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Cell clustering is an essential step in uncovering cellular architectures in single cell RNA-sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. Here, we develop a multi-scale clustering (MSC) approach to construct sparse cell-cell correlation network for identifying de novo cell types and subtypes at multiscale resolution in an unsupervised manner. Based upon simulated, silver and gold standard data as well as real scRNA-seq data in diseases, MSC showed much improved performance in comparison to established benchmark methods, and identified biologically meaningful cell hierarchy to facilitate the discovery of novel disease associated cell subtypes and mechanisms., Competing Interests: Competing interests The authors declare that they have no competing interests. Additional Declarations: No competing interests reported.

Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization., Competing Interests: Declaration of competing interest The A.G.-S. laboratory has received research support from GSK, Pfizer, Senhwa Biosciences, Kenall Manufacturing, Blade Therapeutics, Avimex, Johnson & Johnson, Dynavax, 7Hills Pharma, Pharmamar, ImmunityBio, Accurius, Nanocomposix, Hexamer, N-fold LLC, Model Medicines, Atea Pharma, Applied Biological Laboratories and Merck, outside of the reported work. A.G.-S. has consulting agreements for the following companies involving cash and/or stock: Castlevax, Amovir, Vivaldi Biosciences, Contrafect, 7Hills Pharma, Avimex, Pagoda, Accurius, Esperovax, Farmak, Applied Biological Laboratories, Pharmamar, CureLab Oncology, CureLab Veterinary, Synairgen, Paratus, Pfizer and Prosetta, outside of the reported work. A.G.-S. has been an invited speaker in meeting events organized by Seqirus, Janssen, Abbott and Astrazeneca. A.G.-S. is inventor on patents and patent applications on the use of antivirals and vaccines for the treatment and prevention of virus infections and cancer, owned by the Icahn School of Medicine at Mount Sinai, New York, outside of the reported work. All other authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

HIV-1 latency is a major barrier to curing infections with antiretroviral therapy and, consequently, to eliminating the disease globally. The establishment, maintenance, and potential clearance of latent infection are complex dynamic processes and can be best described with the help of mathematical models followed by experimental validation. Here, we review the use of viral dynamics models for HIV-1, with a focus on applications to the latent reservoir. Such models have been used to explain the multi-phasic decay of viral load during antiretroviral therapy, the early seeding of the latent reservoir during acute infection and the limited inflow during treatment, the dynamics of viral blips, and the phenomenon of post-treatment control. Finally, we discuss that mathematical models have been used to predict the efficacy of potential HIV-1 cure strategies, such as latency-reversing agents, early treatment initiation, or gene therapies, and to provide guidance for designing trials of these novel interventions.

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19., (© 2023 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

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 275 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. A model using both gene expression and physiological factors has the greatest predictive power demonstrating the potential utility of molecular profiling for enhancing prediction of vaccine response. Moreover, expression of genes that are associated with enhanced vaccination response may point to additional biological pathways that contribute to mounting a robust immunological response to the seasonal influenza vaccine.

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.

Sex differences in the pathogenesis of infectious diseases because of 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 biases in influenza pathogenesis., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Costimulation pathways play an essential role in T cell activation, differentiation, and regulation. CD155 expressed on antigen-presenting cells (APCs) interacts with TIGIT, an inhibitory costimulatory molecule, and CD226, an activating costimulatory molecule, on T cells. TIGIT and CD226 are expressed at varying levels depending on the T cell subset and activation state. T follicular helper cells in germinal centers (GC-Tfh) in human tonsils express high TIGIT and low CD226. However, the biological role of the CD155/TIGIT/CD226 axis in human Tfh cell biology has not been elucidated. To address this, we analyzed tonsillar CD4 + T cell subsets cultured with artificial APCs constitutively expressing CD155. Here we show that CD226 signals promote the early phase of Tfh cell differentiation in humans. CD155 signals promoted the proliferation of naïve CD4 + T cells and Tfh precursors (pre-Tfh) isolated from human tonsils and upregulated multiple Tfh molecules and decreased IL-2, a cytokine detrimental for Tfh cell differentiation. Blocking CD226 potently inhibited their proliferation and expression of Tfh markers. By contrast, while CD155 signals promoted the proliferation of tonsillar GC-Tfh cells, their proliferation required only weak CD226 signals. Furthermore, attenuating CD226 signals rather increased the expression of CXCR5, ICOS, and IL-21 by CD155-stimulated GC-Tfh cells. Thus, the importance of CD226 signals changes according to the differentiation stage of human Tfh cells and wanes in mature GC-Tfh cells. High TIGIT expression on GC-Tfh may play a role in attenuating the detrimental CD226 signals post GC-Tfh cell maturation., Competing Interests: Authors AC, NM-O and GC were employed by EMD Serono Research and Development Institute Inc. during the project and have equity interest. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yasutomi, Christiaansen, Imai, Martin-Orozco, Forst, Chen and Ueno.)

Memory T follicular helper (T FH ) 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 T FH cells in germinal centers (GCs) and the generation of memory T FH cells. High Tox2 expression was almost exclusive to GC T FH cells among human tonsillar and blood CD4 + T cell subsets. Tox2 overexpression maintained the expression of T FH -associated genes in T cell receptor–stimulated human GC T FH cells and inhibited their spontaneous conversion into T H 1-like cells. Tox2-deficient mice displayed impaired secondary T FH 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 T FH cell responses and development of memory T FH cells in mice and humans.

Respiratory failure is associated with increased mortality in COVID-19 patients. There are no validated lower airway biomarkers to predict clinical outcome. We investigated whether bacterial respiratory infections were associated with poor clinical outcome of COVID-19 in a prospective, observational cohort of 589 critically ill adults, all of whom required mechanical ventilation. For a subset of 142 patients who underwent bronchoscopy, we quantified SARS-CoV-2 viral load, analysed the lower respiratory tract microbiome using metagenomics and metatranscriptomics and profiled the host immune response. Acquisition of a hospital-acquired respiratory pathogen was not associated with fatal outcome. Poor clinical outcome was associated with lower airway enrichment with an oral commensal (Mycoplasma salivarium). Increased SARS-CoV-2 abundance, low anti-SARS-CoV-2 antibody response and a distinct host transcriptome profile of the lower airways were most predictive of mortality. Our data provide evidence that secondary respiratory infections do not drive mortality in COVID-19 and clinical management strategies should prioritize reducing viral replication and maximizing host responses to SARS-CoV-2., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

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.

Background: The abundance and diversity of antibiotic resistance genes (ARGs) in the human respiratory microbiome remain poorly characterized. In the context of influenza virus infection, interactions between the virus, the host, and resident bacteria with pathogenic potential are known to complicate and worsen disease, resulting in coinfection and increased morbidity and mortality of infected individuals. When pathogenic bacteria acquire antibiotic resistance, they are more difficult to treat and of global health concern. Characterization of ARG expression in the upper respiratory tract could help better understand the role antibiotic resistance plays in the pathogenesis of influenza-associated bacterial secondary infection., Results: Thirty-seven individuals participating in the Household Influenza Transmission Study (HITS) in Managua, Nicaragua, were selected for this study. We performed metatranscriptomics and 16S rRNA gene sequencing analyses on nasal and throat swab samples, and host transcriptome profiling on blood samples. Individuals clustered into two groups based on their microbial gene expression profiles, with several microbial pathways enriched with genes differentially expressed between groups. We also analyzed antibiotic resistance gene expression and determined that approximately 25% of the sequence reads that corresponded to antibiotic resistance genes mapped to Streptococcus pneumoniae and Staphylococcus aureus. Following construction of an integrated network of ARG expression with host gene co-expression, we identified several host key regulators involved in the host response to influenza virus and bacterial infections, and host gene pathways associated with specific antibiotic resistance genes., Conclusions: This study indicates the host response to influenza infection could indirectly affect antibiotic resistance gene expression in the respiratory tract by impacting the microbial community structure and overall microbial gene expression. Interactions between the host systemic responses to influenza infection and antibiotic resistance gene expression highlight the importance of viral-bacterial co-infection in acute respiratory infections like influenza. Video abstract.

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. IMPORTANCE 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., (Copyright © 2020 Wang et al.)

Influenza A viruses (IAVs) have a remarkable tropism in their ability to circulate in both mammalian and avian species. The IAV NS1 protein is a multifunctional virulence factor that inhibits the type I interferon host response through a myriad of mechanisms. How NS1 has evolved to enable this remarkable property across species and its specific impact in the overall replication, pathogenicity, and host preference remain unknown. Here we analyze the NS1 evolutionary landscape and host tropism using a barcoded library of recombinant IAVs. Results show a surprisingly great variety of NS1 phenotypes according to their ability to replicate in different hosts. The IAV NS1 genes appear to have taken diverse and random evolutionary pathways within their multiple phylogenetic lineages. In summary, the high evolutionary plasticity of this viral protein underscores the ability of IAVs to adapt to multiple hosts and aids in our understanding of its global prevalence., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Transcriptional circuit architectures in several organisms have been evolutionarily selected to dictate precise given responses. 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, although 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, although HIV drives molecular innovation to fuel robust gene activation, it experiences transcriptional fragility, thereby influencing viral fate and cure efforts., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.

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.

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., Competing Interests: The authors declare that no competing interests exist.

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.

The plasma membrane contains discrete nanometer-sized domains that are resistant to non-ionic detergents, and which are called detergent resistant membrane domains (DRMDs) or lipid rafts. Exposure of host cells to pathogenic bacteria has been shown to induce the re-distribution of specific host proteins between DRMDs and detergent soluble membranes, which leads to the initiation of cell signaling that enable pathogens to access host cells. DRMDs have been shown to play a role in the invasion of Brucella into host macrophages and the formation of replicative phagosomes called Brucella-containing vacuoles (BCVs). In this study we sought to characterize changes to the protein expression profiles in DRMDs and to respective cellular pathways and networks of Mono Mac 6 cells in response to the adherence of rough VTRM1 and smooth 16 M B. melitensis strains. DRMDs were extracted from Mono Mac 6 cells exposed for 2 minutes at 4°C to Brucella (no infection occurs) and from unexposed control cells. Protein expression was determined using the non-gel based quantitative iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) mass spectrometry technique. Using the identified iTRAQ proteins we performed enrichment analyses and probed constructed human biochemical networks for interactions and metabolic reactions. We identified 149 proteins, which either became enriched, depleted or whose amounts did not change in DRMDs upon Brucella exposure. Several of these proteins were distinctly enriched or depleted in DRMDs upon exposure to rough and smooth B. melitensis strains which results in the differential engagement of cellular pathways and networks immediately upon Brucella encounter. For some of the proteins such as myosin 9, small G protein signaling modulator 3, lysine-specific demethylase 5D, erlin-2, and voltage-dependent anion-selective channel protein 2, we observed extreme differential depletion or enrichment in DRMDs. The identified proteins and pathways could provide the basis for novel ways of treating or diagnosing Brucellosis.

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., (© 2012 Zhang et al.)

Influenza A virus infects 5-20% of the population annually, resulting in ~35,000 deaths and significant morbidity. Current treatments include vaccines and drugs that target viral proteins. However, both of these approaches have limitations, as vaccines require yearly development and the rapid evolution of viral proteins gives rise to drug resistance. In consequence additional intervention strategies, that target host factors required for the viral life cycle, are under investigation. Here we employed arrayed whole-genome siRNA screening strategies to identify cell-autonomous molecular components that are subverted to support H1N1 influenza A virus infection of human bronchial epithelial cells. Integration across relevant public data sets exposed druggable gene products required for epithelial cell infection or required for viral proteins to deflect host cell suicide checkpoint activation. Pharmacological inhibition of representative targets, RGGT and CHEK1, resulted in significant protection against infection of human epithelial cells by the A/WS/33 virus. In addition, chemical inhibition of RGGT partially protected against H5N1 and the 2009 H1N1 pandemic strain. The observations reported here thus contribute to an expanding body of studies directed at decoding vulnerabilities in the command and control networks specified by influenza virulence factors.

Selective autophagy involves the recognition and targeting of specific cargo, such as damaged organelles, misfolded proteins, or invading pathogens for lysosomal destruction. 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 regulation. 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.

A chemical genetics approach was taken to identify inhibitors of NS1, a major influenza A virus virulence factor that inhibits host gene expression. A high-throughput screen of 200,000 synthetic compounds identified small molecules that reversed NS1-mediated inhibition of host gene expression. A counterscreen for suppression of influenza virus cytotoxicity identified naphthalimides that inhibited replication of influenza virus and vesicular stomatitis virus (VSV). The mechanism of action occurs through activation of REDD1 expression and concomitant inhibition of mammalian target of rapamycin complex 1 (mTORC1) via TSC1-TSC2 complex. The antiviral activity of naphthalimides was abolished in REDD1(-/-) cells. Inhibition of REDD1 expression by viruses resulted in activation of the mTORC1 pathway. REDD1(-/-) cells prematurely upregulated viral proteins via mTORC1 activation and were permissive to virus replication. In contrast, cells conditionally expressing high concentrations of REDD1 downregulated the amount of viral protein. Thus, REDD1 is a new host defense factor, and chemical activation of REDD1 expression represents a potent antiviral intervention strategy.

Dense subgraphs of Protein-Protein Interaction (PPI) graphs are assumed to be potential functional modules and play an important role in inferring the functional behavior of proteins. Increasing amount of available PPI data implies a fast, accurate approach of biological complex identification. Therefore, there are different models and algorithms in identifying functional modules. This paper describes a new graph theoretic clustering algorithm that detects densely connected regions in a large PPI graph. The method is based on finding bounded diameter subgraphs around a seed node. The algorithm has the advantage of being very simple and efficient when compared with other graph clustering methods. This algorithm is tested on the yeast PPI graph and the results are compared with MCL, Core-Attachment, and MCODE algorithms.

Background: The recent emergence of the H5N1 influenza virus from avian reservoirs has raised concern about future influenza strains of high virulence emerging that could easily infect humans. We analyzed differential gene expression of lung epithelial cells to compare the response to H5N1 infection with a more benign infection with Respiratory Syncytial Virus (RSV). These gene expression data are then used as seeds to find important nodes by using a novel combination of the Gene Ontology database and the Human Network of gene interactions. Additional analysis of the data is conducted by training support vector machines (SVM) with the data and examining the orientations of the optimal hyperplanes generated., Results: Analysis of gene clustering in the Gene Ontology shows no significant clustering of genes unique to H5N1 response at 8 hours post infection. At 24 hours post infection, however, a number of significant gene clusters are found for nodes representing "immune response" and "response to virus" terms. There were no significant clusters of genes in the Gene Ontology for the control (Mock) or RSV experiments that were unique relative to the H5N1 response. The genes found to be most important in distinguishing H5N1 infected cells from the controls using SVM showed a large degree of overlap with the list of significantly regulated genes. However, though none of these genes were members of the GO clusters found to be significant., Conclusions: Characteristics of H5N1 infection compared to RSV infection show several immune response factors that are specific for each of these infections. These include faster timescales within the cell as well as a more focused activation of immunity factors. Many of the genes that are found to be significantly expressed in H5N1 response relative to the control experiments are not found to cluster significantly in the Gene Ontology. These genes are, however, often closely linked to the clustered genes through the Human Network. This may suggest the need for more diverse annotations of these genes and verification of their action in immune response.

The precise elucidation of the gene concept has become the subject of intense discussion in light of results from several, large high-throughput surveys of transcriptomes and proteomes. In previous work, we proposed an approach for constructing gene concepts that combines genomic heritability with elements of function. Here, we introduce a definition of the gene within a computational framework of cellular interactions. The definition seeks to satisfy the practical requirements imposed by annotation, capture logical aspects of regulation, and encompass the evolutionary property of homology.

In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.

Motivation: A quantitative description of interactions between cell components is a major challenge in Computational Biology. As a method of choice, differential equations are used for this purpose, because they provide a detailed insight into the dynamic behavior of the system. In most cases, the number of time points of experimental time series is usually too small to estimate the parameters of a model of a whole gene regulatory network based on differential equations, such that one needs to focus on subnetworks consisting of only a few components. For most approaches, the set of components of the subsystem is given in advance and only the structure has to be estimated. However, the set of components that influence the system significantly are not always known in advance, making a method desirable that determines both, the components that are included into the model and the parameters., Results: We have developed a method that uses gene expression data as well as interaction data between cell components to define a set of genes that we use for our modeling. In a subsequent step, we estimate the parameters of our model of piecewise linear differential equations and evaluate the results simulating the behavior of the system with our model. We have applied our method to the DNA repair system of Mycobacterium tuberculosis. Our analysis predicts that the gene Rv2719c plays an important role in this system.

Unlike traditional biological research that focuses on a small set of components, systems biology studies the complex interactions between a large number of genes, proteins and other elements of biological networks and systems. Host-pathogen systems biology examines the interactions between the components of two distinct organisms, either a microbial or viral pathogen and its animal host or two different microbial species in a community. With the availability of complete genomic sequences of various hosts and pathogens, together with breakthroughs in proteomics, metabolomics and other experimental areas, the investigation of host-pathogen systems on a multitude of levels of detail has come within reach.

Background: Comparison of metabolic networks is typically performed based on the organisms' enzyme contents. This approach disregards functional replacements as well as orthologies that are misannotated. Direct comparison of the structure of metabolic networks can circumvent these problems., Results: Metabolic networks are naturally represented as directed hypergraphs in such a way that metabolites are nodes and enzyme-catalyzed reactions form (hyper)edges. The familiar operations from set algebra (union, intersection, and difference) form a natural basis for both the pairwise comparison of networks and identification of distinct metabolic features of a set of algorithms. We report here on an implementation of this approach and its application to the procaryotes., Conclusion: We demonstrate that metabolic networks contain valuable phylogenetic information by comparing phylogenies obtained from network comparisons with 16S RNA phylogenies. The algebraic approach to metabolic networks is suitable to study metabolic innovations in two sets of organisms, free living microbes and Pyrococci, as well as obligate intracellular pathogens.

Motivation: The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organism's ability to react to its environment., Results: We detail one computational approach for using gene expression data to identify response networks in an organism. The method is based on the construction of biological networks given different sets of interaction information and the reduction of the said networks to important response sub-networks via the integration of the gene expression data. As an application, the expression data of known stress responders and DNA repair genes in Mycobacterium tuberculosis is used to construct a generic stress response sub-network. This is compared to similar networks constructed from data obtained from subjecting M.tuberculosis to various drugs; we are thus able to distinguish between generic stress response and specific drug response. We anticipate that this approach will be able to accelerate target identification and drug development for tuberculosis in the future., Contact: chris@lanl.gov, Supplementary Information: Supplementary Figures 1 through 6 on drug response networks and differential network analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions. http://www.santafe.edu/~chris/DifferentialNW.

Trends in increased tuberculosis infection and a fatality rate of approximately 23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways 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.

Network Genomics studies genomics and proteomics foundations of cellular networks in biological systems. It complements systems biology in providing information on elements, their interaction and their functional interplay in cellular networks. The relationship between genomic and proteomic high-throughput technologies and computational methods are described, as well as several examples of specific network genomic application are presented.

The information provided by completely sequenced genomes can yield insights into the multi-level organization of organisms and their evolution. At the lowest level of molecular organization individual enzymes are formed, often through assembly of multiple polypeptides. At a higher level, sets of enzymes group into metabolic networks. Much has been learned about the relationship of species from phylogenetic trees comparing individual enzymes. In this article we extend conventional phylogenetic analysis of individual enzymes in different organisms to the organisms' metabolic networks. For this purpose we suggest a method that combines sequence information with information about the underlying reaction networks. A distance between pathways is defined as incorporating distances between substrates and distances between corresponding enzymes. The new analysis is applied to electron-transfer and amino acid biosynthesis networks yielding a more comprehensive understanding of similarities and differences between organisms.

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., (Copyright 2000 Academic Press.)

The abundance of information provided by completely sequenced genomes defines a starting point for new insights in the multilevel organization of organisms and their evolution. At the lowest level enzymes and other protein complexes are formed by aggregating multiple polypeptides. At a higher level enzymes group conceptually into metabolic pathways as part of a dynamic information-processing system, and substrates are processed by enzymes yielding other substrates. A method based on a combination of sequence information with graph topology of the underlying pathway is presented. With this approach pathways of different organisms are related to each other by phylogenetic analysis, extending conventional phylogenetic analysis of individual enzymes. The new method is applied to pathways related to electron transfer and to the Krebs citric acid cycle. In addition to providing a more comprehensive understanding of similarities and differences between organisms, this method indicates different evolutionary rates between substrates and enzymes.

A general system of high-order differential equations describing complex dynamics of replicating biomolecules is given. Symmetry relations and coordinate transformations of general replication systems leading to topologically equivalent systems are derived. Three chaotic attractors observed in Lotka-Volterra equations of dimension n = 3 are shown to represent three cross-sections of one and the same chaotic regime. Also a fractal torus in a generalized three-dimensional Lotka-Volterra Model has been linked to one of the chaotic attractors. The strange attractors are studied in the equivalent four-dimensional catalytic replicator network. The fractal torus has been examined in adapted Lotka-Volterra equations. Analytic expressions are derived for the Lyapunov exponents of the flow in the replicator system. Lyapunov spectra for different pathways into chaos has been calculated. In the generalized Lotka-Volterra system a second inner rest point--coexisting with (quasi)-periodic orbits--can be observed; with an abundance of different bifurcations. Pathways from chaotic tori, via quasi-periodic tori, via limit cycles, via multi-periodic orbits--emerging out of periodic doubling bifurcations--to "simple" chaotic attractors can be found.