Search

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. The code and data are available at: https://github.com/BNLNLP/PPI-Relation-Extraction

Polycistronic gene expression, common in prokaryotes, was thought to be extremely rare in eukaryotes. The development of long-read sequencing of full-length transcript isomers (Iso-Seq) has facilitated a reexamination of that dogma. Using Iso-Seq, we discovered hundreds of examples of polycistronic expression of nuclear genes in two divergent species of green algae: Chlamydomonas reinhardtii and Chromochloris zofingiensis Here, we employ a range of independent approaches to validate that multiple proteins are translated from a common transcript for hundreds of loci. A chromatin immunoprecipitation analysis using trimethylation of lysine 4 on histone H3 marks confirmed that transcription begins exclusively at the upstream gene. Quantification of polyadenylated [poly(A)] tails and poly(A) signal sequences confirmed that transcription ends exclusively after the downstream gene. Coexpression analysis found nearly perfect correlation for open reading frames (ORFs) within polycistronic loci, consistent with expression in a shared transcript. For many polycistronic loci, terminal peptides from both ORFs were identified from proteomics datasets, consistent with independent translation. Synthetic polycistronic gene pairs were transcribed and translated in vitro to recapitulate the production of two distinct proteins from a common transcript. The relative abundance of these two proteins can be modified by altering the Kozak-like sequence of the upstream gene. Replacement of the ORFs with selectable markers or reporters allows production of such heterologous proteins, speaking to utility in synthetic biology approaches. Conservation of a significant number of polycistronic gene pairs between C. reinhardtii, C. zofingiensis, and five other species suggests that this mechanism may be evolutionarily ancient and biologically important in the green algal lineage.

In the Abstract, the url https://modelseed.org has been replaced with https://modelseed.org/biochem.

Epigenetic landscapes can shape physiologic and disease phenotypes. We used integrative, high resolution multi-omics methods to delineate the methylome landscape and characterize the oncogenic drivers of esophageal squamous cell carcinoma (ESCC). We found 98% of CpGs are hypomethylated across the ESCC genome. Hypo-methylated regions are enriched in areas with heterochromatin binding markers (H3K9me3, H3K27me3), while hyper-methylated regions are enriched in polycomb repressive complex (EZH2/SUZ12) recognizing regions. Altered methylation in promoters, enhancers, and gene bodies, as well as in polycomb repressive complex occupancy and CTCF binding sites are associated with cancer-specific gene dysregulation. Epigenetic-mediated activation of non-canonical WNT/β-catenin/MMP signaling and a YY1/lncRNA ESCCAL-1/ribosomal protein network are uncovered and validated as potential novel ESCC driver alterations. This study advances our understanding of how epigenetic landscapes shape cancer pathogenesis and provides a resource for biomarker and target discovery.

Genome signature tags (GSTs) were developed for use in a cost-effective sequencing-based method to identify and quantitatively analyze genomic or mixtures of genomic DNA. The method was successfully used to generate Csp6I-based tags upstream of the 16S rRNA gene and allowed to discriminate between closely related strains of Bacillus cereus and Bacillus anthracis.

CREB transcription factor regulates survival, differentiation and synaptic plasticity, however, the complement of CREB targets that are responsible for these responses are not identified. A unique method to identify CREB targets, called serial analysis of chromatin occupancy (SACO), is developed and results of the study, which is the most exhaustive definition of transcription factor binding sites in metazon species are discussed.

Abstract Background 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. 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. Results From 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. Conclusions Metagenomics 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.

Abstract Throughout immeasurable time, microorganisms evolved and accumulated remarkable physiological and functional heterogeneity, and now constitute the major reserve for genetic diversity on earth. Using metagenomics, namely genetic material recovered directly from environmental samples, this biogenetic diversification can be accessed without the need to cultivate cells. Accordingly, microbial communities and their metagenomes, isolated from biotopes with high turnover rates of recalcitrant biomass, such as lignocellulosic plant cell walls, have become a major resource for bioprospecting; furthermore, this material is a major asset in the search for new biocatalytics (enzymes) for various industrial processes, including the production of biofuels from plant feedstocks. However, despite the contributions from metagenomics technologies consequent upon the discovery of novel enzymes, this relatively new enterprise requires major improvements. In this review, we compare function-based metagenome screening and sequence-based metagenome data mining, discussing the advantages and limitations of both methods. We also describe the unusual enzymes discovered via metagenomics approaches, and discuss the future prospects for metagenome technologies.

Abstract Background Families of homologous enzymes evolved from common progenitors. The availability of multiple sequences representing each activity presents an opportunity for extracting information specifying the functionality of individual homologs. We present a straightforward method for the identification of residues likely to determine class specific functionality in which multiple sequence alignments are converted to an annotated graphical form by the Conserved Property Difference Locator (CPDL) program. Results Three test cases, each comprised of two groups of funtionally-distinct homologs, are presented. Of the test cases, one is a membrane and two are soluble enzyme families. The desaturase/hydroxylase data was used to design and test the CPDL algorithm because a comparative sequence approach had been successfully applied to manipulate the specificity of these enzymes. The other two cases, ATP/GTP cyclases, and MurD/MurE synthases were chosen because they are well characterized structurally and biochemically. For the desaturase/hydroxylase enzymes, the ATP/GTP cyclases and the MurD/MurE synthases, groups of 8 (of ~400), 4 (of ~150) and 10 (of >400) residues, respectively, of interest were identified that contain empirically defined specificity determining positions. Conclusion CPDL consistently identifies positions near enzyme active sites that include those predicted from structural and/or biochemical studies to be important for specificity and/or function. This suggests that CPDL will have broad utility for the identification of potential class determining residues based on multiple sequence analysis of groups of homologous proteins. Because the method is sequence, rather than structure, based it is equally well suited for designing structure-function experiments to investigate membrane and soluble proteins.

Throughout immeasurable time, microorganisms evolved and accumulated remarkable physiological and functional heterogeneity, and now constitute the major reserve for genetic diversity on earth. Using metagenomics, namely genetic material recovered directly from environmental samples, this biogenetic diversification can be accessed without the need to cultivate cells. Accordingly, microbial communities and their metagenomes, isolated from biotopes with high turnover rates of recalcitrant biomass, such as lignocellulosic plant cell walls, have become a major resource for bioprospecting; furthermore, this material is a major asset in the search for new biocatalytics (enzymes) for various industrial processes, including the production of biofuels from plant feedstocks. However, despite the contributions from metagenomics technologies consequent upon the discovery of novel enzymes, this relatively new enterprise requires major improvements. In this review, we compare function-based metagenome screening and sequence-based metagenome data mining, discussing the advantages and limitations of both methods. We also describe the unusual enzymes discovered via metagenomics approaches, and discuss the future prospects for metagenome technologies. [ABSTRACT FROM AUTHOR]

The effects of elevated atmospheric CO2 (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 CO2 and O3 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 CO2. 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 CO2 significantly alters soil microbial community composition. [ABSTRACT FROM AUTHOR]

The repressor element 1 (RE1) silencing transcription factor (REST) helps preserve the identity of nervous tissue by silencing neuronal genes in non-neural tissues. Moreover, in an epithelial model of tumorigenesis, loss of REST function is associated with loss of adhesion, suggesting the aberrant expression of REST-controlled genes encoding this property. To date, no adhesion molecules under REST control have been identified. Here, we used serial analysis of chromatin occupancy to perform genome-wide identification of REST-occupied target sequences (RE1 sites) in a kidney cell line. We discovered novel REST-binding motifs and found that the number of RE1 sites far exceeded previous estimates. A large family of targets encoding adhesion proteins was identified, as were genes encoding signature proteins of neuroendocrine tumors. Unexpectedly, genes considered exclusively non-neuronal also contained an RE1 motif and were expressed in neurons. This supports the model that REST binding is a critical determinant of neuronal phenotype. [ABSTRACT FROM AUTHOR]