Alice Townsend

Summary: The oleoresin defense system of loblolly pine ( Pinus taeda ) protects trees from insects and pathogens and is an important source of renewable biofuels and chemicals, but the genetic basis of oleoresin production is poorly understood. We characterized the genetic architecture of oleoresin flow, resin canal number, stem wood terpene content, and monoterpene composition in two clonal populations of P. taeda . We used quantitative genetic analyses, genome‐wide association studies (GWASs), multiplex network learning, and gene expression profiling to elucidate shared gene networks underlying defense traits and to identify high‐quality candidates for breeding and engineering loblolly pine. Genetic analyses revealed polygenic inheritance and trait‐to‐trait correlations provide strong evidence for shared genes regulating constitutive and induced oleoresin flow. We identified 236 single nucleotide polymorphisms associated with oleoresin flow, resin canal number, and terpene composition and highlight candidate genes likely involved in terpene biosynthesis, cambial meristem reprogramming, and pathogen perception and immune signaling. Fourteen GWAS candidates were methyl jasmonate‐responsive in tissues where resin canals initiate and terpene production occurs. Integrating quantitative genetics, GWAS, gene expression, and multiplex network analyses enabled the prioritization of high‐quality candidate genes. This work advances the development of more resilient loblolly pine optimized for ecological performance, renewable chemical, and biofuel production.

While antiretroviral therapy (ART) has significantly improved disease prognosis in people with HIV (PWH), understanding the biological mechanisms underlying plasma HIV-1 RNA viral load (VL) can inform additional strategies to slow HIV/AIDS disease progression. Here, we integrated multi-omic datasets and used two machine learning network biology tools (GRIN and MENTOR) to identify biological mechanisms associated with VL across 10 cohorts from multiple omics data sets. We integrated the following gene sets: 3 genes from HIV set point VL GWAS, 258 genes whose expression was associated with set point VL in CD4+ T-cells, 143 genes based on DNA methylation associations with VL, and 8 genes previously known to affect the pharmacokinetics of ART. Using GRIN, we retained 194 VL genes based on their high network interconnectivity. We then used MENTOR to collaboratively interpret subsets of these genes and identified the following biological processes: cell cycle checkpoint pathways associated with non-AIDS defining cancers, oxidative stress, viral replication, and interferon signaling. Using these network tools for multi-omic integration, we present a conceptual model of mechanisms underlying HIV VL, and identify drug repurposing candidates to complement existing ART to enhance treatment response and reduce HIV-related comorbidities.

While the proliferation of data-driven omics technologies has continued to accelerate, methods of identifying relationships among large-scale changes from omics experiments have stagnated. It is therefore imperative to develop methods that can identify key mechanisms among one or more omics experiments in order to advance biological discovery. To solve this problem, here we describe the network-based algorithm MENTOR - Multiplex Embedding of Networks for Team-Based Omics Research. We demonstrate MENTOR’s utility as a supervised learning approach to successfully partition a gene set containing multiple ontological functions into their respective functions. Subsequently, we used MENTOR as an unsupervised learning approach to identify important biological functions pertaining to the host genetic architectures in Populus trichocarpa associated with microbial abundance of multiple taxa. Moreover, as open source software designed with scientific teams in mind, we demonstrate the ability to use the output of MENTOR to facilitate distributed interpretation of omics experiments.

Opioid addiction constitutes a public health crisis in the United States and opioids cause the most drug overdose deaths in Americans. Yet, opioid addiction treatments have limited efficacy. To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies (GWAS) of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex (dlPFC) in opioid overdose victims. We identified 211 highly interrelated genes identified by GWAS or dysregulation in the dlPFC of individuals with opioid overdose victims that implicated the Akt, BDNF, and ERK pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. This included drugs used to treat other substance use disorders and antidepressant drugs. Our synthesis of multi-omics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.