Stochiometric quantification of the thiol redox proteome of macrophages reveals subcellular compartmentalization and susceptibility to oxidative perturbations
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
SEARCH · Engineering Papers
Results for “protein quantification”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Electrical sensing, tracking, and actuation of droplets
Devices, techniques, and processes are disclosed that use electrical impedance to detect of the presence and contents of droplets including cells, nucleic acids, proteins, or solute concentrations in an array of retrievable, trackable, trapped droplets in a fluidic system. Electrodes may be positioned underneath individual droplet traps in a microchannel to assay droplet contents and/or actuating droplets for the release of the droplets from corresponding traps. The disclosed technology may be used for detection of the results of solvent extraction processes including time-dependent quantification of metal ion concentration in the aqueous and organic phases, for wastewater treatment, heavy metal detection, pharmaceutical industry, and/or biotechnology, or for environmental monitoring of wastewater for toxic metal, monitoring of biological cell viability and proliferation, monitoring of extraction processes used in heavy metal mining, monitoring of extraction processes used in nuclear fuel processing, monitoring kinetics of enzyme processes, and/or assessing pharmacodynamics and drug efficacy.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantification of Dynamic Scattering Effects in Molecular Crystals using Large Angle Rocking Beam Electron Diffraction
Electron crystallography provides a pathway to solve structure of small crystals (< 1um) in size, and thus overcomes difficult synthesis constraints involved in growing large crystals. Generally, electron diffraction data is collected in the form of integrated intensity using continuous rotation or by precession. The measured intensities in 3D are utilized for structure solution. Using this approach, structure solution of difficult crystals, such as small crystals of zeolites, metal-organic frameworks, molecular crystals, and proteins , can be solved by electron diffraction. However, electron structure solutions are regularly reported with higher Rvalues than x-ray or neutron diffraction. While similar structures are found despite the high R-values, the differences in the measure intensity and theory calculated intensity limit information that can be extracted by electron diffraction. Previous work demonstrated that including multiple-scattering effects significantly reduce the R-values . Thus, it is critical to be able to quantify the dynamical diffraction effects in molecular crystals.
Crystallography↗
Pseudomonas putida membrane vesicles
Cellular and membrane vesicle proteomes from Pseudomonas putida KT2440 and engineered derivative strains. Proteomic quantification by diDO-IPTL (Waldbauer et al. 2017 Analytical Chemistry).
hypervesiculation↗
Computational tools and algorithms for ion mobility spectrometry-mass spectrometry
Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.
59 BASIC BIOLOGICAL SCIENCES↗
A frugal CRISPR kit for equitable and accessible education in gene editing and synthetic biology
Equitable and accessible education in life sciences, bioengineering, and synthetic biology is crucial for training the next generation of scientists, fostering transparency in public decision-making, and ensuring biotechnology can benefit a wide-ranging population. As a groundbreaking technology for genome engineering, CRISPR has transformed research and therapeutics. However, hands-on exposure to this technology in educational settings remains limited due to the extensive resources required for CRISPR experiments. Here, we develop CRISPRkit, an affordable kit designed for gene editing and regulation in high school education. CRISPRkit eliminates the need for specialized equipment, prioritizes biosafety, and utilizes cost-effective reagents. By integrating CRISPRi gene regulation, colorful chromoproteins, cell-free transcription-translation systems, smartphone-based quantification, and an in-house automated algorithm (CRISPectra), our kit offers an inexpensive (~$2) and user-friendly approach to performing and analyzing CRISPR experiments, without the need for a traditional laboratory setup. Experiments conducted by high school students in classroom settings highlight the kit’s utility for reliable CRISPRkit experiments. Furthermore, CRISPRkit provides a modular and expandable platform for genome engineering, and we demonstrate its applications for controlling fluorescent proteins and metabolic pathways such as melanin production. We envision CRISPRkit will facilitate biotechnology education for communities of diverse socioeconomic and geographic backgrounds.
59 BASIC BIOLOGICAL SCIENCES↗
Thermodynamic consequences of Tyr to Trp mutations in the cation–π-mediated binding of trimethyllysine by the HP1 chromodomain
Evolution has converged on cation–p interactions for recognition of quaternary alkyl ammonium groups such as trimethyllysine (Kme3). While computational modelling indicates that Trp provides the strongest cation–p interaction of the native aromatic amino acids, there is limited corroborative data from measurements within proteins. Herein we investigate a Tyr to Trp mutation in the binding pocket of the HP1 chromodomain, a reader protein that recognizes Kme3. Binding studies demonstrate that the Trpmediated cation–p interaction is about -5 kcal mol -1 stronger, and the Y24W crystal structure shows that the mutation is not perturbing. Quantum mechanical calculations indicate that greater enthalpic binding is predominantly due to increased cation–π interactions. NMR studies indicate that differences in the unbound state of the Y24W mutation lead to enthalpy–entropy compensation. These results provide direct experimental quantification of Trp versus Tyr in a cation–π interaction and afford insight into the conservation of aromatic cage residues in Kme3 reader domains.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Enhancing characterization of organic nitrogen components in aerosols and droplets using high-resolution aerosol mass spectrometry
Abstract. This study aims to enhance the understanding and application of the Aerodyne high-resolution aerosol mass spectrometer (HR-AMS) for the comprehensive characterization of organic nitrogen (ON) compounds in aerosol particles and atmospheric droplets. To achieve this goal, we analyzed 75 N-containing organic compounds, representing a diverse range of ambient non-organonitrate ON (NOON) types, including amines, amides, amino acids, N heterocycles, protein, and humic acids. Our results show that NOON compounds can produce significant levels of NHx+ and NOx+ ion fragments, which are typically recognized as ions representative of inorganic nitrogen species. We also identified the presence of CH2N+ at m/z = 28.0187, an ion fragment rarely quantified in ambient datasets due to substantial interference from N2+. As a result, the utilization of an updated calibration factor of 0.79 is necessary for accurate NOON quantification via the HR-AMS. We also assessed the relative ionization efficiencies (RIEs) for various NOON species and found that the average RIE for NOON compounds (1.52 ± 0.58) aligns with the commonly used default value of 1.40 for organic aerosol. Moreover, through a careful examination of the HR-AMS mass spectral features of various NOON types, we propose fingerprint ion series that can aid the NOON speciation analysis. For instance, the presence of CnH2n+2N+ ions is closely linked with amines, with CH4N+ indicating primary amines, C2H6N+ suggesting secondary amines, and C3H8N+ representing tertiary amines. CnH2nNO+ ions (especially for n values of 1–4) are very likely derived from amides. The co-existence of three ions, C2H4NO2+, C2H3NO+, and CH4NO+, serves as an indicator for the presence of amino acids. Additionally, the presence of CxHyN2+ ions indicates the occurrence of 2N-heterocyclic compounds. Notably, an elevated abundance of NH4+ is a distinct signature for amines and amino acids, as inorganic ammonium salts produce only negligible amounts of NH4+ in the HR-AMS. Finally, we quantified the NOON contents in submicron particles (PM1) and fog water in Fresno, California, and PM1 in New York City (NYC). Our results revealed the substantial presence of amino compounds in both Fresno and NYC aerosols, whereas concurrently collected fog water in Fresno contained a broader range of NOON species, including N-containing aromatic heterocycle (e.g., imidazoles) and amides. These findings highlight the significant potential of employing the widespread HR-AMS measurements of ambient aerosols and droplets to enhance our understanding of the sources, transformation processes, and environmental impacts associated with NOON compounds in the atmosphere.
Ge, Xinlei (ORCID:0000000195316478)↗
Systematic and scalable genome-wide essentiality mapping to identify nonessential genes in phages
Phages are one of the key ecological drivers of microbial community dynamics, function, and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor intensive and are generally phage specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage–host combinations that facilitate the identification of nonessential genes. As a proof of concept, we use an arrayed genome-wide CRISPR interference (CRISPRi) assay to map gene essentiality landscape in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification, and tracking of phages in diverse applications.
59 BASIC BIOLOGICAL SCIENCES↗
Quantification of heterogeneity in human CD8 + T cell responses to vaccine antigens: an HLA-guided perspective
Vaccines have historically played a pivotal role in controlling epidemics. Effective vaccines for viruses causing significant human disease, e.g., Ebola, Lassa fever, or Crimean Congo hemorrhagic fever virus, would be invaluable to public health strategies and counter-measure development missions. Here, we propose coverage metrics to quantify vaccine-induced CD8 + T cell-mediated immune protection, as well as metrics to characterize immuno-dominant epitopes, in light of human genetic heterogeneity and viral evolution. Proof-of-principle of our approach and methods are demonstrated for Ebola virus, SARS-CoV-2, and Burkholderia pseudomallei (vaccine) proteins.
60 APPLIED LIFE SCIENCES↗
Development of a Novel Electrochemiluminescence ELISA for Quantification of α-Synuclein Phosphorylated at Ser 129 in Biological Samples
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell
Here, we report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An expectation–maximization framework for comprehensive prediction of isoform-specific functions
Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. We present isoform interpretation, a method that uses expectation–maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function.
59 BASIC BIOLOGICAL SCIENCES↗
Biomolecular budget of persistent, microbial-derived soil organic carbon: The importance of underexplored pools
The details of how soil microorganisms contribute to stable soil organic carbon pools are a pressing knowledge gap with direct implications for soil health and climate mitigation. It is now recognized that microbial necromass contributes substantially to the formation of stable soil carbon. However, the quantification of necromass in soils has largely been limited to model molecules such as aminosugar biomarkers. The abundance and chemical composition of other persistent microbial residues remain unresolved, particularly concerning how these pools may vary with microbial community structure, soil texture, and management practices. We use yearlong soil incubation experiments with an isotopic tracer to quantify the composition of persistent residues derived from microbial communities inhabiting sand or silt dominated soil with annual (corn) or perennial (switchgrass) monocultures. Persistent microbial residues were recovered in diverse soil biomolecular pools including metabolites, proteins, lipids, and mineral-associated organic matter (MAOM). The relative abundances of microbial contributions to necromass pools were consistent across cropping systems and soil textures. The greatest residue accumulation was not recovered in MAOM but in the light density fraction of soil debris that persisted after extraction by chemical fractionation using organic solvents. Necromass abundance was positively correlated with microbial biomass abundance and revealed a possible role of cell wall morphology in enhancing microbial carbon persistence; while gram-negative bacteria accounted for the greatest contribution to microbial-derived carbon by mass at one year, residues from gram-positive Actinobacteria and Firmicutes showed greater durability. Together these results offer a quantitative assessment of the relative importance of diverse molecular classes for generating durable soil carbon.
54 ENVIRONMENTAL SCIENCES↗
Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes
Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure–function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1–3 GPa, which is consistent with crystalline proteins. Altogether, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.
Chemical structure↗
Temporal Sampling of Enzymes from Live Cells by Localized Electroporation and Quantification of Activity by SAMDI Mass Spectrometry
Abstract Measuring changes in enzymatic activity over time from small numbers of cells remains a significant technical challenge. In this work, a method for sampling the cytoplasm of cells is introduced to extract enzymes and measure their activity at multiple time points. A microfluidic device, termed the live cell analysis device (LCAD), is designed, where cells are cultured in microwell arrays fabricated on polymer membranes containing nanochannels. Localized electroporation of the cells opens transient pores in the cell membrane at the interface with the nanochannels, enabling extraction of enzymes into nanoliter‐volume chambers. In the extraction chambers, the enzymes modify immobilized substrates, and their activity is quantified by self‐assembled monolayers for matrix‐assisted laser desorption/ionization (SAMDI) mass spectrometry. By employing the LCAD‐SAMDI platform, protein delivery into cells is demonstrated. Next, it is shown that enzymes can be extracted, and their activity measured without a loss in viability. Lastly, cells are sampled at multiple time points to study changes in phosphatase activity in response to oxidation by hydrogen peroxide. With this unique sampling device and label‐free assay format, the LCAD with SAMDI enables a powerful new method for monitoring the dynamics of cellular activity from small populations of cells.
Mukherjee, Prithvijit↗
Artificial Intelligence for Autonomous Molecular Design: A Perspective
Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.
59 BASIC BIOLOGICAL SCIENCES↗
Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom
Algal blooms produce large quantities of organic matter that is subsequently remineralised by bacterial heterotrophs. Polysaccharide is a primary component of algal biomass. It has been hypothesised that individual bacterial heterotrophic niches during algal blooms are in part determined by the available polysaccharide substrates present. Measurement of the expression of TonB-dependent transporters, often specific for polysaccharide uptake, might serve as a proxy for assessing bacterial polysaccharide consumption over time. To investigate this, we present here high-resolution metaproteomic and metagenomic datasets from bacterioplankton of the 2016 spring phytoplankton bloom at Helgoland island in the southern North Sea, and expression profiles of TonB-dependent transporters during the bloom, which demonstrate the importance of both the Gammaproteobacteria and the Bacteroidetes as degraders of algal polysaccharide. TonB-dependent transporters were the most highly expressed protein class, split approximately evenly between the Gammaproteobacteria and Bacteroidetes , and totalling on average 16.7% of all detected proteins during the bloom. About 93% of these were predicted to take up organic matter, and for about 12% of the TonB-dependent transporters, we predicted a specific target polysaccharide class. Most significantly, we observed a change in substrate specificities of the expressed transporters over time, which was not reflected in the corresponding metagenomic data. From this, we conclude that algal cell wall-related compounds containing fucose, mannose, and xylose were mostly utilised in later bloom stages, whereas glucose-based algal and bacterial storage molecules including laminarin, glycogen, and starch were used throughout. Quantification of transporters could therefore be key for understanding marine carbon cycling.
59 BASIC BIOLOGICAL SCIENCES↗