From Microbes to Microbiomes: Applications for Plant Health and Sustainable Agriculture.

Plant-microbe interaction research has had a transformative trajectory, from individual microbial isolate studies to comprehensive analyses of plant microbiomes within the broader phytobiome framework. Acknowledging the indispensable role of plant microbiomes in shaping plant health, agriculture, and ecosystem resilience, we underscore the urgent need for sustainable crop production strategies in the face of contemporary challenges. We discuss how the synergies between advancements in 'omics technologies and artificial intelligence can help advance the profound potential of plant microbiomes. Furthermore, we propose a multifaceted approach encompassing translational considerations, transdisciplinary research initiatives, public-private partnerships, regulatory policy development, and pragmatic expectations for the practical application of plant microbiome knowledge across diverse agricultural landscapes. We advocate for strategic collaboration and intentional transdisciplinary efforts to unlock the benefits offered by plant microbiomes and address pressing global issues in food security. By emphasizing a nuanced understanding of plant microbiome complexities and fostering realistic expectations, we encourage the scientific community to navigate the transformative journey from discoveries in the laboratory to field applications. As companies specializing in agricultural microbes and microbiomes undergo shifts, we highlight the necessity of understanding how to approach sustainable agriculture with site-specific management solutions. While cautioning against overpromising, we underscore the excitement of exploring the many impacts of microbiome-plant interactions. We emphasize the importance of collaborative endeavors with societal partners to accelerate our collective capacity to harness the diverse and yet-to-be-discovered beneficial activities of plant microbiomes.

Discovery and Characterization of Fluopipamine, a Putative Cellulose Synthase 1 Antagonist within Arabidopsis.

Herbicide-resistant weeds are increasingly a problem in crop fields when exposed to similar chemistry over time. To avoid future yield losses, identifying herbicidal chemistry needs to be accelerated. We screened 50,000 small molecules using a liquid-handling robot and light microscopy focusing on pre-emergent herbicides in the family of cellulose biosynthesis inhibitors. Through phenotypic, chemical, genetic, and in silico methods we uncovered 6-{[4-(2-fluorophenyl)-1-piperazinyl]methyl}-N-(2-methoxy-5-methylphenyl)-1,3,5-triazine-2,4-diamine (fluopipamine). Symptomologies support fluopipamine as a putative antagonist of cellulose synthase enzyme 1 (CESA1) from Arabidopsis (Arabidopsis thaliana). Ectopic lignification, inhibition of etiolation, phenotypes including loss of anisotropic cellular expansion, swollen roots, and live cell imaging link fluopipamine to cellulose biosynthesis inhibition. Radiolabeled glucose incorporation of cellulose decreased in short-duration experiments when seedlings were incubated in fluopipamine. To elucidate the mechanism, ethylmethanesulfonate mutagenized M2 seedlings were screened for fluopipamine resistance. Two loci of genetic resistance were linked to CESA1. In silico docking of fluopipamine, quinoxyphen, and flupoxam against various CESA1 mutations suggests that an alternative binding site at the interface between CESA proteins is necessary to preserve cellulose polymerization in compound presence. These data uncovered potential fundamental mechanisms of cellulose biosynthesis in plants along with feasible leads for herbicidal uses.

MENTOR: Multiplex Embedding of Networks for Team-Based Omics Research.

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.

A multi-ancestry genetic study of pain intensity in 598,339 veterans.

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, ß-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.