Effectiveness and safety of subcutaneous abatacept in biologic-naïve RA patients at Week 52: A Japanese multicentre investigational study (ORIGAMI study).

OBJECTIVES: To evaluate the effectiveness and safety of abatacept over 52 weeks in biologic-naïve rheumatoid arthritis (RA) patients with moderate disease activity in the prospective, 5-year, observational study (ORIGAMI study) in Japan. METHODS: Abatacept (125 mg) was administered subcutaneously once a week. Clinical outcomes included Simplified Disease Activity Index (SDAI) remission at Week 52 (primary endpoint), Japanese Health Assessment Questionnaire (J-HAQ), EuroQol 5-Dimension Questionnaire (EQ-5D), treatment retention, and safety. The results were compared with those of conventional synthetic disease-modifying antirheumatic drug (csDMARD) controls from the ongoing Institute of Rheumatology, Rheumatoid Arthritis (IORRA) registry. RESULTS: Overall, 325 patients were enrolled, with a mean age of 66.9 ± 12.7 years. The proportion of patients achieving SDAI remission (≤3.3) at Week 52 was 18.9% (95% CI: 14.3-23.6) and low disease activity (≤11) was 53.3% (95% CI: 47.4-59.1). A significant improvement was observed in J-HAQ and EQ-5D over 52 weeks in both the abatacept and csDMARD groups. The probability of abatacept treatment retention at Week 52 was 69.9% (95% CI: 64.7-75.5). Adverse events and serious adverse events were reported in 50.0% and 12.1% of patients, respectively. CONCLUSIONS: Abatacept significantly improved disease activity, physical disability, and quality of life for up to 52 weeks in RA patients in a real-world setting.

Long-term intake of Lactobacillus paracasei KW3110 prevents age-related circadian locomotor activity and changes in gut metabolism in physiologically aged mice.

Aging involves age-progressive loss of physiological functions in organs and tissues. We previously showed that Lactobacillus paracasei KW3110 suppressed age-related inflammation and prevented age-related retinal ganglion cell (RGC) loss. As RGCs mediate biological behaviors associated with responses to ambient light, we assessed whether L. paracasei KW3110 affects circadian locomotor activities in physiologically aged mice. The ratio of locomotor activity during the nighttime (active phase) to daytime (inactive phase) significantly decreased in physiologically aged mice compared with young mice: intake of L. paracasei KW3110 prevented this decrease. We also performed metabolomics analysis of cecal contents using both capillary electrophoresis and liquid chromatography time-of-flight mass spectrometry to better understand the benefical effects for aging of L. paracasei KW3110 through a gut retina axis, since our previous study showed that L. paracasei KW3110 mitigated not only age-related expansions of intestinal inflammatory immune cells but age-related alternation of gut microbiome composition. Principal component analysis showed clear changes in metabolites between physiologically aged mice fed a diet containing L. paracasei KW3110 and age-matched control mice. Furthermore, we found that intake of L. paracasei KW3110 mitigated age-related changes in some fatty acids compared with age-matched control mice. Taken together, L. paracasei KW3110 might regulate age-related alternation of metabolites in cecal contents, potentially leading to suppression of age-related decline in physiological functions, including impairment of circadian locomotor activities.

Characterization of genomic DNA of lactic acid bacteria for activation of plasmacytoid dendritic cells.

BACKGROUND: Lactococcus lactis strain Plasma (LC-Plasma) possesses strong stimulatory activity for plasmacytoid dendritic cells (pDCs) via the TLR9-Myd88 pathway. To reveal the effective lactic acid bacteria (LAB) genome structure for pDCs stimulatory activity, we performed in vitro screening, using randomly selected 200 bp DNA fragments from the LC-Plasma genome. RESULTS: We found that the CpG motif copy number in the fragments was positively and significantly correlated with pDCs stimulatory activity (R = 0.491, p < 0.01). However, the determination coefficient (R2) was 0.24, which means other factors affecte activity. We found that the G + C contents of the fragment showed a significant negative correlation with activity (R = - 0.474, p < 0.01). The correlation between pDCs stimulatory activity and the copy number of CpG motifs was greatly increased when DNA fragments were stratified by G + C contents. We also performed bioinformatics analysis and a screening of LAB strains with high pDCs stimulatory activity. Species with a high copy number of CpG motifs in the low-G + C region of their genomes had higher probability of inducing high-pDCs stimulatory activity. L. lactis subsp. lactis, Leuconostoc mesenteroides, and Pediococcus pentosaceus were three typical examples of LAB that had high pDCs stimulatory activity. CONCLUSIONS: Our data suggested that the G + C content of DNA is one of the critical factors for pDCs stimulatory activity by DNA fragments. Furthermore, we found that the copy number in the low-G + C regions strongly affected the pDCs stimulatory activity of whole cells of LAB strains. These results should be useful for the design of new DNA fragments containing CpG motifs. This study also demonstrated an in silico screening method for identifying bacterial species that are able to activate pDCs.

Long-term intake of Lactobacillus paracasei KW3110 prevents age-related chronic inflammation and retinal cell loss in physiologically aged mice.

Age-related chronic inflammation is a major risk factor for the incidence and prevalence of age-related diseases, including infectious and neurodegenerative diseases. We previously reported that a lactic acid bacteria, Lactobacillus paracasei KW3110, activated macrophages and suppressed inflammation in mice and humans. In this study, we investigated whether long-term intake of heat-killed L. paracasei KW3110 modulated age-related inflammation and altered the gut microbiota in physiologically aged mice. Compared with age-matched control mice, fecal analyses of gut microbiota revealed that intake of L. paracasei KW3110 mitigated age-related changes of beneficial bacterial composition, including the Bifidobacteriaceae family. L. paracasei KW3110 intake also mitigated age-related immune defects by reducing the prevalence of interferon-gamma (IFN-γ) -producing inflammatory CD4-positive T cells in the lamina propia of the small intestine, and reduced serum levels of proinflammatory cytokines. Furthermore, L. paracasei KW3110 intake suppressed retinal inflammation by reducing proinflammatory cytokine-producing macrophage, and age-related retinal cell loss. Taken together, these findings suggested that L. paracasei KW3110 mitigated age-related chronic inflammation through modulation of gut microbiota composition and immune system functions in aged mice, and also reduced age-related retinal ganglion cell (RGC) loss. Further studies are needed to evaluate the effect in age-related senescent changes of the retina.

DNA module platform for developing colorimetric aptamer sensors.

Here we present a DNA module platform for developing simple aptamer sensors based on a microarray format combined with secondary structure prediction in silico. The platform comprises four parts: (i) aptamer, (ii) joint module, (iii) terminal stem, and (iv) a DNAzyme that catalyzes a redox reaction controlled by a structural change induced by aptamer/target binding. First, we developed a joint module, capable of sensing a conformational change in the aptamer region, that was linked to the signal transmission activity of a DNAzyme as the reporter in a concentration-dependent manner with the AMP aptamer. This module design was then used to generate an arginine sensor simply by replacing the AMP aptamer region with a previously reported arginine aptamer. Using this DNA module platform, we were also able to customize a microarray containing >10,000 sequences designed by in silico secondary structure prediction and successfully identify a new aptamer against patulin. Our results show that the DNA module platform can be used to readily devise sensors based on known aptamers as well as create new aptamer sensors by array-based screening.

Identification and characterization of Candida utilis multidrug efflux transporter CuCdr1p.

The edible, nitrate assimilating, yeast Candida utilis is a commercial food additive, and it is a potentially useful host for heterologous protein expression. A number of ATP-binding cassette (ABC) transporters are multidrug efflux pumps that can cause multidrug resistance in opportunistic pathogens. In order to develop optimal novel antimicrobial agents it is imperative to understand the structure, function and expression of these transporters. With the ultimate aim of developing an alternative yeast host for the heterologous expression of eukaryotic membrane transporters, and to identify ABC transporters potentially associated with C. utilis multidrug resistance, we classified the entire repertoire of 30 C. utilis ABC proteins. We named the open reading frame most similar to the archetype multidrug efflux pump gene C. albicans CDR1 as CuCDR1 Overexpression of CuCDR1 in Saccharomyces cerevisiae ADΔ caused multidrug resistance similar to that of cells overexpressing CaCDR1 Unlike CaCdr1p, however, the C-terminally green fluorescent protein (GFP) tagged CuCdr1p-GFP was functionally impaired and did not properly localize to the plasma membrane. CuCdr1p function could be recovered however by adding a 15 amino acid linker -GAGGSAGGSGGAGAG- between CuCdr1p and the C-terminal GFP tag.

Draft Genome Sequence of Lactococcus lactis subsp. lactis JCM 5805T, a Strain That Induces Plasmacytoid Dendritic Cell Activation.

Lactococcus lactis subsp. lactis JCM 5805(T) is a dairy lactic acid bacterium that induces plasmacytoid dendritic cell (pDC) activation. Here, we report the 2.55-Mb draft genome and annotation of Lactococcus lactis JCM 5805(T). This genome information will provide further insights into the mechanisms underlying the immunomodulatory function of this strain.

Metabolomic and transcriptomic analysis for rate-limiting metabolic steps in xylose utilization by recombinant Candida utilis.

We have reported that a recombinant Candida utilis strain expressing a Candida shehatae xylose reductase K275R/N277D, a C. shehatae xylitol dehydrogenase, and xylulokinase from Pichia stipitis produced ethanol from xylose, but its productivity was low. In the present study, metabolomic (CE-TOF MS) and transcriptomic (microarray) analyses were performed to characterize xylose metabolism by engineered C. utilis and to identify key genetic changes contributing to efficient xylose utilization. The metabolomic analysis revealed that the xylose-fermenting strain accumulated more pentose phosphate pathway intermediates, more NADH, and more glycolytic intermediates upstream of glyceraldehyde 3-phosphate than the wild-type. Transcriptomic analysis of the strain grown on xylose indicated a significant increase in expression of the genes encoding tricarboxylic acid cycle enzymes, respiratory enzymes, and enzymes involved in ethanol oxidation. To decrease the NADH/NAD(+) ratio and increase the ethanol yield of the fermentation of xylose, ADH1 encoding NADH-dependent alcohol dehydrogenase was overexpressed. The resulting strain exhibited a 17% increase in ethanol production and a 22% decrease in xylitol accumulation relative to control.

Genome and transcriptome analysis of the food-yeast Candida utilis.

The industrially important food-yeast Candida utilis is a Crabtree effect-negative yeast used to produce valuable chemicals and recombinant proteins. In the present study, we conducted whole genome sequencing and phylogenetic analysis of C. utilis, which showed that this yeast diverged long before the formation of the CUG and Saccharomyces/Kluyveromyces clades. In addition, we performed comparative genome and transcriptome analyses using next-generation sequencing, which resulted in the identification of genes important for characteristic phenotypes of C. utilis such as those involved in nitrate assimilation, in addition to the gene encoding the functional hexose transporter. We also found that an antisense transcript of the alcohol dehydrogenase gene, which in silico analysis did not predict to be a functional gene, was transcribed in the stationary-phase, suggesting a novel system of repression of ethanol production. These findings should facilitate the development of more sophisticated systems for the production of useful reagents using C. utilis.

Screening of peptides with a high affinity to bile acids using peptide arrays and a computational analysis.

Bile acid binding peptides have attracted attention for the improvement and prevention of hypercholesterolemia. In this study, screening of bile acid high affinity peptides was investigated using computationally-assisted peptide array analysis. Starting with the screening data obtained from a limited, random 6-mer library (2212 sequences), the peptides with a high affinity to bile acid were characterized by comparison of high- and low-affinity peptides using fuzzy neural network (FNN) analysis. The physical properties of amino acids at specific positions that contribute to bile acid binding activity were extracted as the structural rule; optimization was carried out using three repeated screening cycles of the rule extraction. The extracted structural rule indicates that Trp, Tyr, Phe, Leu, Ile and Val are enriched in bile acid binding peptides. The yields of bile acid binding peptides with an affinity of above the VAWWMY peptide (soystatin, control sequence) were significantly higher in the optimized structural rule (32.5%) compared to that of the random library (3.1%), and 6 peptides were obtained with above 2.0-fold increased binding activity.

Identification of novel candidate genes for treatment response to risperidone and susceptibility for schizophrenia: integrated analysis among pharmacogenomics, mouse expression, and genetic case-control association approaches.

BACKGROUND: Pharmacogenomic approaches based on genomewide sets of single nucleotide polymorphisms (SNPs) are now feasible and offer the potential to uncover variants that influence drug response. METHODS: To detect potential predictor gene variants for risperidone response in schizophrenic subjects, we performed a convergent analysis based on 1) a genomewide (100K SNP) SNP pharmacogenetic study of risperidone response and 2) a global transcriptome study of genes with mRNA levels influenced by risperidone exposure in mouse prefrontal cortex. RESULTS: Fourteen genes were highlighted as of potential relevance to risperidone activity in both studies: ATP2B2, HS3ST2, UNC5C, BAG3, PDE7B, PAICS, PTGFRN, NR3C2, ZBTB20, ST6GAL2, PIP5K1B, EPHA6, KCNH5, and AJAP1. The SNPs related to these genes that were associated in the pharmacogenetic study were further assessed for evidence for association with schizophrenia in up to three case-control series comprising 1564 cases and 3862 controls in total (Japanese [JPN] 1st and 2nd samples and UK sample). Of 14 SNPs tested, one (rs9389370) in PDE7B showed significant evidence for association with schizophrenia in a discovery sample (p(allele) = .026 in JPN_1st, two-tailed). This finding replicated in a joint analysis of two independent case-control samples (p(JPN_2nd+UK) = .008, one-tailed, uncorrected) and in all combined data sets (p(all) = .0014, two-tailed, uncorrected and p(all) = .018, two-tailed, Bonferroni correction). CONCLUSIONS: We identified novel candidate genes for treatment response to risperidone and provide evidence that one of these additionally may confer susceptibility to schizophrenia. Specifically, PDE7B is an attractive candidate gene, although evidence from integrated methodology, including pharmacogenomics, pharmacotranscriptomic, and case-control association approaches.

Screening of peptides with a high affinity for ZnO using spot-synthesized peptide arrays and computational analysis.

Metal-binding peptides have attracted attention for their usefulness in biomineralization processes. In this work screening of high affinity peptides against ZnO was performed using a combinatorial library approach with a peptide array and computational analysis. The computationally assisted peptide screening and design enabled identification of linear peptides with affinity for ZnO with limited experimentation. Starting with the screening data obtained from a random 6-mer library of 420 sequences, the characteristics of the peptides with high ZnO affinity were analyzed using a fuzzy neural network algorithm by comparison of high and low affinity peptides. Three physical properties of amino acids (hydrophobicity, isoelectric point and size) and positional information of each residue were analyzed and a peptide rule with restricted amino acids at certain positions was extracted. The average affinity for ZnO increased 2.0-fold when 300 sequences were synthesized according to the restricted random library, compared with the non-restricted library. In addition, for a peptide library with the amino acids at the restricted sites exchanged the average binding capacity decreased to 0.7-fold. Interestingly, the peptides with high ZnO affinity obtained exhibited binding specificity for ZnO. Computationally assisted screening is an invaluable means for finding peptides with limited experimentation.

Cancer DNA microarray analysis considering multi-subclass with graph-based clustering method.

It is well known that various genes related to cell cycle, cell-cell adhesion, and transcriptional regulation cause the onset of cancer. Moreover, environmental factors including age, sex, and lifestyle can also contribute to the onset of cancer. Therefore, it is difficult to ascertain which factors influence the onset. Thus, patients suffering from same disease can be divided into several distinct groups. In the present study, we applied graph-based clustering to several DNA microarray datasets before the classification analysis. Several clusters formed by the graph-based clustering were used for the construction of multi-class classification model with the k-nearest neighbor and for finding genes, which are specific to a certain cluster, by One vs. Others classification. Using this approach, the classification model was constructed for four microarray datasets, leukemia, breast cancer, prostate cancer, and colon cancer, and the accuracies of classification with k-nearest neighbor were all more than 80%. And in the breast cancer dataset, we succeeded in finding genes that are specific in a cluster consisting of 38 control group samples. These results indicate the importance of sample clustering before classification model construction.

A motif detection and classification method for peptide sequences using genetic programming.

An exploration of common rules (property motifs) in amino acid sequences has been required for the design of novel sequences and elucidation of the interactions between molecules controlled by the structural or physical environment. In the present study, we developed a new method to search property motifs that are common in peptide sequence data. Our method comprises the following two characteristics: (i) the automatic determination of the position and length of common property motifs by calculating the physicochemical similarity of amino acids, and (ii) the quick and effective exploration of motif candidates that discriminates the positives and negatives by the introduction of genetic programming (GP). Our method was evaluated by two types of model data sets. First, the intentionally buried property motifs were searched in the artificially derived peptide data containing intentionally buried property motifs. As a result, the expected property motifs were correctly extracted by our algorithm. Second, the peptide data that interact with MHC class II molecules were analyzed as one of the models of biologically active peptides with buried motifs in various lengths. Twofold MHC class II binding peptides were identified with the rule using our method, compared to the existing scoring matrix method. In conclusion, our GP based motif searching approach enabled to obtain knowledge of functional aspects of the peptides without any prior knowledge.

Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm.

We developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining the model with subsequent repeated experiments. Here we focus on cell-adhesion peptides as a model application of this peptide-screening strategy. Cell-adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell-adhesion peptides was extracted by fuzzy neural network (FNN) analysis. According to this rule, peptides with unfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell-adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array support was significantly high (31%) compared with the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell-adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.

Association study between Apolipoprotein L and schizophrenia by exhaustive and rule-based combination analysis for identification of multilocus interactions.

Several single marker association and haplotypic analyses have been performed to identify susceptible genes for various common diseases, but these approaches using candidate genes did not provide accurate and consistent evidence in each analysis. This inconsistency is partly due to the fact that the common diseases are caused by complex interactions among various genetic factors. Therefore, in this study, to evaluate exhaustive genotype or allele combinations, we applied the binomial and random permutation test (BRP) proposed by Tomita et al. [IPSJ Digital Courier, 2, 691-709 (2006)] for the association analysis between an Apolipoprotein L gene cluster and schizophrenia. Using the seven selected representative single nucleotide polymorphisms (SNPs) based on the results of linkage disequilibrium evaluation, we analyzed 845 schizophrenic patients and 707 healthy controls, and investigated the validation of risk and protective factors with two randomly divided data sets. A comparative study of a method for analyzing the interactions was performed by conventional methods. Even if all the tested methods were used for analysis, the risk factor with a high significance was not commonly selected from both independent data sets. However, the significant interactions for the protective factor against disease development were commonly obtained from both data sets by BRP analysis. In conclusion, although it is considered that the causality of schizophrenia is too complex to identify a susceptible interaction using a small sample size, it was suggested that the healthy controls tend to have the same combination of certain alleles or genotypes for protection from disease development when BRP as a new exhaustive combination analytical method was used.

Subungual glomus tumour: evaluation of ultrasound imaging in preoperative assessment.

For an evaluation of the preoperative assessment of subungual glomus tumours, a non-invasive examination of the lesion is desirable. Previous studies, however, have not clearly demonstrated these findings. We examined two cases of subungual glomus tumours and applied the approximately 5-14 MHz broadband B-mode and C-mode ultrasound imaging methods combined with Color Doppler imaging (CDI) and B-flow imaging (BFI) to clarify the significance of the preoperative assessment. We confirmed the tumour's localization, size and depth, using B-mode and C-mode imaging; and tumour vascularity using CDI and BFI. BFI is a newly developed ultrasound technique that enhances the B-mode imaging quality of the blood flow, including high-frame-rate and high-spatial-resolution imagings. BFI revealed the exact fine blood vessels within the subungual small tumour with no blooming compared to CDI. Our results indicate that these ultrasound scanning imagings are non-invasive and nonionizing evaluation methods, and that an accurate preoperative diagnosis using these methods will result in more effective surgical excision and relief.

Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma.

BACKGROUND: Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN) for predicting development of allergic disease. RESULTS: To predict development of childhood allergic asthma (CAA) and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM) to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV) defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs. CONCLUSION: ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients.