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Objective: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT). Background: Liver cancer is one of the most frequent causes of cancerrelated mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation. Methods: A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT ( > 5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict. Results: HepatoPredict identifies 99% disease-free patients ( > 5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term. Conclusions: HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.

Gut microbiota modulation might constitute a mechanism mediating the effects of beer on health. In this randomized, double-blinded, two-arm parallel trial, 22 healthy men were recruited to drink 330 mL of nonalcoholic beer (0.0% v/v) or alcoholic beer (5.2% v/v) daily during a 4-week follow-up period. Blood and faecal samples were collected before and after the intervention period. Gut microbiota was analyzed by 16S rRNA gene sequencing. Drinking nonalcoholic or alcoholic beer daily for 4 weeks did not increase body weight and body fat mass and did not changed significantly serum cardiometabolic biomarkers. Nonalcoholic and alcoholic beer increased gut microbiota diversity which has been associated with positive health outcomes and tended to increase faecal alkaline phosphatase activity, a marker of intestinal barrier function. These results suggest the effects of beer on gut microbiota modulation are independent of alcohol and may be mediated by beer polyphenols.

Hepatocellular carcinoma (HCC) is amongst the cancers with highest mortality rates and is the most common malignancy of the liver. Early detection is vital to provide the best treatment possible and liquid biopsies combined with analysis of circulating tumour DNA methylation show great promise as a non-invasive approach for early cancer diagnosis and monitoring with low false negative rates. To identify reliable diagnostic biomarkers of early HCC, we performed a systematic analysis of multiple hepatocellular studies and datasets comprising > 1500 genome-wide DNA methylation arrays, to define a methylation signature predictive of HCC in both tissue and cell-free DNA liquid biopsy samples. Our machine learning pipeline identified differentially methylated regions in HCC, some associated with transcriptional repression of genes related with cancer progression, that benchmarked positively against independent methylation signatures. Combining our signature of 38 DNA methylation regions, we derived a HCC detection score which confirmed the utility of our approach by identifying in an independent dataset 96% of HCC tissue samples with a precision of 98%, and most importantly successfully separated cfDNA of tumour samples from healthy controls. Notably, our risk score could identify cell-free DNA samples from patients with other tumours, including colorectal cancer. Taken together, we propose a comprehensive HCC DNA methylation fingerprint and an associated risk score for detection of HCC from tissue and liquid biopsies.

The Cape Verde islands are part of the African Sahelian arid belt that possesses an erratic rain pattern prompting the need for water reservoirs, which are now critical for the country’s sustainability. Worldwide, freshwater cyanobacterial blooms are increasing in frequency due to global climate change and the eutrophication of water bodies, particularly in reservoirs. To date, there have been no risk assessments of cyanobacterial toxin production in these man-made structures. We evaluated this potential risk using 16S rRNA gene amplicon sequencing and full metagenome sequencing in freshwater reservoirs of Cape Verde. Our analysis revealed the presence of several potentially toxic cyanobacterial genera in all sampled reservoirs. Faveta potentially toxic and bloom-forming Microcystis sp., dominated our samples, while a Cryptomonas green algae and Gammaproteobacteria dominated Saquinho and Poilão reservoirs. We reconstructed and assembled the Microcystis genome, extracted from the metagenome of bulk DNA from Faveta water. Phylogenetic analysis of Microcystis cf. aeruginosa CV01’s genome revealed its close relationship with other Microcystis genomes, as well as clustering with other continental African strains, suggesting geographical coherency. In addition, it revealed several clusters of known toxin-producing genes. This survey reinforces the need to better understand the country’s microbial ecology as a whole of water reservoirs on the rise.

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