The host transcriptional response to superinfection by influenza A virus and Streptococcus pneumoniae.

Secondary bacterial challenges during influenza virus infection "superinfection") cause excessive mortality and hospitalization. Here, we present a longitudinal study of bulk gene expression changes in murine lungs during superinfection, with an initial influenza A virus infection and a subsequent Streptococcus pneumoniae infection. In addition to the well-characterized impairment of the host response, we identified superinfection-specific alterations in the global transcriptional program that are linked to the host's ability to resist the pathogens. Particularly, whereas superinfected mice manifested an excessive rapid induction of the resistance-to-infection program, there was a substantial tissue-level rewiring of this program: upon superinfection, interferon-regulated genes were switched from positive to negative correlations with the host's resistance state, whereas genes of fatty acid metabolism switched from negative to positive correlations with resistance states. Thus, the transcriptional resistance state in superinfection is reprogrammed toward repressed interferon signaling and induced fatty acid metabolism. Our findings suggest new insights into a tissue-level remodeling of the host defense upon superinfection, providing promising targets for future therapeutic interventions. IMPORTANCE: Secondary bacterial infections are the most frequent complications during influenza A virus (IAV) pandemic outbreaks, contributing to excessive morbidity and mortality in the human population. Most IAV-related deaths are attributed to Streptococcus pneumoniae (SP) infections, which usually begin within the first week of IAV infection in the respiratory tracts. Here, we focused on longitudinal transcriptional responses during a superinfection model consisting of an SP infection that follows an initial IAV infection, comparing superinfection to an IAV-only infection, an SP-only infection, and control treatments. Our longitudinal data allowed a fine analysis of gene expression changes during superinfection. For instance, we found that superinfected mice exhibited rapid gene expression induction or reduction within the first 12 h after encountering the second pathogen. Cell proliferation and immune response activation processes were upregulated, while endothelial processes, vasculogenesis, and angiogenesis were downregulated, providing promising targets for future therapeutic interventions. We further analyzed the longitudinal transcriptional responses in the context of a previously defined spectrum of the host's resistance state, revealing superinfection-specific reprogramming of resistance states, such as reprogramming of fatty acid metabolism and interferon signaling. The reprogrammed functions are compelling new targets for switching the pathogenic superinfection state into a single-infection state.

Distinct gene programs underpinning disease tolerance and resistance in influenza virus infection.

When challenged with an invading pathogen, the host-defense response is engaged to eliminate the pathogen (resistance) and to maintain health in the presence of the pathogen (disease tolerance). However, the identification of distinct molecular programs underpinning disease tolerance and resistance remained obscure. We exploited transcriptional and physiological monitoring across 33 mouse strains, during in vivo influenza virus infection, to identify two host-defense gene programs-one is associated with hallmarks of disease tolerance and the other with hallmarks of resistance. Both programs constitute generic responses in multiple mouse and human cell types. Our study describes the organizational principles of these programs and validates Arhgdia as a regulator of disease-tolerance states in epithelial cells. We further reveal that the baseline disease-tolerance state in peritoneal macrophages is associated with the pathophysiological response to injury and infection. Our framework provides a paradigm for the understanding of disease tolerance and resistance at the molecular level.

Structure-function conservation between the methyltransferases SETD3 and SETD6.

Among the protein lysine methyltransferases family members, it appears that SETD6 is highly similar and closely related to SETD3. The two methyltransferases show high similarity in their structure, which raised the hypothesis that they share cellular functions. Using a proteomic screen, we identified 52 shared interacting-proteins. Gene Ontology (GO) analysis of the shared proteins revealed significant enrichment of proteins involved in transcription. Our RNA-seq data of SETD6 KO and SETD3 KO HeLa cells identified ∼100 up-regulated and down-regulated shared genes. We have also identified a substantial number of genes that changed dramatically in the double KO cells but did not significantly change in the single KO cells. GO analysis of these genes revealed enrichment of apoptotic genes. Accordingly, we show that the double KO cells displayed high apoptotic levels, suggesting that SETD6 and SETD3 inhibit apoptosis. Collectively, our data strongly suggest a functional link between SETD6 and SETD3 in the regulation of apoptosis.

Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics.

Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.

Cell composition analysis of bulk genomics using single-cell data.

Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.

Dissection of Influenza Infection In Vivo by Single-Cell RNA Sequencing.

The influenza virus is a major cause of morbidity and mortality worldwide. Yet, both the impact of intracellular viral replication and the variation in host response across different cell types remain uncharacterized. Here we used single-cell RNA sequencing to investigate the heterogeneity in the response of lung tissue cells to in vivo influenza infection. Analysis of viral and host transcriptomes in the same single cell enabled us to resolve the cellular heterogeneity of bystander (exposed but uninfected) as compared with infected cells. We reveal that all major immune and non-immune cell types manifest substantial fractions of infected cells, albeit at low viral transcriptome loads relative to epithelial cells. We show that all cell types respond primarily with a robust generic transcriptional response, and we demonstrate novel markers specific for influenza-infected as opposed to bystander cells. These findings open new avenues for targeted therapy aimed exclusively at infected cells.

Chromatin associated SETD3 negatively regulates VEGF expression.

SETD3 is a member of the protein lysine methyltransferase (PKMT) family, which catalyzes the addition of methyl group to lysine residues. Accumulating data suggest that PKMTs are involved in the regulation of a broad spectrum of biological processes by targeting histone and non-histone proteins. Using a proteomic approach, we have identified 172 new SETD3 interacting proteins. We show that SETD3 binds and methylates the transcription factor FoxM1, which has been previously shown to be associated with the regulation of VEGF expression. We further demonstrate that under hypoxic conditions SETD3 is down-regulated. Mechanistically, we find that under basal conditions, SETD3 and FoxM1 are enriched on the VEGF promoter. Dissociation of both SETD3 and FoxM1 from the VEGF promoter under hypoxia correlates with elevated expression of VEGF. Taken together, our data reveal a new SETD3-dependent methylation-based signaling pathway at chromatin that regulates VEGF expression under normoxic and hypoxic conditions.

Proteomic analysis of SETD6 interacting proteins.

SETD6 (SET-domain-containing protein 6) is a mono-methyltransferase that has been shown to methylate RelA and H2AZ. Using a proteomic approach we recently identified several new SETD6 substrates. To identify novel SETD6 interacting proteins, SETD6 was immunoprecipitated (IP) from Human erythromyeloblastoid leukemia K562 cells. SETD6 binding proteins were subjected to mass-spectrometry analysis resulting in 115 new SETD6 binding candidates. STRING database was used to map the SETD6 interactome network. Network enrichment analysis of biological processes with Gene Ontology (GO) database, identified three major groups; metabolic processes, muscle contraction and protein folding.