Interpretation Of Omics Data Research Articles

A novel insight into neurological disorders through HDAC6 protein–protein interactions

Due to its involvement in physiological and pathological processes, histone deacetylase 6 (HDAC6) is considered a promising pharmaceutical target for several neurological manifestations. However, the exact regulatory role of HDAC6 in the central nervous system (CNS) is still not fully understood. Hence, using a semi-automated literature screening technique, we systematically collected HDAC6-protein interactions that are experimentally validated and reported in the CNS. The resulting HDAC6 network encompassed 115 HDAC6-protein interactions divided over five subnetworks: (de)acetylation, phosphorylation, protein complexes, regulatory, and aggresome-autophagy subnetworks. In addition, 132 indirect interactions identified through HDAC6 inhibition were collected and categorized. Finally, to display the application of our HDAC6 network, we mapped transcriptomics data of Alzheimer’s disease, Parkinson’s disease, and Amyotrophic Lateral Sclerosis on the network and highlighted that in the case of Alzheimer’s disease, alterations predominantly affect the HDAC6 phosphorylation subnetwork, whereas differential expression within the deacetylation subnetwork is observed across all three neurological disorders. In conclusion, the HDAC6 network created in the present study is a novel and valuable resource for the understanding of the HDAC6 regulatory mechanisms, thereby providing a framework for the integration and interpretation of omics data from neurological disorders and pharmacodynamic assessments.

Systematic single-cell pathway analysis to characterize early Tcell activation.

Pathway analysis is a key analytical stage in the interpretation of omics data, providing a powerful method for detecting alterations in cellular processes. We recently developed a sensitive and distribution-free statistical framework for multisample distribution testing, which we implement here in the open-source R package single-cell pathway analysis (SCPA). We demonstrate the effectiveness of SCPA over commonly used methods, generate a scRNA-seq Tcell dataset, and characterize pathway activity over early cellular activation. This reveals regulatory pathways in Tcells, including an intrinsic type I interferon system regulating Tcell survival and a reliance on arachidonic acid metabolism throughout Tcell activation. A systems-level characterization of pathway activity in Tcells across multiple tissues also identifies alpha-defensin expression as a hallmark of bone-marrow-derived Tcells. Overall, this work provides a widely applicable tool for single-cell pathway analysis and highlights regulatory mechanisms of Tcells.

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers.

The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles.We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets.This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.

Key questions about the future of laboratory medicine in the next decade of the 21st century: A report from the IFCC-Emerging Technologies Division

This review advances the discussion about the future of laboratory medicine in the 2020s. In five major topic areas: 1. the “big picture” of healthcare; 2. pre-analytical factors; 3. Analytical factors; 4. post-analytical factors; and 5. relationships, which explores a next decade perspective on laboratory medicine and the likely impact of the predicted changes by means of a number of carefully focused questions that draw upon predictions made since 2013. The “big picture” of healthcare explores the effects of changing patient populations, the brain-to-brain loop, direct access testing, robots and total laboratory automation, and green technologies and sustainability. The pre-analytical section considers the role of different sample types, drones, and biobanks. The analytical section examines advances in point-of-care testing, mass spectrometry, genomics, gene and immunotherapy, 3D-printing, and total laboratory quality. The post-analytical section discusses the value of laboratory medicine, the emerging role of artificial intelligence, the management and interpretation of omics data, and common reference intervals and decision limits. Finally, the relationships section explores the role of laboratory medicine scientific societies, the educational needs of laboratory professionals, communication, the relationship between laboratory professionals and clinicians, laboratory medicine financing, and the anticipated economic opportunities and outcomes in the 2020's.

Signalling Network Construction for Modelling Plant Defence Response

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided.