Explore the vast range of titles available.

Despite the fundamental importance of transcription, a comprehensive analysis of RNA polymerase (RNAP) behavior and its role in the nucleoid organization in vivo is lacking. Here, we used superresolution microscopy to study the localization and dynamics of the transcription machinery and DNA in live bacterial cells, at both the single-molecule and the population level. We used photoactivated single-molecule tracking to discriminate between mobile RNAPs and RNAPs specifically bound to DNA, either on promoters or transcribed genes. Mobile RNAPs can explore the whole nucleoid while searching for promoters, and spend 85% of their search time in nonspecific interactions with DNA. On the other hand, the distribution of specifically bound RNAPs shows that low levels of transcription can occur throughout the nucleoid. Further, clustering analysis and 3D structured illumination microscopy (SIM) show that dense clusters of transcribing RNAPs form almost exclusively at the nucleoid periphery. Treatment with rifampicin shows that active transcription is necessary for maintaining this spatial organization. In faster growth conditions, the fraction of transcribing RNAPs increases, as well as their clustering. Under these conditions, we observed dramatic phase separation between the densest clusters of RNAPs and the densest regions of the nucleoid. These findings show that transcription can cause spatial reorganization of the nucleoid, with movement of gene loci out of the bulk of DNA as levels of transcription increase. This work provides a global view of the organization of RNA polymerase and transcription in living cells.

Nucleotide excision repair (NER) removes chemically diverse DNA lesions in all domains of life. In Escherichia coli , UvrA and UvrB initiate NER, although the mechanistic details of how this occurs in vivo remain to be established. Here, we use single-molecule fluorescence imaging to provide a comprehensive characterization of the lesion search, recognition and verification process in living cells. We show that NER initiation involves a two-step mechanism in which UvrA scans the genome and locates DNA damage independently of UvrB. Then UvrA recruits UvrB from solution to the lesion. These steps are coordinated by ATP binding and hydrolysis in the ‘proximal’ and ‘distal’ UvrA ATP-binding sites. We show that initial UvrB-independent damage recognition by UvrA requires ATPase activity in the distal site only. Subsequent UvrB recruitment requires ATP hydrolysis in the proximal site. Finally, UvrA dissociates from the lesion complex, allowing UvrB to orchestrate the downstream NER reactions. Nucleotide excision repair is able to identify and remove a wide range of DNA helix distorting lesions from the genome. Here the authors use single molecule imaging of UvrA and UvrB molecules and suggest a two-step ‘scan and recruit’ model for UvrA function.

Cellular DNA damage is reversed by balanced repair pathways that avoid accumulation of toxic intermediates. Despite their importance, the organization of DNA repair pathways and the function of repair enzymes in vivo have remained unclear because of the inability to directly observe individual reactions in living cells. Here, we used photoactivation, localization, and tracking in live Escherichia coli to directly visualize single fluorescent labeled DNA polymerase I (Pol) and ligase (Lig) molecules searching for DNA gaps and nicks, performing transient reactions, and releasing their products. Our general approach provides enzymatic rates and copy numbers, substrate-search times, diffusion characteristics, and the spatial distribution of reaction sites, at the single-cell level, all in one measurement. Single repair events last 2.1 s (Pol) and 2.5 s (Lig), respectively. Pol and Lig activities increased fivefold over the basal level within minutes of DNA methylation damage; their rates were limited by upstream base excision repair pathway steps. Pol and Lig spent >80% of their time searching for free substrates, thereby minimizing both the number and lifetime of toxic repair intermediates. We integrated these single-molecule observations to generate a quantitative, systems-level description of a model repair pathway in vivo.

Many viruses form highly pleomorphic particles. In influenza, virion structure is of interest not only in the context of virus assembly, but also because pleomorphic variations may correlate with infectivity and pathogenicity. We have used fluorescence super-resolution microscopy combined with a rapid automated analysis pipeline, a method well-suited to the study of large numbers of pleomorphic structures, to image many thousands of individual influenza virions; gaining information on their size, morphology and the distribution of membrane-embedded and internal proteins. We observed broad phenotypic variability in filament size, and Fourier transform analysis of super-resolution images demonstrated no generalized common spatial frequency patterning of HA or NA on the virion surface, suggesting a model of virus particle assembly where the release of progeny filaments from cells occurs in a stochastic way. We also showed that viral RNP complexes are located preferentially within Archetti bodies when these were observed at filament ends, suggesting that these structures may play a role in virus transmission. Our approach therefore offers exciting new insights into influenza virus morphology and represents a powerful technique that is easily extendable to the study of pleomorphism in other pathogenic viruses.

The understanding of cellular mechanisms benefits substantially from accurate determination of protein diffusive properties. Prior work in this field primarily focuses on traditional methods, such as mean square displacements, for calculation of protein diffusion coefficients and biological states. This proves difficult and error-prone for proteins undergoing heterogeneous behaviour, particularly in complex environments, limiting the exploration of new biological behaviours. The importance of determining protein diffusion coefficients, anomalous exponents, and biological behaviours led to the Anomalous Diffusion Challenge 2024, exploring machine learning methods to infer these variables in heterogeneous trajectories with time-dependent changepoints. In response to the challenge, we present M3, a machine learning method for pointwise inference of diffusive coefficients, anomalous exponents, and states along noisy heterogenous protein trajectories. M3 makes use of long short-term memory cells to achieve small mean absolute errors for the diffusion coefficient and anomalous exponent alongside high state accuracies (>90%). Subsequently, we implement changepoint detection to determine timepoints at which protein behaviour changes. M3 removes the need for expert fine-tuning required in most conventional statistical methods while being computationally inexpensive to train. The model finished in the Top 5 of the Anomalous Diffusive Challenge 2024, with small improvements made since challenge closure.

The increasing risk from viral outbreaks such as the ongoing COVID-19 pandemic exacerbates the need for rapid, affordable and sensitive methods for virus detection, identification and quantification; however, existing methods for detecting virus particles in biological samples usually depend on multistep protocols that take considerable time to yield a result. Here, we introduce a rapid fluorescence in situ hybridization (FISH) protocol capable of detecting influenza virus, avian infectious bronchitis virus and SARS-CoV-2 specifically and quantitatively in approximately 20 min, in virus cultures, combined nasal and throat swabs with added virus and likely patient samples without previous purification. This fast and facile workflow can be adapted both as a lab technique and a future diagnostic tool in enveloped viruses with an accessible genome.

Using fluorescence resonance energy transfer to monitor distances within single molecules of abortively initiating transcription initiation complexes, we show that initial transcription proceeds through a \"scrunching\" mechanism, in which RNA polymerase (RNAP) remains fixed on promoter DNA and pulls downstream DNA into itself and past its active center. We show further that putative alternative mechanisms for RNAP active-center translocation in initial transcription, involving \"transient excursions\" of RNAP relative to DNA or \"inchworming\" of RNAP relative to DNA, do not occur. The results support a model in which a stressed intermediate, with DNA-unwinding stress and DNA-compaction stress, is formed during initial transcription, and in which accumulated stress is used to drive breakage of interactions between RNAP and promoter DNA and between RNAP and initiation factors during promoter escape.

Transcription in bacteria is controlled by multiple molecular mechanisms that precisely regulate gene expression. It has been recently shown that initial RNA synthesis by the bacterial RNA polymerase (RNAP) is interrupted by pauses; however, the pausing determinants and the relationship of pausing with productive and abortive RNA synthesis remain poorly understood. Using single-molecule FRET and biochemical analysis, here we show that the pause encountered by RNAP after the synthesis of a 6-nt RNA (ITC6) renders the promoter escape strongly dependent on the NTP concentration. Mechanistically, the paused ITC6 acts as a checkpoint that directs RNAP to one of three competing pathways: productive transcription, abortive RNA release, or a new unscrunching/scrunching pathway. The cyclic unscrunching/scrunching of the promoter generates a long-lived, RNA-bound paused state; the abortive RNA release and DNA unscrunching are thus not as tightly linked as previously thought. Finally, our new model couples the pausing with the abortive and productive outcomes of initial transcription. RNA synthesis by bacterial RNA polymerase is interrupted by pauses but their role in RNA synthesis is poorly understood. Here the authors use single-molecule FRET and biochemical analysis to show that pausing regulates branching between the abortive and productive outcomes of initial transcription.

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli . Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain’s own minimum inhibitory concentration. We used the users’ classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.