as clustering results around known bursting phenomena. Moreover, we demonstrate that mixtures of von Mises-Fisher distributions can be used as a parametric model for these embedding spaces to enable statistics hypothesis testing. This technique may enable new types of comparative studies using neural organoids, and may be critical for creating a representation for quantitative comparison and validation of neural organoid models against human and animal data. Looking ahead, this work could allow the formulation of a new class of experiments investigating the functional impact of toxins, genetic manipulations, or pharmacological interventions on human neurons.

neuroscience↗

bioRxiv2025

Neuromodulation in neural organoids with shell MEAs

Neural organoids (NOs) have emerged as important tissue engineering models for brain sciences and biocomputing. Establishing reliable relationships between stimulation and recording traces of electrical activity is essential to monitor the functionality of NOs, especially as it relates to realizing biocomputing paradigms such as reinforcement learning or stimulus discrimination. While researchers have demonstrated neuromodulation in NOs, they have primarily used 2D microelectrode arrays (MEAs) with limited access to the entire 3D contour of the NOs. Here, we report neuromodulation using tiny mimics of macroscale EEG caps or shell MEAs. Specifically, we observe that stimulating current within a specific range (20 to 30 {micro}A) induced a statistically significant increase in neuron firing rate when comparing the activity five seconds before and after stimulation. We observed neuromodulatory behavior using both three- and 16-electrode shells and could generate 3D spatiotemporal maps of neuromodulatory activity around the surface of the NO. Our studies demonstrate a methodology for investigating 3D spatiotemporal neuromodulation in organoids of broad relevance to biomedical engineering and biocomputing. One-Sentence SummaryNeuromodulation, an essential intelligence feature, was observed using 3D stimulation and recording from neural organoids.

bioengineering↗

bioRxiv2025

Longitudinal Trajectories of Cognition and Neural Metrics as Predictors of Persistent Distressing Psychotic-Like Experiences Across Middle Childhood and Early Adolescence

ObjectivesPsychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and neural metrics over time, which in turn lead to worsening PLEs. Persistence and distress are factors that distinguish more clinically significant PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and structural neural metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. MethodsMultigroup univariate latent growth models examined three waves of cognitive metrics and global structural neural metrics separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901). Models then examined whether changes in cognitive and structural neural metrics over time attenuated associations between genetic liability (i.e., schizophrenia polygenic risk scores/family history) or environmental risk scores (e.g., poverty) and PLE groups. ResultsPersistent distressing PLEs showed greater decreases (i.e., more negative slopes) of cognition and neural metrics over time compared to those in low-level PLE groups. Associations between environmental risk and persistent distressing PLEs were attenuated when accounting for lowered scores over time on cognitive (e.g., picture vocabulary) and to a lesser extent neural (e.g., cortical thickness, volume) metrics. ConclusionsAnalyses provide novel evidence for extant theories that worsening cognition and global structural metrics may partially account for associations between environmental risk with persistent distressing PLEs.

neuroscience↗

bioRxiv2023

Aβ Amyloid Scaffolds the Accumulation of Matrisome and Additional Proteins in Alzheimer's Disease

We report a highly significant correlation between human Alzheimers disease (AD) brain proteome changes and those in CRND8 APP695NL/F transgenic mice. Comparing protein changes observed in the CRND8 mice with co-expression networks derived from human Alzheimers disease (AD), reveals both conserved and divergent module changes. Many proteins in the most highly conserved module (M42, matrisome) accumulate in plaques, cerebrovascular amyloid (CAA), dystrophic neuronal processes, or a combination thereof. Overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), in CRND8 mice brains leads to increased accumulation of A{beta} in plaques and in blood vessels; further, recombinant MDK and PTN enhance A{beta} aggregation into amyloid structures. Multiple M42 proteins bind to fibrillar A{beta}42 and a non-human amyloid fibril in vitro. Supporting this binding data, MDK and PTN co-accumulate with transthyretin (TTR) amyloid in the heart. Notably, our findings establish that proteomic changes in modules observed in human AD brains define an A{beta} amyloid "responsome" that is well conserved from mouse models to humans. Further, distinct amyloid structures appear to serve as scaffolds, facilitating the co-accumulation of proteins with signaling functions, and this co-accumulation may contribute to downstream pathological sequalae. Overall, this contextualized understanding of proteomic changes and their interplay with amyloid deposition provides valuable insights into the complexity of AD pathogenesis and potential biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2023

Data-driven motif discovery in biological neural networks

AO_SCPLOWBSTRACTC_SCPLOWData from a variety of domains are represented as graphs, including social networks, transportation networks, computer networks, and biological networks. A key question spans these domains: are there meaningful repeated subgraphs, or motifs, within the structure of these larger networks? This is a particularly relevant problem when searching for repeated neural circuits in networks of biological neurons, as the field now regularly produces large brain connectivity maps of neurons and synapses, or connectomes. Given acquisition costs, however, these neuron-synapse connectivity maps are mostly one-of-a-kind. With current graph analysis techniques, it is very challenging to discover new "interesting" subgraphs a priori given small sample sizes of host graphs. Another challenge is that for even relatively modest graph sizes, an exhaustive search of all possible subgraphs is computationally intractable. For these reasons, motif discovery in biological graphs remains an unsolved challenge in the field. In this work, we present a motif discovery approach that can derive a list of undirected or directed motifs, with occurrence counts which are statistically significant compared to randomized graphs, from a single graph example. We first address common pitfalls in the current most common approaches when testing for motif statistical significance, and outline a strategy to ameliorate this problem with improved graph randomization techniques. We then propose a progressive-refinement approach for motif discovery, which addresses issues of computational cost. We demonstrate that our sampling correction technique allows for significance testing of target motifs while highlighting misleading conclusions from standard random graph models. Finally, we share our reference implementation, which is available as an open-source Python package, and demonstrate real-world preliminary results on the C. elegans connectome and the ellipsoid body of the Drosophila melanogaster fruit fly connectome.

neuroscience↗

bioRxiv2023

Mechanisms of carbon dioxide detection in the earthworm Dendrobaena veneta.

Carbon dioxide (CO2) is a critical biological signal that is noxious to many animals at high concentrations. The earthworm Dendrobaena veneta lives in subterranean burrows containing high levels of CO2 and respires through its skin. Despite the ecological and agricultural importance of earthworms, relatively little is known about how they make decisions in their environment, including their response to elevated levels of CO2. To examine CO2 detection in this species, we designed the exudate assay, in which we placed an earthworm in a sealed container, exposed it to varying concentrations of CO2 for one minute, and recorded the amount of exudate secreted. Because earthworms excrete exudate in response to noxious stimuli, we hypothesized that the amount of exudate produced was proportional to the amount of irritation. We repeated these experiments after treatment with several blockers for molecules with potential involvement in CO2 detection, including carbonic anhydrases, guanylate cyclase, TRPA1, ASICs, and OTOP channels. We also confirmed the presence of homologous transcripts for each of these gene families in an epithelial transcriptome for D. veneta. Additionally, since organisms often detect CO2 levels indirectly by monitoring the conversion to carbonic acid (a weak acid), we used the exudate assay to evaluate aversion to additional weak acids (formic acid, acetic acid, and propionic acid). Earthworms excreted significantly more exudate in response to CO2 in a dosage-dependent manner, and this response was muted by the general carbonic anhydrase inhibitor acetazolamide, the carbonic anhydrase IX/XII inhibitor indisulam, the calcium channel blocker ruthenium red, the sodium channel blocker amiloride, and the acid-sensing ion channel blocker diminazene aceturate. These data provide evidence of the role of carbonic anhydrase and epithelial sodium channels in earthworm CO2 detection, establish that, similar to other subterranean-dwelling animals, earthworms are extremely tolerant of CO2, and contribute to our understanding of the mechanisms used by earthworms to detect and react to weak acids in their environment. Contribution to the field statementCarbon dioxide (CO2) is a major byproduct of cellular respiration and an important biological signal. The metabolism of animals living underground in burrows can cause the concentration of CO2 to increase above atmospheric CO2. Most animals have multiple molecular mechanisms that detect CO2 and find high concentrations aversive or noxious; some subterranean animals have adaptations that make them more tolerant of concentrated CO2. Earthworms live in burrows, are a keystone species for subterranean ecosystems, and in their lightless environment must rely primarily on tactile and chemical signals. Despite the profound importance of earthworms, few if any of the molecular mechanisms they use to detect these signals have been characterized. Here we use RNA sequencing to develop a list of candidate mechanisms that the European nightcrawler may use to respond to CO2. We also have developed an assay for comparing how noxious different concentrations of volatile compounds are to earthworms. In the present study, we utilize that assay with inhibitors to further narrow the possible multiple molecular mechanisms responsible for the detection of CO2. We suspect that our study is among the first attempts to combine RNA-sequencing technology and pharmacology to describe earthworm sensory biology.

physiology↗

bioRxiv2023

Informing generative replay for continual learning with long-term memory formation in the fruit fly

Continual learning without catastrophic forgetting is a challenge for artificial systems but it is done naturally across a range of biological systems, including in insects. A recurrent circuit has been identified in the fruit fly mushroom body to consolidate long term memories (LTM), but there is not currently an algorithmic understanding of this LTM formation. We hypothesize that generative replay is occurring to consolidate memories in this recurrent circuit, and find anatomical evidence in synapse-level connectivity that supports this hypothesis. Next, we introduce a computational model which combines an initial experience phase and LTM phase to perform generative replay based continual learning. When evaluated on a CIFAR-100 class-incremental continual learning task, the modeled LTM phase increases classification performance by 20% and approaches within 2% of the performance for a non-incremental upper bound baseline. Unique elements of the proposed generative replay model include: 1) coupling high dimensional sparse activation patterns with generative replay and 2) sampling and reconstructing higher level representations for training generative replay (as opposed to reconstructing sensory-level or processed sensory-level representations). Additionally, we make the experimentally testable prediction that a specific set of synapses would need to undergo experience-dependent plasticity during LTM formation to support our generative replay based model.

neuroscience↗

bioRxiv2022

Declining autozygosity over time: an exploration in over 1 million individuals from three diverse cohorts

We hypothesized that overall autozygosity is decreasing over generational time. In this report, we present data that partially support this hypothesis from three large cohorts of diverse ancestries, two from the US (All of Us and the Million Veteran Program, N=82,474 and 622,497, respectively) and one from the UK (UK Biobank, N=380,899). Our results from a mixed-effect meta-analysis demonstrate an overall trend of decreasing autozygosity over generational time (meta-analyzed slope=-0.029, se=0.009, p=6.03e-4). Using a chi-square difference test, we determined that a model including an ancestry-by-country interaction term fit the data best, indicating that ancestry differences in this trend differ by country. We found further evidence to suggest a difference between the US and UK cohorts by meta-analyzing within country, observing a significant negative estimate in the US cohorts (meta-analyzed slope=-0.058, se=0.015, p=1.50e-4) but a non-significant estimate in the UK (meta-analyzed slope=-0.001, se=0.008, p=0.945). We also found that the association between autozygosity and year of birth in the overall meta-analysis was substantially attenuated when accounting for educational attainment and income (meta-analyzed slope=-0.011, se=0.008, p=0.167), suggesting that increases in education and income may partially account for decreasing levels of autozygosity over time. To our knowledge, this is the largest demonstration of decreasing autozygosity over time in a modern sample (birth years 1904-2003), and we speculate that this trend can be attributed to increases in population size, urbanization and panmixia, with differences in demographic and sociocultural processes leading to country-specific differences in the rate of decline.

genomics↗

bioRxiv2022

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

The healthy human brain has long been considered a sterile environment, with the blood brain barrier preventing the formation of a bacterial brain microbiome. Recent electron microscopy (EM) imaging of brain tissue has, however, provided the first preliminary evidence of bacteria in otherwise healthy brain slices. Whether due to contamination, disease, or a previously unknown relationship of bacteria to healthy brain tissue, novel tools are needed to detect and search for bacteria in nanoscale, volumetric EM images. While computer vision tools are widely used in cell segmentation and object detection problems in EM imaging, no bacteria detection tool or dataset exists. Overcoming the rarity of training data, this work presents the first pipeline for training a bacteria detection network for EM images, leveraging existing deep networks for object detection. A deployment and proofreading pipeline is presented, along with characterization of deployment to public EM image datasets. While bacteria in healthy brain tissue were not discovered in this work, this tool presents an opportunity for large scale bacteria search in EM imaging for both scientific discovery and experimental quality control, and serves more generally as a framework for sparse object detection in large imagery datasets.

neuroscience↗

bioRxiv2022

Scalable graph analysis tools for the connectomics community

Neuroscientists now have the opportunity to analyze synaptic resolution connectomes that are larger than the memory on single consumer workstations. As dataset size and tissue diversity have grown, there is increasing interest in conducting comparative connectomics research, including rapidly querying and searching for recurring patterns of connectivity across brain regions and species. There is also a demand for algorithm reuse -- applying methods developed for one dataset to another volume. A key technological hurdle is enabling researchers to efficiently and effectively query these diverse datasets, especially as the raw image volumes grow beyond terabyte sizes. Existing community tools can perform such queries and analysis on smaller scale datasets, which can fit locally in memory, but the path to scaling remains unclear. Existing solutions such as neuPrint or FlyBrainLab enable these queries for specific datasets, but there remains a need to generalize algorithms and standards across datasets. To overcome this challenge, we present a software framework for comparative connectomics and graph discovery to make connectomes easy to analyze, even when larger-than-RAM, and even when stored in disparate datastores. This software suite includes visualization tools, a web portal, a connectivity and annotation query engine, and the ability to interface with a variety of data sources and community tools from the neuroscience community. These tools include MossDB (an immutable datastore for metadata and rich annotations); Grand (for prototyping larger-than-RAM graphs); GrandIso-Cloud (for querying existing graphs that exceed the capabilities of a single work-station); and Motif Studio (for enabling the public to query across connectomes). These tools interface with existing frameworks such as neuPrint, graph databases such as Neo4j, and standard data analysis tools such as Pandas or NetworkX. Together, these tools enable tool and algorithm reuse, standardization, and neuroscience discovery.

neuroscience↗

bioRxiv2021

Exploring the relationships between autozygosity, educational attainment, and cognitive ability in a contemporary, trans-ancestral American sample

Previous studies have found significant associations between estimated autozygosity - the proportion of an individuals genome contained in homozygous segments due to distant inbreeding - and multiple traits, including educational attainment (EA) and cognitive ability. In one study, estimated autozygosity showed a stronger association with parental EA than the subjects own EA. This was likely driven by parental EAs association with mobility: more educated parents tended to migrate further from their hometown, therefore choosing more genetically diverse partners. We examined the associations between estimated autozygosity, cognitive ability, and parental EA in a contemporary sub-sample of adolescents from the Adolescent Brain and Cognitive Development StudySM (ABCD Study(R)) (analytic N=6,504). We found a negative association between autozygosity and child cognitive ability consistent with previous studies, while the associations between autozygosity and parental EA were in the expected direction of effect (with greater levels of autozygosity being associated with lower EA) but the effect sizes were significantly weaker than those estimated in previous work. We also found a lower mean level of autozygosity in the ABCD sample compared to previous autozygosity studies, which may reflect overall decreasing levels of autozygosity over generations. Variation in migration and mobility patterns in the ABCD study compared to other studies may explain the pattern of associations between estimated autozygosity, EA, and cognitive ability in the current study.

genetics↗

bioRxiv2021

Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use

BackgroundRodent paradigms and human genome-wide association studies (GWASs) on drug use have the potential to provide biological insight into the pathophysiology of addiction. MethodsUsing GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP-heritability of these gene-sets using four large human GWASs: 1) alcoholic drinks per week, 2) problematic alcohol use, 3) cigarettes per day and 4) smoking cessation. We benchmarked our findings with curated human alcoholism and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWASs (e.g., height). ResultsThe rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance use GWASs, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. ConclusionOur results suggest that rodent gene expression studies can help to identify genes that capture heritability of substance use traits in humans, yet the specificity to human substance use was less than expected due to various factors such as the genetic architecture of a trait. We outline various limitations, interpretations and considerations for future research.

genetics↗

bioRxiv2021

Online learning for orientation estimation during translation in an insect ring attractor network

Insect neural systems are a promising source of inspiration for new algorithms for navigation, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing of orientation estimation error. Overall, our results here may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments. SummaryAn insect neural connectivity-constrained model performs sensor fusion and online learning for orientation estimation.

neuroscience↗

bioRxiv2020

Adaptation with correlated noise predicts negative interval correlations in neuronal spike trains

Negative correlations in the sequential evolution of interspike intervals (ISIs) are a signature of memory in neuronal spike-trains. They provide coding benefits including firing-rate stabilization, improved detectability of weak sensory signals, and enhanced transmission of information by improving signal-to-noise ratio. Primary electrosensory afferent spike-trains in weakly electric fish fall into two categories based on the pattern of SCCs: non-bursting units have negative SCCs which remain negative but decay to zero with increasing lags (Type I SCCs), and bursting units have oscillatory (alternating sign) SCCs which damp to zero with increasing lags (Type II SCCs). Here, we predict and match observed ISI serial correlations in these afferents using a stochastic dynamic threshold model. We determine SCCs as a function of an arbitrary discrete noise correlation function Rk, where k is a multiple of the mean ISI. The function permits forward and inverse calculations of SCCs. Both types of SCCs can be generated by adding colored noise to the spike threshold with Type I SCCs generated with slow noise and Type II SCCs generated with fast noise. We show that a first-order autoregressive (AR) process with a single parameter is sufficient to predict and accurately match both types of afferent SCCs, the type being determined by the sign of the AR parameter. The predicted and experimentally observed SCCs are in geometric progression. The theory predicts that the limiting sum of SCCs is -0.5 yielding a perfect DC-block in the power spectrum of the spike train. Observed SCCs from afferents have a limiting sum that is slightly larger at -0.475 {+/-} 0.04 (mean {+/-} s.d.). We conclude that the underlying process for generating ISIs may be a simple combination of low-order autoregressive (AR) processes, and discuss the results from the perspective of optimal coding.

neuroscience↗

bioRxiv2020

Multi-omics integration analysis identifies novel genes for alcoholism with potential link to neurodegenerative diseases

SignificanceIdentification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understanding the biology of alcohol use disorder (AUD). MethodsIntegration of "multi-omics" data is often necessary to nominate candidate causal variants and genes and prioritize them for follow up studies. Here, we used Mendelian randomization to integrate AUD and drinks per week (DPW) GWAS summary statistics with the gene expression and methylation quantitative trait loci (eQTLs and mQTLs) in the largest brain and myeloid datasets. We also used AUD-related single cell epigenetic data to nominate candidate causal variants and genes associated with DPW and AUD. ResultsOur multi-omics integration analyses prioritized unique as well as shared genes and pathways among AUD and DPW. The GWAS variants associated with both AUD and DPW showed significant enrichment in the promoter regions of fetal and adult brains. The integration of GWAS SNPs with mQTLs from fetal brain prioritized variants on chromosome 11 in both AUD and DPW GWASs. The co-localized variants were found to be overlapping with promoter marks for SPI1, specifically in human microglia, the myeloid cells of the brain. The co-localized SNPs were also strongly associated with SPI1 mRNA expression in myeloid cells from peripheral blood. The prioritized variant at this locus is predicted to alter the binding site for a transcription factor, RXRA, a key player in the regulation of myeloid cell function. Our analysis also identified MAPT as a candidate causal gene specifically associated with DPW. mRNA expression of MAPT was also correlated with daily amounts of alcohol intake in post-mortem brains (frontal cortex) from alcoholics and controls (N = 92). Results may be queried and visualized in an online public resource of these integrative analysis (https://lcad.shinyapps.io/alc_multiomics/). These results highlight overlap between causal genes for neurodegenerative diseases, alcohol use disorder and alcohol consumption. In conclusionintegrating GWAS summary statistics with multi-omics datasets from multiple sources identified biological similarities and differences between typical alcohol intake and disordered drinking highlighting molecular heterogeneity that might inform future targeted functional and cross-species studies. Interestingly, overlap was also observed with causal genes for neurodegenerative diseases.

genomics↗

bioRxiv2020

Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.

genetics↗

bioRxiv2020

The TRIM9/TRIM67 neuronal interactome reveals novel activators of morphogenesis

TRIM9 and TRIM67 are neuronally-enriched E3 ubiquitin ligases essential for appropriate morphogenesis of cortical and hippocampal neurons and fidelitous responses to the axon guidance cue netrin-1. Deletion of murine Trim9 or Trim67 results in neuroanatomical defects and striking behavioral deficits, particularly in spatial learning and memory. TRIM9 and TRIM67 interact with cytoskeletal and exocytic proteins, but the full interactome is not known. Here we performed the unbiased proximity-dependent biotin identification (BioID) approach to define TRIM9 and TRIM67 protein-protein proximity network in developing cortical neurons and identified neuronal putative TRIM interaction partners. Candidates included cytoskeletal regulators, cytosolic protein transporters, exocytosis and endocytosis regulators, and proteins necessary for synaptic regulation. A subset of high priority candidates was validated, including Myo16, Coro1A, SNAP47, ExoC1, GRIP1, PRG-1, and KIF1A. For a subset of validated candidates, we utilized TIRF microscopy to demonstrate dynamic colocalization with TRIM proteins at the axonal periphery, including at the tips of filopodia. Further analysis demonstrated the RNAi-based knockdown of the unconventional myosin Myo16 in cortical neurons altered axonal branching patterns in a TRIM9 and netrin-1 dependent manner. Future analysis of other validated candidates will likely identify novel proteins and mechanisms by which TRIM9 and TRIM67 regulate neuronal form and function.

cell biology↗

bioRxiv2020

Modulation of metabolic hormone signaling via a circadian hormone and a biogenic amine in Drosophila melanogaster.

In insects, Adipokinetic hormone is the primary hormone responsible for the mobilization of stored energy. While a growing body of evidence has solidified AKHs role in modulating the physiological and behavioral responses to metabolic stress, little is known about the upstream endocrine circuit that directly regulates AKH release. We evaluated the AKH-expressing cell transcriptome to identify potential regulatory elements controlling AKH cell activity, and found that a number of receptors show consistent expression levels, including all known dopamine receptors, dopamine ecdysone receptor (DopEcR), Dopamine 2-like receptor (D2R), Dopamine 1-like receptor 2 (DopR2), DopR, and the Pigment Dispersing Factor (PDFR). We tested the consequences of targeted genetic knockdown and found that RNAi elements targeting each dopamine receptor caused a significant reduction in survival under starvation. In contrast, PDFR knockdown significantly extended lifespan under starvation whereas expression of a tethered PDF in AKH cells resulted in a significantly shorter lifespan during starvation. These manipulations also caused various changes in locomotor activity under starvation. Specifically, there were higher amounts of locomotor activity in dopamine receptor knockdowns, in both replete and starved states. PDFR knockdown resulted in increased locomotion during replete conditions and locomotion levels that were comparable to wild-type during starvation. Expression of a membrane-tethered PDF led to decreased locomotion under baseline and starvation. Next, we used live-cell imaging to evaluate the acute effects of the ligands for these receptors (dopamine, ecdysone, and Pigment Dispersing Factor) on AKH cell activation. Dopamine application led to a transient increase in intracellular calcium in a sugar-dependent manner. Furthermore, we found that co-application of dopamine and ecdysone led to a complete loss of this response, suggesting that these two hormones are acting antagonistically. We also found that PDF application directly led to an increase in cAMP in AKH cells, and that this response was dependent on expression of the PDFR in AKH cells. Together these results suggest a complex circuit in which multiple hormones act on AKH cells to modulate metabolic state.

neuroscience↗