Biology subjects

Kitano, H.

Publications and source records attributed to Kitano, H..

7 recordsLinked to original sources

bioRxiv2025

Clustering Gene Co-expression Using 2D Contour Analysis and Autoencoder-based Embedding

Identifying co-expressed genes is crucial for understanding biological processes; however, common methods such as Pearson and Spearman correlation rely on assumptions of linearity and monotonicity, respectively, that may not hold for complex biological data. To address these limitations, we propose a framework that captures visual features of gene expression profiles without relying solely on correlation-based methods. Our approach involves converting 3D gene expression data, which contain rich information on gene envelopes over time, into 2D contours that retain important visual information. We then train an autoencoder on the 2D contour images and generate embeddings from them, followed by clustering on the generated embeddings. We also introduce a new clustering algorithm, TAHC, based on hierarchical clustering, which performs better than existing methods in higher dimensions while using cosine similarity. We apply this framework to the Percellome database, which contains gene expression data with two variables--time and dosage--across various experimental conditions (e.g., tissue type). The resulting clusters exhibit good visual coherence, with an overall average Pearson correlation coefficient of 0.81, demonstrating the effectiveness of our approach.

pharmacology and toxicology↗

bioRxiv2024

Discovering and exploring the hidden diversity of human gut viruses using highly enriched virome samples

Viruses are an abundant and crucial component of the human microbiome, but accurately discovering them via metagenomics is still challenging. Currently, the available viral reference genomes poorly represent the diversity in microbiome samples, and expanding such a set of viral references is difficult. As a result, many viruses are still undetectable through metagenomics even when considering the power of de novo metagenomic assembly and binning, as viruses lack universal markers. Here, we describe a novel approach to catalog new viral members of the human gut microbiome and show how the resulting resource improves metagenomic analyses. We retrieved >3,000 viral-like particles (VLP) enriched metagenomic samples (viromes), evaluated the efficiency of the enrichment in each sample to leverage the viromes of highest purity, and applied multiple analysis steps involving assembly and comparison with hundreds of thousands of metagenome-assembled genomes to discover new viral genomes. We reported over 162,000 viral sequences passing quality control from thousands of gut metagenomes and viromes. The great majority of the retrieved viral sequences ([~]94.4%) were of unknown origin, most had a CRISPR spacer matching host bacteria, and four of them could be detected in >50% of a set of 18,756 gut metagenomes we surveyed. We included the obtained collection of sequences in a new MetaPhlAn 4.1 release, which can quantify reads within a metagenome matching the known and newly uncovered viral diversity. Additionally, we released the viral database for further virome and metagenomic studies of the human microbiome.

microbiology↗

bioRxiv2022

Human immune and gut microbial parameters associated with inter-individual variations in COVID-19 mRNA vaccine-induced immunity

COVID-19 mRNA vaccines induce protective adaptive immunity against SARS-CoV-2 in most individuals, but there is wide variation in levels of vaccine-induced antibody and T-cell responses. However, factors associated with this inter-individual variation remain unclear. Here, using a systems biology approach based on multi-omics analyses of human blood and stool samples, we find that baseline expression of AP-1 transcription factors, FOS and ATF3, is inversely correlated with BNT162b2 mRNA vaccine-induced T-cell responses. FOS expression is associated with transcription modules related to baseline immunity, but it is negatively associated with those related to T-cell activation upon BNT162b2 mRNA stimulation. Interestingly, the gut microbial fucose/rhamnose degradation pathway is positively correlated with FOS and ATF3 expression and inversely correlated with BNT162b2-induced T-cell responses. Taken together, these results demonstrate that baseline expression of AP-1 genes, which is associated with the gut microbial fucose/rhamnose degradation pathway, is a key negative correlate of BNT162b2-induced T-cell responses.

immunology↗

bioRxiv2021

Deep learning of Cas13 guide activity from high-throughput gene essentiality screening

Transcriptome engineering technologies that can effectively and precisely perturb mammalian RNAs are needed to accelerate biological discovery and RNA therapeutics. However, the broad utility of programmable CRISPR-Cas13 ribonucleases has been hampered by an incomplete understanding of the design rules governing guide RNA activity as well as cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we sought to characterize and develop Cas13d systems for efficient and specific RNA knockdown with low cellular toxicity in human cells. We first quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs in the largest-scale screen to date and systematically evaluated three linear, two ensemble, and two deep learning models to build a guide efficiency prediction algorithm validated across multiple human cell types in orthogonal validation experiments (https://www.RNAtargeting.org). Deep learning model interpretation revealed specific sequence motifs at spacer position 15-24 along with favored secondary features for highly efficient guides. We next identified 46 novel Cas13d orthologs through metagenomic mining for activity and cytotoxicity screening, discovering that the metagenome-derived DjCas13d ortholog achieves low cellular toxicity and high transcriptome-wide specificity when deployed against high abundance transcripts or in sensitive cell types, including human embryonic stem cells, neural progenitor cells, and neurons. Finally, our Cas13d guide efficiency model successfully generalized to DjCas13d, highlighting the utility of a comprehensive approach combining machine learning with ortholog discovery to advance RNA targeting in human cells.

bioengineering↗

bioRxiv2021

Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state

Cell cycle (CC) is a fundamental biological process with robust, cyclical gene expression programs to facilitate cell division. In the immune system, a productive immune response requires the expansion of pathogen-responsive cell types, but whether CC also confers unique gene expression programs that inform the subsequent immunological response remains unclear. Here we demonstrate that single macrophages adopt different plasticity states in CC, which is a major source of heterogeneity in response to polarizing cytokines. Specifically, macrophage plasticity to interferon gamma (IFNG) is substantially reduced, while interleukin 4 (IL-4) can induce S-G2/M-biased gene expression. Additionally, IL-4 polarization shifts the CC-phase distribution of the population towards G2/M phase, providing a mechanism for reduced IFNG-induced repolarization. Finally, we show that macrophages express tissue remodeling genes in the S-G2/M-phases of CC, that can be also detected in vivo during muscle regeneration. Therefore, macrophage inflammatory and regenerative responses are gated by CC in a cyclical phase-dependent manner. HighlightsO_LISingle-cell chromatin maps reveal heterogeneous macrophage polarization states C_LIO_LICell cycle coincides with heterogeneity and alters macrophage plasticity to polarizing cytokines C_LIO_LIMacrophage polarization is a cell cycle phase-dependent immunological process C_LIO_LIS-G2/M-biased gene expression is linked to tissue remodeling and detected in proliferating macrophages during muscle regeneration C_LI

immunology↗

bioRxiv2020

Artificial intelligence based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease

BackgroundIdentification of novel therapeutic targets is a key for successful drug development. However, the cost to experimentally identify therapeutic targets is huge and only 400 genes are targets for FDA-approved drugs. Therefore, it is inevitable to develop powerful computational tools to identify potential novel therapeutic targets. Because proteins make their functions together with their interacting partners, a protein-protein interaction network (PIN) in human could be a useful resource to build computational tools to investigate potential targets for therapeutic drugs. Network embedding methods, especially deep-learning based methods would be useful tools to extract an informative low-dimensional latent space that contains enough information required to fully represent original high-dimensional non-linear data of PINs. ResultsIn this study, we developed a deep learning based computational framework that extracts low-dimensional latent space embedded in high-dimensional data of the human PIN and uses the features in the latent space (latent features) to infer potential novel targets for therapeutic drugs. We examined the relationships between the latent features and the representative network metrics and found that the network metrics can explain a large number of the latent features, while several latent features do not correlate with all the network metrics. The results indicate that the features are likely to capture information that the representative network metrics can not capture, while the latent features also can capture information obtained from the network metrics. Our computational framework uses the latent features together with state-of-the-art machine learning techniques to infer potential drug target genes. We applied our computational framework to prioritized novel putative target genes for Alzheimers disease and successfully identified key genes for potential novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we inferred repositionable candidate-compounds for the disease (e.g., Tamoxifen, Bosutinib, and Dasatinib) DiscussionsOur computational framework could be powerful computational tools to efficiently prioritize new therapeutic targets and drug repositioning. It is pertinent to note here that our computational platform is easily applicable to investigate novel potential targets and repositionable compounds for any diseases, especially for rare diseases.

bioinformatics↗

bioRxiv2018

Network-guided Discovery of Influenza Virus Replication Host Factors

The position of host factors required for viral replication within a human protein-protein interaction (PPI) network can be exploited to identify drug targets that are robust to drug-mediated selective pressure. Host factors can physically interact with viral proteins, be a component of pathways regulated by viruses (where proteins themselves do not interact with viral proteins) or be required for viral replication but unregulated by viruses. Here, we demonstrate a method of combining a human PPI network with virus-host protein interaction data to improve antiviral drug discovery for influenza viruses by identifying target host proteins. Network analysis shows that influenza virus proteins physically interact with host proteins in network positions significant for information flow. We have isolated a subnetwork of the human PPI network which connects virus-interacting host proteins to host factors that are important for influenza virus replication without physically interacting with viral proteins. The subnetwork is enriched for signaling and immune processes. Selecting proteins based on network topology within the subnetwork, we performed an siRNA screen to determine if the subnetwork was enriched for virus replication host factors and if network position within the subnetwork offers an advantage in prioritization of drug targets to control influenza virus replication. We found that the subnetwork is highly enriched for target host proteins - more so than the set of host factors that physically interact with viral proteins. Our findings demonstrate that network positions are a powerful predictor to guide antiviral drug candidate prioritization.\n\nIMPORTANCEIntegrating virus-host interactions with host protein-protein interactions, we have created a method using these established network practices to identify host factors (i.e. proteins) that are likely candidates for antiviral drug targeting. We demonstrate that interaction cascades between host proteins that directly interact with viral proteins and host factors that are important to influenza replication are enriched for signaling and immune processes. Additionally, we show that host proteins that interact with viral proteins are in network locations of power. Finally, we demonstrate a new network methodology to predict novel host factors and validate predictions with an siRNA screen. Our results show that integrating virus-host proteins interactions is useful in the identification of antiviral drug target candidates.

systems biology↗