Pathogen infection induces sickness behaviors by recruiting neuromodulatory systems linked to stress and satiety inC. elegans

Abstract

SUMMARYWhen animals are infected by a pathogen, peripheral sensors of infection signal to the brain to coordinate a set of adaptive behavioral changes known as sickness behaviors. While the pathways that signal from the periphery to the brain have been intensively studied in recent years, how central circuits are reconfigured to elicit sickness behaviors is not well understood. Here we find that neuromodulatory systems linked to stress and satiety are recruited upon infection to drive sickness behaviors inC. elegans. Upon chronic infection by the bacteriumPseudomonas aeruginosaPA14,C. elegansdecrease their feeding behavior, then display reversible bouts of quiescence, and eventually die. The ALA neuron and its neuropeptides FLP-7, FLP-24, and NLP-8, which control stress-induced sleep in uninfected animals, promote the PA14-induced feeding reduction. However, the ALA neuropeptide FLP-13 instead acts to delay quiescence and death in infected animals. Cell-specific genetic perturbations show that the neurons that release FLP-13 to delay quiescence in infected animals are distinct from ALA. A brain-wide imaging screen reveals that infection-induced quiescence involves ASI and DAF-7/TGF-beta, which control satiety-induced quiescence in uninfected animals. Our results suggest that a common set of neuromodulators are recruited across different physiological states, acting from distinct neural sources and in distinct combinations to drive state-dependent behaviors.

Deep Neural Networks to Register and Annotate the Cells of theC. elegansNervous System

“…2B-D shows example GCaMP dataset and GFP control datasets). We term the ANTSUN pipeline with gradient descent registration ANTSUN 1.4 35,46 and the version with BrainAlignNet registration ANTSUN 2.0 (Fig. 2A).…”

“…Data used to train and evaluate the models include previously-published datasets 35,46,59 and newly-collected data. These animals were recorded under similar recording conditions to those described in our previous study 35 .…”

“…We integrated BrainAlignNet into our previously-described ANTSUN pipeline 35,46 (also applied in 59 ). Briefly, the pipeline: (i) performs some image pre-processing such as shear-correction and cropping; (ii) segments the images into neuron ROIs via a 3D U-Net; (iii) finds time points where the worm postures are similar; (iv) performs image registration to define a coordinate mapping between these time points; (v) applies that coordinate mapping to the ROIs; (vi) constructs an ROI similarity matrix storing how likely different ROIs are to correspond to the same neuron; (vii) clusters that matrix to extract neuron identity; (viii) maps the linked ROIs onto the GCaMP data to extract neural traces; and (ix) performs some postprocessing such as background-subtraction and bleach correction to extract neural traces.…”

“…To convert the output into labels, we first ran our previously-described neuron segmentation network 35,46 on the tagRFP channel of the NeuroPAL image. Specifically, since this segmentation network was trained on lower-SNR freely-moving data, we ran it on a lower-SNR copy of the tagRFP channel.…”