Broderick, G. | Search bioRxiv

bioRxiv2022

Inflammation as a Silent Partner in Opioid Addiction: A Regulatory Logic Model

The brain and body consist of complex networks of interconnected feedback and feed forward loops. Because these networks are capable of supporting multiple homeostatic states, a stressor or combination of stressors may cause the network to become "stuck" in a persistent maladaptive state, for example, chronic pain and the potentiation of opioid dependency. The current research uses automated text mining of over 14,000 publications to assemble a regulatory circuit consisting of 44 immune and neurotransmission mediators linked by 188 documented regulatory interactions. Decisional logic parameters dictating the regulatory dynamics available to each network model were estimated such that predicted behavior would adhere to observed pathologies. Analysis of this psycho-neuroimmune network confirmed that a broad family of behavioral kinetics may be equally capable of supporting dynamically stable conditions of chronic pain, persistent depression and addiction behaviors. Despite differences in the predicted course of onset, these models typically point to characteristic patterns of increased inflammatory activity in the brain for each of these pathologies, specifically increased expression of the protein complex NF-kB and inflammatory signaling proteins IL1-B, IL6, and TNF. Potential treatments targeting both addiction and chronic pain may therefore benefit from the use of anti-inflammatory drugs as pharmacological potentiators of current behavioral interventions. Clinical RelevanceThis work establishes a methodology for understanding both illness-specific and shared mechanisms underlying addiction, chronic pain, and depression, and the corresponding expression profiles of psychoneuroimmune markers that might facilitate screening and treatment design.

systems biology↗

bioRxiv2022

Proteomic network analysis of bronchoalveolar lavage fluid in ex-smokers to discover implicated protein targets and novel drug treatments for chronic obstructive pulmonary disease

RationaleBronchoalveolar lavage of the epithelial lining fluid can sample the profound changes in the airway lumen milieu prevalent in Chronic Obstructive Pulmonary Disease (COPD). Characterizing the proteins in bronchoalveolar lavage fluid in COPD with advanced proteomic methods will identify disease-related changes, provide insight into pathogenetic mechanisms and potential therapeutics that will aid in the discovery of more effective therapeutics for COPD. ObjectivesWe compared epithelial lining fluid proteome of ex-smokers with moderate COPD who are not in exacerbation status COPD, to non-smoking healthy control subjects using advanced proteomics methods and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins towards the treatment of COPD. MethodsProteomic profiles of bronchalveolar lavage fluid were obtained from 1) never-smoker control subjects with normal lung function (n=10) or 2) individuals with stable moderate (GOLD stage 2, FEV1 50% - 80% predicted) COPD who were ex-smokers for at least one year (n=10). NIHs Database for Annotation, Visualization and Integrated Discovery (DAVID) and Ingenuitys Ingenuity Pathway Analysis (IPA) were the two bioinformatics tools employed for network analysis on the differentially expressed proteins to identify potential crucial hub proteins. The drug-proteome interaction signature comparison and ranking approach implemented in the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery was utilized to identify potential repurposable drugs for the treatment of COPD based on the BALF proteome. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that would most likely ameloriate inflammatory processes by inhibition or activation of their functions. ResultsProteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were differentially expressed in COPD versus control, including proteins associated with inflammation, structural elements, and energy metabolism. Functional annotation of the differentially expressed proteins by their implicated biological processes, cellular localization, and transcription factor interactions was accomplished via DAVID. Canonical pathways containing the differential expressed proteins were detailed via the Ingenuity Pathway Analysis application. Topological network analysis demonstrated that four proteins act as central node proteins in the inflammatory pathways in COPD. The CANDO multiscale drug discovery platform was used to analyze the behavioral similarity between the interaction signatures of all FDA-approved drugs and the identified BALF proteins. The drugs with the signatures most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The analysis revealed 189 drugs that putatively target the proteins implicated in COPD. The putative COPD drugs that were identified using CANDO were subsequently analyzed using a knowledge based technique to identify an optimal two drug combination that had the most appropriate effect on the central node proteins. ConclusionAnalysis of the BALF proteome revealed novel differentially expressed proteins in the epithelial lining fluid that elucidate COPD pathogenesis. Network analyses identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.

bioinformatics↗

bioRxiv2021

Attractor Landscapes as a Model Selection Criterion in Data Poor Environments

Modeling of systems for which data is limited often leads to underdetermined model identification problems, where multiple candidate models are equally adherent to data. In such situations additional optimality criteria are useful in model selection apart from the conventional minimization of error and model complexity. This work presents the attractor landscape as a domain for novel model selection criteria, where the number and location of attractors impact desirability. A set of candidate models describing immune response dynamics to SARS-CoV infection is used as an example for model selection based on features of the attractor landscape. Using this selection criteria, the initial set of 18 models is ranked and reduced to 7 models that have a composite objective value with a p-value < 0.05. Additionally, the impact of pharmacologically induced remolding of the attractor landscape is presented.

systems biology↗