Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis.

BACKGROUND: Microarray and DNA-sequencing based technologies continue to produce enormous amounts of data on gene expression. This data has great potential to illuminate our understanding of biology and medicine, but the data alone is of limited value without computational tools to allow human investigators to visualize and interpret it in the context of their problem of interest. RESULTS: We created a web server called SHOE that provides an interactive, visual presentation of the available evidence of transcriptional regulation and gene co-expression to facilitate its exploration and interpretation. SHOE predicts the likely transcription factor binding sites in orthologous promoters of humans, mice, and rats using the combined information of 1) transcription factor binding preferences (position-specific scoring matrix (PSSM) libraries such as Transfac32, Jaspar, HOCOMOCO, ChIP-seq, SELEX, PBM, and iPS-reprogramming factor), 2) evolutionary conservation of putative binding sites in orthologous promoters, and 3) co-expression tendencies of gene pairs based on 1,714 normal human cells selected from the Gene Expression Omnibus Database. CONCLUSION: SHOE enables users to explore potential interactions between transcription factors and target genes via multiple data views, discover transcription factor binding motifs on top of gene co-expression, and visualize genes as a network of gene and transcription factors on its native gadget GeneViz, the CellDesigner pathway analyzer, and the Reactome database to search the pathways involved. As we demonstrate here when using the CREB1 and Nf-κB datasets, SHOE can reliably identify experimentally verified interactions and predict plausible novel ones, yielding new biological insights into the gene regulatory mechanisms involved. SHOE comes with a manual describing how to run it on a local PC or via the Garuda platform ( www.garuda-alliance.org ), where it joins other popular gadgets such as the CellDesigner pathway analyzer and the Reactome database, as part of analysis workflows to meet the growing needs of molecular biologists and medical researchers. SHOE is available from the following URL http://ec2-54-150-223-65.ap-northeast-1.compute.amazonaws.com A video demonstration of SHOE can be found here: https://www.youtube.com/watch?v=qARinNb9NtE.

Auxiliary self-supervision to metric learning for music similarity-based retrieval and auto-tagging.

In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Similarity-based retrieval involves automatically analyzing a music track and fetching analogous tracks from a database. Auto-tagging, on the other hand, assesses a music track to deduce associated tags, such as genre and mood. Given the limitations and non-scalability of human supervision signals, it becomes crucial for models to learn from alternative sources to enhance their performance. Contrastive learning-based self-supervised learning, which exclusively relies on learning signals derived from music audio data, has demonstrated its efficacy in the context of auto-tagging. In this work, we propose a model that builds on the self-supervised learning approach to address the similarity-based retrieval challenge by introducing our method of metric learning with a self-supervised auxiliary loss. Furthermore, diverging from conventional self-supervised learning methodologies, we discovered the advantages of concurrently training the model with both self-supervision and supervision signals, without freezing pre-trained models. We also found that refraining from employing augmentation during the fine-tuning phase yields better results. Our experimental results confirm that the proposed methodology enhances retrieval and tagging performance metrics in two distinct scenarios: one where human-annotated tags are consistently available for all music tracks, and another where such tags are accessible only for a subset of music tracks.

Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set.

Rapid progress in technologies opened the new era of computer-leaded analytics, leaving humans more space for experimental design and decision making. Here we demonstrate the machine learning analysis workflow represented by spectral clustering, elucidation of evolutionary conserved transcription regulation, and network analysis using reverse engineering. Analysis of genes induced by the Pentachlorophenol toxic chemical revealed two subnetworks, one orchestrated by Interferon and another by Nuclear receptor factor 2 (NRF2) gene. Furthermore, network-inference based analysis identified a gene network module composed of genes associated with interferon signaling and their regulatory interaction with downstream genes, especially TRIM family proteins involved in responses of innate immune systems.

Corrigendum: Alpha-Arbutin Promotes Wound Healing by Lowering ROS and Upregulating Insulin/IGF-1 Pathway in Human Dermal Fibroblast.

[This corrects the article DOI: 10.3389/fphys.2020.586843.].

Alpha-Arbutin Promotes Wound Healing by Lowering ROS and Upregulating Insulin/IGF-1 Pathway in Human Dermal Fibroblast.

Alpha-arbutin (4-hydroxyphenyl alpha-glucopyranoside) is a known inhibitor of tyrosinase in keratinocytes; however, its effect on other genes and pathways in other skin cells has not been thoroughly investigated. In this study, we investigate the mechanism of alpha-arbutin activity in human dermal fibroblast cultures for 48 h. Results showed that the oxidative stress pathway was activated as alpha-arbutin reduced reactive oxygen species. In addition, we found a high possibility of wound healing and the upregulation of the insulin-like growth factor 1 receptor (IFG1R) pathway. We also investigated the role of the NRF2 gene in mediating the alpha-arbutin response. In silico comparative genomics analysis conducted using our original tool, SHOE, suggested transcription factors with a role in tumor suppression and toxicity response as candidates for regulating the alpha-arbutin-mediated pathway.

A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions.

Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed. Prior methods/tools for time series data, however, do not have the following four major ingredients in analytic and methodological view point: (i) principled time-series feature extraction methods, (ii) variety of manifold learning methods for capturing high-level view of the dataset, (iii) high-end automatic structure extraction, and (iv) friendliness to the biological user community. With a view to meet the requirements, we present AGCT (A Geometric Clustering Tool), a software package used to unravel the complex architecture of large-scale, non-necessarily synchronized time-series gene expression data. AGCT capture signals on exhaustive wavelet expansions of the data, which are then embedded on a low-dimensional non-linear map using manifold learning algorithms, where geometric proximity captures potential interactions. Post-processing techniques, including hard and soft information geometric clustering algorithms, facilitate the summarizing of the complete map as a smaller number of principal factors which can then be formally identified using embedded statistical inference techniques. Three-dimension interactive visualization and scenario recording over the processing helps to reproduce data analysis results without additional time. Analysis of the whole-cell Yeast Metabolic Cycle (YMC) moreover, Yeast Cell Cycle (YCC) datasets demonstrate AGCT's ability to accurately dissect all stages of metabolism and the cell cycle progression, independently of the time course and the number of patterns related to the signal. Analysis of Pentachlorophenol iduced dataset demonstrat how AGCT dissects data to identify two networks: Interferon signaling and NRF2-signaling networks.

An Advanced Omic Approach to Identify Co-Regulated Clusters and Transcription Regulation Network with AGCT and SHOE Methods.

To obtain the global picture of genetic machinery for massive high-throughput gene expression data, novel data-driven unsupervised learning approaches are becoming essentially important. For this purpose, basic analytic workflow has been established and should include two steps: first, unsupervised clustering to identify genes with similar behavior upon exposure to a signal, and second, identification of transcription factors regulating those genes. In this chapter, we will describe an advanced tool that can be used for analyzing and characterizing large-scale time-series gene expression composed of a two-step approach. For the first step, we developed an original method "A Geometric Clustering Tool" (AGCT) that unveils the complex architecture of large-scale time-series gene expression data in a real-time manner using cutting edge techniques of low dimension manifold learning, data clustering, and visualization. For the second step, we established an original method "Sequence Homology in Eukaryotes" (SHOE) executing comparative genomic analysis on humans, mice, and rats.

Comparative genomic analysis of transcription regulation elements involved in human map kinase G-protein coupling pathway.

The identification of cis-elements (motifs) in the regulatory regions of higher eukaryotes is an important and challenging problem in computational biology. Eukaryotic transcriptional regulatory mechanisms pose several difficulties for promoter analysis: including a high variance in the motif locations, frequently large divergence from motif consensus patterns, and a large amount of repetitive elements (confusing to many motif finding procedures). One promising approach to this difficult problem involves cross-species comparison. In this work we analyzed the full-length regulatory regions of genes involved in the G-protein coupling MAP kinase pathway and compared the results with ribosomal genes using human, mouse and rat genomic data. We found 19 high likely transcription factors (TFs) candidates for MAPK and 12 TFs for the ribosomal dataset. In the case of the MAPK dataset, regulatory regions of genes functionally grouped as receptors and MAP-core genes were found mostly highly conserved across the three species.

Chum salmon egg extracts induce upregulation of collagen type I and exert antioxidative effects on human dermal fibroblast cultures.

Components of fish roe possess antioxidant and antiaging activities, making them potentially very beneficial natural resources. Here, we investigated chum salmon eggs (CSEs) as a source of active ingredients, including vitamins, unsaturated fatty acids, and proteins. We incubated human dermal fibroblast cultures for 48 hours with high and low concentrations of CSE extracts and analyzed changes in gene expression. Cells treated with CSE extract showed concentration-dependent upregulation of collagen type I genes and of multiple antioxidative genes, including OXR1, TXNRD1, and PRDX family genes. We further conducted in silico phylogenetic footprinting analysis of promoter regions. These results suggested that transcription factors such as acute myeloid leukemia-1a and cyclic adenosine monophosphate response element-binding protein may be involved in the observed upregulation of antioxidative genes. Our results support the idea that CSEs are strong candidate sources of antioxidant materials and cosmeceutically effective ingredients.

In silico discovery of novel transcription factors regulated by mTOR-pathway activities.

The mammalian target of rapamycine (mTOR) pathway is a key regulator of cellular growth, development, and ageing, and unraveling its control is essential for understanding life and death of biological organisms. A motif-discovery workbench including nine tools was used to identify transcription factors involved in five basic (Insulin, MAPK, VEGF, Hypoxia, and mTOR core) activities of the mTOR pathway. Discovered transcription factors are classified as "process-specific" or "pathway-ubiquitous" with highlights toward their regulating/regulated activities within the mTOR pathway. Our transcription regulation results will facilitate further research on investigating the control mechanism in mTOR pathway.

Reprogramming resistant genes: in-depth comparison of gene expressions among iPS, ES, and somatic cells.

Transcription factor-based reprogramming reverts adult cells to an embryonic state, yielding potential for generating different tissue types. However, recent reports indicated the substantial differences in pattern of gene expression between induced pluripotent stem (iPS) cells and embryonic stem cells (ESC). In this study, we compare gene expression signatures of different iPS and ES cell lines and relate expression profiles of differently expressed genes to their expression status in somatic cells. As a result, we discovered that genes resistant to reprogramming comprise two major clusters, which are reprogramming dependent "Induced Genes" and somatic origin "Inherited Genes," both exhibiting preferences in methylation marks. Closer look into the Induced Genes by means of the transcription regulation analysis predicted several groups of genes with various roles in reprogramming and transcription factor DNA binding model. We believe that our results are a helpful source for biologists for further improvement of iPS cell technology.

G-protein coupled receptor signaling architecture of mammalian immune cells.

A series of recent studies on large-scale networks of signaling and metabolic systems revealed that a certain network structure often called "bow-tie network" are observed. In signaling systems, bow-tie network takes a form with diverse and redundant inputs and outputs connected via a small numbers of core molecules. While arguments have been made that such network architecture enhances robustness and evolvability of biological systems, its functional role at a cellular level remains obscure. A hypothesis was proposed that such a network function as a stimuli-reaction classifier where dynamics of core molecules dictate downstream transcriptional activities, hence physiological responses against stimuli. In this study, we examined whether such hypothesis can be verified using experimental data from Alliance for Cellular Signaling (AfCS) that comprehensively measured GPCR related ligands response for B-cell and macrophage. In a GPCR signaling system, cAMP and Ca2+ act as core molecules. Stimuli-response for 32 ligands to B-Cells and 23 ligands to macrophages has been measured. We found that ligands with correlated changes of cAMP and Ca2+ tend to cluster closely together within the hyperspaces of both cell types and they induced genes involved in the same cellular processes. It was found that ligands inducing cAMP synthesis activate genes involved in cell growth and proliferation; cAMP and Ca2+ molecules that increased together form a feedback loop and induce immune cells to migrate and adhere together. In contrast, ligands without a core molecules response are scattered throughout the hyperspace and do not share clusters. G-protein coupling receptors together with immune response specific receptors were found in cAMP and Ca2+ activated clusters. Analyses have been done on the original software applicable for discovering 'bow-tie' network architectures within the complex network of intracellular signaling where ab initio clustering has been implemented as well. Groups of potential transcription factors for each specific group of genes were found to be partly conserved across B-cell and macrophage. A series of findings support the hypothesis.