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Adeno-associated virus (AAV) vectors have shown promising results in preclinical models, but the genomic consequences of transduction with AAV vectors encoding CRISPR-Cas nucleases is still being examined. In this study, we observe high levels of AAV integration (up to 47%) into Cas9-induced double-strand breaks (DSBs) in therapeutically relevant genes in cultured murine neurons, mouse brain, muscle and cochlea. Genome-wide AAV mapping in mouse brain shows no overall increase of AAV integration except at the CRISPR/Cas9 target site. To allow detailed characterization of integration events we engineer a miniature AAV encoding a 465 bp lambda bacteriophage DNA (AAV-λ465), enabling sequencing of the entire integrated vector genome. The integration profile of AAV-465λ in cultured cells display both full-length and fragmented AAV genomes at Cas9 on-target sites. Our data indicate that AAV integration should be recognized as a common outcome for applications that utilize AAV for genome editing. In-depth characterization of adeno-associated virus (AAV)-mediated CRISPR delivery is still lacking. Here, the authors show high levels of integration into Cas9-induced double-strand breaks (DSBs) in therapeutically relevant genes in vivo.

Interactions between bacteria and the viruses that infect them (i.e., phages) have profound effects on biological processes, but despite their importance, little is known on the general structure of infection and resistance between most phages and bacteria. For example, are bacteria–phage communities characterized by complex patterns of overlapping exploitation networks, do they conform to a more ordered general pattern across all communities, or are they idiosyncratic and hard to predict from one ecosystem to the next? To answer these questions, we collect and present a detailed metaanalysis of 38 laboratory-verified studies of host–phage interactions representing almost 12,000 distinct experimental infection assays across a broad spectrum of taxa, habitat, and mode of selection. In so doing, we present evidence that currently available host–phage infection networks are statistically different from random networks and that they possess a characteristic nested structure. This nested structure is typified by the finding that hard to infect bacteria are infected by generalist phages (and not specialist phages) and that easy to infect bacteria are infected by generalist and specialist phages. Moreover, we find that currently available host–phage infection networks do not typically possess a modular structure. We explore possible underlying mechanisms and significance of the observed nested host–phage interaction structure. In addition, given that most of the available host–phage infection networks examined here are composed of taxa separated by short phylogenetic distances, we propose that the lack of modularity is a scale-dependent effect, and then, we describe experimental studies to test whether modular patterns exist at macroevolutionary scales.

The processes responsible for the evolution of key innovations, whereby lineages acquire qualitatively new functions that expand their ecological opportunities, remain poorly understood. We examined how a virus, bacteriophage λ, evolved to infect its host, Eschenchia coli, through a novel pathway. Natural selection promoted the fixation of mutations in the virus's host-recognition protein, J, that improved fitness on the original receptor, LamB, and set the stage for other mutations that allowed infection through a new receptor, OmpF. These viral mutations arose after the host evolved reduced expression of LamB, whereas certain other host mutations prevented the phage from evolving the new function.This study shows the complex interplay between genomic processes and ecological conditions that favor the emergence of evolutionary innovations.

Bacteriophage infection, a pivotal process in microbiology, initiates with the phage’s tail recognizing and binding to the bacterial cell surface, which then mediates the injection of viral DNA. Although comprehensive studies on the interaction between bacteriophage lambda and its outer membrane receptor, LamB, have provided rich information about the system’s biochemical properties, the precise molecular mechanism remains undetermined. This study revealed the high-resolution cryo-electron microscopy (cryo-EM) structures of the bacteriophage lambda tail complexed with its irreversible Shigella sonnei 3070 LamB receptor and the closed central tail fiber. These structures reveal the complex processes that trigger infection and demonstrate a substantial conformational change in the phage lambda tail tip upon LamB binding. Providing detailed structures of bacteriophage lambda infection initiation, this study contributes to the expanding knowledge of lambda-bacterial interaction, which holds significance in the fields of microbiology and therapeutic development. Here, Ge et al use cryo-electron microscopy to resolve the structure of the bacteriophage lambda tail in complex with its LamB receptor from Shigella sonnei and shed light on the conformational changes that the phage tail fiber undergoes in response to binding.

Prokaryotes acquire virus resistance by integrating short fragments of viral nucleic acid into clusters of regularly interspaced short palindromic repeats (CRISPRs). Here we show how virus-derived sequences contained in CRISPRs are used by CRISPR-associated (Cas) proteins from the host to mediate an antiviral response that counteracts infection. After transcription of the CRISPR, a complex of Cas proteins termed Cascade cleaves a CRISPR RNA precursor in each repeat and retains the cleavage products containing the virus-derived sequence. Assisted by the helicase Cas3, these mature CRISPR RNAs then serve as small guide RNAs that enable Cascade to interfere with virus proliferation. Our results demonstrate that the formation of mature guide RNAs by the CRISPR RNA endonuclease subunit of Cascade is a mechanistic requirement for antiviral defense.

Gene regulatory networks control many cellular processes such as cell cycle, cell differentiation, metabolism and signal transduction. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool. Key Points By combining biological knowledge and experimental data with judicious computational modelling, regulatory networks can be dissected, and analysis results can shed light on life and disease mechanisms. Logical models provide a simplified, yet useful, approach that copes well with partial knowledge. They have been used successfully to identify specific regulatory interactions. Continuous models can describe a wide range of phenomena and can be readily compared to experimental measurements. Single-molecule level models simulate network behaviour at the resolution of individual molecular interactions. They have been successfully applied to regulatory networks that exhibit stochastic behaviour. The advantages and disadvantages of the different approaches are discussed, along with future goals of computational modelling. Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.

DNA flow-stretching is a widely employed, powerful technique for investigating the mechanisms of DNA-binding proteins involved in compacting and organizing chromosomal DNA. We combine single-molecule DNA flow-stretching experiments with Brownian dynamics simulations to study the effect of the crowding agent polyethylene glycol (PEG) in these experiments. PEG interacts with DNA by an excluded volume effect, resulting in compaction of single, free DNA molecules in PEG solutions. In addition, PEG increases the viscosity of the buffer solution. By stretching surface-tethered bacteriophage lambda DNA in a flow cell and tracking the positions of a quantum dot labeled at the free DNA end using total internal reflection fluorescence (TIRF) microscopy, we find that higher PEG concentrations result in increased end-to-end length of flow-stretched DNA and decreased fluctuations of the free DNA end. To better understand our experimental results, we perform Brownian dynamics simulations of a bead-spring chain model of flow-stretched DNA in a viscous buffer that models the excluded volume effect of PEG by an effective attractive interaction between DNA segments. We find quantitative agreement between our model and the experimental results for suitable PEG-DNA interaction parameters.

Bacteriophage lambda is one of the most extensively studied organisms and has been a primary model for understanding basic modes of genetic regulation. Here, we examine the progress of lambda gene expression during phage development by ribosome profiling and, thereby, provide a very-high-resolution view of lambda gene expression. The known genes are expressed in a predictable fashion, authenticating the analysis. However, many previously unappreciated potential open reading frames become apparent in the expression analysis, revealing an unexpected complexity in the pattern of lambda gene function.

Read Until allows real-time selective sequencing on a nanopore sequencer, enabling applications such as target enrichment and amplicon normalization. The Oxford Nanopore Technologies MinION sequencer enables the selection of specific DNA molecules for sequencing by reversing the driving voltage across individual nanopores. To directly select molecules for sequencing, we used dynamic time warping to match reads to reference sequences. We demonstrate our open-source Read Until software in real-time selective sequencing of regions within small genomes, individual amplicon enrichment and normalization of an amplicon set.

Fluorescence polarization microscopy images both the intensity and orientation of fluorescent dipoles and plays a vital role in studying molecular structures and dynamics of bio-complexes. However, current techniques remain difficult to resolve the dipole assemblies on subcellular structures and their dynamics in living cells at super-resolution level. Here we report polarized structured illumination microscopy (pSIM), which achieves super-resolution imaging of dipoles by interpreting the dipoles in spatio-angular hyperspace. We demonstrate the application of pSIM on a series of biological filamentous systems, such as cytoskeleton networks and λ-DNA, and report the dynamics of short actin sliding across a myosin-coated surface. Further, pSIM reveals the side-by-side organization of the actin ring structures in the membrane-associated periodic skeleton of hippocampal neurons and images the dipole dynamics of green fluorescent protein-labeled microtubules in live U2OS cells. pSIM applies directly to a large variety of commercial and home-built SIM systems with various imaging modality. Polarization microscopy has been combined with single-molecule localization, but it’s often limited in either speed or resolution. Here the authors present polarized Structured Illumination Microscopy (pSIM), a method that uses polarized laser excitation to measure dye orientation during fast super-resolution live cell imaging.