Dataset Information

Metatranscriptome analyses indicate resource partitioning between diatoms in the field.

ABSTRACT: Diverse communities of marine phytoplankton carry out half of global primary production. The vast diversity of the phytoplankton has long perplexed ecologists because these organisms coexist in an isotropic environment while competing for the same basic resources (e.g., inorganic nutrients). Differential niche partitioning of resources is one hypothesis to explain this "paradox of the plankton," but it is difficult to quantify and track variation in phytoplankton metabolism in situ. Here, we use quantitative metatranscriptome analyses to examine pathways of nitrogen (N) and phosphorus (P) metabolism in diatoms that cooccur regularly in an estuary on the east coast of the United States (Narragansett Bay). Expression of known N and P metabolic pathways varied between diatoms, indicating apparent differences in resource utilization capacity that may prevent direct competition. Nutrient amendment incubations skewed N/P ratios, elucidating nutrient-responsive patterns of expression and facilitating a quantitative comparison between diatoms. The resource-responsive (RR) gene sets deviated in composition from the metabolic profile of the organism, being enriched in genes associated with N and P metabolism. Expression of the RR gene set varied over time and differed significantly between diatoms, resulting in opposite transcriptional responses to the same environment. Apparent differences in metabolic capacity and the expression of that capacity in the environment suggest that diatom-specific resource partitioning was occurring in Narragansett Bay. This high-resolution approach highlights the molecular underpinnings of diatom resource utilization and how cooccurring diatoms adjust their cellular physiology to partition their niche space.

SUBMITTER: Alexander H

PROVIDER: S-EPMC4418864 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Metatranscriptome analyses indicate resource partitioning between diatoms in the field.

Alexander Harriet H Jenkins Bethany D BD Rynearson Tatiana A TA Dyhrman Sonya T ST

Proceedings of the National Academy of Sciences of the United States of America 20150413 17

Diverse communities of marine phytoplankton carry out half of global primary production. The vast diversity of the phytoplankton has long perplexed ecologists because these organisms coexist in an isotropic environment while competing for the same basic resources (e.g., inorganic nutrients). Differential niche partitioning of resources is one hypothesis to explain this "paradox of the plankton," but it is difficult to quantify and track variation in phytoplankton metabolism in situ. Here, we use ...[more]

PMID: 25870299

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Project description:Analyses of metagenome data (MG) and metatranscriptome data (MT) are often challenged by a paucity of complete reference genome sequences and the uneven/low sequencing depth of the constituent organisms in the microbial community, which respectively limit the power of reference-based alignment and de novo sequence assembly. These limitations make accurate protein family classification and abundance estimation challenging, which in turn hamper downstream analyses such as abundance profiling of metabolic pathways, identification of differentially encoded/expressed genes, and de novo reconstruction of complete gene and protein sequences from the protein family of interest. The profile hidden Markov model (HMM) framework enables the construction of very useful probabilistic models for protein families that allow for accurate modeling of position specific matches, insertions, and deletions. We present a novel homology detection algorithm that integrates banded Viterbi algorithm for profile HMM parsing with an iterative simultaneous alignment and assembly computational framework. The algorithm searches a given profile HMM of a protein family against a database of fragmentary MG/MT sequencing data and simultaneously assembles complete or near-complete gene and protein sequences of the protein family. The resulting program, HMM-GRASPx, demonstrates superior performance in aligning and assembling homologs when benchmarked on both simulated marine MG and real human saliva MG datasets. On real supragingival plaque and stool MG datasets that were generated from healthy individuals, HMM-GRASPx accurately estimates the abundances of the antimicrobial resistance (AMR) gene families and enables accurate characterization of the resistome profiles of these microbial communities. For real human oral microbiome MT datasets, using the HMM-GRASPx estimated transcript abundances significantly improves detection of differentially expressed (DE) genes. Finally, HMM-GRASPx was used to reconstruct comprehensive sets of complete or near-complete protein and nucleotide sequences for the query protein families. HMM-GRASPx is freely available online from http://sourceforge.net/projects/hmm-graspx.

Dataset Information

Metatranscriptome analyses indicate resource partitioning between diatoms in the field.

Publications

Metatranscriptome analyses indicate resource partitioning between diatoms in the field.

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