Killer cell proteases can target viral immediate-early proteins to control human cytomegalovirus infection in a noncytotoxic manner.

Human cytomegalovirus (HCMV) is the most frequent viral cause of congenital defects and can trigger devastating disease in immune-suppressed patients. Cytotoxic lymphocytes (CD8+ T cells and NK cells) control HCMV infection by releasing interferon-γ and five granzymes (GrA, GrB, GrH, GrK, GrM), which are believed to kill infected host cells through cleavage of intracellular death substrates. However, it has recently been demonstrated that the in vivo killing capacity of cytotoxic T cells is limited and multiple T cell hits are required to kill a single virus-infected cell. This raises the question whether cytotoxic lymphocytes can use granzymes to control HCMV infection in a noncytotoxic manner. Here, we demonstrate that (primary) cytotoxic lymphocytes can block HCMV dissemination independent of host cell death, and interferon-α/β/γ. Prior to killing, cytotoxic lymphocytes induce the degradation of viral immediate-early (IE) proteins IE1 and IE2 in HCMV-infected cells. Intriguingly, both IE1 and/or IE2 are directly proteolyzed by all human granzymes, with GrB and GrM being most efficient. GrB and GrM cleave IE1 after Asp398 and Leu414, respectively, likely resulting in IE1 aberrant cellular localization, IE1 instability, and functional impairment of IE1 to interfere with the JAK-STAT signaling pathway. Furthermore, GrB and GrM cleave IE2 after Asp184 and Leu173, respectively, resulting in IE2 aberrant cellular localization and functional abolishment of IE2 to transactivate the HCMV UL112 early promoter. Taken together, our data indicate that cytotoxic lymphocytes can also employ noncytotoxic ways to control HCMV infection, which may be explained by granzyme-mediated targeting of indispensable viral proteins during lytic infection

Highly conserved interaction profiles between clinically relevant mutants of the cytomegalovirus CDK-like kinase pUL97 and human cyclins: functional significance of cyclin H

The complex host interaction network of human cytomegalovirus (HCMV) involves the regulatory protein kinase pUL97, which represents a viral cyclin-dependent kinase (CDK) ortholog. pUL97 interacts with the three human cyclin types T1, H, and B1, whereby the binding region of cyclin T1 and the pUL97 oligomerization region were both assigned to amino acids 231-280. We further addressed the question of whether HCMVs harboring mutations in ORF-UL97, i.e., short deletions or resistance-conferring point mutations, are affected in the interaction with human cyclins and viral replication. To this end, clinically relevant UL97 drug-resistance-conferring mutants were analyzed by whole-genome sequencing and used for genetic marker transfer experiments. The recombinant HCMVs indicated conservation of pUL97-cyclin interaction, since all viral UL97 point mutants continued to interact with the analyzed cyclin types and exerted wild-type-like replication fitness. In comparison, recombinant HCMVs UL97 Δ231-280 and also the smaller deletion Δ236-275, but not Δ241-270, lost interaction with cyclins T1 and H, showed impaired replication efficiency, and also exhibited reduced kinase activity. Moreover, a cellular knock-out of cyclins B1 or T1 did not alter HCMV replication phenotypes or pUL97 kinase activity, possibly indicating alternative, compensatory pUL97-cyclin interactions. In contrast, however, cyclin H knock-out, similar to virus deletion mutants in the pUL97-cyclin H binding region, exhibited strong defective phenotypes of HCMV replication, as supported by reduced pUL97 kinase activity in a cyclin H-dependent coexpression setting. Thus, cyclin H proved to be a very relevant determinant of pUL97 kinase activity and viral replication efficiency. As a conclusion, the results provide evidence for the functional importance of pUL97-cyclin interaction. High selective pressure on the formation of pUL97-cyclin complexes was identified by the use of clinically relevant mutants

Cyclin-Dependent Kinase Activity Controls the Onset of the HCMV Lytic Cycle

The onset of human cytomegalovirus (HCMV) lytic infection is strictly synchronized with the host cell cycle. Infected G0/G1 cells support viral immediate early (IE) gene expression and proceed to the G1/S boundary where they finally arrest. In contrast, S/G2 cells can be infected but effectively block IE gene expression and this inhibition is not relieved until host cells have divided and reentered G1. During latent infection IE gene expression is also inhibited, and for reactivation to occur this block to IE gene expression must be overcome. It is only poorly understood which viral and/or cellular activities maintain the block to cell cycle or latency-associated viral IE gene repression and whether the two mechanisms may be linked. Here, we show that the block to IE gene expression during S and G2 phase can be overcome by both genotoxic stress and chemical inhibitors of cellular DNA replication, pointing to the involvement of checkpoint-dependent signaling pathways in controlling IE gene repression. Checkpoint-dependent rescue of IE expression strictly requires p53 and in the absence of checkpoint activation is mimicked by proteasomal inhibition in a p53 dependent manner. Requirement for the cyclin dependent kinase (CDK) inhibitor p21 downstream of p53 suggests a pivotal role for CDKs in controlling IE gene repression in S/G2 and treatment of S/G2 cells with the CDK inhibitor roscovitine alleviates IE repression independently of p53. Importantly, CDK inhibiton also overcomes the block to IE expression during quiescent infection of NTera2 (NT2) cells. Thus, a timely block to CDK activity not only secures phase specificity of the cell cycle dependent HCMV IE gene expression program, but in addition plays a hitherto unrecognized role in preventing the establishment of a latent-like state

Scenario Analysis as a Tool for Informing the Design of Behaviour Change Interventions

This article presents the design process behind the specification of a behaviour change intervention method to promote energy saving. The amount of energy used for food preparation is highly influenced by people’s behaviours. A user-centred design approach based on scenario analysis was applied to provide understanding of context of use and specification of user requirements. This knowledge was applied to the design of behaviour change interventions to motivate sustainable behaviours

Combinatorial, additive and dose-dependent drug–microbiome associations

Data availability: The source data for the figures are provided at Zenodo (https://doi.org/10.5281/zenodo.4728981). Raw shotgun sequencing data that support the findings of this study have been deposited at the ENA under accession codes PRJEB41311, PRJEB38742 and PRJEB37249 with public access. Raw spectra for metabolomics have been deposited in the MassIVE database under the accession codes MSV000088043 (UPLC–MS/MS) and MSV000088042 (GC–MS). The metadata on disease groups and drug intake are provided in Supplementary Tables 1–3. The demographic, clinical and phenotype metadata, and processed microbiome and metabolome data for French, German and Danish participants are available at Zenodo (https://doi.org/10.5281/zenodo.4674360).Code availability: The new drug-aware univariate biomarker testing pipeline is available as an R package (metadeconfoundR; Birkner et al., manuscript in preparation) at Github (https://github.com/TillBirkner/metadeconfoundR) and at Zenodo (https://doi.org/10.5281/zenodo.4721078). The latest version (0.1.8) of this package was used to generate the data shown in this publication. The code used for multivariate analysis based on the VpThemAll package is available at Zenodo (https://doi.org/10.5281/zenodo.4719526). The phenotype and drug intake metadata, processed microbiome, and metabolome data and code resources are available for download at Zenodo (https://doi.org/10.5281/zenodo.4674360). The code for reproducing the figures is provided at Zenodo (https://doi.org/10.5281/zenodo.4728981).During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1,2,3,4,5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug–host–microbiome interactions in cardiometabolic disease.This work was supported by the European Union’s Seventh Framework Program for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312 (METACARDIS). Part of this work was also supported by the EMBL, by the Metagenopolis grant ANR-11-DPBS-0001, by the H2020 European Research Council (ERC-AdG-669830) (to P.B.), and by grants from the Deutsche Forschungsgemeinschaft (SFB1365 to S.K.F. and L.M.; and SFB1052/3 A1 MS to M.S. (209933838)). Assistance Publique-Hôpitaux de Paris is the promoter of the clinical investigation (MetaCardis). M.-E.D. is supported by the NIHR Imperial Biomedical Research Centre and by grants from the French National Research Agency (ANR-10-LABX-46 (European Genomics Institute for Diabetes)), from the National Center for Precision Diabetic Medicine – PreciDIAB, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by the Hauts-de-France Regional Council (Agreement 20001891/NP0025517) and by the European Metropolis of Lille (MEL, Agreement 2019_ESR_11) and by Isite ULNE (R-002-20-TALENT-DUMAS), also jointly funded by ANR (ANR-16-IDEX-0004-ULNE), the Hauts-de-France Regional Council (20002845) and by the European Metropolis of Lille (MEL). R.J.A. is a member of the Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Bioscience. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research institution at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation

Combinatorial, additive and dose-dependent drug–microbiome associations

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug–host–microbiome interactions in cardiometabolic disease