its intrinsic and associated acetylase activities and/or Portland, Oregon 97239 by interacting with the core transcriptional machinery 2 Biology Department (Vo and Goodman, 2001). Brookhaven National Laboratory The ability of CREB to be activated by multiple signal-Upton, New York 11973 ing pathways has led investigators to examine its role in 3 Department of Public Health long-term adaptative responses to various extracellular and Preventative Medicine stimuli. CREB regulates the differentiation of T cells, Oregon Health and Science University hepatocytes, and spermatocytes, and CREB-dependent Portland, Oregon 97239 gene expression plays a particularly important role in 4 Department of Microbiology and Immunology the central nervous system, where it regulates neuronal Emory School of Medicine survival, memory consolidation, addiction, entrainment Atlanta, Georgia 30322 of the biological clock, and synaptic refinement. Surpris-5Howard Hughes Medical Institute ingly, despite strong evidence for its involvement in gene Department of Neurobiology and Behavior expression, CREB binding to native promoters has been The State University of New York at Stony Brook documented for only a few target genes. Moreover, Stony Brook, New York 11794 many CREB targets (e.g., C/EBP␤, Egr1, and Nurr1) are themselves transcription factors that regulate other genes. Thus, it is often uncertain which genes are acti-Summary vated by CREB directly and which are activated indirectly. The CREB transcription factor regulates differentia-One approach for identifying CREB targets is to meation, survival, and synaptic plasticity. The complement sure the changes in gene expression that occur in reof CREB targets responsible for these responses has sponse to an activating or interfering CREB mutant (Fass not been identified, however. We developed a novel et al., 2003; McClung and Nestler, 2003). A caveat to approach to identify CREB targets, termed serial analthis approach is that the overexpressed protein may ysis of chromatin occupancy (SACO), by combining bind inappropriately or compete for coactivators. Addichromatin immunoprecipitation (ChIP) with a modifitionally, effects can be indirect. For example, one of the cation of SAGE. Using a SACO library derived from rat genes most highly induced by VP16-CREB (a constitu-PC12 cells, we identified ‫000,14ف‬ genomic signature tively active CREB mutant) is ICER, a potent inhibitor tags (GSTs) that mapped to unique genomic loci. CREB of CREB function, which itself alters the expression of binding was confirmed for all loci supported by multi-CREB-regulated genes (Fass et al., 2003). ple GSTs. Of the 6302 loci identified by multiple GSTs, Chromatin immunoprecipitation (ChIP) measures bind-40% were within 2 kb of the transcriptional start of an ing of endogenous transcription factors to native promotannotated gene, 49% were within 1 kb of a CpG island, ers. Theoretically, immunoprecipitated DNA fragments and 72% were within 1 kb of a putative cAMP-response can be cloned and sequenced to determine thei...

The effects of elevated atmospheric CO(2) (560 p.p.m.) and subsequent plant responses on the soil microbial community composition associated with trembling aspen was assessed through the classification of 6996 complete ribosomal DNA sequences amplified from the Rhinelander WI free-air CO(2) and O(3) enrichment (FACE) experiments microbial community metagenome. This in-depth comparative analysis provides an unprecedented, detailed and deep branching profile of population changes incurred as a response to this environmental perturbation. Total bacterial and eukaryotic abundance does not change; however, an increase in heterotrophic decomposers and ectomycorrhizal fungi is observed. Nitrate reducers of the domain bacteria and archaea, of the phylum Crenarchaea, potentially implicated in ammonium oxidation, significantly decreased with elevated CO(2). These changes in soil biota are evidence for altered interactions between trembling aspen and the microorganisms in its surrounding soil, and support the theory that greater plant detritus production under elevated CO(2) significantly alters soil microbial community composition.

This study describes the composition and metabolic potential of a lignocellulosic biomass degrading community that decays poplar wood chips under anaerobic conditions. We examined the community that developed on poplar biomass in a non-aerated bioreactor over the course of a year, with no microbial inoculation other than the naturally occurring organisms on the woody material. The composition of this community contrasts in important ways with biomass-degrading communities associated with higher organisms, which have evolved over millions of years into a symbiotic relationship. Both mammalian and insect hosts provide partial size reduction, chemical treatments (low or high pH environments), and complex enzymatic ‘secretomes’ that improve microbial access to cell wall polymers. We hypothesized that in order to efficiently degrade coarse untreated biomass, a spontaneously assembled free-living community must both employ alternative strategies, such as enzymatic lignin depolymerization, for accessing hemicellulose and cellulose and have a much broader metabolic potential than host-associated communities. This would suggest that such a community would make a valuable resource for finding new catalytic functions involved in biomass decomposition and gaining new insight into the poorly understood process of anaerobic lignin depolymerization. Therefore, in addition to determining the major players in this community, our work specifically aimed at identifying functions potentially involved in the depolymerization of cellulose, hemicelluloses, and lignin, and to assign specific roles to the prevalent community members in the collaborative process of biomass decomposition. A bacterium similar to Magnetospirillum was identified among the dominant community members, which could play a key role in the anaerobic breakdown of aromatic compounds. We suggest that these compounds are released from the lignin fraction in poplar hardwood during the decay process, which would point to lignin-modification or depolymerization under anaerobic conditions.

BackgroundTo efficiently deconstruct recalcitrant plant biomass to fermentable sugars in industrial processes, biocatalysts of higher performance and lower cost are required. The genetic diversity found in the metagenomes of natural microbial biomass decay communities may harbor such enzymes. Our goal was to discover and characterize new glycoside hydrolases (GHases) from microbial biomass decay communities, especially those from unknown or never previously cultivated microorganisms.ResultsFrom the metagenome sequences of an anaerobic microbial community actively decaying poplar biomass, we identified approximately 4,000 GHase homologs. Based on homology to GHase families/activities of interest and the quality of the sequences, candidates were selected for full-length cloning and subsequent expression. As an alternative strategy, a metagenome expression library was constructed and screened for GHase activities. These combined efforts resulted in the cloning of four novel GHases that could be successfully expressed in Escherichia coli. Further characterization showed that two enzymes showed significant activity on p-nitrophenyl-α-L-arabinofuranoside, one enzyme had significant activity against p-nitrophenyl-β-D-glucopyranoside, and one enzyme showed significant activity against p-nitrophenyl-β-D-xylopyranoside. Enzymes were also tested in the presence of ionic liquids.ConclusionsMetagenomics provides a good resource for mining novel biomass degrading enzymes and for screening of cellulolytic enzyme activities. The four GHases that were cloned may have potential application for deconstruction of biomass pretreated with ionic liquids, as they remain active in the presence of up to 20% ionic liquid (except for 1-ethyl-3-methylimidazolium diethyl phosphate). Alternatively, ionic liquids might be used to immobilize or stabilize these enzymes for minimal solvent processing of biomass.

We developed single-point genome signature tags (SP-GSTs), a generally applicable, high-throughput sequencing-based method that targets specific genes to generate identifier tags from well-defined points in a genome. The technique yields identifier tags that can distinguish between closely related bacterial strains and allow for the identification of microbial community members. SP-GSTs are determined by three parameters: (i) the primer designed to recognize a conserved gene sequence, (ii) the anchoring enzyme recognition sequence, and (iii) the type IIS restriction enzyme which defines the tag length. We evaluated the SP-GST method in silico for bacterial identification using the genes rpoC, uvrB, and recA and the 16S rRNA gene. The best distinguishing tags were obtained with the restriction enzyme Csp6I upstream of the 16S rRNA gene, which discriminated all organisms in our data set to at least the genus level and most organisms to the species level. The method was successfully used to generate Csp6I-based tags upstream of the 16S rRNA gene and allowed us to discriminate between closely related strains of Bacillus cereus and Bacillus anthracis. This concept was further used successfully to identify the individual members of a defined microbial community.

To efficiently deconstruct recalcitrant plant biomass to fermentable sugars in industrial processes, biocatalysts of higher performance and lower cost are required. The genetic diversity found in the metagenomes of natural microbial biomass decay communities may harbor such enzymes. The aim of this chapter is to describe strategies, based on metagenomic approaches, for the discovery of glycoside hydrolases (GHases) from microbial biomass decay communities, especially those from unknown or never-been-cultivated microorganisms.

Because protein-protein interactions (PPIs) are crucial to understand living systems, harvesting these data is essential to probe disease development and discern gene/protein functions and biological processes. Some curated datasets contain PPI data derived from the literature and other sources (e.g., IntAct, BioGrid, DIP, and HPRD). However, they are far from exhaustive, and their maintenance is a labor-intensive process. On the other hand, machine learning methods to automate PPI knowledge extraction from the scientific literature have been limited by a shortage of appropriate annotated data. This work presents a unified, multi-source PPI corpora with vetted interaction definitions augmented by binary interaction type labels and a Transformer-based deep learning method that exploits entities' relational context information for relation representation to improve relation classification performance. The model's performance is evaluated on four widely studied biomedical relation extraction datasets, as well as this work's target PPI datasets, to observe the effectiveness of the representation to relation extraction tasks in various data. Results show the model outperforms prior state-of-the-art models.