BIOLOGICAL SYSTEMS IN INTERPLANETARY ENVIRONMENT
Space biology - experiments on beetles and blood pressure - weightlessness and balloon flights
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Space biology - experiments on beetles and blood pressure - weightlessness and balloon flights
The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.
The widespread application of pesticides such as organophosphates, organochlorides, and triazines in modern agriculture has led to their notable presence in soils, water bodies, and food chains, raising concerns about persistence, bioaccumulation, and adverse effects on nontarget organisms. Biotransformation, the enzymatic transformation of xenobiotic compounds by microorganisms, plants, and animals, plays a pivotal role in the degradation and detoxification of these chemicals. This review provides a comprehensive examination of the mechanisms, key enzyme classes (e.g., hydrolases, oxidoreductases, transferases), and environmental factors influencing pesticide biotransformation across different biological systems. Recent advances in omics technologies have revolutionized the understanding of microbial and plant metabolism, while synthetic biology offers opportunities for engineering enhanced degradation capabilities. The environmental fate of transformation products is also discussed, together with a critical analysis of challenges, unresolved questions, and future research directions, offering a holistic perspective on pesticide biotransformation as a key process for mitigating chemical pollution.
Genetic approaches have been traditionally used to understand microbial metabolism, but this process can be slow in nonmodel organisms due to limited genetic tools. An alternative approach is to study metabolism directly in the cell lysate. This avoids the need for genetic tools and is routinely used to study individual enzymatic reactions but is not generally used to study systems-level properties of metabolism. Here we demonstrate a new approach that we call “cell-free systems biology”, where we use well-characterized enzymes and multienzyme cascades to serve as sources or sinks of intermediate metabolites. This allows us to isolate subnetworks within metabolism and study their systems-level properties. To demonstrate this, we worked with a threeenzyme cascade reaction that converts pyruvate to 2,3-butanediol. Although it has been previously used in cell-free systems, its pH dependence was not well characterized, limiting its utility as a sink for pyruvate. We showed that improved proton accounting allowed better prediction of pH changes and that active pH control allowed 2,3-butanediol titers of up to 2.1 M (189 g/L) from acetoin and 1.6 M (144 g/L) from pyruvate. The improved proton accounting provided a crucial insight that preventing the escape of CO 2 from the system largely eliminated the need for active pH control, dramatically simplifying our experimental setup. We then used this cascade reaction to understand limits to product formation in Clostridium thermocellum, an organism with potential applications for cellulosic biofuel production. We showed that the fate of pyruvate is largely controlled by electron availability and that reactions upstream of pyruvate limit overall product formation.
In addition to having applications for waste management issues on planet Earth, microbial systems have application in reducing waste volumes aboard spacecraft. A candidate for such an application is the space station. Many of the planned experiments generate aqueous waste. To recycle air and water the contaminants from previous experiments must be removed before the air and water can be used for other experiments. This can be achieved using microorganisms in a bioreactor. Potential bioreactors (inorganics, organics, and etchants) are discussed. Current technologies that may be applied to waste treatment are described. Examples of how biological systems may be used in treating waste on the space station.
The DOE‐BER “EvoNet” project investigates the genetic and molecular basis of plant resilience in extreme environments. We do this by identifying key genes that enable “extreme survivor” species to thrive in the nitrogen-poor soils of Chile’s hyper-arid Atacama Desert. Our collections focus on 32 Atacama extremophile species, including seven grass species with potential biofuel applications. To identify genes-of-importance to survival we compared genomic and transcriptomic profiles of extremophile species that thrive in the Atacama to those of closely related “sister” species from nitrogen-rich arid and mesic regions of California. Deep RNA sequencing and de novo transcriptome assembly across these triplet species sets supported a phylogenomic framework for identifying positively selected genes associated with adaptive divergence. Our integrative analysis combined ecological and environmental data, metagenomics, evolutionary and systems biology, and metabolomics. This enabled us to create an unprecedented framework for systematically understanding how non-model plants have adapted to survive in extreme conditions. Our resulting database of positively selected ortholog groups in the extremophile plants offers promising targets for engineering crop and biofuel species with enhanced resilience to drought and extreme weather. Additionally, our newest dataset explores and exploits a complementary metabolomic approach. This new aspect provides innovative strategies to manipulate plant cell metabolism, further supporting efforts to improve agricultural productivity in the face of extreme climates. Importantly, our combined evolutionary- and metabolomic-based strategies focused on convergent patterns of adaptation, providing a genetic and metabolomic toolkit for improving crop and biofuel resilience across diverse plant species. Finally, our novel exploration of ecological and evolutionary dynamics delivered to the community a phylogenomic computational pipeline called “PhyloGeneious.” Our continued adaptations of this pipeline are publicly available to expedite evolutionary genomic research for future scientific discoveries. In total, our DOE-BER has provided genomic, metabolomic, and computational strategies to understand how extremophile plants provide evolutionary and physiological targets for improving agricultural and biofuel production.
Various models of biological human life support systems are surveyed. Biological structures, dimensions, and functional parameters of man-chlorella-microorganism models are described. Significant observations and the results obtained from these models are reported.
Here we discuss recent advances in applying ideas of fractals and disordered systems to two topics of biological interest, both topics having common the appearance of scale-free phenomena, i.e., correlations that have no characteristic length scale, typically exhibited by physical systems near a critical point and dynamical systems far from equilibrium. (i) DNA nucleotide sequences have traditionally been analyzed using models which incorporate the possibility of short-range nucleotide correlations. We found, instead, a remarkably long-range power law correlation. We found such long-range correlations in intron-containing genes and in non-transcribed regulatory DNA sequences as well as intragenomic DNA, but not in cDNA sequences or intron-less genes. We also found that the myosin heavy chain family gene evolution increases the fractal complexity of the DNA landscapes, consistent with the intron-late hypothesis of gene evolution. (ii) The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis, whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state. We found, however, that under normal conditions, beat-to-beat fluctuations in heart rate display long-range power law correlations.
Phenylpropanoids are specialized metabolites involved in several aspects of plant growth and development and in the responses of plants to environmental stimuli. These compounds are synthesized from key intermediates of the shikimate pathway, which are structurally modified by the combined activities of lyases, transferases, ligases, reductases and oxygenases, resulting in the organ- and developmental-specific synthesis and accumulation of diverse metabolites (Vogt, 2010). The phenylpropanoid pathway provides the building blocks for lignin, suberin, and condensed tannins that play a role in structural support and mechanical strength. Lignin is a major contributor to feedstock recalcitrance and negatively affects the conversion of plant biomass into downstream products in biorefineries (Liu et al., 2021). Further, this pathway is key for the production of anthocyanins for organ pigmentation, flavonols and flavones for UV protection, various flavonoids and isoflavonoids for plant-microbe interactions, and antimicrobial phytoalexins for protection against pathogens (Deng and Lu, 2017). In addition to their biological functions in planta, phenylpropanoids are economically important metabolites. They constitute important components in the human diet, acting as nutraceutical compounds with antioxidant, chemopreventive, antimitotic, neuroprotective, cardioprotective, and anti-inflammatory activities. Several phenylpropanoids are considered high-value biochemicals employed in the production of fragrances, pharmaceuticals and biopolymers (Lin and Eudes, 2020).
We have developed reaction libraries for predicting transformation products of PFAS in a variety of environmental and biological reaction systems.
This final technical report synthesizes major research results for DOE award DE-SC0022207. The project sought to identify metabolic control points and bottlenecks that regulate flux to free fatty acids (FFAs) in Picosynechococcus sp. PCC 7002 by treating lipid metabolism as modular pathways upstream and downstream of acetyl-CoA.
Invasion of plant-based life support systems by plant pathogens could cause plant disease and disruption of life support capability. Root rot caused by the fungus, Pythium, was observed during tests of prototype plant growth systems containing wheat at the Kennedy Space Center (KSC). We conducted experiments to determine if the presence of complex microbial communities in the plant root zone (rhizosphere) resisted invasion by the Pythium species isolated from the wheat root. Rhizosphere inocula of different complexity (as assayed by community-level physiological profile: CLPP) were developed using a dilution/extinction approach, followed by growth in hydroponic rhizosphere. Pythium growth on wheat roots and concomitant decreases in plant growth were inversely related to the complexity of the inocula during 20-day experiments in static hydroponic systems. Pythium was found on the seeds of several different wheat cultivars used in controlled environmental studies, but it is unclear if the seed-borne fungal strain(s) were identical to the pathogenic strain recovered from the KSC studies. Attempts to control pathogens and their effects in hydroponic life support systems should include early inoculation with complex microbial communities, which is consistent with ecological theory.
Our research endeavors entail ambitious objectives to explore the communities of microorganisms and quantify their chemical input and output to determine specific biochemical activity. To achieve these scientific aims, we are creating advanced algorithms to scrutinize extensive experimental data produced through high-throughput technologies, enabling us to elucidate how biological functions are influenced by environmental factors on various scales, and how microbial systems alter their surroundings. By studying the metabolic interdependence of bacterial communities for their survival and reproductive prosperity, we can gain insights into their biochemical capabilities. To accomplish this, we employed cutting-edge mass spectrometry-based techniques and metabolic fingerprinting methods, which offer high chemical specificity and sensitivity. As our efforts progressed, the project evolved into ENIGMA, encompassing a broader range of technology development, including untargeted metabolomics and its implementation for system-level organism analysis.
Charge-transfer (CT) excited states play an important role in many biological processes. However, many computational approaches often inadequately address the equilibration effects of nuclear and environmental degrees of freedom on these states. One prominent example of systems in which CT states are of utmost importance is reaction centers (RC) in photosystems. Here we use a multiscale approach combined with time-dependent density functional theory to explore the lowest CT excited state of the special pair P D1 –P D2 in the Photosystem II-RC of a cyanobacterium. We find that the nonequilibrium CT excited state resides near the Soret band, making an exciton the lowest-energy excited state. However, accounting for nuclear and state-specific dielectric equilibration along the CT potential energy surface (PES), the CT state P D1 – –P D2 + stabilizes energetically below the excitonic state. Importantly, this underscores the crucial role of state-specific solvation in mapping the PES of CT states, as demonstrated in a simplified dimer model.
Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet’s ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.
NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). The GeneLab Data Systems (GLDS) version 4.0 will be available on October 1st 2019, and will provide the latest in terms of professional state-of-the-art bioinformatics platform for the space biology and radiation community to upload their data into an omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. Started in 2015 as a repository designed to archive omics data from space experiments, GeneLab has expanded its scope to all ionizing radiation omics experiments conducted on the ground and has put considerable effort in providing carefully characterized radiation metadata on all dataset. GeneLab is also providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome) to help users explore important questions: 1) Which genes or proteins are expressed differently in space for various living organisms? 2) What specific DNA mutations or epigenetic changes happen in space or after exposure to ionizing radiation? and 3) How does genetics affect these responses? Processed data available on GeneLab are derived by standard data analysis workflows vetted by hundreds of scientists who volunteered to join one of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). In this presentation, we will discuss how to bridge the gap between irradiation studies performed on earth and biological experiments conducted in space since the early 1990's. We will discuss how radiation dosimetry was estimated for datasets derived from samples collected during the Space Shuttle era or on the International Space Station. Finally, we will address future strategies regarding dose monitoring in future missions into space, inter-agency efforts to unify data under one umbrella, and knowledge dissemination across the radiation research community and the space biology community.
The influence of chemical contaminants in potential biowaste sources upon the design and interface requirements of a biowaste resistojet propulsion system for a space station and/or base are evaluated. Chemical contaminants are defined as all compounds present in biowaste other than carbon dioxide, water, and methane. The latter are the nominal effluent candidates for the biowaste resistojet.