Intrinsically Disordered Regions May Lower the Hydration Free Energy in Proteins: A Case Study of Nudix Hydrolase in the Bacterium Deinococcus radiodurans

The proteome of the radiation- and desiccation-resistant bacterium D. radiodurans features a group of proteins that contain significant intrinsically disordered regions that are not present in non-extremophile homologues. Interestingly, this group includes a number of housekeeping and repair proteins such as DNA polymerase III, nudix hydrolase and rotamase. Here, we focus on a member of the nudix hydrolase family from D. radiodurans possessing low-complexity N- and C-terminal tails, which exhibit sequence signatures of intrinsic disorder and have unknown function. The enzyme catalyzes the hydrolysis of oxidatively damaged and mutagenic nucleotides, and it is thought to play an important role in D. radiodurans during the recovery phase after exposure to ionizing radiation or desiccation. We use molecular dynamics simulations to study the dynamics of the protein, and study its hydration free energy using the GB/SA formalism. We show that the presence of disordered tails significantly decreases the hydration free energy of the whole protein. We hypothesize that the tails increase the chances of the protein to be located in the remaining water patches in the desiccated cell, where it is protected from the desiccation effects and can function normally. We extrapolate this to other intrinsically disordered regions in proteins, and propose a novel function for them: intrinsically disordered regions increase the “surface-properties” of the folded domains they are attached to, making them on the whole more hydrophilic and potentially influencing, in this way, their localization and cellular activity

Proteome sequence features carry signatures of the environmental niche of prokaryotes

<p>Abstract</p> <p>Background</p> <p>Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments.</p> <p>Results</p> <p>We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests.</p> <p>Conclusions</p> <p>To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments.</p

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

Phenotypic and Genetic Consequences of Protein Damage

<div><p>Although the genome contains all the information necessary for maintenance and perpetuation of life, it is the proteome that repairs, duplicates and expresses the genome and actually performs most cellular functions. Here we reveal strong phenotypes of physiological oxidative proteome damage at the functional and genomic levels. Genome-wide mutations rates and biosynthetic capacity were monitored in real time, in single <i>Escherichia coli</i> cells with identical levels of reactive oxygen species and oxidative DNA damage, but with different levels of irreversible oxidative proteome damage (carbonylation). Increased protein carbonylation correlates with a mutator phenotype, whereas reducing it below wild type level produces an anti-mutator phenotype identifying proteome damage as the leading cause of spontaneous mutations. Proteome oxidation elevates also UV-light induced mutagenesis and impairs cellular biosynthesis. In conclusion, protein damage reduces the efficacy and precision of vital cellular processes resulting in high mutation rates and functional degeneracy akin to cellular aging.</p></div

UV-induced mutation frequencies correlate with total proteome carbonylation.

<p>(a) Dose response of UVC induced mutation frequency in <i>E.coli</i> strains with increasing chaperone activity and translation accuracy. (b) Protein carbonylation at the end of the recovery period correlates with the UVC radiation dose. (c) Correlation between the UVC induced mutation frequency and protein carbonylation measured immediately after irradiation. Spontaneous mutation frequencies for each strain are labelled with an X. The results of mutation frequencies are given as median of 3 measurements, each in triplicate. Protein carbonylation measurements are presented as mean of 3 measurement, each in triplicate. Error bars represent the standard deviation. “Oe” stands for over-expression.</p

Trolox reduces the amount of protein carbonylation and the mutation rates.

<p>Parallel decrease in mutation rate (white and black circle) (fraction of cells with a MutL-CFP focus) and in constitutive protein carbonylation (white and black square) in the presence of 1 mM trolox (black symbols) relative to no trolox controls (white symbols). The results are given as mean of 3 measurements, each in triplicate. Error bars represent the standard deviation.</p

Protein damage reduces the efficiency of DNA repair.

<p>The level of 8-oxo-guanine in DNA (a) immediately after irradiation and (b) at the end of the recovery period when it increases differently with the UVC radiation depending on the fidelity of protein biosynthesis and chaperone activity. (c) 8-oxo-guanine level correlates with the protein carbonylation level immediately after irradiation. (d) 8-oxo-guanine level after post-irradiation incubation correlates with protein carbonylation. 8-oxo-guanine and protein carbonylation levels prior to irradiation are labeled with an X. The results are given as mean of 2 measurements, each in triplicate. Error bars represent the standard deviation. R² value of the linear fit is indicated in panel (a). “Oe” stands for over-expression.</p

Editorial for the Special Issue “Adaptation, Aging, and Cell Death in Yeast Stress Response: Models, Mechanisms and Applications”

Every cell experiences different types of stress during its life cycle [...]

Mutation rate correlates with total proteome carbonylation.

<p>(a) The fraction of cells with a MutL-CFP focus (mutation rate) decreases with increasing chaperone activity and translation accuracy in wild type (white bars) and MutH deficient <i>E.coli</i> (black bars). (b) 8-oxo-guanine and ROS (DHR123 fluorescence) levels in each strain, in the absence and presence of 1 mM trolox, (c) Correlation between the fraction of cells with MutL-CFP foci and total proteome carbonylation. Strain identity corresponding to the numbers is listed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003810#pgen-1003810-g001" target="_blank">Figure 1D</a>. Results are given as mean of 3 measurements, each in triplicate. Error bars represent the standard deviation. “Oe” stands for over-expression.</p

Proteome carbonylation correlates with cellular biosynthetic capacity.

<p>Exponentially growing <i>E.coli</i> strains with different levels of chaperone activity and translation errors show different levels of (a) protein carbonylation and (b) bacteriophage λ single burst size. (c) (χ) There is a negative correlation between total proteome carbonylation and biosynthetic capacity measured as single burst size of bacteriophage λ. Strain identity corresponding to the numbers is listed in (d). Results are means of 3 measurements, each in triplicate. The error bars represent the standard deviation. R² value of the linear fit is indicated in panel (c). “Oe” stands for over-expression.</p