Microbiome rescue: directing resilience of environmental microbial communities
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In a 21-d study, 480 Cobb 500 (off-sex) male broiler chicks were used to investigate the effects of feeding different sources and levels of resistant starches (RS) on growth performance, nutrient and energy utilization, and intestinal health in broiler chickens. The birds were allocated to 10 dietary treatments in a 3 × 3 + 1 factorial arrangement. The factors were 3 RS-sources (RSS): banana starch (BS), raw potato starch (RPS), and high-amylose corn starch (HCS); each at 3 levels (RSL) 25, 50, or 100 g/kg plus a corn-soybean meal control diet. Birds and feed were weighed on d 0, 8, and 21. On d 21, samples of jejunal tissue and digesta were collected for chemical analysis. Data were analyzed using the mixed model procedure of JMP with factor levels nested with the control. In the 0 to 21 phase, the birds fed the RPS diets had higher (P = 0.011) FI than those fed HCS or control diets, and FCR was greater (P = 0.030) in birds that received BS diets than in other diets. RSS × RSL was significant (P < 0.05) for total tract nutrient retention, AME, and AMEn on d 21. The starch digestibility was higher (P < 0.001) in birds that received the control diet than in RS diets, and decreased as RS levels increased, except for HCS. The apparent metabolizable energy (AME) and nitrogen-corrected AME (AMEn) were higher (P < 0.001) in birds fed 100 g/kg HCS diet, with both decreasing with increasing levels of BS and RPS, except for HCS. Relative ileal oligosaccharides profile showed significant (P < 0.05) RSS × RSL with a higher relative abundance of Hex(3) (P = 0.01) and Pent(3) (P = 0.001) in HCS diets. In conclusion, RS may influence gut health and growth performance in broiler chickens through modulation of cecal SCFA and nutrient digestion, but these depend largely on the botanical origin and concentrations of individual RS.
Coralline algae are worldwide carbonate builders, considered to be foundational species and biodiversity hotspots. Coralline habitats face increasing pressure from human activities and effects related to Global Change, yet their ecological properties and adaptive responses remain poorly understood. The relationships of the algal microbiota with the mineral bioconstructions, as well as plasticity and resilience of coralline holobionts in a changing environment, are of particular interest. In the Gulf of California, Neogoniolithon trichotomum (Rhodophyta) is the main carbonate builder in tidal pools. We performed a multi-disciplinary assessment of the N. trichotomum microstructure using XRD, SEM microscopy and SR-FTIR spectromicroscopy. In the algal perithallus, magnesium-calcite and aragonite were spatially segregated and embedded in a polysaccharide matrix (rich in sulfated polysaccharides). Mg-calcites (18-19 mol% Mg) were the main mineral components of the thallus overall, followed by iron carbonates related to dolomite (ankerite) and siderite. Minerals of late evaporitic sequences (sylvite and bischofite) were also present, suggesting potential halophilic microenvironments within the algal thalli. The diverse set of abundant halophilic, halotolerant and oligotrophic taxa, whose abundance increase in the summer, further suggests this condition. We created an integrated model, based on environmental parameters and the microbiota distribution, that identified temperature and nutrient availability (particularly nitrate and silicate) as the main parameters related to specific taxa patterns. Among these, Hahella, Granulossicoccus, Ferrimonas, Spongiibacteraceae and cyanobacterial Xenococcaceae and Nostocaceae change significantly between seasons. These bacterial components might play relevant roles in algal plasticity and adaptive responses to a changing environment. This study contributes to the understanding of the interplay of the prokaryotic microbiota with the mineral microenvironments of coralline algae. Because of their carbonates with potential resistance to dissolution in a higher pCO 2 world and their seasonally dynamic bacteria, coralline algae are relevant targets to study coastal resilience and carbonated systems responses to changing environments.
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Nutrient limitation has been shown to reduce bacterial genome size and influence nucleotide composition; however, much of this work has been conducted in marine systems and the factors which shape soil bacterial genomic traits remain largely unknown. Here, for this work, we determined average genome size, GC content, codon usage, and amino acid content from 398 soil metagenomes across a broad geographic range and used machine-learning to determine the environmental parameters that most strongly explain the distribution of these traits. We found that genomic trait averages were most related to pH, which we suggest is primarily due to the correlation of pH with several environmental parameters, particularly soil carbon content. Low pH soils had higher carbon to nitrogen ratios (C:N) and tended to have communities with lower GC content and larger genomes, potentially a response to increased physiological stress and a requirement for metabolic diversity. Conversely, communities in high pH and low soil C:N had smaller genomes and higher GC content—indicating potential resource driven selection against AT base pairs, which have a higher C:N than GC base pairs. Similarly, we found that nutrient conservation also applied to amino acid stoichiometry, where bacteria in soils with low C:N ratios tended to code for amino acids with lower C:N. Together, these relationships point towards fundamental mechanisms that underpin genome size, and nucleotide and amino acid selection in soil bacteria.
Pseudomonas putida KT2440 (hereafter KT2440) is a well-studied platform bacterium for the production of industrially valuable chemicals from heterogeneous mixtures of aromatic compounds obtained from lignin depolymerization. KT2440 can grow on lignin-related monomers, such as ferulate (FA), 4-coumarate (4CA), vanillate (VA), 4-hydroxybenzoate (4HBA), and protocatechuate (PCA). Genes associated with their catabolism are known, but knowledge about the uptake systems remains limited. In this work, we studied the KT2440 transporters of lignin-related monomers and their substrate selectivity. Based on the inhibition by protonophores, we focused on five genes encoding aromatic acid/H+ symporter family transporters categorized into major facilitator superfamily that uses the proton motive force. Furthermore, the mutants of PP_1376 (pcaK) and PP_3349 (hcnK) exhibited significantly reduced growth on PCA/4HBA and FA/4CA, respectively, while no change was observed on VA for any of the five gene mutants. At pH 9.0, the conversion of these compounds by hcnK mutant (FA/4CA) and vanK mutant (VA) was dramatically reduced, revealing that these transporters are crucial for the uptake of the anionic substrates at high pH. Uptake assays using 14 C-labeled substrates in Escherichia coli and biosensor-based assays confirmed that PcaK, HcnK, and VanK have ability to take up PCA, FA/4CA, and VA/PCA, respectively. Additionally, analyses of the predicted protein structures suggest that the size and hydropathic properties of the substrate-binding sites of these transporters determine their substrate preferences. Overall, this study reveals that at physiological pH, PcaK and HcnK have a major role in the uptake of PCA/4HBA and FA/4CA, respectively, and VanK is a VA/PCA transporter. This information can contribute to the engineering of strains for the efficient conversion of lignin-related monomers to value-added chemicals.
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In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. In this study, ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.
Prenol and isoprenol are promising advanced biofuels and serve as biosynthetic precursors for pharmaceuticals, fragrances, and other industrially relevant compounds. Despite engineering improvements that circumvent intermediate cytotoxicity and lower energy barriers, achieving high titer ‘mevalonate (MVA)-derived’ prenol has remained elusive. Difficulty in selective prenol production stems from the necessary isomerization of isopentenyl diphosphate (IPP) to dimethylallyl diphosphate (DMAPP) as well as the intrinsic toxicity of these diphosphate precursors. Here, the expression of specific isopentenyl monophosphate kinases with model-guided enzyme substitution of diphosphate isomerases and phosphatases enabled selective cycling of monophosphates and diphosphates, dramatically improving prenol titers and selectivity in Escherichia coli. Pairing this approach with the canonical MVA pathway resulted in 300 mg/L prenol at a 30:1 ratio with isoprenol. Further pairing with the “IPP-Bypass” pathway resulted in 526 mg/L prenol at a 72:1 ratio with isoprenol, the highest and purest MVA-derived prenol titer to date. Additionally, modifying this “IPP-Repass” for DMAPP production and coexpressing the prenyltransferase acPT1 yielded 48.3 mg/L of the potential therapeutic precursor drupanin from p-coumarate. These novel repass pathways establish a unique strategy for tuning diphosphate precursors to drive isoprenoid biosynthesis and prenylation reactions.
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Dissolved organic matter (DOM) is a large and complex mixture of molecules that fuels biogeochemical reaction in virtually all ecosystems on Earth. However, the relative importance of deterministic and stochastic processes in structuring DOM composition remains poorly characterized. Here we develop a framework for partitioning molecular composition based on key molecular traits, including lability vs. recalcitrance and activity vs. inactivity. Within this framework, we examine the ecological processes governing the assembly of DOM fractions by deploying aquatic microcosms on mountainsides that span gradients of temperature and nutrient loading in subtropical and subarctic ecosystems. Across study regions, deterministic and stochastic processes primarily structure active and inactive fractions, respectively. However, recalcitrant molecules are more deterministically assembled than labile molecules in the inactive fraction. Deterministic processes leading to variable selection generally exhibit more variation across the energy supply gradient for inactive fractions, and their importance increases with energy supply for recalcitrant molecules in both active and inactive fractions. Together, our results indicate that active and inactive fractions of DOM assemblages are structured by contrasting ecological processes, and their recalcitrant components are sensitive to global change. In conclusion, our framework opens new avenues to understand the assembly and turnover of DOM in a changing world, which can be used to predict carbon cycling at local to global scales.