Paleogene geochronology: An integrated approach

Geochronology is the conceptual division of continuous time as measured (geochronometry) by the progression in an ordinal series of events. This is best achieved by an approach which integrates four independent data sets: magnetostratigraphy, seafloor spreading magnetic lineation patterns, biostratigraphy, and isotopic ages. This integrated approach results in an ordinal framework which can measure time with greater resolution, though perhaps less accuracy, than a radiometric approach alone. A comparative analysis of two recently proposed Paleogene geochronologic time scales is presented

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.National Institute of General Medical Sciences (U.S.) (Go grant 1RC2GM092602-01

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.National Institute of General Medical Sciences (U.S.) (Go grant 1RC2GM092602-01

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/human​n. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.National Institutes of Health (U.S.) (U54HG004968

Deep Sequencing of the Oral Microbiome Reveals Signatures of Periodontal Disease

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (∼2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ∼90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes

OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models

Multiple expressions of plume-ridge interaction in the Galapagos : volcanic lineaments and ridge jumps

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 13 (2012): Q05018, doi:10.1029/2012GC004093.Anomalous volcanism and tectonics between near-ridge mantle plumes and mid-ocean ridges provide important insights into the mechanics of plume-lithosphere interaction. We present new observations and analysis of multibeam, side scan sonar, sub-bottom chirp, and total magnetic field data collected during the R/V Melville FLAMINGO cruise (MV1007; May–June, 2010) to the Northern Galápagos Volcanic Province (NGVP), the region between the Galápagos Archipelago and the Galápagos Spreading Center (GSC) on the Nazca Plate, and to the region east of the Galápagos Transform Fault (GTF) on the Cocos Plate. The NGVP exhibits pervasive off-axis volcanism related to the nearby Galápagos hot spot, which has dominated the tectonic evolution of the region. Observations indicate that ~94% of the excess volcanism in our survey area occurs on the Nazca Plate in three volcanic lineaments. Identified faults in the NGVP are consistent with normal ridge spreading except for those within a ~60 km wide swath of transform-oblique faults centered on the GTF. These transform-oblique faults are sub-parallel to the elongation direction of larger lineament volcanoes, suggesting that lineament formation is influenced by the lithospheric stress field. We evaluate current models for lineament formation using existing and new observations as well as numerical models of mantle upwelling and melting. The data support a model where the lithospheric stress field controls the location of volcanism along the lineaments while several processes likely supply melt to these eruptions. Synthetic magnetic models and an inversion for crustal magnetization are used to determine the tectonic history of the study area. Results are consistent with creation of the GTF by two southward ridge jumps, part of a series of jumps that have maintained a plume-ridge separation distance of 145 km to 215 km since ~5 Ma.This work was supported by NSF grant OCE-0926637 and OCE-1030904 to DF and KH. DG’s work was supported by NSF grants EAR- 0838461 and EAR-1145271. Additional support was provided to E.M. by the Deep Ocean Exploration Institute at the Woods Hole Oceanographic Institution.2012-11-3

Accretion, structure and hydrology of intermediate spreading-rate oceanic crust from drillhole experiments and seafloor observations

Downhole measurements recorded in the context of the Ocean Drilling Program in Hole 504B, the deepest hole drilled yet into the oceanic crust, are analyzed in terms of accretion processes of the upper oceanic crust at intermediate spreading-rate. The upper part of the crust is found to support the non steady-state models of crustal accretion developed from seafloor observations (Kappel and Ryan, 1986; Gente, 1987). The continuous and vertical nature of borehole measurements provides stratigraphic and structural data that cannot be obtained solely from seafloor studies and, in turn, these models define a framework to analyze the structural, hydrological, and mineralogical observations made in the hole over the past decade.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43190/1/11001_2005_Article_BF01204282.pd

Advancing microbial sciences by individual-based modelling

Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE)