Biology subjects

Eisen, T.

Publications and source records attributed to Eisen, T..

3 recordsLinked to original sources

bioRxiv2025

Gene-centric metagenomic analyses reveal microbiome functional insights into diseases

The microbiome encodes millions of genes; however, understanding their role in human health remains challenging. Here, we developed MetaGEAR, a gene-centric analysis framework for metagenomic data. MetaGEAR constructs cohort-specific databases for efficient retrieval of gene annotations, abundances, and co-localization, while providing enhanced taxonomic resolution by integrating reference- and assembly-based approaches. This is combined with a Metagenomic Assembled Graph (MAGraph) capturing gene neighborhood information. Using MetaGEAR, we built a multi-cohort database comprising >33 million gene families to investigate microbiome functionality across 24 cohorts of inflammatory bowel disease, colorectal cancer, and healthy populations and identified disease signature genes. Furthermore, the MAGraph revealed mobile genetic elements acting as hubs for tetracycline resistance spreading among Enterococcus, Streptococcus, and Veillonella species. Also, a duplicated nitrate reduction operon in Klebsiella pneumoniae was linked to differential gene expression under stress and virulence-inducing conditions. In summary, gene-centric metagenomic analyses reveal important insights into microbiome functionality in diseases.

microbiology↗

bioRxiv2019

Exploration of the role of CHRNA5-A3-B4 genotype in smoking behaviours

Genome-wide association studies have identified associations between variation at rs16969968/rs1051730 in the CHRNA5-A3-B4 gene cluster and smoking related outcomes. Experiments in rodents have described the nicotinic acetylcholine receptors (nAChRs) subunits encoded by this gene cluster and showed a lack of nicotine aversion in nAChRs deficient animal models. We conducted a nicotine challenge and a smoking topography study in humans, hypothesising that: 1. responses to a nicotine challenge would differ according to the rs16969968/rs1051730 genotype and 2. genotype may influence nicotine intake via smoking topography.\n\nWe used linear regressions to examine associations between rs16969968/rs1051730 genotype and subjective (questionnaires) and objective (physiological parameters) responses following acute nicotine exposure in never smokers (hypothesis 1) or cigarette smoking in current smokers (hypothesis 2). There was evidence to suggest nicotine exposure increases blood pressure and heart rate, and negatively affects mood, but insufficient evidence that these effects differ by genotype. Carriers of the minor allele following smoking one cigarette, exhibited reduced cravings (b=-2.46, 95% CI -4.87 to - 0.06, p=0.04) and inhaled less smoke per cigarette (b=-0.24, 95% CI - 0.43 to - 0.06, p=0.01) and per puff (b=-0.18, 95% CI -0.32 to -0.01, p=0.02). These results suggest that we need to carefully consider the translational value of the findings of aversion behaviour in nAChRs rodent models, and that deeper inhalation does not explain the strong association between rs16969968/rs1051730 genotype and objective biomarkers of tobacco exposure.

neuroscience↗

bioRxiv2019

Comprehensive characterisation of cell-free tumour DNA in plasma and urine of patients with renal tumours

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of [~]33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to [~]50%, including in patients with early-stage and even benign lesions.\n\nA machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.\n\nOverall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.\n\nOne sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

cancer biology↗