Alternative splicing is an important regulatory process in eukaryotes. In plants, the major form of alternative splicing is intron retention. Despite its importance, the global impact of AS on the Arabidopsis proteome has not been investigated. In this study, we address this gap by performing a comprehensive integrated analysis of how changes in AS can affect the Arabidopsis proteome using mutants that disrupt ACINUS and PININ, two evolutionarily conserved alternative splicing factors. We used tandem mass tagging (TMT) with real-time search MS3 (RTS-SPS-MS3) coupled with extensive sample fractionations to achieve very high coverage and accurate protein quantification. We then integrated our proteomic data with transcriptomic data to assess how transcript changes and increased intron retention (IIR) affect the proteome. For differentially expressed transcripts, we have observed a weak to moderate correlation between transcript changes and protein changes. Our studies revealed that some IIRs have no effect on either transcript or protein levels, while some IIRs can significantly affect protein levels. Surprisingly, we found that IIRs have a much smaller effect on increasing protein diversity. Notably, the increased intron retention events detected in the double mutant are also detected in the WT under various biotic or abiotic stresses. We further investigated the characteristics of the retained introns. Our extensive proteomic data help to guide the phenotypic analysis and reveal that collective protein changes contribute to the observed phenotypes of the increased anthocyanin, pale green, reduced growth, and short root observed in the acinus pnn double mutant. Overall, our study provides insight into the intricate regulatory mechanism of intron retention and its impact on protein abundance in plants.

At a rapid pace, biologists are learning the many ways in which resident microbes influence, and sometimes even control, their hosts to shape both health and disease. Understanding the biochemistry behind these interactions promises to reveal completely novel and targeted ways of counteracting disease processes. However, in our protocols and publications, we continue to describe these new results using a language that originated in a completely different context. This language developed when microbial interactions with hosts were perceived to be primarily pathogenic, as threats that had to be vanquished. Biomedicine had one dominating thought: winning this war against microorganisms. Today, we know that beyond their defensive roles, host tissues, especially epithelia, are vital to ensuring association with the normal microbiota, the communities of microbes that persistently live with the host. Thus, we need to adopt a language that better encompasses the newly appreciated importance of host-microbiota associations. We also need a language that frames the onset and progression of pathogenic conditions within the context of the normal microbiota. Such a reimagined lexicon should make it clear, from the very nature of its words, that microorganisms are primarily vital to our health, and only more rarely the cause of disease.This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

All animals and plants likely require interactions with microbes, often in strong, persistent symbiotic associations. While the recognition of this phenomenon has been slow in coming, it will impact most, if not all, subdisciplines of biology.

The symbiotic relationship between the bioluminescent bacterium Vibrio fischeri and the bobtail squid Euprymna scolopes serves as a valuable system to investigate bacterial growth and peptidoglycan (PG) synthesis within animal tissues. To better understand the growth dynamics of V. fischeri in the crypts of the light-emitting organ of its juvenile host, we showed that, after the daily dawn-triggered expulsion of most of the population, the remaining symbionts rapidly proliferate for 6 h. At that point the population enters a period of extremely slow growth that continues throughout the night until the next dawn. Further, we found that PG synthesis by the symbionts decreases as they enter the slow-growing stage. Surprisingly, in contrast to the most mature crypts (i.e., Crypt 1) of juvenile animals, most of the symbiont cells in the least mature crypts (i.e., Crypt 3) were not expelled and, instead, remained in the slow-growing state throughout the day, with almost no cell division. Consistent with this observation, the expression of the gene encoding the PG-remodeling enzyme, L,D-transpeptidase (LdtA), was greatest during the slowly growing stage of Crypt 1 but, in contrast, remained continuously high in Crypt 3. Finally, deletion of the ldtA gene resulted in a symbiont that grew and survived normally in culture, but was increasingly defective in competing against its parent strain in the crypts. This result suggests that remodeling of the PG to generate additional 3-3 linkages contributes to the bacterium's fitness in the symbiosis, possibly in response to stresses encountered during the very slow-growing stage.

Capturing images of the nuclear dynamics within live cells is an essential technique for comprehending the intricate biological processes inherent to plant cell nuclei. While various methods exist for imaging nuclei, including combining fluorescent proteins and dyes with microscopy, there is a dearth of commercially available dyes for live-cell imaging. In Arabidopsis thaliana, we discovered that nuclei emit autofluorescence in the near-infrared (NIR) range of the spectrum and devised a non-invasive technique for the visualization of live cell nuclei using this inherent NIR autofluorescence. Our studies demonstrated the capability of the NIR imaging technique to visualize the dynamic behavior of nuclei within primary roots, root hairs, and pollen tubes, which are tissues that harbor a limited number of other organelles displaying autofluorescence. We further demonstrated the applicability of NIR autofluorescence imaging in various other tissues by incorporating fluorescence lifetime imaging techniques. Nuclear autofluorescence was also detected across a wide range of plant species, enabling analyses without the need for transformation. The nuclear autofluorescence in the NIR wavelength range was not observed in animal or yeast cells. Genetic analysis revealed that this autofluorescence was caused by the phytochrome protein. Our studies demonstrated that nuclear autofluorescence imaging can be effectively employed not only in model plants but also for studying nuclei in non-model plant species.

Comparisons of carbon uptake estimates from bottom-up terrestrial biosphere models (TBMs) to top-down atmospheric inversions help assess how well we understand carbon dioxide (CO2) exchange between the atmosphere and terrestrial biosphere. Previous comparisons have shown varying levels of agreement between bottom-up and top-down approaches, but they have almost exclusively focused on large, aggregated scales (e.g., global or continental), providing limited insights into reasons for the mismatches. Here we explore how consistency, defined as the spread in net ecosystem exchange (NEE) estimates within an ensemble of TBMs or inversions, varies with at finer spatial scales ranging from 1 circle x1 circle to the continent of North America. We also evaluate how well consistency informs accuracy in overall NEE estimates by filtering models based on their agreement with the variability, magnitude, and seasonality in observed atmospheric CO2 drawdowns or enhancements. We find that TBMs produce more consistent estimates of NEE for most regions and at most scales relative to inversions. Filtering models using atmospheric CO2 metrics causes ensemble spread to decrease substantially for TBMs, but not for inversions. This suggests that ensemble spread is likely not a reliable measure of the uncertainty associated with the North American carbon balance at any spatial scale. Promisingly, applying atmospheric CO2 metrics leads to a set of models with converging flux estimates across TBMs and inversions. Overall, we show that multiscale assessment of the agreement between bottom-up and top-down NEE estimates, aided by regional-scale observational constraints is a promising path towards identifying fine-scale sources of uncertainty and improving both ensemble consistency and accuracy. These findings help refine our understanding of biospheric carbon balance, particularly at scales relevant for informing regional carbon-climate feedbacks.

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. Wethen generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.

Gut bacteria are prevalent throughout the Metazoa and form complex microbial communities associated with food breakdown, nutrient provision and disease prevention. How hosts acquire and maintain a consistent bacterial flora remains mysterious even in the best-studied animals, including humans, mice, fishes, squid, bugs, worms and flies. This essay visits the evidence that hosts have co-evolved relationships with specific bacteria and that some of these relationships are supported by specialized physical niches that select, sequester and maintain microbial symbionts. Genetics approaches could uncover the mechanisms for recruiting and maintaining the stable and consistent members of the microbiome.This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a "top 10" list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Dinoflagellate genomes often are very large and difficult to assemble, which has until recently precluded their analysis with modern functional genomic tools. Here, we present a protocol for mapping three-dimensional (3D) genome organization in dinoflagellates and using it for scaffolding their genome assemblies. We describe steps for crosslinking, nuclear lysis, denaturation, restriction digest, ligation, and DNA shearing and purification. We then detail procedures sequencing library generation and computational analysis, including initial Hi-C read mapping and 3D-DNA scaffolding/assembly correction. For complete details on the use and execution of this protocol, please refer to Marinov etal.1.