Transformation between meron and skyrmion topological spin textures in a chiral magnet.

Crystal lattices with tetragonal or hexagonal structure often exhibit structural transitions in response to external stimuli1. Similar behaviour is anticipated for the lattice forms of topological spin textures, such as lattices composed of merons and antimerons or skyrmions and antiskyrmions (types of vortex related to the distribution of electron spins in a magnetic field), but has yet to be verified experimentally2,3. Here we report real-space observations of spin textures in a thin plate of the chiral-lattice magnet Co8Zn9Mn3, which exhibits in-plane magnetic anisotropy. The observations demonstrate the emergence of a two-dimensional square lattice of merons and antimerons from a helical state, and its transformation into a hexagonal lattice of skyrmions in the presence of a magnetic field at room temperature. Sequential observations with decreasing temperature reveal that the topologically protected skyrmions remain robust to changes in temperature, whereas the square lattice of merons and antimerons relaxes to non-topological in-plane spin helices, highlighting the different topological stabilities of merons, antimerons and skyrmions. Our results demonstrate the rich variety of topological spin textures and their lattice forms, and should stimulate further investigation of emergent electromagnetic properties.

Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more suitable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods.

In contrast to RNA-seq analysis, which has various standard methods, no standard methods for identifying differentially methylated cytosines (DMCs) exist. To identify DMCs, we tested principal component analysis and tensor decomposition-based unsupervised feature extraction with optimized standard deviation, which has been shown to be effective for differentially expressed gene (DEG) identification. The proposed method outperformed certain conventional methods, including those that assume beta-binomial distribution for methylation as the proposed method does not require this, especially when applied to methylation profiles measured using high throughput sequencing. DMCs identified by the proposed method also significantly overlapped with various functional sites, including known differentially methylated regions, enhancers, and DNase I hypersensitive sites. The proposed method was applied to data sets retrieved from The Cancer Genome Atlas to identify DMCs using American Joint Committee on Cancer staging system edition labels. This suggests that the proposed method is a promising standard method for identifying DMCs.

Bioinformatic tools for epitranscriptomics.

The epitranscriptome, defined as RNA modifications that do not involve alterations in the nucleotide sequence, is a popular topic in the genomic sciences. Because we need massive computational techniques to identify epitranscriptomes within individual transcripts, many tools have been developed to infer epitranscriptomic sites as well as to process datasets using high-throughput sequencing. In this review, we summarize recent developments in epitranscriptome spatial detection and data analysis and discuss their progression.

Nonreciprocal Phonon Propagation in a Metallic Chiral Magnet.

The phonon magnetochiral effect (MChE) is the nonreciprocal acoustic and thermal transports of phonons caused by the simultaneous breaking of the mirror and time-reversal symmetries. So far, the phonon MChE has been observed only in a ferrimagnetic insulator Cu_{2}OSeO_{3}, where the nonreciprocal response disappears above the Curie temperature of 58 K. Here, we study the nonreciprocal acoustic properties of a room-temperature ferromagnet Co_{9}Zn_{9}Mn_{2} for unveiling the phonon MChE close to room temperature. Surprisingly, the nonreciprocity in this metallic compound is enhanced at higher temperatures and observed up to 250 K. This clear contrast between insulating Cu_{2}OSeO_{3} and metallic Co_{9}Zn_{9}Mn_{2} suggests that metallic magnets have a mechanism to enhance the nonreciprocity at higher temperatures. From the ultrasound and microwave-spectroscopy experiments, we conclude that the magnitude of the phonon MChE of Co_{9}Zn_{9}Mn_{2} mostly depends on the Gilbert damping, which increases at low temperatures and hinders the magnon-phonon hybridization. Our results suggest that the phonon nonreciprocity could be further enhanced by engineering the magnon band of materials.

In Situ Electric-Field Control of THz Nonreciprocal Directional Dichroism in the Multiferroic Ba_{2}CoGe_{2}O_{7}.

Nonreciprocal directional dichroism, also called the optical-diode effect, is an appealing functional property inherent to the large class of noncentrosymmetric magnets. However, the in situ electric control of this phenomenon is challenging as it requires a set of conditions to be fulfilled: Special symmetries of the magnetic ground state, spin excitations with comparable magnetic- and electric-dipole activity, and switchable electric polarization. We demonstrate the isothermal electric switch between domains of Ba_{2}CoGe_{2}O_{7} possessing opposite magnetoelectric susceptibilities. Combining THz spectroscopy and multiboson spin-wave analysis, we show that unbalancing the population of antiferromagnetic domains generates the nonreciprocal light absorption of spin excitations.

Identifying suitable tools for variant detection and differential gene expression using RNA-seq data.

Neurodegenerative diseases are the most predominate brain disorders around the globe and the affected populations are rapidly increasing. Recently, these diseases have been addressed using the data obtained from RNA-sequencing technology to reveal the changes in gene/transcript expression, effect of variants, and pathways involved in disease mechanisms. However, the observations mainly depend on the aligners/tools and the performance of existing RNA-seq tools on hg38 genome assembly has not yet been documented. In this study, we performed a systematic analysis of various spliced aligners, transcript assembling and variant calling tools based on both genomic assemblies (hg19/hg38) from hippocampus brain tissue. This helps to identify the best possible combination tools for hg38 annotation. In order to evaluate the identified variants from various pipelines, we compared them with expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS). In addition, the identified differentially expressed genes (DG) were compared with microarray studies. From our analysis of variant calling, the combination of GATK (Genome Analysis Tool-kit) and STAR (Spliced Transcripts Alignment to a Reference) protocol yields a larger number of GWAS/eQTL variants compared to SAMtools (Sequence Alignment Map). We also identified a higher number of non-coding variants in hg38 compared to hg19 due to enhanced annotation. In the case of various DG pipelines, we found that the Salmon-based hg38 transcriptomic quantification yields a higher number of reported DG compared to other genome-based quantification methods. This study revealed that higher number of reads maps to multiple location of the genome with hg38 compared to hg19, and these spurious multi-mapped reads may affect the gene quantification techniques. We suggest that it is necessary to develop efficient algorithms, which can handle the multi-mapped reads and improve the performance of genome-based alignment quantification.

Evolution of Electronic States and Emergence of Superconductivity in the Polar Semiconductor GeTe by Doping Valence-Skipping Indium.

GeTe is a chemically simple IV-VI semiconductor which bears a rich plethora of different physical properties induced by doping and external stimuli. Here, we report a superconductor-semiconductor-superconductor transition controlled by finely-tuned In doping. Our results reveal the existence of a critical doping concentration x_{c}=0.12 in Ge_{1-x}In_{x}Te, where various properties, including structure, resistivity, charge carrier type, and the density of states, take either an extremum or change their character. At the same time, we find indications of a change in the In-valence state from In^{3+} to In^{1+} with increasing x by core-level photoemission spectroscopy, suggesting that this system is a new promising playground to probe valence fluctuations and their possible impact on structural, electronic, and thermodynamic properties of their host.

Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis.

The selective vulnerability of distinct regions of the brain is a critical factor in neurodegenerative disorders. In Alzheimer's disease (AD), neurons in hippocampus situated in medial temporal lobe are immensely damaged. Identifying tissue-specific variants is essential in order to perceive the selective vulnerability in AD. In current work, we aligned mRNA-seq data with HG19/HG38 genomic assembly and identified specific variations present in temporal, frontal and other lobes of the AD using sequence alignment map tools. We compared the results with the genome-wide association and gene expression quantitative trait loci studies of the various neurological disorders. We also distinguished variants and epitranscriptomic modifications through the RNA-modification database and evaluated the variant effect in the coding/UTR regions. In addition, we developed genetic and functional interaction networks to understand the relationship between predicted vulnerable variations and differentially expressed genes. We found that genes involved in gliogenesis, intermediate filament organization are altered in the temporal lobe. Oxidative phosphorylation, and calcium ion homeostasis are modified in the frontal lobe, and protein degradation, apoptotic signaling are altered in other lobes. From this study, we propose that disruption of glial cell structural integrity, defective gliogenesis, and failure in glia-neuron communication are the primary factors for selective vulnerability.

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.

BACKGROUND: Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug-protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene-gene interactions. RESULTS: In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study. CONCLUSIONS: The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.

Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes.

BACKGROUND: Even though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly. METHODS: A recently proposed principal component analysis (PCA)-based and tensor decomposition (TD)-based unsupervised feature extraction (FE) can solve this problem because these two approaches can deal with gene expression and methylation profiles even when a small number of samples is available. RESULTS: PCA-based and TD-based unsupervised FE methods were applied to the analysis of gene expression and methylation profiles in the brains of two social insects, Polistes canadensis and Dinoponera quadriceps. Genes associated with differential expression and methylation between castes were identified, and analysis of enrichment of Gene Ontology terms confirmed reliability of the obtained sets of genes from the biological standpoint. CONCLUSIONS: Biologically relevant genes, shown to be associated with significant differential gene expression and methylation between castes, were identified here for the first time. The identification of these genes may help understand the mechanisms underlying epigenetic control of development of multiple phenotypes under the same genomic conditions.

Collaborative environmental DNA sampling from petal surfaces of flowering cherry Cerasus × yedoensis 'Somei-yoshino' across the Japanese archipelago.

Recent studies have shown that environmental DNA is found almost everywhere. Flower petal surfaces are an attractive tissue to use for investigation of the dispersal of environmental DNA in nature as they are isolated from the external environment until the bud opens and only then can the petal surface accumulate environmental DNA. Here, we performed a crowdsourced experiment, the "Ohanami Project", to obtain environmental DNA samples from petal surfaces of Cerasus × yedoensis 'Somei-yoshino' across the Japanese archipelago during spring 2015. C. × yedoensis is the most popular garden cherry species in Japan and clones of this cultivar bloom simultaneously every spring. Data collection spanned almost every prefecture and totaled 577 DNA samples from 149 collaborators. Preliminary amplicon-sequencing analysis showed the rapid attachment of environmental DNA onto the petal surfaces. Notably, we found DNA of other common plant species in samples obtained from a wide distribution; this DNA likely originated from the pollen of the Japanese cedar. Our analysis supports our belief that petal surfaces after blossoming are a promising target to reveal the dynamics of environmental DNA in nature. The success of our experiment also shows that crowdsourced environmental DNA analyses have considerable value in ecological studies.

Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is among the severe neuro degenerative diseases that lack widely available effective treatments. As the disease progresses, patients lose the control of voluntary muscles. Although the neuronal degeneration is the cause of this disease, the failure mechanism is still unknown. In order to seek genetic mechanisms that initiate and progress ALS, the association of microRNA (miRNA) expression with this disease was considered. Serum miRNAs from healthy controls, sporadic ALS (sALS), familial ALS (fALS) and ALS mutation carriers were investigated. Principal component analysis (PCA)-based unsupervised feature extraction (FE) was applied to these serum miRNA profiles. As a result, we predict miRNAs that can discriminate patients from healthy controls with high accuracy. Thus, these miRNAs can be potential prognosis miRNA biomarkers for ALS.

Robust metastable skyrmions and their triangular-square lattice structural transition in a high-temperature chiral magnet.

Skyrmions, topologically protected nanometric spin vortices, are being investigated extensively in various magnets. Among them, many structurally chiral cubic magnets host the triangular-lattice skyrmion crystal (SkX) as the thermodynamic equilibrium state. However, this state exists only in a narrow temperature and magnetic-field region just below the magnetic transition temperature Tc, while a helical or conical magnetic state prevails at lower temperatures. Here we describe that for a room-temperature skyrmion material, ß-Mn-type Co 8Zn 8Mn 4, a field-cooling via the equilibrium SkX state can suppress the transition to the helical or conical state, instead realizing robust metastable SkX states that survive over a very wide temperature and magnetic-field region. Furthermore, the lattice form of the metastable SkX is found to undergo reversible transitions between a conventional triangular lattice and a novel square lattice upon varying the temperature and magnetic field. These findings exemplify the topological robustness of the once-created skyrmions, and establish metastable skyrmion phases as a fertile ground for technological applications.

Effects of glucose concentration on osteogenic differentiation of type II diabetes mellitus rat bone marrow-derived mesenchymal stromal cells on a nano-scale modified titanium.

BACKGROUND AND OBJECTIVE: Diabetes mellitus (DM) is a common disease worldwide. Patients with DM have an increased risk of losing their teeth compared with other individuals. Dental implants are a standard of care for treating partial or full edentulism, and various implant surface treatments have recently been developed to increase dental implant stability. However, some studies have reported that DM reduces osseointegration and the success rate of dental implants. The purpose of this study was to determine the effects of high glucose levels for hard tissue formation on a nano-scale modified titanium surface. MATERIAL AND METHODS: Titanium disks were heated at 600°C for 1 h after treatment with or without 10 m NaOH solution. All disks were incubated with type II DM rat bone marrow-derived mesenchymal stromal cells before exposure to one of four concentrations of glucose (5.5, 8.0, 12.0 or 24.0 mm). The effect of different glucose concentrations on bone marrow-derived mesenchymal stromal cell osteogenesis and inflammatory cytokines on the nano-scale modified titanium surface was evaluated. RESULTS: Alkaline phosphatase activity decreased with increasing glucose concentration. In contrast, osteocalcin production and calcium deposition were significantly decreased at 8.0 mm glucose, but increased with glucose concentrations over 8.0 mm. Differences in calcium/phosphate ratio associated with the various glucose concentrations were similar to osteocalcin production and calcium deposition. Inflammatory cytokines were expressed at high glucose concentrations, but the nano-scale modified titanium surface inhibited the effect of high glucose concentrations. CONCLUSION: High glucose concentration increased hard tissue formation, but the quality of the mineralized tissue decreased. Furthermore, the nano-scale modified titanium surface increased mineralized tissue formation and anti-inflammation, but the quality of hard tissue was dependent on glucose concentration.

Expression of Serum Exosomal and Esophageal MicroRNA in Rat Reflux Esophagitis.

Gastroesophageal reflux disease (GERD) is a common upper gastrointestinal disease. However, the role of exosomal microRNAs (miRNAs) and esophageal miRNAs in GERD has not been studied. A rat model of acid reflux esophagitis was used to establish a novel diagnosis marker for GERD and examine dynamics of miRNA expression in GERD. Rats were sacrificed 3 (acute phase), 7 (sub-acute phase) and 21 days (chronic phase) after induction of esophagitis. Exosomes were extracted from serum, and the expression patterns of serum miRNAs were analyzed. Four upregulated miRNAs (miR-29a-3p, 128-3p, 223-3p and 3473) were identified by microarray analysis. The expression levels of exosomal miR-29a-3p were significantly higher in the chronic phase of reflux esophagitis compared with controls, and increased expression of miR-29a-3p was specific to chronic reflux esophagitis. Esophageal miR-223-3p expression was higher compared with controls, and gradually decreased from acute to chronic phase in esophagitis. In conclusion, exosomal miR-29a-3p and esophageal miR-223-3p might play roles in GERD.

Microarray analysis of circulating microRNAs in familial Mediterranean fever.

OBJECTIVES: Familial Mediterranean fever (FMF) is an autoinflammatory disease caused by mutations in MEFV. Mutations in exon 10 are associated with typical FMF phenotypes, whereas the pathogenic role of variants in exons 2 and 3 remains uncertain. Recent evidence suggests that circulating microRNAs (miRNAs) are potentially useful biomarkers in several diseases. Therefore, their expression was assessed in FMF. METHODS: The subjects were 24 patients with FMF who were between attacks: eight with exon 10 mutations (group A), eight with exon 3 mutations (group B), and eight without exon 3 or 10 mutations (group C). We also investigated eight cases of PFAPA as disease controls. Exosome-rich fractionated RNA was subjected to miRNA profiling by microarray. RESULTS: Using the expression patterns of 26 miRNAs, we classified FMF (groups A, B, and C) and PFAPA with 78.1% accuracy. In FMF patients, groups A and B, A and C, and B and C were distinguished with 93.8, 87.5, and 100% accuracy using 24, 30, and 25 miRNA expression patterns, respectively. CONCLUSIONS: These findings suggest that expression patterns of circulating miRNAs differ among FMF subgroups based on MEFV mutations between FMF episodes. These patterns may serve as a useful biomarker for detecting subgroups of FMF.

MicroRNA expression in hepatocellular carcinoma after the eradication of chronic hepatitis virus C infection using interferon therapy.

AIM: Hepatocellular carcinoma (HCC) develops in up to 5% of patients after the successful treatment of chronic hepatitis C virus (HCV) infection using interferon therapy. The aim of this study was to characterize miRNA expression in liver tissues from patients who achieved a sustained viral response (SVR). METHODS: Seventy-one patients with resected HCC were enrolled into the present study: 61 HCC from patients with continuously infected HCV (HCV-HCC) and 10 from patients who had achieved SVR (SVR-HCC). We also included non-tumor tissues (SVR-NT) from four patients with SVR-HCC, and liver tissue (SVR-CH) from four SVR patients without HCC. Total RNA was extracted from liver samples. The miRNA expression patterns were analyzed using microarrays. In addition, target gene expression was quantified after miRNA overexpression in HEK293 cells. RESULTS: We could discriminate between SVR-HCC and HCV-HCC with 75.36% accuracy using the expression pattern of six specific miRNA. The expression levels of 37 miRNA were significantly lower in HCV-HCC than in SVR-HCC, whereas the expression of 25 miRNA was significantly higher in HCV-HCC than SVR-HCC (P < 1.0E-05). The expression of thrombospondin 1 was regulated in an opposing manner by miR-30a-3p in SVR-HCC and HCV-HCC. In non-tumor tissues, the expression pattern of seven miRNA could distinguish between SVR-CH and SVR-NT with 87.50% accuracy. CONCLUSION: Comprehensive miRNA expression analyses could not only differentiate between SVR-HCC and HCV-HCC but also forecast hepatocarcinogenesis after achieving SVR.

Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction.

MicroRNA(miRNA)-mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA-mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA-mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.

Identification of aberrant gene expression associated with aberrant promoter methylation in primordial germ cells between E13 and E16 rat F3 generation vinclozolin lineage.

BACKGROUND: Transgenerational epigenetics (TGE) are currently considered important in disease, but the mechanisms involved are not yet fully understood. TGE abnormalities expected to cause disease are likely to be initiated during development and to be mediated by aberrant gene expression associated with aberrant promoter methylation that is heritable between generations. However, because methylation is removed and then re-established during development, it is not easy to identify promoter methylation abnormalities by comparing normal lineages with those expected to exhibit TGE abnormalities. METHODS: This study applied the recently proposed principal component analysis (PCA)-based unsupervised feature extraction to previously reported and publically available gene expression/promoter methylation profiles of rat primordial germ cells, between E13 and E16 of the F3 generation vinclozolin lineage that are expected to exhibit TGE abnormalities, to identify multiple genes that exhibited aberrant gene expression/promoter methylation during development. RESULTS: The biological feasibility of the identified genes were tested via enrichment analyses of various biological concepts including pathway analysis, gene ontology terms and protein-protein interactions. All validations suggested superiority of the proposed method over three conventional and popular supervised methods that employed t test, limma and significance analysis of microarrays, respectively. The identified genes were globally related to tumors, the prostate, kidney, testis and the immune system and were previously reported to be related to various diseases caused by TGE. CONCLUSIONS: Among the genes reported by PCA-based unsupervised feature extraction, we propose that chemokine signaling pathways and leucine rich repeat proteins are key factors that initiate transgenerational epigenetic-mediated diseases, because multiple genes included in these two categories were identified in this study.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

BACKGROUND: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. RESULTS: Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. CONCLUSIONS: Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.