LncRNA Malat1 suppresses pyroptosis and T cell-mediated killing of incipient metastatic cells.

The contribution of antitumor immunity to metastatic dormancy is poorly understood. Here we show that the long noncoding RNA Malat1 is required for tumor initiation and metastatic reactivation in mouse models of breast cancer and other tumor types. Malat1 localizes to nuclear speckles to couple transcription, splicing and mRNA maturation.

Engineered hydrogel reveals contribution of matrix mechanics to esophageal adenocarcinoma and identifies matrix-activated therapeutic targets.

Increased extracellular matrix (ECM) stiffness has been implicated in esophageal adenocarcinoma (EAC) progression, metastasis, and resistance to therapy. However, the underlying protumorigenic pathways are yet to be defined. Additional work is needed to develop physiologically relevant in vitro 3D culture models that better recapitulate the human tumor microenvironment and can be used to dissect the contributions of matrix stiffness to EAC pathogenesis.

p53 Gain-of-Function Mutation Induces Metastasis via BRD4-Dependent CSF-1 Expression.

Unlabelled: TP53 mutations are frequent in esophageal squamous cell carcinoma (ESCC) and other SCCs and are associated with a proclivity for metastasis. Here, we report that colony-stimulating factor-1 (CSF-1) expression is upregulated significantly in a p53-R172H-dependent manner in metastatic lung lesions of ESCC. The p53-R172H-dependent CSF-1 signaling, through its cognate receptor CSF-1R, increases tumor cell invasion and lung metastasis, which in turn is mediated in part through Stat3 phosphorylation and epithelial-to-mesenchymal transition (EMT).

Inferring targeting modes of Argonaute-loaded tRNA fragments.

Transfer RNA fragments (tRFs) are an emerging class of small RNA molecules derived from mature or precursor tRNAs. They are found across a wide range of organisms and tissues, in small RNA fraction or loaded to Argonaute in numbers comparable to microRNAs. Their functions and mechanisms of action are largely unknown, and results obtained on individual tRFs are often hard to generalize.

ARIADNA: machine learning method for ancient DNA variant discovery.

Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, these methods have been used in many published studies, sometimes with additions of arbitrary filters or modifications, designed to overcome aDNA degradation and contamination problems. The general lack of best practices for aDNA mutation calling may lead to inaccurate results.