Mesoscopic Model and Free Energy Landscape for Protein-DNA Binding Sites: Analysis of Cyanobacterial Promoters

<div><p>The identification of protein binding sites in promoter sequences is a key problem to understand and control regulation in biochemistry and biotechnological processes. We use a computational method to analyze promoters from a given genome. Our approach is based on a physical model at the mesoscopic level of protein-DNA interaction based on the influence of DNA local conformation on the dynamics of a general particle along the chain. Following the proposed model, the joined dynamics of the protein particle and the DNA portion of interest, only characterized by its base pair sequence, is simulated. The simulation output is analyzed by generating and analyzing the Free Energy Landscape of the system. In order to prove the capacity of prediction of our computational method we have analyzed nine promoters of <i>Anabaena</i> PCC 7120. We are able to identify the transcription starting site of each of the promoters as the most populated macrostate in the dynamics. The developed procedure allows also to characterize promoter macrostates in terms of thermo-statistical magnitudes (free energy and entropy), with valuable biological implications. Our results agree with independent previous experimental results. Thus, our methods appear as a powerful complementary tool for identifying protein binding sites in promoter sequences.</p></div

Thermo-statistical properties of studied promoters.

<p>Occupancy probabilities and thermo-statistical magnitudes of the TSS and other relevant sites of the promoter sequences. NS stands for nonspecific sites defined in the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003835#s4" target="_blank">discussion</a> section. As already stated, each site is labelled starting from the position on the gene ().</p><p>Thermo-statistical properties of studied promoters.</p

First four PCA eigenvectors calculated for three different complete genes.

<p>The promoter region -with the TSS highlighted- and the codifying region are pointed out. Most of the fluctuations appear localized in the promoter region, meaning that bubbles tend to form mostly here. This feature manifests the different mechanical behavior of the promoter and codifying regions, suggesting its key role in the DNA-protein interaction.</p

Hierarchical free energy dendrogram for three selected promoters.

<p>Basins of attraction separated by barriers lower than are clustered to define macrostates of the system. Their weight is indicated in the plot together with a representation of the physical state they represent, typically the particle located in a certain site where a bubble opens.</p

DNA opening versus protein position.

<p>Base pair mean opening (upper panels) and particle histogram (lower panels) calculated for each of the nine studied promoters. The horizontal axis represent the base pair positions counted from the coding starting point ATG (). We use this criterion to label the binding sites of the simulated promoters. The experimentally identified TSSs are shaded and their exact location marked with solid bars. In every case a peak appears close to each TSS, meaning this region is “softer” and thus likely to form bubbles, supporting their key role in regulatory processes. The total A-T content of <i>Anabaena</i> PCC7120 genome is around <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003835#pcbi.1003835-Anabaena1" target="_blank">[18]</a>. The A-T content of each analyzed sequence is: <i>alr0750</i> (61%); <i>argC</i> (64%); <i>nifB</i> (68%); <i>conR</i> (57%); <i>furA</i> (66%); <i>furB</i> (65%); <i>petH</i> (62%); <i>petF</i> (63%); <i>ntcA</i> (65%).</p

Simplified illustration of the DNA-particle interaction model.

<p>The one-dimensional chain (solid spheres) models the DNA chain considering a single relevant degree of freedom per base pair, and two phenomenological potentials [ and ]. The brownian particle, with coordinate (dim ellipse), diffuses along the chain interacting with open regions through the potential .</p

(A) Time to first therapy (TFT) and (B) overall survival (OS) of 197 patients with 11q deletion CLL and <40% or ≥40% FISH losses.

<p>(A) Time to first therapy (TFT) and (B) overall survival (OS) of 197 patients with 11q deletion CLL and <40% or ≥40% FISH losses.</p

Multivariate Cox regression analysis of overall survival in 11q- CLL patients with respect to the number of losses detected by FISH: <40% (n = 51) or ≥40% (n = 146).^*

<p>*The following covariates were included in the final model: age, sex, Binet stage, splenomegaly, extended lymphadenopathies, LDH, β₂ microglobulin, CD38, ZAP70, <i>IGHV</i> mutation status and percentage 11q deleted nuclei.</p><p>Multivariate Cox regression analysis of overall survival in 11q- CLL patients with respect to the number of losses detected by FISH: <40% (n = 51) or ≥40% (n = 146).^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143073#t002fn001" target="_blank">*</a>}</p

A Low Frequency of Losses in 11q Chromosome Is Associated with Better Outcome and Lower Rate of Genomic Mutations in Patients with Chronic Lymphocytic Leukemia

<div><p>To analyze the impact of the 11q deleted (11q-) cells in CLL patients on the time to first therapy (TFT) and overall survival (OS), 2,493 patients with CLL were studied. 242 patients (9.7%) had 11q-. Fluorescence <i>in situ</i> hybridization (FISH) studies showed a threshold of 40% of deleted cells to be optimal for showing that clinical differences in terms of TFT and OS within 11q- CLLs. In patients with ≥40% of losses in 11q (11q-H) (74%), the median TFT was 19 months compared with 44 months in CLL patients with <40% del(11q) (11q-L) (<i>P</i><0.0001). In the multivariate analysis, only the presence of 11q-L, mutated <i>IGHV</i> status, early Binet stage and absence of extended lymphadenopathy were associated with longer TFT. Patients with 11q-H had an OS of 90 months, while in the 11q-L group the OS was not reached (<i>P</i> = 0.008). The absence of splenomegaly (<i>P</i> = 0.02), low LDH (<i>P</i> = 0.018) or β2M (<i>P</i> = 0.006), and the presence of 11q-L (<i>P</i> = 0.003) were associated with a longer OS. In addition, to detect the presence of mutations in the <i>ATM</i>, <i>TP53</i>, <i>NOTCH1</i>, <i>SF3B1</i>, <i>MYD88</i>, <i>FBXW7</i>, <i>XPO1</i> and <i>BIRC3</i> genes, a select cohort of CLL patients with losses in 11q was sequenced by next-generation sequencing of amplicons. Eighty % of CLLs with 11q- showed mutations and fewer patients with low frequencies of 11q- had mutations among genes examined (50% <i>vs</i> 94.1%, <i>P</i> = 0.023). In summary, CLL patients with <40% of 11q- had a long TFT and OS that could be associated with the presence of fewer mutated genes.</p></div

Multivariate Cox regression analysis of time to first therapy in 11q- CLL patients with respect to the number of losses detected by FISH: <40% (n = 51) or ≥40% (n = 146).^*

<p>*The following covariates were included in the final model: age, sex, Binet stage, splenomegaly, extended lymphadenopathies, LDH, β₂ microglobulin, CD38, ZAP70, <i>IGHV</i> mutation status and percentage 11q deleted nuclei.</p><p>Multivariate Cox regression analysis of time to first therapy in 11q- CLL patients with respect to the number of losses detected by FISH: <40% (n = 51) or ≥40% (n = 146).^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143073#t002fn001" target="_blank">*</a>}</p