Small proteins (10–200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ∼2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10–200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

Summary: The rapidly growing industry of crop biostimulants leverages the application of plant growth promoting rhizobacteria (PGPR) to promote plant growth and health. However, introducing nonnative rhizobacteria may impact other aspects of ecosystem functioning and have legacy effects; these potential consequences are largely unexplored. Nontarget consequences of PGPR may include changes in resident microbiomes, nutrient cycling, pollinator services, functioning of other herbivores, disease suppression, and organic matter persistence. Importantly, we lack knowledge of whether these ecosystem effects may manifest in adjacent ecosystems. The introduced PGPR can leave a functional legacy whether they persist in the community or not. Legacy effects include shifts in resident microbiomes and their temporal dynamics, horizontal transfer of genes from the PGPR to resident taxa, and changes in resident functional groups and interaction networks. Ecosystem functions may be affected by legacies PGPR leave following niche construction, such as when PGPR alter soil pH that in turn alters biogeochemical cycling rates. Here, we highlight new research directions to elucidate how introduced PGPR impact resident microbiomes and ecosystem functions and their capacity for legacy effects.

Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving bioeconomic growth. Trichoderma species are well-studied model fungal organisms used for their biocontrol properties with great potential to alleviate the use of agrochemicals in agriculture. However, identifying and characterizing effective natural products in novel species or strains as biological control products remains a meticulous process with many known challenges to be navigated. Integration of recent advancements in various “omics” technologies, next generation biodesign, machine learning, and artificial intelligence approaches could greatly advance bioprospecting goals. Herein, we propose a roadmap for assessing the potential impact of already known or newly discovered Trichoderma species for biocontrol applications. By screening publicly available Trichoderma genome sequences, we first highlight the prevalence of putative biosynthetic gene clusters and antimicrobial peptides among genomes as an initial step toward predicting which organisms could increase the diversity of natural products. Next, we discuss high-throughput methods for screening organisms to discover and characterize natural products and how these findings impact both fundamental and applied research fields.

Crassulacean acid metabolism (CAM) enhances plant water-use efficiency through an inverse day/night pattern of stomatal closure/opening that facilitates nocturnal CO2 uptake. CAM has evolved independently in over 35 plant lineages, accounting for ~ 6% of all higher plants. Agave species are highly heat- and drought-tolerant, and have been domesticated as model CAM crops for beverage, fiber, and biofuel production in semi-arid and arid regions. However, the genomic basis of evolutionary innovation of CAM in genus Agave is largely unknown. Using an approach that integrated genomics, gene co-expression networks, comparative genomics and protein structure analyses, we investigated the molecular evolution of CAM as exemplified in Agave. Comparative genomics analyses among C3, C4 and CAM species revealed that core metabolic components required for CAM have ancient genomic origins traceable to non-vascular plants while regulatory proteins required for diel re-programming of metabolism have a more recent origin shared among C3, C4 and CAM species. We showed that accelerated evolution of key functional domains in proteins responsible for primary metabolism and signaling, together with a diel re-programming of the transcription of genes involved in carbon fixation, carbohydrate processing, redox homeostasis, and circadian control is required for the evolution of CAM in Agave. Furthermore, we highlighted the potential candidates contributing to the adaptation of CAM functional modules. This work provides evidence of adaptive evolution of CAM related pathways. We showed that the core metabolic components required for CAM are shared by non-vascular plants, but regulatory proteins involved in re-reprogramming of carbon fixation and metabolite transportation appeared more recently. We propose that the accelerated evolution of key proteins together with a diel re-programming of gene expression were required for CAM evolution from C3 ancestors in Agave.

Background: Microbe-microbe interactions between members of the plant rhizosphere are important but remain poorly understood. A more comprehensive understanding of the molecular mechanisms used by microbes to cooperate, compete, and persist has been challenging because of the complexity of natural ecosystems and the limited control over environmental factors. One strategy to address this challenge relies on studying complexity in a progressive manner, by first building a detailed understanding of relatively simple subsets of the community and then achieving high predictive power through combining different building blocks (e.g., hosts, community members) for different environments. Herein, we coupled this reductionist approach with high-resolution mass spectrometry-based metaproteomics to study molecular mechanisms driving community assembly, adaptation, and functionality for a defined community of ten taxonomically diverse bacterial members of Populus deltoides rhizosphere co-cultured either in a complex or defined medium. Results: Metaproteomics showed this defined community assembled into distinct microbiomes based on growth media that eventually exhibit composition and functional stability over time. The community grown in two different media showed variation in composition, yet both were dominated by only a few microbial strains. Proteome-wide interrogation provided detailed insights into the functional behavior of each dominant member as they adjust to changing community compositions and environments. The emergence and persistence of select microbes in these communities were driven by specialization in strategies including motility, antibiotic production, altered metabolism, and dormancy. Protein-level interrogation identified post-translational modifications that provided additional insights into regulatory mechanisms influencing microbial adaptation in the changing environments. Conclusions: This study provides high-resolution proteome-level insights into our understanding of microbe-microbe interactions and highlights specialized biological processes carried out by specific members of assembled microbiomes to compete and persist in changing environmental conditions. Emergent properties observed in these lower complexity communities can then be re-evaluated as more complex systems are studied and, when a particular property becomes less relevant, higher-order interactions can be identified.