A Run-Length Encoding Approach for Path Analysis of C. elegans

The nematode Caenorhabditis elegans explores the environment using a combination of different movement patterns, which include straight movement, reversal, and turns. We propose to quantify C. elegans movement behavior using a computer vision approach based on run-length encoding of step-length data. In this approach, the path of C. elegans is encoded as a string of characters, where each character represents a path segment of a specific type of movement. With these encoded string data, we perform k-means cluster analysis to distinguish movement behaviors resulting from different genotypes and food availability. We found that shallow and sharp turns are the most critical factors in distinguishing the differences among the movement behaviors. To validate our approach, we examined the movement behavior of tph-1 mutants that lack an enzyme responsible for serotonin biosynthesis. A k-means cluster analysis with the path string-encoded data showed that tph-1 movement behavior on food is similar to that of wild-type animals off food. We suggest that this run-length encoding approach is applicable to trajectory data in animal or human mobility data

A Markovian dynamics for C.elegansC. elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm $C. elegans$, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion, and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both ``runs-and-pirouettes'' as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.Comment: 28 pages, 14 figure

Annotation of two large contiguous regions from the Haemonchus contortus genome using RNA-seq and comparative analysis with Caenorhabditis elegans

The genomes of numerous parasitic nematodes are currently being sequenced, but their complexity and size, together with high levels of intra-specific sequence variation and a lack of reference genomes, makes their assembly and annotation a challenging task. Haemonchus contortus is an economically significant parasite of livestock that is widely used for basic research as well as for vaccine development and drug discovery. It is one of many medically and economically important parasites within the strongylid nematode group. This group of parasites has the closest phylogenetic relationship with the model organism Caenorhabditis elegans, making comparative analysis a potentially powerful tool for genome annotation and functional studies. To investigate this hypothesis, we sequenced two contiguous fragments from the H. contortus genome and undertook detailed annotation and comparative analysis with C. elegans. The adult H. contortus transcriptome was sequenced using an Illumina platform and RNA-seq was used to annotate a 409 kb overlapping BAC tiling path relating to the X chromosome and a 181 kb BAC insert relating to chromosome I. In total, 40 genes and 12 putative transposable elements were identified. 97.5% of the annotated genes had detectable homologues in C. elegans of which 60% had putative orthologues, significantly higher than previous analyses based on EST analysis. Gene density appears to be less in H. contortus than in C. elegans, with annotated H. contortus genes being an average of two-to-three times larger than their putative C. elegans orthologues due to a greater intron number and size. Synteny appears high but gene order is generally poorly conserved, although areas of conserved microsynteny are apparent. C. elegans operons appear to be partially conserved in H. contortus. Our findings suggest that a combination of RNA-seq and comparative analysis with C. elegans is a powerful approach for the annotation and analysis of strongylid nematode genomes

Unexpected cell type-dependent effects of autophagy on polyglutamine aggregation revealed by natural genetic variation in C. elegans.

BACKGROUND: Monogenic protein aggregation diseases, in addition to cell selectivity, exhibit clinical variation in the age of onset and progression, driven in part by inter-individual genetic variation. While natural genetic variants may pinpoint plastic networks amenable to intervention, the mechanisms by which they impact individual susceptibility to proteotoxicity are still largely unknown. RESULTS: We have previously shown that natural variation modifies polyglutamine (polyQ) aggregation phenotypes in C. elegans muscle cells. Here, we find that a genomic locus from C. elegans wild isolate DR1350 causes two genetically separable aggregation phenotypes, without changing the basal activity of muscle proteostasis pathways known to affect polyQ aggregation. We find that the increased aggregation phenotype was due to regulatory variants in the gene encoding a conserved autophagy protein ATG-5. The atg-5 gene itself conferred dosage-dependent enhancement of aggregation, with the DR1350-derived allele behaving as hypermorph. Surprisingly, increased aggregation in animals carrying the modifier locus was accompanied by enhanced autophagy activation in response to activating treatment. Because autophagy is expected to clear, not increase, protein aggregates, we activated autophagy in three different polyQ models and found a striking tissue-dependent effect: activation of autophagy decreased polyQ aggregation in neurons and intestine, but increased it in the muscle cells. CONCLUSIONS: Our data show that cryptic natural variants in genes encoding proteostasis components, although not causing detectable phenotypes in wild-type individuals, can have profound effects on aggregation-prone proteins. Clinical applications of autophagy activators for aggregation diseases may need to consider the unexpected divergent effects of autophagy in different cell types

Olfactory learning alters navigation strategies and behavioral variability in C. elegans

Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butanone gradient or ignore it, respectively. However, the exact change in navigation strategy in response to learning is still unknown. Here we study the learned odor navigation in worms by combining precise experimental measurement and a novel descriptive model of navigation. Our model consists of two known navigation strategies in worms: biased random walk and weathervaning. We infer weights on these strategies by applying the model to worm navigation trajectories and the exact odor concentration it experiences. Compared to naive worms, appetitive trained worms up-regulate the biased random walk strategy, and aversive trained worms down-regulate the weathervaning strategy. The statistical model provides prediction with $>90 \%$ accuracy of the past training condition given navigation data, which outperforms the classical chemotaxis metric. We find that the behavioral variability is altered by learning, such that worms are less variable after training compared to naive ones. The model further predicts the learning-dependent response and variability under optogenetic perturbation of the olfactory neuron AWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream from AWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guided navigation. Together, we provide a new paradigm to quantify flexible navigation algorithms and pinpoint the underlying neural substrates

On Weighted k-mer Dictionaries

We consider the problem of representing a set of k-mers and their abundance counts, or weights, in compressed space so that assessing membership and retrieving the weight of a k-mer is efficient. The representation is called a weighted dictionary of k-mers and finds application in numerous tasks in Bioinformatics that usually count k-mers as a pre-processing step. In fact, k-mer counting tools produce very large outputs that may result in a severe bottleneck for subsequent processing. In this work we extend the recently introduced SSHash dictionary (Pibiri, Bioinformatics 2022) to also store compactly the weights of the k-mers. From a technical perspective, we exploit the order of the k-mers represented in SSHash to encode runs of weights, hence allowing (several times) better compression than the empirical entropy of the weights. We also study the problem of reducing the number of runs in the weights to improve compression even further and illustrate a lower bound for this problem. We propose an efficient, greedy, algorithm to reduce the number of runs and show empirically that it performs well, i.e., very similarly to the lower bound. Lastly, we corroborate our findings with experiments on real-world datasets and comparison with competitive alternatives. Up to date, SSHash is the only k-mer dictionary that is exact, weighted, associative, fast, and small

Temporal Processing by Caenorhabditis elegans Sensory Neurons

Caenorhabditis elegans is a promising organism for trying to understand how nervous systems generate real-time behavior. Its low neuron count suggests that we may be able to observe all of the constituents of the computation of sophisticated sensorimotor behavior. However, its appropriateness as a system for quantitative dynamical study has yet to be established. We show that C. elegans chemosensory neurons can operate in a highly deterministic and low-noise mode, and they act as reliable linear filters of their input. We then use dynamical systems analysis in combination with classical genetic perturbation to uncover cellular and circuit mechanisms of temporal processing. This work should firmly establish C. elegans as a viable platform for applying quantitative dynamical systems methods to understanding how a nervous system processes sensory information, integrates it with an evolving internal state, and produces goal-directed, coordinated behavior

Network Designs Via Signaling Dynamics On Geometric Dynamic Graphs

Artificial neural networks are treated as black boxes. Generally,only the states of a subset of the network are considered to determine its efficacy, while the relationship between a neural network’s topology and its function remains under-theorized. For my analysis, I use a new class of event-driven recurrent neural networks—a geometric dynamic network modeled on canonical neurobiological signaling principles that allows to directly encode input data into its evolving dynamics—to forward a new type of machine learning approach. I accomplish this by first, mapping causal neuronal signal flows in the C. elegans connectome to show how the dynamic evolution of signal flows results in a unique internal representation of particular input data. Second, I propose two distinct approaches to determine the upper-bound for the amount of network dynamics needed for capturing the signaling evolution of the system. Using the upper-bound values, I construct a mathematical object representing the causal neuronal signaling dynamics, and delineate the interaction of sub-sub structures at various scales/heights of sub-graphs. Finally, based on recent theoretical propositions regarding optimal signaling in a geometric dynamic network, I show that neurons modify their axonal morphology so that the propagation time of an action potential, and the membrane’s refractory period become balanced. Thus, this work not only lays the foundation to construct and analyze a new class of artificial neural networks whose overall behavior and underlying dynamics are transparently coupled, it also provides fertile grounds for future work on biologically inspired artificial intelligence

Regulation of two motor patterns enables the gradual adjustment of locomotion strategy in Caenorhabditis elegans

In animal locomotion a tradeoff exists between stereotypy and flexibility: fast long-distance travelling (LDT) requires coherent regular motions, while local sampling and area-restricted search (ARS) rely on flexible movements. We report here on a posture control system in C. elegans that coordinates these needs. Using quantitative posture analysis we explain worm locomotion as a composite of two modes: regular undulations versus flexible turning. Graded reciprocal regulation of both modes allows animals to flexibly adapt their locomotion strategy under sensory stimulation along a spectrum ranging from LDT to ARS. Using genetics and functional imaging of neural activity we characterize the counteracting interneurons AVK and DVA that utilize FLP-1 and NLP-12 neuropeptides to control both motor modes. Gradual regulation of behaviors via this system is required for spatial navigation during chemotaxis. This work shows how a nervous system controls simple elementary features of posture to generate complex movements for goal-directed locomotion strategies

Transcription, signaling receptor activity, oxidative phosphorylation, and fatty acid metabolism mediate the presence of closely related species in distinct intertidal and cold-seep habitats

Bathyal cold seeps are isolated extreme deep-sea environments characterized by low species diversity while biomass can be high. The Hakon Mosby mud volcano (Barents Sea, 1,280 m) is a rather stable chemosynthetic driven habitat characterized by prominent surface bacterial mats with high sulfide concentrations and low oxygen levels. Here, the nematode Halomonhystera hermesithrives in high abundances (11,000 individuals 10 cm(-2)). Halomonhystera hermesi is a member of the intertidal Halomonhystera disjuncta species complex that includes five cryptic species (GD 1-5). GD1-5's common habitat is characterized by strong environmental fluctuations. Here, we compared the transcriptomes of H. hermesi and GD1, H. hermesi's closest relative. Genes encoding proteins involved in oxidative phosphorylation are more strongly expressed in H. hermesi than in GD1, and many genes were only observed in H. hermesi while being completely absent in GD1. Both observations could in part be attributed to high sulfide concentrations and low oxygen levels. Additionally, fatty acid elongation was also prominent in H. hermesi confirming the importance of highly unsaturated fatty acids in this species. Significant higher amounts of transcription factors and genes involved in signaling receptor activity were observed in GD1 (many of which were completely absent in H. hermesi), allowing fast signaling and transcriptional reprogramming which can mediate survival in dynamic intertidal environments. GC content was approximately 8% higher in H. hermesi coding unigenes resulting in differential codon usage between both species and a higher proportion of amino acids with GC-rich codons in H. hermesi. In general our results showed that most pathways were active in both environments and that only three genes are under natural selection. This indicates that also plasticity should be taken in consideration in the evolutionary history of Halomonhystera species. Such plasticity, as well as possible preadaptation to low oxygen and high sulfide levels might have played an important role in the establishment of a cold-seep Halomonhystera population