Preferred Orientation of a Physisorbed Molecular Catalyst and Implications for Selectivity
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Single atom, low valent transition metals are important for heterogeneous catalysis but are challenging to generate and stabilize in a well-defined manner. Herein, we explored the functionalization of silica with well-defined N-heterocyclic phosphenium (NHP) ions to heterogenize low-valent metals. The surface electro-statically bound [NHP] + coordinate to Pt(0) precursors resulting in well-defined, chemisorbed [(NHP)Pt(0)L n ] + sites. The resulting materials catalyze the hydrosilylation of alkynes and exhibit activities and selectivities that rival the current industry standard homogeneous catalysts. The catalysts leach Pt limiting their recyclability; however, recycling studies support that the high regioselectivities arise from heterogeneous sites and Pt particles do not form on the surface. Here we suspect that this phosphenium-based immobilization strategy will result in stable, tunable, low valent heterogeneous transition metal catalysts in a wider array of catalytic reactions.
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Soil viruses have received increased interest in recent years because of their abundance and unelucidated potential as drivers for organic carbon derived from fungal and bacterial necromass. With decreasing levels of rainfall and climbing instances of unseasonal conditions, it becomes crucial to our goals for negative carbon emissions to understand how soil carbon flux is affected. Here we leveraged metatranscriptomic data from a section of research plot of Hopland Research and Extension in northern California with a suite of viral detection tools: VirSorter, DRAMv, and VContact2. We aimed to cross analyze moisture regiments with viral populations. We find that viral diversity is driven by site, like bacteria. We found that viral diversity at face value showed some differentiation but did not significantly differ by legacy moisture content. Additionally, there was detection of auxiliary metabolic genes in a viral sequence which may further support virus’s direct input into the soil carbon pool.
In the opening weeks of 1998 a news article in the British journal Nature reported that NASA was about to enter biology in a big way. A "virtual" Astrobiology Institute was gearing up for business, and NASA administrator Dan Goldin told his external advisory council that he would like to see spending on the new institute eventually reach $100 million per year. "You just wait for the screaming from the physical scientists (when that happens)," Goldin was quoted as saying. Nevertheless, by the time of the second Astrobiology Science Conference in 2002, attended by seven hundred scientists from many disciplines, NASA spending on astrobiology had reached nearly half that amount and was growing at a steady pace. Under NASA leadership numerous institutions around the world applied the latest scientific techniques in the service of astrobiology's ambitious goal: the study of what NASA's 1996 Strategic Plan termed the "living universe." This goal embraced nothing less than an understanding of the origin, history, and distribution of life in the universe, including Earth. Astrobiology, conceived as a broad interdisciplinary research program, held the prospect of being the science for the twenty-first century which would unlock the secrets to some of the great questions of humanity. It is no surprise that these age-old questions should continue into the twenty-first century. But that the effort should be spearheaded by NASA was not at all obvious to those - inside and outside the agency - who thought NASA's mission was human spaceflight, rather than science, especially biological science. NASA had, in fact, been involved for four decades in "exobiology," a field that embraced many of the same questions but which had stagnated after the 1976 Viking missions to Mars. In this volume we tell the colorful story of the rise of the discipline of exobiology, how and why it morphed into astrobiology at the end of the twentieth century, and why NASA was the engine for both the discipline's founding and for its transformation.
Chemostat growth chambers measure optical density over time and require manual calculation of growth rates. Here, we present chemostat_regression, R software that enables users to automatically identify chemostat cycles and estimate growth rate using a linear regression approach. We describe steps for creating requisite software environment(s), formatting input data, executing the software via command line/RStudio/R-Shiny, interpreting results, assessing the validity of results, and modifying input parameters.
Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single-cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF), which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation, and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle, we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined that mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex, drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimer's disease and other neurological disorders.
During microbial infection, mammalian hosts reduce the availability of free metals such as zinc in a process known as nutritional immunity. Pathogens counteract nutritional immunity by expressing gene products that enhance growth in metal-limited conditions. One of the most transcriptionally induced genes in zinc-limited Neisseria gonorrhoeae , ngo1049, encodes a DUF4198 family protein we have named Zcp. This family of proteins is widely distributed in Gram-negative bacteria. Here, we provide the first structural, biochemical, and functional characterization of a DUF4198 protein. Zcp is a periplasmic, homodimeric substrate-binding protein (SBP), which binds one zinc ion per subunit with submicromolar affinity. We identified a zinc binding pocket in each subunit, composed of three histidine residues. Zcp enables maximal growth of N. gonorrhoeae in zinc-limited conditions but is dispensable for zinc uptake, in contrast to the cluster A-I SBP ZnuA, which is required for zinc import. The growth defect of zcp mutant N. gonorrhoeae is rescued by zinc supplementation. Zcp associates with proteins with roles in maintaining cell envelope integrity, and N. gonorrhoeae lacking zcp is more sensitive to envelope-targeting antimicrobials. Zcp enables infectivity of human epithelial cells and neutrophils by zinc-limited N. gonorrhoeae . We conclude that N. gonorrhoeae produces Zcp to buffer periplasmic zinc, which enables ZnuA to balance import of different metals and ensures the bioavailability of zinc for extracytoplasmic zinc-requiring proteins, as part of the coordinated response to host-imposed nutritional immunity.
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Cells’ structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.
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Co-variation of traits provides fundamental insights into constraints governing their evolution. An inverse correlation between glucose uptake rates (GURs) and cell surface area-to-volume (SA:V) ratios across 11 yeast species was recently reported. Here, we expand the analysis to 282 species to assess the generalizability of this correlation across Saccharomycotina yeasts and the contribution of shared evolutionary history to the co-variation of these traits. Using phylogenetic regression models, we found extracellular acidification rates (ECARs, used as a proxy for GURs) were weakly, but significantly, correlated with SA:V across Saccharomycotina. ECARs were also correlated with genome sizes and growth rates. Our findings support the reported correlation between GURs and SA:V ratios, but suggest other associated traits, including genome sizes. Specifically, yeasts that consume glucose faster tend to have lower SA:V, faster growth rates, and larger genomes, suggesting a trait syndrome governing several metabolic, genomic, and morphological traits across the yeast subphylum.
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In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of a Rhizosolenia sp. bloom, the initiation and complete bloom cycle of Chaetoceros socialis (8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22–1.2 µm), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.
Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling.
Abstract Motivation The detection of APOBEC3F- and APOBEC3G-induced mutations in virus sequences is useful for identifying hypermutated sequences. These sequences are not representative of viral evolution and can therefore alter the results of downstream sequence analyses if included. We previously published the software Hypermut, which detects hypermutation events in sequences relative to a reference. Two versions of this method are available as a webtool. Neither of these methods consider multistate characters or gaps in the sequence alignment. Results Here, we present an updated, user-friendly web and command-line version of Hypermut with functionality to handle multistate characters and gaps in the sequence alignment. This tool allows for straightforward integration of hypermutation detection into sequence analysis pipelines. As with the previous tool, while the main purpose is to identify G to A hypermutation events, any mutational pattern and context can be specified. Availability and implementation Hypermut 3 is written in Python 3. It is available as a command-line tool at https://github.com/MolEvolEpid/hypermut3 and as a webtool at https://www.hiv.lanl.gov/content/sequence/HYPERMUT/hypermutv3.html.
Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.
Microbial conversion of lignocellulosic biomass represents an alternative route for production of biofuels and bioproducts. While researchers have mostly focused on engineering strains such as Rhodotorula toruloides for better bisabolene production as a sustainable aviation fuel, less is known about the impact of the feedstock heterogeneity on bisabolene production. Critical material attributes like feedstock composition, nutritional content, and inhibitory compounds can all influence bioconversion. Further, the given feedstocks can have a marked influence on selection of suitable pretreatment and hydrolysis technologies, optimizing the fermentation conditions, and possibly even modifying the microorganism's metabolic pathways, to better utilize the available feedstock. Here, this work aimed to examine and understand how variations in corn stover batches, anatomical fractions, and storage conditions impact the efficiency of bisabolene production by R. toruloides. All of these represent different facets of feedstock heterogeneity. Deacetylation, mechanical refining, and enzymatic hydrolysis of these variable feedstocks served as the basis of this research. The resulting hydrolysates were converted to bisabolene via fermentation, a sustainable aviation fuel precursor, using an engineered R. toruloides strain. This study showed that different sources of feedstock heterogeneity can influence microbial growth and product titer in counterintuitive ways, as revealed through global analysis of protein expression. The maximum bisabolene produced by R. toruloides was on the stalk fraction of corn stover hydrolysate (8.89 ± 0.47 g/L). Further, proteomics analysis comparing the protein expression between the anatomic fractions showed that proteins relating to carbohydrate metabolism, energy production, and conversion as well as inorganic ion transport metabolism were either significantly upregulated or downregulated. Specifically, downregulation of proteins related to the iron–sulfur cluster in stalk fraction suggests a coordinated response by R. toruloides to maintain overall metabolic balance, and this was corroborated by the concentration of iron in the feedstocks.