Simultaneous infection by multiple parasite species is ubiquitous in nature. Interactions among co-infecting parasites may have important consequences for disease severity, transmission and community-level responses to perturbations. However, our current view of parasite interactions in nature comes primarily from observational studies, which may be unreliable at detecting interactions. We performed a perturbation experiment in wild mice, by using an anthelminthic to suppress nematodes, and monitored the consequences for other parasite species. Overall, these parasite communities were remarkably stable to perturbation. Only one non-target parasite species responded to deworming, and this response was temporary: we found strong, but short-lived, increases in the abundance of Eimeria protozoa, which share an infection site with the dominant nematode species, suggesting local, dynamic competition. These results, providing a rare and clear experimental demonstration of interactions between helminths and co-infecting parasites in wild vertebrates, constitute an important step towards understanding the wider consequences of similar drug treatments in humans and animals.

Evolutionary ecology predicts that parasite life-history traits, including a parasite's survivorship and fecundity within a host, will evolve in response to selection and that their evolution will be constrained by trade-offs between traits. Here, we test these predictions using a nematode parasite of rats, Strongyloides ratti, as a model. We performed a selection experiment by passage of parasite progeny from either early in an infection ('fast' lines) or late in an infection ('slow' lines). We found that parasite fecundity responded to selection but that parasite survivorship did not. We found a trade-off mediated via conspecific density-dependent constraints; namely, that fast lines exhibit higher density-independent fecundity than slow lines, but fast lines suffered greater reduction in fecundity in the presence of density-dependent constraints than slow lines. We also found that slow lines both stimulate a higher level of IgG1, which is a marker for a Th2-type immune response, and show less of a reduction in fecundity in response to IgG1 levels than for fast lines. Our results confirm the general prediction that parasite life-history traits can evolve in response to selection and indicate that such evolutionary responses may have significant implications for the epidemiology of infectious disease.

Purpose: Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles. Experimental Design: To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients' genomic profiles and prognoses. Results: Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964–0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953–0.993), and predicted stage I ccRCC patients' survival outcomes (log-rank test P = 0.02). Our machine learning approaches further identified histopathology image features indicative of copy-number alterations (AUC > 0.7 in multiple genes in patients with ccRCC) and tumor mutation burden. Conclusions: Our results suggest that convolutional neural networks can extract histologic signals predictive of patients' diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.

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Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.

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Objective: A leading theory for ovarian carcinogenesis proposes that the inflammation associated with incessant ovulation is a driver of oncogenesis. Consistent with this theory, epidemiological studies have shown that nonsteroidal anti-inflammatory drugs (NSAIDs) decrease ovarian cancer risk. Previous studies suggest, however, that the antineoplastic activity of NSAIDs does not require the traditional cyclooxygenase (COX) enzyme inhibition, and rather may be exerted through phosphodiesterase (PDE) inhibition. PDEs represent a potentially unique chemopreventive target for ovarian cancer given that ovulation is regulated by cyclic nucleotide signaling. This study evaluates the effects of phosphodiesterase 10A (PDE10A) inhibition as a novel chemopreventive and therapeutic approach for ovarian cancer. Methods: We investigated the effects of PDE10A small molecule inhibitors, Pf-2545920 and MCI-030 (a novel non-COX inhibitory NSAID-derivative), and PDE10A knockout by CRISPR/Cas9 gene editing in various ovarian cancer cell lines using in vitro assays that measured cell proliferation, cell viability, cell cycle arrest, apoptosis, cell migration and invasion. Downstream signaling pathways affected by PDE10A inhibition and gene knockout (KO) were assessed by western-blotting, confocal microscopy, and RNA sequencing. Results: Analysis of The Cancer Genome Atlas (TCGA) ovarian cancer database and an institutional cohort of ovarian cancer patients revealed that high PDE10A mRNA expression was associated with significantly worse overall survival. PDE10A expression was also positively correlated with oncogenic and inflammatory signaling pathways in the TCGA dataset. PDE10A inhibition with Pf-2545920 or MCI-030 decreased ovarian cancer cell proliferation, while inducing cell cycle arrest and apoptosis. Using inhibitors to block PKA (H89) and PKG (KT5823) kinase activity, we demonstrated that the pro-apoptotic effects of PDE10A inhibition were mediated by activation of cGMP/PKG and cAMP/PKA signaling. SKOV3 and OV-90 PDE10A KO cells showed decreased colony formation, cell proliferation, and migration and invasion properties compared to their wild-type (WT) counterparts. Moreover, PDE10A inhibition decreased Wnt-induced β-catenin nuclear translocation as well as decreased EGF-mediated activation of RAS/MAPK and AKT pathways in ovarian cancer cells. RNA sequencing of SKOV3 PDE10A KO clones revealed that pathways associated with cancer, Wnt and TGF-β signaling were downregulated compared to WT control cells. Conclusions: Our data demonstrate that PDE10A has pro-tumorigenic effects in ovarian cancer cells and its high expression in ovarian cancer patients is associated with poor prognosis. Altogether, our results warrant future studies of PDE10A as a novel target for ovarian cancer chemoprevention and/or treatment. Citation Format: Rebecca M. Barber, Elaine Gavin, Alla Musiyenko, Wito Richter, Kevin J. Lee, Annelise Wilhite, Joel F. Andrews, Steve McClellan, Ileana Aragon, Antonio Ward, Xi Chen, Adam Keeton, Kristy Berry, Gary A. Piazza, Jennifer M. Scalici, Luciana Madeira da Silva. PDE10A as a novel target to suppress Wnt/β-catenin signaling and other oncogenic pathways in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1213.

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