Biology subjects

Hadadi, N.

Publications and source records attributed to Hadadi, N..

12 recordsLinked to original sources

bioRxiv2022

NICEgame: A workflow for annotating the knowledge gaps in metabolic reconstructions, using known and hypothetical reactions

Advances in medicine and biotechnology rely on the further understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant biochemical knowledge gaps remain uncharacterized. Several approaches have been developed during the past years to identify missing metabolic annotations in genome-scale. However, these approaches suggest missing metabolic reactions within a limited set of already characterized metabolic capabilities. In this study, we introduce NICEgame (Network Integrated Computational Explorer for Gap Annotation of Metabolism), a workflow to characterize missing metabolic capabilities in genome-scale metabolic models using the ATLAS of Biochemistry. NICEgame suggests alternative sets of known and hypothetical reactions to resolve gaps in metabolic networks, assesses their thermodynamic feasibility, and suggests candidate genes and proteins to catalyze the introduced reactions. We use gene essentiality data use to identify metabolic gaps in the latest genome-scale model of Escherichia coli, iML1515. We apply our gap-filling approach and further enhance its genome annotation, by suggesting reactions and putative genes to resolve 46 % of the false negative gene essentiality predictions.

systems biology↗

bioRxiv2021

Comparative multi-tissue profiling reveals extensive tissue-specificity in transcriptome reprogramming upon cold exposure

Cold exposure is an extensively used intervention for enhancing thermogenic and mitochondrial activity in adipose tissues. As such, it has been suggested as a potential lifestyle intervention for body weight maintenance. The metabolic consequences of cold acclimation are not limited to the adipose tissues, however the impact on rest of the tissues in context of their gene expression profile remains unclear. Here we provide a systematic characterization of cold exposure-mediated effects in a comparative multi-tissue RNA sequencing approach using wide range of organs including spleen, bone marrow, spinal cord, brain, hypothalamus, ileum, liver, subcutaneous-, visceral- and brown adipose tissues. Our findings highlight that transcriptional responses to cold exposure exhibit high degree of tissue-specificity both at the gene level and at GO enrichment gene sets, which is not directed by the basal gene expression pattern exhibited by the various organs. Our study places the cold adaptation of individual tissues in a whole-organism framework and provides an integrative transcriptional analysis necessary for understanding the cold exposure-mediated biological reprograming.

physiology↗

bioRxiv2020

Cold Exposure Protects from Neuroinflammation Through Immunologic Reprogramming

Autoimmunity is energetically costly, but the impact of a metabolically active state on immunity and immune-mediated diseases is unclear. Ly6Chi monocytes are key effectors in CNS autoimmunity with elusive role in priming naive autoreactive T cells. Here we provide unbiased analysis of the immune changes in various compartments during cold exposure, and show that this energetically costly stimulus markedly ameliorates active experimental autoimmune encephalomyelitis (EAE). Cold exposure decreases MHCII on monocytes at steady-state and in various inflammatory mouse models, and suppresses T cell priming and pathogenicity through the modulation of monocytes. Genetic, or antibody-mediated monocyte depletion, or adoptive transfer of Th1- or Th17-polarized cells for EAE abolish the cold-induced effects on T cells or EAE, respectively. These findings provide a mechanistic link between environmental temperature and neuroinflammation, and suggest competition between cold-induced metabolic adaptations and autoimmunity as energetic trade-off beneficial for the immune-mediated diseases.

immunology↗

bioRxiv2020

Database for drug metabolism and comparisons, NICEdrug.ch, aids discovery and design

The discovery of a drug requires over a decade of enormous research and financial investments--and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch database, which incorporates 250,000 bio-active molecules, and studied their metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. Clustering based on this fingerprint in statins identified drugs for repurposing. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1,300 drugs and food molecules to target COVID-19 and explains their inhibitory mechanisms. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

pharmacology and toxicology↗

bioRxiv2019

Acetobacteraceae in the honey bee gut comprise two distant clades with diverging metabolism and ecological niches

Various bacteria of the family Acetobacteraceae are associated with the gut environment of insects. Honey bees harbor two distinct Acetobacteraceae in their gut, Alpha2.1 and Alpha2.2. While Alpha2.1 seems to be a gut specialist, Alpha2.2 is also found in the diet (e.g. royal jelly), the hypopharyngeal glands, and the larvae of honey bees. Here, we combined amplicon and genome sequencing to better understand functional differences associated with the ecology of Alpha2.1 and Alpha2.2. We find that the two phylotypes are differentially distributed along the worker and queen bee gut. Phylogenetic analysis shows that Alpha2.2 is nested within the acetic acid bacteria and consists of two separate sub-lineages, whereas Alpha2.1 belongs to a basal lineage with an unusual GC content for Acetobacteraceae. Gene content analysis revealed major differences in the central carbon and respiratory metabolism between the two phylotypes. While Alpha2.2 encodes two periplasmic dehydrogenases to carry out oxidative fermentation, Alpha2.1 lacks this capability, but instead harbors a diverse set of cytoplasmic dehydrogenases. These differences are accompanied by the loss of the TCA cycle in Alpha2.2, but not in Alpha2.1. We speculate that Alpha2.2 has specialized for fast-resource utilization through incomplete carbohydrate oxidation, giving it an advantage in sugar-rich environments such as royal jelly. On the contrary, the broader metabolic range of Alpha2.1 may provide an advantage in the worker bee hindgut, where competition with other bacteria and flexibility in resource utilization may be relevant for persistence. Our results show that bacteria belonging to the same family may utilize vastly different strategies to colonize niches associated with the animal gut.

genomics↗

bioRxiv2019

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

systems biology↗

bioRxiv2019

Close-Range Interactions Favor Growth in Random-Paired Extracted Soil Bacteria

Species interactions at the cellular level are thought to govern the formation and functioning of microbial communities, but direct measurements of species interactions are difficult to perform between the hundreds of different species that constitute most microbial ecosystems. We developed a methodology to examine interactive growth of random cell pairs encapsulated inside 40-70 {micro}m diameter agarose beads. We focused on a sandy soil as a test microbial ecosystem. By using gentle washing procedures, we detached microbial cells from sand and encapsulated them either in the absence or presence of pure culture inoculants. We then tested whether inoculants had on average positive or negative effects on the growth of resident community members depending on the growth substrate. Surprisingly, all the tested inoculants (including Pseudomonas veronii 1YdBTEX2, Pseudomonas putida F1, Pseudomonas protegens CHA0 and Escherichia coli MG1655) stimulated the growth of 40-80 percent of sand-derived cells when grown pair-wise in close proximity (i.e., within the same bead). This was true essentially irrespective of the growth substrate. Beneficial inoculant-sand cell partnerships resulted in up to 100-fold increase in productivity of the sand cell partner and up to 100-fold decrease in that of the inoculant. However, the maximum productivity attained by inoculant-sand cell partners within beads did not surpass that of inoculants alone. Further surprisingly, random pairs of sand cells encapsulated within the same bead also benefited growth in comparison to individual sand cells in a mutualistic manner (i.e., productivity when grown together was greater than the sum of individual productivities), but less than productivities observed in partnerships with the tested inoculants. This suggests that partnerships between inoculants and sand cells are not so much characterized by competition for substrate as by carbon loss through metabolite provision of the inoculant to sand cells (competitive exploitation).

microbiology↗

bioRxiv2018

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was also able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.\n\nAuthor SummaryThe recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in high-throughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between \"data generation\" and \"data integration.\" reconstructed model to predict the observed physiology, e.g., growth phase through omics data integration. In this study, we present a new method named REMI (Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints in genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid to understanding the interactions between different types of omics data.

systems biology↗

bioRxiv2018

Genomic changes underlying host specialization in the bee gut symbiont Lactobacillus Firm5

Bacteria that engage in longstanding associations with particular hosts are expected to evolve host-specific adaptations that limit their capacity to thrive in other environments. Consistent with this, many gut symbionts seem to have a limited host range, based on community profiling and phylogenomics. However, few studies have experimentally investigated host specialization of gut symbionts and underlying mechanisms have largely remained elusive. Here, we studied host specialization of a dominant gut symbiont of social bees, Lactobacillus Firm5. We show that Firm5 strains isolated from honey bees and bumble bees separate into deep-branching phylogenetic lineages. Despite their divergent evolution, colonization experiments show that bumble bee strains are capable of colonizing the honey bee gut. However, they were less successful than honey bee strains, and competition with honey bee strains completely abolished their colonization, whereas honey bee strains were able to coexist. This suggests that both host selection and interbacterial competition play important roles for host specialization. Using comparative genomics of 27 Firm5 isolates, we identified candidate genomic changes underlying host specialization. We found that honey bee strains harbored a larger and more diverse gene pool of carbohydrate-related functions than bumble bee strains. As dietary-derived carbohydrates are the main energy source for strains of the Firm5 phylotype, the metabolic flexibility of honey bee strains may give these bacteria a competitive advantage over bumble bee strains in colonizing the gut niche and hence contribute to host specialization.

microbiology↗

bioRxiv2018

Heterologous Expression of Pseudomonas putida Methyl-Accepting Chemotaxis Proteins Yields Escherichia coli Chemotactic to Aromatic Compounds

Escherichia coli, commonly used in chemotaxis studies, is attracted mostly by amino acids, sugars and peptides. We envisioned modifying chemotaxis specificity of E. coli by expressing heterologous chemoreceptors from Pseudomonas putida enabling attraction either to toluene or benzoate. The mcpT gene encoding the type 40H methyl-accepting chemoreceptor for toluene from Pseudomonas putida MT53 and the pcaY gene for the type 40H receptor for benzoate and related molecules from P. putida F1 were expressed from the trg promoter on a plasmid in motile wild-type E. coli MG1655. E. coli cells expressing McpT accumulated in chemoattraction assays to sources with 60-200 M toluene; less strongly than the response to 100 M serine, but statistically significantly stronger than to sources without any added attractant. An McpT-mCherry fusion protein was detectably expressed in E. coli and yielding weak but distinguishable membrane and polar foci in 1% of cells. E. coli expressing PcaY showed weak attraction to 0.1-1 mM benzoate but 50-70% of cells localized the PcaY-mCherry fusion to their membrane. We conclude that implementing heterologous receptors in the E. coli chemotaxis network is possible and, upon improvement of the compatibility of the type 40H chemoreceptors, may bear interest for biosensing.\n\nIMPORTANCEBacterial chemotaxis might be harnessed for the development of rapid biosensors, in which chemical availability is deduced from cell accumulation to chemoattractants over time. Chemotaxis of Escherichia coli has been well-studied, but the bacterium is not attracted to chemicals of environmental concern, such as aromatic solvents. We show here that heterologous chemoreceptors for aromatic compounds from Pseudomonas putida at least partly functionally complement the E. coli chemotaxis network, yielding cells attracted to toluene or benzoate. Complementation was still inferior to native chemoattractants like serine, but our study demonstrates the potential for obtaining selective sensing for aromatic compounds in E. coli.

microbiology↗

bioRxiv2017

Knowledge of the Neighborhood of the Reactive Site up to Three Atoms Can Predict Biochemistry and Protein Sequences

Thousands of biochemical reactions with characterized activities are orphan, meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for these reactions, and this method introduces, for the first time, information about the enzyme binding pocket into reaction similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized, nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest protein sequences and genes that catalyze the most similar non-orphan reactions as candidates for also catalyzing the orphan ones.\n\nWe performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from KEGG 2011 (a comprehensive enzymatic reaction database) that became non-orphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 out of 379 novel reactions in 2014 that were later catalogued in KEGG 2018, BridgIT predicted the exact or highly similar enzyme sequences.\n\nBridgIT requires knowledge about only three connecting bonds around the atoms of the reactive sites to correctly identify protein sequences for 93% of analyzed enzymatic reactions. Increasing to six connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.\n\nSIGNIFICANCE STATEMENTRecent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them \"orphan\". Nearly half of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, a novel computational method, BridgIT, identified a potential enzyme sequence to orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze non-natural transformations.

bioinformatics↗

bioRxiv2017

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia towards sustainable production of the 2nd generation of biofuels, with Methyl Ethyl Ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methyl-butanenitrile for further study. We reconstructed 3679610 novel biosynthetic pathways towards these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18622 were found to be most biologically feasible ones based on thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC=\"FIGDIR/small/209569_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@15bb5corg.highwire.dtl.DTLVardef@bfc12borg.highwire.dtl.DTLVardef@1230500org.highwire.dtl.DTLVardef@1d74d2_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

synthetic biology↗