The Caenorhabditis elegans TDRD5/7-like protein, LOTR-1, interacts with the helicase ZNFX-1 to balance epigenetic signals in the germline.

LOTUS and Tudor domain containing proteins have critical roles in the germline. Proteins that contain these domains, such as Tejas/Tapas in Drosophila, help localize the Vasa helicase to the germ granules and facilitate piRNA-mediated transposon silencing. The homologous proteins in mammals, TDRD5 and TDRD7, are required during spermiogenesis. Until now, proteins containing both LOTUS and Tudor domains in Caenorhabditis elegans have remained elusive. Here we describe LOTR-1 (D1081.7), which derives its name from its LOTUS and Tudor domains. Interestingly, LOTR-1 docks next to P granules to colocalize with the broadly conserved Z-granule helicase, ZNFX-1. The Tudor domain of LOTR-1 is required for its Z-granule retention. Like znfx-1 mutants, lotr-1 mutants lose small RNAs from the 3' ends of WAGO and mutator targets, reminiscent of the loss of piRNAs from the 3' ends of piRNA precursor transcripts in mouse Tdrd5 mutants. Our work shows that LOTR-1 acts with ZNFX-1 to bring small RNA amplifying mechanisms towards the 3' ends of its RNA templates.

DevStaR: high-throughput quantification of C. elegans developmental stages.

We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.