Identification of Bacterial Membrane Selectivity of Romo1-Derived Antimicrobial Peptide AMPR-22 via Molecular Dynamics

The abuse or misuse of antibiotics has caused the emergence of extensively drug-resistant (XDR) bacteria, rendering most antibiotics ineffective and increasing the mortality rate of patients with bacteremia or sepsis. Antimicrobial peptides (AMPs) are proposed to overcome this problem; however, many AMPs have attenuated antimicrobial activities with hemolytic toxicity in blood. Recently, AMPR-11 and its optimized derivative, AMPR-22, were reported to be potential candidates for the treatment of sepsis with a broad spectrum of antimicrobial activity and low hemolytic toxicity. Here, we performed molecular dynamics (MD) simulations to clarify the mechanism of lower hemolytic toxicity and higher efficacy of AMPR-22 at an atomic level. We found four polar residues in AMPR-11 bound to a model mimicking the bacterial inner/outer membranes preferentially over eukaryotic plasma membrane. AMPR-22 whose polar residues were replaced by lysine showed a 2-fold enhanced binding affinity to the bacterial membrane by interacting with bacterial specific lipids (lipid A or cardiolipin) via hydrogen bonds. The MD simulations were confirmed experimentally in models that partially mimic bacteremia conditions in vitro and ex vivo. The present study demonstrates why AMPR-22 showed low hemolytic toxicity and this approach using an MD simulation would be helpful in the development of AMPs.     

A Fully-Human Antibody Specifically Targeting a Membrane-Bound Fragment of CADM1 Potentiates the T Cell-Mediated Death of Human Small-Cell Lung Cancer Cells

Small-cell lung cancer (SCLC) is the most aggressive form of lung cancer and the leading cause of global cancer-related mortality. Despite the earlier identification of membrane-proximal cleavage of cell adhesion molecule 1 (CADM1) in cancers, the role of the membrane-bound fragment of CAMD1 (MF-CADM1) is yet to be clearly identified. In this study, we first isolated MF-CADM1-specific fully human single-chain variable fragments (scFvs) from the human synthetic scFv antibody library using the phage display technology. Following the selected scFv conversion to human immunoglobulin G1 (IgG1) scFv-Fc antibodies (K103.1–4), multiple characterization studies, including antibody cross-species reactivity, purity, production yield, and binding affinity, were verified. Finally, via intensive in vitro efficacy and toxicity evaluation studies, we identified K103.3 as a lead antibody that potently promotes the death of human SCLC cell lines, including NCI-H69, NCI-H146, and NCI-H187, by activated Jurkat T cells without severe endothelial toxicity. Taken together, these findings suggest that antibody-based targeting of MF-CADM1 may be an effective strategy to potentiate T cell-mediated SCLC death, and MF-CADM1 may be a novel potential therapeutic target in SCLC for antibody therapy.     

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.     

The Role of Polymorphisms at the Interleukin-1, Interleukin-4, GATA-3 and Cyclooxygenase-2 Genes in Non-Surgical Periodontal Therapy

Periodontitis is a multifactorial disease. The aim of this explorative study was to investigate the role of Interleukin-(IL)-1, IL-4, GATA-3 and Cyclooxygenase-(COX)-2 polymorphisms after non-surgical periodontal therapy with adjunctive systemic antibiotics (amoxicillin/metronidazole) and subsequent maintenance in a Caucasian population. Analyses were performed using blood samples from periodontitis patients of a multi-center trial (ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00707369","term_id":"NCT00707369"}}NCT00707369=ABPARO-study). Polymorphisms were analyzed using quantitative real-time PCR. Clinical attachment levels (CAL), percentage of sites showing further attachment loss (PSAL) ≥1.3 mm, bleeding on probing (BOP) and plaque score were assessed. Exploratory statistical analysis was performed. A total of 209 samples were genotyped. Patients carrying heterozygous genotypes and single-nucleotide-polymorphisms (SNP) on the GATA-3-IVS4 +1468 gene locus showed less CAL loss than patients carrying wild type. Heterozygous genotypes and SNPs on the IL-1A-889, IL-1B +3954, IL-4-34, IL-4-590, GATA-3-IVS4 +1468 and COX-2-1195 gene loci did not influence CAL. In multivariate analysis, CAL was lower in patients carrying GATA-3 heterozygous genotypes and SNPs than those carrying wild-types. For the first time, effects of different genotypes were analyzed in periodontitis progression after periodontal therapy and during supportive treatment using systemic antibiotics demonstrating a slight association of GATA-3 gene locus with CAL. This result suggests that GATA-3 genotypes are a contributory but non-essential risk factor for periodontal disease progression.     

Development of Deep-Learning-Based Single-Molecule Localization Image Analysis

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging.     

HDAC8-Selective Inhibition by PCI-34051 Enhances the Anticancer Effects of ACY-241 in Ovarian Cancer Cells

HDAC6 is overexpressed in ovarian cancer and is known to be correlated with tumorigenesis. Accordingly, ACY-241, a selective HDAC6 inhibitor, is currently under clinical trial and has been tested in combination with various drugs. HDAC8, another member of the HDAC family, has recently gained attention as a novel target for cancer therapy. Here, we evaluated the synergistic anticancer effects of PCI-34051 and ACY-241 in ovarian cancer. Among various ovarian cancer cells, PCI-34051 effectively suppresses cell proliferation in wild-type p53 ovarian cancer cells compared with mutant p53 ovarian cancer cells. In ovarian cancer cells harboring wild-type p53, PCI-34051 in combination with ACY-241 synergistically represses cell proliferation, enhances apoptosis, and suppresses cell migration. The expression of pro-apoptotic proteins is synergistically upregulated, whereas the expressions of anti-apoptotic proteins and metastasis-associated proteins are significantly downregulated in combination treatment. Furthermore, the level of acetyl-p53 at K381 is synergistically upregulated upon combination treatment. Overall, co-inhibition of HDAC6 and HDAC8 through selective inhibitors synergistically suppresses cancer cell proliferation and metastasis in p53 wild-type ovarian cancer cells. These results suggest a novel approach to treating ovarian cancer patients and the therapeutic potential in developing HDAC6/8 dual inhibitors.     

A formal model of creative decision making

Real-world tasks are often complex, uncertain, and constrained. The complexity arises from the need to consider numerous factors that are of varying degrees of relevance to the problem at hand. The uncertainty springs from imperfect information concerning the state of the world, the repertoire of feasible alternatives, and the consequences of each action. The constraints are attributable to time, money, and computational resources as well as individual tastes and societal values.Despite the rich nature of practical tasks, previous work in decision making—whether in engineering, statistics, management, or economics—has focused solely on partial aspects of the problem. This state of affairs is reflected in the nomenclature, which involves categories such as “constrained optimization” or “decisions under uncertainty”.If real-world tasks are to be addressed in a coherent fashion, it is imperative to develop a systematic framework providing an integrated view. The framework may then serve as the foundation for a general theory of decision making that can capture the full richness of realistic problems. This paper explores how these goals might be achieved.Algebraic and stochastic models of innovative decision making are presented. This is followed by an examination of idea generation in product design. Finally, suggestions are made for extending the work along both theoretical and empirical lines.     

Elastic password authentication scheme using the Passcell-based virtual scroll wheel

User authentication such as password setting has become increasingly important for the secure management of the information stored in mobile devices. However, in the password authentication schemes used in mobile devices, enhancing security reduces their usability, and passwords become hard to memorize. In addition, enhancing their usability makes them vulnerable to shoulder-surfing or recording attacks involving stealing a glance at the authentication process through the system interface. In this paper, we propose a password authentication scheme that uses a virtual scroll wheel, called WheelLock, to ensure appropriate usability and prevent brute force, shoulder-surfing, and recording attacks.     

Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure

This study focuses on the use of genetic programming to automate the design of robust analog circuits. We define two complementary types of failure modes: partial short-circuit and partial disconnect, and demonstrated novel circuits that are resilient across a spectrum of fault levels. In particular, we focus on designs that are uniformly robust, and unlike designs based on redundancy, do not have any single point of failure. We also explore the complementary problem of designing tamper-proof circuits that are highly sensitive to any change or variation in their operating conditions. We find that the number of components remains similar both for robust and standard circuits, suggesting that the robustness does not necessarily come at significant increased circuit complexity. A number of fitness criteria, including surrogate models and co-evolution were used to accelerate the evolutionary process. A variety of circuit types were tested, and the practicality of the generated solutions was verified by physically constructing the circuits and testing their physical robustness.