On the Use of Coarse-Grained Thermodynamic Landscapes to Efficiently Estimate Folding Kinetics for RNA Molecules

Thesis advisor: Peter CloteRNA folding pathways play an important role in various biological processes, such as 1) the conformational switch in spliced leader RNA from Leptomonas collosoma, which controls transsplicing of a portion of the 5’ exon, and 2) riboswitches–portions of the 5’ untranslated region of mRNA that regulate genes by allostery. Since RNA folding pathways are determined by the thermodynamic landscape, we have developed a number of novel algorithms—including FFTbor and FFTbor2D—which efficiently compute the coarse-grained energy landscape for a given RNA sequence. These energy landscapes can then be used to produce a model for RNA folding kinetics that can compute both the mean first passage time (MFPT) and equilibrium time in a deterministic and efficient manner, using a new software package we call Hermes. The speed of the software provided within Hermes—namely FFTmfpt and FFTeq—present what we believe to be the first suite of kinetic analysis tools for RNA sequences that are suitable for high throughput usage, something we believe to be of interest in the field of synthetic design.Thesis (PhD) — Boston College, 2015.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Biology

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Using the fast fourier transform to accelerate the computational search for RNA conformational switches.

Using complex roots of unity and the Fast Fourier Transform, we design a new thermodynamics-based algorithm, FFTbor, that computes the Boltzmann probability that secondary structures differ by [Formula: see text] base pairs from an arbitrary initial structure of a given RNA sequence. The algorithm, which runs in quartic time O(n(4)) and quadratic space O(n(2)), is used to determine the correlation between kinetic folding speed and the ruggedness of the energy landscape, and to predict the location of riboswitch expression platform candidates. A web server is available at http://bioinformatics.bc.edu/clotelab/FFTbor/

Run times in seconds for RNAbor and FFTbor, on random RNA of length

<p><b> in step size of 20 nt. Each algorithm was run with the empty initial structure </b><b>, see rows RNAbor (empty), FFTbor (empty), and with the minimum free energy structure as the initial structure </b><b>, see rows RNAbor (MFE) and FFTbor (MFE).</b> Note that for both RNAbor and FFTbor, the run time increases when is the MFE structure, rather than the empty structure. Notice the radical improvement in the run time of FFTbor over that of RNAbor.</p

Rfam consensus structures (Rfam) and minimum free energy (MFE) secondary structures for two thiamine pyrophosphate (TPP) riboswitch aptamers, chosen at random from RF00059 Rfam family seed alignment [<b>23</b>].

<p>Using pairwise BLAST <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050506#pone.0050506-Altschul1" target="_blank">[14]</a>, there is no sequence similarity, although the secondary structures are very similar, as shown in this figure. <i>(A)</i> Rfam consensus structure for BX842649.1/277414-277318. <i>(B)</i> MFE structure for BX842649.1/277414-277318. <i>(C)</i> Rfam consensus structure for AACY022101973.1/389-487. <i>(D)</i> Rfam consensus structure for AACY022101973.1/389-487.</p

Pearson correlation between various aspects of selenocysteine insertion sequences from the seed alignment of Rfam family RF00031 [23].

<p>For each of the 61 RNA sequences, we ran FFTbor, starting from empty initial structure , and we ran a Monte Carlo folding algorithm, developed by E. Freyhult and P. Clote (unpublished). Using the Monte Carlo algorithm, we determined the mean first passage time (MFPT), defined as the average taken over 50 runs, of the number of Monte Carlo steps taken to fold the empty structure into the MFE structure, where an absolute upper bound of 5 million steps was allowed in the simulation. From the output of FFTbor, we computed <i>(1)</i> the mean number () of base pairs per structure, taken over the ensemble of all secondary structures for the given sequence, <i>(2)</i> the standard deviation () of the number of base pairs per structure, <i>(3)</i> the coefficient of variation , <i>(4)</i> the RNA sequence length, and <i>(5)</i> the minimum free energy (MFE). Additionally, we computed the logarithm base 10 of mean first passage time (log10MFPT), taken over 50 Monte Carlo runs per sequence (log base 10 of the standard deviation of number of Monte Carlo steps per run was approximately 9% of log10MFPT on average). The table shows the correlation between each of these aspects. Some correlations are obvious – for example, <i>(i)</i> the standard deviation is highly correlated with the coefficient of variation ; <i>(ii)</i> the mean is negatively correlated with the coefficient of variation ; <i>(iii)</i> the mean is negatively correlated with the minimum free energy (MFE) – if most low energy structures in the ensemble have many base pairs, then it is likely that the minimum free energy is very low (i.e. since MFE is negative, the absolute value of MFE increases); <i>(iv)</i> sequence length is negatively correlated with MFE – as sequence length increases, the minimum free energy (MFE) decreases. However, it may appear surprising that <i>(v)</i> the mean number of base pairs per structure is independent of MFPT (correlation ), although <i>(vi)</i> MFE is correlated with MFPT (correlation ) – i.e. from <i>(iii)</i>, lower MFE is correlated with a larger average number of base pairs per structure, from <i>(vi)</i> higher MFE is correlated with longer folding time, but from <i>(v)</i> the average number of base pairs per structure is independent of folding time. The most important insight from this table is that <i>(vii)</i> standard deviation is correlated with mean first passage time – the correlation is statistically significant, with one-tailed -value of .</p

<i>(Left)</i> Table showing parallel run times in seconds for FFTbor, using OpenMP

<p><a href="http://openmp.org/" target="_blank">http://openmp.org/</a><b>.</b> Column headers 1,2 indicate the number of cores used in the computational experiment. For each sequence length , five random RNAs were generated using equal probability for each nucleotide A,C,G,U. Run time in seconds, plus or minus one standard deviation, are given for a 24-core AMD Opteron 6172 with 2.10 GHz and 64 GB RAM, with only 1 (resp. 2) cores used. <i>(Right)</i> Graph showing parallel run time of FFTbor on an AMD Opteron 6172 with 2.10 GHz and 64 GB RAM, using respectively 1,2,3,4,6,9,12,15,20 cores.</p

FFTbor output for the RNA attenuator for the phenylalanyl-tRNA synthetase (pheST) operon in <i>E. coli</i> K-12 substr.

<p>DH10B, located adjacent to the phenylalanyl-tRNA synthetase operon leader, with GenBank accession code CP000948.1/1887748-1887820 (complement). The -axis represents base pair distance to the minimum free energy structure ; -axis represents Boltzmann probability that a structure has base pair distance to . <i>(Left)</i> Probability that base pair distance to MFE structure is . <i>(Center)</i> Cumulative probability that base pair distance to MFE structure is at most . <i>(Right)</i> Finite difference (Derivative) of probability that base pair distance to MFE structure is .</p