Failed "nonaccelerating" models of prokaryote gene regulatory networks

Much current network analysis is predicated on the assumption that important biological networks will either possess scale free or exponential statistics which are independent of network size allowing unconstrained network growth over time. In this paper, we demonstrate that such network growth models are unable to explain recent comparative genomics results on the growth of prokaryote regulatory gene networks as a function of gene number. This failure largely results as prokaryote regulatory gene networks are "accelerating" and have total link numbers growing faster than linearly with network size and so can exhibit transitions from stationary to nonstationary statistics and from random to scale-free to regular statistics at particular critical network sizes. In the limit, these networks can undergo transitions so marked as to constrain network sizes to be below some critical value. This is of interest as the regulatory gene networks of single celled prokaryotes are indeed characterized by an accelerating quadratic growth with gene count and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. We develop two "nonaccelerating" network models of prokaryote regulatory gene networks in an endeavor to match observation and demonstrate that these approaches fail to reproduce observed statistics.Comment: Corrected error in biological input parameter: 13 pages, 9 figure

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting "accelerating" growth have total link numbers growing faster than linearly with network size and can exhibit transitions from stationary to nonstationary statistics and from random to scale-free to regular statistics at particular critical network sizes. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value.Comment: Corrected error in biological input parameter: 15 pages, 10 figure

Increasing biological complexity is positively correlated with the relative genome-wide expansion of non-protein-coding DNA sequences

Background: Prior to the current genomic era it was suggested that the number of protein-coding genes that an organism made use of was a valid measure of its complexity. It is now clear, however, that major incongruities exist and that there is only a weak relationship between biological complexity and the number of protein coding genes. For example, using the protein-coding gene number as a basis for evaluating biological complexity would make urochordates and insects less complex than nematodes, and humans less complex than rice. Results: We analyzed the ratio of noncoding to total genomic DNA (ncDNA/tgDNA) for 85 sequenced species and found that this ratio correlates well with increasing biological complexity. The ncDNA/tgDNA ratio is generally contained within the bandwidth of 0.05-0.24 for prokaryotes, but rises to 0.26-0.52 in unicellular eukaryotes, and to 0.62-0.985 for developmentally complex multicellular organisms. Significantly, prokaryotic species display a non-uniform species distribution approaching the mean of 0.1177 ncDNA/tgDNA (p=1.58 x 10^-13), and a nonlinear ncDNA/tgDNA relationship to genome size (r=0.15). Importantly, the ncDNA/tgDNA ratio corrects for ploidy, and is not substantially affected by variable loads of repetitive sequences. Conclusions: We suggest that the observed noncoding DNA increases and compositional patterns are primarily a function of increased information content. It is therefore possible that introns, intergenic sequences, repeat elements, and genomic DNA previously regarded as genetically inert may be far more important to the evolution and functional repertoire of complex organisms than has been previously appreciated.Comment: 25 pages, 2 figures, 1 tabl

Effect of site-specific mutations in different phosphotransfer domains of the chemosensory protein ChpA on Pseudomonas aeruginosa motility

The virulence of Pseudomonas aeruginosa and other surface pathogens involves the coordinate expression of a wide range of virulence determinants, including type IV pili. These surface filaments are important for the colonization of host epithelial tissues and mediate bacterial attachment to, and translocation across, surfaces by a process known as twitching motility. This process is controlled in part by a complex signal transduction system whose central component, ChpA, possesses nine potential sites of phosphorylation, including six histidine-containing phosphotransfer (HPt) domains, one serine-containing phosphotransfer domain, one threonine-containing phosphotransfer domain, and one CheY-like receiver domain. Here, using site-directed mutagenesis, we show that normal twitching motility is entirely dependent on the CheY-like receiver domain and partially dependent on two of the HPt domains. Moreover, under different assay conditions, point mutations in several of the phosphotransfer domains of ChpA give rise to unusual "swarming" phenotypes, possibly reflecting more subtle perturbations in the control of P. aeruginosa motility that are not evident from the conventional twitching stab assay. Together, these results suggest that ChpA plays a central role in the complex regulation of type IV pilus-mediated motility in P. aeruginos

RNA Regulatory Networks 2.0

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The central role of RNA molecules in cell biology has been an expanding subject of study since the proposal of the "RNA world" hypothesis 60 years ago [...].info:eu-repo/semantics/publishedVersio

Transcriptome-wide identification of A > I RNA editing sites by inosine specific cleavage

Adenosine to inosine (A > I) RNA editing, which is catalyzed by the ADAR family of proteins, is one of the fundamental mechanisms by which transcriptomic diversity is generated. Indeed, a number of genome-wide analyses have shown that A > I editing is not limited to a few mRNAs, as originally thought, but occurs widely across the transcriptome, especially in the brain. Importantly, there is increasing evidence that A > I editing is essential for animal development and nervous system function. To more efficiently characterize the complete catalog of ADAR events in the mammalian transcriptome we developed a high-throughput protocol to identify A > I editing sites, which exploits the capacity of glyoxal to protect guanosine, but not inosine, from RNAse T1 treatment, thus facilitating extraction of RNA fragments with inosine bases at their termini for high-throughput sequencing. Using this method we identified 665 editing sites in mouse brain RNA, including most known sites and suite of novel sites that include nonsynonymous changes to protein-coding genes, hyperediting of genes known to regulate p53, and alterations to non-protein-coding RNAs. This method is applicable to any biological system for the de novo discovery of A > I editing sites, and avoids the complicated informatic and practical issues associated with editing site identification using traditional RNA sequencing data. This approach has the potential to substantially increase our understanding of the extent and function of RNA editing, and thereby to shed light on the role of transcriptional plasticity in evolution, development, and cognition

The relationship between transcription initiation RNAs and CCCTC-binding factor (CTCF) localization

Background: Transcription initiation RNAs (tiRNAs) are nuclear localized 18 nucleotide RNAs derived from sequences immediately downstream of RNA polymerase II (RNAPII) transcription start sites. Previous reports have shown that tiRNAs are intimately correlated with gene expression, RNA polymerase II binding and behaviors, and epigenetic marks associated with transcription initiation, but not elongation. Results: In the present work, we show that tiRNAs are commonly found at genomic CCCTC-binding factor (CTCF) binding sites in human and mouse, and that CTCF sites that colocalize with RNAPII are highly enriched for tiRNAs. To directly investigate the relationship between tiRNAs and CTCF we examined tiRNAs originating near the intronic CTCF binding site in the human tumor suppressor gene, p21 (cyclin-dependent kinase inhibitor 1A gene, also known as CDKN1A). Inhibition of CTCF-proximal tiRNAs resulted in increased CTCF localization and increased p21 expression, while overexpression of CTCF-proximal tiRNA mimics decreased CTCF localization and p21 expression. We also found that tiRNA-regulated CTCF binding influences the levels of trimethylated H3K27 at the alternate upstream p21 promoter, and affects the levels of alternate p21 (p21) transcripts. Extending these studies to another randomly selected locus with conserved CTCF binding we found that depletion of tiRNA alters nucleosome density proximal to sites of tiRNA biogenesis. Conclusions: Taken together, these data suggest that tiRNAs modulate local epigenetic structure, which in turn regulates CTCF localization

Accelerating, hyperaccelerating, and decelerating networks

Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyperaccelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits