30 research outputs found

    From bit to it: How a complex metabolic network transforms information into living matter

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    Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide. I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells. The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective

    Suppression of Plant Resistance Gene-Based Immunity by a Fungal Effector

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    The innate immune system of plants consists of two layers. The first layer, called basal resistance, governs recognition of conserved microbial molecules and fends off most attempted invasions. The second layer is based on Resistance (R) genes that mediate recognition of effectors, proteins secreted by pathogens to suppress or evade basal resistance. Here, we show that a plant-pathogenic fungus secretes an effector that can both trigger and suppress R gene-based immunity. This effector, Avr1, is secreted by the xylem-invading fungus Fusarium oxysporum f.sp. lycopersici (Fol) and triggers disease resistance when the host plant, tomato, carries a matching R gene (I or I-1). At the same time, Avr1 suppresses the protective effect of two other R genes, I-2 and I-3. Based on these observations, we tentatively reconstruct the evolutionary arms race that has taken place between tomato R genes and effectors of Fol. This molecular analysis has revealed a hitherto unpredicted strategy for durable disease control based on resistance gene combinations

    Insertion of an Esterase Gene into a Specific Locust Pathogen (Metarhizium acridum) Enables It to Infect Caterpillars

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    An enduring theme in pathogenic microbiology is poor understanding of the mechanisms of host specificity. Metarhizium is a cosmopolitan genus of invertebrate pathogens that contains generalist species with broad host ranges such as M. robertsii (formerly known as M. anisopliae var. anisopliae) as well as specialists such as the acridid-specific grasshopper pathogen M. acridum. During growth on caterpillar (Manduca sexta) cuticle, M. robertsii up-regulates a gene (Mest1) that is absent in M. acridum and most other fungi. Disrupting M. robertsii Mest1 reduced virulence and overexpression increased virulence to caterpillars (Galleria mellonella and M. sexta), while virulence to grasshoppers (Melanoplus femurrubrum) was unaffected. When Mest1 was transferred to M. acridum under control of its native M. robertsii promoter, the transformants killed and colonized caterpillars in a similar fashion to M. robertsii. MEST1 localized exclusively to lipid droplets in M. robertsii conidia and infection structures was up-regulated during nutrient deprivation and had esterase activity against lipids with short chain fatty acids. The mobilization of stored lipids was delayed in the Mest1 disruptant mutant. Overall, our results suggest that expression of Mest1 allows rapid hydrolysis of stored lipids, and promotes germination and infection structure formation by M. robertsii during nutrient deprivation and invasion, while Mest1 expression in M. acridum broadens its host range by bypassing the regulatory signals found on natural hosts that trigger the mobilization of endogenous nutrient reserves. This study suggests that speciation in an insect pathogen could potentially be driven by host shifts resulting from changes in a single gene

    Dynamic Chromatin Localization of Sirt6 Shapes Stress- and Aging-Related Transcriptional Networks

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    The sirtuin Sirt6 is a NAD-dependent histone deacetylase that is implicated in gene regulation and lifespan control. Sirt6 can interact with the stress-responsive transcription factor NF-κB and regulate some NF-κB target genes, but the full scope of Sirt6 target genes as well as dynamics of Sirt6 occupancy on chromatin are not known. Here we map Sirt6 occupancy on mouse promoters genome-wide and show that Sirt6 occupancy is highly dynamic in response to TNF-α. More than half of Sirt6 target genes are only revealed upon stress-signaling. The majority of genes bound by NF-κB subunit RelA recruit Sirt6, and dynamic Sirt6 relocalization is largely driven in a RelA-dependent manner. Integrative analysis with global gene expression patterns in wild-type, Sirt6−/−, and double Sirt6−/− RelA−/− cells reveals the epistatic relationships between Sirt6 and RelA in shaping diverse temporal patterns of gene expression. Genes under the direct joint control of Sirt6 and RelA include several with prominent roles in cell senescence and organismal aging. These data suggest dynamic chromatin relocalization of Sirt6 as a key output of NF-κB signaling in stress response and aging

    Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

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    Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation

    Deep-Sequencing Analysis of the Mouse Transcriptome Response to Infection with Brucella melitensis Strains of Differing Virulence

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    Brucella melitensis is an important zoonotic pathogen that causes brucellosis, a disease that affects sheep, cattle and occasionally humans. B. melitensis strain M5-90, a live attenuated vaccine cultured from B. melitensis strain M28, has been used as an effective tool in the control of brucellosis in goats and sheep in China. However, the molecular changes leading to attenuated virulence and pathogenicity in B. melitensis remain poorly understood. In this study we employed the Illumina Genome Analyzer platform to perform genome-wide digital gene expression (DGE) analysis of mouse peritoneal macrophage responses to B. melitensis infection. Many parallel changes in gene expression profiles were observed in M28- and M5-90-infected macrophages, suggesting that they employ similar survival strategies, notably the induction of anti-inflammatory and antiapoptotic factors. Moreover, 1019 differentially expressed macrophage transcripts were identified 4 h after infection with the different B. melitensis strains, and these differential transcripts notably identified genes involved in the lysosome and mitogen-activated protein kinase (MAPK) pathways. Further analysis employed gene ontology (GO) analysis: high-enrichment GOs identified endocytosis, inflammatory, apoptosis, and transport pathways. Path-Net and Signal-Net analysis highlighted the MAPK pathway as the key regulatory pathway. Moreover, the key differentially expressed genes of the significant pathways were apoptosis-related. These findings demonstrate previously unrecognized changes in gene transcription that are associated with B. melitensis infection of macrophages, and the central signaling pathways identified here merit further investigation. Our data provide new insights into the molecular attenuation mechanism of strain M5-90 and will facilitate the generation of new attenuated vaccine strains with enhanced efficacy

    Whole genome identification of Mycobacterium tuberculosis vaccine candidates by comprehensive data mining and bioinformatic analyses

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    <p>Abstract</p> <p>Background</p> <p><it>Mycobacterium tuberculosis</it>, the causative agent of tuberculosis (TB), infects ~8 million annually culminating in ~2 million deaths. Moreover, about one third of the population is latently infected, 10% of which develop disease during lifetime. Current approved prophylactic TB vaccines (BCG and derivatives thereof) are of variable efficiency in adult protection against pulmonary TB (0%–80%), and directed essentially against early phase infection.</p> <p>Methods</p> <p>A genome-scale dataset was constructed by analyzing published data of: (1) global gene expression studies under conditions which simulate intra-macrophage stress, dormancy, persistence and/or reactivation; (2) cellular and humoral immunity, and vaccine potential. This information was compiled along with revised annotation/bioinformatic characterization of selected gene products and <it>in silico </it>mapping of T-cell epitopes. Protocols for scoring, ranking and prioritization of the antigens were developed and applied.</p> <p>Results</p> <p>Cross-matching of literature and <it>in silico</it>-derived data, in conjunction with the prioritization scheme and biological rationale, allowed for selection of 189 putative vaccine candidates from the entire genome. Within the 189 set, the relative distribution of antigens in 3 functional categories differs significantly from their distribution in the whole genome, with reduction in the Conserved hypothetical category (due to improved annotation) and enrichment in Lipid and in Virulence categories. Other prominent representatives in the 189 set are the PE/PPE proteins; iron sequestration, nitroreductases and proteases, all within the Intermediary metabolism and respiration category; ESX secretion systems, resuscitation promoting factors and lipoproteins, all within the Cell wall category. Application of a ranking scheme based on qualitative and quantitative scores, resulted in a list of 45 best-scoring antigens, of which: 74% belong to the dormancy/reactivation/resuscitation classes; 30% belong to the Cell wall category; 13% are classical vaccine candidates; 9% are categorized Conserved hypotheticals, all potentially very potent T-cell antigens.</p> <p>Conclusion</p> <p>The comprehensive literature and <it>in silico</it>-based analyses allowed for the selection of a repertoire of 189 vaccine candidates, out of the whole-genome 3989 ORF products. This repertoire, which was ranked to generate a list of 45 top-hits antigens, is a platform for selection of genes covering all stages of <it>M. tuberculosis </it>infection, to be incorporated in rBCG or subunit-based vaccines.</p

    Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

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    Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.
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