This article summarizes our progress with RegulonDB (http://regulondb.ccg.unam.mx/) during the past 2 years. We have kept up-to-date the knowledge from the published literature regarding transcriptional regulation in Escherichia coli K-12. We have maintained and expanded our curation efforts to improve the breadth and quality of the encoded experimental knowledge, and we have implemented criteria for the quality of our computational predictions. Regulatory phrases now provide high-level descriptions of regulatory regions. We expanded the assignment of quality to various sources of evidence, particularly for knowledge generated through high-throughput (HT) technology. Based on our analysis of most relevant methods, we defined rules for determining the quality of evidence when multiple independent sources support an entry. With this latest release of RegulonDB, we present a new highly reliable larger collection of transcription start sites, a result of our experimental HT genome-wide efforts. These improvements, together with several novel enhancements (the tracks display, uploading format and curational guidelines), address the challenges of incorporating HT-generated knowledge into RegulonDB. Information on the evolutionary conservation of regulatory elements is also available now. Altogether, RegulonDB version 8.0 is a much better home for integrating knowledge on gene regulation from the sources of information currently available.

RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference database of the best-known regulatory network of any free-living organism, that of Escherichia coli K-12. The major conceptual change since 3 years ago is an expanded biological context so that transcriptional regulation is now part of a unit that initiates with the signal and continues with the signal transduction to the core of regulation, modifying expression of the affected target genes responsible for the response. We call these genetic sensory response units, or Gensor Units. We have initiated their high-level curation, with graphic maps and superreactions with links to other databases. Additional connectivity uses expandable submaps. RegulonDB has summaries for every transcription factor (TF) and TF-binding sites with internal symmetry. Several DNA-binding motifs and their sizes have been redefined and relocated. In addition to data from the literature, we have incorporated our own information on transcription start sites (TSSs) and transcriptional units (TUs), obtained by using high-throughput whole-genome sequencing technologies. A new portable drawing tool for genomic features is also now available, as well as new ways to download the data, including web services, files for several relational database manager systems and text files including BioPAX format.

Despite almost 40 years of molecular genetics research in Escherichia coli a major fraction of its Transcription Start Sites (TSSs) are still unknown, limiting therefore our understanding of the regulatory circuits that control gene expression in this model organism. RegulonDB (http://regulondb.ccg.unam.mx/) is aimed at integrating the genetic regulatory network of E. coli K12 as an entirely bioinformatic project up till now. In this work, we extended its aims by generating experimental data at a genome scale on TSSs, promoters and regulatory regions. We implemented a modified 5′ RACE protocol and an unbiased High Throughput Pyrosequencing Strategy (HTPS) that allowed us to map more than 1700 TSSs with high precision. From this collection, about 230 corresponded to previously reported TSSs, which helped us to benchmark both our methodologies and the accuracy of the previous mapping experiments. The other ca 1500 TSSs mapped belong to about 1000 different genes, many of them with no assigned function. We identified promoter sequences and type of σ factors that control the expression of about 80% of these genes. As expected, the housekeeping σ70 was the most common type of promoter, followed by σ38. The majority of the putative TSSs were located between 20 to 40 nucleotides from the translational start site. Putative regulatory binding sites for transcription factors were detected upstream of many TSSs. For a few transcripts, riboswitches and small RNAs were found. Several genes also had additional TSSs within the coding region. Unexpectedly, the HTPS experiments revealed extensive antisense transcription, probably for regulatory functions. The new information in RegulonDB, now with more than 2400 experimentally determined TSSs, strengthens the accuracy of promoter prediction, operon structure, and regulatory networks and provides valuable new information that will facilitate the understanding from a global perspective the complex and intricate regulatory network that operates in E. coli.

Wetlands constitute the main natural source of methane on Earth due to their high content of natural organic matter (NOM), but key drivers, such as electron acceptors, supporting methanotrophic activities in these habitats are poorly understood. We performed anoxic incubations using freshly collected sediment, along with water samples harvested from a tropical wetland, amended with 13 C-methane (0.67 atm) to test the capacity of its microbial community to perform anaerobic oxidation of methane (AOM) linked to the reduction of the humic fraction of its NOM. Collected evidence demonstrates that electron-accepting functional groups (e.g., quinones) present in NOM fueled AOM by serving as a terminal electron acceptor. Indeed, while sulfate reduction was the predominant process, accounting for up to 42.5% of the AOM activities, the microbial reduction of NOM concomitantly occurred. Furthermore, enrichment of wetland sediment with external NOM provided a complementary electron-accepting capacity, of which reduction accounted for ϳ100 nmol 13 CH 4 oxidized · cm Ϫ3 · day Ϫ1 . Spectroscopic evidence showed that quinone moieties were heterogeneously distributed in the wetland sediment, and their reduction occurred during the course of AOM. Moreover, an enrichment derived from wetland sediments performing AOM linked to NOM reduction stoichiometrically oxidized methane coupled to the reduction of the humic analogue anthraquinone-2,6-disulfonate. Microbial populations potentially involved in AOM coupled to microbial reduction of NOM were dominated by divergent biota from putative AOMassociated archaea. We estimate that this microbial process potentially contributes to the suppression of up to 114 teragrams (Tg) of CH 4 · year Ϫ1 in coastal wetlands and more than 1,300 Tg · year Ϫ1 , considering the global wetland area. IMPORTANCEThe identification of key processes governing methane emissions from natural systems is of major importance considering the global warming effects triggered by this greenhouse gas. Anaerobic oxidation of methane (AOM) coupled to the microbial reduction of distinct electron acceptors plays a pivotal role in mitigating methane emissions from ecosystems. Given their high organic content, wetlands constitute the largest natural source of atmospheric methane. Nevertheless, processes controlling methane emissions in these environments are poorly understood. Here, we provide tracer analysis with 13 CH 4 and spectroscopic evidence revealing that AOM linked to the microbial reduction of redox functional groups in natural organic matter (NOM) prevails in a tropical wetland. We suggest that microbial reduction of NOM may largely contribute to the suppression of methane emissions from

The current DNA sequencing technologies and their high-throughput yield, allowed the thrive of genomic and transcriptomic experiments but it also have generated big data problem. Due to this exponential growth of sequencing data, also the complexity of managing, processing and interpreting it in order to generate results, has raised. Therefore, the demand of easy-to-use friendly software and websites to run bioinformatic tools is imminent. In particular, RNA-Seq and differential expression analysis have become a popular and useful method to evaluate the genetic expression change in any organism. However, many scientists struggle with the data analysis since most of the available tools are implemented in a UNIX-based environment. Therefore, we have developed the web server IDEAMEX (Integrative Differential Expression Analysis for Multiple EXperiments). The IDEAMEX pipeline needs a raw count table for as many desired replicates and conditions, allowing the user to select which conditions will be compared, instead of doing all-vs.-all comparisons. The whole process consists of three main steps (1) Data Analysis: that allows a preliminary analysis for quality control based on the data distribution per sample, using different types of graphs; (2) Differential expression: performs the differential expression analysis with or without batch effect error awareness, using the bioconductor packages, NOISeq, limma-Voom, DESeq2 and edgeR, and generate reports for each method; (3) Result integration: the obtained results the integrated results are reported using different graphical outputs such as correlograms, heatmaps, Venn diagrams and text lists. Our server allows an easy and friendly visualization for results, providing an easy interaction during the analysis process, as well as error tracking and debugging by providing output log files. The server is currently available and can be accessed at http://www.uusmb.unam.mx/ideamex/ where the documentation and example input files are provided. We consider that this web server can help other researchers with no previous bioinformatic knowledge, to perform their analyses in a simple manner.