Latency-associated nuclear antigen (LANA) mediates γ2-herpesvirus genome persistence and regulates transcription. We describe the crystal structure of the murine gammaherpesvirus-68 LANA C-terminal domain at 2.2 Å resolution. The structure reveals an alpha-beta fold that assembles as a dimer, reminiscent of Epstein-Barr virus EBNA1. A predicted DNA binding surface is present and opposite this interface is a positive electrostatic patch. Targeted DNA recognition substitutions eliminated DNA binding, while certain charged patch mutations reduced bromodomain protein, BRD4, binding. Virus containing LANA abolished for DNA binding was incapable of viable latent infection in mice. Virus with mutations at the charged patch periphery exhibited substantial deficiency in expansion of latent infection, while central region substitutions had little effect. This deficiency was independent of BRD4. These results elucidate the LANA DNA binding domain structure and reveal a unique charged region that exerts a critical role in viral latent infection, likely acting through a host cell protein(s).

Many pathogens, including Kaposi’s sarcoma herpesvirus (KSHV), lack tractable small animal models. KSHV persists as a multi-copy, nuclear episome in latently infected cells. KSHV latency-associated nuclear antigen (kLANA) binds viral terminal repeat (kTR) DNA to mediate episome persistence. Model pathogen murine gammaherpesvirus 68 (MHV68) mLANA acts analogously on mTR DNA. kLANA and mLANA differ substantially in size and kTR and mTR show little sequence conservation. Here, we find kLANA and mLANA act reciprocally to mediate episome persistence of TR DNA. Further, kLANA rescued mLANA deficient MHV68, enabling a chimeric virus to establish latent infection in vivo in germinal center B cells. The level of chimeric virus in vivo latency was moderately reduced compared to WT infection, but WT or chimeric MHV68 infected cells had similar viral genome copy numbers as assessed by immunofluorescence of LANA intranuclear dots or qPCR. Thus, despite more than 60 Ma of evolutionary divergence, mLANA and kLANA act reciprocally on TR DNA, and kLANA functionally substitutes for mLANA, allowing kLANA investigation in vivo. Analogous chimeras may allow in vivo investigation of genes of other human pathogens.

Viruses have evolved mechanisms to hijack components of cellular E3 ubiquitin ligases, thus modulating the ubiquitination pathway. However, the biological relevance of such mechanisms for viral pathogenesis in vivo remains largely unknown. Here, we utilized murid herpesvirus 4 (MuHV-4) infection of mice as a model system to address the role of MuHV-4 latency-associated nuclear antigen (mLANA) E3 ligase activity in gammaherpesvirus latent infection. We show that specific mutations in the mLANA SOCS box (V199A, V199A/L202A, or P203A/P206A) disrupted mLANA's ability to recruit Elongin C and Cullin 5, thereby impairing the formation of the Elongin BC/Cullin 5/SOCS (EC 5 S mLANA ) complex and mLANA's E3 ligase activity on host NF-B and Myc. Although these mutations resulted in considerably reduced mLANA binding to viral terminal repeat DNA as assessed by electrophoretic mobility shift assay (EMSA), the mutations did not disrupt mLANA's ability to mediate episome persistence. In vivo, MuHV-4 recombinant viruses bearing these mLANA SOCS box mutations exhibited a deficit in latency amplification in germinal center (GC) B cells. These findings demonstrate that the E3 ligase activity of mLANA contributes to gammaherpesvirus-driven GC B cell proliferation. Hence, pharmacological inhibition of viral E3 ligase activity through targeting SOCS box motifs is a putative strategy to control gammaherpesvirus-driven lymphoproliferation and associated disease. A s obligatory intracellular parasites, viruses have evolved mechanisms to modulate ubiquitination, which is an essential regulatory mechanism in eukaryotes, controlling a wide range of cellular pathways. Ubiquitination occurs through a threeenzyme cascade involving an E1 ubiquitin-activating enzyme, an E2 ubiquitin-conjugating enzyme, and an E3 ubiquitin ligase enzyme (1). E3 ligases bind to the E2-ubiquitin intermediate and the substrate, catalyzing the transfer of ubiquitin to the substrate target lysine. Many E3 ligases have been described, such as Cullin 5-RING E3 ligases (CRL5), also known as Elongin BC/Cullin 5/SOCS (EC 5 S) E3 ligases. They are multisubunit complexes containing a scaffold protein (Cullin 5) attached to a RING finger protein (Rbx) (Cullin 5-Rbx module), an adaptor heterodimer (Elongin B/C), and a substrate recognition protein (suppressor of cytokine signaling [SOCS] box protein). The last component bridges the substrate of ubiquitination and the E3 ligase complex by interacting with Elongin B/C and Cullin 5 through a SOCS box motif (2-4). IMPORTANCE The gammaherpesviruses Epstein-Barr virus (EBV) and Kaposi's sarcoma-associated herpesvirus (KSHV) cause lifelong persistent infection and play causative roles in several human malignancies. Colonization of B cells is crucial for virusCertain viruses encode proteins with SOCS box motifs to hijack the components of cellular E3 ligases, thus modulating the ubiquitination pathway. Examples are latency-associated nuclear antigen (LANA) of Kaposi's sarcoma-associated herpesvirus (KSHV) (5, 6) and murid he...

We report herein a set of 3′-azido-3′-deoxythymidine (AZT) derivatives based on triazoles and triazolium salts for HIV-1 infection. The compounds were synthesized via click chemistry with Cu(I) and Ru(II) catalysts. Triazolium salts were synthesized by reaction with methyl iodide or methyl triflate in good yields. The antiviral activity of the compounds was tested using two methodologies: In method one the activity was measured on infected cells; in method two a pre-exposure prophylaxis experimental model was employed. For method one the activity of the compounds was moderate, and in general the triazolium salts showed a decreased activity in relation to their triazole precursors. With method two the antiviral activity was higher. All compounds were able to decrease the infection, with two compounds able to clear almost all the infection, while a lower antiviral activity was noted for the triazolium salts. These results suggest that these drugs could play an important role in the development of pre-exposure prophylaxis therapies.

Background The provision of seamless public transport supply requires a complete understanding of the real traffic dynamics, comprising origin-to-destination multimodal mobility patterns along the transport network. However, most current solutions are centred on the volumetric analysis of passengers’ flows, generally neglecting transfer, walking, and waiting needs, as well as the changes in the mobility patterns with the calendar and user profile. These challenges prevent a comprehensive assessment of the routing and scheduling vulnerabilities of (multimodal) public transport networks. Research aims/questions The research presented in this paper aims at addressing the above challenges by proposing a novel approach that extends dynamic Origin-Destination (OD) matrix inference to dynamic OD matrix inference with aggregated statistics, highlighting vulnerabilities and multimodal mobility patterns from individual trip record data. Methodology Given specific spatial and temporal criteria, the proposed methodology extends dynamic Origin-Destination (OD) matrices with aggregated statistics, using smart-card validations gathered from (multimodal) public transport networks. More specifically, three major contributions are tackled; i) the data enrichment in the OD matrices with statistical information besides trip volume (e.g., transfer and trip features); ii) the detection of vulnerabilities on the network pertaining to walking distances and trip durations in a user-centric way and iii) the decomposition of traffic flows in accordance with calendrical rules and user (passenger) profiles. The set of contributions are validated on the bus-and-metro public transport network in the city of Lisbon. Results The proposed approach for inferring OD matrices yields four unique contributions. First, we allow inference to consider multimodal commuting patterns, detecting individual trips undertaken along with different operators. Second, we support dynamic matrices’ OD inference along with parameterizable time intervals and calendrical rules, and further support the decomposition of traffic flows according to the user profile. Third, we allow parameterization of the desirable spatial granularity and visualisation preferences. Fourth, our solution efficiently computes several statistics that support OD matrix analysis, helping with the detection of vulnerabilities throughout the transport network. More specifically, statistical indicators related to travellers’ functional mobility needs (commuters for working purposes, etc.), walking distances and trip durations are supported. The inferred dynamic OD matrices are the outcome of a developed software with strict guarantees of usability. Results from the case study using data gathered from the two main public transport operators (Bus and Metro) in the city of Lisbon show that 77.3% of alighting stops can be estimated with a high confidence degree from bus smart-card data. The inferred OD matrices (Bus and Metro) in the city of Lisbon reveal vulnerabilities along specific OD pairs, offering the bus public operators in Lisbon new knowledge and a means to better understand dynamics and validate OD assumptions.

Background European cities are placing a larger emphasis on urban data consolidation and analysis for optimizing public transport in response to changing urban mobility dynamics. Despite the existing efforts, traffic data analysis often disregards vital situational context, including large-scale events, weather factors, traffic generation poles, social distancing norms, or traffic interdictions. Some of these sources of context data are still private, dispersed, or unavailable for the purpose of planning or managing urban mobility. Addressing the above observation, the Lisbon city Council has already established efforts for gathering historic and prospective sources of situational context in standardized semi-structured repositories, triggering new opportunities for context-aware traffic data analysis. Research questions The work presented in this paper aims at tackling the following main research question: How to incorporate historical and prospective sources of situational context into descriptive and predictive models of urban traffic data? Methodology We propose a methodology anchored in data science methods to integrate situational context in the descriptive and predictive models of traffic data, with a focus on the three following major spatiotemporal traffic data structures: i) georeferenced time series data; ii) origin-destination tensor data; iii) raw traffic event data. Second, we introduce additional principles for the online consolidation and labelling of heterogeneous sources of situational context from public repositories. Third, we quantify the impact produced by situational context aspects on public passenger transport data gathered from smart card validations along the bus (CARRIS), subway (METRO) and bike sharing (GIRA) modes in the city of Lisbon. Results The gathered results stress the importance of incorporating historical and prospective context data for a guided description and prediction of urban mobility dynamics, irrespective of the underlying data representation. Overall, the research offers the following major contributions: A novel methodology on how to acquire, consolidate and incorporate different sources of context for the context-enriched analysis of traffic data; The instantiation of the proposed methodology in the city of Lisbon, discussing the role of recent initiatives for the ongoing monitoring of relevant context data sources within semi-structured repositories, and further showing how these initiatives can be extended for the context-sensitive modelling of traffic data for descriptive and predictive ends; A roadmap of practical illustrations quantifying impact of different context factors (including weather, traffic interdictions and public events) on different transportation modes using different spatiotemporal traffic data structures; and A review of state-of-the-art contributions on context-enriched traffic data analysis. The contributions reported in this work are anchored in the empirical observations gathered along the first stage of the ILU project (see footnote 1), providing a study case of interest to be followed by other European cities.

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