67

Human Genome Center

Laboratory of Functional Analysis In Silico
機能解析イン・シリコ分野
Professor
Kenta Nakai, Ph.D.
Associate Professor Sung-Joon Park, Ph.D.
Assistant Professor Martin Loza, Ph.D.

教 授
准教授
助 教

博士（理学）
中 井 謙 太博士（工学）朴聖 俊
博士（生命機能学） ローザ マーティン

Laboratory of Genome Database
ゲノムデータベース分野
Professor

Kenta Nakai, Ph.D.

教

授博士（理学）中井謙

太The mission of our laboratory is to conduct computational (“in silico”) studies on thefunctional aspects of genome information. At present, we mainly focus on theanalysis of regulatory information of gene expression in the non-coding region,
using a variety of next generation sequencing (NGS) data. In addition, we are
actively collaborating with researchers from various fields.

1.  Epigenetic characterization of housekeeping
core promoters and their importance in tumor
suppression
Martin Loza, Alexis Vandenbon1, and Kenta Nakai
1
Institute for Life and Medical Sciences, Kyoto University, Japan.
In this research, we elucidate the presence ofaround 11,000 housekeeping cis-regulatory elements(HK-CREs) and describe their main characteristics.
Besides the trivial promoters of housekeeping genes,
most HK-CREs reside in promoter regions and are involved in a broader role beyond housekeeping gene
regulation. HK-CREs are conserved regions rich inunmethylated CpG sites. Their distribution highly
correlates with that of protein-coding genes, and theyinteract with many genes over long distances. We ob-served reduced activity of a subset of HK-CREs in diverse cancer subtypes due to aberrant methylation,
particularly those located in chromosome 19 and associated with zinc finger genes. Further analysis ofsamples from 17 cancer subtypes showed a significantly increased survival probability of patients withhigher expression of these genes, suggesting them ashousekeeping tumor suppressor genes. Overall, ourwork unravels the presence of housekeeping CREs indispensable for the maintenance and stability of cells.
2.  A graph-embedding approach dissecting enhancer-cofactor-promoter interactions
Sung-Joon Park and Kenta Nakai
The spatial genome organization mediates thefunctional impact of distal chromosomal interactions.

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Particularly, enhancer-promoter interactions havebeen intensively studied by cutting-edge computational algorithms. However, we still have a very limited understanding of how enhancer signals transmit
to their target promoters through highly intricate regulatory networks. Here, we developed a new computational framework. The method combined a regression modeling that predicts gene expression byinferring important promoter-distal and -proximal
gene regulators with a graph-embedding algorithm
that detects cell-type specific and conserved regulatory interactions in complex gene regulatory networks.
We applied the method to human naïve and germinal
center B cells. Consequently, we identified sets of promoter-distal transcription factors and architectural
cofactor proteins, which are appropriately co-regulated to prevent malignancy. These results highlight theimportance of understanding the cis- and trans-regulatory interactions composed in the transcriptional
domain. Our approach offers an alternative method
to understanding enhancer biology mediated by protein-protein interactions in the 3D genome organization.
3.  HyGAnno: Hybrid graph neural network-based
cell type annotation for single-cell ATAC sequencing data
Weihang Zhang, Yang Cui, Bowen Liu, Martin
Loza, Sung-Joon Park and Kenta Nakai
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics
data. Although various computational approaches
have been proposed for single-cell RNA sequencing(scRNA-seq) annotation, high-quality cell labels arestill lacking in single-cell ATAC sequencing (scATAC-seq) data because of extreme sparsity and inconsistent chromatin accessibility between datasets.
Here, we present a novel automated cell annotation
method called HyGAnno that transfers cell type information from a well-labeled scRNA-seq reference
to an unlabeled scATAC-seq target via a parallelgraph neural network in a semi-supervised manner.
Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno integrates
genome-wide accessibility peak features to facilitate
the training process. In addition, HyGAnno reconstructs a reference-target cell graph that can be used
to detect cells with low prediction reliability according to their specific graph connectivity patterns. HyGAnno was tested using large datasets and demonstrated the advantages of accurate cell annotation,interpretable cell embedding, robustness to noisy reference data, and adaptability to tumor tissues.

4.  
Spatial Transcriptomics Analysis via Image-Aided Graph Contrastive Learning for Domain Exploration and Alignment-Free Integration
Yitao Yang, Yang Cui, Xin Zeng, Yubo Zhang, Martin Loza, Sung-Joon Park and Kenta Nakai
Biological tissues, as intricate networks of variedcell types, perform essential life functions throughunique spatial configurations. Recent spatial transcriptomics techniques, such as 10x Visium, havegreatly enhanced our ability to map genetic data
within these configurations, offering deeper insights
into the genetic makeup of tissues in health and disease and advancing our grasp of molecular and physiological intricacies. We propose Spatial Transcriptomics and Image-based Graph learning (STAIG), a
deep learning framework for advanced spatial region
identification in spatial transcriptomics. STAIG integrates gene expression, spatial coordinates, and histological images using graph contrastive learning, excelling in feature extraction and enhancing analysis
on datasets with or without histological images. It hasoutperformed existing methods in recognizing spatial regions within Visium and other platform datasets. STAIG was specifically engineered to counterbatch effects and to allow for the integration of tissue
slices without pre-alignment. In applications to human breast cancer and zebrafish melanoma, STAIG
has identified regions with high precision, uncovering new insights into tumor microenvironments.
STAIG thus offers a versatile tool for probing cellular
structures and interactions, enriching our understanding of spatial transcriptomics.
5.  TF-EPI: An Interpretable Enhancer Promoter
Interaction Detection Method Based on Large
Language Model
Bowen Liu, Weihang Zhang, Xin Zeng, Martin
Loza, Sung-Joon Park and Kenta Nakai
The detection of enhancer-promoter interactions(EPIs) is crucial for understanding gene expression
regulation, disease mechanisms, and more. In thisstudy, we developed TF-EPI, a deep learning model
based on Transformer, designed to detect these interactions solely from DNA sequences. The performanceof TF-EPI surpassed other state-of-the-art methods in
multiple benchmark datasets. Importantly, by utilizing the attention mechanism of the Transformer, we
identified distinct cell type-specific motifs and sequences in enhancers and promoters, which we validated against databases like Jaspar and UniBind,
highlighting the potential of our method in discovering new biological insights. Moreover, our analysis of
the transcription factors corresponding to these motif
and short sequence pairs revealed the heterogeneity

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and commonality of gene regulatory mechanisms
and demonstrated the ability to identify TFs relevantto the source information of the cell line. Finally, theintroduction of transfer learning can mitigate thechallenges posed by cell type-specific gene regulation, yielding enhanced accuracy in cross-cell line EPI
detection results. Overall, our work unveils important sequences information for the investigation ofenhancer-promoter pairs based on the attention
mechanism of Transformer, which provides an important milestone in the investigation of cis-regulatory grammar.
6.  Computational Transcriptomic Analysis Identifies a Novel Immune-dysregulated TNBC Subtype Regulated by STAT3
Yang Cui and Kenta Nakai
Triple-negative breast cancer (TNBC) is the mostaggressive subtype of breast cancer and frequently
appears to be resistant to treatment due to its extensive intratumoral heterogeneity. The tumor microenvironment (TME) plays a critical role in TNBC immunomodulation and progression. In this study, weleveraged TNBC bulk RNA-seq data and TNBC scRNA-seq data to investigate the TNBC TME. Utilizing
CIBERSORTx to deconvolve the bulk RNA-seq datawith scRNA-seq as a reference, we identified cellcompositions within the TME. Non-negative matrix
factorization further revealed three unique clusters,with patients in Cluster 2 (C2) exhibiting significantly
shorter survival times. Next, we trained a random forest classifier on TNBC bulk RNA-seq data to identify
the C2 patients within the scRNA-seq dataset. We
found a reduced abundance of immune cells, including CD4+ T cells, CD8+ T cells, DC, Macrophage, andNK cells, and an increase in breast cancer cells in C2,
indicating an immunosuppressive environment. We
further conducted cell-cell communication (CCC)
analysis and found compromised MHC-I and MHCII signaling in CD8+ T cells and macrophages, respectively, in C2. Followed SCENIC analysis unveiled thegene regulatory network of the CD8+ T cells in C2,
revealing an enrichment of regulon STAT3, which is
associated with T cell dysfunction. Our study unveils
an immunodysregulated subtype within TNBC, andprovides valuable insights into its heterogeneity.
7.  Development of the splice site prediction model that can account for long-distance effects
using Transformer.
Yuna Miyachi and Kenta Nakai
RNA splicing is an essential process in the regulation of gene expression. Recently, deep learning
methods have been applied to predict splice sites andelucidate their complex mechanisms. However, there
are no existing methods that account for both longrange sequence effects and high interpretability.
To solve this problem, we created a new splice site
prediction model consisting of a convolution layer
and an attention mechanism, taking as input sequences up to 100k in length. The combination of the convolutional layer and the attention mechanism allowed
the model to learn both local and global features. The
results show that the proposed model outperforms
the state-of-the-art SpliceAI model in terms of precision and F1 score on a dataset of Gencode annotations. We also performed predictions on an aberrant
splicing dataset generated from DBASS and compared the performance of the proposed method with
SpliceAI. We found that our model is more sensitive
to changes in splice sites than SpliceAI if the threshold is set sufficiently low. We also examined which
sequences are important for splicing by analyzing theattention weights. We found several exonic splicing
enhancer candidates in addition to the GT-AG rule,
which is known as a consensus sequence for donor
and acceptor sites of intron. Altogether, our proposedmodel has high interpretability and achieved highprediction performance, which could be useful for
disease diagnosis and mechanistic elucidation, suchas the detection of pathogenic mutations that cause
aberrant splicing.
8.  Comparative Single-cell Analyses of Humanand Mouse Dendritic Cell Progenitors
Phit Ling Tan, Florent Ginhoux2, Kenta Nakai
2
Singapore Immunology Network (SIgN), A*STAR
The dendritic cells (DC) population comprises aheterogeneous family of immune cells, including
plasmacytoid DC (pDC) and two subsets of conventional DC (cDC1 and cDC2). Despite the well characterization of mature DC, the origins and differentiation pathways of human DC are still not clearly
elucidated as compared to the mouse counterpart. In
this study, we compare human and mouse bone marrow cell types in order to find out the progenitors ofdifferentiated DC populations. We integrated multiple public datasets via Mutual Nearest Neighbors(Haghverdi et al., 2018). We also predicted transcription factor (TF) regulons and gene regulatory networks with SCENIC+ (Bravo and De Winter et al.,2023). In the future, we intend to identify the homologous TFs of the same lineage across humans andmouse to understand the genetic mechanisms of DC
specification.
9.  Discovery of human oncogenes guided by bioinformatics analysis of multi-omics data.
Martin Loza, Alexis Vandenbon1, Gözde Korkmaz3,and Kenta Nakai

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School of Medicine, Koç University, Turkey

3

Cis-regulatory elements, as enhancers and promoters, orchestrate the spatial and temporal expression of genes and are indispensable for organismaldevelopment and disease protection. In our latest
publication (Loza et al., 2023), we uncovered the existence of a subset of housekeeping regulatory elementswith potential tumor suppression capabilities.
Functional genomics that is based on functional
phenotypic screening has become highly prominent
in cancer-focused studies to identify the function ofgenes and regulatory elements essential for the malignant phenotype. In the last decade, CRISPR-based
screening has been stated as a fundamental method
for genome-wide loss-of-function (LOF) screening.
Pooled library approaches make it possible to uncover novel genes that promote malignancy, and until
now, several studies have demonstrated the applicability of CRISPR-based genome-wide LOF screenings
to identify novel oncogenic genes.
Altogether, the scope of this study is to validate
the functionality of the housekeeping core promotersidentified as potential tumor suppressors. Using
CRISPR-based screening, we will validate their targets as potential human oncogenes for diverse cancer
sub-types.
10. Comparative single-cell transcriptomic analysis reveals key differentiation drivers and potential origin of vertebrate retina
Xin Zeng, Fuki Gyoja4, Yang Cui, Martin Loza,
Takehiro G. Kusakabe4, Kenta Nakai
4
Faculty of Science and Engineering, Konan University
The retina plays a vital role in capturing and processing visual information in vertebrates, allowing
them to adapt to diverse environments. Single-cell expression profiles of the vertebrate retina have been
described; however, a deeper understanding of theexpression patterns in the context of development
and evolution among homologous cell types is lacking. To identify such shared patterns, we examined
and compared approximately 230,000 retinal cells
from three species: mouse, chicken, and zebrafish. We
found significant similarities among homologous cell
types, indicating inherent regulatory patterns. To understand these shared patterns, we constructed gene
regulatory networks for each developmental stage forthe three species. We identified 690 regulons with 381
regulators across the three species. In addition, we
identified ten common cell type-specific regulatorsand 16 preserved regulons. Using RNA velocity analysis, we pinpointed conserved driver genes key to retinal cell differentiation in both mouse and zebrafish,
and by intersecting regulators, we extracted the cru-

cial regulators that facilitate these differentiation processes. Finally, investigation of the potential origins
of photoreceptors and retinal ganglion cells by examining conserved expression patterns among the threevertebrate species and the invertebrate Ciona intestinalis revealed functional similarities in light transduction mechanisms between Ciona photoreceptor-related cells and vertebrate retinal cells. Ourfindings offer insights into the conserved regulatory
frameworks intrinsic, evolutionarily preserved differentiation programs, and ancestral origins of vertebrate retinal cells.
11. 
Exploring the role of hysteresis in macrophage lipid metabolism and robustness of
repolarization memory
Yubo Zhang, Kei Ishida5, Wenbo Yang, Yutaro Kumagai6, Sung-Joon Park, Jun Kunisawa6 and Kenta
Nakai
5
National Institutes of Biomedical Innovation,
Health and Nutrition (NIBIOHN)
6National Institute of Advanced Industrial Scienceand Technology (AIST)
Macrophages exhibit polarization, leading tofunctionally distinct phenotypic changes. This process of polarization and subsequent de-/re-polarization results in hysteresis, where the cells retain genetic and epigenetic markers from previous states,
impacting their core functions. To probe the role ofhysteresis in lipid metabolism, we utilized computational analysis on an extensive public dataset. Our
approach involved reanalyzing 154 RNA-seq datasets
pertaining to (re)polarized macrophages, including
those with lipid accumulation. We applied Weighted
Gene Correlation Network Analysis (WGCNA) andconducted functional enrichment analysis. This led tothe identification of 10 significant gene modules, including one uniquely associated with polarized macrophages without de-/re-polarization history, linked
to cholesterol biosynthesis. Conversely, a module enriched in inflammatory and LPS response was closely
related to cells with a history of specific macrophagede-/re-polarization. To further validate the expression
trends of hysteresis genes in de-/repolarized macrophages, we are using lab techniques like PCR andWestern Blot, aligning these with prior research. We
extended the de-/repolarization duration to bolster
the credibility of our results. Our research aims to establish the reproducibility of the hysteresis effect andhighlight the importance of its inhibition in regulating lipid metabolism. The complex regulation of cellular memory, however, still presents significant unknowns. Herein, we delve into our ongoing efforts toelucidate the role of macrophage hysteresis. This research was presented at InCOB2023.

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12. Identification of transcription factors that
contribute to enhancer-promoter communication in mESCs by heterogeneous graph neural network
Yubo Zhang, Xufeng Shu7, Yitao Yang, Wenbo Yang,
Piero Carninci7 and Kenta Nakai
7
RIKEN Center for Integrative Medical Sciences
Transcription factors (TFs) play a critical role inthe communication between enhancers and promoters, essential for gene expression in mouse embryonicstem cells (mESCs). Moving beyond the traditional
focus on pairwise TF interactions, our research delves
into a more sophisticated network involving multiple
interactions and transcriptional condensates. We employ state-of-the-art sequencing methods and deeplearning techniques to unravel complex TF interactions within enhancer-promoter loops, aiming to uncover crucial, yet hidden, TF pairs that influence
mESC identity and differentiation. In this endeavor,
we use CAGE-seq to identify promoter and enhancer
regions in mESCs precisely and complement this withRADICL-seq and PCHi-C for detecting chromatinloops. By integrating computational analysis of TF
binding sites with existing protein-protein interaction
data, we create an extensive network graph featuring
enhancers, promoters, and TFs. The innovative
co-contrastive learning method, HeCo, allows us to
produce simplified representations of TF nodes. Thismethodology aids in predicting missing links in the
TF network, which we hypothesize to be critical in enhancer-promoter interactions. These predictions, including 20 high-confidence TF-TF interaction pairs,
are currently being validated through experimental
wet-lab techniques. Our overarching objective is tolead the development of a detailed graph that maps
the Enhancer-TFs-Promoter/Genes regulatory network in mESCs. This project is aimed at pinpointing
essential TFs and regulatory components involved inenhancer-promoter communications and loop formations. Through this, we hope to facilitate the prediction and discovery of novel TF interactions, thereby
advancing the understanding of complex biologicalnetworks in stem cell research.
13. Identification of COPS5 as a novel biomarker
of diffuse large B cell lymphoma by a machine-learning algorithm
Yutong Dai, Sung-Joon Park, Jingmei Li8, Keita
Yamamoto8, Susumu Goyama8 and Kenta Nakai
8
Graduate School of Frontier Sciences, The University of Tokyo
The transition from generalized medicine to precision medicine has been pivotal in cancer research,emphasizing the need for biomarkers. Despite challenges in identifying meaningful biomarkers, ma-chine-learning approaches for biomarker discovery
have been widely adopted. Particularly, Deep learning, while powerful, faces criticism for its interpretability and performance issues due to missing data.
To address these challenges, our study employs
the Joint Non-Negative Matrix Factorization (JNMF),
an unsupervised algorithm, to unravel cancer biomarkers from multi-omics data. Unlike prior pan-cancer studies, our approach integrates JNMF, gene signature and pathway analysis, and patient data
validation, ensuring high interpretability and clinicalrelevance. The JNMF efficiently handles sparse datasets, demonstrated through simulations and the Cancer Cell Line Encyclopedia (CCLE) datasets. The analysis identifies 40 reproducible and robust feature sets,termed Common Pattern Modules (CPMs), revealing
cancer-specific patterns. Notably, the hematopoietic
and lymphoid-specific CPM highlights genomic features of diffuse large B-cell lymphoma (DLBCL).
Pathway analysis associates DLBCL key pathways,
including NF-κB, TP53, JAK-STAT, and PI3K, with
COPS5 identified as a key upstream regulator of DLBCL-related pathways in this CPM. Survival analysis
using The Cancer Genome Atlas (TCGA) and Gene
Expression Omnibus (GEO) data establishes COPS5
overexpression as a predictor of poor patient survival, corroborating its role in DLBCL. Moreover, Consistent with our observation of overexpression of
COPS5, MYC, BCL6, TP53, and STAT3 in DLBCL-specific CPM, a positive correlation between COPS5 expression levels and these genes was also observed in
DLBCL patients. More significantly, CRISPR/Cas9
knockout experiments further validate COPS5’s significance in promoting malignant growth in mature
B-cell neoplasms.
In conclusion, the integrated analysis unveils DLBCL genomic characteristics, spotlighting COPS5 as apotential prognostic biomarker. The methodology
holds promise for extracting clinically significant biomarkers from diverse omics data, advancing precision medicine in cancer research.
14. 
GlycoMSParser: an automated pipeline forcomprehensive permethylated glycan analysis
Huan-Chuan Tseng, Wei-Kang Tseng9, Yuen-Hsien
Tseng10, Kenta Nakai
9
National Chengchi University, Taiwan
10
National Taiwan Normal University, Taiwan
We’re trying to establish a pipeline that is applicable to automatically annotate glycans (polysaccharides) extracted from proteins obtained from biological samples, which were generally analyzed andannotated manually. Software for annotating spectra
based on in silico calculated glycan fragments and database search still needs man to operate it with proper
knowledge and conditions based on the experimental

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method. In our research, we developed one non-machine learning algorithm that can annotate glycan assisted with glycosyltransferase gene expression (if itexists and the functions are known) and can predict
and annotate similar glycan structures under thesame experimental method in unknown samples.
We’re still improving the whole pipeline and testing itwith more data and parameters to get more comprehensive and convincing results for publishing this research.
15. 
Cross-sample genetic correlations reveal a
causal relationship between human complex
traitsYi Zheng, Zheng Ning11, Yudi Pawitan11, Xia Shen11,
Kenta Nakai
11
Karolinska Institutet, Sweden
Ascertaining causality in human complex traits is
crucial for understanding biology and disease prevention. In this research, we introduce a novel concept termed cross-sample genetic correlation (X-rG).
It is calculated from GWAS summary statistics of two
phenotypes from different cohorts and serves as anindicator to identify potential causal relationships between complex traits. Through mathematical modeling and numerical simulations, we have found thatwhen the difference in genetic correlation between
two cohorts is attributable to causal effects, a significant disparity in X-rG values is observed. Conversely,
variation in genetic correlations due to pleiotropy
leads to symmetric X-rG values. Leveraging this feature, we screened for asymmetry in X-rG among 211
complex traits between male and female cohorts in
the UK Biobank, identifying 207 pairs of traits withpotential causal relationships. The findings underscore the pivotal roles of education and occupation
type in exposure and outcome, respectively, which
are beneficial for improving health indicators andwell-being. This method provides an effective complement to causal reasoning for complex traits anddiseases and has significant implications for the development of precision medicine strategies.
16. Discovery of antibodies with potential therapeutic applications aided by machine learningand artificial intelligence.
Diana Hinojosa-Trujillo12, Bowen Liu, Weihang
Zhang, Melissa Garcia-Vega12, Sofia Hernandez-Valenzuela12, Monica Resendiz-Sandoval12, Martin
Loza, and Jesus Hernandez12
12
Centro de Investigacion en Alimentacion y Desarrollo, Mexico
The use of antibodies to improve human health isan emerging area of biotechnology and immunology.
They represent a therapeutic strategy with several ap-

plications for managing various types of cancer, infectious and inflammatory diseases, and other noninfectious diseases. Several pharmaceutics and researchgroups have worked in the last years to find new therapeutic applications for antibodies, including antibody modifications to enhance their efficacy and stability and facilitate their administration. One criticalstep in this area is antibody discovery, which represents the major challenge of the field. The new computational and experimental methodologies offer important advantages for discovering and testing new
antibodies. Single-cell RNAseq is the best example ofthese new tools since it allows isolating antibody sequences from B-cells of individuals immunized withthe therapeutic target. However, analyzing a significant number of sequences takes time and effort. The
use of computational approaches like deep learning,machine learning, and artificial intelligence can potentially assist in discovering new antibodies, providing a tool for the prediction of neutralizing profiles,
thus virtually reducing the time, effort, and resources
invested.
In this project, we aim to build a bioinformatics
pipeline based on artificial intelligence to discover
new neutralizing antibodies against SARS-CoV-2.
The final platform could be extended to assist in the
discovery of antibodies against other diseases using
public libraries or new ones generated by Prof.
Hernández´s Laboratory.
17. 
Identifying pioneer molecular modulating
global chromatin accessibility by genomewide ATAC-see screening and ATAC-seq analysis
Sung-Joon Park, Yasuyuki Ohkawa13, Kenta Nakai,
Yusuke Miyanari14
13
Medical Institute of Bioregulation, Kyushu University
14
WPI Nano Life Science Institute, Kanazawa University
The dynamics of chromatin accessibility play a
crucial role in a local environment for accomplishingvarious biological functions. Here, to understand themolecular mechanisms underlying the control ofchromatin accessibility, we conducted a genome-wide
CRISPR screening combined with an optimized ATAC-see that covers more than 19,000 human genes in
eHAP cells, along with developing a computational
analytic pipeline. This examination detected genes
previously unknown to modulate global chromatin
accessibility, including TFDP1, HNRNPU, EIF3D,
and THAP11. Particularly, the transcription factor
TFDP1 markedly impacted global chromatin accessibility through transcriptional regulation of canonicalhistones. That is, the TFDP1-depleted cells showed
increased global chromatin accessibility and enhanced the efficiency of DNA-associated applications,

73
including iPSC reprogramming. These results highlight the potential that the manipulation of chromatin
accessibility by altering key molecules is a promising
tool for enhancing the efficiency of various cell engineering applications.
18. 
Resting heart rate and risk of dementia: a
Mendelian Randomization Study in the International Genomics of Alzheimer9s Projectand UK Biobank
Xingxing Chen15,16, Yi Zheng, Jun Wang17, Blake
Yue18,19, Xian Zhang16, Kenta Nakai , Lijing L
Yan15,16,20,21
15
School of Public Health, Wuhan University, China
16
Duke Kunshan University, Global Health ResearchCenter, China
17
Huazhong University of Science and Technology,
China
18
Edith Cowan University, Australia
19
Auckland University of Technology, New Zealand20
Duke University, USA
21
Peking University, China
Observational studies have demonstrated that ahigher resting heart rate (RHR) is associated with anincreased risk of dementia. However, it is not clear
whether the association is causal. This study aimed todetermine the causal effects of higher genetically predicted RHR on the risk of dementia. We performed ageneralized summary Mendelian randomization(GSMR) analysis to analyze the corresponding effects
of higher RHR on the following different outcomes: 1)
diagnosis of AD (International Genomics of Alzheimer’s Project), 2) family history (maternal and paternal)of AD from UK Biobank, 3) combined meta-analysis
including these three GWAS results. Further analyses
were conducted to determine the possibility of reverse causal association by adjusting for RHR modifying medication. The results of GSMR showed nosignificant causal effect of higher genetically predicted RHR on the risk of AD (βGSMR = 0.12, P = 0.30).
GSMR applied to the maternal family history of AD(βGSMR = -0.18, P = 0.13) and to the paternal family history of AD (βGSMR = -0.14, P = 0.39) showed the same
results. Furthermore, the results were robust after adjusting for RHR modifying drugs (βGSMR = -0.03, P =0.72). In conclusion, our study did not find any evidence that supports a causal effect of higher RHR on
dementia. Previous observational associations between RHR and dementia are likely attributed to thecorrelation between RHR and other cardiovasculardiseases.
19. DeepBIO: An automated and interpretabledeep-learning platform for high-throughput
biological sequence prediction, functional annotation, and visualization analysis

Ruheng Wang22, Yi iang22, Junru Jin22, Chenglin
Yin22, Haoqing Yu22, Fengsheng Wang22, Jiuxin
Feng22, Ran Su23, Kenta Nakai, Quan Zou24, & Leyi
Wei22
22
Shandong University, China
23
Tianjin University, China
24
University of Electronic Science and Technology ofChina, China
DeepBIO is the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. It is a
one-stop-shop web service that enables researchers todevelop new deep-learning architectures to answer
any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42
state-of-the-art deep-learning algorithms for modeltraining, comparison, optimization, and evaluation ina fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictivemodels covering several aspects, such as model interpretability, feature analysis, and functional sequential
region discovery. We expect DeepBIO to ensure thereproducibility of deep-learning biological sequence
analysis, lessen the programming and hardware burden for biologists, and provide meaningful functional
insights at both the sequence level and base level from
biological sequences alone. DeepBIO is publicly available at https://inner.wei-group.net/DeepBIO.
20. 19n01, a broadly neutralizing antibody against
omicron BA.1, BA.2, BA.4/5, and other SARSCoV2 variants of concern
Melissa Garcia-Vega12, Edgar A. Melgoza-Gonzalez12, Sofia Hernandez-Valenzuela12, Diana Hinojosa-Trujillo12, Monica Resendiz-Sandoval12, Mara
Anais Llamas-Covarrubias25, Martin Loza-Lopez,
Olivia Valenzuela26, Alan Soto-Gaxiola27, Miguel A.
Hernandez-Onate28, Veronica Mata-Haro28, Irene
Cassaniti39, Jose Camilla Sammartino29, Alessandro
Ferrari29, Luca Simonelli30, Mattia Pedotti30, Rui
Sun11, Fanglei Zuo11, Fausto Baldanti29, Luca Varani30, Harold Marcotte11, Qiang Pan-Hammarstrom11,
and Jesus Hernandez1225
Research Institute for Microbial Diseases, Osaka
University
26
Division de Ciencias de la Salud, Universidad deSonora, Mexico
27
Secretaria de Salud del Estado de Sonora, Mexico
28
Centro de Investigacion en Alimentacion y Desarrollo, Mexico
29
Fondazione IRCCS Policlinico San Matteo, Italy
30
Universita` della Svizzera italiana (USI), Italy
This study reports the isolation and characterization of a human monoclonal antibody (mAb) called
19n01. This mAb was isolated by using single-cell
RNAseq of B cells from donors infected with the an-74
cestral strain. This mAb possesses a potent and broad
capacity to bind and neutralize all previously circulating variants of concern (VOCs), including Omicron
sublineages BA.1, BA.2, and BA.4/5. The pseudovirus
neutralization assay revealed robust neutralization
capacity against the G614 strain, BA.1, BA.2, andBA.4/5, with inhibitory concentration (IC50) values
ranging from 0.0035 to 0.0164 mg/mL. The microneutralization assay using the G614 strain and VOCs

demonstrated IC50 values of 0.013–0.267 mg/mL. Biophysical and structural analysis showed that 19n01
cross-competes with ACE2 binding to the receptor-binding domain (RBD), and the kinetic parameters confirmed the high affinity against the Omicron
sublineages (KD of 61 and 30 nM for BA.2 and BA.4/5,
respectively). These results suggest that the 19n01 is aremarkably potent and broadly reactive mAb.

Publication list
Ruheng Wang, Yi iang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su,
Kenta Nakai, Quan Zou, & Leyi Wei. DeepBIO: An
automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation, and visualization
analysis. Nucl. Acids Res., 51(7), 3017-3029 (2023).
(Breakthrough Article)
Shu Liu, Fujio Toriumi, Xin Zeng, Mao Nishiguchi,
and Kenta Nakai. SignedS2V: Structural embedding method for signed networks. in (Hocine Cherifi, Rosario Nunzio Mantegna, Luis M. Rocha,
Chantal Cherifi, & Salvatore Miccichè eds.) Complex Networks and Their Applications XI. (Proceedings of The Eleventh International Conference on
Complex Networks and Their Applications: COMPLEXNETWORKS 2022) Volume 1, pp.337-349 (2023).
Springer ISBN: 978-3-031-21126-3
Esra Büşra Işık, Michelle D. Brazas, Russell Schwartz,
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