Biology subjects

Greber, U. F.

Publications and source records attributed to Greber, U. F..

4 recordsLinked to original sources

bioRxiv2020

Cell-to-cell and genome-to-genome variability of Adenovirus transcription tuned by the cell cycle

In clonal cultures, not all cells are equally susceptible to virus infection. Underlying mechanisms of infection variability are poorly understood. Here, we developed image-based single cell measurements to scrutinize the heterogeneity of adenovirus (AdV) infection. AdV delivers, transcribes and replicates a linear double-stranded DNA genome in the nucleus. We measured the abundance of viral transcripts by single-molecule RNA fluorescence in situ hybridization (FISH), and the incoming ethynyl-deoxy-cytidine (EdC)-tagged viral genome by copper(I)-catalyzed azide-alkyne cycloaddition (click) reaction. The early transcripts increased from 2-12 hours, the late ones from 12-23 hours post infection (pi), indicating distinct accumulation kinetics. Surprisingly, the expression of the immediate early transactivator gene E1A only moderately correlated with the number of viral genomes in the cell nucleus, although the incoming viral DNA remained largely intact until 7 hours pi. Genome-to-genome heterogeneity was found at the level of viral transcription, as indicated by colocalization with the large intron containing early region E4 transcripts, uncorrelated to the multiplicity of incoming genomes in the nucleus. In accordance, individual genomes exhibited heterogeneous replication activity, as shown by single-strand DNA-FISH and immunocytochemistry. These results indicate that the variability in viral gene expression and replication are not due to defective genomes but due to host cell heterogeneity. By analyzing the cell cycle state, we found that G1 cells exhibited the highest E1A expression, and significantly increased the correlation between E1A expression and viral genome copy numbers. This combined image-based single molecule procedure is ideally suited to explore the cell-to-cell variability in viral infection, including transcriptional activators and repressors, RNA splicing mechanisms, and the impact of the 3-dimensional nuclear topology on gene regulation. Author SummaryAdenoviruses (AdV) are ubiquitous pathogens in vertebrates. They persist in infected people, and cause unpredictable outbreaks, morbidity and mortality across the globe. Here we report that the common human AdV type C5 (AdV-C5) gives rise to considerable infection variability at the level of single cells in culture, and that a major underlying reason is the cell-to-cell heterogeneity. By combining sensitive single molecule in situ technology for detecting the incoming viral DNA and newly synthesized viral transcripts we show that viral gene expression is heterogeneous between infected human cells, as well as individual genomes. We report a moderate correlation between the number of viral genomes in the nucleus and immediate early E1A transcripts. This correlation is increased in the G1 phase of the cell cycle, where the E1A transcripts were found to be more abundant than in any other cell cycle phase. Our results demonstrate the importance of cell-to-cell variability measurements for understanding transcription and replication in viral infections.

microbiology↗

bioRxiv2020

Double-stranded RNA bending by AU-tract sequences

Sequence-dependent structural deformations of the DNA double helix (dsDNA) have been extensively studied, where adenine tracts (A-tracts) provide a striking example for global bending in the molecule. In contrast to dsDNA, much less is known about how the nucleotide sequence affects bending deformations of double-stranded RNA (dsRNA). Using all-atom microsecond long molecular dynamics simulations we found a sequence motif consisting of alternating adenines and uracils, or AU-tracts, that bend the dsRNA helix by locally compressing the major groove. We experimentally tested this prediction using atomic force microscopy (AFM) imaging of long dsRNA molecules containing phased AU-tracts. AFM images revealed a clear intrinsic bend in these AU-tracts molecules, as quantified by a significantly lower persistence length compared to dsRNA molecules of arbitrary sequence. The bent structure of AU-tracts here described might play a role in sequence-specific recognition of dsRNAs by dsRNA-interacting proteins or impact the folding of RNA into intricate tertiary and quaternary structures.

biophysics↗

bioRxiv2019

Dynamic competition for hexon binding between core protein VII and lytic protein VI promotes adenovirus maturation and entry

Adenovirus minor coat protein VI contains a membrane-disrupting peptide which is inactive when VI is bound to hexon trimers. Protein VI must be released during entry to ensure endosome escape. Hexon:VI stoichiometry has been uncertain, and only fragments of VI have been identified in the virion structure. Recent findings suggest an unexpected relationship between VI and the major core protein, VII. According to the high resolution structure of the mature virion, VI and VII may compete for the same binding site in hexon; and non-infectious human adenovirus type 5 particles assembled in the absence of VII (Ad5-VII-) are deficient in proteolytic maturation of protein VI and endosome escape. Here we show that Ad5-VII- particles are trapped in the endosome because they fail to increase VI exposure during entry. This failure was not due to increased particle stability, because capsid disruption happened at lower thermal or mechanical stress in Ad5-VII- compared to wildtype (Ad5-wt) particles. Cryo-EM difference maps indicated that VII can occupy the same binding pocket as VI in all hexon monomers, strongly arguing for binding competition. In the Ad5-VII- map, density corresponding to the immature amino-terminal region of VI indicates that in the absence of VII the lytic peptide is trapped inside the hexon cavity, and clarifies the hexon:VI stoichiometry conundrum. We propose a model where dynamic competition between proteins VI and VII for hexon binding facilitates the complete maturation of VI, and is responsible for releasing the lytic protein from the hexon cavity during entry and stepwise uncoating. Significance StatementCorrect assembly of an adenovirus infectious particle involves the highly regulated interaction of more than ten different proteins as well as the viral genome. Here we examine the interplay between two of these proteins: the major core protein VII, involved in genome condensation, and the multifunctional minor coat protein VI. Protein VI binds to the inner surface of adenovirus hexons (trimers of the major coat protein) and contains a lytic peptide which must be released during entry to ensure endosome rupture. We present data supporting a dynamic competition model between proteins VI and VII for hexon binding during assembly. This competition facilitates the release of the lytic peptide from the hexon cavity and ensures virus escape from the early endosome.

microbiology↗

bioRxiv2019

Deep learning of virus infections reveals mechanics of lytic cells

Imaging across scales gives insight into disease mechanisms in organisms, tissues and cells. Yet, rare infection phenotypes, such as virus-induced cell lysis have remained difficult to study. Here, we developed fixed and live cell imaging modalities and a deep learning approach to identify herpesvirus and adenovirus infections in the absence of virus-specific stainings. Procedures comprises staining of infected nuclei with DNA-dyes, fluorescence microscopy, and validation by virus-specific live-cell imaging. Deep learning of multi-round infection phenotypes identified hallmarks of adenovirus-infected cell nuclei. At an accuracy of >95%, the procedure predicts two distinct infection outcomes 20 hours prior to lysis, nonlytic (nonspreading) and lytic (spreading) infections. Phenotypic prediction and live-cell imaging revealed a faster enrichment of GFP-tagged virion proteins in lytic compared to nonlytic infected nuclei, and distinct mechanics of lytic and nonlytic nuclei upon laser-induced ruptures. The results unleash the power of deep learning based prediction in unraveling rare infection phenotypes.

cell biology↗