Search

Vinculin binds to specific sites of mechanically unfolded talin rod domains to reinforce the coupling of the cell's exterior to its force generation machinery. Force-dependent vinculin–talin complexation and dissociation was previously observed as contraction or extension of the unfolded talin domains respectively using magnetic tweezers. However, the structural mechanism underlying vinculin recognition of unfolded vinculin binding sites (VBSs) in talin remains unknown. Using molecular dynamics simulations, we demonstrate that a VBS dynamically refolds under force, and that vinculin can recognize and bind to partially unfolded VBS states. Vinculin binding enables refolding of the mechanically strained VBS and stabilizes its folded α-helical conformation, providing resistance against mechanical stress. Together, these results provide an understanding of a recognition mechanism of proteins unfolded by force and insight into the initial moments of how vinculin binds unfolded talin rod domains during the assembly of this mechanosensing meshwork. Author summary: Cells attach to and interact with their surroundings, and sense mechanical signals from the microenvironment. Vinculin and talin are classical examples of mechanosensitive proteins that complex in a force-dependent manner. Talin domains unfold under mechanical loads exposing buried vinculin binding sites, namely single α-helices. Such mechanical stretching enables recognition by vinculin, however, the structural states of vinculin's binding site when under force and the complexation mechanisms are poorly understood. We used molecular dynamics simulations to study the behavior of a single vinculin binding helix under mechanical load and during the initial moments of the vinculin–talin binding process. Our results reveal the highly dynamic nature of a vinculin binding helix under mechanical load. Depending on the force magnitude, the helix frequently reforms to a partially folded conformation on the nanosecond to microsecond timescale. Crucially, vinculin recognizes such partially folded helices in talin and promotes their refolding against force. Our findings are beyond the resolution limits imposed by experimental single molecule methods, suggesting a model which may be useful as a general mechanism for protein-protein binding under mechanical load. [ABSTRACT FROM AUTHOR]

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets. Author summary: Diverse new technologies are being developed for single-molecule protein sequencing, capable of identifying and quantifying mixtures of proteins at the level of individual molecules. There are many biochemical challenges intrinsic to high-throughput studies of proteins at such high sensitivity arising from their heterogeneous chemistries, sizes, and abundances. Beyond these challenges, the technologies themselves involve complex multi-step analytical processes. Thus, in developing and optimizing these technologies, it is important to consider the accuracy of each step and to have reliable approaches for estimating these accuracies. We focus on one particular single-molecule sequencing technology known as flourosequencing. We report and validate two methods for simultaneously determining the error-rates of each of the various steps of the fluorosequencing process. These new error estimation techniques will help researchers to better interpret the effects of changes to the chemistry and sample preparation used in fluorosequencing so that these steps can be improved. Further, more accurate determination of error rates will aid in the creation of better tools for the interpretation of this data. [ABSTRACT FROM AUTHOR]

The chemical etching of germanium in Br2 environment at elevated temperatures is described by the Michaelis–Menten equation. The validity limit of Michaelis–Menten kinetics is subjected to the detailed analysis. The steady-state etching rate requires synergy of two different process parameters. High purity gas should be directed to the substrate on which intermediate reaction product does not accumulate. Theoretical calculations indicate that maximum etching rate is maintained when 99.89% of the germanium surface is covered by the reaction product, and 99.9999967% of the incident Br2 molecules are reflected from the substrate surface. Under these conditions, single GeBr2 molecule is formed after 30 million collisions of Br2 molecules with the germanium surface. [ABSTRACT FROM AUTHOR]

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses. Author summary: Associating diseases and other metabolic disruptions with physiological markers is key for diagnostic and personalised medicine. These markers can be metabolites—small molecules involved in every living being's metabolism, and can be measured in biofluids such as blood or urine using metabolic profiling (metabolomics). Nevertheless, this experimental metabolomics design needs to be tailor made for each disease to ensure that most relevant metabolites will be detected. The selection of metabolites to analyse for future experiments can be time-consuming and expensive. In this paper, we build upon an existing computational method for simulating metabolite changes in a human model. This provides a prediction of the change in biofluid abundance of every known metabolite involved in human metabolism in a potentially large number of metabolic situations. The newly introduced method produces a change score and a rank for each metabolite in each condition. We show the strong potential of the approach by comparing predictions with experimental results. [ABSTRACT FROM AUTHOR]

At the transition to stationary phase, a subpopulation of Bacillus subtilis cells can enter the developmental state of competence, where DNA is taken up through the cell envelope, and is processed to single stranded DNA, which is incorporated into the genome if sufficient homology between sequences exists. We show here that the initial step of transport across the cell wall occurs via a true pilus structure, with an average length of about 500 nm, which assembles at various places on the cell surface. Once assembled, the pilus remains at one position and can be retracted in a time frame of seconds. The major pilin, ComGC, was studied at a single molecule level in live cells. ComGC was found in two distinct populations, one that would correspond to ComGC freely diffusing throughout the cell membrane, and one that is relatively stationary, likely reflecting pilus-incorporated molecules. The ratio of 65% diffusing and 35% stationary ComGC molecules changed towards more stationary molecules upon addition of external DNA, while the number of pili in the population did not strongly increase. These findings suggest that the pilus assembles stochastically, but engages more pilin monomers from the membrane fraction in the presence of transport substrate. Our data support a model in which transport of environmental DNA occurs through the entire cell surface by a dynamic pilus, mediating efficient uptake through the cell wall into the periplasm, where DNA diffuses to a cell pole containing the localized transport machinery mediating passage into the cytosol. Author summary: Horizontal gene transfer (HGT) is important for bacterial evolution and the adaption to new environments. Through HGT, genes can be transferred between bacteria, which can lead to antibiotic resistance development, especially critical with regards to pathogenic bacterial species. One mechanism of HGT is natural competence, through which bacteria can take up DNA under natural conditions and incorporate it into their chromosomes. For DNA uptake, a complex will be formed that is conserved among Gram-positive and Gram-negative bacteria, in spite of different envelope architectures. We show that in Bacillus subtilis, where the part of the uptake complex spanning the cell membrane localizes to a single cell pole, transport through the cell wall is mediated by a visible pilus structure that can extend and retract to pull DNA into the periplasm. Thus, DNA uptake from the environment is not limited to polar sites, but is channelled into the cytosol at the pole. Single molecule dynamics of ComGC revealed a static, likely pilus-bound, and a mobile fraction of ComGC within the cell membrane. Addition of environmental DNA increased the static fraction of molecules, showing that pilus dynamics respond to DNA binding. [ABSTRACT FROM AUTHOR]

High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome. Author summary: DNA methylation is an epigenetic DNA modification that is often associated with the repression of gene expression. Correct patterning of DNA methylation is crucial in development and for normal cellular function. Aberrant patterns of DNA methylation are also observed in diseases such as cancer. Here, we examine DNA methylation patterns within long DNA molecules to identify how methylation heterogeneity varies throughout the human genome within single molecules. We find that single molecule DNA methylation heterogeneity varies widely, with some genomic regions, particularly those away from genes, having very heterogeneous patterns. In contrast, we find that the regions of the genome around active genes have more homogeneous, uniform methylation states. When we examined the nature of heterogeneous methylation patterns, we find they have an underlying periodicity. We also find that this periodicity is reflective of the single molecule placement of nucleosomes, proteins that are important in the packaging of DNA. Overall, our study indicates that DNA methylation heterogeneity shows wide variation across the human genome and that DNA packaging could play a role in shaping DNA methylation patterns at the level of single molecules. [ABSTRACT FROM AUTHOR]

Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to existing ExM protocols is the equipping of different kinds of molecules, with different kinds of anchoring moieties, so they can all be pulled apart from each other by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables proteins and RNAs to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and reduces cost (by 2-10-fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this united ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructures, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues and may support the visualization of other kinds of biomolecular species as well. uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues. [ABSTRACT FROM AUTHOR]

The transcriptional activator p53 is a tumor suppressor protein that controls cellular pathways important for cell fate decisions, including cell cycle arrest, senescence, and apoptosis. It functions as a tetramer by binding to specific DNA sequences known as response elements (REs) to control transcription via interactions with co-regulatory complexes. Despite its biological importance, the mechanism by which p53 binds REs remains unclear. To address this, we have used an in vitro single molecule fluorescence approach to quantify the dynamic binding of full-length human p53 to five native REs in real time under equilibrium conditions. Our approach enabled us to quantify the oligomeric state of DNA-bound p53. We found little evidence that dimer/DNA complexes form as intermediates en route to binding or dissociation of p53 tetramer/DNA complexes. Interestingly, however, at some REs dimers can rapidly exchange from tetramer/DNA complexes. Real time kinetic measurements enabled us to determine rate constants for association and dissociation at all five REs, which revealed two kinetically distinct populations of tetrameric p53/RE complexes. For the less stable population, the rate constants for dissociation were larger at REs closest to consensus, showing that the more favorable binding sequences form the least kinetically stable complexes. Together our single molecule measurements provide new insight into mechanisms by which tetrameric p53 forms complexes on different native REs. [ABSTRACT FROM AUTHOR]

Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data's structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them. Author summary: Decoding biochemical reaction networks–the number of species, reactions connecting them and reaction rates–from snapshot data, such as data drawn from single molecule fluorescence in situ hybridization (smFISH) experiments, is of critical interest. Yet, this task's challenges are currently addressed with network specification heuristics since: 1) network size cannot be specified independently of rates; 2) rates may be separated by orders of magnitude, generating stiff ODEs; 3) uncertainty is not propagated into networks and parameters. We present, inspired by recent computational statistics tools, a method to simultaneously deduce reaction networks and associated rates from snapshot data while propagating error over all unknowns. We achieve this by treating network models as random variables and, within the Bayesian nonparametric paradigm, develop a posterior over models themselves. This multidimensional posterior naturally contains multiple hills and valleys, so we propose a combination of samplers allowing for the first simultaneous and self-consistent inference of networks and their associated rates. Our method's ability to treat arbitrary numbers of states contrasts the current state of the art and may modify previous biological conclusions based on network-determination heuristics. We demonstrate on method to synthetic data mimicking smFISH experiments and demonstrate its improvement over naive MCMC schemes. [ABSTRACT FROM AUTHOR]

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates. Author summary: Scientists often wish to know which proteins, and at what quantities, are present in a sample. The field of proteomics offers a number of technologies that aid in this, such as tandem mass spectrometry and immunoassays, that provide different tradeoffs between sensitivity, throughput, and generality. One new technology, known as fluorosequencing, detects and provides partial sequences for individual peptide or protein molecules from a sample in a highly parallelized fashion. However, as only partial sequences are measured, the resulting sequencing reads must be matched to a reference database of possible proteins, such as might be obtained from the human genome. We describe a suitable computer algorithm for performing this matching of fluorosequencing reads to a reference database while accounting for the most prevalent types of sequencing errors. We detail its performance and implementation, and describe a number of uncommon algorithmic improvements and approximations which allow this approach to scale to classification against the whole human proteome. The resulting software, known as whatprot, allows researchers to interpret fluorosequencing reads and better apply this emergent single molecule protein sequencing technology. [ABSTRACT FROM AUTHOR]

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a two-step statistical testing scheme for comparing biomolecule dynamics observed in different experimental conditions without having to identify or make strong prior assumptions about the model generating the recorded random walks. We first train a graph neural network to perform simulation-based inference and thus learn a rich summary statistics vector describing individual trajectories. We then compare trajectories obtained in different biological conditions using a non-parametric maximum mean discrepancy (MMD) statistical test on their so-obtained summary statistics. This procedure allows us to characterise sets of random walks regardless of their generating models, without resorting to model-specific physical quantities or estimators. We first validate the relevance of our approach on numerically simulated trajectories. This demonstrates both the statistical power of the MMD test and the descriptive power of the learnt summary statistics compared to estimates of physical quantities. We then illustrate the ability of our framework to detect changes in α-synuclein dynamics at synapses in cultured cortical neurons, in response to membrane depolarisation, and show that detected differences are largely driven by increased protein mobility in the depolarised state, in agreement with previous findings. The method provides a means of interpreting the differences it detects in terms of single trajectory characteristics. Finally, we emphasise the interest of performing various comparisons to probe the heterogeneity of experimentally acquired datasets at different levels of granularity (e.g., biological replicates, fields of view, and organelles). Author summary: The continuous improvement of methods for single molecule tracking in live cells are driving our understanding of how biomolecules move inside cells. Analysing trajectories of single molecules is complicated by their highly erratic and noisy nature and thus requires the use of statistical models of their motion. However, it is often not possible to unambiguously determine a model from a set of short and noisy trajectories. Furthermore, the heterogeneous nature of the cellular environment means that the molecules' motion is often not properly described by a single model. In this paper we develop a new statistical testing scheme to detect differences in biomolecule dynamics within organelles without needing to identify a model of their motion. We train a graph neural network on large-scale simulations of random walks to learn a latent representation that captures relevant physical properties of a trajectory. We use a kernel-based statistical test within that latent space to compare the properties of two sets of trajectories recorded under different biological conditions. We apply our approach to detect differences in the dynamics of α-synuclein, a presynaptic protein, in axons and boutons during synaptic stimulation. This represents an important step towards automated single-molecule-based read-out of pharmacological action. [ABSTRACT FROM AUTHOR]

How the complexity of primitive self-replication molecules develops through Darwinian evolution remains a mystery with regards to the origin of life. Theoretical studies have proposed that coevolution with parasitic replicators increases network complexity by inducing inter-dependent replication. Particularly, Takeuchi and Hogeweg proposed a complexification process of replicator networks by successive appearance of a parasitic replicator followed by the addition of a new host replicator that is resistant to the parasitic replicator. However, the feasibility of such complexification with biologically relevant molecules is still unknown owing to the lack of an experimental model. Here, we investigated the plausible complexification pathway of host-parasite replicators using both an experimental host-parasite RNA replication system and a theoretical model based on the experimental system. We first analyzed the parameter space that allows for sustainable replication in various replication networks ranging from a single molecule to three-member networks using computer simulation. The analysis shows that the most plausible complexification pathway from a single host replicator is the addition of a parasitic replicator, followed by the addition of a new host replicator that is resistant to the parasite, consistent with the previous study by Takeuchi and Hogeweg. We also provide evidence that the pathway actually occurred in our previous evolutionary experiment. These results provide experimental evidence that a population of a single replicator spontaneously evolves into multi-replicator networks through coevolution with parasitic replicators. Author summary: How primitive simple self-replication molecules develop their complexity through evolution is one of the largest mysteries in the origin of life. The largest obstacle in the development of complexity is parasitic replicators, which spontaneously appear and destroy inter-molecular cooperative networks, such as hypercycles, and simplify the replication system. However, Takeuchi and Hogeweg found that parasitic replicators could increase the complexity of replication network by working as a "niche" for multiple host replicators. This idea provides an attractive answer to the long-standing mystery, that is, how complexity of a molecular replication system develops, although experimental evidence is lacking. In the present study, we performed a theoretical analysis of an RNA replication system using computer simulation, together with experimental verification, to understand the reason for sustainable co-replication of multiple replicators. We also found that the most plausible route for complexity in the host–parasite replication network is the addition of the parasite first, followed by a new host that is resistant to the parasite. These results provide both theoretical and experimental evidence that parasitic replicators mediate the development of complexity in replication networks through Darwinian evolution. [ABSTRACT FROM AUTHOR]

The epigenetics of bacteria, and bacteria with a reduced genome in particular, is of great interest, but is still poorly understood. Mycoplasma gallisepticum, a representative of the class Mollicutes, is an excellent model of a minimal cell because of its reduced genome size, lack of a cell wall, and primitive cell organization. In this study we investigated DNA modifications of the model object Mycoplasma gallisepticum and their roles. We identified DNA modifications and methylation motifs in M. gallisepticum S6 at the genome level using single molecule real time (SMRT) sequencing. Only the ANCNNNNCCT methylation motif was found in the M. gallisepticum S6 genome. The studied bacteria have one functional system for DNA modifications, the Type I restriction-modification (RM) system, MgaS6I. We characterized its activity, affinity, protection and epigenetic functions. We demonstrated the protective effects of this RM system. A common epigenetic signal for bacteria is the m6A modification we found, which can cause changes in DNA-protein interactions and affect the cell phenotype. Native methylation sites are underrepresented in promoter regions and located only near the -35 box of the promoter, which does not have a significant effect on gene expression in mycoplasmas. To study the epigenetics effect of m6A for genome-reduced bacteria, we constructed a series of M. gallisepticum strains expressing EGFP under promoters with the methylation motifs in their different elements. We demonstrated that m6A modifications of the promoter located only in the -10-box affected gene expression and downregulated the expression of the corresponding gene. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. Methods and findings: A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity. Conclusions: The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity. [ABSTRACT FROM AUTHOR]

Antiretroviral therapy (ART) effectively inhibits HIV-1 replication but is not curative due to the persistence of a latent viral reservoir in resting CD4+ T cells. This reservoir is a major barrier to cure. Sequencing studies have revealed that the population of proviruses persisting in ART-treated individuals is dominated by defective proviruses that cannot give rise to viral rebound due to fatal defects including large deletions and APOBEC3-mediated hypermutation. Near full genome sequencing (nFGS) of individual proviruses is used in reservoir assays to provide an estimate of the fraction of proviruses that are intact. nFGS methods rely on a long-distance outer PCR capturing most (~9 kb) of the genome, followed by nested inner PCRs. The outer PCR is carried out at limit dilution, and interpretation of the results is based on the assumption that all proviruses are quantitatively captured. Here, we evaluate nFGS methods using the intact proviral DNA assay (IPDA), a multiplex digital droplet PCR assay that quantitates intact and defective proviruses with single molecule sensitivity using only short, highly efficient amplicons. We analyzed proviral templates of known sequence to avoid the additional complication of sequence polymorphism. With the IPDA, we quantitated molecular yields at each step of nFGS methods. We demonstrate that nFGS methods are inefficient and miss ~70% of full-length proviruses due to amplification failure at the initial outer PCR step. In contrast, proviruses with large internal deletions encompassing 70% of the genome can be quantitatively amplified under the same conditions. Accurate measurement of the latent reservoir of HIV-1 is essential for evaluating the efficacy of cure strategies, and the bias against full length proviruses in nFGS methods must be considered. Author summary: Despite antiretroviral therapy, HIV-1 persists in a small population of resting memory CD4+ T cells carrying a latent viral genome. This latent reservoir is the major barrier to cure. Accurate reservoir assays are critical for evaluating therapies aimed at reducing the reservoir. Sequencing studies have shown defective proviruses greatly outnumbered the intact, replication-competent proviruses responsible for viral rebound, and thus reservoir assays that rely on near full-genome sequencing (nFGS) have provided important qualitative information about intact and defective proviruses. However, it is assumed that all proviruses are amplified with equal efficiency in nFGS methods, regardless of sequence length. Here, we rigorously measure the efficiency with which nFGS methods detect intact and defective proviruses using a highly efficient multiplex digital droplet PCR assay, the intact proviral DNA assay. This assay allows direct counting of input proviral template molecules as well as PCR amplified products generated with different nFGS methods. We determined that nFGS methods do not provide an accurate quantitative measure of intact proviruses. Only ~30% of intact proviruses were detected, while proviruses with large internal deletions were amplified at expected frequencies. Our study demonstrates that nFGS methods do not provide accurate quantitative information about the size and composition of the latent reservoir. [ABSTRACT FROM AUTHOR]

Centromeres are key elements for chromosome segregation. Canonical centromeres are built over long-stretches of tandem repetitive arrays. Despite being quite abundant compared to other loci, centromere sequences overall still represent only 2 to 5% of the human genome, therefore studying their genetic and epigenetic features is a major challenge. Furthermore, sequencing of centromeric regions requires high coverage to fully analyze length and sequence variations, and this can be extremely costly. To bypass these issues, we have developed a technique, named CenRICH, to enrich for centromeric DNA from human cells based on selective restriction digestion and size fractionation. Combining restriction enzymes cutting at high frequency throughout the genome, except within most human centromeres, with size-selection of fragments >20 kb, resulted in over 25-fold enrichment in centromeric DNA. High-throughput sequencing revealed that up to 60% of the DNA in the enriched samples is made of centromeric repeats. We show that this method can be used in combination with long-read sequencing to investigate the DNA methylation status of certain centromeres and, with a specific enzyme combination, also of their surrounding regions (mainly HSATII). Finally, we show that CenRICH facilitates single-molecule analysis of replicating centromeric fibers by DNA combing. This approach has great potential for making sequencing of centromeric DNA more affordable and efficient and for single DNA molecule studies. Author summary: Centromeres are the portions of the chromosomes required for the correct partitioning of genetic material into the daughter cells. In humans, centromeric DNA is made of highly repetitive DNA sequences that hindered its precise molecular characterization until very recently with the development of pivotal technological advances. However, these approaches require the analysis of the whole human genome, while centromeres only represent less than 5%. For this reason, detailed characterization of human centromeres is still very expensive in terms of cost, timing and data analysis. We propose a method called CenRICH that allows to enrich and purify for human centromeric DNA. We prove that this method provides several advantages: 1) it drastically reduces the cost of centromere sequencing; 2) it can be used to study the epigenetic status of centromeres with high level of resolution; 3) it is suitable for single molecule visualization with advanced microscopy techniques. Therefore, CenRICH is a powerful tool to facilitate many future studies in the ever-expanding field of centromere biology, with potential application in study of genetic disease. [ABSTRACT FROM AUTHOR]

Development of automated analysis tools for "single ion channel" recording is hampered by the lack of available training data. For machine learning based tools, very large training sets are necessary with sample-by-sample point labelled data (e.g., 1 sample point every 100microsecond). In an experimental context, such data are labelled with human supervision, and whilst this is feasible for simple experimental analysis, it is infeasible to generate the enormous datasets that would be necessary for a big data approach using hand crafting. In this work we aimed to develop methods to generate simulated ion channel data that is free from assumptions and prior knowledge of noise and underlying hidden Markov models. We successfully leverage generative adversarial networks (GANs) to build an end-to-end pipeline for generating an unlimited amount of labelled training data from a small, annotated ion channel "seed" record, and this needs no prior knowledge of theoretical dynamical ion channel properties. Our method utilises 2D CNNs to maintain the synchronised temporal relationship between the raw and idealised record. We demonstrate the applicability of the method with 5 different data sources and show authenticity with t-SNE and UMAP projection comparisons between real and synthetic data. The model would be easily extendable to other time series data requiring parallel labelling, such as labelled ECG signals or raw nanopore sequencing data. [ABSTRACT FROM AUTHOR]

The pathogenic bacterium Yersina pestis is protected from macrophage engulfment by a capsule like antigen, F1, formed of long polymers of the monomer protein, Caf1. However, despite the importance of this pathogen, the mechanism of protection was not understood. Here we demonstrate how F1 protects the bacteria from phagocytosis. First, we show that Escherichia coli expressing F1 showed greatly reduced adherence to macrophages. Furthermore, the few cells that did adhere remained on the macrophage surface and were not engulfed. We then inserted, by mutation, an "RGDS" integrin binding motif into Caf1. This did not change the number of cells adhering to macrophages but increased the fraction of adherent cells that were engulfed. Therefore, F1 protects in two separate ways, reducing cell adhesion, possibly by acting as a polymer brush, and hiding innate receptor binding sites needed for engulfment. F1 is very robust and we show that E. coli expressing weakened mutant polymers are engulfed like the RGDS mutant. This suggests that innate attachment sites on the native cell surface are exposed if F1 is weakened. Single-molecule force spectroscopy (SMFS) experiments revealed that wild-type F1 displays a very high mechanical stability of 400 pN. However, the mechanical resistance of the destabilised mutants, that were fully engulfed, was only 20% weaker. By only marginally exceeding the mechanical force applied to the Caf1 polymer during phagocytosis it may be that the exceptional tensile strength evolved to resist the forces applied at this stage of engulfment. Author summary: Macrophages, a type of white blood cell, form an important element of our immune defence. They interrogate other cells' surfaces for molecular clues and ingest those presenting a threat in a process known as phagocytosis. Not surprisingly, pathogenic bacteria have developed ways to evade this fate. The plague bacterium, Yersinia pestis, produces the long polymeric F1 coat protein which enables it to avoid ingestion, but the mechanism was unclear. We show that equipping Escherichia coli cells with an F1 coat protected them from phagocytosis by two separate mechanisms, reducing contact with the macrophage surface and hiding the signals that tell the macrophages they are targets. F1 is also a very stable protein polymer and using single molecule force spectroscopy we showed it also has a very high resistance to pulling forces. Surprisingly, mutations which reduced this by only 20% caused adherent bacteria to be fully ingested, indicating that cells are subject to significant forces prior to recognition and ingestion. Thus, F1 has evolved three notable properties (i) physical; creation of a hydrated polymer brush to inhibit surface interactions, (ii) chemical; absence of molecular recognition clues needed for engulfment and (iii) mechanical; strength that maintains the camouflage layer during surface stretching. [ABSTRACT FROM AUTHOR]

Tokay Gecko (Gekko gecko) is a rare and endangered medicinal animal in China. Its dry body has been used as an anti-asthmatic agent for two thousand years. To date, the genome and transcriptome of this species remain poorly understood. Here, we adopted single molecule real-time (SMRT) sequencing to obtain full-length transcriptome data and characterized the transcriptome structure. We identified 882,273 circular consensus (CCS) reads, including 746,317 full-length nonchimeric (FLNC) reads. The transcript cluster analysis revealed 212,964 consensus sequences, including 203,994 high-quality isoforms. In total, 111,372 of 117,888 transcripts were successfully annotated against eight databases (Nr, eggNOG, Swiss-Prot, GO, COG, KOG, Pfam and KEGG). Furthermore, 23,877 alternative splicing events, 169,128 simple sequence repeats (SSRs), 10,437 lncRNAs and 7,932 transcription factors were predicted across all transcripts. To our knowledge, this report is the first to document the G. gecko transcriptome using SMRT sequencing. The full-length transcript data might accelerate transcriptome research and lay the foundation for further research on G. gecko. [ABSTRACT FROM AUTHOR]

Single molecule localization microscopy (SMLM) has the potential to resolve structural details of biological samples at the nanometer length scale. Compared to room temperature experiments, SMLM performed under cryogenic temperature achieves higher photon yields and, hence, higher localization precision. However, to fully exploit the resolution it is crucial to account for the anisotropic emission characteristics of fluorescence dipole emitters with fixed orientation. In case of slight residual defocus, localization estimates may well be biased by tens of nanometers. We show here that astigmatic imaging in combination with information about the dipole orientation allows to extract the position of the dipole emitters without localization bias and down to a precision of 1 nm, thereby reaching the corresponding Cramér Rao bound. The approach is showcased with simulated data for various dipole orientations, and parameter settings realistic for real life experiments. [ABSTRACT FROM AUTHOR]

O-methylation of small molecules is a common modification widely present in most organisms. Type III polyketides undergo O-methylation at hydroxyl end to play a wide spectrum of roles in bacteria, plants, algae, and fungi. Mycobacterium marinum harbours a distinctive genomic cluster with a type III pks gene and genes for several polyketide modifiers including a methyltransferase gene, mmar_2193. This study reports functional analyses of MMAR_2193 and reveals multi-methylating potential of the protein. Comparative sequence analyses revealed conservation of catalytically important motifs in MMAR_2193 protein. Homology-based structure-function and molecular docking studies suggested type III polyketide cores as possible substrates for MMAR_2193 catalysis. In vitro enzymatic characterization revealed the capability of MMAR_2193 protein to utilize diverse polyphenolic substrates to methylate several hydroxyl positions on a single substrate molecule. High-resolution mass spectrometric analyses identified multi-methylations of type III polyketides in cell-free reconstitution assays. Notably, our metabolomics analyses identified some of these methylated molecules in biofilms of wild type Mycobacterium marinum. This study characterizes a novel mycobacterial O-methyltransferase protein with multi-methylating enzymatic ability that could be exploited to generate a palette of structurally distinct bioactive molecules. [ABSTRACT FROM AUTHOR]

Objective: We have used long-read single molecule, real-time (SMRT) sequencing to fully characterize a ~12Mb genomic region on chromosome Xq24-q27, significantly linked to bipolar disorder (BD) in an extended family from a genetic sub-isolate. This family segregates BD in at least four generations with 24 affected individuals. Methods: We selected 16 family members for targeted sequencing. The selected individuals either carried the disease haplotype, were non-carriers of the disease haplotype, or served as married-in controls. We designed hybrid capture probes enriching for 5-9Kb fragments spanning the entire 12Mb region that were then sequenced to screen for candidate structural variants (SVs) that could explain the increased risk for BD in this extended family. Results: Altogether, 201 variants were detected in the critically linked region. Although most of these represented common variants, three variants emerged that showed near-perfect segregation among all BD type I affected individuals. Two of the SVs were identified in or near genes belonging to the RNA Binding Motif Protein, X-Linked (RBMX) gene family—a 330bp Alu (subfamily AluYa5) deletion in intron 3 of the RBMX2 gene and an intergenic 27bp tandem repeat deletion between the RBMX and G protein-coupled receptor 101 (GPR101) genes. The third SV was a 50bp tandem repeat insertion in intron 1 of the Coagulation Factor IX (F9) gene. Conclusions: Among the three genetically linked SVs, additional evidence supported the Alu element deletion in RBMX2 as the leading candidate for contributing directly to the disease development of BD type I in this extended family. [ABSTRACT FROM AUTHOR]

Optical DNA mapping (ODM) is based on fluorescent labeling, stretching and imaging of single DNA molecules to obtain sequence-specific fluorescence profiles, DNA barcodes. These barcodes can be mapped to theoretical counterparts obtained from DNA reference sequences, which in turn allow for DNA identification in complex samples and for detecting structural changes in individual DNA molecules. There are several types of DNA labeling schemes for ODM and for each labeling type one or several types of match scoring methods are used. By combining the information from multiple labeling schemes one can potentially improve mapping confidence; however, combining match scores from different labeling assays has not been implemented yet. In this study, we introduce two theoretical methods for dealing with analysis of DNA molecules with multiple label types. In our first method, we convert the alignment scores, given as output from the different assays, into p-values using carefully crafted null models. We then combine the p-values for different label types using standard methods to obtain a combined match score and an associated combined p-value. In the second method, we use a block bootstrap approach to check for the uniqueness of a match to a database for all barcodes matching with a combined p-value below a predefined threshold. For obtaining experimental dual-labeled DNA barcodes, we introduce a novel assay where we cut plasmid DNA molecules from bacteria with restriction enzymes and the cut sites serve as sequence-specific markers, which together with barcodes obtained using the established competitive binding labeling method, form a dual-labeled barcode. All experimental data in this study originates from this assay, but we point out that our theoretical framework can be used to combine data from all kinds of available optical DNA mapping assays. We test our multiple labeling frameworks on barcodes from two different plasmids and synthetically generated barcodes (combined competitive-binding- and nick-labeling). It is demonstrated that by simultaneously using the information from all label types, we can substantially increase the significance when we match experimental barcodes to a database consisting of theoretical barcodes for all sequenced plasmids. [ABSTRACT FROM AUTHOR]

Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations. [ABSTRACT FROM AUTHOR]

The intestine is lined with isolated lymphoid follicles (ILFs) that facilitate sampling of luminal antigens to elicit immune responses. Technical challenges related to the scarcity and small sizes of ILFs and their follicle-associated epithelium (FAE) impeded the characterization of their spatial gene expression programs. Here, we combined RNA sequencing of laser capture microdissected tissues with single-molecule transcript imaging to obtain a spatial gene expression map of the ILF and its associated FAE in the mouse small intestine. We identified zonated expression programs in both follicles and FAEs, with a decrease in enterocyte antimicrobial and absorption programs and a partial induction of expression programs normally observed at the villus tip. We further identified Lepr+ subepithelial telocytes at the FAE top, which are distinct from villus tip Lgr5+ telocytes. Our analysis exposes the epithelial and mesenchymal cell states associated with ILFs. The intestine is lined with isolated lymphoid follicles that facilitate sampling of luminal antigens to elicit immune responses. This study combines spatial transcriptomics and single molecule transcript imaging to identify spatially localized cellular states in intestinal follicles and their associated epithelium. [ABSTRACT FROM AUTHOR]

Numerous stages of organismal development rely on the cellular interpretation of gradients of secreted morphogens including members of the Bone Morphogenetic Protein (BMP) family through transmembrane receptors. Early gradients of BMPs drive dorsal/ventral patterning throughout the animal kingdom in both vertebrates and invertebrates. Growing evidence in Drosophila, zebrafish, murine and other systems suggests that BMP ligand heterodimers are the primary BMP signaling ligand, even in systems in which mixtures of BMP homodimers and heterodimers are present. Signaling by heterodimers occurs through a hetero-tetrameric receptor complex comprising of two distinct type one BMP receptors and two type II receptors. To understand the system dynamics and determine whether kinetic assembly of heterodimer-heterotetramer BMP complexes is favored, as compared to other plausible BMP ligand-receptor configurations, we developed a kinetic model for BMP tetramer formation based on current measurements for binding rates and affinities. We find that contrary to a common hypothesis, heterodimer-heterotetramer formation is not kinetically favored over the formation of homodimer-tetramer complexes under physiological conditions of receptor and ligand concentrations and therefore other mechanisms, potentially including differential kinase activities of the formed heterotetramer complexes, must be the cause of heterodimer-heterotetramer signaling primacy. Further, although BMP complex assembly favors homodimer and homomeric complex formation over a wide range of parameters, ignoring these signals and instead relying on the heterodimer improves the range of morphogen interpretation in a broad set of conditions, suggesting a performance advantage for heterodimer signaling in patterning multiple cell types in a gradient. Author summary: TGF-β signaling is an important cell signaling system through which cells respond to external information. In the TGF-β system, signaling is initiated when a ligand dimer pair binds to a receptor tetramer. Interestingly, in numerous developmental contexts, TGF-β signaling has a greater response to heterodimeric ligands (dimers of multiple ligands), as compared to homomeric ligands (dimers made of two molecules of a single ligand. However, neither the cause of heterodimer signaling primacy, nor the systemic effects of heterodimeric vs homomeric signaling are understood. We use a biophysically-informed computational modeling approach to investigate the system dynamics of heterodimer-heterotetramer BMP signaling, to understand the cause and consequence of the requirement for BMP2/7-mediated signaling in dorsoventral patterning in zebrafish development. Using our model, we demonstrate that BMP heterodimer signaling complex formation is not kinetically favored over homodimer signaling complexes, suggesting subfunctionalization of BMP receptors may be required to explain heterodimer signaling. Additionally, we show that heterodimer signaling provides a performance advantage via increased range of morphogen interpretation. Our findings provide insight into the systems principles involved in developmental signaling. [ABSTRACT FROM AUTHOR]

We report a new subgroup of Type III Restriction-Modification systems that use m4C methylation for host protection. Recognition specificities for six such systems, each recognizing a novel motif, have been determined using single molecule real-time DNA sequencing. In contrast to all previously characterized Type III systems which modify adenine to m6A, protective methylation of the host genome in these new systems is achieved by the N4-methylation of a cytosine base in one strand of an asymmetric 4 to 6 base pair recognition motif. Type III systems are heterotrimeric enzyme complexes containing a single copy of an ATP-dependent restriction endonuclease-helicase (Res) and a dimeric DNA methyltransferase (Mod). The Type III Mods are beta-class amino-methyltransferases, examples of which form either N6-methyl adenine or N4-methyl cytosine in Type II RM systems. The Type III m4C Mod and Res proteins are diverged, suggesting ancient origin or that m4C modification has arisen from m6A MTases multiple times in diverged lineages. Two of the systems, from thermophilic organisms, required expression of both Mod and Res to efficiently methylate an E. coli host, unlike previous findings that Mod alone is proficient at modification, suggesting that the division of labor between protective methylation and restriction activities is atypical in these systems. Two of the characterized systems, and many homologous putative systems, appear to include a third protein; a conserved putative helicase/ATPase subunit of unknown function and located 5' of the mod gene. The function of this additional ATPase is not yet known, but close homologs co-localize with the typical Mod and Res genes in hundreds of putative Type III systems. Our findings demonstrate a rich diversity within Type III RM systems. [ABSTRACT FROM AUTHOR]

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the 'NeuroCAAS' cloud platform. Author summary: Spatial transcriptomics technologies allow us to simultaneously detect multiple molecular targets in the context of intact tissues. These experiments yield images that answer two questions: which kinds of molecules are present, and where are they located in the tissue? In many experiments (e.g., mapping RNA expression in fine neuronal processes), it is desirable to increase the signal density relative to the imaging resolution. This may lead to mixing of signals from multiple RNA molecules into single imaging voxels; thus we need to demix the signals from these images. Here we introduce BarDensr, a new computational method to perform this demixing. The method is based on a forward model of the imaging process, followed by a convex optimization approach to approximately 'invert' mixing induced during imaging. This new approach leads to significantly improved performance in demixing imaging data with dense expression and/or low spatial resolution. [ABSTRACT FROM AUTHOR]

The HIV capsid self-assembles a protective conical shell that simultaneously prevents host sensing whilst permitting the import of nucleotides to drive DNA synthesis. This is accomplished through the construction of dynamic, highly charged pores at the centre of each capsid multimer. The clustering of charges required for dNTP import is strongly destabilising and it is proposed that HIV uses the metabolite IP6 to coordinate the pore during assembly. Here we have investigated the role of inositol phosphates in coordinating a ring of positively charged lysine residues (K25) that forms at the base of the capsid pore. We show that whilst IP5, which can functionally replace IP6, engages an arginine ring (R18) at the top of the pore, the lysine ring simultaneously binds a second IP5 molecule. Dose dependent removal of K25 from the pore severely inhibits HIV infection and concomitantly prevents DNA synthesis. Cryo-tomography reveals that K25A virions have a severe assembly defect that inhibits the formation of mature capsid cones. Monitoring both the kinetics and morphology of capsids assembled in vitro reveals that while mutation K25A can still form tubes, the ability of IP6 to drive assembly of capsid cones has been lost. Finally, in single molecule TIRF microscopy experiments, capsid lattices in permeabilised K25 mutant virions are rapidly lost and cannot be stabilised by IP6. These results suggest that the coordination of IP6 by a second charged ring in mature hexamers drives the assembly of conical capsids capable of reverse transcription and infection. Author summary: HIV protects its RNA genome while copying it into DNA by carrying out reverse transcription inside its capsid. This is accomplished by importing nucleotides through highly charged pores at the centre of each capsid multimer. These pores contain two rings of positively charged residues–R18 and K25 –but assembling capsids with these features is challenging because they are intrinsically destabilising. Here we show that the metabolite IP6 coordinates both residues within the pore to drive the assembly of stable capsids capable of nucleotide import. R18 or K25 mutants lose infectivity and the ability to synthesise DNA but have differing assembly phenotypes. Mutant K25A is unable to undergo efficient capsid assembly, while replacing K25 with a neutral polar residue partially restores assembly but not infectivity. We propose that IP6-driven assembly is conserved by HIV not because it is the only way to build a capsid, but because it allows the construction of a capsid with a charged pore that can import nucleotides. [ABSTRACT FROM AUTHOR]

Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide 'lifeact'. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a dSTORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments. [ABSTRACT FROM AUTHOR]

The coronavirus causing the COVID-19 pandemic, SARS-CoV-2, uses −1 programmed ribosomal frameshifting (−1 PRF) to control the relative expression of viral proteins. As modulating −1 PRF can inhibit viral replication, the RNA pseudoknot stimulating −1 PRF may be a fruitful target for therapeutics treating COVID-19. We modeled the unusual 3-stem structure of the stimulatory pseudoknot of SARS-CoV-2 computationally, using multiple blind structural prediction tools followed by μs-long molecular dynamics simulations. The results were compared for consistency with nuclease-protection assays and single-molecule force spectroscopy measurements of the SARS-CoV-1 pseudoknot, to determine the most likely conformations. We found several possible conformations for the SARS-CoV-2 pseudoknot, all having an extended stem 3 but with different packing of stems 1 and 2. Several conformations featured rarely-seen threading of a single strand through junctions formed between two helices. These structural models may help interpret future experiments and support efforts to discover ligands inhibiting −1 PRF in SARS-CoV-2. Author summary: The coronavirus that causes COVID-19 controls the production of key viral proteins through a process known as programmed ribosomal frameshifting. Frameshifting is triggered by a particular structure in the viral RNA, a pseudoknot, which is a promising drug target. Here we model the structure of this pseudoknot through atomistic molecular dynamics simulations. Surprisingly, we find that the pseudoknot can take on distinct fold topologies, two of which involve unusual threading of a single strand of RNA through helical junctions, something not seen before in frameshifting pseudoknots. All of the folds are generally consistent with previous experimental studies of the closely-related SARS coronavirus pseudoknot. These results should assist in the analysis and interpretation of future experimental studies of the pseudoknot structure, and support structure-based drug-discovery efforts. [ABSTRACT FROM AUTHOR]

Endogenous genotoxic stress occurs in healthy cells due to competition between DNA replication machinery, and transcription and topographic relaxation processes. This causes replication fork stalling and regression, which can further collapse to form single-ended double strand breaks (seDSBs). Super-resolution microscopy has made it possible to directly observe replication stress and DNA damage inside cells, however new approaches to sample preparation and analysis are required. Here we develop and apply multicolor single molecule microscopy to visualize individual replication forks under mild stress from the trapping of Topoisomerase I cleavage complexes, a damage induction which closely mimics endogenous replicative stress. We observe RAD51 and RAD52, alongside RECQ1, as the first responder proteins to stalled but unbroken forks, whereas Ku and MRE11 are initially recruited to seDSBs. By implementing novel super-resolution imaging assays, we are thus able to discern closely related replication fork stress motifs and their repair pathways. Author summary: Damage to the genetic code embedded in an organism's DNA can result in mutation or cell death which, in turn, can lead to disease and dysfunction. DNA damage is the main cause of many human diseases including cancer, and some forms of neurodegeneration and immune dysfunction. DNA double strand breaks (DSBs), which occur a handful of times in each replicating cell each day, are especially deleterious due to the difficulty of their repair. The main endogenous cause of DSBs is the breakdown of DNA replication forks however there is increasing evidence that damage and stress at these forks can also result in unbroken intermediate structures which avoid DSB formation such as fork regression. We have developed and applied new assays for labelling and visualizing damaged replication forks using super-resolution microscopy. This has enabled us to differentiate between broken and unbroken forks and to discern the different proteins that are recruited for DSB and regressed fork repair. Our data further demonstrate that these assays are widely applicable to DNA damage research and offer a new approach to mapping the spatiotemporal repair of individual damage events inside cells. [ABSTRACT FROM AUTHOR]

Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python. Author summary: We have developed an accurate, highly-efficient and flexible algorithm for quantifying spatial clustering in large, 3-dimensional single-molecule localization microscopy (SMLM) datasets. Our method, FOCAL3D, is provided as an open-source software package written in Python. FOCAL3D scales linearly with the number of localizations and the algorithmic parameters may be systematically optimized so that the resulting analysis is insensitive to variation over a range of parameter choices. We initially validate the performance and parametric insensitivity of FOCAL3D on simulated datasets, then apply the algorithm to 3-dimensional, astigmatic dSTORM images of the nuclear pore complex in human osteosarcoma cells. [ABSTRACT FROM AUTHOR]

Populations of bacteria often undergo a lag in growth when switching conditions. Because growth lags can be large compared to typical doubling times, variations in growth lag are an important but often overlooked component of bacterial fitness in fluctuating environments. We here explore how growth lag variation is determined for the archetypical switch from glucose to lactose as a carbon source in Escherichia coli. First, we show that single-cell lags are bimodally distributed and controlled by a single-molecule trigger. That is, gene expression noise causes the population before the switch to divide into subpopulations with zero and nonzero lac operon expression. While "sensorless" cells with zero preexisting lac expression at the switch have long lags because they are unable to sense the lactose signal, any nonzero lac operon expression suffices to ensure a short lag. Second, we show that the growth lag at the population level depends crucially on the fraction of sensorless cells and that this fraction in turn depends sensitively on the growth condition before the switch. Consequently, even small changes in basal expression can significantly affect the fraction of sensorless cells, thereby population lags and fitness under switching conditions, and may thus be subject to significant natural selection. Indeed, we show that condition-dependent population lags vary across wild E. coli isolates. Since many sensory genes are naturally low expressed in conditions where their inducer is not present, bimodal responses due to subpopulations of sensorless cells may be a general mechanism inducing phenotypic heterogeneity and controlling population lags in switching environments. This mechanism also illustrates how gene expression noise can turn even a simple sensory gene circuit into a bet hedging module and underlines the profound role of gene expression noise in regulatory responses. Is ignorance bliss for some bacterial cells? Single-cell analysis of the archetypical switch from glucose to lactose as a carbon source in E. coli shows that bacteria can exhibit stochastic bimodal responses to external stimuli because the corresponding sensory circuit is so lowly expressed that some cells are effectively blind to the stimulus. [ABSTRACT FROM AUTHOR]

The activity of antimicrobial peptides (AMPs) has significant bacterial species bias, the mechanisms of which are not fully understood. We employed single-molecule tracking to measure the affinity of three different AMPs to hybrid supported bilayers composed of lipid A extracted from four different Gram negative bacteria and observed a strong empirical anticorrelation between the affinity of a particular AMP to a given lipid A layer and the activity of that AMP towards the bacterium from which that lipid A was extracted. This suggested that the species bias of AMP activity is directly related to AMP interactions with bacterial outer membranes, despite the fact that the mechanism of antimicrobial activity occurs at the inner membrane. The trend also suggested that the interactions between AMPs and the outer membrane lipid A (even in the absence of other components, such as lipopolysaccharides) capture effects that are relevant to the minimum inhibitory concentration. [ABSTRACT FROM AUTHOR]

In the modern genomic era, scientists without extensive bioinformatic training need to apply high-power computational analyses to critical tasks like phage genome annotation. At the Center for Phage Technology (CPT), we developed a suite of phage-oriented tools housed in open, user-friendly web-based interfaces. A Galaxy platform conducts computationally intensive analyses and Apollo, a collaborative genome annotation editor, visualizes the results of these analyses. The collection includes open source applications such as the BLAST+ suite, InterProScan, and several gene callers, as well as unique tools developed at the CPT that allow maximum user flexibility. We describe in detail programs for finding Shine-Dalgarno sequences, resources used for confident identification of lysis genes such as spanins, and methods used for identifying interrupted genes that contain frameshifts or introns. At the CPT, genome annotation is separated into two robust segments that are facilitated through the automated execution of many tools chained together in an operation called a workflow. First, the structural annotation workflow results in gene and other feature calls. This is followed by a functional annotation workflow that combines sequence comparisons and conserved domain searching, which is contextualized to allow integrated evidence assessment in functional prediction. Finally, we describe a workflow used for comparative genomics. Using this multi-purpose platform enables researchers to easily and accurately annotate an entire phage genome. The portal can be accessed at https://cpt.tamu.edu/galaxy-pub with accompanying user training material. Author summary: In the modern genomic era, scientists without extensive bioinformatic training need to apply advanced computational analyses to genome annotation. At the Center for Phage Technology, we use two open source, web-based platforms: Galaxy, for reproducible computational analyses, and Apollo, a collaborative genome annotation editor, to facilitate annotation of phage genomes. Unlike the genomes of most cellular life forms, phage genomes are usually a single contiguous molecule <200,000 bases in length. Their size allows high standards for complete, evidence-based annotations, and is amenable to genomics education settings. The Galaxy and Apollo system described here is used for original biological research and development of new bioinformatic tools to analyze many individual phage genomes, as well as clusters of related phages. Our robust suite of phage-oriented tools moves all aspects of control and choice into the user's court. In comparison to widely-used automated and fast command-line annotation methods, our integrated and flexible approach benefits from trained human intervention to result in high-quality final annotations. We have educated a steady stream of scientists, including both undergraduate and graduate students, informally and through formal university course offerings using Galaxy-Apollo to annotate phage genomes, deposit the data in public sequence repositories, and publish the results. [ABSTRACT FROM AUTHOR]

We present the software platform 2CALM that allows for a comparative analysis of 3D localisation microscopy data representing protein distributions in two biological samples. The in-depth statistical analysis reveals differences between samples at the nanoscopic level using parameters such as cluster-density and -curvature. An automatic classification system combines multiplex and multi-level statistical approaches into one comprehensive parameter for similarity testing of the compared samples. We demonstrated the biological importance of 2CALM, comparing the protein distributions of CD41 and CD62p on activated platelets in a 3D artificial clot. Additionally, using 2CALM, we quantified the impact of the inflammatory cytokine interleukin-1β on platelet activation in clots. The platform is applicable to any other cell type and biological system and can provide new insights into biological and medical applications. Author summary: Single-molecule localisation microscopy (LM) became more accessible over the past years. Companies and research facilities developed instruments for 3D/2D LM, but there is a lack of comparison methods for LM-images. Our tool offers a new comparative analysis of LM data. Our system is capable of showing the difference on the level of single molecule clusters and provides information on differences between images on various scales. We determine if clusters formed of molecule localisations are comparable. We display the difference on density or the shape of these formed clusters for a certain dimension. Since the comparison is performed for all dimensions, the differences between cluster properties are observed very precisely. Thereby, we learn if clusters of molecules are formed and how they differ in both samples. For a fast/concluding comparison, we have added tools, which derives a percentage of equality of both images based on all comparison results. The tool is useful for comparison of images of molecules in cells, which are expected to differ from each other (exemplified with platelets). The differences might be caused by drug treatment, different disease progress or other environmental/genetical issues. The analysis is performed at a single cell level as well as cell cluster level. [ABSTRACT FROM AUTHOR]

Background: The fungal toxin acts as effective, low-cost chemical substances for pest control worldwide and also an alternative to synthetic insecticides. This study assessed the larvicidal potential of Metarhizium anisopliae fungi derived metabolites against Aedes aegypti, Anopheles stephensi, Culex quinquefasciatus and non-targeted organisms at 24hr post treatment. Method: Isolation of entomopathogenic fungi M. anisopliae from natural traps confirmed by using 18s rDNA biotechnological tools. Crude extracts from M. anisopliae solvent extraction and their secondary metabolites were bio-assayed following WHO standard procedures against Ae. aegypti, An. stephensi and Cx. quinquefasciatus, Artemia nauplii, Eudrilus eugeniae, and Solanum lycopersicum after 24 hr exposure. Histopathological analysis of E. eugeniae treated with fungi metabolites toxicity compared to those treated with Monocrotophos after 24hrpost-treatment. M. anisopliae metabolites were characterized using GC-MS and FT-IR analysis. Results: The larvicidal activity was recorded in highest concentration of 75μg/ml, with 85%, 97% and 89% mortality in Ae. aegypti, An. stephensi and Cx. quinquefasciatus respectively. M. anisopliae metabolites produced LC50 values in Ae. aegypti, 59.83μg/ml, in An. stephensi, 50.16μg/ml and in Cx. quinquefasciatus, 51.15μg/ml respectively. M. anisopliae metabolites produced lower toxic effects on A. nauplii, LC50 values were, 54.96μg/ml respectively. Bio-indicator toxicity results show 18% and 58% mortality was recorded in E. eugeniae and A. nauplii and also there is no phytotoxicity that was observed on S. lycopersicum L. under semi-field condition. E. eugeniae histopathological studies shows fungal metabolites showed lower sub-lethal effects compared to synthetic chemical pesticide at 24hrs of the treatment. The GC-MS and FT-IR analysis identified five major components of active ingredients. Conclusion: Findings of this study indicate that, M. anisopliae ethyl acetate derived secondary metabolites are effective against larvae of Ae. aegypti, An. stephensi and Cx. quinquefasciatus mosquito species, lower toxicity effects were observed on non-target organisms such as, Artemia nauplii, Eudrilus eugeniae as well as, no toxicity effect were observed on Solanum lycopersicum. Further research should be conducted in laboratory for separation of single pure molecule and be tested semifield conditions. [ABSTRACT FROM AUTHOR]

Intrinsic fluorescence of biological material, also called auto-fluorescence, is a well-known phenomenon and has in recent years been used for imaging, diagnostics and cell viability studies. Here we show that in addition to commonly observed auto-fluorescence, intrinsic anti-Stokes emission can also be observed under 560 nm or 633 nm excitation. The anti-Stokes emission is shown to be spatially located on/in the mitochondria. The findings presented here show that sensitive imaging experiments e.g. single molecule experiments or two-photon excitation imaging can be compromised if intracellular anti-Stokes emission is not accounted for. On the other hand, we suggest that this anti-Stokes emission could be exploited as an additional modality for mitochondria visualization and cell viability investigation even in systems that are already labeled with commonly used fluorophores that rely on normal Stokes-based detection. [ABSTRACT FROM AUTHOR]

Large expansions of microsatellite DNA cause several neurological diseases. In Spinocerebellar ataxia type 10 (SCA10), the repeat interruptions change disease phenotype; an (ATTCC)n or a (ATCCT)n/(ATCCC)n interruption within the (ATTCT)n repeat is associated with the robust phenotype of ataxia and epilepsy while mostly pure (ATTCT)n may have reduced penetrance. Large repeat expansions of SCA10, and many other microsatellite expansions, can exceed 10,000 base pairs (bp) in size. Conventional next generation sequencing (NGS) technologies are ineffective in determining internal sequence contents or size of these expanded repeats. Using repeat primed PCR (RP-PCR) in conjunction with a high-sensitivity pulsed-field capillary electrophoresis fragment analyzer (FEMTO-Pulse, Agilent, Santa Clara, CA) (RP-FEMTO hereafter), we successfully determined sequence content of large expansion repeats in genomic DNA of SCA10 patients and transformed yeast artificial chromosomes containing SCA10 repeats. This RP-FEMTO is a simple and economical methodology which could complement emerging NGS for very long sequence reads such as Single Molecule, Real-Time (SMRT) and nanopore sequencing technologies. [ABSTRACT FROM AUTHOR]

Detailed comprehensive knowledge of the structures of individual long-range telomere-terminal haplotypes are needed to understand their impact on telomere function, and to delineate the population structure and evolution of subtelomere regions. However, the abundance of large evolutionarily recent segmental duplications and high levels of large structural variations have complicated both the mapping and sequence characterization of human subtelomere regions. Here, we use high throughput optical mapping of large single DNA molecules in nanochannel arrays for 154 human genomes from 26 populations to present a comprehensive look at human subtelomere structure and variation. The results catalog many novel long-range subtelomere haplotypes and determine the frequencies and contexts of specific subtelomeric duplicons on each chromosome arm, helping to clarify the currently ambiguous nature of many specific subtelomere structures as represented in the current reference sequence (HG38). The organization and content of some duplicons in subtelomeres appear to show both chromosome arm and population-specific trends. Based upon these trends we estimate a timeline for the spread of these duplication blocks. Author summary: The ends of human chromosomes have caps called telomeres that are essential. These telomeres are influenced by the portions of DNA next to them, a region known as the subtelomere. We need to better understand the subtelomeric region to understand how it impacts the telomeres. This subtelomeric region is not well described in the current references. This is due to large variations in this region and portions that are repeated many times, making current sequencing technologies struggle to capture these regions. Many of these variations are evolutionary recent. Here we use 154 different samples from the 26 geographic regions of the world to gain a better understanding of the variation in these regions. We found many new haplotypes and clarified the haplotypes existing in the current reference. We then examined population and chromosome specific trends. [ABSTRACT FROM AUTHOR]

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, β-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (rSNAPsim), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. rSNAPsim is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git. Translation is an essential step in which ribosomes decipher mRNA sequences to manufacture proteins. Recent advances in time-lapse fluorescence microscopy allow live-cell quantification of translation dynamics at the resolution of single mRNA molecules. Here, we develop a flexible computational framework to reproduce and interpret such experiments. We use this framework to explore how well different single-mRNA translation experiment designs would perform to estimate key translation parameters. We then integrate experimental data from the most flexible design with our stochastic model framework to reproduce the statistics and temporal dynamics of nascent protein elongation for three different human genes. Our validated computational method is packaged with a simple graphical user interface that (1) starts with mRNA sequences, (2) generates discrete, codon-dependent translation models, (3) provides visualization of ribosome movement as trajectories or kymographs, and (4) allows the user to estimate how optical single-mRNA translation experiments would be affected by different genetic alterations (e.g., codon substitutions) or environmental perturbations (e.g., tRNA titrations or drug treatments). [ABSTRACT FROM AUTHOR]

In single molecule fluorescence studies, background emission from labeled substrates often restricts their concentrations to non-physiological nanomolar values. One approach to address this challenge is the use of zero-mode waveguides (ZMWs), nanoscale holes in a thin metal film that physically and optically confine the observation volume allowing much higher concentrations of fluorescent substrates. Standard fabrication of ZMWs utilizes slow and costly E-beam nano-lithography. Herein, ZMWs are made using a self-assembled mask of polystyrene microspheres, enabling fabrication of thousands of ZMWs in parallel without sophisticated equipment. Polystyrene 1 μm dia. microbeads self-assemble on a glass slide into a hexagonal array, forming a mask for the deposition of metallic posts in the inter-bead interstices. The width of those interstices (and subsequent posts) is adjusted within 100–300 nm by partially fusing the beads at the polystyrene glass transition temperature. The beads are dissolved in toluene, aluminum or gold cladding is deposited around the posts, and those are dissolved, leaving behind an array ZMWs. Parameter optimization and the performance of the ZMWs are presented. By using colloidal self-assembly, typical laboratories can make use of sub-wavelength ZMW technology avoiding the availability and expense of sophisticated clean-room environments and equipment. [ABSTRACT FROM AUTHOR]

We propose and theoretically study an approach to massively parallel single molecule peptide sequencing, based on single molecule measurement of the kinetics of probe binding (Havranek, et al., 2013) to the N-termini of immobilized peptides. Unlike previous proposals, this method is robust to both weak and non-specific probe-target affinities, which we demonstrate by applying the method to a range of randomized affinity matrices consisting of relatively low-quality binders. This suggests a novel principle for proteomic measurement whereby highly non-optimized sets of low-affinity binders could be applicable for protein sequencing, thus shifting the burden of amino acid identification from biomolecular design to readout. Measurement of probe occupancy times, or of time-averaged fluorescence, should allow high-accuracy determination of N-terminal amino acid identity for realistic probe sets. The time-averaged fluorescence method scales well to weakly-binding probes with dissociation constants of tens or hundreds of micromolar, and bypasses photobleaching limitations associated with other fluorescence-based approaches to protein sequencing. We argue that this method could lead to an approach with single amino acid resolution and the ability to distinguish many canonical and modified amino acids, even using highly non-optimized probe sets. This readout method should expand the design space for single molecule peptide sequencing by removing constraints on the properties of the fluorescent binding probes. [ABSTRACT FROM AUTHOR]

Background: Timely diagnosis of tuberculosis disease is critical for positive patient outcomes, yet potentially millions go undiagnosed or unreported each year. Sputum is widely used as the testing input, but limited by its complexity, heterogeneity, and sourcing problems. Finding methods to interrogate noninvasive, non-sputum clinical specimens is indispensable to improving access to tuberculosis diagnosis and care. In this work, economical plasmonic gratings were used to analyze tuberculosis biomarker lipoarabinomannan (LAM) from clinical urine samples by single molecule fluorescence assay (FLISA) and compared with gold standard sputum GeneXpert MTB/ RIF, culture, and reference ELISA testing results. Methods and Findings: In this study, twenty sputum and urine sample sets were selected retrospectively from a repository of HIV-negative patient samples collected before initiation of anti-tuberculosis therapy. GeneXpert MTB/RIF and culture testing of patient sputum confirmed the presence or absence of pulmonary tuberculosis while all patient urines were reference ELISA LAM-negative. Plasmonic gratings produced by low-cost soft lithography were bound with anti-LAM capture antibody, incubated with patient urine samples, and biotinylated detection antibody. Fluorescently labeled streptavidin revealed single molecule emission by epifluorescence microscope. Using a 1 fg/mL baseline for limit of detection, single molecule FLISA demonstrated good qualitative agreement with gold standard tests on 19 of 20 patients, including accurately predicting the gold-standard-negative patients, while one gold-standard-positive patient produced no observable LAM in urine. Conclusions: Single molecule FLISA by plasmonic grating demonstrated the ability to quantify tuberculosis LAM from complex urine samples of patients from a high endemic setting with negligible interference from the complex media itself. Moreover, agreement with patient diagnoses by gold standard testing suggests that single molecule FLISA could be used as a highly sensitive test to diagnose tuberculosis noninvasively. [ABSTRACT FROM AUTHOR]

In the last 20 years, atomic force microscopy (AFM) has emerged as a ubiquitous technique in biological research, allowing the analysis of biological samples under near-physiological conditions from single molecules to living cells. Despite its growing use, the low process throughput remains a major drawback. Here, we propose a solution validated on a device allowing a fully automated, multi-sample analysis. Our approach is mainly designed to study samples in fluid and biological cells. As a proof of concept, we demonstrate its feasibility applied to detect and scan both fixed and living bacteria before completion of data processing. The effect of two distinct treatments (i.e. gentamicin and heating) is then evidenced on physical parameters of fixed Yersinia pseudotuberculosis bacteria. The multi-sample analysis presented allows an increase in the number of scanned samples while limiting the user’s input. Importantly, cantilever cleaning and control steps are performed regularly–as part of the automated process–to ensure consistent scanning quality. We discuss how such an approach is paving the way to AFM developments in medical and clinical fields, in which statistical significance of results is a prerequisite. [ABSTRACT FROM AUTHOR]

The initial steps in the synthesis of leukotrienes are the translocation of 5-lipoxygenase (5-LO) to the nuclear envelope and its subsequent association with its scaffold protein 5-lipoxygenase-activating protein (FLAP). A major gap in our understanding of this process is the knowledge of how the organization of 5-LO and FLAP on the nuclear envelope regulates leukotriene synthesis. We combined single molecule localization microscopy with Clus-DoC cluster analysis, and also a novel unbiased cluster analysis to analyze changes in the relationships between 5-LO and FLAP in response to activation of RBL-2H3 cells to generate leukotriene C4. We identified the time-dependent reorganization of both 5-LO and FLAP into higher-order assemblies or clusters in response to cell activation via the IgE receptor. Clus-DoC analysis identified a subset of these clusters with a high degree of interaction between 5-LO and FLAP that specifically correlates with the time course of LTC4 synthesis, strongly suggesting their role in the initiation of leukotriene biosynthesis. [ABSTRACT FROM AUTHOR]

Mutagenic translesion DNA polymerase V (UmuD′2C) is induced as part of the DNA damage-induced SOS response in Escherichia coli, and is subjected to multiple levels of regulation. The UmuC subunit is sequestered on the cell membrane (spatial regulation) and enters the cytosol after forming a UmuD′2C complex, ~ 45 min post-SOS induction (temporal regulation). However, DNA binding and synthesis cannot occur until pol V interacts with a RecA nucleoprotein filament (RecA*) and ATP to form a mutasome complex, pol V Mut = UmuD′2C-RecA-ATP. The location of RecA relative to UmuC determines whether pol V Mut is catalytically on or off (conformational regulation). Here, we present three interrelated experiments to address the biochemical basis of conformational regulation. We first investigate dynamic deactivation during DNA synthesis and static deactivation in the absence of DNA synthesis. Single-molecule (sm) TIRF-FRET microscopy is then used to explore multiple aspects of pol V mut dynamics. Binding of ATP/ATPγS triggers a conformational switch that reorients RecA relative to UmuC to activate pol V Mut. This process is required for polymerase-DNA binding and synthesis. Both dynamic and static deactivation processes are governed by temperature and time, in which on → off switching is “rapid” at 37°C (~ 1 to 1.5 h), “slow” at 30°C (~ 3 to 4 h) and does not require ATP hydrolysis. Pol V Mut retains RecA in activated and deactivated states, but binding to primer-template (p/t) DNA occurs only when activated. Studies are performed with two forms of the polymerase, pol V Mut-RecA wt, and the constitutively induced and hypermutagenic pol V Mut-RecAE38K/ΔC17. We discuss conformational regulation of pol V Mut, determined from biochemical analysis in vitro, in relation to the properties of pol V Mut in RecA wild-type and SOS constitutive genetic backgrounds in vivo. [ABSTRACT FROM AUTHOR]

Interactions between membrane proteins are poorly understood despite their importance in cell signaling and drug development. Here, we present a co-immunoimmobilization assay (Co-II) enabling the direct observation of membrane protein interactions in single living cells that overcomes the limitations of currently prevalent proximity-based indirect methods. Using Co-II, we investigated the transient homodimerizations of epidermal growth factor receptor (EGFR) and beta-2 adrenergic receptor (β2-AR) in living cells, revealing the differential regulation of these receptors’ dimerizations by molecular conformations and microenvironment in a plasma membrane. Co-II should provide a simple, rapid, and robust platform for visualizing both weak and strong protein interactions in the plasma membrane of living cells. [ABSTRACT FROM AUTHOR]

The first draft genome sequencing of the non-model fungal pathogen Pyrenochaeta lycopersici showed an expansion of gene families associated with heterokaryon incompatibility and lacking of mating-type genes, providing insights into the genetic basis of this “imperfect” fungus which lost the ability to produce the sexual stage. However, due to the Illumina short-read technology, the draft genome was too fragmented to allow a comprehensive characterization of the genome, especially of the repetitive sequence fraction. In this work, the sequencing of another P. lycopersici isolate using long-read Single Molecule Real-Time sequencing technology was performed with the aim of obtaining a gapless genome. Indeed, a gapless genome assembly of 62.7 Mb was obtained, with a fraction of repetitive sequences representing 30% of the total bases. The gene content of the two P. lycopersici isolates was very similar, and the large difference in genome size (about 8 Mb) might be attributable to the high fraction of repetitive sequences detected for the new sequenced isolate. The role of repetitive elements, including transposable elements, in modulating virulence effectors is well established in fungal plant pathogens. Moreover, transposable elements are of fundamental importance in creating and re-modelling genes, especially in imperfect fungi. Their abundance in P. lycopersici, together with the large expansion of heterokaryon incompatibility genes in both sequenced isolates, suggest the presence of possible mechanisms alternative to gene re-assorting mediated by sexual recombination. A quite large fraction (~9%) of repetitive elements in P. lycopersici, has no homology with known classes, strengthening this hypothesis. The availability of a gapless genome of P. lycopersici allowed the in-depth analysis of its genome content, by annotating functional genes and TEs. This goal will be an important resource for shedding light on the evolution of the reproductive and pathogenic behaviour of this soilborne pathogen and the onset of a possible speciation within this species. [ABSTRACT FROM AUTHOR]