Human Tumor-Infiltrating MAIT Cells Display Hallmarks of Bacterial Antigen Recognition in Colorectal Cancer.

Growing evidence indicates a role for the gut microbiota in modulating anti-tumor treatment efficacy in human cancer. Here we study mucosa-associated invariant T (MAIT) cells to look for evidence of bacterial antigen recognition in human colon, lung, and kidney carcinomas. Using mass cytometry and single-cell mRNA sequencing, we identify a tumor-infiltrating MAIT cell subset expressing CD4 and Foxp3 and observe high expression of CD39 on MAIT cells from colorectal cancer (CRC) only, which we show in vitro to be expressed specifically after TCR stimulation. We further reveal that these cells are phenotypically and functionally exhausted. Sequencing data show high bacterial infiltration in CRC tumors and highlight an enriched species, Fusobacteria nucleatum, with capability to activate MAIT cells in a TCR-dependent way. Our results provide evidence of a MAIT cell response to microbial antigens in CRC and could pave the way for manipulating MAIT cells or the microbiome for cancer therapy.

The Drosophila Dicer-1 Partner Loquacious Enhances miRNA Processing from Hairpins with Unstable Structures at the Dicing Site.

In Drosophila, Dicer-1 binds Loquacious-PB (Loqs-PB) as its major co-factor. Previous analyses indicated that loqs mutants only partially impede miRNA processing, but the activity of minor isoforms or maternally deposited Loqs was not eliminated in these studies. We addressed this by generating a cell line from loqs-null embryos and found that only ∼40% of miRNAs showed clear Loqs dependence. Genome-wide comparison of the hairpin structure and Loqs dependence suggested that Loqs substrates are influenced by base-pairing status at the dicing site. Artificial alteration of base-pairing stability at this position in model miRNA hairpins resulted in predicted changes in Loqs dependence, providing evidence for this hypothesis. Finally, we found that evolutionarily young miRNA genes tended to be Loqs dependent. We propose that Loqs may have roles in assisting the de novo emergence of miRNA genes by facilitating dicing of suboptimal hairpin substrates.

Error criteria for cross validation in the context of chaotic time series prediction.

The prediction of a chaotic time series over a long horizon is commonly done by iterating one-step-ahead prediction. Prediction can be implemented using machine learning methods, such as radial basis function networks. Typically, cross validation is used to select prediction models based on mean squared error. The bias-variance dilemma dictates that there is an inevitable tradeoff between bias and variance. However, invariants of chaotic systems are unchanged by linear transformations; thus, the bias component may be irrelevant to model selection in the context of chaotic time series prediction. Hence, the use of error variance for model selection, instead of mean squared error, is examined. Clipping is introduced, as a simple way to stabilize iterated predictions. It is shown that using the error variance for model selection, in combination with clipping, may result in better models.

Postprocessing methods for finding the embedding dimension of chaotic time series.

One problem when using the global false nearest-neighbors (GFNN) method and Cao's method to estimate embedding dimension is that their effectiveness is affected by the ratio of signal power to noise power (SNR). Simple models are proposed to explain the curves commonly obtained when using the GFNN method and Cao's method. Methods are proposed for systematically estimating the embedding dimension. Prior information is incorporated to improve the estimates.