Life detection by means of metabolic experiments.
Life detection instrument incorporating metabolism and growth experiments including detection of metabolism of radioactive substrates, photosynthesis, etc
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Life detection instrument incorporating metabolism and growth experiments including detection of metabolism of radioactive substrates, photosynthesis, etc
Chronic hypercapnia effects on pH level, Ca and P metabolism and electrolyte metabolism in normal sedentary man
Age and sex features of strontium metabolism have been analyzed on studies of the population residing on the banks of the Techa river which was contaminated by fission products during the years 1949-1956. Measurements of 90Sr body burden have been performed since 1974 using a whole-body counter, and these have made it possible to estimate age-specific long-term retention and elimination rates for men and women. Regarding the retention that correlated with the respective maturation ages, distinct sex differences have been observed for adolescents, whereas only postmenopausal women showed a sharp increase of their elimination rates. There were no differences concerning the reproductive ages. Our experimental findings have a clear physiological interpretation and can be used to develop metabolic models for bone-seeking radionuclides.
Living organisms control their cellular biological clocks to maintain functional oscillation of the redox cycle, also called the "metabolic cycle" or "respiratory cycle". Organization of cellular processes requires parallel processing on a synchronized time-base. These clocks coordinate the timing of all biochemical processes in the cell, including energy production, DNA replication, and RNA transcription. When this universal time keeping function is perturbed by exogenous induction of reactive oxygen species (ROS), the rate of metabolism changes. This causes oxidative stress, aging and mutations. Therefore, good temporal coordination of the redox cycle not only actively prevents chemical conflict between the reductive and oxidative partial reactions; it also maintains genome integrity and lifespan. Moreover, this universal biochemical rhythm can be disrupted by ROS induction in vivo. This in turn can be achieved by blocking the electron transport chain either endogenously or exogenously by various metabolites, e.g. hydrogen sulfide (H2S), highly diffusible drugs, and carbon monoxide (CO). Alternatively, the electron transport in vivo can be attenuated via a coherent or interfering transfer of energy from exogenous ultralow frequency (ULF) and extremely low frequency (ELF) electromagnetic (EM) fields, suggesting that-on Earth-such ambient fields are an omnipresent (and probably crucially important) factor for the time-setting basis of universal biochemical reactions in living cells. Our work demonstrated previously un-described evidence for quantum effects in biology by electromagnetic coupling below thermal noise at the universal electron transport chain (ETC) in vivo.
The goal of the BioSentinel small satellite payload is to measure the effect of deep space radiation on the growth and metabolic activity of yeast cells. Raw test data is generated by fluidics cards containing yeast cells rehydrated at different periods, with metabolic activity measured by the reduction of alamarBlue. Each card well has a sensor array that measures the amount of red, green, and infrared light transmitted through the yeast culture. This illumination data is then converted to absorbance values, which are further converted into concentrations. The ultimate objective is to convert these concentrations into biologically-relevant metrics that can be compared against one another to determine changes due to differential radiation exposure. Beginning with IR absorbance data (corresponding to cell density) from ground studies, three parameters from a sigmoidal growth curve were extracted and analyzed: 𝜆 (lag phase), 𝜇 (max growth rate), and A (max cell growth). The data was fit to the Gompertz model of microbial growth using non-linear regression (Minitab), as the fit error was reduced compared to the simpler logistic growth curve. Graphs showed that the data contained a discrepancy (drift) in the lag phase that is attributable to a slow, constant loss of moisture. Correcting this discrepancy by fitting the first 25 hours of the data to a power function and subtracting these values from the absorbance readings obtained a better statistical fit to the growth curve in the lag phase. A power fit was selected over a linear fit because it reflected the effects of constant volume loss. This correction to the BioSentinel data analysis pipeline will enable quantitative statistical analysis of the effect of different levels of deep space radiation on yeast cells. Future work includes automation of drift correction and curve modeling to extract these parameters directly from data.
Long-duration spaceflight beyond Earth's magnetosphere poses serious health risks, including muscle atrophy, bone loss, liver and kidney damage, and the Spaceflight-Associated Neuro-ocular Syndrome (SANS). RNA-seq of mice aboard the International Space Station (ISS) for 37 days revealed extraordinary hypermutation in tissue-specific genes, with guanine base conversion predominating, potentially contributing to spaceflight-associated health risks. Our results suggest that the genome-wide accelerated mutation that we measured, seemingly independent of radiation dose, was induced by oxidative damage from higher atmospheric carbon dioxide (CO 2 ) levels and increased reactive oxygen species (ROS) on the ISS. This accelerated mutation, faster via RNA transcription than replication and more numerous than by radiation alone, unveils novel hotspots in the mammalian proteome. Notably, these hotspots correlate with commonly mutated genes across various human cancers, highlighting the ISS as a crucial platform for studying accelerated mutation, genome instability, and the induction of disease-causing mutations in model organisms. Our results suggest that metabolic processes can contribute to somatic mutation, and thus may play a role in the development of cancer. A metabolic link to genetic instability potentially has far-reaching implications for various diseases, with implications for human health on Earth and in space.
Abstract BACKGROUND Phytophthora capsici , a refractory and model oomycete plant pathogen, especially threatens multiple vegetable crops. A limited number of chemical pesticides play a vital role in controlling oomycete plant diseases. However, this approach often leads to excessive use of chemical agent, exacerbates environmental issues and more and more drug‐resistant strains of oomycete. Therefore, it is imperative to devise innovative solutions that can effectively address the infection of oomycete while maintaining high levels of environmental sustainability and low toxicity. RESULTS In this study, g‐C 3 N 4 @ZnO heterostructure was synthesized and characterized. The g‐C 3 N 4 @ZnO showed higher toxicity on Phytophthora capsici than graphitic carbon nitride (g‐C 3 N 4 ) nanosheets and zinc oxide (ZnO) nanoparticles in vitro and in vivo . Except the hyphal growth of Phytophthora capsici , their germination rate of spores, sporangium formation and number of spores were all suppressed by g‐C 3 N 4 @ZnO heterostructure. Furthermore, we found that this g‐C 3 N 4 @ZnO heterostructure has higher photocatalytic activity under visible light, which potentially enhanced the reactive oxygen species (ROS) mediated stress on Phytophthora capsici . Ultrastructural morphology, global changes of gene expression and weighted gene co‐expression network analysis all supported that the anti‐oomycete activity of g‐C 3 N 4 @ZnO was manifested in the destruction of membrane system and inhibition of multiple metabolisms of Phytophthora capsici under visible irradiation, which also could be attributed to the ROS and zinc ion (Zn 2+ ) mediated stress. CONCLUSION This works offers a novel oomycete disease management strategy by using g‐C 3 N 4 @ZnO, which were attributed to the ROS stress, destruction of membrane system and inhibition of multiple metabolisms. © 2023 Society of Chemical Industry.
Position-specific 13 C/ 12 C ratios within amino acids remain largely unexplored in environmental samples due to methodological limitations. We hypothesized that natural-abundance isotope patterns in serine may serve as a proxy for plant metabolic fluxes including photorespiration. Here we describe an Orbitrap method optimized for the position-specific carbon isotope analysis of serine to test our hypothesis and discuss the generalizability of this method to other amino acids. Position-specific carbon isotope ratios of serine were measured using a Thermo Scientific™ Q Exactive™ GC Orbitrap™. Amino acids were hydrolyzed from Arabidopsis biomass, purified from potential matrix interferences, and derivatized alongside standards. Derivatized serine (N,O-bis(trifluoroacetyl)methyl ester) was isolated using gas chromatography, trapped in a reservoir, and purged into the electron ionization source over tens of minutes, producing fragment ions containing different combinations of atoms from the serine-derivative molecule. The 13 C/ 12 C ratios of fragments with monoisotopic masses of 110.0217, 138.0166, and 165.0037 Da were monitored in the mass analyzer and used to calculate position-specific δ 13 C values relative to a working standard. This methodology constrains position-specific δ 13 C values for nanomole amounts of serine isolated from chemically complex mixtures. The δ 13 C values of fragment ions of serine were characterized with ≤1‰ precisions, leading to propagated standard errors of 0.7–5‰ for each carbon position. Position-specific δ 13 C values differed by up to ca 28 ± 5‰ between serine molecules hydrolyzed from plants grown under contrasting pCO 2 , selected to promote different fluxes through photosynthesis and photorespiration. The method was validated using pure serine standards characterized offline. Here this study presents the first Orbitrap-based measurements of natural-abundance, position-specific carbon isotope variation in an amino acid isolated from a biological matrix. We present a method for the precise characterization of isotope ratios in serine and propose applications probing metabolism in plants. We discuss the potential for extending these approaches to other amino acids, paving the way for novel applications.
Abstract Circular RNAs (circRNAs) are closed‐loop RNAs forming a covalent bond between their 3′ and 5′ ends, the back splice junction (BSJ), rendering them resistant to exonucleases and thus more stable compared to linear RNAs. Identification of circRNAs and distinction from their cognate linear RNA is only possible by sequencing the BSJ that is unique to the circRNA. CircRNAs are involved in the regulation of their cognate RNAs by increasing transcription rates, RNA stability, and alternative splicing. We have identified circRNAs from C. sativa that are associated with the regulation of germination, light response, and lipid metabolism. We sequenced light‐grown and etiolated seedlings after 5 or 7 days post‐germination and identified a total of 3447 circRNAs from 2763 genes. Most circRNAs originate from a single homeolog of the three subgenomes from allohexaploid camelina and correlate with higher ratios of alternative splicing of their cognate genes. A network analysis shows the interactions of select miRNA:circRNA:mRNAs for regulation of transcript stabilities where circRNA can act as a competing endogenous RNA. Several key lipid metabolism genes can generate circRNA, and we confirmed the presence of KASII circRNA as a true circRNA. CircRNA in camelina can be a novel target for breeding and engineering efforts.
Biomass can be converted to various types of products in biological and metabolic processes. For an in-depth understanding of biomass conversion, quantitative and qualitative information of products in these conversion processes are essential. Here we introduce analytical techniques including high-performance liquid chromatography (HPLC), gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) for biomass-based products characterization in biological and metabolic processes.
Yarrowia lipolytica has endogenous metabolism to use complex sugars derived from lignocellulosic biomass. However, many of these pathways are cryptic and hence either inactive or inefficient for xylose, arabinose, and cellobiose assimilation. Furthermore, we present collective methods to activate and elucidate these endogenous sugar pathways by performing short-term growth adaptation, determining the pathway efficiency, and conducting transcriptomic, enzymatic, and metabolic analyses to identify rate limiting steps for enhanced sugar consumption.
The inhibition of acetylcholinesterase is regarded as the primary toxic mechanism of action for chemical warfare agents. Recently, there have been numerous reports suggesting that metabolic processes could significantly contribute to toxicity. As such, we applied a multi-omics pipeline to generate a detailed cascade of molecular events temporally occurring in guinea pigs exposed to VX. Proteomic and metabolomic profiling resulted in the identification of several enzymes and metabolic precursors involved in glycolysis and the TCA cycle. All lines of experimental evidence indicated that there was a blockade of the TCA cycle at isocitrate dehydrogenase 2, which converts isocitrate to α-ketoglutarate. Using a primary beating cardiomyocyte cell model, we were able to determine that the supplementation of α-ketoglutarate subsequently rescued cells from the acute effects of VX poisoning. This study highlights the broad impacts that VX has and how understanding these mechanisms could result in new therapeutics such as α-ketoglutarate.
Ure2 regulates nitrogen catabolite repression in Saccharomyces cerevisiae. Deletion of URE2 induces a physiological state mimicking the nitrogen starvation and autophagic responses. Previous work has shown that deletion of URE2 increases the fermentation rate of some wine-producing strains of S. cerevisiae. In this work, we investigated the effect of URE2 deletion (ΔURE2) on the metabolism of S. cerevisiae. During growth on glucose, the ΔURE2 mutant grew less well than the wild type; however, it produced ethanol at higher rates. To better under the behavior of this mutant, we performed transcriptomics and metabolomics. Analysis of the RNA sequencing results and metabolite levels indicate that the mutant strain exhibited characteristics of both nitrogen starvation and autophagy. In addition, many pyruvate decarboxylase and alcohol dehydrogenase isoforms were expressed at higher rates than the wild type. The mutant also accumulated less trehalose and glycogen, and produced more lipids. These results suggest that URE2 may be a promising target for metabolic engineering in S. cerevisiae and potentially other strains of yeast as well for the production of lipid-based fuels and chemicals.
2,3-Butanediol (2,3-BDO) is a promising commodity chemical with various industrial applications. While petroleum-based chemical processes currently dominate the industrial production of 2,3-BDO, fermentation-based production of 2,3-BDO pro- vides an attractive alternative to chemical-based processes with regards to economic and environmental sustainability. The achievement of high 2,3-BDO titer, yield, and productivity in microbial fermentation is a prerequisite for the production of 2,3-BDO at large scales. Also, enantiopure production of 2,3-BDO production is desirable because 2,3-BDO stereoisomers have unique physicochemical properties. Pursuant to these goals, many metabolic engineering strategies to improve 2,3-BDO pro- duction from inexpensive sugars by Klebsiella oxytoca, Bacillus species, and Saccharomyces cerevisiae have been developed. Furthermore, this review summarizes the recent advances in metabolic engineering of non-pathogenic microorganisms to enable efficient and enantiopure production of 2,3-BDO.
Consolidated bioprocessing (CBP) of lignocellulosic biomass uses cellulolytic microorganisms to enable enzyme production, saccharification, and fermentation to produce biofuels, biochemicals, and biomaterials in a single step. However, understanding and redirecting metabolisms of these microorganisms compatible with CBP are limited. Here, a cellulolytic thermophile Clostridium thermocellum was engineered and demonstrated to be compatible with CBP integrated with a Co-solvent Enhanced Lignocellulosic Fractionation (CELF) pretreatment for conversion of hardwood poplar into short-chain esters with industrial use as solvents, flavors, fragrances, and biofuels. The recombinant C. thermocellum engineered with deletion of carbohydrate esterases and stable overexpression of alcohol acetyltransferases improved ester production without compromised deacetylation activities. These esterases were discovered to exhibit promiscuous thioesterase activities and their deletion enhanced ester production by rerouting the electron and carbon metabolism. Ester production was further improved up to 80-fold and ester composition could be modulated by deleting lactate biosynthesis and using poplar with different pretreatment severity.
Many fungal species have been used in industrial production for biofuels and bioproducts. Developing strains with better performance in biomanufacturing requires systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated in several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. In this report we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test- learn cycles, ultimately advancing the field of fungal biomanufacturing.
Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.
Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A.