A functional logic for neurotransmitter corelease in the cholinergic forebrain pathway.

The cholinergic system of the basal forebrain plays an integral part in behaviors ranging from attention to learning, partly by altering the impact of noise in neural populations. The circuit computations underlying cholinergic actions are confounded by recent findings that forebrain cholinergic neurons corelease both acetylcholine (ACh) and GABA. We have identified that corelease of ACh and GABA by cholinergic inputs to the claustrum, a structure implicated in the control of attention, has opposing effects on the electrical activity of claustrum neurons that project to cortical vs. subcortical targets. These actions differentially alter neuronal gain and dynamic range in the two types of neurons. In model networks, the differential effects of ACh and GABA toggle network efficiency and the impact of noise on population dynamics between two different projection subcircuits. Such cholinergic switching between subcircuits provides a potential logic for neurotransmitter corelease in implementing behaviorally relevant computations.

A genomic enhancer signature associates with hepatocellular carcinoma prognosis.

Background & Aims: Lifestyle and environmental-related exposures are important risk factors for hepatocellular carcinoma (HCC), suggesting that epigenetic dysregulation significantly underpins HCC. We profiled 30 surgically resected tumours and the matched adjacent normal tissues to understand the aberrant epigenetic events associated with HCC. Methods: We identified tumour differential enhancers and the associated genes by analysing H3K27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) and Hi-C/HiChIP data from the resected tumour samples of 30 patients with early-stage HCC. This epigenome dataset was analysed with previously reported genome and transcriptome data of the overlapping group of patients from the same cohort. We performed patient-specific differential expression testing using multiregion sequencing data to identify genes that undergo both enhancer and gene expression changes. Based on the genes selected, we identified two patient groups and performed a recurrence-free survival analysis. Results: We observed large-scale changes in the enhancer distribution between HCC tumours and the adjacent normal samples. Many of the gain-in-tumour enhancers showed corresponding upregulation of the associated genes and vice versa, but much of the enhancer and gene expression changes were patient-specific. A subset of the upregulated genes was activated in a subgroup of patients' tumours. Recurrence-free survival analysis revealed that the patients with a more robust upregulation of those genes showed a worse prognosis. Conclusions: We report the genomic enhancer signature associated with differential prognosis in HCC. Findings that cohere with oncofoetal reprogramming in HCC were underpinned by genome-wide enhancer rewiring. Our results present the epigenetic changes in HCC that offer the rational selection of epigenetic-driven gene targets for therapeutic intervention or disease prognostication in HCC. Impact and Implications: Lifestyle and environmental-related exposures are the important risk factors of hepatocellular carcinoma (HCC), suggesting that tumour-associated epigenetic dysregulations may significantly underpin HCC. We profiled tumour tissues and their matched normal from 30 patients with early-stage HCC to study the dysregulated epigenetic changes associated with HCC. By also analysing the patients' RNA-seq and clinical data, we found the signature genes - with epigenetic and transcriptomic dysregulation - associated with worse prognosis. Our findings suggest that systemic approaches are needed to consider the surrounding cellular environmental and epigenetic changes in HCC tumours.

Multi-region sampling with paired sample sequencing analyses reveals sub-groups of patients with novel patient-specific dysregulation in Hepatocellular Carcinoma.

BACKGROUND: Conventional differential expression (DE) testing compares the grouped mean value of tumour samples to the grouped mean value of the normal samples, and may miss out dysregulated genes in small subgroup of patients. This is especially so for highly heterogeneous cancer like Hepatocellular Carcinoma (HCC). METHODS: Using multi-region sampled RNA-seq data of 90 patients, we performed patient-specific differential expression testing, together with the patients' matched adjacent normal samples. RESULTS: Comparing the results from conventional DE analysis and patient-specific DE analyses, we show that the conventional DE analysis omits some genes due to high inter-individual variability present in both tumour and normal tissues. Dysregulated genes shared in small subgroup of patients were useful in stratifying patients, and presented differential prognosis. We also showed that the target genes of some of the current targeted agents used in HCC exhibited highly individualistic dysregulation pattern, which may explain the poor response rate. DISCUSSION/CONCLUSION: Our results highlight the importance of identifying patient-specific DE genes, with its potential to provide clinically valuable insights into patient subgroups for applications in precision medicine.

Overcoming Interpretability in Deep Learning Cancer Classification.

Since its inception, deep learning has revolutionized the field of machine learning and data-driven science. One such data-driven science to be transformed by deep learning is genomics. In the past decade, numerous genomics studies have adopted deep learning and its applications range from predicting regulatory elements to cancer classification. Despite its dominating efficacy in these applications, deep learning is not without drawbacks. A prominent shortcoming of deep learning is the lack of interpretability. Hence, the main objective of this study is to address this obstacle in the deep learning cancer classification. Here we adopt a feature importance scoring methodology (Gradient-based class activation mapping or Grad-CAM) on a quasi-recurrent neural network model that classify cancer based on FASTA sequencing data. In this study, we managed to formulate a nucleotide-to-genomic-region Grad-CAM scoring methodology, as well as, validate the use this methodology for the chosen model. Consequently, this allows for the utilization of the Grad-CAM scoring methodology for feature importance in deep learning cancer classification. The results from our study identify potential novel candidate genes, genomic elements, and mechanisms for future cancer research.