Cytotoxic Staphylococcus aureus PSMα3 inhibits the aggregation of human insulin in vitro.

Phenol-soluble modulins (PSMs) are extracellular short amphipathic peptides secreted by the bacteria Staphylococcus aureus (S. aureus). They play an essential role in the bacterial lifecycle, biofilm formation, and stabilisation. From the PSM family, PSMα3 has been of special interest recently due to its cytotoxicity and highly stable α-helical conformation, which also remains in its amyloid fibrils. In particular, PSMα3 fibrils were shown to be composed of self-associating "sheets" of α-helices oriented perpendicular to the fibril axis, mimicking the architecture of canonical cross-ß fibrils. Therefore, they were called cross-α-fibrils. PSMα3 was synthesised and verified for identity with wild-type sequences (S. aureus). Then, using several experimental techniques, we evaluated its propensity for in vitro aggregation. According to our findings, synthetic PSMα3 (which lacks the N-terminal formyl groups found in bacteria) does not form amyloid fibrils and maintains α-helical conformation in a soluble monomeric form for several days of incubation. We also evaluated the influence of PSMα3 on human insulin fibrillation in vitro, using a variety of experimental approaches in combination with computational molecular studies. First, it was shown that PSMα3 drastically inhibits the fibrillation of human insulin. The anti-fibrillation effect of PSMα3 was concentration-dependent and required a concentration ratio of PSMα3: insulin equal to or above 1 : 100. Molecular modelling revealed that PSMα3 most likely inhibits the production of insulin primary nuclei by competing for residues involved in its dimerization.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA.

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

PACT - Prediction of amyloid cross-interaction by threading.

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

The role of tandem repeats in bacterial functional amyloids.

Repetitivity and modularity of proteins are two related notions incorporated into multiple evolutionary concepts. We discuss whether they may also be essential for functional amyloids. Amyloids are proteins that create very regular and usually highly insoluble fibrils, which are often associated with neurodegeneration. However, recent discoveries showed that amyloid structure of a protein could also be beneficial and desired, e.g., to promote cell adhesion. Functional amyloids are proteins which differ in their characteristics from pathological amyloids, so that the fibril formation could be more under control of an organism. We propose that repeats in the sequence could regulate the aggregation propensity of these proteins. The inclusion of multiple symmetric interactions, due to the presence of the repeats, could be supporting and strengthening the desirable structural properties of functional amyloids. Our results show that tandem repeats in bacterial functional amyloids have a distinct characteristic. The pattern of repeats supports the appropriate level of fibril formation and better controllability of fibril stability. The repeats tend to be more imperfect, which attenuates excessive aggregation propensity. Their desired structure and function are also reinforced by their amino acid profile. Although in the study we focused on bacterial functional amyloids, due to their importance in biofilm formation, we propose that similar mechanisms could be employed in other functional amyloids which are designed by evolution to aggregate in a desirable manner, but not necessarily in pathological amyloids.

AmyloGraph: a comprehensive database of amyloid-amyloid interactions.

Information about the impact of interactions between amyloid proteins on their fibrillization propensity is scattered among many experimental articles and presented in unstructured form. We manually curated information located in almost 200 publications (selected out of 562 initially considered), obtaining details of 883 experimentally studied interactions between 46 amyloid proteins or peptides. We also proposed a novel standardized terminology for the description of amyloid-amyloid interactions, which is included in our database, covering all currently known types of such a cross-talk, including inhibition of fibrillization, cross-seeding and other phenomena. The new approach allows for more specific studies on amyloids and their interactions, by providing very well-defined data. AmyloGraph, an online database presenting information on amyloid-amyloid interactions, is available at (http://AmyloGraph.com/). Its functionalities are also accessible as the R package (https://github.com/KotulskaLab/AmyloGraph). AmyloGraph is the only publicly available repository for experimentally determined amyloid-amyloid interactions.

A spatiotemporal reconstruction of the C. elegans pharyngeal cuticle reveals a structure rich in phase-separating proteins.

How the cuticles of the roughly 4.5 million species of ecdysozoan animals are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. Forty-eight percent of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families whose transcripts are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs, which are introduced for the first time here. All four families have sequence properties that drive phase separation and we demonstrate phase separation for one exemplar in vitro. This systematic analysis represents the first blueprint for cuticle construction and highlights the massive contribution that phase-separating materials make to the structure.

Topological analysis as a tool for detection of abnormalities in protein-protein interaction data.

MOTIVATION: Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis. RESULTS: We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences. AVAILABILITY AND IMPLEMENTATION: A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. CONTACT: alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins.

Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli.

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1-R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment's propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Water Pores in Planar Lipid Bilayers at Fast and Slow Rise of Transmembrane Voltage.

Basic understanding of the barrier properties of biological membranes can be obtained by studying model systems, such as planar lipid bilayers. Here, we study water pores in planar lipid bilayers in the presence of transmembrane voltage. Planar lipid bilayers were exposed to fast and slow linearly increasing voltage and current signals. We measured the capacitance, breakdown voltage, and rupture time of planar lipid bilayers composed of 1-pamitoyl 2-oleoyl phosphatidylcholine (POPC), 1-pamitoyl 2-oleoyl phosphatidylserine (POPS), and a mixture of both lipids in a 1:1 ratio. Based on the measurements, we evaluated the change in the capacitance of the planar lipid bilayer corresponding to water pores, the radius of water pores at membrane rupture, and the fraction of the area of the planar lipid bilayer occupied by water pores.planar lipid bilayer capacitance, which corresponds to water pores, water pore radius at the membrane rupture, and a fraction of the planar lipid bilayer area occupied by water pores. The estimated pore radii determining the rupture of the planar lipid bilayer upon fast build-up of transmembrane voltage are 0.101 nm, 0.110 nm, and 0.106 nm for membranes composed of POPC, POPS, and POPC:POPS, respectively. The fraction of the surface occupied by water pores at the moment of rupture of the planar lipid bilayer The fraction of an area that is occupied by water pores at the moment of planar lipid bilayer rupture is in the range of 0.1-1.8%.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data.

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.

Ultrathin glass fiber microprobe for electroporation of arbitrary selected cell groups.

A new type of ultrathin fiber microprobe for selective electroporation is reported. The microprobe is 10 cm long and has a diameter of 350 µm. This microprobe is a low cost tool, which allows electroporation of an arbitrary selected single cell or groups of cells among population with use of a standard microscope and cell culture plates. The microprobe in its basic form contains two metal microelectrodes made of a silver-copper alloy, running along the fiber, each with a diameter of 23 µm. The probe was tested in vitro on a population of normal and cancer cells. Successful targeted electroporation was observed by means of accumulation of trypan blue (TB) dye marker in the cell. The electroporation phenomenon was also verified with propidium iodide and AnnexinV in fluorescent microscopy.

PATH - Prediction of Amyloidogenicity by Threading and Machine Learning.

Amyloids are protein aggregates observed in several diseases, for example in Alzheimer's and Parkinson's diseases. An aggregate has a very regular beta structure with a tightly packed core, which spontaneously assumes a steric zipper form. Experimental methods enable studying such peptides, however they are tedious and costly, therefore inappropriate for genomewide studies. Several bioinformatic methods have been proposed to evaluate protein propensity to form an amyloid. However, the knowledge of aggregate structures is usually not taken into account. We propose PATH (Prediction of Amyloidogenicity by THreading) - a novel structure-based method for predicting amyloidogenicity and show that involving available structures of amyloidogenic fragments enhances classification performance. Experimental aggregate structures were used in templatebased modeling to recognize the most stable representative structural class of a query peptide. Several machine learning methods were then applied on the structural models, using their energy terms. Finally, we identified the most important terms in classification of amyloidogenic peptides. The proposed method outperforms most of the currently available methods for predicting amyloidogenicity, with its area under ROC curve equal to 0.876. Furthermore, the method gave insight into significance of selected structural features and the potentially most stable structural class of a peptide fragment if subjected to crystallization.

Proapoptotic activity induced by photodynamic reaction with novel cyanine dyes in caspase-3-deficient human breast adenocarcinoma cell lines (MCF/WT and MCF/DX).

Photodynamic therapy (PDT) is currently one of the cancer treatment options. PDT requires the application of a photosensitizer (such as: porphyrins, chlorines, and phthalocyanines) that selectively targets malignant cells. It is a dilemma to find a proper photosensitizer. In our study, we have tested a new in-vitro group of cyanine dyes. These dyes are widely applied in biotechnology as fluorescent markers. Two malignant adenocarcinoma cell lines (MCF-7/WT and MCF-7/DOX) were investigated using photodynamic reaction (PDR) with four cyanine dyes (KF-570, HM-118, FBF-749, and ER-139). KF-570 and HM-118 were irradiated with red light (630 nm), whereas FBF-749 and ER-139 with green light (435 nm). To evaluate PDR efficiency, a clonogenic test was conducted. Apoptosis was investigated by TUNEL and NCA (neutral comet) assays. Proteins selected as indicators of the apoptotic pathway (AIF, sPLA2, Smac/Diablo) and intracellular response markers (SOD-1 and GST-pi) were detected using western blot. The highest number of apoptotic cells (ca. 100%) was observed after PDR with HM-118 and KF-570 in both conducted tests, in both cell lines. The results showed that HM-118 and KF-570 cyanine dyes demonstrated a major phototoxic effect causing apoptosis in doxorubicin-resistant and sensitive cell lines.

Effect of electrochemotherapy with betulinic acid or cisplatin on regulation of heat shock proteins in metastatic human carcinoma cells in vitro.

Betulinic acid (BTA) is naturally occurring triterpene that has received interest as a novel therapeutic substance with cytotoxicity towards a number of cancer cell lines. Despite the wide spectrum of biological and pharmacological effects, its effect may be limited its lipophobic properties. Therefore, strategies to improve the access of BTA to the cells are required to enhance the anticancer effects. Electroporation (EP) enables increased inflow of drugs into cancer cells, even at low doses, which may reduce the side effects caused by high doses of chemotherapy. The potential application of BTA in electrochemotherapy (ECT) in metastatic type of cancers was investigated in the present study. The efficacy of BTA with EP was estimated using a cell survival assay (MTT assay), microscopical morphology analysis and the immunocytochemical expression of heat shock proteins (HSPs). HSPs are molecules that protect the cell from harmful environmental, chemical and physical stresses, and ensure cell survival, recovery and proper functioning. HSP expression is induced various stress factors. Therefore, the expression of HSP27 and HSP70 was evaluated after cells were exposed to an external pulsed electric field and anticancer drugs. Facilitated drug delivery and the anticancer effect on metastatic tumor cells were evaluated in vitro. The effect of BTA was compared with cisplatin (CP), a standard cytostatic agent. Two different metastatic cancer cell lines were used, an ovary adenocarcinoma cell line (SW626) and melanoma cell line (Me45). BTA combined with EP exhibited similar efficacy to CP with EP after 24 and 48 h in SW626 and Me45 cancer cells. Me45 cells also had high HSP27 and low HSP70 immunosignals post­ECT treatment. ECT caused increased expression of HSP27 and HSP70 proteins in SW626 cells, which were less sensitive to ECT than the Me45 melanoma cell line. The results indicate that BTA may be efficiently applied instead of CP in ECT approaches, but its activity differs between tumor cell lines.

Biological effects in photodynamic treatment combined with electropermeabilization in wild and drug resistant breast cancer cells.

Electrochemotherapy became one of the therapeutic protocols successfully used in oncology. However, biological effects occurring in cells, especially those which are drug resistant, have not been studied thoroughly. This study presents response of wild and drug resistant breast cancer cells to classical photodynamic therapy with Photofrin or experimental photodynamic therapy with cyanine IR-775, combined with electroporation. Photodynamic reaction or electroporation alone had no cytotoxic effect, but their combination significantly disturbed cellular functions. Applying electroporation allowed the drugs to increase its accumulation, especially for a poorly permeant cyanine in drug resistant cells. FACS analysis showed that even at relatively mild electric field, ca. 90% of cells were permeabilized. High intracellular concentration of drugs triggered the cellular defense system through increased expression of glutathione S-transferase and multidrug resistance proteins (MDR1 and MRP7), particularly in drug resistant cells. Finally, expressively decreased cell metabolism and proliferation, as well as formation of apoptotic bodies and fragmentation of cells were observed after the combined treatment. The results show that electroporation can be used for effective delivery of photosensitizers, even to drug resistant breast cancer cells, which was not tested before. This shows that electro-photodynamic treatment could be a promising approach to overcome a problem of drug resistance in cancer cells.

Automated method to differentiate between native and mirror protein models obtained from contact maps.

Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters-the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.

Role of Bioinformatics in the Study of Ionic Channels.

Ionic channels belong to the group of the most important proteins. Not only do they enable transmembrane transport but they are also the key factors for proper cell function. Mutations changing their structure and functionality often lead to severe diseases called channelopathies. On the other hand, transmembrane channels are very difficult objects for experimental studies. Only 2% of experimentally identified structures are transmembrane proteins, while genomic studies show that transmembrane proteins make up 30% of all coded proteins. This gap could be diminished by bioinformatical methods which enable modeling unknown protein structures, functions, transmembrane location, and ligand binding. Several in silico methods dedicated to transmembrane proteins have been developed; some general methods could also be used. They provide the information unavailable from experiments. Current modeling tools use a variety of computational methods, which provide results of surprisingly high quality.

Amyloidogenic motifs revealed by n-gram analysis.

Amyloids are proteins associated with several clinical disorders, including Alzheimer's, and Creutzfeldt-Jakob's. Despite their diversity, all amyloid proteins can undergo aggregation initiated by short segments called hot spots. To find the patterns defining the hot spots, we trained predictors of amyloidogenicity, using n-grams and random forest classifiers. Since the amyloidogenicity may not depend on the exact sequence of amino acids but on their more general properties, we tested 524,284 reduced amino acid alphabets of different lengths (three to six letters) to find the alphabet providing the best performance in cross-validation. The predictor based on this alphabet, called AmyloGram, was benchmarked against the most popular tools for the detection of amyloid peptides using an external data set and obtained the highest values of performance measures (AUC: 0.90, MCC: 0.63). Our results showed sequential patterns in the amyloids which are strongly correlated with hydrophobicity, a tendency to form ß-sheets, and lower flexibility of amino acid residues. Among the most informative n-grams of AmyloGram we identified 15 that were previously confirmed experimentally. AmyloGram is available as the web-server: http://smorfland.uni.wroc.pl/shiny/AmyloGram/ and as the R package AmyloGram. R scripts and data used to produce the results of this manuscript are available at http://github.com/michbur/AmyloGramAnalysis .

The Effect of Millisecond Pulsed Electric Fields (msPEF) on Intracellular Drug Transport with Negatively Charged Large Nanocarriers Made of Solid Lipid Nanoparticles (SLN): In Vitro Study.

Drug delivery technology is still a dynamically developing field of medicine. The main direction in nanotechnology research (nanocarriers, nanovehicles, etc.) is efficient drug delivery to target cells with simultaneous drug reduction concentration. However, nanotechnology trends in reducing the carrier sizes to several nanometers limit the volume of the loaded substance and may pose a danger of uncontrolled access into the cells. On the other hand, nanoparticles larger than 200 nm in diameter have difficulties to undergo rapid diffusional transport through cell membranes. The main advantage of large nanoparticles is higher drug encapsulation efficiency and the ability to deliver a wider array of drugs. Our present study contributes a new approach with large Tween 80 solid lipid nanoparticles SLN (i.e., hydrodynamic GM-SLN-glycerol monostearate, GM, as the lipid and ATO5-SLNs-glyceryl palmitostearate, ATO5, as the lipid) with diameters DH of 379.4 nm and 547 nm, respectively. They are used as drug carriers alone and in combination with electroporation (EP) induced by millisecond pulsed electric fields. We evaluate if EP can support the transport of large nanocarriers into cells. The study was performed with two cell lines: human colon adenocarcinoma LoVo and hamster ovarian fibroblastoid CHO-K1 with coumarin 6 (C6) as a fluorescent marker for encapsulation. The biological safety of the potential treatment procedure was evaluated with cell viability after their exposure to nanoparticles and EP. The EP efficacy was evaluated by FACS method. The impact on intracellular structure organization of cytoskeleton was visualized by CLSM method with alpha-actin and beta-tubulin. The obtained results indicate low cytotoxicity of both carrier types, free and loaded with C6. The evaluation of cytoskeleton proteins indicated no intracellular structure damage. The intracellular uptake and accumulation show that SLNs do not support transport of C6 coumarin. Only application of electroporation improved the transport of encapsulated and free C6 into both treated cell lines.