Sergio Branciamore

CpG island clusters and pro-epigenetic selection for CpGs in protein-coding exons of HOX and other transcription factors

CpG dinucleotides contribute to epigenetic mechanisms by being the only site for DNA methylation in mammalian somatic cells. They are also mutation hotspots and ∼5-fold depleted genome-wide. We report here a study focused on CpG sites in the coding regions of Hox and other transcription factor genes, comparing methylated genomes of Homo sapiens, Mus musculus, and Danio rerio with nonmethylated genomes of Drosophila melanogaster and Caenorhabditis elegans. We analyzed 4-fold degenerate, synonymous codons with the potential for CpG. That is, we studied “silent” changes that do not affect protein products but could damage epigenetic marking. We find that DNA-binding transcription factors and other developmentally relevant genes show, only in methylated genomes, a bimodal distribution of CpG usage. Several genetic code-based tests indicate, again for methylated genomes only, that the frequency of silent CpGs in Hox genes is much greater than expectation. Also informative are NCG-GNN and NCC-GNN codon doublets, for which an unusually high rate of G to C and C to G transversions was observed at the third (silent) position of the first codon. Together these results are interpreted as evidence for strong “pro-epigenetic” selection acting to preserve CpG sites in coding regions of many genes controlling development. We also report that DNA-binding transcription factors and developmentally important genes are dramatically overrepresented in or near clusters of three or more CpG islands, suggesting a possible relationship between evolutionary preservation of CpG dinucleotides in both coding regions and CpG islands.

The Origin of Life: Chemical Evolution of a Metabolic System in a Mineral Honeycomb?

For the RNA-world hypothesis to be ecologically feasible, selection mechanisms acting on replicator communities need to be invoked and the corresponding scenarios of molecular evolution specified. Complementing our previous models of chemical evolution on mineral surfaces, in which selection was the consequence of the limited mobility of macromolecules attached to the surface, here we offer an alternative realization of prebiotic group-level selection: the physical encapsulation of local replicator communities into the pores of the mineral substrate. Based on cellular automaton simulations we argue that the effect of group selection in a mineral honeycomb could have been efficient enough to keep prebiotic ribozymes of different specificities and replication rates coexistent, and their metabolic cooperation protected from extensive molecular parasitism. We suggest that mutants of the mild parasites persistent in the metabolic system can acquire useful functions such as replicase activity or the production of membrane components, thus opening the way for the evolution of the first autonomous protocells on Earth.

Looking for the Primordial Genetic Honeycomb

All life forms on Earth share the same biological program based on the DNA/RNA genomes and proteins. The genetic information, recorded in the nucleotide sequence of the DNA and RNA molecule, supplies the language of life which is transferred through the different generations, thus ensuring the perpetuation of genetic information on Earth. The presence of a genetic system is absolutely essential to life. Thus, the appearance in an ancestral era of a nucleic acid-like polymer able to undergo Darwinian evolution indicates the beginning of life on our planet. The building of primordial genetic molecules, whatever they were, required the presence of a protected environment, allowing the synthesis and concentration of precursors (nucleotides), their joining into larger molecules (polynucleotides), the protection of forming polymers against degradation (i.e. by cosmic and UV radiation), thus ensuring their persistence in a changing environment, and the expression of the “biological” potential of the molecule (its capacity to self-replicate and evolve). Determining how these steps occurred and how the primordial genetic molecules originated on Earth is a very difficult problem that still must be resolved. It has long been proposed that surface chemistry, i.e. on clay minerals, could have played a crucial role in the prebiotic formation of molecules basic to life. In the present work, we discuss results obtained in different fields that strengthen the hypothesis of a clay-surface-mediated origin of genetic material.

Enhanced evolution by stochastically variable modification of epigenetic marks in the early embryo

Significance In this paper we investigate by quantitative modeling the effect on evolution of epigenetic variation during a window of opportunity in the early embryo. It is generally accepted that generation of new functions is primarily driven by gene duplication. However, pseudogenization (degradation of a new gene copy) is statistically much more likely than gaining a new function, and thus this remains a serious conceptual problem. We find that epigenetic variation, even in a constant environment, can essentially eliminate the pseudogenization problem and dramatically improve the efficacy of evolution by gene duplication. Evolution by gene duplication is generally accepted as one of the crucial driving forces for the gain of new complexity and functions, but the formation of pseudogenes remains a problem for this mechanism. Here we expand on earlier ideas that epigenetic modifications can drive neo- and subfunctionalization in evolution by gene duplication. We explore the effects of stochastic epigenetic modifications on the evolution (and thus development) of complex organisms in a constant environment. Modeling is done both using a modified genetic drift analytical treatment and computer simulations, which were found to agree. A transposon silencing model is also explored. Some key assumptions made include (i) stochastic, incomplete removal (or addition) of repressive epigenetic marks takes place during a window(s) of opportunity in the zygote and early embryo; (ii) there is no statistical variation of the marks after the window closes; and (iii) the genes affected are sensitive to dosage. Our genetic drift treatment takes into account that after gene duplication the prevailing case upon which selection operates is a duplicate/singlet heterozygote; to the best of our knowledge, this has not been considered in previous treatments. We conclude from our modeling that stochastic epigenetic modifications, with rates consistent with experimental observation, can both increase the rate of gene fixation and decrease pseudogenization, thus dramatically improving the efficacy of evolution by gene duplication. We also find that a transposon silencing model is advantageous for fixation of recessive genes in diploid organisms, especially with large effective population sizes.

Combined action of a bacterial monooxygenase and a fungal laccase for the biodegradation of mono- and poly-aromatic hydrocarbons.

The combined action of a wide substrate range toluene o-xylene monooxygenase from Pseudomonas sp. OX1, able to convert many aromatic compounds into mono- and di-hydroxylated derivatives, and fungal laccases from Pleurotus ostreatus which oxidize these hydroxylated products yielding polymers with reduced toxicity is described. This strategy permits to overcome many of the substrate specificity problems and dead end toxic products formation generally encountered in complex bacterial biodegradation pathways. Toluene and naphthalene degradations were tested as representative of mono- and poly-aromatic pollutants. The combined biological action was optimized in micellar and microemulsion systems able to increase the bioavailability of the hydrophobic aromatic pollutants. This approach allows efficient hydroxylations of hydrophobic substrates thus favoring the further action of fungal oxidases.

The origin of the 5S ribosomal RNA molecule could have been caused by a single inverse duplication: strong evidence from its sequences.

The secondary structure of the 5S ribosomal RNA (5S rRNA) molecule shows a high degree of symmetry. In order to explain the origin of this symmetry, it has been conjectured that one half of the 5S rRNA molecule was its precursor and that an indirect duplication of this precursor created the other half and thus the current symmetry of the molecule. Here, we have subjected to an empirical test both the indirect duplication model, analysing a total of 684 5S rRNA sequences for complementarity between the two halves of the 5S rRNA, and the direct duplication model analysing in this case the similarity between the two halves of this molecule. In intra- and inter-molecule and intra- and inter-domain comparisons, we find a high statistical support to the hypothesis of a complementarity relationship between the two halves of the 5S rRNA molecule, denying vice versa the hypothesis of similarity between these halves. Therefore, these observations corroborate the indirect duplication model at the expense of the direct duplication model, as reason of the origin of the 5S rRNA molecule. More generally, we discuss and favour the hypothesis that all RNAs and proteins, which present symmetry, did so through gene duplication and not by gradualistic accumulation of few monomers or segments of molecule into a gradualistic growth process. This would be the consequence of the very high propensity that nucleic acids have to be subjected to duplications.

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative) positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.

Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling

Significance We report here that a recently developed Bayesian network (BN) methodology and software platform yield useful information when applied to the analysis of intrachromosomal interaction datasets combined with Encyclopedia of DNA Elements publicly available datasets for the B-lymphocyte cell line GM12878. Of 106 variables analyzed, interaction strength between DNA segments was found to be directly dependent on only four types of variables: distance, Rad21 or SMC3 (cohesin components), transcription at transcription start sites, and the number of CCCTC-binding factor (CTCF)–cohesin complexes between interacting DNA segments. The importance of directionally oriented ctcf motifs was confirmed not only for loops but also for enhancer–promoter interactions. Purely data-driven BN analyses also identified known critical, lineage-determining transcription factors (TFs) as well as some potentially new dependencies between TFs. Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)–cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF–cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA–DNA, protein–DNA, and protein–protein interactions.

Frequent monoallelic or skewed expression for developmental genes in CNS-derived cells and evidence for balancing selection

Significance While most mammalian genes are expressed from both chromosomal copies, many autosomal genes randomly express only one allele in a given cell, resulting in somatic cellular mosaicism. To better understand the mechanisms, developmental aspects, and evolution of autosomal monoallelic expression (MAE), we used nucleotide polymorphism differences between hybrid mice to analyze MAE of clonal neural stem cell lines as they differentiated to astrocytes. We found that genes showing MAE are highly enriched among developmental stage-specific genes. Genes showing strong skewed expression are similarly enriched. We also found evidence suggestive of balancing selection not just for genes with MAE but also, for developmental stage-specific genes. Cellular mosaicism due to monoallelic autosomal expression (MAE), with cell selection during development, is becoming increasingly recognized as prevalent in mammals, leading to interest in understanding its extent and mechanism(s). We report here use of clonal cell lines derived from the CNS of adult female F1 hybrid (C57BL/6 X JF1) mice to characterize MAE as neural stem cells (nscs) differentiate to astrocyte-like cells (asls). We found that different subsets of genes show MAE in the two populations of cells; in each case, there is strong enrichment for genes specific to the respective developmental state. Genes that exhibit MAE are 22% of nsc-specific genes and 26% of asl-specific genes. Moreover, the promoters of genes with MAE have reduced CpG dinucleotides but increased CpG differences between the two parental mouse strains. Extending the study of variability to wild populations of mice, we found evidence for balancing selection as a contributing force in evolution of those genes showing developmental specificity (i.e., expressed in either nsc or asl), not just for genes showing MAE. Furthermore, we found that genes showing skewed allelic expression (SKE) were similarly enriched among cell type-specific genes and also showed a heightened probability of balancing selection. Thus, developmental stage-specific genes and genes with MAE or SKE seem to make up overlapping classes subject to selection for increased diversity. The implications of these results for development and evolution are discussed in the context of a model with stochastic epigenetic modifications taking place only during a relatively brief developmental window.

Aging in a Relativistic Biological Space-Time

Here we present a theoretical and mathematical perspective on the process of aging. We extend the concepts of physical space and time to an abstract, mathematically-defined space, which we associate with a concept of “biological space-time” in which biological dynamics may be represented. We hypothesize that biological dynamics, represented as trajectories in biological space-time, may be used to model and study different rates of biological aging. As a consequence of this hypothesis, we show how dilation or contraction of time analogous to relativistic corrections of physical time resulting from accelerated or decelerated biological dynamics may be used to study precipitous or protracted aging. We show specific examples of how these principles may be used to model different rates of aging, with an emphasis on cancer in aging. We discuss how this theory may be tested or falsified, as well as novel concepts and implications of this theory that may improve our interpretation of biological aging.