Cofactors in Coronavirus Entry

Viruses have evolved complex ways to penetrate host barriers and cause disease. One of the most important barriers the virus has to cross is the cellular membrane. Enveloped viruses accomplish this task by viral glycoprotein-mediated binding to host cells and fusion of virus and host cell membranes. For the coronaviruses, viral spike (S) proteins execute these cell entry functions. In my dissertation research I focused on understanding the coronavirus spike proteins as well as other cofactors required for S-mediated entry into cells. The S proteins are set apart from other viral and cellular membrane fusion proteins by their extensively palmitoylated membrane-associated tails. In our experiments, substitution of alanines for the cysteines that are subject to palmitoylation had effects on both S incorporation into virions and S- mediated membrane fusions. In specifically dissecting the effects of endodomain mutations on the fusion process, we used antiviral peptides that bind only to folding intermediates in the S-mediated fusion process, and found that mutants lacking three palmitoylated cysteines remained in transitional folding states nearly ten times longer than native S proteins. This slower refolding was also reflected in the paucity of post-fusion six-helix bundle configurations amongst the mutant S proteins. Viruses with fewer palmitoylated S protein cysteines entered cells slowly and had reduced specific infectivities. These findings indicate that lipid adducts anchoring S proteins into virus membranes are necessary for the rapid, productive S protein refolding events that culminate in membrane fusions. The membrane fusion process also requires an S protein conformational flexibility that is facilitated by proteolytic cleavages. The severe acute respiratory syndrome (SARS) coronavirus S proteins rely on host cell proteases for fusion activation. I identified the human lung transmembrane serine protease, TMPRSS2, as an important factor for SARS coronavirus entry. TMPRSS2 co-localized on cell surfaces with the virus receptor ACE2, and enhanced the cell entry of both SARS S - pseudotyped HIV and authentic SARS-CoV. Enhanced entry correlated with TMPRSS2-mediated proteolysis of both S and ACE2. These findings indicate that a cell-surface complex comprising a primary receptor and a separate endoprotease operate as portals for activation of SARS coronavirus cell entry

Optimized two-dimensional thin layer chromatography to monitor the intracellular concentration of acetyl phosphate and other small phosphorylated molecules

Acetyl phosphate (acetyl-P) serves critical roles in coenzyme A recycling and ATP synthesis. It is the intermediate of the Pta-AckA pathway that inter-converts acetyl-coenzyme A and acetate. Acetyl-P also can act as a global signal by donating its phosphoryl group to specific two-component response regulators. This ability derives from its capacity to store energy in the form of a high-energy phosphate bond. This bond, while critical to its function, also destabilizes acetyl-P in cell extracts. This lability has greatly complicated biochemical analysis, leading in part to widely varying acetyl-P measurements. We therefore developed an optimized protocol based on two-dimensional thin layer chromatography that includes metabolic labeling under aerated conditions and careful examination of the integrity of acetyl-P within extracts. This protocol results in greatly improved reproducibility, and thus permits precise measurements of the intracellular concentration of acetyl-P, as well as that of other small phosphorylated molecules

A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2

We apply graph-based approaches to identify H-bond clusters in protein complexes. Three conformations of spike protein S have distinct H-bond clusters at key sites. Hydrogen-bond clusters could govern structural plasticity of spike protein S. Protein S binds to ACE2 receptor via H-bond clusters extending deep across interface.Corona virus spike protein S is a large homo-trimeric protein anchored in the membrane of the virion particle. Protein S binds to angiotensin-converting-enzyme 2, ACE2, of the host cell, followed by proteolysis of the spike protein, drastic protein conformational change with exposure of the fusion peptide of the virus, and entry of the virion into the host cell. The structural elements that govern conformational plasticity of the spike protein are largely unknown. Here, we present a methodology that relies upon graph and centrality analyses, augmented by bioinformatics, to identify and characterize large H-bond clusters in protein structures. We apply this methodology to protein S ectodomain and find that, in the closed conformation, the three protomers of protein S bring the same contribution to an extensive central network of H-bonds, and contribute symmetrically to a relatively large H-bond cluster at the receptor binding domain, and to a cluster near a protease cleavage site. Markedly different H-bonding at these three clusters in open and pre-fusion conformations suggest dynamic H-bond clusters could facilitate structural plasticity and selection of a protein S protomer for binding to the host receptor, and proteolytic cleavage. From analyses of spike protein sequences we identify patches of histidine and carboxylate groups that could be involved in transient proton binding.PSI COVID19 Emergency Science FundSpanish Ministry of Science, Innovation and Universities RTI2018-098983-B-I00Excellence Initiative of the German Federal and State Governments via the Freie Universitat BerlinGerman Research Foundation (DFG) SFB 107

Spatiotemporal Analysis of Hepatitis C Virus Infection

Hepatitis C virus (HCV) entry, translation, replication, and assembly occur with defined kinetics in distinct subcellular compartments. It is unclear how HCV spatially and temporally regulates these events within the host cell to coordinate its infection. We have developed a single molecule RNA detection assay that facilitates the simultaneous visualization of HCV (+) and (−) RNA strands at the single cell level using high-resolution confocal microscopy. We detect (+) strand RNAs as early as 2 hours post-infection and (−) strand RNAs as early as 4 hours post-infection. Single cell levels of (+) and (−) RNA vary considerably with an average (+):(−) RNA ratio of 10 and a range from 1–35. We next developed microscopic assays to identify HCV (+) and (−) RNAs associated with actively translating ribosomes, replication, virion assembly and intracellular virions. (+) RNAs display a defined temporal kinetics, with the majority of (+) RNAs associated with actively translating ribosomes at early times of infection, followed by a shift to replication and then virion assembly. (−) RNAs have a strong colocalization with NS5A, but not NS3, at early time points that correlate with replication compartment formation. At later times, only ~30% of the replication complexes appear to be active at a given time, as defined by (−) strand colocalization with either (+) RNA, NS3, or NS5A. While both (+) and (−) RNAs colocalize with the viral proteins NS3 and NS5A, only the plus strand preferentially colocalizes with the viral envelope E2 protein. These results suggest a defined spatiotemporal regulation of HCV infection with highly varied replication efficiencies at the single cell level. This approach can be applicable to all plus strand RNA viruses and enables unprecedented sensitivity for studying early events in the viral life cycle

Spatiotemporal Analysis of Hepatitis C Virus Infection

<div><p>Hepatitis C virus (HCV) entry, translation, replication, and assembly occur with defined kinetics in distinct subcellular compartments. It is unclear how HCV spatially and temporally regulates these events within the host cell to coordinate its infection. We have developed a single molecule RNA detection assay that facilitates the simultaneous visualization of HCV (+) and (−) RNA strands at the single cell level using high-resolution confocal microscopy. We detect (+) strand RNAs as early as 2 hours post-infection and (−) strand RNAs as early as 4 hours post-infection. Single cell levels of (+) and (−) RNA vary considerably with an average (+):(−) RNA ratio of 10 and a range from 1–35. We next developed microscopic assays to identify HCV (+) and (−) RNAs associated with actively translating ribosomes, replication, virion assembly and intracellular virions. (+) RNAs display a defined temporal kinetics, with the majority of (+) RNAs associated with actively translating ribosomes at early times of infection, followed by a shift to replication and then virion assembly. (−) RNAs have a strong colocalization with NS5A, but not NS3, at early time points that correlate with replication compartment formation. At later times, only ~30% of the replication complexes appear to be active at a given time, as defined by (−) strand colocalization with either (+) RNA, NS3, or NS5A. While both (+) and (−) RNAs colocalize with the viral proteins NS3 and NS5A, only the plus strand preferentially colocalizes with the viral envelope E2 protein. These results suggest a defined spatiotemporal regulation of HCV infection with highly varied replication efficiencies at the single cell level. This approach can be applicable to all plus strand RNA viruses and enables unprecedented sensitivity for studying early events in the viral life cycle.</p></div

Colocalization of (+) and (−) strand HCV RNAs with active translating ribosomes.

<p><b>A</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5 and at 48 hours post-infection cells were left untreated or pre-treated with anisomycin (Ani) (competitive inhibitor of puromycin; 9.4 uM) followed by puromycin (Puro) labeling and digitonin extraction before fixation. Cells were processed for immunofluorescence using the anti-puromycin PMY-2A4 monoclonal antibody. Scale bar is 5 μm. <b>B</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5 and at the indicated times post-infection the cells were fixed and processed for strand specific RNA detection followed by immunofluorescence staining for puromycylated ribosomes. Scale bar is 5 μm. Insets represent 10 times magnification of the merged image. Solid arrows point to (+) strand RNA colocalizing with ribosomes; arrowheads point to (−) strand RNA colocalizing with ribosomes. <b>C</b>. Quantitation of % colocalization in (B). Each error bar indicates standard deviation from 25 different images. <b>D</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5 and at 6 hpi the cells were fixed and processed for strand specific RNA detection followed by immunofluorescence staining for calnexin.</p

Colocalization of (+) and (−) strand HCV RNAs with core protein and virion E2.

<p><b>A</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5, fixed at the indicated times post-infection and processed for strand specific RNA detection followed by immunofluorescence staining for core. Scale bar is 5 um. Solid arrows indicate (+) strand (red) colocalization; arrowheads indicate (−) strand (magenta) colocalization with core (green). Insets represent 10 times magnification of the merged image. The asterisk indicates juxtaposition of (−) strand with core. <b>B</b>. Quantification of images in panel A. <b>C</b>. A merged image of core colocalization with (+) and (−) HCV RNAs at 48 hpi is shown together with an ImageJ color intensity plot for the white line drawn in the merged image. <b>D, E</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5 and at 48 and 72 hpi the cells were fixed and processed for strand specific RNA detection followed by immunofluorescence staining for E2 protein using CBH-5 antibody and quantified. Insets represent 10 times magnification of the merged image.</p

Colocalization of (−) and (+) HCV RNA strands.

<p><b>A</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5 and at the indicated times post infection cells were fixed and processed for strand specific RNA detection. Scale bar is 5 μm. Solid arrows point to (+) strand RNA colocalizing with (−) strand RNA. <b>B</b>. Quantitation of % colocalization in (A).</p

Colocalization of (+) and (−) strand HCV RNAs with NS5A and NS3.

<p>Huh-7.5 cells were infected with HCV at MOI = 1.5 and at the indicated times post-infection the cells were fixed and processed for strand specific RNA detection followed by immunofluorescence staining for <b>A</b>. NS5A or <b>C</b>. NS3. For 6 and 12 hpi samples, antibody signal was amplified using the tyramide signal amplification kit (TSA) as described in the materials and methods section. Scale bar is 5 μm. <b>B</b>. Quantitation of (A) <b>D</b>. Quantitation of (C). Each error bar indicates standard deviation from 25 different images. Scale bar is 5 μm. Insets represent 10 times magnification of the merged image. Solid arrows point to (+) strand RNA colocalizing with NS5A and NS3; arrowheads point to (−) strand RNA colocalizing with NS5A and NS3.</p

Kinetic analysis of genomic RNA fate.

<p><b>A</b>. Huh-7.5 cells were infected with HCV at MOI = 1.5. RNA was collected at the indicated time points post-infection and quantified with real-time RT-PCR. Error bar, standard deviation. <b>B</b>. Intra- and extra-cellular viral supernatants from infections in panel A were collected at the indicated time points and titered by limiting dilution assay. Shown are the averages of two sets of titer data. Error bar, standard deviation. <b>C</b>. Total (+) puncta or <b>D</b>. Percent of (+) strand puncta colocalizing with translation (puromycylated ribosomes), replication (NS5A + NS3), assembly (core) and virion (E2) markers over the indicated time course (Figs. <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004758#ppat.1004758.g003" target="_blank">3</a>–<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004758#ppat.1004758.g006" target="_blank">6</a>) were plotted using the smooth graph function in Microsoft Excel. <b>E</b>. Percent of (−) strand puncta colocalizing with active replication compartments (NS5A), active replication ((+) strands) and active replication (NS3) over the indicated time course plotted using the smooth graph function in Microsoft Excel.</p