Even though people worldwide tend to consume probiotic products for their beneficial health effects on a daily basis, recently, concerns were outlined regarding the uptake and potential intestinal colonisation of the bacteria that they carry. These bacteria are capable of executing horizontal gene transfer (HGT) which facilitates the movement of various genes, including antimicrobial resistance genes (ARGs), among the donor and recipient bacterial populations. Within our study, 47 shotgun sequencing datasets deriving from various probiotic samples (isolated strains and metagenomes) were bioinformatically analysed. We detected more than 70 ARGs, out of which rpoB mutants conferring resistance to rifampicin, tet(W/N/W) and potentially extended-spectrum beta-lactamase (ESBL) coding TEM-116 were the most common. Numerous ARGs were associated with integrated mobile genetic elements, plasmids or phages promoting the HGT. Our findings raise clinical and public health concerns as the consumption of probiotic products may lead to the transfer of ARGs to human gut bacteria.

Androgen receptor (AR) in prostate cancer (PCa) can drive transcriptional repression of multiple genes including MYC, and supraphysiological androgen is effective in some patients. Here, we show that this repression is independent of AR chromatin binding and driven by coactivator redistribution, and through chromatin conformation capture methods show disruption of the interaction between the MYC super-enhancer within the PCAT1 gene and the MYC promoter. Conversely, androgen deprivation in vitro and in vivo increases MYC expression. In parallel, global AR activity is suppressed by MYC overexpression, consistent with coactivator redistribution. These suppressive effects of AR and MYC are mitigated at shared AR/MYC binding sites, which also have markedly higher levels of H3K27 acetylation, indicating enrichment for functional enhancers. These findings demonstrate an intricate balance between AR and MYC, and indicate that increased MYC in response to androgen deprivation contributes to castration-resistant PCa, while decreased MYC may contribute to responses to supraphysiological androgen therapy.

Purpose: Neuroendocrine prostate cancer (NEPC) is a resistance phenotype that emerges in men with metastatic castration-resistant prostate adenocarcinoma (CR-PRAD) and has important clinical implications, but is challenging to detect in practice. Herein, we report a novel tissue–informed epigenetic approach to noninvasively detect NEPC. Experimental Design: We first performed methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) on a training set of tumors, identified differentially methylated regions between NEPC and CR-PRAD, and built a model to predict the presence of NEPC (termed NEPC Risk Score). We then performed MeDIP-seq on cell-free DNA (cfDNA) from two independent cohorts of men with NEPC or CR-PRAD and assessed the accuracy of the model to predict the presence NEPC. Results: The test cohort comprised cfDNA samples from 48 men, 9 with NEPC and 39 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 4.3 × 10–7) and discriminated between NEPC and CR-PRAD with high accuracy (AUROC 0.96). The optimal NEPC Risk Score cutoff demonstrated 100% sensitivity and 90% specificity for detecting NEPC. The independent, multi-institutional validation cohort included cfDNA from 53 men, including 12 with NEPC and 41 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 7.5×10–12) and perfectly discriminated NEPC from CR-PRAD (AUROC 1.0). Applying the predefined NEPC Risk Score cutoff to the validation cohort resulted in 100% sensitivity and 95% specificity for detecting NEPC. Conclusions: Tissue-informed cfDNA methylation analysis is a promising approach for noninvasive detection of NEPC in men with advanced prostate cancer.

The WNT signaling pathway has an essential role in colorectal carcinogenesis and progression, which involves a cascade of genetic and epigenetic changes. We aimed to analyze DNA methylation affecting the WNT pathway genes in colorectal carcinogenesis in promoter and gene body regions using whole methylome analysis in 9 colorectal cancer, 15 adenoma, and 6 normal tumor adjacent tissue (NAT) samples by methyl capture sequencing. Functional methylation was confirmed on 5-aza-2′-deoxycytidine-treated colorectal cancer cell line datasets. In parallel with the DNA methylation analysis, mutations of WNT pathway genes (APC, β-catenin/CTNNB1) were analyzed by 454 sequencing on GS Junior platform. Most differentially methylated CpG sites were localized in gene body regions (95% of WNT pathway genes). In the promoter regions, 33 of the 160 analyzed WNT pathway genes were differentially methylated in colorectal cancer vs. normal, including hypermethylated AXIN2, CHP1, PRICKLE1, SFRP1, SFRP2, SOX17, and hypomethylated CACYBP, CTNNB1, MYC; 44 genes in adenoma vs. NAT; and 41 genes in colorectal cancer vs. adenoma comparisons. Hypermethylation of AXIN2, DKK1, VANGL1, and WNT5A gene promoters was higher, while those of SOX17, PRICKLE1, DAAM2, and MYC was lower in colon carcinoma compared to adenoma. Inverse correlation between expression and methylation was confirmed in 23 genes, including APC, CHP1, PRICKLE1, PSEN1, and SFRP1. Differential methylation affected both canonical and noncanonical WNT pathway genes in colorectal normal-adenoma-carcinoma sequence. Aberrant DNA methylation appears already in adenomas as an early event of colorectal carcinogenesis.

Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.