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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.

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.

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

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. Contact: {radde.gebert}@zpr.uni-koeln.de, chris@lanl.gov

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.

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.

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.

With the human genome sequence available and with an exponentially growing number of completely sequenced genomes from various model organisms, the research paradigm is shifted towards the analysis of the function of interacting genes and gene-product. The reduced estimate of 30,000-40,000 genes in the human genome strongly indicates that the functional complexity expected for an organism such as human may originate in locations and processes beyond the identified genes. Functional genomics emerges as valuable research area for the identification of key-players in the complex processes within a living organism. It is complemented by Systems Biology [7,103,104], a new research area that combines data obtained from Functional Genomics analysis with biocomputation and simulation of cellular networks. Both Functional Genomics and Systems Biology outline the analysis, prediction and control of complex biological systems.

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.

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.

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

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.

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.

Background: Tryptophan synthase consists of two subunits, and . Two distinct subgroups of chain exist. The major group (TrpEb_1) includes the well-studied chain of Salmonella typhimurium. The minor group of chain (TrpEb_2) is most frequently found in the Archaea. Most of the amino-acid residues important for catalysis are highly conserved between both TrpE subfamilies. Results: Conserved amino-acid residues of TrpEb_1 that make allosteric contact with the TrpEa subunit (the chain) are absent in TrpEb_2. Representatives of Archaea, Bacteria and higher plants all exist that possess both TrpEb_1 and TrpEb_2. In those prokaryotes where two trpEb genes coexist, one is usually trpEb_1 and is adjacent to trpEa, whereas the second is trpEb_2 and is usually unlinked with other tryptophan-pathway genes. Conclusions: TrpEb_1 is nearly always partnered with TrpEa in the tryptophan synthase reaction. However, by default at least six lineages of the Archaea are likely to use TrpEb_2 as the functional chain, as TrpEb_1 is absent. The six lineages show a distinctive divergence within the overall TrpEa phylogenetic tree, consistent with the lack of selection for amino-acid residues in TrpEa that are otherwise conserved for interfacing with TrpEb_1. We suggest that the standalone function of TrpEb_2 might be to catalyze the serine deaminase reaction, an established catalytic capability of tryptophan synthase chains. A coincident finding of interest is that the Archaea seem to use the citramalate pathway, rather than threonine deaminase (IlvA), to initiate the pathway of isoleucine biosynthesis.