Publications by authors named "Anat Kreimer"

19 Publications

  • Page 1 of 1

Author Correction: lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements.

Nat Protoc 2020 Oct 30. Epub 2020 Oct 30.

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41596-020-00422-zDOI Listing
October 2020

Evaluation of Davis et al.: Exploring Sequence of Determinants of Transcriptional Regulation-The Case of c-AMP Response Element.

Cell Syst 2020 07 22;11(1):2-4. Epub 2020 Jul 22.

Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Boston, MA, USA. Electronic address:

One snapshot of the peer review process for "Dissection of c-AMP Response Element Architecture by Using Genomic and Episomal Massively Parallel Reporter Assays" (Davis et al., 2020).
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http://dx.doi.org/10.1016/j.cels.2020.07.001DOI Listing
July 2020

lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements.

Nat Protoc 2020 08 8;15(8):2387-2412. Epub 2020 Jul 8.

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.

Massively parallel reporter assays (MPRAs) can simultaneously measure the function of thousands of candidate regulatory sequences (CRSs) in a quantitative manner. In this method, CRSs are cloned upstream of a minimal promoter and reporter gene, alongside a unique barcode, and introduced into cells. If the CRS is a functional regulatory element, it will lead to the transcription of the barcode sequence, which is measured via RNA sequencing and normalized for cellular integration via DNA sequencing of the barcode. This technology has been used to test thousands of sequences and their variants for regulatory activity, to decipher the regulatory code and its evolution, and to develop genetic switches. Lentivirus-based MPRA (lentiMPRA) produces 'in-genome' readouts and enables the use of this technique in hard-to-transfect cells. Here, we provide a detailed protocol for lentiMPRA, along with a user-friendly Nextflow-based computational pipeline-MPRAflow-for quantifying CRS activity from different MPRA designs. The lentiMPRA protocol takes ~2 months, which includes sequencing turnaround time and data processing with MPRAflow.
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http://dx.doi.org/10.1038/s41596-020-0333-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550205PMC
August 2020

Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction.

Cell Stem Cell 2019 Nov 17;25(5):713-727.e10. Epub 2019 Oct 17.

Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA; Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA. Electronic address:

Epigenomic regulation and lineage-specific gene expression act in concert to drive cellular differentiation, but the temporal interplay between these processes is largely unknown. Using neural induction from human pluripotent stem cells (hPSCs) as a paradigm, we interrogated these dynamics by performing RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and assay for transposase accessible chromatin using sequencing (ATAC-seq) at seven time points during early neural differentiation. We found that changes in DNA accessibility precede H3K27ac, which is followed by gene expression changes. Using massively parallel reporter assays (MPRAs) to test the activity of 2,464 candidate regulatory sequences at all seven time points, we show that many of these sequences have temporal activity patterns that correlate with their respective cell-endogenous gene expression and chromatin changes. A prioritization method incorporating all genomic and MPRA data further identified key transcription factors involved in driving neural fate. These results provide a comprehensive resource of genes and regulatory elements that orchestrate neural induction and illuminate temporal frameworks during differentiation.
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http://dx.doi.org/10.1016/j.stem.2019.09.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6850896PMC
November 2019

MPRAnalyze: statistical framework for massively parallel reporter assays.

Genome Biol 2019 09 2;20(1):183. Epub 2019 Sep 2.

Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California, USA.

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.
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http://dx.doi.org/10.1186/s13059-019-1787-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717970PMC
September 2019

Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.

Hum Mutat 2019 09 18;40(9):1299-1313. Epub 2019 Jun 18.

Department of Electrical Engineering and Computer Sciences, Center for Computational Biology, University of California, Berkeley, California.

Deciphering the potential of noncoding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA sequences and their variants in a single experiment. With increasing number of publically available MPRA data sets, one can now develop data-driven models which, given a DNA sequence, predict its regulatory activity. Here, we performed a comprehensive meta-analysis of several MPRA data sets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across data sets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell-type-specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" Challenge for predicting effects of single-nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest.
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http://dx.doi.org/10.1002/humu.23820DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771677PMC
September 2019

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay.

Hum Mutat 2019 09 23;40(9):1280-1291. Epub 2019 Jun 23.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.

The integrative analysis of high-throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five disease-associated human enhancers and nine disease-associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cell-types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of disease-associated genetic variation.
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http://dx.doi.org/10.1002/humu.23797DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879779PMC
September 2019

Use antibiotics in cell culture with caution: genome-wide identification of antibiotic-induced changes in gene expression and regulation.

Sci Rep 2017 08 8;7(1):7533. Epub 2017 Aug 8.

Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.

Standard cell culture guidelines often use media supplemented with antibiotics to prevent cell contamination. However, relatively little is known about the effect of antibiotic use in cell culture on gene expression and the extent to which this treatment could confound results. To comprehensively characterize the effect of antibiotic treatment on gene expression, we performed RNA-seq and ChIP-seq for H3K27ac on HepG2 cells, a human liver cell line commonly used for pharmacokinetic, metabolism and genomic studies, cultured in media supplemented with penicillin-streptomycin (PenStrep) or vehicle control. We identified 209 PenStrep-responsive genes, including transcription factors such as ATF3 that are likely to alter the regulation of other genes. Pathway analyses found a significant enrichment for "xenobiotic metabolism signaling" and "PXR/RXR activation" pathways. Our H3K27ac ChIP-seq identified 9,514 peaks that are PenStrep responsive. These peaks were enriched near genes that function in cell differentiation, tRNA modification, nuclease activity and protein dephosphorylation. Our results suggest that PenStrep treatment can significantly alter gene expression and regulation in a common liver cell type such as HepG2, advocating that antibiotic treatment should be taken into account when carrying out genetic, genomic or other biological assays in cultured cells.
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http://dx.doi.org/10.1038/s41598-017-07757-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548911PMC
August 2017

Predicting gene expression in massively parallel reporter assays: A comparative study.

Hum Mutat 2017 09 9;38(9):1240-1250. Epub 2017 Mar 9.

Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, California.

In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.
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http://dx.doi.org/10.1002/humu.23197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560998PMC
September 2017

Impact of pre-adapted HIV transmission.

Nat Med 2016 06 16;22(6):606-13. Epub 2016 May 16.

Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.

Human leukocyte antigen class I (HLA)-restricted CD8(+) T lymphocyte (CTL) responses are crucial to HIV-1 control. Although HIV can evade these responses, the longer-term impact of viral escape mutants remains unclear, as these variants can also reduce intrinsic viral fitness. To address this, we here developed a metric to determine the degree of HIV adaptation to an HLA profile. We demonstrate that transmission of viruses that are pre-adapted to the HLA molecules expressed in the recipient is associated with impaired immunogenicity, elevated viral load and accelerated CD4(+) T cell decline. Furthermore, the extent of pre-adaptation among circulating viruses explains much of the variation in outcomes attributed to the expression of certain HLA alleles. Thus, viral pre-adaptation exploits 'holes' in the immune response. Accounting for these holes may be key for vaccine strategies seeking to elicit functional responses from viral variants, and to HIV cure strategies that require broad CTL responses to achieve successful eradication of HIV reservoirs.
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http://dx.doi.org/10.1038/nm.4100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899163PMC
June 2016

NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation.

BMC Bioinformatics 2015 May 17;16:164. Epub 2015 May 17.

Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.

Background: Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.

Results: NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms' niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds.

Conclusions: The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.
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http://dx.doi.org/10.1186/s12859-015-0588-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4434858PMC
May 2015

Co-regulated transcripts associated to cooperating eSNPs define Bi-fan motifs in human gene networks.

PLoS Genet 2014 Sep 11;10(9):e1004587. Epub 2014 Sep 11.

Department of Computer Science, Columbia University, New York, New York, United States of America.

Associations between the level of single transcripts and single corresponding genetic variants, expression single nucleotide polymorphisms (eSNPs), have been extensively studied and reported. However, most expression traits are complex, involving the cooperative action of multiple SNPs at different loci affecting multiple genes. Finding these cooperating eSNPs by exhaustive search has proven to be statistically challenging. In this paper we utilized availability of sequencing data with transcriptional profiles in the same cohorts to identify two kinds of usual suspects: eSNPs that alter coding sequences or eSNPs within the span of transcription factors (TFs). We utilize a computational framework for considering triplets, each comprised of a SNP and two associated genes. We examine pairs of triplets with such cooperating source eSNPs that are both associated with the same pair of target genes. We characterize such quartets through their genomic, topological and functional properties. We establish that this regulatory structure of cooperating quartets is frequent in real data, but is rarely observed in permutations. eSNP sources are mostly located on different chromosomes and away from their targets. In the majority of quartets, SNPs affect the expression of the two gene targets independently of one another, suggesting a mutually independent rather than a directionally dependent effect. Furthermore, the directions in which the minor allele count of the SNP affects gene expression within quartets are consistent, so that the two source eSNPs either both have the same effect on the target genes or both affect one gene in the opposite direction to the other. Same-effect eSNPs are observed more often than expected by chance. Cooperating quartets reported here in a human system might correspond to bi-fans, a known network motif of four nodes previously described in model organisms. Overall, our analysis offers insights regarding the fine motif structure of human regulatory networks.
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http://dx.doi.org/10.1371/journal.pgen.1004587DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161301PMC
September 2014

Variants in exons and in transcription factors affect gene expression in trans.

Genome Biol 2013 Jul 11;14(7):R71. Epub 2013 Jul 11.

Background: In recent years many genetic variants (eSNPs) have been reported as associated with expression of transcripts in trans. However, the causal variants and regulatory mechanisms through which they act remain mostly unknown. In this paper we follow two kinds of usual suspects: SNPs that alter coding regions or transcription factors, identifiable by sequencing data with transcriptional profiles in the same cohort. We show these interpretable genomic regions are enriched for eSNP association signals, thereby naturally defining source-target gene pairs. We map these pairs onto a protein-protein interaction (PPI) network and study their topological properties.

Results: For exonic eSNP sources, we report source-target proximity and high target degree within the PPI network. These pairs are more likely to be co-expressed and the eSNPs tend to have a cis effect, modulating the expression of the source gene. In contrast, transcription factor source-target pairs are not observed to have such properties, but instead a transcription factor source tends to assemble into units of defined functional roles along with its gene targets, and to share with them the same functional cluster of the PPI network.

Conclusions: Our results suggest two modes of trans regulation: transcription factor variation frequently acts via a modular regulation mechanism, with multiple targets that share a function with the transcription factor source. Notwithstanding, exon variation often acts by a local cis effect, delineating shorter paths of interacting proteins across functional clusters of the PPI network.
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http://dx.doi.org/10.1186/gb-2013-14-7-r71DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054683PMC
July 2013

NetCmpt: a network-based tool for calculating the metabolic competition between bacterial species.

Bioinformatics 2012 Aug 4;28(16):2195-7. Epub 2012 Jun 4.

Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.

Unlabelled: NetCmpt is a tool for calculating the competitive potential between pairs of bacterial species. The score describes the effective metabolic overlap (EMO) between two species, derived from analyzing the topology of the corresponding metabolic models. NetCmpt is based on the EMO algorithm, developed and validated in previous studies. It takes as input lists of species-specific enzymatic reactions (EC numbers) and generates a matrix of the potential competition scores between all pairwise combinations.

Availability And Implementation: NetCmpt is provided as both a web tool and a software package, designed for the use of non-computational biologists. The NetCmpt web tool, software, examples, and documentation are freely available online at http://app.agri.gov.il/shiri/NetComp.php.
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http://dx.doi.org/10.1093/bioinformatics/bts323DOI Listing
August 2012

Inference of modules associated to eQTLs.

Nucleic Acids Res 2012 Jul 24;40(13):e98. Epub 2012 Mar 24.

Department of Biomedical Informatics, Columbia University, New York 10032, USA.

Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.
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http://dx.doi.org/10.1093/nar/gks269DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3401454PMC
July 2012

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species.

PLoS Comput Biol 2010 Feb 26;6(2):e1000690. Epub 2010 Feb 26.

The Blavatnik School of Computer Sciences, Faculty of Life Sciences, Ramat Aviv, Israel.

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlation = approximately 0.6) with the intrinsic metabolic capacities of a species-higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets.
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http://dx.doi.org/10.1371/journal.pcbi.1000690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829043PMC
February 2010

The large-scale organization of the bacterial network of ecological co-occurrence interactions.

Nucleic Acids Res 2010 Jul 1;38(12):3857-68. Epub 2010 Mar 1.

Blavatnik School of Computer Sciences, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel.

In their natural environments, microorganisms form complex systems of interactions. Understating the structure and organization of bacterial communities is likely to have broad medical and ecological consequences, yet a comprehensive description of the network of environmental interactions is currently lacking. Here, we mine co-occurrences in the scientific literature to construct such a network and demonstrate an expected pattern of association between the species' lifestyle and the recorded number of co-occurring partners. We further focus on the well-annotated gut community and show that most co-occurrence interactions of typical gut bacteria occur within this community. The network is then clustered into species-groups that significantly correspond with natural occurring communities. The relationships between resource competition, metabolic yield and growth rate within the clusters correspond with the r/K selection theory. Overall, these results support the constructed clusters as a first approximation of a bacterial ecosystem model. This comprehensive collection of predicted communities forms a new data resource for further systematic characterization of the ecological design principals shaping communities. Here, we demonstrate its utility for predicting cooperation and inhibition within communities.
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http://dx.doi.org/10.1093/nar/gkq118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896517PMC
July 2010

Metabolic-network-driven analysis of bacterial ecological strategies.

Genome Biol 2009 5;10(6):R61. Epub 2009 Jun 5.

The Blavatnik School of Computer Sciences, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel.

Background: The growth-rate of an organism is an important phenotypic trait, directly affecting its ability to survive in a given environment. Here we present the first large scale computational study of the association between ecological strategies and growth rate across 113 bacterial species, occupying a variety of metabolic habitats. Genomic data are used to reconstruct the species' metabolic networks and habitable metabolic environments. These reconstructions are then used to investigate the typical ecological strategies taken by organisms in terms of two basic species-specific measures: metabolic variability--the ability of a species to survive in a variety of different environments; and co-habitation score vector--the distribution of other species that co-inhabit each environment.

Results: We find that growth rate is significantly correlated with metabolic variability and the level of co-habitation (that is, competition) encountered by an organism. Most bacterial organisms adopt one of two main ecological strategies: a specialized niche with little co-habitation, associated with a typically slow rate of growth; or ecological diversity with intense co-habitation, associated with a typically fast rate of growth.

Conclusions: The pattern observed suggests a universal principle where metabolic flexibility is associated with a need to grow fast, possibly in the face of competition. This new ability to produce a quantitative description of the growth rate-metabolism-community relationship lays a computational foundation for the study of a variety of aspects of the communal metabolic life.
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http://dx.doi.org/10.1186/gb-2009-10-6-r61DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718495PMC
September 2009

The evolution of modularity in bacterial metabolic networks.

Proc Natl Acad Sci U S A 2008 May 6;105(19):6976-81. Epub 2008 May 6.

School of Mathematical Science, Tel Aviv University, Tel Aviv 69978, Israel.

Deciphering the modular organization of metabolic networks and understanding how modularity evolves have attracted tremendous interest in recent years. Here, we present a comprehensive large scale characterization of modularity across the bacterial tree of life, systematically quantifying the modularity of the metabolic networks of >300 bacterial species. Three main determinants of metabolic network modularity are identified. First, network size is an important topological determinant of network modularity. Second, several environmental factors influence network modularity, with endosymbionts and mammal-specific pathogens having lower modularity scores than bacterial species that occupy a wider range of niches. Moreover, even among the pathogens, those that alternate between two distinct niches, such as insect and mammal, tend to have relatively high metabolic network modularity. Third, horizontal gene transfer is an important force that contributes significantly to metabolic modularity. We additionally reconstruct the metabolic network of ancestral bacterial species and examine the evolution of modularity across the tree of life. This reveals a trend of modularity decrease from ancestors to descendants that is likely the outcome of niche specialization and the incorporation of peripheral metabolic reactions.
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http://dx.doi.org/10.1073/pnas.0712149105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2383979PMC
May 2008