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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