Rakku sisenevad peptiidid: tsütotoksilisus, immunogeensus ning rakendamine tuumor-spetsiifilise transportvektorina

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Viimastel aastatel, suur tähelepanu kasvajate ravis on pööratud mitmesugustele terapeutilise toimega ravimainete sisestamise strateegiatele, mis ühendavad endas nii ravimite spetsiifilist edastamist kui ka vähivastase kemoteraapiat. Rakku sisenevad peptiidid (RSPd või CPP-s, cell-penetrating peptides) osutunud väga tõhusateks vektoriteks selliste terapeutiliste ravimite sisseviimisel nagu proteiine ning nukleiinhappeid (plasmiidne DNA (pDNA) ja splaissingut korrigeerivaid oligonukleotiide (SKO)). RSPde kasutamine võib olla piiratud nende poolt põhjustatud kõrvaltoimete tõttu: tsütotoksilised efektid ning immunvastuse esilekutsumine. Käesoleva töö peamiseks eesmärgiks oli analüüsida uute RSPde, PepFect-ide immunogeenset ja tsütotoskilist aktiivsust in vitro ja in vivo; uurida p53 analoogsete peptiidide tsütotoksilisust ning apoptootilist toimet rinnavähi rakumudelil ning välja töötada uus RSP, mis on võimeline sisenema ja spetsiifiliselt ära tundma peaajuvähi rakke gliobalstoomi (U87 MG) rakumudelil nii in vitro kui ka in vivo. Esimeses töös näidati, et uued RSPd PepFect-id, mis põhinevad peptiidi transportaan järjestusel, ei avaldanud tsütotoksilist ega immunogeenset toimet immuunsüsteemi rakkudele peptiidi kontsentratsioonidel 5 µM ja 10 µM in vitro tingimustes. Sarnane tulemus saadi ka immunokompetentsete hiirte süstimisel, kui kasutatud annused olid suuremad kui 5 mg/kg kohta. Teises töös disainiti ning sünteesiti lühikesi p53-analoogseid peptiide, mis lisaks efektiivsele rakku sisenemisele avaldasid ka apoptootilist toimet rinnavähi rakkudele in vitro (seerumivabas keskkonnas). Lühikesed p53-analoogsed peptiidid omavad head perspektiivi vähivastase strateegia arendamiseks. Kolmandas töös kirjeldati uue transportvektori, gHoPe2, väljatöötamist, mis on võimeline spetsiifiliselt sihtmärkrakkudesse sisenema. Demonstreeriti ka selle potentsiaalseid kasutusvõimalusi ning toimet ainete transpordil vähirakkudesse. Neljandas töös näidati, et mitteamfipaatse ja sünteetilise (RxR)4 peptiidi stearüülhappega N-terminaalse modifitseerimine tagab efektiivse ning mitte toksilise nukleiinhappete transporti rakkudesse. Käesolev töö annab täiendava ülevaate vähispetsiifilistel RSPdel põhinevatest transportsüsteemidest, hinnates nende toksilisust, immunogeenset aktiivsust ning spetsiifilist transporti kasvajarakkudesse.The delivery of therapeutic agents for the hard-to-treat tumors which comprise both targeting and anticancer chemotherapy strategies, have received much attention. Application of cell-penetrating peptides is one of very promising strategies for targeted drug delivery. However, similar issues as for any other drug delivery system: cytotoxicity and the tendency to induce innate immune response may limit their use. The current thesis is focused on the characterization of immunogenic and cytotoxic activities of cell-penetrating peptides PepFects, on the cytotoxic and apoptotic activities of newly designed cell-penetrating p53 analogues and on the development of a peptide-based glioma-targeted drug delivery vector. In Paper I, the cytotoxic and immunogenic activities of PepFect peptides and PepFect/nucleic acid complexes were analyzed in vitro and in vivo. The current study confirmed that PepFect peptides possess promising potential in nucleic acid delivery without evidence of undesired cytotoxicity and inflammation at the concentrations of 10 μM and 5 μM, respectively in vitro and at a dose of 5 mg/kg in vivo. In Paper II, we generated short p53-derived cell-penetrating protein analogues and analyzed their internalization efficacy, cytotoxic and apoptotic activities in the neuroblastoma and breast cancer cell lines. Even though the effect was observed under serum-free conditions, the novel protein analogues demonstrated selective apoptotic activity in the p53-non-active breast cancer cell line suggesting promising perspectives for a future anticancer strategy. In Paper III, a novel tumor-selective peptide-based drug delivery platform gHoPe2 has been developed demonstrating potential application of CPP-s for tumor-targeted delivery. In Paper IV, we showed that N-terminal modification of non-amphipathic (RxR)4 with stearic acid moiety resulted in efficient peptide vector for nucleic acids delivery in non-toxic manner. In conclusion, the current work provides evidence for the development of apoptotic protein-derived cell-penetrating peptides and tumor-targeted cell-penetrating peptide-based drug delivery vector with reduced toxicity and immunogenicity

High-throughput functional genomics using CRISPR–Cas9

Forward genetic screens are powerful tools for the discovery and functional annotation of genetic elements. Recently, the RNA-guided CRISPR (clustered regularly interspaced short palindromic repeat)-associated Cas9 nuclease has been combined with genome-scale guide RNA libraries for unbiased, phenotypic screening. In this Review, we describe recent advances using Cas9 for genome-scale screens, including knockout approaches that inactivate genomic loci and strategies that modulate transcriptional activity. We discuss practical aspects of screen design, provide comparisons with RNA interference (RNAi) screening, and outline future applications and challenges.Klarman Family Foundation (Fellowship)Massachusetts Institute of Technology. Simons Center for the Social Brain (Postdoctoral Fellowship)National Human Genome Research Institute (U.S.) (K99-HG008171)National Institute of Mental Health (U.S.) (DP1-MH100706)National Institute of Neurological Disorders and Stroke (U.S.) (R01-NS07312401)National Science Foundation (U.S.) (Waterman Award)W. M. Keck FoundationDamon Runyon Cancer Research FoundationKinship Foundation. Searle Scholars ProgramKlingenstein FoundationVallee FoundationMerkin FoundationSimons FoundationNew York Stem Cell Foundatio

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Optimization of molecular tools for high-throughput genetic screening

Forward genetic screening allows for the identification of any genes important for a particular biological process or phenotype. While the power of this approach is broadly agreed on, the efficacy of currently available tools limits the strength of conclusions drawn from these experiments. This thesis describes a method to optimize molecular tools for high-throughput screening, both for shRNA and sgRNA based reagents. Using large shRNA efficacy datasets, we first designed an algorithm predicting the potency of shRNAs based on sequence determinants. Combined with a novel shRNA backbone that further improves the processing of synthetic shRNAs, we built a library of potent shRNAs to reliably and efficiently knock-down any gene in the human and mouse genomes. We then went on to apply a similar approach to identify sgRNAs with increased activity. We complemented this with conservation and repair prediction to increase the likelihood of generating functional knock-outs. With these tools in hand, we constructed, sequence-verified and validated arrayed shRNA and sgRNA libraries targeting any protein coding gene in the human genome. These resources allow large-scale screens to be performed in a multiplexed or arrayed format in a variety of biological contexts. I have also applied these tools to identify therapeutic targets to circumvent cancer resistance to treatment in two different contexts. To overcome the shortfalls of single target therapy, I have developed multiplexed multidimensional shRNA screening strategy, where two genes are knocked down simultaneously in each cell. This strategy allows the identification of gene pairs that could be targeted in tandem to maximize therapeutic benefits. As a proof of concept, I have used it with a subset of druggable genes in melanoma cell lines. Moreover, we have applied our genome wide shRNA libraries to a different resistance context, stroma-mediated resistance to gemcitabine in PDAC. In this project, we performed screens in a PDAC-CAF coculture setting to try and identify cancer vulnerabilities specifically in the presence of stroma. Overall, the tools developed in this thesis allow for the efficient knockdown or knockout of any gene, both in an individual or combinatorial setting. Apart from providing a resource that will be useful for many fields, we have performed several proof-of-concept studies where we have applied our tools to identify potential cancer drug targets.Cancer Research U

Nat Rev Genet

Forward genetic screens are powerful tools for the discovery and functional annotation of genetic elements. Recently, the RNA-guided CRISPR (clustered regularly interspaced short palindromic repeat)-associated Cas9 nuclease has been combined with genome-scale guide RNA libraries for unbiased, phenotypic screening. In this Review, we describe recent advances using Cas9 for genome-scale screens, including knockout approaches that inactivate genomic loci and strategies that modulate transcriptional activity. We discuss practical aspects of screen design, provide comparisons with RNA interference (RNAi) screening, and outline future applications and challenges.DP1 MH100706/MH/NIMH NIH HHS/United StatesDP1\ue2\u20ac\u2018MH100706/DP/NCCDPHP CDC HHS/United StatesK99 HG008171/HG/NHGRI NIH HHS/United StatesK99\ue2\u20ac\u2018HG008171/HG/NHGRI NIH HHS/United StatesR01\ue2\u20ac\u2018NS07312401/NS/NINDS NIH HHS/United States2015-11-01T00:00:00Z25854182PMC450323

RNA Interference for Improving the Outcome of Islet Transplantation and Polymeric Micelle‑based Nanomedicine for Cancer Therapy

Ex vivo gene transfer has been used to improve the outcome of islet transplantation for treating type I diabetes. RNA interference is an effective approach for reducing gene expressions at the mRNA level. The application of RNA interference to improve the outcome of islet transplantation was reviewed in Chapter 2, where I summarized biological obstacles to islet transplantation, various types of RNAi techniques, combinatorial RNAi in islet transplantation, and different delivery strategies. Upregulation of inducible nitric oxide synthase (iNOS) and subsequent product of radical nitric oxide (NO) impair islet β cell function. Therefore, we hypothesized that iNOS gene silencing could prevent β cell death. In Chapter 3, we designed and used siRNA to silence iNOS gene in a rat β cell line and human islets. We found that siRNA inhibited rat iNOS gene expression and NO production in rat β cells in a dose and sequence dependent manner. iNOS gene silencing also protected these β cells from inflammatory cytokine‑induced apoptosis and increased their capacity to secret insulin. Although there was also dose and sequence dependent iNOS gene silencing and NO production in human islets, the effect of iNOS gene on apoptosis of islets was only moderate, as evidenced by 25‑30% reduction in caspase 3 activity and in the percentage of apoptotic cells. Since an islet is a cluster of 20‑1000 cells, the transfection efficiency of lipid/siRNA complexes into human islets was only 21‑28%, compared to effective transfection efficiency (\u3e 90%) in β cells. Gene delivery vectors which can express a growth factor gene to promote revascularization and silence of proapoptotic genes might be good for ex vivo genetic modification of islet prior to transplantation. Thus, in Chapter 4, we constructed bipartite plasmid vectors to co‑express a vascular endothelial growth factor (VEGF) cDNA and shRNA targeting iNOS gene. Firstly, shRNA sequences against human iNOS gene were screened. Then, we determined the effect of different promoters and shRNA backbones on gene silencing. The shRNA with H1, U6 and CMV promoters showed similar efficiency in iNOS gene silencing. In addition, a conventional shRNA showed better silencing of iNOS gene, compared to shRNA containing mir375 and mir30 backbones. A bipartite plasmid was also constructed with mir30‑shRNA and a VEGF cDNA controlled by a single CMV promoter. This plasmid showed a better silencing effect compared with plasmid without VEGF cDNA. In conclusion, we have successfully constructed bipartite vectors co‑expressing a VEGF cDNA and a shRNA against iNOS gene. These vectors could be an attractive candidate to improve the survival of transplanted islets. The second part of research was focused on the study of polymeric micelle formulations for treating cancers. Two key elements were integrated in my research projects: development of polymeric micelle delivery systems; discovery of new therapeutics for better treatment of cancers. The project described in Chapter 5 was carried out in collaboration with Dr. Miller\u27s group at The University of Tennessee Health Science Center. In this study, we showed that SMART‑100 effectively inhibited HepG2 cell proliferation and was able to circumvent multiple drug resistance (MDR) in cancer cells. SMART‑100 inhibited P‑gp activity, which may be responsible for its ability to overcome MDR. Since SMART‑100 is poorly soluble in water, it was formulated in poly(ethylene glycol)‑b‑poly(D, L‑lactide) (PEG‑PLA) micelles. The solubility of SMART‑100 was increased by more than 1.1x105folds. SMART‑100 loaded PEG‑PLA micelles could effectively inhibit HepG2 cell growth and arrest cell cycle progression at G2/M phase, followed by cell apoptosis. Increased caspase 3 activity was also observed when HepG2 cells were treated with SMART‑100. The anticancer activity of SMART‑100 loaded PEG‑PLA micelles was also evaluated on luciferase expressing C4‑2‑Luc cell lines by IVIS imaging. Our results suggest that SMART‑100 has the potential to treat resistant cancers and PEG‑PLA micelles can be used to formulate SMART‑100. We are not only interested in using commercial polymers but also interested in designing new polymers for micellar drug delivery. Therefore, the objective of study described in Chapter 6 was to design lipopolymers for hydrophobic drug delivery. In this study, poly(ethylene glycol)‑block‑poly(2‑methyl‑2‑carboxyl‑propylene carbonate‑graft‑dodecanol) (PEG-PCD) lipopolymers were synthesized and characterized by 1H NMR, FTIR, GPC, and DSC. The critical micelle concentration (CMC) of PEG‑PCD micelles was around 10-8 M and decreased with increasing length of hydrophobic block. PEG‑PCD micelles could efficiently load a model drug embelin into its hydrophobic core and significantly improve its solubility. The drug loading capacity was dependent on the polymer core structure, but the length of hydrophobic core had little effect. PEG‑PCD formed both spherical and cylindrical micelles, which were dependent on the copolymer structure and composition. Lipopolymers PEG‑PCD with various hydrophobic core lengths showed similar drug release profiles, which were slower than that of poly(ethylene glycol)‑block‑poly(2‑methyl‑2‑benzoxycarbonyl‑propylene carbonate) (PEG‑PBC) micelles. Embelin loaded PEG‑PCD micelles showed significant inhibition of C4‑2 prostate cancer cell proliferation, while no obvious cellular toxicity was observed for blank micelles. In Chapter 7, we studied the use of paclitaxel and lapatinib loaded lipopolymer micelles for treating MDR prostate cancers. Although paclitaxel remains effective in treating prostate cancer, its prolonged treatment develops MDR due to the over‑expression of P‑gp. Our hypothesis is that combination of paclitaxel and lapatinib, which is a potent P‑gp inhibitor, can overcome MDR in prostate cancers. Paclitaxel and lapatinib loaded lipopolymer micelle formulations were developed and evaluated in vitro with cell‑based assay. The paclitaxel and lapatinib combination effectively inhibited in vitro MDR cancer cell proliferation, induced cell cycle perturbation and cell apoptosis. In contrast, monotherapy with paclitaxel or lapatinib alone showed minimal anticancer effect. The combination therapy was further investigated in vivo with athymic nude mice xenograft MDR tumor model. Similar to in vitro study, paclitaxel (5 mg/kg) and lapatinib (5 mg/Kg) combination therapy significantly inhibited tumor growth in vivo when compared to high dose paclitaxel (10 mg/kg) monothearpy. These studies indicate that the paclitaxel and lapatinib loaded PEG‑PCD lipopolymer micelle formulation could be used to treat MDR prostate cancers

Genome-wide Predictive Simulation on the Effect of Perturbation and the Cause of Phenotypic variations with Network Biology Approach

Thanks to modern high-throughput technologies such as microarray-based gene expression profiling, a large amount of molecular profile data have been generated in several disease related contexts. Despite the fact that these data likely contain systems-level information about disease regulation, revealing the underlying dynamics between genes and mechanisms of gene regulation in genome wide way remains a major challenge. Understanding these mechanisms in genome-wide fashion and the resulting dynamical behavior is a key goal of the nascent field of systems biology. One approach to dissect the logic of the cell, is to use reverse engineering algorithms that infer regulatory interactions form molecular profile data. In this context, use of information theoretic approaches has been very successful: for instance, the ARACNe algorithm has been able to successfully infer transcriptional interactions between transcription factors and their target genes; similarly, the MINDy algorithm has identified post-translational modulators of transcription factor activity by multivariate analysis of large gene expression profile datasets. Many methods have been proposed to improve ARACNe both from a computational efficiency perspective and in terms of increasing the accuracy of the predicted interactions. Yet, the main core of ARACNe, i.e., the data processing inequality (DPI), has remained virtually unaffected even though modern information theory has extended the DPI theorem into higher-order interactions. First, we introduce an improvement of ARACNe, hARACNe, which recursively applies a higher-order DPI analysis. We show that the new algorithm successfully detects false positive feed-forward loops involving more than three genes. Second, we extend the MINDy algorithm using co-information as a novel metric, thus replacing the conditional mutual information and significantly improving the algorithm"™s predictions. Largely, two ultimate goals of systems perturbation studies are to reveal how human diseases are connected with the genes, and to find regulatory mechanism that determine disease cell behavior. However, these goals remain daunting: even the most talented researchers still have to rely on laborious genetic screens and very simplified hypotheses about effects of given perturbation have been experimentally validated and roughly analyzed with very limited regulatory sub-network such as pathway. To overcome these limitations, use of gene regulatory network is explored in this thesis research. Specifically, we propose creation of a new algorithm that can accurately predict cell state in genome-wide fashion following perturbation of individual genes, such as from silencing or ectopic expression experiments. Furthermore, experimentally validated methods to predict genome-wide changes in a cellular system following a genetic perturbation (e.g., gene silencing or ectopic expression) are still unavailable, and even though phenotypic variations are experimentally profiled and gene signatures are selected by being statistically tested, finding the exact regulator which systematically causes significant variations of gene signature is still quite challenging. In this research, I introduce and experimentally validate a probabilistic Bayesian method to simulate the propagation of genetic perturbations on integrated gene regulatory networks inferred by the hARACNe and coMINDy algorithms from human B cell data. With the same predictive framework, we also computationally predict the master driver (regulator) that is most likely to have produced the observed variations in gene expression levels; these studies as a systematized pre-screening process before genetic manipulation. I predict in silico the effect of silencing of several genes as well as the cause of phenotypic variations. Performance analysis, tested by Gene Set Enrichment Analysis (GSEA), shows that the new methods are highly predictive, thus providing an initial step toward building predictive probabilistic regulatory models, which may be applicable as pre-screening steps in perturbation studies

Molecular mechanisms and targets of new anticancer treatments

The work presented in this thesis is an effort to decipher and understand the mechanism of action (MOA) of anticancer agents by building on and complementing chemical proteomics methods. The backbone of the thesis relies on a recent method called Functional Identification of Target by Expression Proteomics (FITExP) developed in Zubarev lab, where drug induced proteomic signatures are analyzed in various cell lines and top differentially regulated proteins with consistent behavior are determined, among which the drug target and mechanistic proteins are usually present. FITExP relies on the assumption that proteins most affected with a perturbation have a higher probability of being involved in that process. In this regard, Paper I aimed to enhance the performance of FITExP analysis by merging proteomic data from drug-treated matrix attached and detached cells. This is while the majority if not all proteomics and molecular biology experiments are performed in matrix attached cells, as the general belief is that detached cells lose their structural integrity and do not harbor valuable information. However, detached cells are those that are more sensitive to chemotherapeutics and might reflect the proteome changes better. The comparative proteomics of living and dying cells improved FITExP performance with regards to identification of targets and provided insight about proteins involved in cellular life and death decisions. Furthermore, the orthogonal partial least squares-discriminant analysis (OPLS-DA) paradigm presented in this study, was used throughout the thesis for contrasting and visualizing the proteomic signature of a molecule against others, to reveal targets and specific proteins changing in response to the molecule of interest. In Paper II, as a further development of FITExP and to demonstrate its applicability in a broader context, we built a proteome signature library of 56 clinical and experimental anticancer agents in A549 lung adenocarcinoma cell line. This resource called ProTargetMiner can be used for different purposes. The proximity of compounds in hierarchical clustering or t-SNE could be used for prediction of the mechanism of new compounds. Contrasting each molecule against other treatments using the OPLS-DA scheme presented in Paper I, revealed drug targets, mechanistic proteins, resistance factors, drug metabolizing enzymes and effects on protein complexes. Representative examples were used to demonstrate that the specificity factors extracted from the OPLS-DA models can help identify subtle but biologically significant processes, even when such an effect is as low as 15% fold change. Furthermore, we showed that the inclusion of 8-10 contrasting molecules in the OPLS-DA models can produce enough specificity for drug target deconvolution, which offered a miniaturization opportunity. Therefore, we built three deeper datasets using 9 compounds that showed the most diverse proteome changes in the orthogonal space in three cell lines from major cancer types: A549 lung, MCF-7 breast and RKO colon cancers. These datasets provide a unique depth of 7398, 8735 and 8551 respectively, with no missing values. Subsequently, a Shiny package was created in R, which can employ these datasets as a resource and merge it with user data and provide OPLS-DA output and target deconvolution opportunity for new compounds. Finally, using the original ProTargetMiner data, we also built a first of its kind proteomic correlation database which can find applications in deciphering the function of uncharacterized proteins. Moreover, the resource helped to identify a set of core or untouchable proteins with stable expression across all the treatments, revealing essential functions within the cells. Such proteins could be used as house-keeping controls in molecular biology experiments. In paper III, we combined FITExP with other chemical proteomics tools Thermal Proteome Profiling (TPP) and multiplexed redox proteomics, to study the target and mechanism space of auranofin. This would also allow to assess the power, orthogonality and complementarity of these techniques in the realm of chemical proteomics. TPP is a recently developed technique that can monitor changes in the stability of proteins upon binding to small molecules. Redox proteomics is a method by which the oxidation level of protein cysteinome can be quantitatively analyzed. Auranofin is an FDA-approved anti-inflammatory drug for treatment of rheumatoid arthritis, but due to its potent antitumor activity, it is currently in clinical trials against cancer. Although several MOAs have been suggested for auranofin, uncertainties exist regarding its cellular targets; therefore, this molecule was chosen as a challenging candidate to test the chemical proteomics tools. A combination of the above mentioned tools confirmed thioredoxin reductase 1 (TXNRD1) (ranking 3rd) as the cognate target of auranofin and demonstrated that perturbation of oxidoreductase pathway is the main route of auranofin cytotoxicity. We next showed that changes in the redox state of specific cysteines can be linked to protein stability in TPP. Some of these cysteines were mapped to the active sites of redox-active enzymes. In Paper IV, using quantitative multiplexed proteomics, we helped to show that b-AP15, a bis-benzylidine piperidone compound inhibiting deubiquitinases USP14 and UCHL5, produces a similar perturbation signature as bortezomib in colon cancer cells. However, in comparison with bortezomib, b-AP15 induces chaperone expression to a significantly higher level and leads to a more extensive accumulation of polyubiqutinated proteins. The polyubiqutinated proteins co-localize with mitochondrial membrane and subsequently reduce oxidative phosphorylation. These results help define the atypical cell death induced by b-AP15 and describe why this molecule is effective against apoptosis resistant cells in variety of tumor models. Finally, in Paper V, we extended the applications of TPP and combined it with specificity concept for proteome-wide discovery of specific protein substrates for enzymes. We developed a universal method called System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA) that relies on the hypothesis that enzymatic post-translational modification of substrate proteins can potentially change their stability against thermal denaturation. Furthermore, we applied the concept of specificity similar to the above papers, to reveal potential substrates using OPLS-DA. SIESTA was applied to two enzyme systems, namely TXNRD1 and poly-(ADP-ribose) polymerase-10 (PARP10), identifying known and putative candidate substrates. A number of these candidate proteins were validated as PARP10 substrates by targeted mass spectrometry, chemiluminescence and other assays. SIESTA is an unbiased and system wide approach and its broad application can improve our understanding of enzyme function in homeostasis and disease. In turn, specific protein substrates can serve as readouts in high throughput screening and facilitate drug discovery. Taken together, in this thesis, FITExP methodology was improved in two directions. In paper I, we improved the performance of FITExP by combining the proteomics data from detached and attached cells. In Paper II, we demonstrated how the proteomics data on a multitude of drugs in a single cell line enables the discovery of compound targets and MOA. Furthermore, we built an R Shiny package which can serve as a resource for the cancer community in target and MOA deconvolution. In Papers III and IV, we applied an arsenal of chemical proteomics tools for characterization of two anticancer compounds. In Paper V, we expanded the applications of TPP to identification of specific protein substrates for enzymes in a system-wide manner

Membrane-active peptides from structural viral proteins : identifying novel delivery vectors for gene therapy

Péptidos activos em membranas são relevantes em diversos campos da biomedicina. Os péptidos translocadores de membranas (CPPs), em particular, são promissores na administração de fármacos, incluindo em terapia génica. O presente trabalho teve por objectivo identificar novas sequências CPPs em proteínas estruturais de vírus utilizando técnicas bioinformáticas e validação experimental. 270 proteínas virais foram examinadas para reconhecimento de potenciais CPPs, tendo sido identificadas 2400 sequências putativas. 14 CPPs de vírus foram seleccionados para ensaios in vitro como vectores para carga génica, utilizando oligonucleótidos de ssDNA como modelo. A eficiência de entrega foi monitorizada por espectroscopia de fluorescência, citometria de fluxo e microscopia confocal. Adicionalmente, efectuaram-se ensaios biofísicos para compreender propriedades físico-químicas necessárias para entrega celular eficiente dos CPPs. Consequentemente, medidas de potencial de membrana com di-8-ANEPPS foram utilizados ao estudar afinidade de CPPs para membranas. Foi usado dicroísmo circular para inferir estruturas secundárias induzidas em CPPs por membranas lipídicas. A conjugação entre CPPs de vírus e oligonucleótidos foi também avaliada por dispersão dinâmica de luz para aferir a formação de complexos entre vectores e carga transportada. Seis dos péptidos demonstraram eficiência na entrega de ssDNA a células. Dados biofísicos demonstraram que a eficiência da entrega de CPPs está dependente das interacções entre CPPs e lípidos, assim como da capacidade de conjugação com a carga a transportar. Dois CPPs foram particularmente eficientes e deverão continuar sob desenvolvimento e caracterização. Proteínas estruturais de vírus são uma fonte viável de CPPs, e podem ser exploradas para outras biotecnologias de péptidos activos em membranas, nomeadamente péptidos antimicrobianos.Membrane-active peptides provide wide therapeutic potential in several biomedical areas. Among these, cell-penetrating peptides (CPPs) are highly promising molecules in drug delivery, particularly when applied to gene therapy applications. This work aimed to identify novel CPP sequences in structural viral proteins using bioinformatics, followed by experimental validation. 270 structural viral proteins were screened for the existence of potential CPP sequences, which resulted in the identification of 2400 putative sequences. A subset of 14 viral CPPs was selected for in vitro testing as gene cargo vectors using a 15-mer ssDNA oligonucleotide as a model. Delivery efficiency was monitored by fluorescence spectroscopy, flow cytometry and confocal microscopy. Furthermore, biophysical assays were conducted to understand the physical-chemical properties required for effective CPP cellular delivery. As such, membrane dipole potential sensing, using di-8-ANEPPS, was employed to study the affinity of CPPs towards lipid membranes. Circular dichroism was used to infer about lipid membrane-induced CPP secondary structure. Moreover, conjugation between each viral CPP and oligonucleotides was evaluated by dynamic light scattering to infer about the proper formation of vector:cargo complexes. Six peptides demonstrated clear efficiency in delivering ssDNA into cells. Biophysical data showed that the molecular determinants required for an efficient CPP are dependent on CPP-lipid interactions and proper conjugation with the cargo to deliver. Thus, two CPPs were particularly efficient and should be considered for future development and characterization. Structural viral proteins are a viable source for new CPPs, which may also be explored for other membrane-active peptide biotechnologies, namely antimicrobial peptides