Julio Collado-Vides

Biology faces the challenge of articulating general principles capable of organizing and conceptually integrating its immense diversity. Since the operon and allosteric models of the 1960s, the regulatory landscape has expanded dramatically. Building on classical allosteric regulation, I propose the allostery principle, which stipulates that the combinatorial diversity in gene regulation arises from the use of two independent sites that can link any two molecular species within a signaling process, independent of specific mechanistic implementations. I provide empirical evidence that the allostery principle has been implemented repeatedly in six distinct molecular platforms during evolution, generating symbolic genetic explosions that expanded the combinatorial diversity of gene regulation. Four involve bacterial genetic platforms, including protein regulators, promoters, and small RNAs. Two involve eukaryotic platforms: intrinsically disordered proteins coupled with alternative splicing, and the extensive complex multicellular eukaryotic regulation mediated by non-coding RNAs. Remarkably, the central Merge operation within the minimalist generative model of the human language faculty-another symbolic explosion in evolution-conceptually satisfies the allostery principle. I further propose that regulatory signaling fluxes constitute a theory physically instantiated within organisms' architecture, functioning as a compressed representation with a strong explanatory power of its capabilities. The concept of missing regulatability provides the required falsifiability for this theoretical construct. Gene regulation implements physiological capabilities whose global significance derives from alignment with universally encoded biological ends-survival, reproduction, development, and differentiation. A system that responds to the environment making the adequate changes to achieve its final ends is showing the ability to instantiate intelligent actions. Therefore, gene regulation provides a form of intelligence to biological systems. This definition matches with intelligence in physical symbol systems formalized early in artificial intelligence. Adequate interpretations of these results and their implications are discussed. "It is the union of passionate interest in the detailed facts with equal devotion to abstract generalization which forms the novelty in our present society" (Whitehead 1925).

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We use these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We use BoltzNet to quantitatively design novel binding sites, which we validate with biophysical experiments on purified protein. We generate models for 124 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

Cryptic prophages (CPs) are elements of bacterial genomes acquired from bacteriophage that infect the host cell and ultimately become stably integrated within the host genome. While some proteins encoded by CPs can modulate host phenotypes, the potential for Transcription Factors (TFs) encoded by CPs to impact host physiology by regulating host genes has not been thoroughly investigated. In this work, we report hundreds of host genes regulated by DicC, a DNA-binding TF encoded in the Qin prophage of Esherichia coli . We identified host-encoded regulatory targets of DicC that could be linked to known phenotypes of its induction. We also demonstrate that a DicC-induced growth defect is largely independent of other Qin prophage genes. Our data suggest a greater role for cryptic prophage TFs in controlling bacterial host gene expression than previously appreciated.

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. Here, we present for the first time a detailed analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of Escherichia coli K-12. An RI groups the transcription factor, its effect (positive or negative) and the regulated target, a promoter, a gene or transcription unit. We improved the evidence codes so that specific methods are incorporated and classified into independent groups. On this basis we updated the computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. These updates enabled us to map the RI set to the current collection of HT TF-binding datasets from ChIP-seq, ChIP-exo, gSELEX and DAP-seq in RegulonDB, enriching in this way the evidence of close to one-quarter (1329) of RIs from the current total 5446 RIs. Based on the new computational capabilities of our improved annotation of evidence sources, we can now analyze the internal architecture of evidence, their categories (experimental, classical, HT, computational), and confidence levels. This is how we know that the joint contribution of HT and computational methods increase the overall fraction of reliable RIs (the sum of confirmed and strong evidence) from 49% to 71%. Thus, the current collection has 3912 reliable RIs, with 2718 or 70% of them with classical evidence which can be used to benchmark novel HT methods. Users can selectively exclude the method they want to benchmark, or keep for instance only the confirmed interactions. The recovery of regulatory sites in RegulonDB by the different HT methods ranges between 33% by ChIP-exo to 76% by ChIP-seq although as discussed, many potential confounding factors limit their interpretation. The collection of improvements reported here provides a solid foundation to incorporate new methods and data, and to further integrate the diverse sources of knowledge of the different components of the transcriptional regulatory network. There is no other genomic database that offers this comprehensive high-quality architecture of knowledge supporting a corpus of transcriptional regulatory interactions.

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.

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. As new methodologies emerge, a natural step is to compare their results with those from established methodologies, such as the classic methods of molecular biology used to characterize transcription factor binding sites, promoters, or transcription units. In the case of Escherichia coli K-12, the best-studied microorganism, for the last 30 years we have continuously gathered such knowledge from original scientific publications, and have organized it in two databases, RegulonDB and EcoCyc. Furthermore, since RegulonDB version 11.0 (1), we offer comprehensive datasets of binding sites from chromatin immunoprecipitation combined with sequencing (ChIP-seq), ChIP combined with exonuclease digestion and next-generation sequencing (ChIP-exo), genomic SELEX screening (gSELEX), and DNA affinity purification sequencing (DAP-seq) HT technologies, as well as additional datasets for transcription start sites, transcription units and RNA sequencing (RNA-seq) expression profiles. Here, we present for the first time an analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of E. coli K-12. An RI is formed by the transcription factor, its positive or negative effect on a promoter, a gene or transcription unit. We improved the evidence codes so that the specific methods are described, and we classified them into seven independent groups. This is the basis for our updated computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. We compare the confidence levels of the RI collection before and after adding HT evidence illustrating how knowledge will change as more HT data and methods appear in the future. Users can generate subsets filtering out the method they want to benchmark and avoid circularity, or keep for instance only the confirmed interactions. The comparison of different HT methods with the available datasets indicate that ChIP-seq recovers the highest fraction (>70%) of binding sites present in RegulonDB followed by gSELEX, DAP-seq and ChIP-exo. There is no other genomic database that offers this comprehensive high-quality anatomy of evidence supporting a corpus of transcriptional regulatory interactions.

Genomics has set the basis for a variety of methodologies that produce high-throughput datasets identifying the different players that define gene regulation, particularly regulation of transcription initiation and operon organization. These datasets are available in public repositories, such as the Gene Expression Omnibus, or ArrayExpress. However, accessing and navigating such a wealth of data is not straightforward. No resource currently exists that offers all available high and low-throughput data on transcriptional regulation in Escherichia coli K-12 to easily use both as whole datasets, or as individual interactions and regulatory elements. RegulonDB (https://regulondb.ccg.unam.mx) began gathering high-throughput dataset collections in 2009, starting with transcription start sites, then adding ChIP-seq and gSELEX in 2012, with up to 99 different experimental high-throughput datasets available in 2019. In this paper we present a radical upgrade to more than 2000 high-throughput datasets, processed to facilitate their comparison, introducing up-to-date collections of transcription termination sites, transcription units, as well as transcription factor binding interactions derived from ChIP-seq, ChIP-exo, gSELEX and DAP-seq experiments, besides expression profiles derived from RNA-seq experiments. For ChIP-seq experiments we offer both the data as presented by the authors, as well as data uniformly processed in-house, enhancing their comparability, as well as the traceability of the methods and reproducibility of the results. Furthermore, we have expanded the tools available for browsing and visualization across and within datasets. We include comparisons against previously existing knowledge in RegulonDB from classic experiments, a nucleotide-resolution genome viewer, and an interface that enables users to browse datasets by querying their metadata. A particular effort was made to automatically extract detailed experimental growth conditions by implementing an assisted curation strategy applying Natural language processing and machine learning. We provide summaries with the total number of interactions found in each experiment, as well as tools to identify common results among different experiments. This is a long-awaited resource to make use of such wealth of knowledge and advance our understanding of the biology of the model bacterium E. coli K-12.

Knowledge of biological organisms at the molecular level that has been gathered is now organized into databases, often within ontological frameworks. To enable computational comparisons of annotations across different genomes and organisms, controlled vocabularies have been essential, as is the case in the functional annotation classifications used for bacteria, such as MultiFun and the more widely used Gene Ontology. The function of individual gene products as well as the processes in which collections of them participate constitute a wealth of classes that describe the biological role of gene products in a large number of organisms in the three kingdoms of life. In this contribution, we highlight from a qualitative perspective some limitations of these frameworks and discuss challenges that need to be addressed to bridge the gap between annotation as currently captured by ontologies and databases and our understanding of the basic principles in the organization and functioning of organisms; we illustrate these challenges with some examples in bacteria. We hope that raising awareness of these issues will encourage users of Gene Ontology and similar ontologies to be careful about data interpretation and lead to improved data representation.