Deep sequencing of proteotoxicity modifier genes uncovers a Presenilin-2/beta-amyloid-actin genetic risk module shared among alpha-synucleinopathies.

Whether neurodegenerative diseases linked to misfolding of the same protein share genetic risk drivers or whether different protein-aggregation pathologies in neurodegeneration are mechanistically related remains uncertain. Conventional genetic analyses are underpowered to address these questions. Through careful selection of patients based on protein aggregation phenotype (rather than clinical diagnosis) we can increase statistical power to detect associated variants in a targeted set of genes that modify proteotoxicities. Genetic modifiers of alpha-synuclein (ɑS) and beta-amyloid (Aß) cytotoxicity in yeast are enriched in risk factors for Parkinson's disease (PD) and Alzheimer's disease (AD), respectively. Here, along with known AD/PD risk genes, we deeply sequenced exomes of 430 ɑS/Aß modifier genes in patients across alpha-synucleinopathies (PD, Lewy body dementia and multiple system atrophy). Beyond known PD genes GBA1 and LRRK2, rare variants AD genes (CD33, CR1 and PSEN2) and Aß toxicity modifiers involved in RhoA/actin cytoskeleton regulation (ARGHEF1, ARHGEF28, MICAL3, PASK, PKN2, PSEN2) were shared risk factors across synucleinopathies. Actin pathology occurred in iPSC synucleinopathy models and RhoA downregulation exacerbated ɑS pathology. Even in sporadic PD, the expression of these genes was altered across CNS cell types. Genome-wide CRISPR screens revealed the essentiality of PSEN2 in both human cortical and dopaminergic neurons, and PSEN2 mutation carriers exhibited diffuse brainstem and cortical synucleinopathy independent of AD pathology. PSEN2 contributes to a common-risk signal in PD GWAS and regulates ɑS expression in neurons. Our results identify convergent mechanisms across synucleinopathies, some shared with AD.

Computational Identification of Stearic Acid as a Potential PDK1 Inhibitor and In Vitro Validation of Stearic Acid as Colon Cancer Therapeutic in Combination with 5-Fluorouracil.

BACKGROUND: Colorectal cancer is the third largest cause of cancer-related mortality worldwide. Although current treatments with chemotherapeutics have allowed for management of colorectal cancer, additional novel treatments are essential. Intervening with the metabolic reprogramming observed in cancers called "Warburg Effect," is one of the novel strategies considered to combat cancers. In the metabolic reprogramming pathway, pyruvate dehydrogenase kinase (PDK1) plays a pivotal role. Identification and characterization of a PDK1 inhibitor is of paramount importance. Further, for efficacious treatment of colorectal cancers, combinatorial regimens are essential. To this end, we opted to identify a PDK1 inhibitor using computational structure-based drug design FINDSITEcomb and perform combinatorial studies with 5-FU for efficacious treatment of colorectal cancers. METHODS: Using computational structure-based drug design FINDSITEcomb, stearic acid (SA) was identified as a possible PDK1 inhibitor. Elucidation of the mechanism of action of SA was performed using flow cytometry, clonogenic assays. RESULTS: When the growth inhibitory potential of SA was tested on colorectal adenocarcinoma (DLD-1) cells, a 50% inhibitory concentration (IC50) of 60 µM was recorded. Moreover, SA inhibited the proliferation potential of DLD-1 cells as shown by the clonogenic assay and there was a sustained response even after withdrawal of the compound. Elucidation of the mechanism of action revealed, that the inhibitory effect of SA was through the programmed cell death pathway. There was increase in the number of apoptotic and multicaspase positive cells. SA also impacted the levels of the cell survival protein Bcl-2. With the aim of achieving improved treatment for colorectal cancer, we opted to combine 5-fluorouracil (5-FU), the currently used drug in the clinic, with SA. Combining SA with 5-FU, revealed a synergistic effect in which the IC50 of 5-FU decreased from 25 to 6 µM upon combination with 60 µM SA. Further, SA did not inhibit non-tumorigenic NIH-3T3 proliferation. CONCLUSIONS: We envision that this significant decrease in the IC50 of 5-FU could translate into less side effects of 5-FU and increase the efficacy of the treatment due to the multifaceted action of SA. The data generated from the current studies on the inhibition of colorectal adenocarcinoma by SA discovered by the use of the computational program as well as synergistic action with 5-FU should open up novel therapeutic options for the management of colorectal adenocarcinomas.

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis.

BACKGROUND: Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient's condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). METHODS: Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction. RESULTS: For breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641 ± 0.031 with ConcatAE, and 0.63 ± 0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583 ± 0.058) or miRNA expression (0.616 ± 0.057). CONCLUSIONS: In conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.

A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data.

Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, indicating a need to identify prognostic biomarkers. By integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)), it is likely to improve the accuracy of patient survival predictions compared to prediction using single modality data. Therefore, we propose to develop a machine learning pipeline using decision-level integration of multi-omics tumor data from The Cancer Genome Atlas (TCGA) to predict the overall survival of breast cancer patients. With multi-omics data consisting of gene expression, methylation, miRNA expression, and CNVs, the top performing model predicted survival with an accuracy of 85% and area under the curve (AUC) of 87%. Furthermore, the model was able to identify which modalities best contributed to prediction performance, identifying methylation, miRNA, and gene expression as the best integrated classification combination. Our method not only recapitulated several breast cancer-specific prognostic biomarkers that were previously reported in the literature but also yielded several novel biomarkers. Further analysis of these biomarkers could lend insight into the molecular mechanisms that lead to poor survival.