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Due to the importance of proteases in regulating cellular processes, the development of protease inhibitors has garnered great attention. Peptide-based aldehydes are a class of compounds that exhibit inhibitory activities against various proteases and proteasomes in the context of anti-proliferative treatments for cancer and other diseases. More than a dozen peptide-based natural products containing aldehydes have been discovered such as chymostatin, leupeptin, and fellutamide; however, the biosynthetic origin of the aldehyde functionality has yet to be elucidated. Herein we describe the discovery of a new group of lipopeptide aldehydes, the flavopeptins, and the corresponding biosynthetic pathway arising from an orphan gene cluster in Streptomyces sp. NRRL-F6652, a close relative of Streptomyces flavogriseus ATCC 33331. This research was initiated using a proteomics approach that screens for expressed enzymes involved in secondary metabolism in microorganisms. Flavopeptins are synthesized through a non-ribosomal peptide synthetase containing a terminal NAD(P)H-dependent reductase domain likely for the reductive release of the peptide with a C-terminal aldehyde. Solid-phase peptide synthesis of several flavopeptin species and derivatives enabled structural verification and subsequent screening of biological activity. Flavopeptins exhibit sub-micromolar inhibition activities against cysteine proteases such as papain and calpain as well as the human 20S proteasome. They also show anti-proliferative activities against multiple myeloma and lymphoma cell lines. [ABSTRACT FROM AUTHOR]

To date, the proposed mechanisms of nitrogenase-driven photosynthetic H2 production by the diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142 have assumed that reductant and ATP requirements are derived solely from glycogen oxidation and cyclic-electron flow around photosystem I. Through genome-scale transcript and protein profiling, this study presents and tests a new hypothesis on the metabolic relationship between oxygenic photosynthesis and nitrogenase-mediated H2 production in Cyanothece 51142. Our results show that net-positive rates of oxygenic photosynthesis and increased expression of photosystem II reaction centers correspond and are synchronized with nitrogenase expression and H2 production. These findings provide a new and more complete view on the metabolic processes contributing to the energy budget of photosynthetic H2 production and highlight the role of concurrent photocatalytic H2O oxidation as a participating process. [ABSTRACT FROM AUTHOR]

The accumulation of Fe and Mn in seasonally stratified drinking water reservoirs adversely impacts water quality. To control issues with Fe and Mn at the source, some drinking water utilities have deployed hypolimnetic oxygenation systems to create well-oxygenated conditions in the water column that are favorable for the oxidation, and thus removal, of Fe and Mn. However, in addition to being controlled by dissolved oxygen (DO), Fe and Mn concentrations are also influenced by pH and metal-oxidizing microorganisms. We studied the response of Fe and Mn concentrations to hypolimnetic oxygenation in a shallow drinking water reservoir in Vinton, Virginia, USA by sequentially activating and deactivating an oxygenation system over two summers. We found that maintaining well-oxygenated conditions effectively prevented the accumulation of soluble Fe in the hypolimnion. However, while the rate of Mn oxidation increased under well-oxygenated conditions, soluble Mn still accumulated in the slightly acidic to neutral (pH 5.6 to 7.5) hypolimnion. In parallel, we conducted laboratory incubation experiments, which showed that the presence of Mn-oxidizing microorganisms increased the rate of Mn oxidation in comparison with rates under oxic, abiotic conditions. Combined, our field and laboratory results demonstrate that increasing DO concentrations in the water column is important for stimulating the oxidation of Fe and Mn, but that the successful management of Mn is also tied to the activity of Mn-oxidizing organisms in the water column and favorable (neutral to alkaline) pH. [ABSTRACT FROM AUTHOR]

The development of low dissolved oxygen (DO) concentrations in the hypolimnion of drinking water reservoirs during thermal stratification can lead to the reduction of oxidized, insoluble iron (Fe) and manganese (Mn) in sediments to soluble forms, which are then released into the water column. As metals degrade drinking water quality, robust measurements of metal fluxes under changing oxygen conditions are critical for optimizing water treatment. In this study, we conducted benthic flux chamber experiments in summer 2018 to directly quantify Fe and Mn fluxes at the sediment-water interface under different DO and redox conditions of a eutrophic drinking water reservoir with an oxygenation system (Falling Creek Reservoir, Vinton, VA, USA). Throughout the experiments, we monitored DO, oxidation-reduction potential (ORP), water temperature, and pH in the chambers and compared the metal fluxes in the chambers with time-series of fluxes calculated using a hypolimnetic mass balance method. Our results showed that metal fluxes were highly variable during the monitoring period and were sensitive to redox conditions in the water column at the sediment-water interface. The time-series changes in fluxes and relationship to redox conditions are suggestive of "hot moments", short time periods of intense biogeochemical cycling. Although the metal concentrations and fluxes are specific to this site, the approaches for examining relationships between metals, oxygen concentrations and overall redox conditions can be applied by water utilities to improve water quality management of Fe and Mn. Image 1 • We measured metal fluxes across sediment-water interface of a eutrophic reservoir. • Fluxes (Fe, Mn) were measured under different redox conditions using two methods. • Time series of fluxes were highly variable, reflecting hot moments of metal cycling. • Fluxes were sensitive to hypolimnetic oxygen and redox potential (ORP). • Under anoxic conditions, ORP is a useful indicator of metal fluxes. [ABSTRACT FROM AUTHOR]

Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both the overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded a coverage of ∼380 kinases, a > 60% increase compared to using a data-dependent acquisition (DDA)-MS method, and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.

Small-cell lung cancer (SCLC) accounts for nearly 15% of all lung cancers. Although patients respond to first-line therapy readily, rapid relapse is inevitable, with few treatment options in the second-line setting. Here, we describe SCLC cell lines harboring amplification of MYC and MYCN but not MYCL1 or non-amplified MYC cell lines exhibit superior sensitivity to treatment with the pan-BET bromodomain protein inhibitor mivebresib (ABBV075). Silencing MYC and MYCN partially rescued SCLC cell lines harboring these respective amplifications from the antiproliferative effects of mivebresib. Further characterization of genome-wide binding of MYC, MYCN, and MYCL1 uncovered unique enhancer and epigenetic preferences. Implications: Our study suggests that chromatin landscapes can establish cell states with unique gene expression programs, conveying sensitivity to epigenetic inhibitors such as mivebresib., (©2024 The Authors; Published by the American Association for Cancer Research.)

Per- and polyfluoroalkyl substances (PFAS) are a widespread and persistent class of contaminants posing significant environmental and human health concerns. Comprehensive understanding of the modes of action underlying toxicity among structurally diverse PFAS is mostly lacking. To address this need, we recently reported on our application of developing zebrafish to evaluate a large library of PFAS for developmental toxicity. In the present study, we prioritized 15 bioactive PFAS that induced significant morphological effects and performed RNA-sequencing to characterize early transcriptional responses at a single timepoint (48 h post fertilization) after early developmental exposures (8 h post fertilization). Internal concentrations of 5 of the 15 PFAS were measured from pooled whole fish samples across multiple timepoints between 24-120 h post fertilization, and additional temporal transcriptomics at several timepoints (48-96 h post fertilization) were conducted for Nafion byproduct 2. A broad range of differentially expressed gene counts were identified across the PFAS exposures. Most PFAS that elicited robust transcriptomic changes affected biological processes of the brain and nervous system development. While PFAS disrupted unique processes, we also found that similarities in some functional head groups of PFAS were associated with the disruption in expression of similar gene sets. Body burdens after early developmental exposures to select sulfonic acid PFAS, including Nafion byproduct 2, increased from the 24-96 h post fertilization sampling timepoints and were greater than those of sulfonamide PFAS of similar chain lengths. In parallel, the Nafion byproduct 2-induced transcriptional responses increased between 48 and 96 h post fertilization. PFAS characteristics based on toxicity, transcriptomic effects, and modes of action will contribute to further prioritization of PFAS structures for testing and informed hazard assessment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Rericha, St. Mary, Truong, McClure, Martin, Leonard, Thunga, Simonich, Waters, Field and Tanguay.)

Targeted protein degradation (TPD) is a therapeutic approach that leverages the cell's natural machinery to degrade targets instead of inhibiting them. This is accomplished by using mono- or bifunctional small molecules designed to induce the proximity of target proteins and E3 ubiquitin ligases, leading to ubiquitination and subsequent proteasome-dependent degradation of the target. One of the most significant attributes of the TPD approach is its proposed catalytic mechanism of action, which permits substoichiometric exposure to achieve the desired pharmacological effects. However, apart from one in vitro study, studies supporting the catalytic mechanism of degraders are largely inferred based on potency. A more comprehensive understanding of the degrader catalytic mechanism of action can help aspects of compound development. To address this knowledge gap, we developed a workflow for the quantitative measurement of the catalytic rate of degraders in cells. Comparing a selective and promiscuous BTK degrader, we demonstrate that both compounds function as efficient catalysts of BTK degradation, with the promiscuous degrader exhibiting faster rates due to its ability to induce more favorable ternary complexes. By leveraging computational modeling, we show that the catalytic rate is highly dynamic as the target is depleted from cells. Further investigation of the promiscuous kinase degrader revealed that the catalytic rate is a better predictor of optimal degrader activity toward a specific target compared to degradation magnitude alone. In summary, we present a versatile method for mapping the catalytic activity of any degrader for TPD in cells.

The activated B cell (ABC) subset of diffuse large B-cell lymphoma (DLBCL) is characterized by chronic B-cell receptor signaling and associated with poor outcomes when treated with standard therapy. In ABC-DLBCL, MALT1 is a core enzyme that is constitutively activated by stimulation of the B-cell receptor or gain-of-function mutations in upstream components of the signaling pathway, making it an attractive therapeutic target. We discovered a novel small-molecule inhibitor, ABBV-MALT1, that potently shuts down B-cell signaling selectively in ABC-DLBCL preclinical models leading to potent cell growth and xenograft inhibition. We also identified a rational combination partner for ABBV-MALT1 in the BCL2 inhibitor, venetoclax, which when combined significantly synergizes to elicit deep and durable responses in preclinical models. This work highlights the potential of ABBV-MALT1 monotherapy and combination with venetoclax as effective treatment options for patients with ABC-DLBCL., (©2024 The Authors; Published by the American Association for Cancer Research.)

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes-more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.IMPORTANCEWe address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1,512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. Our work reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step toward comprehending the intricate world of soil microorganisms and its role in the global climate., Competing Interests: The authors declare no conflict of interest.

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

The rhizosphere constitutes a dynamic interface between plant hosts and their associated microbial communities. Despite the acknowledged potential for enhancing plant fitness by manipulating the rhizosphere, the engineering of the rhizosphere microbiome through inoculation has posed significant challenges. These challenges are thought to arise from the competitive microbial ecosystem where introduced microbes must survive, and the absence of adaptation to the specific metabolic and environmental demands of the rhizosphere. Here, we engineered a synthetic rhizosphere community (SRC1) with the anticipation that it would exhibit a selective advantage in colonizing the host Sorghum bicolor, thereby potentially fostering its growth. SRC1 was assembled from bacterial isolates identified either for their potential role in community cohesion through network analysis or for their ability to benefit from host-specific exudate compounds. The growth performance of SRC1 was assessed in vitro on solid media, in planta under gnotobiotic laboratory conditions, and in the field. Our findings reveal that SRC1 cohesion is most robust when cultivated in the presence of the plant host under laboratory conditions, with lineages being lost from the community when grown either in vitro or in a native field setting. We establish that SRC1 effectively promotes the growth of both above- and below-ground plant phenotypes in both laboratory and native field contexts. Furthermore, in laboratory conditions, these growth enhancements correlate with the transcriptional dampening of lignin biosynthesis in the host. Collectively, these results underscore the potential utility of synthetic microbial communities for modulating crop performance in controlled and native environments alike., (Published by Oxford University Press on behalf of the International Society for Microbial Ecology 2024.)

Recent advances in microbial ecology and synthetic biology have the potential to mitigate damage caused by anthropogenic activities that are deleteriously impacting Earth's soil ecosystems. Here, we discuss challenges and opportunities for harnessing natural and synthetic soil microbial communities, focusing on plant growth promotion under different scenarios. We explore current needs for microbial solutions in soil ecosystems, how these solutions are being developed and applied, and the potential for new biotechnology breakthroughs to tailor and target microbial products for specific applications. We highlight several scientific and technological advances in soil microbiome engineering, including characterization of microbes that impact soil ecosystems, directing how microbes assemble to interact in soil environments, and the developing suite of gene-engineering approaches. This Review underscores the need for an interdisciplinary approach to understand the composition, dynamics and deployment of beneficial soil microbiomes to drive efforts to mitigate or reverse environmental damage by restoring and protecting healthy soil ecosystems., (© 2023. Battelle Memorial Institute, under exclusive licence to Springer Nature America, Inc.)

Bacteriophages are abundant in soils. However, the majority are uncharacterized, and their hosts are unknown. Here, we apply high-throughput chromosome conformation capture (Hi-C) to directly capture phage-host relationships. Some hosts have high centralities in bacterial community co-occurrence networks, suggesting phage infections have an important impact on the soil bacterial community interactions. We observe increased average viral copies per host (VPH) and decreased viral transcriptional activity following a two-week soil-drying incubation, indicating an increase in lysogenic infections. Soil drying also alters the observed phage host range. A significant negative correlation between VPH and host abundance prior to drying indicates more lytic infections result in more host death and inversely influence host abundance. This study provides empirical evidence of phage-mediated bacterial population dynamics in soil by directly capturing specific phage-host interactions., (© 2023. Battelle Memorial Institute.)

Crop plants and undomesticated resilient species employ different strategies to regulate their energy resources and growth. Most crop species are sensitive to stress and prioritise rapid growth to maximise yield or biomass production. In contrast, resilient plants grow slowly, are small, and allocate their resources for survival in challenging environments. One small group of plants, termed resurrection plants, survive desiccation of their vegetative tissue and regain full metabolic activity upon watering. However, the precise molecular mechanisms underlying this extreme tolerance remain unknown. In this study, we employed a transcriptomics and metabolomics approach, to investigate the mechanisms of desiccation tolerance in Tripogon loliiformis, a modified desiccation-tolerant plant, that survives gradual but not rapid drying. We show that T. loliiformis can survive rapid desiccation if it is gradually dried to 60% relative water content (RWC). Furthermore, the gene expression data showed that T. loliiformis is genetically predisposed for desiccation in the hydrated state, as evidenced by the accumulation of MYB, NAC, bZIP, WRKY transcription factors along with the phytohormones, abscisic acid, salicylic acid, amino acids (e.g., proline) and TCA cycle sugars during initial drying. Through network analysis of co-expressed genes, we observed differential responses to desiccation between T. loliiformis shoots and roots. Dehydrating shoots displayed global transcriptional changes across broad functional categories, although no enrichment was observed during drying. In contrast, dehydrating roots showed distinct network changes with the most significant differences occurring at 40% RWC. The cumulative effects of the early stress responses may indicate the minimum requirements of desiccation tolerance and enable T. loliiformis to survive rapid drying. These findings potentially hold promise for identifying biotechnological solutions aimed at developing drought-tolerant crops without growth and yield penalties., (© 2023. The Author(s).)

Multi-omic analyses can provide information on the potential for activity within a microbial community but often lack specificity to link functions to cell, primarily offer potential for function or rely on annotated databases. Functional assays are necessary for understanding in situ microbial activity to better describe and improve microbiome biology. Targeting enzyme activity through activity-based protein profiling enhances the accuracy of functional studies. Here, we introduce a pipeline of coupling activity-based probing with fluorescence-activated cell sorting, culturing, and downstream activity assays to isolate and examine viable populations of cells expressing a function of interest. We applied our approach to a soil microbiome using two activity-based probes to enrich for communities with elevated activity for lignocellulose-degradation phenotypes as determined by four fluorogenic kinetic assays. Our approach efficiently separated and identified microbial members with heightened activity for glycosyl hydrolases, and by expanding this workflow to various probes for other function, this process can be applied to unique phenotype targets of interest., (© 2023. ISME Publications B.V.)

Introduction: The application of RNA-sequencing has led to numerous breakthroughs related to investigating gene expression levels in complex biological systems. Among these are knowledge of how organisms, such as the vertebrate model organism zebrafish ( Danio rerio ), respond to toxicant exposure. Recently, the development of 3' RNA-seq has allowed for the determination of gene expression levels with a fraction of the required reads compared to standard RNA-seq. While 3' RNA-seq has many advantages, a comparison to standard RNA-seq has not been performed in the context of whole organism toxicity and sparse data. Methods and results: Here, we examined samples from zebrafish exposed to perfluorobutane sulfonamide (FBSA) with either 3' or standard RNA-seq to determine the advantages of each with regards to the identification of functionally enriched pathways. We found that 3' and standard RNA-seq showed specific advantages when focusing on annotated or unannotated regions of the genome. We also found that standard RNA-seq identified more differentially expressed genes (DEGs), but that this advantage disappeared under conditions of sparse data. We also found that standard RNA-seq had a significant advantage in identifying functionally enriched pathways via analysis of DEG lists but that this advantage was minimal when identifying pathways via gene set enrichment analysis of all genes. Conclusions: These results show that each approach has experimental conditions where they may be advantageous. Our observations can help guide others in the choice of 3' RNA-seq vs standard RNA sequencing to query gene expression levels in a range of biological systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 McClure, Rericha, Waters and Tanguay.)

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. There is growing interest in the use of GO and extensive in vitro and in vivo studies have been conducted to assess its nanotoxicity. Although it is known that GO can alter the composition of the gut microbiota in mice and zebrafish, studies on the potential impacts of GO on the human gut microbiome are largely lacking. This study addresses an important knowledge gap by investigating the impact of GO exposure- at low (25 mg/L) and high (250 mg/L) doses under both fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial communitys' structure and function, using an in vitro model. This model includes simulated oral, gastric, small intestinal phase digestion of GO followed by incubation in a colon bioreactor. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 mg/L GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 mg/L GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 mg/L GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 mg/L GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism at both low and high GO concentrations., Competing Interests: Declaration of Competing Interest There are no conflicts to declare., (Copyright © 2023 Battelle Memorial Institute, The Author(s). Published by Elsevier B.V. All rights reserved.)

Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop "Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research" gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety., (© 2023 The Authors. Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

Introduction: Understanding how microorganisms within a soil community interact to support collective respiration and growth remains challenging. Here, we used a model substrate, chitin, and a synthetic Model Soil Consortium (MSC-2) to investigate how individual members of a microbial community contribute to decomposition and community growth. While MSC-2 can grow using chitin as the sole carbon source, we do not yet know how the growth kinetics or final biomass yields of MSC-2 vary when certain chitin degraders, or other important members, are absent., Methods: To characterize specific roles within this synthetic community, we carried out experiments leaving out members of MSC-2 and measuring biomass yields and CO 2 production. We chose two members to iteratively leave out (referred to by genus name): Streptomyces, as it is predicted via gene expression analysis to be a major chitin degrader in the community, and Rhodococcus as it is predicted via species co-abundance analysis to interact with several other members., Results: Our results showed that when MSC-2 lacked Streptomyces, growth and respiration of the community was severely reduced. Removal of either Streptomyces or Rhodococcus led to major changes in abundance for several other species, pointing to a comprehensive shifting of the microbial community when important members are removed, as well as alterations in the metabolic profile, especially when Streptomyces was lacking. These results show that when keystone, chitin degrading members are removed, other members, even those with the potential to degrade chitin, do not fill the same metabolic niche to promote community growth. In addition, highly connected members may be removed with similar or even increased levels of growth and respiration., Discussion: Our findings are critical to a better understanding of soil microbiology, specifically in how communities maintain activity when biotic or abiotic factors lead to changes in biodiversity in soil systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 McClure, Garcia, Couvillion, Farris and Hofmockel.)

Soil microorganisms provide key ecological functions that often rely on metabolic interactions between individual populations of the soil microbiome. To better understand these interactions and community processes, we used chitin, a major carbon and nitrogen source in soil, as a test substrate to investigate microbial interactions during its decomposition. Chitin was applied to a model soil consortium that we developed, "model soil consortium-2" (MSC-2), consisting of eight members of diverse phyla and including both chitin degraders and nondegraders. A multiomics approach revealed how MSC-2 community-level processes during chitin decomposition differ from monocultures of the constituent species. Emergent properties of both species and the community were found, including changes in the chitin degradation potential of Streptomyces species and organization of all species into distinct roles in the chitin degradation process. The members of MSC-2 were further evaluated via metatranscriptomics and community metabolomics. Intriguingly, the most abundant members of MSC-2 were not those that were able to metabolize chitin itself, but rather those that were able to take full advantage of interspecies interactions to grow on chitin decomposition products. Using a model soil consortium greatly increased our knowledge of how carbon is decomposed and metabolized in a community setting, showing that niche size, rather than species metabolic capacity, can drive success and that certain species become active carbon degraders only in the context of their surrounding community. These conclusions fill important knowledge gaps that are key to our understanding of community interactions that support carbon and nitrogen cycling in soil. IMPORTANCE The soil microbiome performs many functions that are key to ecology, agriculture, and nutrient cycling. However, because of the complexity of this ecosystem we do not know the molecular details of the interactions between microbial species that lead to these important functions. Here, we use a representative but simplified model community of bacteria to understand the details of these interactions. We show that certain species act as primary degraders of carbon sources and that the most successful species are likely those that can take the most advantage of breakdown products, not necessarily the primary degraders. We also show that a species phenotype, including whether it is a primary degrader or not, is driven in large part by the membership of the community it resides in. These conclusions are critical to a better understanding of the soil microbial interaction network and how these interactions drive central soil microbiome functions.

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly., (© 2022 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants and are associated with human disease. Canonically, many PAHs induce toxicity via activation of the aryl hydrocarbon receptor (AHR) pathway. While the interaction between PAHs and the AHR is well-established, understanding which AHR-regulated transcriptional effects directly result in observable phenotypes and which are adaptive or benign is important to better understand PAH toxicity. Retene is a frequently detected PAH in environmental sampling and has been associated with AHR2-dependent developmental toxicity in zebrafish, though its mechanism of toxicity has not been fully elucidated. To interrogate transcriptional changes causally associated with retene toxicity, we conducted whole-animal RNA sequencing at 48 h post-fertilization after exposure to eight retene concentrations. We aimed to identify the most sensitive transcriptomic responses and to determine whether this approach could uncover gene sets uniquely differentially expressed at concentrations which induce a phenotype. We identified a concentration-response relationship for differential gene expression in both number of differentially expressed genes (DEGs) and magnitude of expression change. Elevated expression of cyp1a at retene concentrations below the threshold for teratogenicity suggested that while cyp1a expression is a sensitive biomarker of AHR activation, it may be too sensitive to serve as a biomarker of teratogenicity. Genes differentially expressed at only non-teratogenic concentrations were enriched for transforming growth factor-β (TGF-β) signaling pathway disruption while DEGs identified at only teratogenic concentrations were significantly enriched for response to xenobiotic stimulus and reduction-oxidation reaction activity. DEGs which spanned both non-teratogenic and teratogenic concentrations showed similar disrupted biological processes to those unique to teratogenic concentrations, indicating these processes were disrupted at low exposure concentrations. Gene co-expression network analysis identified several gene modules, including those associated with PAHs and AHR2 activation. One, Module 7, was strongly enriched for AHR2-associated genes and contained the strongest responses to retene. Benchmark concentration (BMC) of Module seven genes identified a median BMC of 7.5 µM, nearly the highest retene concentration with no associated teratogenicity, supporting the hypothesis that Module seven genes are largely responsible for retene toxicity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wilson, McClure, Waters, Simonich and Tanguay.)

Oxygen availability is decreasing in many lakes and reservoirs worldwide, raising the urgency for understanding how anoxia (low oxygen) affects coupled biogeochemical cycling, which has major implications for water quality, food webs, and ecosystem functioning. Although the increasing magnitude and prevalence of anoxia has been documented in freshwaters globally, the challenges of disentangling oxygen and temperature responses have hindered assessment of the effects of anoxia on carbon, nitrogen, and phosphorus concentrations, stoichiometry (chemical ratios), and retention in freshwaters. The consequences of anoxia are likely severe and may be irreversible, necessitating ecosystem-scale experimental investigation of decreasing freshwater oxygen availability. To address this gap, we devised and conducted REDOX (the Reservoir Ecosystem Dynamic Oxygenation eXperiment), an unprecedented, 7-year experiment in which we manipulated and modeled bottom-water (hypolimnetic) oxygen availability at the whole-ecosystem scale in a eutrophic reservoir. Seven years of data reveal that anoxia significantly increased hypolimnetic carbon, nitrogen, and phosphorus concentrations and altered elemental stoichiometry by factors of 2-5× relative to oxic periods. Importantly, prolonged summer anoxia increased nitrogen export from the reservoir by six-fold and changed the reservoir from a net sink to a net source of phosphorus and organic carbon downstream. While low oxygen in freshwaters is thought of as a response to land use and climate change, results from REDOX demonstrate that low oxygen can also be a driver of major changes to freshwater biogeochemical cycling, which may serve as an intensifying feedback that increases anoxia in downstream waterbodies. Consequently, as climate and land use change continue to increase the prevalence of anoxia in lakes and reservoirs globally, it is likely that anoxia will have major effects on freshwater carbon, nitrogen, and phosphorus budgets as well as water quality and ecosystem functioning., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Interleukin-1 receptor-associated kinase 3 (IRAK3) is a pseudokinase mediator in the human inflammatory pathway, and ablation of its function is associated with enhanced antitumor immunity. Traditionally, pseudokinases have eluded "druggability" and have not been considered tractable targets in the pharmaceutical industry. Herein we disclose a CRISPR/Cas9-mediated knockout of IRAK3 in monocyte-derived dendritic cells that results in an increase in IL-12 production upon lipopolysaccharide (LPS) stimulation. Furthermore, we disclose and characterize Degradomer D-1, which displays selective proteasomal degradation of IRAK3 and reproduces the 1L-12p40 increases observed in the CRISPR/Cas9 knockout.

Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1-7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables., (© 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

Background: Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response., Results: Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure., Conclusions: Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity., (© 2021. The Author(s).)

A near-complete diploid nuclear genome and accompanying circular mitochondrial and chloroplast genomes have been assembled from the elite commercial diatom species Nitzschia inconspicua. The 50 Mbp haploid size of the nuclear genome is nearly double that of model diatom Phaeodactylum tricornutum, but 30% smaller than closer relative Fragilariopsis cylindrus. Diploid assembly, which was facilitated by low levels of allelic heterozygosity (2.7%), included 14 candidate chromosome pairs composed of long, syntenic contigs, covering 93% of the total assembly. Telomeric ends were capped with an unusual 12-mer, G-rich, degenerate repeat sequence. Predicted proteins were highly enriched in strain-specific marker domains associated with cell-surface adhesion, biofilm formation, and raphe system gliding motility. Expanded species-specific families of carbonic anhydrases suggest potential enhancement of carbon concentration efficiency, and duplicated glycolysis and fatty acid synthesis pathways across cytosolic and organellar compartments may enhance peak metabolic output, contributing to competitive success over other organisms in mixed cultures. The N. inconspicua genome delivers a robust new reference for future functional and transcriptomic studies to illuminate the physiology of benthic pennate diatoms and harness their unique adaptations to support commercial algae biomass and bioproduct production., (© 2021. The Author(s).)

The soil microbiome represents one of the most complex microbial communities on the planet, encompassing thousands of taxa and metabolic pathways, rendering holistic analyses computationally intensive and difficult. Here, we developed an alternative approach in which the complex soil microbiome was broken into components ("functional modules"), based on metabolic capacities, for individual characterization. We hypothesized that reproducible, low-complexity communities that represent functional modules could be obtained through targeted enrichments and that, in combination, they would encompass a large extent of the soil microbiome diversity. Enrichments were performed on a starting soil inoculum with defined media based on specific carbon substrates, antibiotics, alternative electron acceptors under anaerobic conditions, or alternative growing conditions reflective of common field stresses. The resultant communities were evaluated through 16S rRNA amplicon sequencing. Less permissive modules (anaerobic conditions, complex polysaccharides, and certain stresses) resulted in more distinct community profiles with higher richness and more variability between replicates, whereas modules with simple substrates were dominated by fewer species and were more reproducible. Collectively, approximately 27% of unique taxa present in the liquid soil extract control were found across functional modules. Taxa that were underrepresented or undetected in the source soil were also enriched across the modules. Metatranscriptomic analyses were carried out on a subset of the modules to investigate differences in functional gene expression. These results demonstrate that by dissecting the soil microbiome into discrete components it is possible to obtain a more comprehensive view of the soil microbiome and its biochemical potential than would be possible using more holistic analyses. IMPORTANCE The taxonomic and functional diversity inherent to the soil microbiome complicate assessments of the metabolic potential carried out by the community members. An alternative approach is to break down the soil microbiome into reduced-complexity subsets based on metabolic capacities (functional modules) prior to sequencing and analysis. Here, we demonstrate that this approach successfully identified specific phylogenetic and biochemical traits of the soil microbiome that otherwise remained hidden from a more top-down analysis.

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community's functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes' response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.

Over the last several years transcriptomic analysis of bacterial pathogens has become easier and less expensive. This technique is used to determine expression levels for all genes of a particular species or collection of species under a given condition, including genes that are not yet known to exist. While transcriptomics can be a powerful tool to better understand bacterial regulatory responses to specific host environments, the experimental approach and data analysis must be performed correctly to ensure robust, accurate, and translational results. Here, we describe experimental protocols related to transcriptomic analysis of the sexually transmitted disease pathogen Neisseria gonorrhoeae. Methods are described for the extraction of high-quality RNA, examination of RNA to ensure quality, the generation of cDNA libraries for sequencing and the downstream analysis of raw sequencing data to determine gene expression levels. Much of this work can be carried out with equipment and reagents that are readily available, and the methods can be performed by a majority of laboratory groups. RNA-seq and transcriptomic analyses are set to become even more common in the coming years. The protocols described here will provide a standardized set of methods for applying this powerful technique to the study of N. gonorrhoeae under a variety of conditions.

Background: Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications., Results: We evaluated the effect of bootstrap aggregation on inferred networks using commonly applied network inference methods in terms of stability, or resilience to perturbations in the underlying expression data, a metric for accuracy, and functional enrichment of edge interactions., Conclusion: Bootstrap aggregation results in improved stability and, depending on the size of the input dataset, a marginal improvement to accuracy assessed by each method's ability to link genes in the same functional pathway.

Metalimnetic oxygen minimum zones (MOMs) commonly develop during the summer stratified period in freshwater reservoirs because of both natural processes and water quality management. While several previous studies have examined the causes of MOMs, much less is known about their effects, especially on reservoir biogeochemistry. MOMs create distinct redox gradients in the water column which may alter the magnitude and vertical distribution of dissolved methane (CH 4 ) and carbon dioxide (CO 2 ). The vertical distribution and diffusive efflux of CH 4 and CO 2 was monitored for two consecutive open-water seasons in a eutrophic reservoir that develops MOMs as a result of the operation of water quality engineering systems. During both summers, elevated concentrations of CH 4 accumulated within the anoxic MOM, reaching a maximum of 120 μM, and elevated concentrations of CO 2 accumulated in the oxic hypolimnion, reaching a maximum of 780 μM. Interestingly, the largest observed diffusive CH 4 effluxes occurred before fall turnover in both years, while peak diffusive CO 2 effluxes occurred both before and during turnover. Our data indicate that MOMs can substantially change the vertical distribution of CH 4 and CO 2 in the water column in reservoirs, resulting in the accumulation of CH 4 in the metalimnion (vs. at the sediments) and CO 2 in the hypolimnion., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The advent of high-throughput 'omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.

The magnitude of lateral dissolved inorganic carbon (DIC) export from terrestrial ecosystems to inland waters strongly influences the estimate of the global terrestrial carbon dioxide (CO 2 ) sink. At present, no reliable number of this export is available, and the few studies estimating the lateral DIC export assume that all lakes on Earth function similarly. However, lakes can function along a continuum from passive carbon transporters (passive open channels) to highly active carbon transformers with efficient in-lake CO 2 production and loss. We developed and applied a conceptual model to demonstrate how the assumed function of lakes in carbon cycling can affect calculations of the global lateral DIC export from terrestrial ecosystems to inland waters. Using global data on in-lake CO 2 production by mineralization as well as CO 2 loss by emission, primary production, and carbonate precipitation in lakes, we estimated that the global lateral DIC export can lie within the range of [Formula: see text] to [Formula: see text] Pg C yr -1 depending on the assumed function of lakes. Thus, the considered lake function has a large effect on the calculated lateral DIC export from terrestrial ecosystems to inland waters. We conclude that more robust estimates of CO 2 sinks and sources will require the classification of lakes into their predominant function. This functional lake classification concept becomes particularly important for the estimation of future CO 2 sinks and sources, since in-lake carbon transformation is predicted to be altered with climate change.

Genome sequencing has revealed that a far greater number of natural product biosynthetic pathways exist than there are known natural products. To access these molecules directly and deterministically, a new generation of heterologous expression methods is needed. Cell-free protein synthesis has not previously been used to study nonribosomal peptide biosynthesis, and provides a tunable platform with advantages over conventional methods for protein expression. Here, we demonstrate the use of cell-free protein synthesis to biosynthesize a cyclic dipeptide with correct absolute stereochemistry. From a single-pot reaction, we measured the expression of two nonribosomal peptide synthetases larger than 100 kDa, and detected high-level production of a diketopiperazine. Using quantitative LC-MS and synthetically prepared standard, we observed production of this metabolite at levels higher than previously reported from cell-based recombinant expression, approximately 12 mg/L. Overall, this work represents a first step to apply cell-free protein synthesis to discover and characterize new natural products.

Micronutrients have demonstrated promise in managing inattention and emotional dysregulation in children with attention-deficit/hyperactivity disorder (ADHD). One plausible pathway by which micronutrients improve symptoms is the gut microbiome. This study examines changes in fecal microbial composition and diversity after micronutrient supplementation in children with ADHD ( N  = 44) and highlights potential mechanisms responsible for the behavioral improvement, as determined by blinded clinician-rated global improvement response to micronutrients. Participants represent a sub-group of the Micronutrients for ADHD in Youth (MADDY) study, a double blind randomized controlled trial in which participants received micronutrients or placebo for 8 weeks, followed by an 8-week open extension. Stool samples collected at baseline, week 8, and week 16 were analyzed using 16S rRNA amplicon sequencing targeting the V4 hypervariable region. Pairwise compositional analyses investigated changes in fecal microbial composition between micronutrients versus placebo and responders versus non-responders. A significant change in microbial evenness, as measured by alpha diversity, and beta-diversity, as measured by Bray-Curtis, was observed following micronutrients supplementation. The phylum Actinobacteriota decreased in the micronutrients group compared to placebo. Two butyrate-producing bacterial families: Rikenellaceae and Oscillospiraceae , exhibited a significant increase in change following micronutrients between responders versus non-responders. These findings suggest that micronutrients modulated the composition of the fecal microbiota and identified specific bacterial changes associated with micronutrient responders.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is a powerful technique for the profiling of protein expression in cells in a high-throughput fashion. Herein we report a protocol using LC-MS/MS-based proteomics for the screening of enzymes involved in natural product biosynthesis, such as nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) from bacterial strains. Taking advantage of the large size of modular NRPSs and PKSs (often >200 kDa), size-based separation (SDS-PAGE) is employed prior to LC-MS/MS analysis. Based upon the protein identifications obtained through software search, we can accurately pinpoint the expressed NRPS and/or PKS gene clusters from a given strain and growth condition. The proteomics screening result can be used to guide the discovery of potentially new nonribosomal peptide and polyketide natural products.

The microbial world offers a rich source of bioactive compounds for those able to sift through it. Technologies capable of quantitatively detecting natural products while simultaneously identifying known compounds would expedite the search for new pharmaceutical leads. Prior efforts have targeted histone deacetylases in fungi to globally activate the production of new secondary metabolites, yet no study has directly assessed its effects with minimal bias at the metabolomic level. Using untargeted metabolomics, we monitored changes in >1000 small molecules secreted from the model fungus, Aspergillus nidulans, following genetic or chemical reductions in histone deacetylase activity (HDACi). Through quantitative, differential analyses, we found that nearly equal numbers of compounds were up- and down-regulated by >100 fold. We detected products from both known and unknown biosynthetic pathways and discovered that A. nidulans is capable of producing fellutamides, proteasome inhibitors whose expression was induced by ∼100 fold or greater upon HDACi. This work adds momentum to an "omics"-driven resurgence in natural products research, where direct detection replaces bioactivity as the primary screen for new pharmacophores.