Biology subjects

Neu, D.

Publications and source records attributed to Neu, D..

3 recordsLinked to original sources

bioRxiv2023

Oncogenic reactivation of young L1s is a hallmark of colon cancer

Transposable elements become increasingly active in both cancerous and aging cells, driven by loss of DNA methylation as cells divide. Here we leverage the epigenomes of colon cancers with matched adjacent tissue, in addition to non-cancerous normals and cell line models, to assess the role of transposable elements as drivers or passengers in cancer development. Using the baseline of activity from normal and adjacent tissue, we show that the youngest subfamilies of the LINE1 (L1) family exhibit a degree of activity and recurrence across patients that goes beyond what is expected from hypomethylation and cell division, suggesting an additional mechanism of oncogenic reactivation. We characterize this mechanism and find that the loss of the tumor suppressor PLZF drives young L1 reactivation in a cell-division-independent manner. PLZF de-repression exposes abundant motifs for tumor core factors in the L1 5UTR. Active young L1s act as oncogenic enhancers, interacting with oncogenes via gained chromatin loops. We uncover oncogenic L1 reactivation as a hallmark of colon cancer, where young L1s activate universally in our cohort at high levels of recurrence, act as enhancers to oncogenes, and become wired into the core regulatory circuitry of colon cancer.

genomics↗

bioRxiv2018

Mismatch-repair signature mutations activate gene enhancers across colorectal cancer epigenomes

Commonly-mutated genes have been found for many cancers, but less is known about mutations in cis-regulatory elements. We leverage gains in tumor-specific enhancer activity, coupled with allele-biased mutation detection from H3K27ac ChIP-seq data, to pinpoint potential enhancer-activating mutations in colorectal cancer (CRC). Analysis of a genetically-diverse cohort of CRC specimens revealed that microsatellite instable (MSI) samples have a high indel rate within active enhancers. Enhancers with indels show evidence of positive selection, increased target gene expression, and a subset is highly recurrent. The indels affect short homopolymer tracts of A/T and increase affinity for FOX transcription factors. We further demonstrate that signature mismatch-repair (MMR) mutations activate enhancers using a xenograft tumor metastasis model, where mutations are induced naturally via CRISPR/Cas9 inactivation of MLH1 prior to tumor cell injection. Our results suggest that MMR signature mutations activate or augment enhancers in CRC tumor epigenomes to provide a selective advantage.

cancer biology↗

bioRxiv2018

Thought experiment: Decoding cognitive processes from the fMRI data of one individual

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

neuroscience↗