RhizoBindingSites v2.0 Is a Bioinformatic Database of DNA Motifs Potentially Involved in Transcriptional Regulation Deduced From Their Genomic Sites.

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, Rhizobium leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

Rhizobium etli CFN42 and Sinorhizobium meliloti 1021 bioinformatic transcriptional regulatory networks from culture and symbiosis.

Rhizobium etli CFN42 proteome-transcriptome mixed data of exponential growth and nitrogen-fixing bacteroids, as well as Sinorhizobium meliloti 1021 transcriptome data of growth and nitrogen-fixing bacteroids, were integrated into transcriptional regulatory networks (TRNs). The one-step construction network consisted of a matrix-clustering analysis of matrices of the gene profile and all matrices of the transcription factors (TFs) of their genome. The networks were constructed with the prediction of regulatory network application of the RhizoBindingSites database (http://rhizobindingsites.ccg.unam.mx/). The deduced free-living Rhizobium etli network contained 1,146 genes, including 380 TFs and 12 sigma factors. In addition, the bacteroid R. etli CFN42 network contained 884 genes, where 364 were TFs, and 12 were sigma factors, whereas the deduced free-living Sinorhizobium meliloti 1021 network contained 643 genes, where 259 were TFs and seven were sigma factors, and the bacteroid Sinorhizobium meliloti 1021 network contained 357 genes, where 210 were TFs and six were sigma factors. The similarity of these deduced condition-dependent networks and the biological E. coli and B. subtilis independent condition networks segregates from the random Erdös-Rényi networks. Deduced networks showed a low average clustering coefficient. They were not scale-free, showing a gradually diminishing hierarchy of TFs in contrast to the hierarchy role of the sigma factor rpoD in the E. coli K12 network. For rhizobia networks, partitioning the genome in the chromosome, chromids, and plasmids, where essential genes are distributed, and the symbiotic ability that is mostly coded in plasmids, may alter the structure of these deduced condition-dependent networks. It provides potential TF gen-target relationship data for constructing regulons, which are the basic units of a TRN.

Host population structure and species resolution reveal prophage transmission dynamics.

Much knowledge about bacteriophages has been obtained via genomics and metagenomics over the last decades. However, most studies dealing with prophage diversity have rarely conducted phage species delimitation (aspect 1) and have hardly integrated the population structure of the host (aspect 2). Yet, these two aspects are essential in assessing phage diversity. Here, we implemented an operational definition of phage species (clustering at 95% identity, 90% coverage) and integrated the host's population structure to understand prophage diversity better. Gathering the most extensive data set of Acinetobacter baumannii phages (4,152 prophages + 122 virulent phages, distributed in 46 countries in the world), we show that 91% (875 out of 963) of the prophage species have four or fewer prophages per species, and just five prophage species have more than 100 prophages. Most prophage species have a narrow host range and are geographically restricted; yet, very few have a broad host range being well spread in distant lineages of A. baumannii. These few broad host range prophage species are not only cosmopolitan but also the most abundant species. We also noted that polylysogens had very divergent prophages, belonging to different prophage species, and prophages can easily be gained and lost within the bacterial lineages. Finally, even with this extensive data set, the prophage diversity has not been fully grasped. Our study highlights how integrating the host population structure and a solid operational definition of phage species allows us to better appreciate phage diversity and its transmission dynamics. IMPORTANCE: Much knowledge about bacteriophages has been obtained via genomics and metagenomics over the last decades. However, most studies dealing with prophage diversity have rarely conducted phage species delimitation (aspect 1) and have hardly integrated the population structure of the host (aspect 2). Yet, these two aspects are essential in assessing phage diversity. Here, we implemented an operational definition of phage species (clustering at 95% identity, 90% coverage) and integrated the host's population structure to understand prophage diversity better. Gathering the most extensive data set of Acinetobacter baumannii phages, we show that most prophage species have four or fewer prophages per species, and just five prophage species have more than 100 prophages. Most prophage species have a narrow host range and are geographically restricted; yet, very few have a broad host range being well spread in distant lineages of A. baumannii. These few broad host range prophage species are cosmopolitan and the most abundant species. Prophages in the same bacterial genome are very divergent, and prophages can easily be gained and lost within the bacterial lineages. Finally, even with this extensive data set, the prophage diversity has not been fully grasped. This study shows how integrating the host population structure and clustering at the species level allows us to better appreciate phage diversity and its transmission dynamics.

Mobility networks in Greater Mexico City.

Based on more than 11 billion geolocated cell phone records from 33 million different devices, daily mobility networks were constructed over a 15-month period for Greater Mexico City, one of the largest and most diverse metropolitan areas globally. The time frame considered spans the entire year of 2020 and the first three months of 2021, enabling the analysis of population movement dynamics before, during, and after the COVID-19 health contingency. The nodes within the 456 networks represent the basic statistical geographic areas (AGEBs) established by the National Institute of Statistics, Geography, and Informatics (INEGI) in Mexico. This framework facilitates the integration of mobility data with numerous indicators provided by INEGI. Edges connecting these nodes represent movement between AGEBs, with edge weights indicating the volume of trips from one AGEB to another. This extensive dataset allows researchers to uncover travel patterns, cross-reference data with socio-economic indicators, and conduct segregation studies, among other potential analyses.

RegulonDB v12.0: a comprehensive resource of transcriptional regulation in E. coli K-12.

RegulonDB is a database that contains the most comprehensive corpus of knowledge of the regulation of transcription initiation of Escherichia coli K-12, including data from both classical molecular biology and high-throughput methodologies. Here, we describe biological advances since our last NAR paper of 2019. We explain the changes to satisfy FAIR requirements. We also present a full reconstruction of the RegulonDB computational infrastructure, which has significantly improved data storage, retrieval and accessibility and thus supports a more intuitive and user-friendly experience. The integration of graphical tools provides clear visual representations of genetic regulation data, facilitating data interpretation and knowledge integration. RegulonDB version 12.0 can be accessed at https://regulondb.ccg.unam.mx.

Intermunicipal travel networks of Mexico during the COVID-19 pandemic.

Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society's local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020-2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

RhizoBindingSites, a Database of DNA-Binding Motifs in Nitrogen-Fixing Bacteria Inferred Using a Footprint Discovery Approach.

Basic knowledge of transcriptional regulation is needed to understand the mechanisms governing biological processes, i.e., nitrogen fixation by Rhizobiales bacteria in symbiosis with leguminous plants. The RhizoBindingSites database is a computer-assisted framework providing motif-gene-associated conserved sequences potentially implicated in transcriptional regulation in nine symbiotic species. A dyad analysis algorithm was used to deduce motifs in the upstream regulatory region of orthologous genes, and only motifs also located in the gene seed promoter with a p-value of 1e-4 were accepted. A genomic scan analysis of the upstoream sequences with these motifs was performed. These predicted binding sites were categorized according to low, medium and high homology between the matrix and the upstream regulatory sequence. On average, 62.7% of the genes had a motif, accounting for 80.44% of the genes per genome, with 19613 matrices (a matrix is a representation of a motif). The RhizoBindingSites database provides motif and gene information, motif conservation in the order Rhizobiales, matrices, motif logos, regulatory networks constructed from theoretical or experimental data, a criterion for selecting motifs and a guide for users. The RhizoBindingSites database is freely available online at rhizobindingsites.ccg.unam.mx.

RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more.

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.