Protein target search diffusion-association/dissociation free energy landscape around DNA binding site with flanking sequences

In this work we present a minimal structure-based model of protein diffusional search along local DNA amid protein binding and unbinding events on the DNA, taking into account protein-DNA electrostatic interactions and hydrogen-bonding (HB) interactions or contacts at the interface. We accordingly constructed the protein diffusion-association/dissociation free energy surface and mapped it to 1D as the protein slides along DNA, maintaining the protein-DNA interfacial HB contacts that presumably dictate the DNA sequence information detection. Upon DNA helical path correction, the protein 1D diffusion rates along local DNA can be physically derived to be consistent with experimental measurements. We also show that the sequence-dependent protein sliding or stepping patterns along DNA are regulated by collective interfacial HB dynamics, which also determines the ruggedness of the protein diffusion free energy landscape on the local DNA. In comparison, protein association or binding with DNA are generically dictated by the protein-DNA electrostatic interactions, with an interaction zone of nanometers around DNA. Extra degrees of freedom (DOFs) of the protein such as rotations and conformational fluctuations can be well accommodated within the protein-DNA electrostatic interaction zone. As such we demonstrate that the protein binding or association free energy profiling along DNA smoothens over the 1D diffusion free energy landscape, which leads to population variations for an order of magnitude upon a marginal free energetic smoothening around the specific or consensus sites. We further show that the protein unbinding or dissociation from a comparatively high-binding affinity DNA site is dominated by lateral diffusion to the flanking low-affinity sites. The results predict that experimental characterizations on the relative protein-DNA binding affinities or population profiling on the DNA are systematically and physically impacted by the extra DOFs of protein motions aside from 1D translation or helical tracking, as well as from flanking DNA sequences due to protein 1D diffusion and nonspecific binding/unbinding.

Anti-correlation of LacI association and dissociation rates observed in living cells

The rate at which transcription factors (TFs) bind their cognate sites has long been assumed to be limited by diffusion, and thus independent of binding site sequence. Here, we systematically test this assumption using cell-to-cell variability in gene expression as a window into the in vivo association and dissociation kinetics of the model transcription factor LacI. Using a stochastic model of the relationship between gene expression variability and binding kinetics, we performed single-cell gene expression measurements to infer association and dissociation rates for a set of 35 different LacI binding sites. We found that both association and dissociation rates differed significantly between binding sites, and moreover observed a clear anticorrelation between these rates across varying binding site strengths. These results contradict the long-standing hypothesis that TF binding site strength is primarily dictated by the dissociation rate, but may confer the evolutionary advantage that TFs do not get stuck in near-operator sequences while searching.

Analysis of Fluorescent Proteins for Observing Single Gene Locus in a Live and Fixed Escherichia coli Cell

Fluorescent proteins (FPs) are essential tools for advanced microscopy techniques such as super-resolution imaging, single-particle tracking, and quantitative single-molecule counting. Various FPs fused to DNA-binding proteins have been used to observe the subcellular location and movement of specific gene loci in living and fixed bacterial cells. However, quantitative assessments of the properties of FPs for gene locus measurements are still lacking. Here, we assessed various FPs to observe specific gene loci in live and fixed Escherichia coli cells using a fluorescent repressor-operator binding system (FROS), tet operator-Tet repressor proteins (TetR). Tsr-fused FPs were used to assess the intensity and photostability of various FPs (five red FPs: mCherry2, FusionRed, mRFP, mCrimson3, and dKatushka; and seven yellow FPs: SYFP2, Venus, mCitrine, YPet, mClover3, mTopaz, and EYFP) at the single-molecule level in living cells. These FPs were then used for gene locus measurements using FROS. Our results indicate that TetR-mCrimson3 (red) and TetR-EYFP (yellow) had better properties for visualizing gene loci than the other TetR-FPs. Furthermore, fixation procedures affected the clustering of diffusing TetR-FPs and altered the locations of the TetR-FP foci. Fixation with formaldehyde consistently disrupted proper DNA locus observations using TetR-FPs. Notably, the foci measured using TetR-mCrimson3 remained close to their original positions in live cells after glyoxal fixation. This in vivo study provides a cell-imaging guide for the use of FPs for gene-locus observation in E. coli and a scheme for evaluating the use of FPs for other cell-imaging purposes.

Single-Molecule Spectroscopy and Super-Resolution Mapping of Physicochemical Parameters in Living Cells

By superlocalizing the positions of millions of single molecules over many camera frames, a class of super-resolution fluorescence microscopy methods known as single-molecule localization microscopy (SMLM) has revolutionized how we understand subcellular structures over the past decade. In this review, we highlight emerging studies that transcend the outstanding structural (shape) information offered by SMLM to extract and map physicochemical parameters in living mammalian cells at single-molecule and super-resolution levels. By encoding/decoding high-dimensional information-such as emission and excitation spectra, motion, polarization, fluorescence lifetime, and beyond-for every molecule, and mass accumulating these measurements for millions of molecules, such multidimensional and multifunctional super-resolution approaches open new windows into intracellular architectures and dynamics, as well as their underlying biophysical rules, far beyond the diffraction limit.

Modeling Control of Supercoiling Dynamics and Transcription Using DNA-Binding Proteins

Nearly all natural and synthetic gene networks rely on the fundamental process of transcription to enact biological feedback, genetic programs, and living circuitry. In this work, we investigate the efficacy of controlling transcription using a new biophysical mechanism, control of localized supercoiling near a gene of interest. We postulate a basic reaction network model for describing the general phenomenon of transcription and introduce a separate set of equations to describe the dynamics of supercoiling. We show that supercoiling and transcription introduce a shared reaction flux term in the model dynamics and illustrate how the modulation of supercoiling can be used to control transcription rates. We show the supercoiling-transcription model can be written as a nonlinear state-space model, with a radial basis function nonlinearity to capture the empirical relationship between supercoiling and transcription rates. We show the system admits a single, globally exponentially stable equilibrium point. Notably, we show that mRNA steady-state levels can be controlled directly by increasing a length-scale parameter for genetic spacing. Finally, we build a mathematical model to explore the use of a DNA binding protein, to define programmable boundary conditions on supercoiling propagation, which we show can be used to control transcriptional bursting or pulsatile transcriptional response. We show there exists a stabilizing control law for mRNA tracking, using the method of control Lyapunov functions and illustrate these results with numerical simulations.

A Comprehensive Exploration of Caspase Detection Methods: From Classical Approaches to Cutting-Edge Innovations

The activation of caspases is a crucial event and an indicator of programmed cell death, also known as apoptosis. These enzymes play a central role in cancer biology and are considered one promising target for current and future advancements in therapeutic interventions. Traditional methods of measuring caspase activity such as antibody-based methods provide fundamental insights into their biological functions, and are considered essential tools in the fields of cell and cancer biology, pharmacology and toxicology, and drug discovery. However, traditional methods, though extensively used, are now recognized as having various shortcomings. In addition, these methods fall short of providing solutions to and matching the needs of the rapid and expansive progress achieved in studying caspases. For these reasons, there has been a continuous improvement in detection methods for caspases and the network of pathways involved in their activation and downstream signaling. Over the past decade, newer methods based on cutting-edge state-of-the-art technologies have been introduced to the biomedical community. These methods enable both the temporal and spatial monitoring of the activity of caspases and their downstream substrates, and with enhanced accuracy and precision. These include fluorescent-labeled inhibitors (FLIs) for live imaging, single-cell live imaging, fluorescence resonance energy transfer (FRET) sensors, and activatable multifunctional probes for in vivo imaging. Recently, the recruitment of mass spectrometry (MS) techniques in the investigation of these enzymes expanded the repertoire of tools available for the identification and quantification of caspase substrates, cleavage products, and post-translational modifications in addition to unveiling the complex regulatory networks implicated. Collectively, these methods are enabling researchers to unravel much of the complex cellular processes involved in apoptosis, and are helping generate a clearer and comprehensive understanding of caspase-mediated proteolysis during apoptosis. Herein, we provide a comprehensive review of various assays and detection methods as they have evolved over the years, so to encourage further exploration of these enzymes, which should have direct implications for the advancement of therapeutics for cancer and other diseases.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Diffusion-Controlled Reactions: An Overview

We review the milestones in the century-long development of the theory of diffusion-controlled reactions. Starting from the seminal work by von Smoluchowski, who recognized the importance of diffusion in chemical reactions, we discuss perfect and imperfect surface reactions, their microscopic origins, and the underlying mathematical framework. Single-molecule reaction schemes, anomalous bulk diffusions, reversible binding/unbinding kinetics, and many other extensions are presented. An alternative encounter-based approach to diffusion-controlled reactions is introduced, with emphasis on its advantages and potential applications. Some open problems and future perspectives are outlined.

Single-molecule tracking of PprI in D. radiodurans without interference of autoblinking

Autoblinking is a widespread phenomenon and exhibits high level of intensity in some bacteria. In Deinococcus radiodurans (D. radiodurans), strong autoblinking was found to be indistinguishable from PAmCherry and greatly prevented single-molecule tracking of proteins of interest. Here we employed the bright photoswitchable fluorescent protein mMaple3 to label PprI, one essential DNA repair factor, and characterized systematically the fluorescence intensity and bleaching kinetics of both autoblinking and PprI-mMaple3 molecules within cells grown under three different conditions. Under minimal media, we can largely separate autoblinking from mMaple3 molecules and perform reliably single-molecule tracking of PprI in D. radiodurans, by means of applying signal-to-noise ratio and constraining the minimal length for linking the trajectories. We observed three states of PprI molecules, which bear different subcellular localizations and distinct functionalities. Our strategy provides a useful means to study the dynamics and distributions of proteins of interest in bacterial cells with high level of autoblinking.

Parameter estimation of the fractional Ornstein-Uhlenbeck process based on quadratic variation

Modern experiments routinely produce extensive data of the diffusive dynamics of tracer particles in a large range of systems. Often, the measured diffusion turns out to deviate from the laws of Brownian motion, i.e., it is anomalous. Considerable effort has been put in conceiving methods to extract the exact parameters underlying the diffusive dynamics. Mostly, this has been done for unconfined motion of the tracer particle. Here, we consider the case when the particle is confined by an external harmonic potential, e.g., in an optical trap. The anomalous particle dynamics is described by the fractional Ornstein-Uhlenbeck process, for which we establish new estimators for the parameters. Specifically, by calculating the empirical quadratic variation of a single trajectory, we are able to recover the subordination process governing the particle motion and use it as a basis for the parameter estimation. The statistical properties of the estimators are evaluated from simulations.

Critical Assessment of Condensate Boundaries in Dual-Color Single Particle Tracking

Biomolecular condensates are membraneless cellular compartments generated by phase separation that regulate a broad variety of cellular functions by enriching some biomolecules while excluding others. Live-cell single particle tracking of individual fluorophore-labeled condensate components has provided insights into a condensate's mesoscopic organization and biological functions, such as revealing the recruitment, translation, and decay of RNAs within ribonucleoprotein (RNP) granules. Specifically, during dual-color tracking, one imaging channel provides a time series of individual biomolecule locations, while the other channel monitors the location of the condensate relative to these molecules. Therefore, an accurate assessment of a condensate's boundary is critical for combined live-cell single particle-condensate tracking. Despite its importance, a quantitative benchmarking and objective comparison of the various available boundary detection methods is missing due to the lack of an absolute ground truth for condensate images. Here, we use synthetic data of defined ground truth to generate noise-overlaid images of condensates with realistic phase separation parameters to benchmark the most commonly used methods for condensate boundary detection, including an emerging machine-learning method. We find that it is critical to carefully choose an optimal boundary detection method for a given dataset to obtain accurate measurements of single particle-condensate interactions. The criteria proposed in this study to guide the selection of an optimal boundary detection method can be broadly applied to imaging-based studies of condensates.

Bleaching-resistant, Near-continuous Single-molecule Fluorescence and FRET Based on Fluorogenic and Transient DNA Binding

Photobleaching of fluorescent probes limits the observation span of typical single-molecule fluorescence measurements and hinders observation of dynamics at long timescales. Here, we present a general strategy to circumvent photobleaching by replenishing fluorescent probes via transient binding of fluorogenic DNAs to complementary DNA strands attached to a target molecule. Our strategy allows observation of near-continuous single-molecule fluorescence for more than an hour, a timescale two orders of magnitude longer than the typical photobleaching time of single fluorophores under our conditions. Using two orthogonal sequences, we show that our method is adaptable to Förster Resonance Energy Transfer (FRET) and that can be used to study the conformational dynamics of dynamic structures, such as DNA Holliday junctions, for extended periods. By adjusting the temporal resolution and observation span, our approach enables capturing the conformational dynamics of proteins and nucleic acids over a wide range of timescales.

Photophysics of Two Indole-Based Cyan Fluorophores

For the purpose of searching for new biological fluorophore, we assess the photophysical properties of two indole derivatives, 4-cyano-7-azaindole (4CN7AI) and 1-methyl-4-cyano-7-azaindole (1M4CN7AI), in a series of solvents. We find that (1) the absorption spectra of both derivatives are insensitive to solvents and are red-shifted from that of indole, having a maximum absorption wavelength of ca. 318 nm and a broad profile that extends beyond 370 nm; (2) both derivatives emit in the blue to green spectral range with a large Stokes shift, for example, in H₂O, the maximum emission wavelength of 4CN7AI (1M4CN7AI) is at ca. 455 nm (470 nm); (3) 4CN7AI has a higher fluorescence quantum yield (QY) and a longer fluorescence lifetime (τ_F) in aprotic solvents than in protic solvents, for example, QY (τ_F) = 0.72 ± 0.04 (7.6 ± 0.8 ns) in tetrahydrofuran and QY (τ_F) = 0.29 ± 0.03 (6.2 ± 0.6 ns) in H₂O; (4) in all of the solvents used except H₂O, the fluorescence QY (τ_F) of 1M4CN7AI is equal to or higher (longer) than 0.69 ± 0.03 (11.2 ± 0.7 ns). Taken together, these results suggest that the corresponding non-natural amino acids, 4-cyano-7-azatryptophan and 1-methyl-4-cyano-7-azatryptophan, could be useful as biological fluorophores.

Single-Molecule Displacement Mapping Indicates Unhindered Intracellular Diffusion of Small (≲1 kDa) Solutes

While fundamentally important, the intracellular diffusion of small (≲1 kDa) solutes has been difficult to elucidate due to challenges in both labeling and measurement. Here we quantify and spatially map the translational diffusion patterns of small solutes in mammalian cells by integrating several recent advances. In particular, by executing tandem stroboscopic illumination pulses down to 400 μs separation, we extend single-molecule displacement/diffusivity mapping (SMdM), a super-resolution diffusion quantification tool, to small solutes with high diffusion coefficients D of >300 μm2/s. We thus show that for multiple water-soluble dyes and dye-tagged nucleotides, intracellular diffusion is dominated by vast regions of high diffusivity ∼60-70% of that in vitro, up to ∼250 μm2/s in the fastest cases. Meanwhile, we also visualize sub-micrometer foci of substantial slowdowns in diffusion, thus underscoring the importance of spatially resolving the local diffusion behavior. Together, these results suggest that the intracellular diffusion of small solutes is only modestly scaled down by the slightly higher viscosity of the cytosol over water but otherwise not further hindered by macromolecular crowding. We thus lift a paradoxically low speed limit for intracellular diffusion suggested by previous experiments.

Size-Dependent Suppression of Molecular Diffusivity in Expandable Hydrogels: A Single-Molecule Study

By repurposing the recently popularized expansion microscopy to control the meshwork size of hydrogels, we examine the size-dependent suppression of molecular diffusivity in the resultant tuned hydrogel nanomatrices over a wide range of polymer fractions of ∼0.14-7 wt %. With our recently developed single-molecule displacement/diffusivity mapping (SMdM) microscopy methods, we thus show that with a fixed meshwork size, larger molecules exhibit more impeded diffusion and that, for the same molecule, diffusion is progressively more suppressed as the meshwork size is reduced; this effect is more prominent for the larger molecules. Moreover, we show that the meshwork-induced obstruction of diffusion is uncoupled from the suppression of diffusion due to increased solution viscosities. Thus, the two mechanisms, respectively, being diffuser-size-dependent and independent, may separately scale down molecular diffusivity to produce the final diffusion slowdown in complex systems like the cell.

Single-molecule displacement mapping indicates unhindered intracellular diffusion of small (<~1 kDa) solutes

While fundamentally important, the intracellular diffusion of small (<~1 kDa) solutes has been difficult to elucidate due to challenges in both labeling and measurement. Here we quantify and spatially map the translational diffusion patterns of small solutes in mammalian cells by integrating several recent advances. In particular, by executing tandem stroboscopic illumination pulses down to 400-μs separation, we extend single-molecule displacement/diffusivity mapping (SM d M), a super-resolution diffusion quantification tool, to small solutes with high diffusion coefficients D of >300 μm ² /s. We thus show that for multiple water-soluble dyes and dye-tagged nucleotides, intracellular diffusion is dominated by vast regions of high diffusivity ~60-70% of that in vitro , up to ~250 μm ² /s in the fastest cases. Meanwhile, we also visualize sub-micrometer foci of substantial slowdowns in diffusion, thus underscoring the importance of spatially resolving the local diffusion behavior. Together, these results suggest that the intracellular diffusion of small solutes is only modestly scaled down by the slightly higher viscosity of the cytosol over water, but otherwise not further hindered by macromolecular crowding. We thus lift a paradoxically low speed limit for intracellular diffusion suggested by previous experiments.

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Conformation of von Willebrand factor in shear flow revealed with stroboscopic single-molecule imaging

von Willebrand factor (VWF) is a multimeric blood protein that acts as a mechanical probe, responding to changes in flow to initiate platelet plug formation. Previously, our laboratory tests had shown that using single-molecule imaging that shear stress can extend surface-tethered VWF, but paradoxically, we found that the required shear stress was higher than reported for free-in-flow VWF, an observation inconsistent with basic physical principles. To resolve this inconsistency critical to VWF's molecular mechanism, we measured free-VWF extension in shear flow using pulsed laser stroboscopic imaging of single molecules. Here, laser pulses of different durations are used to capture multiple images of the same molecule within each frame, enabling accurate length measurements in the presence of motion blur. At high shear stresses, we observed a mean shift in VWF extension of <200 nm, much shorter than the multiple-micron extensions previously reported with no evidence for the predicted sharp globule-stretch conformational transition. Modeling VWF with a Brownian dynamics simulation, our results were consistent with VWF behaving as an uncollapsed polymer rather than the theorized compact ball. The muted response of free VWF to high shear rates implies that the tension experienced by free VWF in physiological shear flow is lower than indicated by previous reports and that tethering to platelets or the vessel wall is required to mechanically activate VWF adhesive function for primary hemostasis.

Development of single-molecule ubiquitination mediated fluorescence complementation to visualize protein ubiquitination dynamics in dendrites

The ubiquitination/proteasome system is important for the spatiotemporal control of protein synthesis and degradation at synapses, while dysregulation may underlie autism spectrum disorders (ASDs). However, methods allowing direct visualization of the subcellular localization and temporal dynamics of protein ubiquitination are lacking. Here we report the development of Single-Molecule Ubiquitin Mediated Fluorescence Complementation (SM-UbFC) as a method to visualize and quantify the dynamics of protein ubiquitination in dendrites of live neurons in culture. Using SM-UbFC, we demonstrate that the rate of PSD-95 ubiquitination is elevated in dendrites of FMR1 KO neurons compared with wild-type controls. We further demonstrate the rapid ubiquitination of the fragile X messenger ribonucleoprotein, FMRP, and the AMPA receptor subunit, GluA1, which are known to be key events in the regulation of synaptic protein synthesis and plasticity. SM-UbFC will be useful for future studies on the regulation of synaptic protein homeostasis.

Fluorogenic Dimers as Bright Switchable Probes for Enhanced Super-Resolution Imaging of Cell Membranes

Super-resolution fluorescence imaging based on single-molecule localization microscopy (SMLM) enables visualizing cellular structures with nanometric precision. However, its spatial and temporal resolution largely relies on the brightness of ON/OFF switchable fluorescent dyes. Moreover, in cell plasma membranes, the single-molecule localization is hampered by the fast lateral diffusion of membrane probes. Here, to address these two fundamental problems, we propose a concept of ON/OFF switchable probes for SMLM (points accumulation for imaging in nanoscale topography, PAINT) based on fluorogenic dimers of bright cyanine dyes. In these probes, the two cyanine units connected with a linker were modified at their extremities with low-affinity membrane anchors. Being self-quenched in water due to intramolecular dye H-aggregation, they displayed light up on reversible binding to lipid membranes. The charged group in the linker further decreased the probe affinity to the lipid membranes, thus accelerating its dynamic reversible ON/OFF switching. The concept was validated on cyanines 3 and 5. SMLM of live cells revealed that the new probes provided higher brightness and ∼10-fold slower diffusion at the cell surface, compared to reference probes Nile Red and DiD, which boosted axial localization precision >3-fold down to 31 nm. The new probe allowed unprecedented observation of nanoscale fibrous protrusions on plasma membranes of live cells with 40 s time resolution, revealing their fast dynamics. Thus, going beyond the brightness limit of single switchable dyes by cooperative dequenching in fluorogenic dimers and slowing down probe diffusion in biomembranes open the route to significant enhancement of super-resolution fluorescence microscopy of live cells.

Comparison of bacterial diversity, root exudates and soil enzymatic activities in the rhizosphere of AVP1-transgenic and non-transgenic wheat (Triticum aestivum L.)

AIMS

Soil microbial communities are among the most diverse communities that might be affected due to transgenic crops. Therefore, risk assessment studies on transgenes are essentially required as any adverse effects may depend not only on the specific gene and crop involved but also on soil conditions.

METHODS AND RESULTS

The present study deals with the comparison of bacterial populations, root exudates, and activities of soil enzymes in non-transgenic and AVP1-transgenic wheat rhizosphere, overexpressing vacuolar H+pyrophosphatase for salinity and drought stress tolerance. Amounts of organic acids and sugars produced as root exudates and activities of dehydrogenase, phosphatase, and protease enzymes in soil solution showed no significant differences in AVP1-transgenic and non-transgenic wheat rhizosphere, except for urease and phenol oxidase activities. The higher copy number of nifH gene showed the abundance of nitrogen-fixing bacteria in the rhizosphere of AVP1-transgenic wheat compared with non-transgenic wheat. nifH gene sequence analysis indicated the common diazotrophic genera Azospirillum, Bradyrhizobium, Rhizobium, and Pseudomonas in AVP1-transgenic and non-transgenic wheat except for Zoogloea detected only in non-transgenic wheat. Using 454-pyrosequencing of 16S rRNA gene from soil DNA, a total of 156, 282 sequences of 18 phyla were obtained, which represented bacterial (128,006), Archeal (7,928), and unclassified (21,568) sequences. Proteobacteria, Crenarchaeota, and Firmicutes were the most abundant phyla in transgenic and non-transgenic wheat rhizosphere. Further comparison of different taxonomic units at the genus level showed similar distribution in transgenic and non-transgenic wheat rhizosphere.

CONCLUSION

We conclude that AVP1 gene in transgenic wheat has no apparent adverse effects on the soil environment and different bacterial communities. However, bacterial community depends on several other factors not only genetic composition of the host plants.

SIGNIFICANCE OF THE STUDY

The present research supports introduction and cultivation of transgenic plants in agricultural systems without any adverse effects on indigenous bacterial communities and soil ecosystem.

A Critical Review on the Sensing, Control, and Manipulation of Single Molecules on Optofluidic Devices

Single-molecule techniques have shifted the paradigm of biological measurements from ensemble measurements to probing individual molecules and propelled a rapid revolution in related fields. Compared to ensemble measurements of biomolecules, single-molecule techniques provide a breadth of information with a high spatial and temporal resolution at the molecular level. Usually, optical and electrical methods are two commonly employed methods for probing single molecules, and some platforms even offer the integration of these two methods such as optofluidics. The recent spark in technological advancement and the tremendous leap in fabrication techniques, microfluidics, and integrated optofluidics are paving the way toward low cost, chip-scale, portable, and point-of-care diagnostic and single-molecule analysis tools. This review provides the fundamentals and overview of commonly employed single-molecule methods including optical methods, electrical methods, force-based methods, combinatorial integrated methods, etc. In most single-molecule experiments, the ability to manipulate and exercise precise control over individual molecules plays a vital role, which sometimes defines the capabilities and limits of the operation. This review discusses different manipulation techniques including sorting and trapping individual particles. An insight into the control of single molecules is provided that mainly discusses the recent development of electrical control over single molecules. Overall, this review is designed to provide the fundamentals and recent advancements in different single-molecule techniques and their applications, with a special focus on the detection, manipulation, and control of single molecules on chip-scale devices.

Optical Fluorescence Imaging of Native Proteins Using a Fluorescent Probe with a Cell-Membrane-Permeable Carboxyl Group

Although various methods for selective protein tagging have been established, their ap plications are limited by the low fluorescent tagging efficiency of specific terminal regions of the native proteins of interest (NPIs). In this study, the highly sensitive fluorescence imaging of single NPIs was demonstrated using a eukaryotic translation mechanism involving a free carboxyl group of a cell-permeable fluorescent dye. In living cells, the carboxyl group of cell-permeable fluorescent dyes reacted with the lysine residues of acceptor peptides (AP or AVI-Tag). Genetically encoded recognition demonstrated that the efficiency of fluorescence labeling was nearly 100%. Nickel-nitrilotriacetic acid (Ni-NTA) beads bound efficiently to a single NPI for detection in a cell without purification. Our labeling approach satisfied the necessary conditions for measuring fluorescently labeled NPI using universal carboxyl fluorescent dyes. This approach is expected to be useful for resolving complex biological/ecological issues and robust single-molecule analyses of dynamic processes, in addition to applications in ultra-sensitive NPIs detection using nanotechnology.

What Is Motion? Recent Advances in the Study of Molecular Movement Patterns of the Peptidoglycan Synthesis Machines

How proteins move through space and time is a fundamental question in biology. While great strides have been made toward a mechanistic understanding of protein movement, many questions remain. We discuss the biological implications of motion in the context of the peptidoglycan (PG) synthesis machines. We reviewed systems in several bacteria, including Escherichia coli, Bacillus subtilis, and Streptococcus pneumoniae, and present a comprehensive view of our current knowledge regarding movement dynamics. Discrepancies are also addressed because "one size does not fit all". For bacteria to divide, new PG is synthesized and incorporated into the growing cell wall by complex multiprotein nanomachines consisting of PG synthases (transglycosylases [TG] and/or transpeptidases [TP]) as well as a variety of regulators and cytoskeletal factors. Advances in imaging capabilities and labeling methods have revealed that these machines are not static but rather circumferentially transit the cell via directed motion perpendicular to the long axis of model rod-shaped bacteria such as E. coli and B. subtilis. The enzymatic activity of the TG:TPs drives motion in some species while motion is mediated by FtsZ treadmilling in others. In addition, both directed and diffusive motion of the PG synthases have been observed using single-particle tracking technology. Here, we examined the biological role of diffusion regarding transit. Lastly, findings regarding the monofunctional transglycosylases (RodA and FtsW) as well as the Class A PG synthases are discussed. This minireview serves to showcase recent advances, broach mechanistic unknowns, and stimulate future areas of study.

Scanning Electron Microscopy of Escherichia coli Encapsulated in a Spacerized Graphene Sandwich

Electron microscopy of biological materials such as bacteria allows multifaceted analysis to understand their structure and function with high resolution, which is difficult to achieve with optical microscopy. However, the samples are damaged or broken by electron beam irradiation and by the vacuum environment. Here, we observed bacteria in a suspension encapsulated in a graphene sandwich that prevents electron beam damage without the need for fixation. Specifically, we demonstrated in situ scanning electron microscopy observation of Escherichia coli in a graphene sandwich containing a perforated membrane as a spacer, encapsulating non-immobilized E. coli between the graphene layers. However, E. coli activity, such as division, was not observed, although the irradiated cells grew slightly when re-suspended under optimal culture conditions. Our findings suggest that the graphene sandwich methodology enables the observation of wet E. coli cells by electron microscopy but requires refinement to allow the live imaging of biological materials.

Detecting Individual Proteins and Their Surface Charge Variations in Solution by the Potentiometric Nanoimpact Method

Label-free detection and analysis of proteins in their natural form and their dynamic interactions with substrates at the single-molecule level are important for both fundamental studies and various applications. Herein, we demonstrate a simple potentiometric method to achieve this goal by detecting the native charge of protein in solution by utilizing the principle of single-entity electrochemistry techniques. When a charged protein moves near the vicinity of a floating carbon nanoelectrode connected to a high-impedance voltage meter, the distinct local electrostatic potential changes induced by the transient collision event of protein, also called the "nanoimpact" event, can be captured by the nanoelectrode as a potential probe. This potentiometric method is highly sensitive for charged proteins, and low-molecular-weight proteins less than 10 kDa can be detected in low-salt-concentration electrolytes. By analyzing the shape and magnitude of the recorded time-resolved potential change and its time derivative, we can reveal the charge and motion of the protein in the nonspecific protein-surface interaction event. The charge polarity variations of the proteins at different pH values were also successfully probed. Compared with synthetic spherical nanoparticles, the statistical analysis of many single-molecule nanoimpact events revealed a large variation in the recorded transient potential signals, which may be attributed to the intrinsic protein dynamics and surface charge heterogeneity, as suggested by the finite element method and molecular dynamic simulations.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data

Background

Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.

Results

We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.

Conclusions

ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04409-9.

Etching-Engineered Low-Voltage Dielectrophoretic Nanotweezers for Trapping of Single Molecules

Understanding the functions of biomolecules at the single-molecule level is crucial due to their important and diverse roles in cell regulation. Recently, nanotweezers made of dual carbon nanoelectrodes have been developed for single-cell biopsies by applying a high alternating voltage. However, high electric voltage can induce Joule heating, water electrolysis, and other side effects on cell activity, which may be unfavorable for cellular applications. Here, we report a low-voltage nanotweezer for trapping of single DNA molecules using etching-engineered nanoelectrodes which effectively reduce the minimum trapping voltage by six times. Meanwhile, the low-voltage nanotweezer displays an improved trapping stiffness. Based on the finite element method simulations, we attribute the mechanism for the low-voltage nanotweezers to the increase in spatial heterogeneity and nonuniformity of electric field by etching of quartz near the nanoelectrodes. This work opens a new dimension for noninvasive single-molecule manipulation in solution and potential applications in single-cell biopsies.

Precision of Protein Thermometry

Temperature sensing is a ubiquitous cell behavior, but the fundamental limits to the precision of temperature sensing are poorly understood. Unlike in chemical concentration sensing, the precision of temperature sensing is not limited by extrinsic fluctuations in the temperature field itself. Instead, we find that precision is limited by the intrinsic copy number, turnover, and binding kinetics of temperature-sensitive proteins. Developing a model based on the canonical TlpA protein, we find that a cell can estimate temperature to within 2%. We compare this prediction with in vivo data on temperature sensing in bacteria.

Single Molecules Are Your Quanta: A Bottom-Up Approach toward Multidimensional Super-resolution Microscopy

The rise of single-molecule localization microscopy (SMLM) and related super-resolution methods over the past 15 years has revolutionized how we study biological and materials systems. In this Perspective, we reflect on the underlying philosophy of how diffraction-unlimited pictures containing rich spatial and functional information may gradually emerge through the local accumulation of single-molecule measurements. Starting with the basic concepts, we analyze the uniqueness of and opportunities in building up the final picture one molecule at a time. After brief introductions to the more established multicolor and three-dimensional measurements, we highlight emerging efforts to extend SMLM to new dimensions and functionalities as fluorescence polarization, emission spectra, and molecular motions, and discuss rising opportunities and future directions. With single molecules as our quanta, the bottom-up accumulation approach provides a powerful conduit for multidimensional microscopy at the nanoscale.

Nonequilibrium model of short-range repression in gene transcription regulation

Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data.

Recent advances in integrated solid-state nanopore sensors

The advent of single-molecule probing techniques has revolutionized the biomedical and life science fields and has spurred the development of a new class of labs-on-chip based on powerful biosensors. Nanopores represent one of the most recent and most promising single molecule sensing paradigms that is seeing increased chip-scale integration for improved convenience and performance. Due to their physical structure, nanopores are highly sensitive, require low sample volume, and offer label-free, amplification-free, high-throughput real-time detection and identification of biomolecules. Over the last 25 years, nanopores have been extensively employed to detect a variety of biomolecules with a growing range of applicatons ranging from nucleic acid sequencing to ultrasensitive diagnostics to single-molecule biophysics. Nanopores, in particular those in solid-state membranes, also have the potential for integration with other technologies such as optics, plasmonics, microfluidics, and optofluidics to perform more complex tasks for an ever-expanding demand. A number of breakthrough results using integrated nanopore platforms have already been reported, and more can be expected as nanopores remain the focus of innovative research and are finding their way into commercial instruments. This review provides an overview of different aspects and challenges of nanopore technology with a focus on chip-scale integration of solid-state nanopores for biosensing and bioanalytical applications.

BiPSim: a flexible and generic stochastic simulator for polymerization processes

Detailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology.

Detection of Anomalous Diffusion with Deep Residual Networks

Identification of the diffusion type of molecules in living cells is crucial to deduct their driving forces and hence to get insight into the characteristics of the cells. In this paper, deep residual networks have been used to classify the trajectories of molecules. We started from the well known ResNet architecture, developed for image classification, and carried out a series of numerical experiments to adapt it to detection of diffusion modes. We managed to find a model that has a better accuracy than the initial network, but contains only a small fraction of its parameters. The reduced size significantly shortened the training time of the model. Moreover, the resulting network has less tendency to overfitting and generalizes better to unseen data.

Anomalous diffusion in a bench-scale pulsed fluidized bed

We present our analysis on microrheology of a bench-scale pulsed fluidized bed, which represents a weakly confined system. Nonlinear gas-particle and particle-particle interactions resulting from pulsed flow are associated with harmonic and subharmonic modes. While periodic structured bubble patterns are observed at the mesoscale, particle-scale measurements reveal anomalous diffusion in the driven granular medium. We use single-particle tracks to analyze ergodicity and ageing properties at two pulsing frequencies having remarkably different mesoscale features. The scaling of ensemble-averaged mean-squared displacement is not unique. The distribution of time-averaged mean-squared displacements is non-Gaussian, asymmetric, and has a finite trivial contribution from particles in crowded quasistatic surroundings. Results indicate weak ergodicity breaking, which along with ageing characterizes the nonstationary and out-of-equilibrium dynamics.

Tools and Methods for Investigating Synthetic Metal-Catalyzed Reactions in Living Cells

Although abiotic catalysts are capable of promoting numerous new-to-nature reactions, only a small subset has so far been successfully integrated into living systems. Research in intracellular catalysis requires an interdisciplinary approach that takes advantage of both chemical and biological tools as well as state-of-the-art instrumentations. In this perspective, we will focus on the techniques that have made studying metal-catalyzed reactions in cells possible using representative examples from the literature. Although the lack of quantitative data in vitro and in vivo has somewhat limited progress in the catalyst development process, recent advances in characterization methods should help overcome some of these deficiencies. Given its tremendous potential, we believe that intracellular catalysis will play a more prominent role in the development of future biotechnologies and therapeutics.

Clustering single-cell RNA-seq data by rank constrained similarity learning

MOTIVATION

Recent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved.

RESULTS

Here, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman's rank correlations of a cell's expression vector with those of other cells to measure its global similarity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics.

AVAILABILITY

The RCSL algorithm is implemented in R and can be freely downloaded at https://cran.r-project.org/web/packages/RCSL/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

On the Role of Magnesium Ions in the DNA-Scissoring Activity of the Restriction Endonuclease ApaI: Stochastic Kinetics from a Single Molecule to Mesoscopic Paradigm

Biochemical reactions occurring inside cells have significant stochastic signatures due to the low copy number of reacting species. Kinetics of DNA cleavage by restriction endonucleases are no exception as established by single-molecule experiments. Here, we propose a simple reaction scheme to understand the role of the cofactor magnesium ion in the action of the endonuclease ApaI. The methodology is based on the waiting time distribution of cleavage product formation that enables us to determine the corresponding rate both analytically and numerically. The theory is developed at the single-molecule level and then generalized to the biologically relevant case of a population of DNA-endonuclease complexes present inside a cell. The theoretical rate versus cofactor concentration curve is matched with relevant single-molecule experimental data that reveals positive cooperativity of cofactor binding and provides a reliable estimate of model parameters. Furthermore, a parameter range is identified where the dispersion of the waiting time, measured using the coefficient of variation, is significantly lower than the Poisson limit and becomes minimum at the in vivo magnesium ion concentration level. Such low dispersion can play a role in the robust DNA-scissoring activity of ApaI under in vivo conditions.

Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments

DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biological processes. Single-particle tracking allows for direct visualization of protein-DNA kinetics, however, identifying whether a molecule is bound to DNA can be non-trivial. Further complications arise when tracking molecules for extended durations in processes with slow kinetics. We developed a machine learning approach, termed Bound2Learn, using output from a widely used tracking software, to robustly classify tracks in order to accurately estimate residence times. We validated our approach in silico, and in live-cell data from Escherichia coli and Saccharomyces cerevisiae. Our method has the potential for broad utility and is applicable to other organisms.

Testing of Multifractional Brownian Motion

Fractional Brownian motion (FBM) is a generalization of the classical Brownian motion. Most of its statistical properties are characterized by the self-similarity (Hurst) index 0<H<1. In nature one often observes changes in the dynamics of a system over time. For example, this is true in single-particle tracking experiments where a transient behavior is revealed. The stationarity of increments of FBM restricts substantially its applicability to model such phenomena. Several generalizations of FBM have been proposed in the literature. One of these is called multifractional Brownian motion (MFBM) where the Hurst index becomes a function of time. In this paper, we introduce a rigorous statistical test on MFBM based on its covariance function. We consider three examples of the functions of the Hurst parameter: linear, logistic, and periodic. We study the power of the test for alternatives being MFBMs with different linear, logistic, and periodic Hurst exponent functions by utilizing Monte Carlo simulations. We also analyze mean-squared displacement (MSD) for the three cases of MFBM by comparing the ensemble average MSD and ensemble average time average MSD, which is related to the notion of ergodicity breaking. We believe that the presented results will be helpful in the analysis of various anomalous diffusion phenomena.

Bacterial Vivisection: How Fluorescence-Based Imaging Techniques Shed a Light on the Inner Workings of Bacteria

The rise in fluorescence-based imaging techniques over the past 3 decades has improved the ability of researchers to scrutinize live cell biology at increased spatial and temporal resolution. In microbiology, these real-time vivisections structurally changed the view on the bacterial cell away from the "watery bag of enzymes" paradigm toward the perspective that these organisms are as complex as their eukaryotic counterparts. Capitalizing on the enormous potential of (time-lapse) fluorescence microscopy and the ever-extending pallet of corresponding probes, initial breakthroughs were made in unraveling the localization of proteins and monitoring real-time gene expression. However, later it became clear that the potential of this technique extends much further, paving the way for a focus-shift from observing single events within bacterial cells or populations to obtaining a more global picture at the intra- and intercellular level. In this review, we outline the current state of the art in fluorescence-based vivisection of bacteria and provide an overview of important case studies to exemplify how to use or combine different strategies to gain detailed information on the cell's physiology. The manuscript therefore consists of two separate (but interconnected) parts that can be read and consulted individually. The first part focuses on the fluorescent probe pallet and provides a perspective on modern methodologies for microscopy using these tools. The second section of the review takes the reader on a tour through the bacterial cell from cytoplasm to outer shell, describing strategies and methods to highlight architectural features and overall dynamics within cells.

Inchworm stepping of Myc-Max heterodimer protein diffusion along DNA

Oncogenic protein Myc serves as a transcription factor to control cell metabolisms. Myc dimerizes via leucine zipper with its associated partner protein Max to form a heterodimer structure, which then binds target DNA sequences to regulate gene transcription. The regulation depends on Myc-Max binding to DNA and searching for target sequences via diffusional motions along DNA. Here, we conduct structure-based molecular dynamics (MD) simulations to investigate the diffusion dynamics of the Myc-Max heterodimer along DNA. We found that the heterodimer protein slides on the DNA in a rotation-uncoupled manner in coarse-grained simulations, as its two helical DNA binding basic regions (BRs) alternate between open and closed conformations via inchworm stepping motions. In such motions, the two BRs of the heterodimer step across the DNA strand one by one, with step sizes reaching about half of a DNA helical pitch length. Atomic MD simulations of the Myc-Max heterodimer in complex with DNA have also been conducted. Hydrogen bond interactions are revealed between the two BRs and two complementary DNA strands, respectively. In the non-specific DNA binding, the BR from Myc shows an onset of stepping on one association DNA strand and starts detaching from the other strand. Overall, our simulation studies suggest that the inchworm stepping motions of the Myc-Max heterodimer can be achieved during the protein diffusion along DNA.

Interactions between DNA and the Hfq Amyloid-like Region Trigger a Viscoelastic Response

Molecular transport of biomolecules plays a pivotal role in the machinery of life. Yet, this role is poorly understood due the lack of quantitative information. Here, the role and properties of the C-terminal region of Escherichia coli Hfq is reported, involved in controlling the flow of a DNA solution. A combination of experimental methodologies has been used to probe the interaction of Hfq with DNA and to measure the rheological properties of the complex. A physical gel with a temperature reversible elasticity modulus is formed due to the formation of noncovalent cross-links. The mechanical response of the complexes shows that they are inhomogeneous soft solids. Our experiments indicate that the Hfq C-terminal region could contribute to the genome's mechanical response. The reported viscoelasticity of the DNA-protein complex might have implications for cellular processes involving molecular transport of DNA or segments thereof.

Identification of Nonradiative Decay Pathways in Cy3

Photoexcited fluorescent markers are extensively used in spectroscopy, imaging, and analysis of biological systems. The performance of fluorescent markers depends on high levels of emission, which are limited by competing nonradiative decay pathways. Small-molecule fluorescent dyes have been increasingly used as markers due to their high and stable emission. Despite their prevalence, the nonradiative decay pathways of these dyes have not been determined. Here, we investigate these pathways for a widely used indocarbocyanine dye, Cy3, using transient grating spectroscopy. We identify a nonradiative decay pathway via a previously unknown dark state formed within ∼1 ps of photoexcitation. Our experiments, in combination with electronic structure calculations, suggest that the generation of the dark state is mediated by picosecond vibrational mode coupling, likely via a conical intersection. We further identify the vibrational modes, and thus structural elements, responsible for the formation and dynamics of the dark state, providing insight into suppressing nonradiative decay pathways in fluorescent markers such as Cy3.

Kinetic Modeling of the Genetic Information Processes in a Minimal Cell

JCVI-syn3A is a minimal bacterial cell with a 543 kbp genome consisting of 493 genes. For this slow growing minimal cell with a 105 min doubling time, we recently established the essential metabolism including the transport of required nutrients from the environment, the gene map, and genome-wide proteomics. Of the 452 protein-coding genes, 143 are assigned to metabolism and 212 are assigned to genetic information processing. Using genome-wide proteomics and experimentally measured kinetic parameters from the literature we present here kinetic models for the genetic information processes of DNA replication, replication initiation, transcription, and translation which are solved stochastically and averaged over 1,000 replicates/cells. The model predicts the time required for replication initiation and DNA replication to be 8 and 50 min on average respectively and the number of proteins and ribosomal components to be approximately doubled in a cell cycle. The model of genetic information processing when combined with the essential metabolic and cell growth networks will provide a powerful platform for studying the fundamental principles of life.

Transcription and translation contribute to gene locus relocation to the nucleoid periphery in E. coli

Transcription by RNA polymerase (RNAP) is coupled with translation in bacteria. Here, we observe the dynamics of transcription and subcellular localization of a specific gene locus (encoding a non-membrane protein) in living E. coli cells at subdiffraction-limit resolution. The movement of the gene locus to the nucleoid periphery correlates with transcription, driven by either E. coli RNAP or T7 RNAP, and the effect is potentiated by translation.

Decoding the grammar of transcriptional regulation from RNA polymerase measurements: models and their applications

The genomic revolution has indubitably brought about a paradigm shift in the field of molecular biology, wherein we can sequence, write and re-write genomes. In spite of achieving such feats, we still lack a quantitative understanding of how cells integrate environmental and intra-cellular signals at the promoter and accordingly regulate the production of messenger RNAs. This current state of affairs is being redressed by recent experimental breakthroughs which enable the counting of RNA polymerase molecules (or the corresponding nascent RNAs) engaged in the process of transcribing a gene at the single-cell level. Theorists, in conjunction, have sought to unravel the grammar of transcriptional regulation by harnessing the various statistical properties of these measurements. In this review, we focus on the recent progress in developing falsifiable models of transcription that aim to connect the molecular mechanisms of transcription to single-cell polymerase measurements. We discuss studies where the application of such models to the experimental data have led to novel mechanistic insights into the process of transcriptional regulation. Such interplay between theory and experiments will likely contribute towards the exciting journey of unfurling the governing principles of transcriptional regulation ranging from bacteria to higher organisms.

Amino Acids and Ribose: Drivers of Protein and RNA Fermentation by Ingested Bacteria of a Primitive Gut Ecosystem

Animal health is linked to gut ecosystems whose primary function is normally the digestion of dietary matter. Earthworms are representative of one of the oldest known animal lineages and, despite their primitive nature, have unique environmental impact by virtue of their dietary consumption of their habitat, i.e., soil-associated matter. A resident gut community is a hallmark of many gut ecosystems of evolutionarily more advanced animals, but the alimentary canal of earthworms is dominated by ingested transient soil microbes. Protein and RNA are (i) the primary organic components of microbial cells that are subject to lysis during gut passage and (ii) fermentable dietary substrates in the alimentary canal. This study examined the gut-associated fermentation of constituents of these biopolymers to determine how their fermentation is integrated to the microbiological dynamics of the gut and might contribute to earthworm-linked transformations of organic matter in the terrestrial biosphere.

Earthworms are among the most primitive animals and are of fundamental importance to the turnover of organic matter in the terrestrial biosphere. These invertebrates ingest materials that are colonized by microbes, some of which are subject to disruption by the crop/gizzard or other lytic events during gut passage. Protein and RNA are dominant polymers of disrupted microbial cells, and these biopolymers facilitate robust fermentations by surviving ingested bacteria. To further resolve these fermentations, amino acids and ribose (as fermentable constituents of protein and RNA, respectively) were evaluated as potential drivers of fermentation in gut content of the model earthworm Lumbricus terrestris (taxa were examined with 16S rRNA-based analyses). Of eight amino acids tested, glutamate, aspartate, and threonine were most stimulatory and yielded dissimilar fermentations facilitated by contrasting taxa (e.g., glutamate stimulated the Fusobacteriaceae and yielded H₂ and formate, whereas aspartate stimulated the Aeromonadaceae and yielded succinate and propionate). A marginal Stickland fermentation was associated with the Peptostreptococcaceae and Lachnospiraceae. Ribose fermentation yielded a complex product profile facilitated primarily by the Aeromonadaceae. The transient nature of succinate was linked to its decarboxylation to propionate and the Fusobacteriaceae, whereas the transient nature of formate was linked to formate-hydrogen lyase activity and the Peptostreptococcaceae. These findings reinforce the likelihood that (i) the animal host and hosted fermentative bacteria compete for the constituents of protein and RNA in the alimentary canal and (ii) diverse gut fermenters engaged in the fermentation of these constituents produce products that can be utilized by earthworms.

IMPORTANCE Animal health is linked to gut ecosystems whose primary function is normally the digestion of dietary matter. Earthworms are representative of one of the oldest known animal lineages and, despite their primitive nature, have unique environmental impact by virtue of their dietary consumption of their habitat, i.e., soil-associated matter. A resident gut community is a hallmark of many gut ecosystems of evolutionarily more advanced animals, but the alimentary canal of earthworms is dominated by ingested transient soil microbes. Protein and RNA are (i) the primary organic components of microbial cells that are subject to lysis during gut passage and (ii) fermentable dietary substrates in the alimentary canal. This study examined the gut-associated fermentation of constituents of these biopolymers to determine how their fermentation is integrated to the microbiological dynamics of the gut and might contribute to earthworm-linked transformations of organic matter in the terrestrial biosphere.