Improved genome annotation through untargeted detection of pathway-specific metabolites

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.</p> <p>Results</p> <p>Of 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.</p> <p>Conclusions</p> <p>To facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (¹⁵N) or carbon (¹³C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.</p

Assessment of heterologous butyrate and butanol pathway activity by measurement of intracellular pathway intermediates in recombinant Escherichia coli

In clostridia, n-butanol production from carbohydrates at yields of up to 76% of the theoretical maximum and at titers of up to 13 g/L has been reported. However, in Escherichia coli, several groups have reported butyric acid or butanol production from recombinant expression of clostridial genes, at much lower titers and yields. To pinpoint deficient steps in the recombinant pathway, we developed an analytical procedure for the determination of intracellular pools of key pathway intermediates and applied the technique to the analysis of three sets of E. coli strains expressing various combinations of butyrate biosynthesis genes. Low expression levels of the hbd-encoded S-3-hydroxybutyryl-CoA dehydrogenase were insufficient to convert acetyl-CoA to 3-hydroxybutyryl-CoA, indicating that hbd was a rate-limiting step in the production of butyryl-CoA. Increasing hbd expression alleviated this bottleneck, but in resulting strains, our pool size measurements and thermodynamic analysis showed that the reaction step catalyzed by the bcd-encoded butyryl-CoA dehydrogenase was rate-limiting. E. coli strains expressing both hbd and ptb-buk produced crotonic acid as a byproduct, but this byproduct was not observed with expression of related genes from non-clostridial organisms. Our thermodynamic interpretation of pool size measurements is applicable to the analysis of other metabolic pathways

Risk Factors for Being Seronegative following SARS-CoV-2 Infection in a Large Cohort of Health Care Workers in Denmark

Most individuals seroconvert after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but being seronegative is observed in 1 to 9%. We aimed to investigate the risk factors associated with being seronegative following PCR-confirmed SARS-CoV-2 infection. In a prospective cohort study, we screened health care workers (HCW) in the Capital Region of Denmark for SARS-CoV-2 antibodies. We performed three rounds of screening from April to October 2020 using an enzyme-linked immunosorbent assay (ELISA) method targeting SARS-CoV-2 total antibodies. Data on all participants’ PCR for SARS-CoV-2 RNA were captured from national registries. The Kaplan-Meier method and Cox proportional hazards models were applied to investigate the probability of being seronegative and the related risk factors, respectively. Of 36,583 HCW, 866 (2.4%) had a positive PCR before or during the study period. The median (interquartile range [IQR]) age of 866 HCW was 42 (31 to 53) years, and 666 (77%) were female. After a median of 132 (range, 35 to 180) days, 21 (2.4%) of 866 were seronegative. In a multivariable model, independent risk factors for being seronegative were self-reported asymptomatic or mild infection hazard ratio (HR) of 6.6 (95% confidence interval [CI], 2.6 to 17; P < 0.001) and body mass index (BMI) of ≥30, HR 3.1 (95% CI, 1.1 to 8.8; P = 0.039). Only a few (2.4%) HCW were not seropositive. Asymptomatic or mild infection as well as a BMI above 30 were associated with being seronegative. Since the presence of antibodies against SARS-CoV-2 reduces the risk of reinfection, efforts to protect HCW with risk factors for being seronegative may be needed in future COVID-19 surges. IMPORTANCE Most individuals seroconvert after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but negative serology is observed in 1 to 9%. We found that asymptomatic or mild infection as well as a BMI above 30 were associated with being seronegative. Since the presence of antibodies against SARS-CoV-2 reduces the risk of reinfection, efforts to protect HCW with risk factors for being seronegative may be needed in future COVID-19 surges

Genome-wide screens identify Toxoplasma gondii determinants of parasite fitness in IFNγ-activated murine macrophages

Macrophages play an essential role in the early immune response against Toxoplasma and are the cell type preferentially infected by the parasite in vivo. Interferon gamma (IFNγ) elicits a variety of anti-Toxoplasma activities in macrophages. Using a genome-wide CRISPR screen we identify 353 Toxoplasma genes that determine parasite fitness in naїve or IFNγ-activated murine macrophages, seven of which are further confirmed. We show that one of these genes encodes dense granule protein GRA45, which has a chaperone-like domain, is critical for correct localization of GRAs into the PVM and secretion of GRA effectors into the host cytoplasm. Parasites lacking GRA45 are more susceptible to IFNγ-mediated growth inhibition and have reduced virulence in mice. Together, we identify and characterize an important chaperone-like GRA in Toxoplasma and provide a resource for the community to further explore the function of Toxoplasma genes that determine fitness in IFNγ-activated macrophages

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

High-throughput platforms for metabolomics.

Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis

An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses.

Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50&nbsp;GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration

An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses.

Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50&nbsp;GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration

Selection and optimization of gene targets for the metabolic engineering of Microorganisms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2009.Includes bibliographical references.This thesis is about identifying genetic interventions that improve the performance of targeted pathways in the metabolism of the bacterium Escherichia coli. Three case studies illustrate three disparate approaches to identifying genetic interventions: (i) combining metabolomic measurements with thermodynamic calculations to identify rate-limiting reaction steps in a target pathway; (ii) use of stoichiometric, optimization-based models of metabolism to predict target genetic interventions in silico; and (iii) the mutagenesis of promoter sequences to fine-tune the expression level of rate-limiting genes. These techniques can be classified by both the number of strain modifications created, and the number of variables measured in each. Taken together, the cases suggest that the best methods for identifying genetic interventions balance the number of strain modifications with the number of measured variables. The first case is butyrate production in recombinant E. coli. A strain of E. coli deleted for the production of lactate, ethanol, and acetate was designed to minimize competing pathways for carbon, and was unexpectedly found to exhibit oxygen auxotrophy. Expression of genes from Clostridium acetobutylicum resulted in production of 3-hydroxybutyric acid, but not butyric acid.(cont.) The clostridial genes ptb and buk were capable of producing S-3-hydroxybutyric acid from the butyrate pathway intermediate metabolite S-3-hydroxybutyryl-CoA. In parallel, the intracellular concentrations of pathway metabolites was measured for a set of strains expressing the clostridial butanol biosynthesis pathway in various configurations. Comparison of measured pool sizes and pool sizes for thermodynamic equilibrium pinpointed the butyryl-CoA dehydrogenase step, encoded by bcd, as a bottleneck enzyme. Thus, points for genetic intervention are ptb, buk, and bcd. The second case is tyrosine overproduction in E. coli. Constraints-based models of E. coli metabolism proved incapable of predicting gene knockout targets. Therefore, to understand factors underlying tyrosine overproduction, the intracellular concentrations of amino acids were measured. In tyrosine overproducers, the intracellular concentrations of most proteinogenic amino acids were vastly perturbed relative to non-producing strains. This fact and thermodynamic considerations suggested that the transamination of p-hydroxyphenylpyruvate to tyrosine was near equilibrium, and thus that nitrogen supply may be limiting tyrosine production. Culture media amended with glutamate or glutamine, but not with a-ketoglutarate or other organic acids, increased tyrosine production in these strains more than 8-fold, showing that interventions which affect nitrogen supply are attractive targets for engineering tyrosine overproduction in E. coli. The last case addresses the question of what types of intervention are best. A series of 22 promoters with well-characterized, variable strengths was created by mutagenesis. This library was used to replace promoters for key genes in the biosynthesis of lycopene or biomass from glucose. These metabolic phenotypes exhibited strain-dependent optima with respect to the expression levels of the key rate-controlling genes genes. Promoter engineering thus shows that subtle genetic interventions can have profound effects on pathway function.by Curt R. Fischer.Ph.D