Recent advances and fundamentals of microalgae cultivation technology.

Microalgae are considered to be a promising group of organisms for fuel production, waste processing, pharmaceutical applications, and as a source of food components. Unicellular algae are worth being considered because of their capacity to produce comparatively large amounts of lipids, proteins, and vitamins while requiring little room for growth. They can also grow on waste and fix CO2 and nitrogen compounds. However, production costs limit the industrial use of microalgae to the most profitable applications including micronutrient production and fish farming. Therefore, novel microalgae based technologies require an increase of the production efficiencies or values. Here we review the recent studies focused on getting strains with novel characteristics or cultivating techniques that improve production's robustness or efficiency and categorize these findings according to the fundamental factors that determine microalgae growth. Improvements of light and nutrient delivery, as well as other aspects of photobioreactor design, have shown the highest average increase in productivity. Other methods, such as an improvement of phosphorus or CO2 fixation and temperature adaptation have been found to be less effective. Furthermore, interactions with particular bacteria may promote the growth of microalgae, although bacterial and grazer contaminations must be managed to avoid culture failure. The competitiveness of the algal products will increase if these discoveries are applied to industrial settings.

Biogeographic Analysis Suggests Two Types of Planktonic Prokaryote Communities in the Barents Sea.

The Barents Sea is one of the most rapidly changing Arctic regions, with an unprecedented sea ice decline and increase in water temperature and salinity. We have studied the diversity of prokaryotic communities using 16S metabarcoding in the western and northeastern parts of the Barents Sea along the Kola Section and the section from Novaya Zemlya to Franz Joseph Land. The hypothesis-independent clustering method revealed the existence of two distinct types of communities. The most common prokaryotic taxa were shared between two types of communities, but their relative abundance was different. It was found that the geographic location of the sampling sites explained more than 30% of the difference between communities, while no statistically significant correlation between environmental parameters and community composition was found. The representatives of the Psychrobacter, Sulfitobacter and Polaribacter genera were dominant in samples from both types of communities. The first type of community was also dominated by members of Halomonas, Pseudoalteromonas, Planococcaceae and an unclassified representative of the Alteromonadaceae family. The second type of community also had a significant proportion of Nitrincolaceae, SAR92, SAR11 Clade I, NS9, Cryomorphaceae and SUP05 representatives. The origin of these communities can be explained by the influence of environmental factors or by the different origins of water masses. This research highlights the importance of studying biogeographic patterns in the Barents Sea in comparison with those in the North Atlantic and Arctic Ocean prokaryote communities.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Pan-cancer antagonistic inhibition pattern of ATM-driven G2/M checkpoint pathway vs other DNA repair pathways.

DNA repair mechanisms keep genome integrity and limit tumor-associated alterations and heterogeneity, but on the other hand they promote tumor survival after radiation and genotoxic chemotherapies. We screened pathway activation levels of 38 DNA repair pathways in nine human cancer types (gliomas, breast, colorectal, lung, thyroid, cervical, kidney, gastric, and pancreatic cancers). We took RNAseq profiles of the experimental 51 normal and 408 tumor samples, and from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases - of 500/407 normal and 5752/646 tumor samples, and also 573 normal and 984 tumor proteomic profiles from Proteomic Data Commons portal. For all the samplings we observed a congruent trend that all cancer types showed inhibition of G2/M arrest checkpoint pathway compared to the normal samples, and relatively low activities of p53-mediated pathways. In contrast, other DNA repair pathways were upregulated in most of the cancer types. The G2/M checkpoint pathway was statistically significantly downregulated compared to the other DNA repair pathways, and this inhibition was strongly impacted by antagonistic regulation of (i) promitotic genes CCNB and CDK1, and (ii) GADD45 genes promoting G2/M arrest. At the DNA level, we found that ATM, TP53, and CDKN1A genes accumulated loss of function mutations, and cyclin B complex genes - transforming mutations. These findings suggest importance of activation for most of DNA repair pathways in cancer progression, with remarkable exceptions of G2/M checkpoint and p53-related pathways which are downregulated and neutrally activated, respectively.

Experimentally Deduced Criteria for Detection of Clinically Relevant Fusion 3' Oncogenes from FFPE Bulk RNA Sequencing Data.

Drugs targeting receptor tyrosine kinase (RTK) oncogenic fusion proteins demonstrate impressive anti-cancer activities. The fusion presence in the cancer is the respective drug prescription biomarker, but their identification is challenging as both the breakpoint and the exact fusion partners are unknown. RNAseq offers the advantage of finding both fusion parts by screening sequencing reads. Paraffin (FFPE) tissue blocks are the most common way of storing cancer biomaterials in biobanks. However, finding RTK fusions in FFPE samples is challenging as RNA fragments are short and their artifact ligation may appear in sequencing libraries. Here, we annotated RNAseq reads of 764 experimental FFPE solid cancer samples, 96 leukemia samples, and 2 cell lines, and identified 36 putative clinically relevant RTK fusions with junctions corresponding to exon borders of the fusion partners. Where possible, putative fusions were validated by RT-PCR (confirmed for 10/25 fusions tested). For the confirmed 3'RTK fusions, we observed the following distinguishing features. Both moieties were in-frame, and the tyrosine kinase domain was preserved. RTK exon coverage by RNAseq reads upstream of the junction site were lower than downstream. Finally, most of the true fusions were present by more than one RNAseq read. This provides the basis for automatic annotation of 3'RTK fusions using FFPE RNAseq profiles.

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways.

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.

OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization.

OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein-protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level.

Previously, we have shown that the aggregation of RNA-level gene expression profiles into quantitative molecular pathway activation metrics results in lesser batch effects and better agreement between different experimental platforms. Here, we investigate whether pathway level of data analysis provides any advantage when comparing transcriptomic and proteomic data. We compare the paired proteomic and transcriptomic gene expression and pathway activation profiles obtained for the same human cancer biosamples in The Cancer Genome Atlas (TCGA) and the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, for a total of 755 samples of glioblastoma, breast, liver, lung, ovarian, pancreatic, and uterine cancers. In a CPTAC assay, expression levels of 15,112 protein-coding genes were profiled using the Thermo QE series of mass spectrometers. In TCGA, RNA expression levels of the same genes were obtained using the Illumina HiSeq 4000 engine for the same biosamples. At the gene level, absolute gene expression values are compared, whereas pathway-grade comparisons are made between the pathway activation levels (PALs) calculated using average sample-normalized transcriptomic and proteomic profiles. We observed remarkably different average correlations between the primary RNA- and protein expression data for different cancer types: Spearman Rho between 0.017 (p = 1.7 × 10−13) and 0.27 (p < 2.2 × 10−16). However, at the pathway level we detected overall statistically significantly higher correlations: averaged Rho between 0.022 (p < 2.2 × 10−16) and 0.56 (p < 2.2 × 10−16). Thus, we conclude that data analysis at the PAL-level yields results of a greater similarity when comparing high-throughput RNA and protein expression profiles.

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples.

Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)-0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)-0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas.

Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.

Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma.

Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.

Human blood serum can donor-specifically antagonize effects of EGFR-targeted drugs on squamous carcinoma cell growth.

Many patients fail to respond to EGFR-targeted therapeutics, and personalized diagnostics is needed to identify putative responders. We investigated 1630 colorectal and lung squamous carcinomas and 1357 normal lung and colon samples and observed huge variation in EGFR pathway activation in both cancerous and healthy tissues, irrespectively on EGFR gene mutation status. We investigated whether human blood serum can affect squamous carcinoma cell growth and EGFR drug response. We demonstrate that human serum antagonizes the effects of EGFR-targeted drugs erlotinib and cetuximab on A431 squamous carcinoma cells by increasing IC50 by about 2- and 20-fold, respectively. The effects on clonogenicity varied significantly across the individual serum samples in every experiment, with up to 100% differences. EGF concentration could explain many effects of blood serum samples, and EGFR ligands-depleted serum showed lesser effect on drug sensitivity.

Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways.

Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.

RNA Sequencing-Based Identification of Ganglioside GD2-Positive Cancer Phenotype.

The tumor-associated ganglioside GD2 represents an attractive target for cancer immunotherapy. GD2-positive tumors are more responsive to such targeted therapy, and new methods are needed for the screening of GD2 molecular tumor phenotypes. In this work, we built a gene expression-based binary classifier predicting the GD2-positive tumor phenotypes. To this end, we compared RNA sequencing data from human tumor biopsy material from experimental samples and public databases as well as from GD2-positive and GD2-negative cancer cell lines, for expression levels of genes encoding enzymes involved in ganglioside biosynthesis. We identified a 2-gene expression signature combining ganglioside synthase genes ST8SIA1 and B4GALNT1 that serves as a more efficient predictor of GD2-positive phenotype (Matthews Correlation Coefficient (MCC) 0.32, 0.88, and 0.98 in three independent comparisons) compared to the individual ganglioside biosynthesis genes (MCC 0.02-0.32, 0.1-0.75, and 0.04-1 for the same independent comparisons). No individual gene showed a higher MCC score than the expression signature MCC score in two or more comparisons. Our diagnostic approach can hopefully be applied for pan-cancer prediction of GD2 phenotypes using gene expression data.

Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer.

Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.

Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs.

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Cytotoxic Potential of the Novel Horseshoe Crab Peptide Polyphemusin III.

Biological activity of the new antimicrobial peptide polyphemusin III from the horseshoe crab Limulus polyphemus was examined against bacterial strains and human cancer, transformed, and normal cell cultures. Polyphemusin III has the amino acid sequence RRGCFRVCYRGFCFQRCR and is homologous to other ß-hairpin peptides from the horseshoe crab. Antimicrobial activity of the peptide was evaluated and MIC (minimal inhibitory concentration) values were determined. IC50 (half-maximal inhibitory concentration) values measured toward human cells revealed that polyphemusin III showed a potent cytotoxic activity at concentrations of <10 µM. Polyphemusin III caused fast permeabilization of the cytoplasmic membrane of human leukemia cells HL-60, which was measured with trypan blue exclusion assay and lactate dehydrogenase-release assay. Flow cytometry experiments for annexin V-FITC/ propidium iodide double staining revealed that the caspase inhibitor, Z-VAD-FMK, did not abrogate disruption of the plasma membrane by polyphemusin III. Our data suggest that polyphemusin III disrupts the plasma membrane integrity and induces cell death that is apparently not related to apoptosis. In comparison to known polyphemusins and tachyplesins, polyphemusin III demonstrates a similar or lower antimicrobial effect, but significantly higher cytotoxicity against human cancer and transformed cells in vitro.

Acquired resistance to tyrosine kinase inhibitors may be linked with the decreased sensitivity to X-ray irradiation.

Acquired resistance to chemotherapy and radiation therapy is one of the major obstacles decreasing efficiency of treatment of the oncologic diseases. In this study, on the two cell lines (ovarian carcinoma SKOV-3 and neuroblastoma NGP-127), we modeled acquired resistance to five target anticancer drugs. The cells were grown on gradually increasing concentrations of the clinically relevant tyrosine kinase inhibitors (TKIs) Sorafenib, Pazopanib and Sunitinib, and rapalogs Everolimus and Temsirolimus, for 20 weeks. After 20 weeks of culturing, the half-inhibitory concentrations (IC50) increased by 25 - 186% for the particular combinations of the drugs and cell types. We next subjected cells to 10 Gy irradiation, a dose frequently used in clinical radiation therapy. For the SKOV-3, but not NGP-127 cells, for the TKIs Sorafenib, Pazopanib and Sunitinib, we noticed statistically significant increase in capacity to repair radiation-induced DNA double strand breaks compared to naïve control cells not previously treated with TKIs. These peculiarities were linked with the increased activation of ATM DNA repair pathway in the TKI-treated SKOV-3, but not NGP-127 cells. Our results provide a new cell culture model for studying anti-cancer therapy efficiency and evidence that there may be a tissue-specific radioresistance emerging as a side effect of treatment with TKIs.

Comparative in vitro study on cytotoxicity of recombinant ß-hairpin peptides.

Natural antimicrobial peptides (AMPs) are important components of the innate immune system with a wide spectrum of biological activity. In this study, we investigated the cytotoxic effect of three recombinant ß-hairpin cationic AMPs: arenicin-1 from the polychaeta Arenicola marina, tachyplesin I from the horseshoe crab Tachypleus tridentatus, and gomesin from the spider Acanthoscurria gomesiana. All the three ß-hairpin AMPs were overexpressed in Escherichia coli. Different cell lines were incubated with various concentrations of the investigated AMPs in order to evaluate their cytotoxic activity. Double staining with subsequent flow cytometric analysis was used to determine the predominant way of cell death mediated by each AMP. Hemolytic activity of the peptides was tested against fresh human red blood cells. Our results indicated that all the three AMPs exhibited significant cytotoxic effect against cancer cells that varied depending on the cell line type and, in most cases, on the presence of serum components. Flow cytometric analysis implicitly indicated that tachyplesin I mostly promoted late apoptosis/necrosis, while arenicin-1 and gomesin induced early apoptosis under the same conditions. Tachyplesin I proved to be the most promising therapeutic candidate as it displayed the highest specific cytotoxicity against cancer cell lines, independent of the serum presence.

Anticancer Activity of the Goat Antimicrobial Peptide ChMAP-28.

Cytotoxic effect of the antimicrobial peptide ChMAP-28 from leucocytes of the goat Capra hircus was examined against five cancer and two normal human cell lines. ChMAP-28 has the amino acid sequence GRFKRFRKKLKRLWHKVGPFVGPILHY and is homologous to other α-helical mammalian antimicrobial peptides. ChMAP-28 shows considerably higher cytotoxicity against cultured tumor cells than toward normal cells at concentrations of <10 µM. Our findings suggest that ChMAP-28 can initiate necrotic death of cancer cells. Its cytotoxic effect is accomplished due to disruption of the plasma membrane integrity and is not abrogated by the addition of the caspase inhibitor Z-VAD-FMK. ChMAP-28 causes permeabilization of cytoplasmic membrane of human leukemia cells HL-60 already after 15 min of incubation. Here, we show that ChMAP-28 has one of the highest antitumor activity in vitro among all known antimicrobial peptides. We speculate that the observed specificity of ChMAP-28 cytotoxic effect against tumor cells is due to its relatively low hydrophobicity and high cationicity. In the meantime, this peptide has low hemolytic activity, which generates a potential for its use as a therapeutic agent.