Metastasis, the leading cause of death in cancer patients, requires the invasion of tumor cells through the stroma in response to migratory cues, in part provided by the extracellular matrix (ECM). Recent advances in proteomics have led to the identification of hundreds of ECM proteins, which are more abundant in tumors relative to healthy tissue. Our goal was to develop a pipeline to easily predict which ECM proteins are more likely to have an effect on cancer invasion and metastasis. We evaluated the effect of four ECM proteins upregulated in breast tumor tissue in multiple human breast cancer cell lines in three assays. There was no linear relationship between cell adhesion to ECM proteins and ECM-driven 2D cell migration speed, persistence, or 3D invasion. We then used classifiers and partial-least squares regression analysis to identify which metrics best predicted ECM-driven 2D migration and 3D invasion responses. We find that ECM-driven 2D cell migration speed or persistence did not predict 3D invasion in response to the same cue. However, cell adhesion, and in particular cell elongation and shape irregularity, accurately predicted the magnitude of ECM-driven 2D migration and 3D invasion. Our models successfully predicted the effect of novel ECM proteins in a cell-line specific manner. Overall, our studies identify the cell morphological features that determine 3D invasion responses to individual ECM proteins. This platform will help provide insight into the functional role of ECM proteins abundant in tumor tissue and help prioritize strategies for targeting tumor-ECM interactions to treat metastasis.

As the catalogue of sequenced genomes and metagenomes continues to grow, massively parallel approaches for the comprehensive and functional analysis of gene products and regulatory elements are becoming increasingly valuable. Current strategies to synthesize or clone complex libraries of DNA sequences are limited by the length of the DNA targets, throughput and cost. Here, we show that long-adapter single-strand oligonucleotide (LASSO) probes can capture and clone thousands of kilobase DNA fragments in a single reaction. As a proof-of-principle, we simultaneously cloned >3,000 bacterial open reading frames (ORFs) from E. coli genomic DNA (spanning 400–5,000 bp targets). Targets were enriched up to a median of ~60-fold compared to non-targeted genomic regions. At a cutoff of 3 times the median non-target reads per kilobase of genetic element per million reads, ~75% of the targeted ORFs were successfully captured. We also show that LASSO probes can clone human ORFs from complementary DNA, and an ORF library from a human-microbiome sample. LASSO probes could be used for the preparation of long-read sequencing libraries and for massively multiplexed cloning.

Motivation Leveraging cross-species information in protein function prediction can add significant power to network-based protein function prediction methods, because so much functional information is conserved across at least close scales of evolution. We introduce MUNDO, a new cross-species co-embedding method that combines a single network embedding method with a co-embedding method to predict functional annotations in a target species, leveraging also functional annotations in a model species network. Results Across a wide range of parameter choices, MUNDO performs best at predicting annotations in the mouse network, when trained on mouse and human PPI networks, in the human network, when trained on human and mouse PPIs, and in Baker’s yeast, when trained on Fission and Baker’s yeast, as compared to competitor methods. MUNDO also outperforms all the cross-species methods when predicting in Fission yeast when trained on Fission and Baker’s yeast; however, in this single case, discarding the information from the other species and using annotations from the Fission yeast network alone usually performs best. Availability All code is publicly available and can be accessed here: github.com/v0rtex20k/MUNDO Supplementary information Supplementary data are available at Bioinformatics Advances online.