Study of extracellular matrix synthesis in C. elegans

The epithelial monolayer of cells surrounding the animal, the hypodermis, Synthesises five cuticles during the nematode life cycle. The first cuticle is formed within the egg, prior to hatching, and the remainder towards the end of each larval stage. Because of the structural role of the cuticle, mutations in genes involved in assembly of this ECM can cause a spectrum of effects from lethality late in embryogenesis to alterations in the nematode shape. The severity of phenotype correlates with the severity of cuticle synthesis defects. Accordingly, two distinct mutant alleles that cause death after embryonic elongation, possibly due to failure in synthesising an intact cuticle, were characterised . One mutant, ij15, was isolated from a forward genetic screen previously performed (I. Johnstone, Glasgow University, Glasgow, UK). ij15 defines mutationally the gene stc-1, which encodes a HSP70-like protein possibly localised in the secretory pathway. The other mutant, h402, defines mutationally the gene let-607. A second let-607 allele, h189, which results in larval lethaity at the L2 stage was also analysed in this study. let-607 corresponds to the predicted gene F57B10.1, which encodes a putative bZIP transcription factor. Both stc-1 and let-607 are expressed in the hypodermis at all developmental stages. Furthermore, disruption of the function of either stc-1 or let-607 by mutation or RNAi affects cuticle synthesis in different ways. Thus, stc-1 and let-607 encode for a HSP70-like protein and a putative bZIP transcription factor required for synthesis of the cuticular ECM in C. elegans. In addition, this study defines C. elegans mutant phenotypes that can be used as indicators for gene products with controlling roles in the synthesis of this ECM

MicroRNAs in C. elegans Aging: Molecular Insurance for Robustness?

The last decade has witnessed a revolution in our appreciation of the extensive regulatory gene expression networks modulated by small untranslated RNAs. microRNAs (miRNAs), ~22 nt RNAs that bind imperfectly to partially homologous sites on target mRNAs to regulate transcript expression, are now known to influence a broad range of biological processes germane to development, homeostatic regulation and disease. It has been proposed that miRNAs ensure biological robustness, and aging has been described as a progressive loss of system and cellular robustness, but relatively little work to date has addressed roles of miRNAs in longevity and healthspan (the period of youthful vigor and disease resistance that precedes debilitating decline in basic functions). The C. elegans model is highly suitable for testing hypotheses regarding miRNA impact on aging biology: the lifespan of the animal is approximately three weeks, there exist a wealth of genetic mutations that alter lifespan through characterized pathways, biomarkers that report strong healthspan have been defined, and many miRNA genes have been identified, expression-profiled, and knocked out. 50/114 C. elegans miRNAs change in abundance during adult life, suggesting significant potential to modulate healthspan and lifespan. Indeed, miRNA lin-4 has been elegantly shown to influence lifespan and healthspan via its lin-14 mRNA target and the insulin signaling pathway. 27 of the C. elegans age-regulated miRNAs have sequence similarity with both fly and human miRNAs. We review current understanding of a field poised to reveal major insights into potentially conserved miRNA-regulated networks that modulate aging

Sequence Relationships among C. elegans, D. melanogaster and Human microRNAs Highlight the Extensive Conservation of microRNAs in Biology

microRNAs act in a prevalent and conserved post-transcriptional gene regulatory mechanism that impacts development, homeostasis and disease, yet biological functions for the vast majority of miRNAs remain unknown. Given the power of invertebrate genetics to promote rapid evaluation of miRNA function, recently expanded miRNA identifications (miRBase 10.1), and the importance of assessing potential functional redundancies within and between species, we evaluated miRNA sequence relationships by 5′ end match and overall homology criteria to compile a snapshot overview of miRNA families within the C. elegans and D. melanogaster genomes that includes their identified human counterparts. This compilation expands literature documentation of both the number of families and the number of family members, within and between nematode and fly models, and highlights sequences conserved between species pairs or among nematodes, flies and humans. Themes that emerge include the substantial potential for functional redundancy of miRNA sequences within species (84/139 C. elegans miRNAs and 70/152 D. melanogaster miRNAs share significant homology with other miRNAs encoded by their respective genomes), and the striking extent to which miRNAs are conserved across species—over half (73/139) C. elegans miRNAs share sequence homology with miRNAs encoded also in both fly and human genomes. This summary analysis of mature miRNA sequence relationships provides a quickly accessible resource that should facilitate functional and evolutionary analyses of miRNAs and miRNA families

CeleST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion.

In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim "gaits" in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes "graceful" from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible

CeleST reveals novel information on aging phenotypes.

<p><b>A–J</b>, Age-associated changes in swimming parameters in wild-type adults. <b>A</b>, Wave initiation rate; <b>B</b>, Body wave number; <b>C</b>, Asymmetry; <b>D</b>, Stretch; <b>E</b>, Attenuation; <b>F</b>, Reverse swimming; <b>G</b>, Curling; <b>H</b>, Travel speed; <b>I</b>, Brush stroke; and <b>J</b>, Activity index. from 9 independent trials, for each age day 4 and day 11. Data for ages ranging from day 4 to day 20 are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s004" target="_blank">Figure S4</a>. <b>K–M</b>, CeleST reports great differences in graceful agers vs. poor agers for measures that change with age. We selected animals that appeared to have robust crawling capacity (Class A, graceful agers) and those that had decrepit crawling capacity (Class C, poor agers) at day 11 and compared swim behavior. <b>K</b>, Activity index; <b>L</b>, Asymmetry; <b>M</b>, Attenuation. from 3 independent trials, for each class. Data for all ten measures in this series are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s005" target="_blank">Figure S5</a>. <b>N</b>, Locomotory changes under life-extending and progeric insulin signaling pathway manipulation suggest complex influences of signaling over the lifetime. Activity index, WT: blue line (middle dashed line); long lived <i>age-1(hx546)</i>: green (top line); short-lived <i>daf-16(mgDf50)</i>: red (bottom line). Note that the <i>age-1</i> mutant has a higher activity index in young adult life as compared to WT, which suggests differences in swim performance are not limited to aging. Also, at day 15, WT and <i>age-1</i> scores appear increased, which we suggest reflects the preferential death of the poorest swimmers, rather than an actual increase in average swimming of individuals. in each data point from 4 independent trials. Data on all measures are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s006" target="_blank">Figure S6</a>. Error bars show SEM, *** , **** .</p

Examples of ten CeleST measure outputs for an individual <i>C. elegans</i> swim trial.

<p>All measures reflect analysis of a 30/sec, with <b>C–G</b> calculated from analysis of radius of curvature over 12 body segments (curvature plot vs. time example is in <b>B</b>). For <b>C–G</b> and <b>M–O</b>, instantaneous values are plotted in black; the median value for each swim is drawn in red, and the 10–90 percentile range of values is shown in gray; median and range over the 30 s trial are listed on the right. Note that although this panel demonstrates analysis of measurements of a single animal swimming, the CeleST program score multiple animals simultaneously and can compile data from thousands of individual swim trials (examples in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi-1003702-g003" target="_blank">Figure 3</a>). <b>A</b>, Scored animal at three indicated time points in the video, with the curvature measure superimposed on the body; color key shown in <b>B</b>. <b>B</b>, Curvature heat map of an individual swim trial. Map is of curvature at a given body point (Y axis) as a function of time (X axis), with head curvature score at the top and tail curvature at the bottom on the Y axis, deep bend in one direction dark blue, and in the other direction dark red. Lines indicate posture of the animal depicted at that time point in panel <b>A</b>. Note that the posture of an individual at any point in time could be reconstructed from the measure of curvature over body position. Further details on each parameter measurement <b>C–O</b> are given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s009" target="_blank">Text S1</a>. Informative videos (Videos S2 01–10) that feature extreme examples for each measure can be found on <a href="http://celest.mbb.rutgers.edu" target="_blank">http://celest.mbb.rutgers.edu</a>.</p

CeleST analysis reveals features of <i>C. elegans</i> swimming considerable individuality, gait preference, and reverse swimming.

<p><b>A–C</b>, <i>C. elegans</i> exhibit diverse swimming abilities, despite genetic and environmental homogeneity. CeleST can plot scores for two parameters against each other, for example: <b>A</b>, Travel speed vs. Asymmetry; <b>B</b>, Body wave number vs. Activity index; <b>C</b>, Brush stroke vs. Stretch. Data for WT 4-day old animals from 9 independent trials are plotted. <b>D</b>, <i>C. elegans</i> swim at specific wave initiation rates. We plotted in the form of line histograms the distribution of median Wave initiation rates (WIR) in wild-type animals as occurs over a 30 second interval. WIR values are binned to integers and the plot line delineates the contour of the bins in the histogram. X axis is median WIR, Y axis is the number of individuals exhibiting the indicated WIR. Data in this panel are combined to represent 3,372 animals ranging from 4 to 20 days old from 9 independent trials to emphasize the peaked distribution. Although animals in this large population do swim over the range of possible <u>median</u> WIRs (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s001" target="_blank">Figure S1F</a> for example), CeleST analysis reveals an unexpected bias for particular “gaits” in a subset of the population (about 14% total appear in favored WIRs). Older animals swim at lower median WIRs than young adult animals, but the preferred WIRs remain. WIR distributions for specific individual ages are depicted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s001" target="_blank">Figure S1</a>. Note that mean WIR rates do not exhibit a distribution bias (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s001" target="_blank">Figure S1F</a>), so this study emphasizes the value of also considering median scores in swim behavioral analysis. <b>E–G</b>, For brief periods, swimming animals reverse, with the tail initiating the body wave. Reverse swimming is illustrated in Videos S3 and S4 on <a href="http://celest.mbb.rutgers.edu" target="_blank">http://celest.mbb.rutgers.edu</a>. In 4-day old animals, the <i>glr-1(ky176)</i> mutant, lacking a neuronal glutamate receptor, reversal frequency is increased relative to WT (<b>E</b>), although the trend to increased time spent in reverse is not statistically significant () (<b>F</b>). Unexpectedly, <i>glr-1</i> mutants swim more symmetrically than WT at day 4 (<b>G</b>). from 3 independent trials for each strain. Data for all 10 measures, young and old age are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003702#pcbi.1003702.s002" target="_blank">Figure S2</a>. Error bars show SEM, **** .</p

Summary of CeleST components and usage.

<p>Input files are videos of multiple swimming <i>C. elegans</i>. Files are stored in a database that records identifying features (strain, date, etc.) to permit easy selection of animals to be compared by analysis. After selection of animals to be compared, swimmers are automatically tracked from videos, and computation from curvature data or posture is used to score ten swim measures in 30 second swim trials (see description in text). Measures from the scored animals are compiled and can be exported in several alternative data analysis formats, including dot plots, line graphs, histograms and two dimensional comparison (ellipses indicate the principal directions and the standard deviations of the data). Statistical analysis is automated. A dynamic demonstration of CeleST tracking and computing of measures can be found in Video S1 1–4 on <a href="http://celest.mbb.rutgers.edu" target="_blank">http://celest.mbb.rutgers.edu</a>.</p