Bottom-up single-molecule strategy for understanding subunit function of tetrameric β-galactosidase

Single molecule delivery into living cells

Controlled manipulation of cultured cells by delivery of exogenous macromolecules is a cornerstone of experimental biology. Here we describe a platform that uses nanopipettes to deliver defined numbers of macromolecules into cultured cell lines and primary cells at single molecule resolution. In the nanoinjection platform, the nanopipette is used as both a scanning ion conductance microscope (SICM) probe and an injection probe. The SICM is used to position the nanopipette above the cell surface before the nanopipette is inserted into the cell into a defined location and to a predefined depth. We demonstrate that the nanoinjection platform enables the quantitative delivery of DNA, globular proteins, and protein fibrils into cells with single molecule resolution and that delivery results in a phenotypic change in the cell that depends on the identity of the molecules introduced. Using experiments and computational modeling, we also show that macromolecular crowding in the cell increases the signal-to-noise ratio for the detection of translocation events, thus the cell itself enhances the detection of the molecules delivered.

Monomeric Esterase: Insights into Cooperative Behavior, Hysteresis/Allokairy

Herein, we present a novel esterase enzyme, Ade1, isolated from a metagenomic library of Amazonian dark earths soils, demonstrating its broad substrate promiscuity by hydrolyzing ester bonds linked to aliphatic groups. The three-dimensional structure of the enzyme was solved in the presence and absence of substrate (tributyrin), revealing its classification within the α/β-hydrolase superfamily. Despite being a monomeric enzyme, enzymatic assays reveal a cooperative behavior with a sigmoidal profile (initial velocities vs substrate concentrations). Our investigation brings to light the allokairy/hysteresis behavior of Ade1, as evidenced by a transient burst profile during the hydrolysis of substrates such as p-nitrophenyl butyrate and p-nitrophenyl octanoate. Crystal structures of Ade1, coupled with molecular dynamics simulations, unveil the existence of multiple conformational structures within a single molecular state (E̅1). Notably, substrate binding induces a loop closure that traps the substrate in the catalytic site. Upon product release, the cap domain opens simultaneously with structural changes, transitioning the enzyme to a new molecular state (E̅2). This study advances our understanding of hysteresis/allokairy mechanisms, a temporal regulation that appears more pervasive than previously acknowledged and extends its presence to metabolic enzymes. These findings also hold potential implications for addressing human diseases associated with metabolic dysregulation.

Digital Cascade Assays for ADP- or ATP-Producing Enzymes Using a Femtoliter Reactor Array Device

Digital enzyme assays are emerging biosensing methods for highly sensitive quantitative analysis of biomolecules with single-molecule detection sensitivity. However, current digital enzyme assays require a fluorogenic substrate for detection, which limits the applicability of this method to certain enzymes. ATPases and kinases are representative enzymes for which fluorogenic substrates are not available; however, these enzymes form large domains and play a central role in biology. In this study, we implemented a fluorogenic cascade reaction in a femtoliter reactor array device to develop a digital bioassay platform for ATPases and kinases. The digital cascade assay enabled quantitative measurement of the single-molecule activity of F1-ATPase, the catalytic portion of ATP synthase. We also demonstrated a digital assay for human choline kinase α. Furthermore, we developed a digital cascade assay for ATP-synthesizing enzymes and demonstrated a digital assay for pyruvate kinase. These results show the high versatility of this assay platform. Thus, the digital cascade assay has great potential for the highly sensitive detection and accurate characterization of various ADP- and ATP-producing enzymes, such as kinases, which may serve as disease biomarkers.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Disulfide Bonds Are Not Necessary for Intrinsic TNSALP Activity

Recent developments in single-molecule enzymology (SME) have allowed for the observation of subpopulations present in enzyme ensembles. Tissue-nonspecific alkaline phosphatase (TNSALP), a homodimeric monophosphate esterase central to bone metabolism, has become a model enzyme for SME studies. TNSALP contains two internal disulfide bonds that are critical for its effective dimerization; mutations in its disulfide bonding framework have been reported in patients with hypophosphatasia, a rare disease characterized by impaired bone and tooth mineralization. In this paper, we present the kinetics of these mutants and show that these disulfide bonds are not crucial for TNSALP enzymatic function. This surprising result reveals that the enzyme's active conformation does not rely on its disulfide bonds. We posit that the signs and symptoms seen in hypophosphatasia are likely not primarily due to impaired enzyme function, but rather decreased enzyme expression and trafficking.

Enzyme-based digital bioassay technology - key strategies and future perspectives

Digital bioassays based on single-molecule enzyme reactions represent a new class of bioanalytical methods that enable the highly sensitive detection of biomolecules in a quantitative manner. Since the first reports of these methods in the 2000s, there has been significant growth in this new bioanalytical strategy. The principal strategy of this method is to compartmentalize target molecules in micron-sized reactors at the single-molecule level and count the number of microreactors showing positive signals originating from the target molecule. A representative application of digital bioassay is the digital enzyme-linked immunosorbent assay (ELISA). Owing to their versatility, various types of digital ELISAs have been actively developed. In addition, some disease markers and viruses possess catalytic activity, and digital bioassays for such enzymes and viruses have, thus, been developed. Currently, with the emergence of new microreactor technologies, the targets of this methodology are expanding from simple enzymes to more complex systems, such as membrane transporters and cell-free gene expression. In addition, multiplex or multiparametric digital bioassays have been developed to assess precisely the heterogeneities in sample molecules/systems that are obscured by ensemble measurements. In this review, we first introduce the basic concepts of digital bioassays and introduce a range of digital bioassays. Finally, we discuss the perspectives of new classes of digital bioassays and emerging fields based on digital bioassay technology.

Single enzyme electroanalysis

Traditional studies of enzymatic activity rely on the combined kinetics of millions of enzyme molecules to produce a product, an experimental approach that may wash out heterogeneities that exist between individual enzymes. Evaluating these properties on an enzyme-by-enzyme basis represents an unambiguous means of elucidating heterogeneities; however, the quantification of enzymatic activity at the single-enzyme level is fundamentally limited by the maximum catalytic rate, k_cat, inherent to a given enzyme. For electrochemical methods measuring current, single enzymes must turn over greater than 10⁷ molecules per second to produce a measurable signal on the order of 10^-12 A. Enzymes with this capability are extremely rare in nature, with typical k_cat values for biologically relevant enzymes falling between 1 and 10 000 s^-1. Thus, clever amplification strategies are necessary to electrochemically detect the vast majority of enzymes. This review details the progress toward the electroanalytical detection and evaluation of single enzyme kinetics largely focused on the nanoimpact method, a chronoamperometric detection strategy that monitors the change in the current-time profile associated with stochastic collisions of freely diffusing entities (e.g., enzymes) onto a microelectrode or nanoelectrode surface. We discuss the experimental setups and methods developed in the last decade toward the quantification of single molecule enzymatic rates. Special emphasis is given to the limitations of measurement science in the observation of single enzyme activity and feasible methods of signal amplification with reasonable bandwidth.

Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages

MOTIVATION

Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes.

RESULTS

We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria.

AVAILABILITY AND IMPLEMENTATION

Code, models and tutorials are available online (https://github.com/networkbiolab/atlas).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-Molecule Analysis Determines Isozymes of Human Alkaline Phosphatase in Serum

Alkaline phosphatase (ALP) is an important biomarker, as high levels of ALP in blood can indicate liver disease or bone disorders. However, current clinical blood tests only measure the total concentration of ALP but are unable to distinguish enzyme isotypes. Here, we demonstrate a novel and rapid approach to profile various ALP isozymes in blood via a single-molecule-analysis platform. The microarray platform provides enzyme kinetics of hundreds of individual molecules at high throughput. Using these single molecule kinetics, we characterize the different activity profiles of ALP isotypes. By analyzing both healthy and disease samples, we found the single molecule activity distribution of ALP in serum reflects the health status of patients. This result demonstrates the potential utility of the method for improving the conventional ALP test, as well as for analyzing other enzymatic biomarkers, including enzyme isotypes.

Single-molecule measurements in microwells for clinical applications

The ability to detect and analyze proteins, nucleic acids, and other biomolecules is critical for clinical diagnostics and for understanding the underlying mechanisms of disease. Current detection methods in clinical and research laboratories rely upon bulk measurement techniques such as immunoassays, polymerase chain reaction, and mass spectrometry to detect these biomarkers. However, many potentially useful protein or nucleic acid biomarkers in blood, saliva, or other biofluids exist at concentrations well below the detection limits of current methods, necessitating the development of more sensitive technologies. Single-molecule measurements are poised to address this challenge, vastly improving sensitivity for detecting low abundance biomarkers and rare events within a population. Microwell arrays have emerged as a powerful tool for single-molecule measurements, enabling ultrasensitive detection of disease-relevant biomolecules in easily accessible biofluids. This review discusses the development, fundamentals, and clinical applications of microwell-based single-molecule methods, as well as challenges and future directions for translating these methods to the clinic.

Single-Molecule Mechanistic Study of Enzyme Hysteresis

Hysteresis is an important feature of enzyme-catalyzed reactions, as it reflects the influence of enzyme regulation in the presence of ligands such as substrates or allosteric molecules. In typical kinetic studies of enzyme activity, hysteretic behavior is observed as a "lag" or "burst" in the time course of the catalyzed reaction. These lags and bursts are due to the relatively slow transition from one state to another state of the enzyme molecule, with different states having different kinetic properties. However, it is difficult to understand the underlying mechanism of hysteresis by observing bulk reactions because the different enzyme molecules in the population behave stochastically. In this work, we studied the hysteretic behavior of mutant β-glucuronidase (GUS) using a high-throughput single-molecule array platform and investigated the effect of thermal treatment on the hysteresis.