Module-based regularization improves Gaussian graphical models when observing noisy data

Researchers often represent relations in multi-variate correlational data using Gaussian graphical models, which require regularization to sparsify the models. Acknowledging that they often study the modular structure of the inferred network, we suggest integrating it in the cross-validation of the regularization strength to balance under- and overfitting. Using synthetic and real data, we show that this approach allows us to better recover and infer modular structure in noisy data compared with the graphical lasso, a standard approach using the Gaussian log-likelihood when cross-validating the regularization strength

A type I interferon footprint in pre-operative biopsies is an independent biomarker that in combination with CD8+ T cell quantification can improve the prediction of response to neoadjuvant treatment of rectal adenocarcinoma

ABSTRACTTailored treatment for patients with rectal cancer requires clinically available markers to predict their response to neoadjuvant treatment. The quantity of tumor-infiltrating lymphocytes (TILs) in pre-operative tumor biopsies has been suggested to predict a favorable response, but opposing results exist. A biopsy-adapted Immunoscore (ISB) based on TILs has recently emerged as a promising predictor of tumor regression and prognosis in (colo)rectal cancer. We aimed to refine the ISB for prediction of response using multiplex immunofluorescence (mIF) on pre-operative rectal cancer biopsies. We combined the distribution and density of conventional T cell subsets and γδT cells with a type I Interferon (IFN)-driven response assessed using Myxovirus resistance protein A (MxA) expression. We found that pathological complete response (pCR) following neoadjuvant treatment was associated with type I IFN. Stratification of patients according to the density of CD8+ in the entire tumor tissue and MxA+ cells in tumor stroma, where equal weight was assigned to both parameters, resulted in improved predictive quality compared to the ISB. This novel stratification approach using these two independent parameters in pre-operative biopsies could potentially aid in identifying patients with a good chance of achieving a pCR following neoadjuvant treatment

Pseudomonas syringae infectivity correlates to altered transcript and metabolite levels of Arabidopsis mediator mutants

Rapid metabolic responses to pathogens are essential for plant survival and depend on numerous transcription factors. Mediator is the major transcriptional co-regulator for integration and transmission of signals from transcriptional regulators to RNA polymerase II. Using four Arabidopsis Mediator mutants, med16, med18, med25 and cdk8, we studied how differences in regulation of their transcript and metabolite levels correlate to their responses to Pseudomonas syringae infection. We found that med16 and cdk8 were susceptible, while med25 showed increased resistance. Glucosinolate, phytoalexin and carbohydrate levels were reduced already before infection in med16 and cdk8, but increased in med25, which also displayed increased benzenoids levels. Early after infection, wild type plants showed reduced glucosinolate and nucleoside levels, but increases in amino acids, benzenoids, oxylipins and the phytoalexin camalexin. The Mediator mutants showed altered levels of these metabolites and in regulation of genes encoding key enzymes for their metabolism. At later stage, mutants displayed defective levels of specific amino acids, carbohydrates, lipids and jasmonates which correlated to their infection response phenotypes. Our results reveal that MED16, MED25 and CDK8 are required for a proper, coordinated transcriptional response of genes which encode enzymes involved in important metabolic pathways for Arabidopsis responses to Pseudomonas syringae infections