Networks and games for precision medicine

A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Aging-related modifications to G protein-coupled receptor signaling diversity

Aging is a highly complex molecular process, affecting nearly all tissue systems in humans and is the highest risk factor in developing neurodegenerative disorders such as Alzheimer's and Parkinson's disease, cardiovascular disease and Type 2 diabetes mellitus. The intense complexity of the aging process creates an incentive to develop more specific drugs that attenuate or even reverse some of the features of premature aging. As our current pharmacopeia is dominated by therapeutics that target members of the G protein-coupled receptor (GPCR) superfamily it may be prudent to search for effective anti-aging therapeutics in this fertile domain. Since the first demonstration of GPCR-based β-arrestin signaling, it has become clear that an enhanced appreciation of GPCR signaling diversity may facilitate the creation of therapeutics with selective signaling activities. Such 'biased' ligand signaling profiles can be effectively investigated using both standard molecular biological techniques as well as high-dimensionality data analyses. Through a more nuanced appreciation of the quantitative nature across the multiple dimensions of signaling bias that drugs possess, researchers may be able to further refine the efficacy of GPCR modulators to impact the complex aberrations that constitute the aging process. Identifying novel effector profiles could expand the effective pharmacopeia and assist in the design of precision medicines. This review discusses potential non-G protein effectors, and specifically their potential therapeutic suitability in aging and age-related disorders.

ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.

Personalized or Precision Medicine? The Example of Cystic Fibrosis

The advent of the knowledge on human genetics, by the identification of disease-associated variants, culminated in the understanding of human variability. With the genetic knowledge, the specificity of the clinical phenotype and the drug response of each individual were understood. Using the cystic fibrosis (CF) as an example, the new terms that emerged such as personalized medicine and precision medicine can be characterized. The genetic knowledge in CF is broad and the presence of a monogenic disease caused by mutations in the CFTR gene enables the phenotype–genotype association studies (including the response to drugs), considering the wide clinical and laboratory spectrum dependent on the mutual action of genotype, environment, and lifestyle. Regarding the CF disease, personalized medicine is the treatment directed at the symptoms, and this treatment is adjusted depending on the patient’s phenotype. However, more recently, the term precision medicine began to be widely used, although its correct application and understanding are still vague and poorly characterized. In precision medicine, we understand the individual as a response to the interrelation between environment, lifestyle, and genetic factors, which enabled the advent of new therapeutic models, such as conventional drugs adjustment by individual patient dosage and drug type and response, development of new drugs (read through, broker, enhancer, stabilizer, and amplifier compounds), genome editing by homologous recombination, zinc finger nucleases, TALEN (transcription activator-like effector nuclease), CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats-CRISPR-associated endonuclease 9), and gene therapy. Thus, we introduced the terms personalized medicine and precision medicine based on the CF.

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.