Predictability of Genetic Interactions from Functional Gene Modules

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability

MOTIVATION

Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict a binary outcome (viable or not, and negatively interacting or not). Third, most are uninterpretable black box machine learning models.

RESULTS

Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability.

AVAILABILITY AND IMPLEMENTATION

All code and processed datasets used to generate results and figures in this manuscript are available at our Github repository at https://github.com/KISRDevelopment/cell_viability_paper. The repository also contains a link to the GI prediction website that lets users search for GIs using the MN models.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PARP1 as a therapeutic target in acute myeloid leukemia and myelodysplastic syndrome

Poly(ADP-ribose) polymerase 1 (PARP1) is a key mediator of various forms of DNA damage repair and plays an important role in the progression of several cancer types. The enzyme is activated by binding to DNA single-strand and double-strand breaks. Its contribution to chromatin remodeling makes PARP1 crucial for gene expression regulation. Inhibition of its activity with small molecules leads to the synthetic lethal effect by impeding DNA repair in the treatment of cancer cells. At first, PARP1 inhibitors (PARPis) were developed to target breast cancer mutated cancer cells. Currently, PARPis are being studied to be used in a broader variety of patients either as single agents or in combination with chemotherapy, antiangiogenic agents, ionizing radiation, and immune checkpoint inhibitors. Ongoing clinical trials on olaparib, rucaparib, niraparib, veliparib, and the recent talazoparib show the advantage of these agents in overcoming PARPi resistance and underline their efficacy in targeted treatment of several hematologic malignancies. In this review, focusing on the crucial role of PARP1 in physiological and pathological effects in myelodysplastic syndrome and acute myeloid leukemia, we give an outline of the enzyme's mechanisms of action and its role in the pathophysiology and prognosis of myelodysplastic syndrome/acute myeloid leukemia and we analyze the available data on the use of PARPis, highlighting their promising advances in clinical application.

Transcriptomic-Based Identification of the Immuno-Oncogenic Signature of Cholangiocarcinoma for HLC-018 Multi-Target Therapy Exploration

Cholangiocarcinomas (CHOLs), hepatobiliary malignancies, are characterized by high genetic heterogeneity, a rich tumor microenvironment, therapeutic resistance, difficulty diagnosing, and poor prognoses. Current knowledge of genetic alterations and known molecular markers for CHOL is insufficient, necessitating the need for further evaluation of the genome and RNA expression data in order to identify potential therapeutic targets, clarify the roles of these targets in the tumor microenvironment, and explore novel therapeutic drugs against the identified targets. Consequently, in our attempt to explore novel genetic markers associated with the carcinogenesis of CHOL, five genes (SNX15, ATP2A1, PDCD10, BET1, and HMGA2), collectively termed CHOL-hub genes, were identified via integration of differentially expressed genes (DEGs) from relatively large numbers of samples from CHOL GEO datasets. We further explored the biological functions of the CHOL-hub genes and found significant enrichment in several biological process and pathways associated with stem cell angiogenesis, cell proliferation, and cancer development, while the interaction network revealed high genetic interactions with a number of onco-functional genes. In addition, we established associations between the CHOL-hub genes and tumor progression, metastasis, tumor immune and immunosuppressive cell infiltration, dysfunctional T-cell phenotypes, poor prognoses, and therapeutic resistance in CHOL. Thus, we proposed that targeting CHOL-hub genes could be an ideal therapeutic approach for treating CHOLs, and we explored the potential of HLC-018, a novel benzamide-linked small molecule, using molecular docking of ligand-receptor interactions. To our delight, HLC-018 was well accommodated with high binding affinities to binding pockets of CHOL-hub genes; more importantly, we found specific interactions of HLC-018 with the conserved sequence of the AT-hook DNA-binding motif of HMGA2. Altogether, our study provides insights into the immune-oncogenic phenotypes of CHOL and provides valuable information for our ongoing experimental validation.

Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data)

Background

Despite the successful mapping of genes involved in the determinism of numerous traits, a large part of the genetic variation remains unexplained. A possible explanation is that the simple models used in many studies might not properly fit the actual underlying situations. Consequently, various methods have attempted to deal with the simultaneous mapping of genomic regions, assuming that these regions might interact, leading to a complex determinism for various traits. Despite some successes, no gold standard methodology has emerged. Actually, combining several interaction mapping methods might be a better strategy, leading to positive results over a larger set of situations. Our work is a step in that direction.

Results

We first have demonstrated why aggregating results from several distinct methods might increase the statistical power while controlling the type I error. We have illustrated the approach using 6 existing methods (namely: MDR, Boost, BHIT, KNN-MDR, MegaSNPHunter and AntEpiSeeker) on simulated and real data sets. We have used a very simple aggregation strategy: a majority vote across the best loci combinations identified by the individual methods. In order to assess the performances of our aggregation approach in problems where most individual methods tend to fail, we have simulated difficult situations where no marginal effects of individual genes exist and where genetic heterogeneity is present. we have also demonstrated the use of the strategy on real data, using a WTCCC dataset on rheumatoid arthritis.

Since we have been using simplistic assumptions to infer the expected power of the aggregation method, the actual power we estimated from our simulations has turned out to be a bit smaller than theoretically expected. Results nevertheless have shown that grouping the results of several methods is advantageous in terms of power, accuracy and type I error control. Furthermore, as more methods should become available in the future, using a grouping strategy will become more advantageous since adding more methods seems to improve the performances of the aggregated method.

Conclusions

The aggregation of methods as a tool to detect genetic interactions is a potentially useful addition to the arsenal used in complex traits analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2447-0) contains supplementary material, which is available to authorized users.

Systems healthcare: a holistic paradigm for tomorrow

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer's and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine.

PARP inhibitors: Synthetic lethality in the clinic

PARP inhibitors (PARPi), a cancer therapy targeting poly(ADP-ribose) polymerase, are the first clinically approved drugs designed to exploit synthetic lethality, a genetic concept proposed nearly a century ago. Tumors arising in patients who carry germline mutations in either BRCA1 or BRCA2 are sensitive to PARPi because they have a specific type of DNA repair defect. PARPi also show promising activity in more common cancers that share this repair defect. However, as with other targeted therapies, resistance to PARPi arises in advanced disease. In addition, determining the optimal use of PARPi within drug combination approaches has been challenging. Nevertheless, the preclinical discovery of PARPi synthetic lethality and the route to clinical approval provide interesting lessons for the development of other therapies. Here, we discuss current knowledge of PARP inhibitors and potential ways to maximize their clinical effectiveness.