Fertilization in C. elegans requires an intact C-terminal RING finger in sperm protein SPE-42

<p>Abstract</p> <p>Background</p> <p>The <it>C. elegans </it>sperm protein SPE-42, a membrane protein of unknown structure and molecular function, is required for fertilization. Sperm from worms with <it>spe-42 </it>mutations appear normal but are unable to fertilize eggs. Sequence analysis revealed the presence of 8 conserved cysteine residues in the C-terminal cytoplasmic domain of this protein suggesting these residues form a zinc-coordinating RING finger structure.</p> <p>Results</p> <p>We made an <it>in silico </it>structural model of the SPE-42 RING finger domain based on primary sequence analysis and previously reported RING structures. To test the model, we created <it>spe-42 </it>transgenes coding for mutations in each of the 8 cysteine residues predicted to coordinate Zn⁺⁺ions in the RING finger motif. Transgenes were crossed into a <it>spe-42 </it>null background and protein function was measured by counting progeny. We found that all 8 cysteines are required for protein function. We also showed that sequence differences between the C-terminal 29 and 30 amino acids in <it>C. elegans </it>and <it>C. briggsae </it>SPE-42 following the RING finger domain are not responsible for the failure of the <it>C. briggsae </it>SPE-42 homolog to rescue <it>C. elegans spe-42 </it>mutants.</p> <p>Conclusions</p> <p>The results suggest that a <it>bona fide </it>RING domain is present at the C-terminus of the SPE-42 protein and that this motif is required for sperm-egg interactions during <it>C. elegans </it>fertilization. Our structural model of the RING domain provides a starting point for further structure-function analysis of this critical region of the protein. The C-terminal domain swap experiment suggests that the incompatibility between the <it>C. elegans </it>and <it>C. briggsae </it>SPE-42 proteins is caused by small amino acid differences outside the C-terminal domain.</p

spe-10 Encodes a DHHC–CRD Zinc-Finger Membrane Protein Required for Endoplasmic Reticulum/Golgi Membrane Morphogenesis During Caenorhabditis elegans Spermatogenesis

C. elegans spermatogenesis employs lysosome-related fibrous body–membranous organelles (FB–MOs) for transport of many cellular components. Previous work showed that spe-10 mutants contain FB–MOs that prematurely disassemble, resulting in defective transport of FB components into developing spermatids. Consequently, spe-10 spermatids are smaller than wild type and contain defective FB–MO derivatives. In this article, we show that spe-10 encodes a four-pass integral membrane protein that has a DHHC–CRD zinc-finger motif. The DHHC–CRD motif is found in a large, diverse family of proteins that have been implicated in palmitoyl transfer during protein lipidation. Seven spe-10 mutants were analyzed, including missense, nonsense, and deletion mutants. An antiserum to SPE-10 showed significant colocalization with a known marker for the FB–MOs during wild-type spermatogenesis. In contrast, the spe-10(ok1149) deletion mutant lacked detectable SPE-10 staining; this mutant lacks a spe-10 promoter and most coding sequence. The spe-10(eb64) missense mutation, which changes a conserved residue within the DHHC–CRD domain in all homologues, behaves as a null mutant. These results suggest that wild-type SPE-10 is required for the MO to properly deliver the FB to the C. elegans spermatid and the DHHC–CRD domain is essential for this function

Supplemental Material for Ratliff et al., 2018

Supplemental Figures S1-S5<br>Table S1: mib-1 mutations<br>Table S2: Primers Used for PCR and CRISPR Genome Engineering<br>Movies S1-S4<br

Validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on L3 abdominal CT images

Background: body composition assessment using abdominal computed tomography (CT) images is increasingly applied in clinical and translational research. Manual segmentation of body compartments on L3 CT images is time-consuming and requires significant expertise. Robust high-throughput automated segmentation is key to assess large patient cohorts and ultimately, to support implementation into routine clinical practice. By training a deep learning neural network (DLNN) with several large trial cohorts and performing external validation on a large independent cohort, we aim to demonstrate the robust performance of our automatic body composition segmentation tool for future use in patients.Methods: L3 CT images and expert-drawn segmentations of skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue of patients undergoing abdominal surgery were pooled (n = 3,187) to train a DLNN. The trained DLNN was then externally validated in a cohort with L3 CT images of patients with abdominal cancer (n = 2,535). Geometric agreement between automatic and manual segmentations was evaluated by computing two-dimensional Dice Similarity (DS). Agreement between manual and automatic annotations were quantitatively evaluated in the test set using Lin’s Concordance Correlation Coefficient (CCC) and Bland-Altman’s Limits of Agreement (LoA).Results: the DLNN showed rapid improvement within the first 10,000 training steps and stopped improving after 38,000 steps. There was a strong concordance between automatic and manual segmentations with median DS for skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue of 0.97 (interquartile range, IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: skeletal muscle 0.964 (0.959-0.968), visceral adipose tissue 0.998 (0.998-0.998), and subcutaneous adipose tissue 0.992 (0.991-0.993). Bland-Altman metrics (relative to approximate median values in parentheses) indicated only small and clinically insignificant systematic offsets : 0.23 HU (0.5%), 1.26 cm2.m-2 (2.8%), -1.02 cm2.m-2 (1.7%), and 3.24 cm2.m-2 (4.6%) for skeletal muscle average radiodensity, skeletal muscle index, visceral adipose tissue index, and subcutaneous adipose tissue index, respectively. Assuming the decision thresholds by Martin et al. for sarcopenia and low muscle radiation attenuation, results for sensitivity (0.99 and 0.98 respectively), specificity (0.87 and 0.98 respectively), and overall accuracy (0.93) were all excellent.Conclusion: we developed and validated a deep learning model for automated analysis of body composition of patients with cancer. Due to the design of the DLNN, it can be easily implemented in various clinical infrastructures and used by other research groups to assess cancer patient cohorts or develop new models in other fields