INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.

RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks.

MOTIVATION: Computational methods for the prediction of protein-protein interactions (PPIs), while important tools for researchers, are plagued by challenges in generalizing to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases. RESULTS: In this study, we introduce RAPPPID, a method for the Regularized Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin Averaged Weight-Dropped Long Short-Term memory network which employs multiple regularization methods during training time to learn generalized weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID's performance holds regardless of the particular proteins in the testing set and its performance is higher for experimentally supported edges. This study serves to demonstrate that appropriate regularization is an important component of overcoming the challenges of creating models for PPI prediction that generalize to unseen proteins. Additionally, as part of this study, we provide datasets corresponding to several data splits of various strictness, in order to facilitate assessment of PPI reconstruction methods by others in the future. AVAILABILITY AND IMPLEMENTATION: Code and datasets are freely available at https://github.com/jszym/rapppid and Zenodo.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multicellular detachment generates metastatic spheroids during intra-abdominal dissemination in epithelial ovarian cancer.

Ovarian cancer is the most lethal gynecological cancer, where survival rates have had modest improvement over the last 30 years. Metastasis of cancer cells is a major clinical problem, and patient mortality occurs when ovarian cancer cells spread beyond the confinement of ovaries. Disseminated ovarian cancer cells typically spread within the abdomen, where ascites accumulation aids in their transit. Metastatic ascites contain multicellular spheroids, which promote chemo-resistance and recurrence. However, little is known about the origin and mechanisms through which spheroids arise. Using live-imaging of 3D culture models and animal models, we report that epithelial ovarian cancer (EOC) cells, the most common type of ovarian cancer, can spontaneously detach as either single cells or clusters. We report that clusters are more resistant to anoikis and have a potent survival advantage over single cells. Using in vivo lineage tracing, we found that multicellular spheroids arise preferentially from collective detachment, rather than aggregation in the abdomen. Finally, we report that multicellular spheroids from collective detachment are capable of seeding intra-abdominal metastases that retain intra-tumoral heterogeneity from the primary tumor.

Progressive polarity loss and luminal collapse disrupt tissue organization in carcinoma.

Epithelial cancers (carcinoma) account for 80%-90% of all cancers. The development of carcinoma is associated with disrupted epithelial organization and solid ductal structures. The mechanisms underlying the morphological development of carcinoma are poorly understood, but it is thought that loss of cell polarity is an early event. Here we report the characterization of the development of human breast lesions leading to carcinoma. We identified a unique mechanism that generates solid ducts in carcinoma through progressive loss of polarity and collapse of the luminal architecture. This program initiates with asymmetric divisions of polarized cells that generate a stratified epithelium containing both polarized and depolarized cells. Stratified regions form cords that penetrate into the lumen, subdividing it into polarized secondary lumina. The secondary lumina then collapse with a concomitant decrease in RhoA and myosin II activity at the apical membrane and ultimately lose apical-basal polarity. By restoring RhoA activity in mice, ducts maintained lumen and cell polarity. Notably, disrupted tissue architecture through luminal collapse was reversible, and ducts with a lumen were re-established after oncogene suppression in vivo. This reveals a novel and common mechanism that contributes to carcinoma development by progressively disrupting cell and tissue organization.