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Molecular responses to influenza A virus (IAV) infections vary between mammalian species. To identify conserved and species-specific molecular responses, we perform a comparative study of transcriptomic data derived from blood cells, primary epithelial cells, and lung tissues collected from IAV-infected humans, ferrets, and mice. The molecular responses in the human host have unique functions such as antigen processing that are not observed in mice or ferrets. Highly conserved gene coexpression modules across the three species are enriched for IAV infection–induced pathways including cell cycle and interferon (IFN) signaling. TDRD7 is predicted as an IFN-inducible host factor that is up-regulated upon IAV infection in the three species. TDRD7 is required for antiviral IFN response, potentially modulating IFN signaling via the JAK/STAT/IRF9 pathway. Identification of the common and species-specific molecular signatures, networks, and regulators of IAV infection provides insights into host-defense mechanisms and will facilitate the development of novel therapeutic interventions against IAV infection.

Seasonal influenza is a primary public health burden in the USA and globally. Annual vaccination programs are designed on the basis of circulating influenza viral strains. However, the effectiveness of the seasonal influenza vaccine is highly variable between seasons and among individuals. A number of factors are known to influence vaccination effectiveness including age, sex, and comorbidities. Here, we sought to determine whether whole blood gene expression profiling prior to vaccination is informative about pre-existing immunological status and the immunological response to vaccine. We performed whole transcriptome analysis using RNA sequencing (RNAseq) of whole blood samples obtained prior to vaccination from participants enrolled in an annual influenza vaccine trial. Serological status prior to vaccination and 28 days following vaccination was assessed using the hemagglutination inhibition assay (HAI) to define baseline immune status and the response to vaccination. We find evidence that genes with immunological functions are increased in expression in individuals with higher pre-existing immunity and in those individuals who mount a greater response to vaccination. Using a random forest model we find that this set of genes can be used to predict vaccine response with a performance similar to a model that incorporates physiological and prior vaccination status alone. Our study shows that increased expression of immunological genes, possibly reflecting greater plasmablast cell populations, prior to vaccination is associated with an enhanced response to vaccine. Furthermore, in the absence of physiological information and vaccination history, whole blood gene expression signatures are informative about the immunological response of an individual to seasonal influenza vaccination.

Memory T follicular helper (TFH) cells play an essential role to induce secondary antibody response by providing help to memory and naïve B cells. Here, we show that the transcription factor Tox2 is vital for the maintenance of TFH cells in germinal centers (GCs) and the generation of memory TFH cells. High Tox2 expression was almost exclusive to GC TFH cells among human tonsillar and blood CD4+ T cell subsets. Tox2 overexpression maintained the expression of TFH-associated genes in T cell receptor–stimulated human GC TFH cells and inhibited their spontaneous conversion into TH1-like cells. Tox2-deficient mice displayed impaired secondary TFH cell expansion upon reimmunization with an antigen and upon secondary infection with a heterologous influenza virus. Collectively, our study shows that Tox2 is highly integrated into establishment of durable GC TFH cell responses and development of memory TFH cells in mice and humans., 効率よい抗体反応の形成に必要なTリンパ球因子の発見. 京都大学プレスリリース. 2021-10-15., Molecules for building stronger antibodies. 京都大学プレスリリース. 2021-10-15.

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Due to its rapid surge, there is a shortage of information on viral behavior and host response after SARS-CoV-2 infection. Here we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. We particularly focus on key-regulators, cell-receptors, and host-processes that are hijacked by the virus for its advantage. ACE2-controlled processes involve a key-regulator CD300e (a TYROBP receptor) and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigate the age-dependency of such receptors and identify the adipose and the brain as potentially contributing tissues for the disease’s severity in old patients. In contrast, several other tissues in the young population are more susceptible to SARS-CoV-2 infection. In summary, this present study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific age dependence of the cell receptors involved in COVID-19.

SUMMARYSex differences in the pathogenesis of infectious diseases due to differential immune responses between females and males have been well documented for multiple pathogens. However, the molecular mechanism underlying the observed sex differences in influenza virus infection remains poorly understood. In this study, we used a network-based approach to characterize the blood transcriptome collected over the course of infection with influenza A virus from female and male ferrets to dissect sex-biased gene expression. We identified significant differences in the temporal dynamics and regulation of immune responses between females and males. Our results elucidate sex-differentiated pathways involved in the unfolded protein response (UPR), lipid metabolism, and inflammatory responses, including a female-biased IRE1/XBP1 activation and male-biased crosstalk between metabolic reprogramming and IL-1 and AP-1 pathways. Overall, our study provides molecular insights into sex differences in transcriptional regulation of immune responses and contributes to a better understanding of sex bias in influenza pathogenesis.

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal (Mycoplasma salivarium), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.

Defective influenza virus particles generated during viral replication carry incomplete viral genomes and can interfere with the replication of competent viruses. These defective genomes are thought to modulate the disease severity and pathogenicity of an influenza virus infection. Different defective viral genomes also introduce another source of variation across a heterogeneous cell population. Evaluating the impact of defective virus genomes on host cell responses cannot be fully resolved at the population level, requiring single-cell transcriptional profiling. Here, we characterized virus and host transcriptomes in individual influenza virus-infected cells, including those of defective viruses that arise during influenza A virus infection. We established an association between defective virus transcription and host responses and validated interfering and immunostimulatory functions of identified dominant defective viral genome species in vitro. This study demonstrates the intricate effects of defective viral genomes on host transcriptional responses and highlights the importance of capturing host-virus interactions at the single-cell level., Virus and host factors contribute to cell-to-cell variation in viral infections and determine the outcome of the overall infection. However, the extent of the variability at the single-cell level and how it impacts virus-host interactions at a system level are not well understood. To characterize the dynamics of viral transcription and host responses, we used single-cell RNA sequencing to quantify at multiple time points the host and viral transcriptomes of human A549 cells and primary bronchial epithelial cells infected with influenza A virus. We observed substantial variability in viral transcription between cells, including the accumulation of defective viral genomes (DVGs) that impact viral replication. We show (i) a correlation between DVGs and virus-induced variation of the host transcriptional program and (ii) an association between differential inductions of innate immune response genes and attenuated viral transcription in subpopulations of cells. These observations at the single-cell level improve our understanding of the complex virus-host interplay during influenza virus infection.

SUMMARYTranscriptional circuit architectures can be evolutionarily selected to precisely dictate a given response. Unlike these cellular systems, HIV is regulated through a complex circuit composed of two successive phases (host and viral), which create a positive feedback loop facilitating viral replication. However, it has long remained unclear whether both phases operate identically and to what extent the host phase influences the entire circuit. Here we report that while the host phase is regulated by a checkpoint whereby KAP1 mediates transcription activation, the virus evolved a minimalist system bypassing KAP1. Given the complex circuit’s architecture, cell-to-cell KAP1 fluctuations impart heterogeneity in the host transcriptional responses thus affecting the feedback loop. Mathematical modeling of a complete circuit reveals how these oscillations ultimately influence homogeneous reactivation potential of a latent virus. Thus, while HIV drives molecular innovation to fuel robust gene activation, it experiences transcriptional fragility thereby influencing viral fate and cure efforts.In BriefHIV evolved a minimalist but robust transcriptional circuit bypassing host regulatory checkpoints; however, the fragility of the circuit in the host phase (which primes HIV for activation) largely affects proviral transcription and fate.HighlightsThe host and viral phases of the HIV transcriptional circuit have different functional requirementsHIV evolved a minimalist program to robustly bypass host cell regulatory checkpointsA mathematical model reveals that the host phase is subject to transcriptional circuit fragilityHost transcriptional circuit fragility influences the viral feedback and latency reversal potential

It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.

Influenza A virus, with the limited coding capacity of 10–14 proteins, requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system, but also provides further targets for antiviral drug development. To uncover novel pathways and key targets of influenza infection, we assembled a large amount of data from 12 cell-based gene-expression studies of influenza infection for an integrative network analysis. We systematically identified differentially expressed genes and gene co-expression networks induced by influenza infection. We revealed the dedicator of cytokinesis 5 (DOCK5) played potentially an important role for influenza virus replication. CRISPR/Cas9 knockout of DOCK5 reduced influenza virus replication, indicating that DOCK5 is a key regulator for the viral life cycle. DOCK5’s targets determined by the DOCK5 knockout experiments strongly validated the predicted gene signatures and networks. This study systematically uncovered and validated fundamental patterns of molecular responses, intrinsic structures of gene co-regulation, and novel key targets in influenza virus infection., Virology: Molecular Networks and Targets in Influenza Infection Molecular response to influenza infection involves a large number of interacting pathways in the form of complex molecular networks. Most studies on influenza infection have largely focused on testing specific molecules and hypotheses with limited data. Therefore, a global picture of molecular interactions in influenza infection is missing. In this study, we performed an integrative network analysis on a large amount of data from 12 cell-based gene expression studies of influenza infections. By combining differential expression, co-expression networks, and gene knockout experiments, we uncovered and validated fundamental patterns of molecular responses, intrinsic structures of gene co-regulation, and novel key targets in influenza infection. Our findings pave the way for other functional investigations into identifying novel therapeutic targets against influenza infection.

Seasonal flu affects 5–20% of the human population each year. Although mortality rates are typically

Altering pyrimidine levels in influenza virus– or VSV-infected cells reverses the inhibitory effect of viral proteins NS1 and M on host mRNA nuclear export, restoring expression of host antiviral factors., The NS1 protein of influenza virus is a major virulence factor essential for virus replication, as it redirects the host cell to promote viral protein expression. NS1 inhibits cellular messenger ribonucleic acid (mRNA) processing and export, down-regulating host gene expression and enhancing viral gene expression. We report in this paper the identification of a nontoxic quinoline carboxylic acid that reverts the inhibition of mRNA nuclear export by NS1, in the absence or presence of the virus. This quinoline carboxylic acid directly inhibited dihydroorotate dehydrogenase (DHODH), a host enzyme required for de novo pyrimidine biosynthesis, and partially reduced pyrimidine levels. This effect induced NXF1 expression, which promoted mRNA nuclear export in the presence of NS1. The release of NS1-mediated mRNA export block by DHODH inhibition also occurred in the presence of vesicular stomatitis virus M (matrix) protein, another viral inhibitor of mRNA export. This reversal of mRNA export block allowed expression of antiviral factors. Thus, pyrimidines play a necessary role in the inhibition of mRNA nuclear export by virulence factors.

Selective autophagy involves the recognition and targeting of specific cargo, such as damaged organelles, misfolded proteins, or invading pathogens for lysosomal destruction1,2,3,4. Yeast genetic screens have identified proteins required for different forms of selective autophagy, including cytoplasm-to-vacuole targeting, pexophagy and mitophagy, and mammalian genetic screens have identified proteins required for autophagy regulation5. However, there have been no systematic approaches to identify molecular determinants of selective autophagy in mammalian cells. Here, to identify mammalian genes required for selective autophagy, we performed a high-content, image-based, genome-wide small interfering RNA screen to detect genes required for the colocalization of Sindbis virus capsid protein with autophagolysosomes. We identified 141 candidate genes required for viral autophagy, which were enriched for cellular pathways related to messenger RNA processing, interferon signalling, vesicle trafficking, cytoskeletal motor function and metabolism. Ninety-six of these genes were also required for Parkin-mediated mitophagy, indicating that common molecular determinants may be involved in autophagic targeting of viral nucleocapsids and autophagic targeting of damaged mitochondria. Murine embryonic fibroblasts lacking one of these gene products, the C2-domain containing protein, SMURF1, are deficient in the autophagosomal targeting of Sindbis and herpes simplex viruses and in the clearance of damaged mitochondria. Moreover, SMURF1-deficient mice accumulate damaged mitochondria in the heart, brain and liver. Thus, our study identifies candidate determinants of selective autophagy, and defines SMURF1 as a newly recognized mediator of both viral autophagy and mitophagy.

HIV encodes Tat, a small protein that facilitates viral transcription by binding an RNA structure (trans-activating RNA [TAR]) formed on nascent viral pre-messenger RNAs. Besides this well-characterized mechanism, Tat appears to modulate cellular transcription, but the target genes and molecular mechanisms remain poorly understood. We report here that Tat uses unexpected regulatory mechanisms to reprogram target immune cells to promote viral replication and rewire pathways beneficial for the virus. Tat functions through master transcriptional regulators bound at promoters and enhancers, rather than through cellular ‘TAR-like’ motifs, to both activate and repress gene sets sharing common functional annotations. Despite the complexity of transcriptional regulatory mechanisms in the cell, Tat precisely controls RNA polymerase II recruitment and pause release to fine-tune the initiation and elongation steps in target genes. We propose that a virus with a limited coding capacity has optimized its genome by evolving a small but ‘multitasking’ protein to simultaneously control viral and cellular transcription. DOI: http://dx.doi.org/10.7554/eLife.08955.001, eLife digest The human immunodeficiency virus (HIV) reproduces and spreads throughout the body by hijacking human immune cells and causing them to copy the virus’s genetic information. As the virus multiplies, it also causes the death of the immune system cells that help the human body recognize and eliminate viruses. This allows the virus to multiply unchecked. Studies of the genetic material of HIV – which is in the form of single-stranded RNA molecules and contains only a handful of genes – have begun to reveal how the virus can wreak such havoc to the human immune system. A small protein encoded by the virus, called Tat, boosts the expression of HIV genes in infected immune cells by binding to a structure that forms on newly synthesized viral RNAs. Recent evidence suggests that HIV also changes the expression of human genes to make immune cells more hospitable to the virus. However, it was not known exactly which specific genes are targeted, or how the virus alters their expression. Now, Reeder, Kwak et al. reveal how the Tat protein alters the expression of more than 400 human genes. Rather than bind to the same structure seen in newly forming HIV RNAs, Tat turns on or off the expression of its human target genes by interacting with proteins that regulate human gene expression. In doing so, Tat is able to precisely control the activity of an enzyme called RNA Polymerase II that is necessary for the early steps of gene expression. Tat’s multitasking ability – boosting HIV gene expression at the same time as reprogramming human gene expression – helps explain how a virus with so little genetic material of its own can perform such a wide range of activities in infected cells. The work of Reeder, Kwak et al. suggests that Tat reshapes the human genome to position target genes in ways that allow them to be efficiently turned on or off. Future studies will further reveal how Tat accomplishes this genome remodeling during different stages of infection. In addition, further research is also necessary to look closely into the sets of genes targeted by Tat to find patterns of genes that work together to alter cell behavior, and investigate how these new behaviors allow HIV to thrive. DOI: http://dx.doi.org/10.7554/eLife.08955.002

We provide a systematic analysis of a biological system, the microbial pathogen Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and biocomputational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane and cytosol) of Mtb. We report the compartmentalization of 1,044 identified proteins using proteomics methods. Genome-based biological network analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

In silico biology, the computer aided analysis of biological systems, is a relatively young research area. It was first coined from theoretical and computational biology. Biology, like many other fields, has recently experienced an increased use of systems mathematics and computer simulation. With the availability of the human genome sequence and an exponentially growing number of completely sequenced genomes from various model organisms, along with expression and proteomic data at hand, the research paradigm has shifted towards systems analysis and simulation. Computer aided modeling of complex biomolecules and assemblies are already routinely performed. Nowadays, theoretical description and computer simulation of cellular components in larger intraand inter-cellular networks is of growing importance. Together with classic biological and clinical experiments, as well as data from functional genomics, in silico biology will take the leading role in the analysis of biological systems [1].

MicroRNAs (miRNAs) have been shown to regulate viral infection, but the miRNAs that target intracellular sensors and adaptors of innate immunity have not been fully uncovered. Here we conduct an miRNA mimic screen and validation with miRNA inhibitors in cells infected with vesicular stomatitis virus (VSV) to identify miRNAs that regulate viral-host interactions. We identify miR-576-3p as a robust regulator of infection by VSV and other RNA and DNA viruses. While an miR-576-3p mimic sensitizes cells to viral replication, inhibition of endogenous miR-576-3p prevents infection. miR-576-3p is induced by IRF3 concomitantly with interferon and targets STING, MAVS and TRAF3, which are critical factors for interferon expression. Interestingly, miR-576-3p and its binding sites are primate-specific and miR-576-3p levels are reduced in inflammatory diseases. These findings indicate that induction of miR-576-3p by IRF3 triggers a feedback mechanism to reduce interferon expression and set an antiviral response threshold to likely avoid excessive inflammation.

Folding of RNA sequences into secondary structures is viewed as a map that assigns a uniquely defined base pairing pattern to every sequence. The mapping is non-invertible since many sequences fold into the same minimum free energy (secondary) structure or shape. The pre-images of this map, called neutral networks, are uniquely associated with the shapes and vice versa. Random graph theory is used to construct networks in sequence space which are suitable models for neutral networks. The theory of molecular quasispecies has been applied to replication and mutation on single-peak fitness landscapes. This concept is extended by considering evolution on degenerate multi-peak landscapes which originate from neutral networks by assuming that one particular shape is fitter than all the others. On such a single-shape landscape the superior fitness value is assigned to all sequences belonging to the master shape. All other shapes are lumped together and their fitness values are averaged in a way that is reminiscent of mean field theory. Replication and mutation on neutral networks are modeled by phenomenological rate equations as well as by a stochastic birth-and-death model. In analogy to the error threshold in sequence space the phenotypic error threshold separates two scenarios: (i) a stationary (fittest) master shape surrounded by closely related shapes and (ii) populations drifting through shape space by a diffusion-like process. The error classes of the quasispecies model are replaced by distance classes between the master shape and the other structures. Analytical results are derived for single-shape landscapes, in particular, simple expressions are obtained for the mean fraction of master shapes in a population and for phenotypic error thresholds. The analytical results are complemented by data obtained from computer simulation of the underlying stochastic processes. The predictions of the phenomenological approach on the single-shape landscape are very well reproduced by replication and mutation kinetics of tRNA(phe). Simulation of the stochastic process at a resolution of individual distance classes yields data which are in excellent agreement with the results derived from the birth-and-death model.

In this paper, we consider the evolutionary dynamics of catalytically active species with a distinct genotype–phenotype relationship. Folding landscapes of RNA molecules serve as a paradigm for this relationship with essential neutral properties. The landscape itself is partitioned by phenotypes (realized as RNA secondary structures). To each genotype (represented as a sequence) a structure is assigned in a unique way. The set of all sequences which map into a particular structure is modeled as a random graph in sequence space (the so-called neutral network ). A catalytic network is realized as a random digraph with maximal out-degree two and secondary structures as vertex sets. A population of catalytic RNA molecules shows significantly different behavior compared to a deterministic description: hypercycles are able to co-exist and out-compete a parasite with superior catalytic support. A “switching” between different dynamic organizations of the network can be observed, dynamical stability of hypercyclic organizations against errors and the existence of an error-threshold of catalysis can be reported.

Applied Molecular evolution, especially in vitro techniques developed in the last few years have revolutionized the design of functional biopolymers. SELEX techniques for example have become particularly popular. These methods have been enormously successful, yet this success is not trivial: the huge number of sequences being searched, the low concentrations of individual species and the bias and noise inherent in the techniques would seem to make these experiments very difficult. The theory presented here will give a comprehensive description of directed molecular evolution of RNA molecules in detail and biopolymers in general. Essential for an understanding is an adequate genotype–phenotype relationship. This relationship exhibits neutral properties which explain the success of these experimental methods. Influences of the initial library, the error rate, the cycle length and other parameters on the experiments are characterized. New problems arise in extending the concept of evolutionary optimization of biopolymers in a Darwinian scenario to complex reaction networks.

A three-class model of society (farmers, bandits and rulers) is considered in order to explain alternation between despotism and anarchy in ancient China. In the absence of authority, the dynamics of farmers and bandits are governed by the well-known prey-predator interactions. Rulers impose taxes on farmers and punish bandits by execution. Thus, farmers are a sort of renewable resource which is exploited both by bandits and by rulers. Assuming that the dynamics of rulers is slow compared with those of farmers and bandits, slow-fast limit cycles can be identified through a singular perturbation approach. This provides a possible explanation for the accomplishment of an endogenously generated dynastic cycle, i.e. a periodic switching of society between despotism and anarchy. Moreover, there is numerical evidence for the occurrence of a cascade of period-doubling bifurcations leading to chaotic behaviour.