How stress can affect your sex appeal.

In this issue of Developmental Cell, Toker et al. show that in C. elegans, stress-induced sperm defects lead to epigenetically heritable increased sexual attractiveness and increased mating between hermaphrodites and males. This effect is proposed to aid in evolutionary adaptation to stressful conditions by increasing genetic variation.

Membrane-associated cytoplasmic granules carrying the Argonaute protein WAGO-3 enable paternal epigenetic inheritance in Caenorhabditis elegans.

Epigenetic inheritance describes the transmission of gene regulatory information across generations without altering DNA sequences, enabling offspring to adapt to environmental conditions. Small RNAs have been implicated in this, through both the oocyte and the sperm. However, as much of the cellular content is extruded during spermatogenesis, it is unclear whether cytoplasmic small RNAs can contribute to epigenetic inheritance through sperm. Here we identify a sperm-specific germ granule, termed the paternal epigenetic inheritance (PEI) granule, that mediates paternal epigenetic inheritance by retaining the cytoplasmic Argonaute protein WAGO-3 during spermatogenesis in Caenorhabditis elegans. We identify the PEI granule proteins PEI-1 and PEI-2, which have distinct functions in this process: granule formation, Argonaute selectivity and subcellular localization. We show that PEI granule segregation is coupled to the transport of sperm-specific secretory vesicles through PEI-2 in an S-palmitoylation-dependent manner. PEI-like proteins are found in humans, suggesting that the identified mechanism may be conserved.

The double-stranded DNA-binding proteins TEBP-1 and TEBP-2 form a telomeric complex with POT-1.

Telomeres are bound by dedicated proteins, which protect them from DNA damage and regulate telomere length homeostasis. In the nematode Caenorhabditis elegans, a comprehensive understanding of the proteins interacting with the telomere sequence is lacking. Here, we harnessed a quantitative proteomics approach to identify TEBP-1 and TEBP-2, two paralogs expressed in the germline and embryogenesis that associate to telomeres in vitro and in vivo. tebp-1 and tebp-2 mutants display strikingly distinct phenotypes: tebp-1 mutants have longer telomeres than wild-type animals, while tebp-2 mutants display shorter telomeres and a Mortal Germline. Notably, tebp-1;tebp-2 double mutant animals have synthetic sterility, with germlines showing signs of severe mitotic and meiotic arrest. Furthermore, we show that POT-1 forms a telomeric complex with TEBP-1 and TEBP-2, which bridges TEBP-1/-2 with POT-2/MRT-1. These results provide insights into the composition and organization of a telomeric protein complex in C. elegans.

Protease-mediated processing of Argonaute proteins controls small RNA association.

Small RNA pathways defend the germlines of animals against selfish genetic elements, yet pathway activities need to be contained to prevent silencing of self genes. Here, we reveal a proteolytic mechanism that controls endogenous small interfering (22G) RNA activity in the Caenorhabditis elegans germline to protect genome integrity and maintain fertility. We find that DPF-3, a P-granule-localized N-terminal dipeptidase orthologous to mammalian dipeptidyl peptidase (DPP) 8/9, processes the unusually proline-rich N termini of WAGO-1 and WAGO-3 Argonaute (Ago) proteins. Without DPF-3 activity, these WAGO proteins lose their proper complement of 22G RNAs. Desilencing of repeat-containing and transposon-derived transcripts, DNA damage, and acute sterility ensue. These phenotypes are recapitulated when WAGO-1 and WAGO-3 are rendered resistant to DPF-3-mediated processing, identifying them as critical substrates of DPF-3. We conclude that N-terminal processing of Ago proteins regulates their activity and promotes silencing of selfish genetic elements by ensuring Ago association with appropriate small RNAs.

Intrinsically disordered protein PID-2 modulates Z granules and is required for heritable piRNA-induced silencing in the Caenorhabditis elegans embryo.

In Caenorhabditis elegans, the piRNA (21U RNA) pathway is required to establish proper gene regulation and an immortal germline. To achieve this, PRG-1-bound 21U RNAs trigger silencing mechanisms mediated by RNA-dependent RNA polymerase (RdRP)-synthetized 22G RNAs. This silencing can become PRG-1-independent and heritable over many generations, a state termed RNA-induced epigenetic gene silencing (RNAe). How and when RNAe is established, and how it is maintained, is not known. We show that maternally provided 21U RNAs can be sufficient for triggering RNAe in embryos. Additionally, we identify PID-2, a protein containing intrinsically disordered regions (IDRs), as a factor required for establishing and maintaining RNAe. PID-2 interacts with two newly identified and partially redundant eTudor domain-containing proteins, PID-4 and PID-5. PID-5 has an additional domain related to the X-prolyl aminopeptidase APP-1, and binds APP-1, implicating potential N-terminal proteolysis in RNAe. All three proteins are required for germline immortality, localize to perinuclear foci, affect size and appearance of RNA inheritance-linked Z granules, and are required for balancing of 22G RNA populations. Overall, our study identifies three new proteins with crucial functions in C. elegans small RNA silencing.

Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours.

Purpose: Hippocampus contouring for radiotherapy planning is performed on MR image data due to poor anatomical visibility on computed tomography (CT) data. Deep learning methods for direct CT hippocampus auto-segmentation exist, but use MR-based training contours. We investigate if these can be replaced by CT-based contours without loss in segmentation performance. This would remove the MR not only from inference but also from training. Approach: The hippocampus was contoured by medical experts on MR and CT data of 45 patients. Convolutional neural networks (CNNs) for hippocampus segmentation on CT were trained on CT-based or propagated MR-based contours. In both cases, their predictions were evaluated against the MR-based contours considered as the ground truth. Performance was measured using several metrics, including Dice score, surface distances, and contour Dice score. Bayesian dropout was used to estimate model uncertainty. Results: CNNs trained on propagated MR contours (median Dice 0.67) significantly outperform those trained on CT contours (0.59) and also experts contouring manually on CT (0.59). Differences between the latter two are not significant. Training on MR contours results in lower model uncertainty than training on CT contours. All contouring methods (manual or CNN) on CT perform significantly worse than a CNN segmenting the hippocampus directly on MR (median Dice 0.76). Additional data augmentation by rigid transformations improves the quantitative results but the difference remains significant. Conclusions: CT-based training contours for CT hippocampus segmentation cannot replace propagated MR-based contours without significant loss in performance. However, if MR-based contours are used, the resulting segmentations outperform experts in contouring the hippocampus on CT.

Generalization vs. Specificity: In Which Cases Should a Clinic Train its Own Segmentation Models?

As artificial intelligence for image segmentation becomes increasingly available, the question whether these solutions generalize between different hospitals and geographies arises. The present study addresses this question by comparing multi-institutional models to site-specific models. Using CT data sets from four clinics for organs-at-risk of the female breast, female pelvis and male pelvis, we differentiate between the effect from population differences and differences in clinical practice. Our study, thus, provides guidelines to hospitals, in which case the training of a custom, hospital-specific deep neural network is to be advised and when a network provided by a third-party can be used. The results show that for the organs of the female pelvis and the heart the segmentation quality is influenced solely on bases of the training set size, while the patient population variability affects the female breast segmentation quality above the effect of the training set size. In the comparison of site-specific contours on the male pelvis, we see that for a sufficiently large data set size, a custom, hospital-specific model outperforms a multi-institutional one on some of the organs. However, for small hospital-specific data sets a multi-institutional model provides the better segmentation quality.

Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT.

AIM: The segmentation of organs from a CT scan is a time-consuming task, which is one hindrance for adaptive radiation therapy. Through deep learning, it is possible to automatically delineate organs. Metrics like dice score do not necessarily represent the impact for clinical practice. Therefore, a clinical evaluation of the deep neural network is needed to verify the segmentation quality. METHODS: In this work, a novel deep neural network is trained on 300 CT and 300 artificially generated pseudo CBCTs to segment bladder, prostate, rectum and seminal vesicles from CT and cone beam CT scans. The model is evaluated on 45 CBCT and 5 CT scans through a clinical review performed by three different clinics located in Europe, North America and Australia. RESULTS: The deep learning model is scored either equally good (prostate and seminal vesicles) or better (bladder and rectum) than the structures from routine clinical practice. No or minor corrections are required for 97.5% of the segmentations of the bladder, 91.5% of the prostate, 94% of the rectum and seminal vesicles. Overall, for 82.5% of the patients none of the organs need major corrections or a redraw. CONCLUSION: This study shows that modern deep neural networks are capable of producing clinically applicable organ segmentation for the male pelvis. The model is able to produce acceptable structures as frequently as current clinical routine. Therefore, deep neural networks can simplify the clinical workflow by offering initial segmentations. The study further shows that to retain the clinicians' personal preferences a structure review and correction is necessary for structures both created by other clinicians and deep neural networks.

A Full-Image Deep Segmenter for CT Images in Breast Cancer Radiotherapy Treatment.

Radiation therapy is one of the key cancer treatment options. To avoid adverse effects in the healthy tissue, the treatment plan needs to be based on accurate anatomical models of the patient. In this work, an automatic segmentation solution for both female breasts and the heart is constructed using deep learning. Our newly developed deep neural networks perform better than the current state-of-the-art neural networks while improving inference speed by an order of magnitude. While manual segmentation by clinicians takes around 20 min, our automatic segmentation takes less than a second with an average of 3 min manual correction time. Thus, our proposed solution can have a huge impact on the workload of clinical staff and on the standardization of care.

Oscillating Ferrofluid Droplet Microrheology of Liquid-Immersed Sessile Droplets.

The damped oscillations of liquid-immersed ferrofluid sessile droplets were studied with high-speed imaging experiments and analytical modeling to develop a novel microrheology technique. Droplet oscillations were induced with an external magnetic field, thereby avoiding transients in the resulting vibrational response of the droplet. By following the droplet relaxation with a high-speed camera, the frequency and relaxation time of the damped harmonic oscillations were measured. We extend upon existing analytical theories to describe our liquid-immersed sessile droplet system, and directly quantify the droplet relaxation with the viscosity of the internal and external fluid as well as the interfacial tension between these. The easily controllable magnetic droplets make our oscillating ferrofluid droplet technique a potential candidate for high-throughput microrheology and tensiometry in the future.