Extracellular polymeric substances in electroactive biofilms play a crucial role in improving the efficiency of microbial fuel and electrolysis cells.

In microbial electrochemical cells (MECs), electroactive microbial biofilms can transmit electrons from organic molecules to anodes. To further understand the production of anodic biofilms, it is essential to investigate the composition and distribution of extracellular polymeric substance (EPS) in the MECs. In this study, the structure of EPS was examined in microbial electrolysis cells from mixed cultures forming biofilm using carbon fiber fabric anode. EPS was extracted from the anode biofilm of microbial electrolysis cells inoculated with mixed microbial culture. The anode biofilm yielded 0.4 mg of EPS, of which 51.2% was humic substance, 16.2% was protein, 12.6% was carbohydrates, and 20% consisted of undetermined substances. Using epifluorescence microscopy, the composition of bacterial cells and their location inside EPS were studied, and the distribution of microbial communities was compared based on current density results in the presence of various carbohydrates. On the electrode surface, bacteria and EPS gathered or overlapped in various locations can affect microbial electrochemical performance. Our findings showed that EPS formation in electroactive biofilms would be important for enhanced efficiency of electricity- or hydrogen-producing microbial electrolysis cells.

Success rate of rapid DNA technology in kinship analysis for forensic purpose.

Reducing the time required for DNA analysis in forensic genetics can yield significant benefits, both in determining genealogical relationships for legal proceedings and in criminal cases. Swift identification of individuals plays a pivotal role in solving crimes and apprehending perpetrators. Additionally, in situations like mass disasters, prompt victim identification holds utmost importance. The Rapid DNA technology, introduced in 2012 to expedite DNA analysis, has evolved to streamline the process into a single step. This advancement not only minimizes the risk of human error and contamination, but also boasts a remarkable time advantage, delivering results in as little as 90 min. In this study, DNA profiles of 30 families (consisting of mothers, fathers, and children) were analyzed using the RapidHITTM ID System. The system automatically calculated maternity-paternity probabilities to assess the suitability of Rapid DNA technology for kinship analysis. For validation, DNA profiles were also generated using the 3500 GA method. The study revealed that 9 out of 30 families exhibited discrepancies in DNA profiling, leading to inaccuracies in automatic kinship analysis. Consequently, while the method offers rapid and user-friendly advantages for forensic sciences, the software underlying the system requires re-evaluation. Issues such as maternal-paternal exclusion in kinship analyses, arising from challenges like un-called alleles, warrant further attention.

Synergistic Effect of Apigenin and Curcumin on Apoptosis, Paraptosis and Autophagy-related Cell Death in HeLa Cells.

BACKGROUND/AIM: We aimed to investigate the synergistic effects of apigenin and curcumin on the cross-talk between apoptosis and autophagic cell death, as well as on paraptosis in HeLa cells. MATERIALS AND METHODS: Cell viability was measured using the MTT assay. Synergistic effects were measured using the Bliss independence model. qRT-PCR was used to study the expression of genes related to apoptosis, autophagic cell death, and cross-talk. GRP78/BiP immunostaining was used to identify endoplasmic reticulum (ER) stress. RESULTS: Treatment with a combination of apigenin and curcumin increased the expression levels of genes related to cell death in HeLa cells 1.29- to 27.6-fold. The combination of curcumin and apigenin showed a synergistic anti-tumor effect via cross-talk between processes leading to apoptosis and autophagic cell death, as well as ER stress-associated paraptosis. GRP78 expression was down-regulated, and massive cytoplasmic vacuolization was observed in HeLa cells. CONCLUSION: The combination of curcumin and apigenin is an effective potential therapeutic for cervical cancers.

Gene editing and RNAi approaches for COVID-19 diagnostics and therapeutics.

The novel coronavirus pneumonia (COVID-19) is a highly infectious acute respiratory disease caused by Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV-2) (Prec Clin Med 2020;3:9-13, Lancet 2020;395:497-506, N. Engl J Med 2020a;382:1199-207, Nature 2020;579:270-3). SARS-CoV-2 surveillance is essential to controlling widespread transmission. However, there are several challenges associated with the diagnostic of the COVID-19 during the current outbreak (Liu and Li (2019), Nature 2020;579:265-9, N. Engl J Med 2020;382:727-33). Firstly, the high number of cases overwhelms diagnostic test capacity and proposes the need for a rapid solution for sample processing (Science 2018;360:444-8). Secondly, SARS-CoV-2 is closely related to other important coronavirus species and subspecies, so detection assays can give false-positive results if they are not efficiently specific to SARS-CoV-2. Thirdly, patients with suspected SARS-CoV-2 infection sometimes have a different respiratory viral infection or co-infections with SARS-CoV-2 and other respiratory viruses (MedRxiv 2020a;1-18). Confirmation of the COVID-19 is performed mainly by virus isolation followed by RT-PCR and sequencing (N. Engl J Med 2020;382:727-33, MedRxiv 2020a, Turkish J Biol 2020;44:192-202). The emergence and outbreak of the novel coronavirus highlighted the urgent need for new therapeutic technologies that are fast, precise, stable, easy to manufacture, and target-specific for surveillance and treatment. Molecular biology tools that include gene-editing approaches such as CRISPR-Cas12/13-based SHERLOCK, DETECTR, CARVER and PAC-MAN, antisense oligonucleotides, antisense peptide nucleic acids, ribozymes, aptamers, and RNAi silencing approaches produced with cutting-edge scientific advances compared to conventional diagnostic or treatment methods could be vital in COVID-19 and other future outbreaks. Thus, in this review, we will discuss potent the molecular biology approaches that can revolutionize diagnostic of viral infections and therapies to fight COVID-19 in a highly specific, stable, and efficient way.

Antimicrobial Drug Interactions: Systematic Evaluation of Protein and Nucleic Acid Synthesis Inhibitors.

Antimicrobial multidrug resistance and its transmission among strains are serious problems. Success rate is decreased and treatment options are narrowed due to increasing bacterial multidrug resistance. On the other hand, the need for long-term efforts to discover new antibiotics and difficulties finding new treatment protocols make this problem more complex. Combination therapy, especially with synergistic use of antimicrobials is a rational treatment option with huge benefits. Thus, screening antibiotic interactions is crucial for finding better treatment options. Clinicians currently use combinatorial antibiotic treatment as an effective treatment option. However, antibiotics can show synergistic or antagonistic interactions when used together. In our study, we aimed to investigate interactions of antibiotics with different mechanisms of action. Antibiotics, which act as protein synthesis inhibitors (P) and nucleic acid synthesis inhibitors (N) were used in our study. We tested 66 (PN), 15 (NN), and 55 (PP) drug pairs on the Escherichia coli strain. The Loewe additivity model was used and alpha scores were calculated for analysis of interactions of drug combinations. Drug interactions were categorized as synergistic or antagonistic. Accordingly, pairwise combinations of protein synthesis inhibitors (PP) showed stronger synergistic interactions than those of nucleic acid synthesis inhibitors (NN) and nucleic acid synthesis-protein synthesis inhibitors (PN). As a result, the importance of mechanisms of action of drugs is emphasized in the selection of synergistic drug combinations.

Design of high-order antibiotic combinations against M. tuberculosis by ranking and exclusion.

Combinations of more than two drugs are routinely used for the treatment of pathogens and tumors. High-order combinations may be chosen due to their non-overlapping resistance mechanisms or for favorable drug interactions. Synergistic/antagonistic interactions occur when the combination has a higher/lower effect than the sum of individual drug effects. The standard treatment of Mycobacterium tuberculosis (Mtb) is an additive cocktail of three drugs which have different targets. Herein, we experimentally measured all 190 pairwise interactions among 20 antibiotics against Mtb growth. We used the pairwise interaction data to rank all possible high-order combinations by strength of synergy/antagonism. We used drug interaction profile correlation as a proxy for drug similarity to establish exclusion criteria for ideal combination therapies. Using this ranking and exclusion design (R/ED) framework, we modeled ways to improve the standard 3-drug combination with the addition of new drugs. We applied this framework to find the best 4-drug combinations against drug-resistant Mtb by adding new exclusion criteria to R/ED. Finally, we modeled alternating 2-order combinations as a cycling treatment and found optimized regimens significantly reduced the overall effective dose. R/ED provides an adaptable framework for the design of high-order drug combinations against any pathogen or tumor.

Hydrogen production profiles using furans in microbial electrolysis cells.

Microbial electrochemical cells including microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) are novel biotechnological tools that can convert organic substances in wastewater or biomass into electricity or hydrogen. Electroactive microbial biofilms used in this technology have ability to transfer electrons from organic compounds to anodes. Evaluation of biofilm formation on anode is crucial for enhancing our understanding of hydrogen generation in terms of substrate utilization by microorganisms. In this study, furfural and hydroxymethylfurfural (HMF) were analyzed for hydrogen generation using single chamber membrane-free MECs (17 mL), and anode biofilms were also examined. MECs were inoculated with mixed bacterial culture enriched using chloroethane sulphonate. Hydrogen was succesfully produced in the presence of HMF, but not furfural. MECs generated similar current densities (5.9 and 6 mA/cm2 furfural and HMF, respectively). Biofilm samples obtained on the 24th and 40th day of cultivation using aromatic compounds were evaluated by using epi-fluorescent microscope. Our results show a correlation between biofilm density and hydrogen generation in single chamber MECs.

Chemogenomics and orthology-based design of antibiotic combination therapies.

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical-genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug-interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less-studied pathogens by leveraging chemogenomics data in model organisms.

Screening of new antileukemic agents from essential oils of algae extracts and computational modeling of their interactions with intracellular signaling nodes.

Microalgae are very rich in bioactive compounds, minerals, polysaccharides, poly-unsaturated fatty acids and vitamins, and these rich constituents make microalgae an important resource for the discovery of new bioactive compounds with applications in biotechnology. In this study, we studied the antileukemic activity of several chosen microalgae species at the molecular level and assessed their potential for drug development. Here we identified Stichococcus bacillaris, Phaeodactylum tricornutum, Microcystis aeruginosa and Nannochloropsis oculata microalgae extracts with possible antileukemic agent potentials. Specifically we studied the effects of these extracts on intracellular signal nodes and apoptotic pathways. We characterized the composition of essential oils of these fifteen different algae extracts using gas chromatography-mass spectrometry (GC-MS). Finally, to identify potential molecular targets causing the phenotypic changes in leukemic cell lines, we docked a selected group of these essential oils to several key intracellular proteins. According to results of rank score algorithm, five of these essential oils analyzed might be considered as in silico plausible candidates to be used as antileukemic agents.

Cytotoxic effect of extract from Dunaliella salina against SH-SY5Y neuroblastoma cells.

Cytotoxic effects of essential oils extracted from Dunaliella salina on SH-SY5Y human neuroblastoma cells were investigated in this study. GC-MS analysis was used for determination of the composition of essential oils found in Dunaliella salina extract. All experimented concentrations of Dunaliella salina extract on SH-SY5Y human neuroblastoma cells were significantly more cytotoxic than the tested concentrations of the extract on ECV304 human endothelial cells used as a control. Fifthy compounds were detected in GC-MS analysis of the extract, and five major compounds were predominantly found as follows: octadecanoic acid, methyl ester (27.43%); hexadecanoic acid, methyl ester (Cas) methyl palmitate (24.82%); 9,12,15-octadecatrienoic acid, ethyl ester, (Z,Z,Z)- (7.39%); octadecanoic acid (5.03%), pentadecanoic acid (3.60%). The cytotoxic activity of Dunaliella salina extract on SH-SY5Y human neuroblastoma cells might be due to high concentrations of octadecanoic acid and hexadecanoic acid. Furthermore, results indicate that the extract demonstrates some proliferative effect on ECV304 cells in a dose-dependent manner between 0.25 and 5 µg/ml. These results suggest that Dunaliella salina may have anticancer potential against human neuroblastoma cells.

Target-independent prediction of drug synergies using only drug lipophilicity.

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds ("drugs") previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms.

Large-scale identification and analysis of suppressive drug interactions.

One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate.

Oxidative stress is a mediator for increased lipid accumulation in a newly isolated Dunaliella salina strain.

Green algae offer sustainable, clean and eco-friendly energy resource. However, production efficiency needs to be improved. Increasing cellular lipid levels by nitrogen depletion is one of the most studied strategies. Despite this, the underlying physiological and biochemical mechanisms of this response have not been well defined. Algae species adapted to hypersaline conditions can be cultivated in salty waters which are not useful for agriculture or consumption. Due to their inherent extreme cultivation conditions, use of hypersaline algae species is better suited for avoiding culture contamination issues. In this study, we identified a new halophilic Dunaliella salina strain by using 18S ribosomal RNA gene sequencing. We found that growth and biomass productivities of this strain were directly related to nitrogen levels, as the highest biomass concentration under 0.05 mM or 5 mM nitrogen regimes were 495 mg/l and 1409 mg/l, respectively. We also confirmed that nitrogen limitation increased cellular lipid content up to 35% under 0.05 mM nitrogen concentration. In order to gain insight into the mechanisms of this phenomenon, we applied fluorometric, flow cytometric and spectrophotometric methods to measure oxidative stress and enzymatic defence mechanisms. Under nitrogen depleted cultivation conditions, we observed increased lipid peroxidation by measuring an important oxidative stress marker, malondialdehyde and enhanced activation of catalase, ascorbate peroxidase and superoxide dismutase antioxidant enzymes. These observations indicated that oxidative stress is accompanied by increased lipid content in the green alga. In addition, we also showed that at optimum cultivation conditions, inducing oxidative stress by application of exogenous H2O2 leads to increased cellular lipid content up to 44% when compared with non-treated control groups. Our results support that oxidative stress and lipid overproduction are linked. Importantly, these results also suggest that oxidative stress mediates lipid accumulation. Understanding such relationships may provide guidance for efficient production of algal biodiesels.

Rediscovery of historical Vitis vinifera varieties from the South Anatolia region by using amplified fragment length polymorphism and simple sequence repeat DNA fingerprinting methods.

Anatolia played an important role in the diversification and spread of economically important Vitis vinifera varieties. Although several biodiversity studies have been conducted with local cultivars in different regions of Anatolia, our aim is to gain a better knowledge on the biodiversity of endangered historical V. vinifera varieties in the northern Adana region of southern Anatolia, particularly those potentially displaying viticulture characteristics. We also demonstrate the genetic relatedness in a selected subset of widely cultivated and commercialized V. vinifera collection cultivars, which were obtained from the National Grapevine Germplasm located at the Institute of Viticulture, Turkey. In the present study, microsatellites were used in narrowing the sample size from 72 accessions down to a collection of 27 varieties. Amplified fragment length polymorphisms were then employed to determine genetic relatedness among this collection and local V. vinifera cultivars. The unweighted pair group method with arithmetic mean cluster and principal component analyses revealed that Saimbeyli local cultivars form a distinct group, which is distantly related to a selected subset of V. vinifera collection varieties from all over Turkey. To our knowledge, this is the first study conducted with these cultivars. Further preservation and use of these potential viticultural varieties will be helpful to avoid genetic erosion and to promote continued agriculture in the region.