Cakmakci, D. | bioRxiv Science

bioRxiv2023

Differential Chromatin Architecture and Risk Variants in Deep Layer Excitatory Neurons and Grey Matter Microglia Contribute to Major Depressive Disorder

Major depressive disorder (MDD) associated genetic variants reside primarily in the non-coding, regulatory genome. Here we investigate genome-wide regulatory differences and putative gene-regulatory effects of disease risk-variants by examining chromatin accessibility combined with single-cell gene-expression profiles in over 200,000 cells from the dorsolateral prefrontal cortex (DLPFC) of 84 individuals with MDD and neurotypical controls. MDD-associated accessibility alterations were prominent in deep-layer excitatory neurons characterized by transcription factor (TF) motif accessibility and binding of nuclear receptor (NR)4A2, an activity-dependent TF responsive to pathological stress. The same neurons were significantly enriched for MDD-associated genetic variation disrupting cis-regulatory sites and TF binding associated with genes involved in synaptic communication. Furthermore, a grey matter microglial cluster exhibited differentially closed chromatin in MDD affecting binding sites bound by TFs known to regulate immune homeostasis. In summary, our study points to specific cell types and regulatory mechanisms whereby genetic variation may increase predisposition to MDD.

genomics↗

bioRxiv2022

Pathway-informed deep learning model for survivalanalysis and pathological classification of gliomas

Online assessment of tumor characteristics during surgery is important and has the potential to establish an intraoperative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based online tumor pathology prediction, their model complexity and, in turn, the predictive performance is limited by the small dataset sizes. Furthermore, the information conveyed by the feedback provided on the tumor tissue could be improved both in terms of content and accuracy. In this study, we propose a metabolic pathway-informed deep learning model, PiDeeL, to perform survival analysis and pathology assessment based on metabolite concentrations. We show that incorporating pathway information into the model architecture substantially reduces parameter complexity and achieves better survival analysis and pathological classification performance. With these design decisions, we show that PiDeeL improves tumor pathology prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve (AUC-ROC) by 3.38% and the Area Under the Precision-Recall Curve (AUC-PR) by 4.06%. Similarly, with respect to the time-dependent concordance index (c-index), we observe that PiDeeL achieves better survival analysis performance (improvement up to 4.3%) when compared to the state-of-the-art. Moreover, we show that importance analyses performed on input metabolite features as well as pathway-specific hidden-layer neurons of PiDeeL provide insights into tumor metabolism. We foresee that the use of this model in the surgery room will help surgeons adjust the surgery plan on the fly and will result in better prognosis estimates tailored to surgical procedures. AvailabilityThe code is released at https://github.com/ciceklab/PiDeeL. The data used in this study is released at https://zenodo.org/record/7228791. Contactcicek@cs.bilkent.edu.tr Supplementary informationSupplementary data are available at Briefings in Bioinformatics online.

bioinformatics↗