Organism-Level Analysis of Vaccination Reveals Networks of Protection across Tissues.

A fundamental challenge in immunology is to decipher the principles governing immune responses at the whole-organism scale. Here, using a comparative infection model, we observe immune signal propagation within and between organs to obtain a dynamic map of immune processes at the organism level. We uncover two inter-organ mechanisms of protective immunity mediated by soluble and cellular factors. First, analyzing ligand-receptor connectivity across tissues reveals that type I IFNs trigger a whole-body antiviral state, protecting the host within hours after skin vaccination. Second, combining parabiosis, single-cell analyses, and gene knockouts, we uncover a multi-organ web of tissue-resident memory T cells that functionally adapt to their environment to stop viral spread across the organism. These results have implications for manipulating tissue-resident memory T cells through vaccination and open up new lines of inquiry for the analysis of immune responses at the organism level.

HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.

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 are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.

Epigenetic characterization of housekeeping core promoters and their importance in tumor suppression.

In this research, we elucidate the presence of around 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 in unmethylated CpG sites. Their distribution highly correlates with that of protein-coding genes, and they interact with many genes over long distances. We observed 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 of samples from 17 cancer subtypes showed a significantly increased survival probability of patients with higher expression of these genes, suggesting them as housekeeping tumor suppressor genes. Overall, our work unravels the presence of housekeeping CREs indispensable for the maintenance and stability of cells.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop 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 model training, comparison, optimization and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis and functional sequential region discovery. Additionally, DeepBIO supports nine base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility 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.

The development of next-generation sequencing techniques has led to an exponential increase in the amount of biological sequence data accessible. It naturally poses a fundamental challenge­how to build the relationships from such large-scale sequences to their functions. In this work, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. It enables researchers to develop new deep-learning architectures to answer any biological question in a fully automated pipeline. We expect DeepBIO to ensure the reproducibility of deep-learning-based 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.

Protein design via deep learning.

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Single-cell transcriptomic analysis of B cells reveals new insights into atypical memory B cells in COVID-19.

Here, we performed single-cell RNA sequencing of S1 and receptor binding domain protein-specific B cells from convalescent COVID-19 patients with different clinical manifestations. This study aimed to evaluate the role and developmental pathway of atypical memory B cells (MBCs) in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The results revealed a proinflammatory signature across B cell subsets associated with disease severity, as evidenced by the upregulation of genes such as GADD45B, MAP3K8, and NFKBIA in critical and severe individuals. Furthermore, the analysis of atypical MBCs suggested a developmental pathway similar to that of conventional MBCs through germinal centers, as indicated by the expression of several genes involved in germinal center processes, including CXCR4, CXCR5, BCL2, and MYC. Additionally, the upregulation of genes characteristic of the immune response in COVID-19, such as ZFP36 and DUSP1, suggested that the differentiation and activation of atypical MBCs may be influenced by exposure to SARS-CoV-2 and that these genes may contribute to the immune response for COVID-19 recovery. Our study contributes to a better understanding of atypical MBCs in COVID-19 and the role of other B cell subsets across different clinical manifestations.

scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.

With the advent of single-cell RNA sequencing (scRNA-seq), one major challenging is the so-called 'dropout' events that distort gene expression and remarkably influence downstream analysis in single-cell transcriptome. To address this issue, much effort has been done and several scRNA-seq imputation methods were developed with two categories: model-based and deep learning-based. However, comprehensively and systematically comparing existing methods are still lacking. In this work, we use six simulated and two real scRNA-seq datasets to comprehensively evaluate and compare a total of 12 available imputation methods from the following four aspects: (i) gene expression recovering, (ii) cell clustering, (iii) gene differential expression, and (iv) cellular trajectory reconstruction. We demonstrate that deep learning-based approaches generally exhibit better overall performance than model-based approaches under major benchmarking comparison, indicating the power of deep learning for imputation. Importantly, we built scIMC (single-cell Imputation Methods Comparison platform), the first online platform that integrates all available state-of-the-art imputation methods for benchmarking comparison and visualization analysis, which is expected to be a convenient and useful tool for researchers of interest. It is now freely accessible via https://server.wei-group.net/scIMC/.

Predicting protein-peptide binding residues via interpretable deep learning.

SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on third-party tools or complex data preprocessing for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. To address the limitations, we propose PepBCL, a novel BERT (Bidirectional Encoder Representation from Transformers) -based contrastive learning framework to predict the protein-peptide binding residues based on protein sequences only. PepBCL is an end-to-end predictive model that is independent of feature engineering. Specifically, we introduce a well pre-trained protein language model that can automatically extract and learn high-latent representations of protein sequences relevant for protein structures and functions. Further, we design a novel contrastive learning module to optimize the feature representations of binding residues underlying the imbalanced dataset. We demonstrate that our proposed method significantly outperforms the state-of-the-art methods under benchmarking comparison, and achieves more robust performance. Moreover, we found that we further improve the performance via the integration of traditional features and our learnt features. Interestingly, the interpretable analysis of our model highlights the flexibility and adaptability of deep learning-based protein language model to capture both conserved and non-conserved sequential characteristics of peptide-binding residues. Finally, to facilitate the use of our method, we establish an online predictive platform as the implementation of the proposed PepBCL, which is now available at http://server.wei-group.net/PepBCL/. AVAILABILITY AND IMPLEMENTATION: https://github.com/Ruheng-W/PepBCL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OpenContami: a web-based application for detecting microbial contaminants in next-generation sequencing data.

SUMMARY: Microorganisms infect and contaminate eukaryotic cells during the course of biological experiments. Because microbes influence host cell biology and may therefore lead to erroneous conclusions, a computational platform that facilitates decontamination is indispensable. Recent studies show that next-generation sequencing (NGS) data can be used to identify the presence of exogenous microbial species. Previously, we proposed an algorithm to improve detection of microbes in NGS data. Here, we developed an online application, OpenContami, which allows researchers easy access to the algorithm via interactive web-based interfaces. We have designed the application by incorporating a database comprising analytical results from a large-scale public dataset and data uploaded by users. The database serves as a reference for assessing user data and provides a list of genera detected from negative blank controls as a 'blacklist', which is useful for studying human infectious diseases. OpenContami offers a comprehensive overview of exogenous species in NGS datasets; as such, it will increase our understanding of the impact of microbial contamination on biological and pathological traits. AVAILABILITY AND IMPLEMENTATION: OpenContami is freely available at: https://openlooper.hgc.jp/opencontami/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Jmjd3-Irf4 axis regulates M2 macrophage polarization and host responses against helminth infection.

Polarization of macrophages to M1 or M2 cells is important for mounting responses against bacterial and helminth infections, respectively. Jumonji domain containing-3 (Jmjd3), a histone 3 Lys27 (H3K27) demethylase, has been implicated in the activation of macrophages. Here we show that Jmjd3 is essential for M2 macrophage polarization in response to helminth infection and chitin, though Jmjd3 is dispensable for M1 responses. Furthermore, Jmjd3 (also known as Kdm6b) is essential for proper bone marrow macrophage differentiation, and this function depends on demethylase activity of Jmjd3. Jmjd3 deficiency affected trimethylation of H3K27 in only a limited number of genes. Among them, we identified Irf4 as encoding a key transcription factor that controls M2 macrophage polarization. Collectively, these results show that Jmjd3-mediated H3K27 demethylation is crucial for regulating M2 macrophage development leading to anti-helminth host responses.

HHEX promotes myeloid transformation in cooperation with mutant ASXL1.

Additional sex combs-like 1 (ASXL1), an epigenetic modulator, is frequently mutated in myeloid neoplasms. Recent analyses of mutant ASXL1 conditional knockin (ASXL1-MT-KI) mice suggested that ASXL1-MT alone is insufficient for myeloid transformation. In our previous study, we used retrovirus-mediated insertional mutagenesis, which exhibited the susceptibility of ASXL1-MT-KI hematopoietic cells to transform into myeloid leukemia cells. In this screening, we identified the hematopoietically expressed homeobox (HHEX) gene as one of the common retrovirus integration sites. In this study, we investigated the potential cooperation between ASXL1-MT and HHEX in myeloid leukemogenesis. Expression of HHEX enhanced proliferation of ASXL1-MT-expressing HSPCs by inhibiting apoptosis and blocking differentiation, whereas it showed only modest effect in normal HSPCs. Moreover, ASXL1-MT and HHEX accelerated the development of RUNX1-ETO9a and FLT3-ITD leukemia. Conversely, HHEX depletion profoundly attenuated the colony-forming activity and leukemogenicity of ASXL1-MT-expressing leukemia cells. Mechanistically, we identified MYB and ETV5 as downstream targets for ASXL1-MT and HHEX by using transcriptome and chromatin immunoprecipitation-next-generation sequencing analyses. Moreover, we found that expression of ASXL1-MT enhanced the binding of HHEX to the promoter loci of MYB or ETV5 via reducing H2AK119ub. Depletion of MYB or ETV5 induced apoptosis or differentiation in ASXL1-MT-expressing leukemia cells, respectively. In addition, ectopic expression of MYB or ETV5 reversed the reduced colony-forming activity of HHEX-depleted ASXL1-MT-expressing leukemia cells. These findings indicate that the HHEX-MYB/ETV5 axis promotes myeloid transformation in ASXL1-mutated preleukemia cells.

A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations.

BACKGROUND: Understanding the functional effects of non-coding variants is important as they are often associated with gene-expression alteration and disease development. Over the past few years, many computational tools have been developed to predict their functional impact. However, the intrinsic difficulty in dealing with the scarcity of data leads to the necessity to further improve the algorithms. In this work, we propose a novel method, employing a semi-supervised deep-learning model with pseudo labels, which takes advantage of learning from both experimentally annotated and unannotated data. RESULTS: We prepared known functional non-coding variants with histone marks, DNA accessibility, and sequence context in GM12878, HepG2, and K562 cell lines. Applying our method to the dataset demonstrated its outstanding performance, compared with that of existing tools. Our results also indicated that the semi-supervised model with pseudo labels achieves higher predictive performance than the supervised model without pseudo labels. Interestingly, a model trained with the data in a certain cell line is unlikely to succeed in other cell lines, which implies the cell-type-specific nature of the non-coding variants. Remarkably, we found that DNA accessibility significantly contributes to the functional consequence of variants, which suggests the importance of open chromatin conformation prior to establishing the interaction of non-coding variants with gene regulation. CONCLUSIONS: The semi-supervised deep learning model coupled with pseudo labeling has advantages in studying with limited datasets, which is not unusual in biology. Our study provides an effective approach in finding non-coding mutations potentially associated with various biological phenomena, including human diseases.

T cell receptor stimulation-induced epigenetic changes and Foxp3 expression are independent and complementary events required for Treg cell development.

The transcription factor Foxp3 is essential for the development of regulatory T (Treg) cells, yet its expression is insufficient for establishing the Treg cell lineage. Here we showed that Treg cell development was achieved by the combination of two independent processes, i.e., the expression of Foxp3 and the establishment of Treg cell-specific CpG hypomethylation pattern. Both events were induced by T cell receptor stimulation. The Treg cell-type CpG hypomethylation began in the thymus and continued to proceed in the periphery and could be fully established without Foxp3. The hypomethylation was required for Foxp3(+) T cells to acquire Treg cell-type gene expression, lineage stability, and full suppressive activity. Thus, those T cells in which the two events have concurrently occurred are developmentally set into the Treg cell lineage. This model explains how Treg cell fate and plasticity is controlled and can be exploited to generate functionally stable Treg cells.

Inferring the choreography of parental genomes during fertilization from ultralarge-scale whole-transcriptome analysis.

Fertilization precisely choreographs parental genomes by using gamete-derived cellular factors and activating genome regulatory programs. However, the mechanism remains elusive owing to the technical difficulties of preparing large numbers of high-quality preimplantation cells. Here, we collected >14 × 10(4) high-quality mouse metaphase II oocytes and used these to establish detailed transcriptional profiles for four early embryo stages and parthenogenetic development. By combining these profiles with other public resources, we found evidence that gene silencing appeared to be mediated in part by noncoding RNAs and that this was a prerequisite for post-fertilization development. Notably, we identified 817 genes that were differentially expressed in embryos after fertilization compared with parthenotes. The regulation of these genes was distinctly different from those expressed in parthenotes, suggesting functional specialization of particular transcription factors prior to first cell cleavage. We identified five transcription factors that were potentially necessary for developmental progression: Foxd1, Nkx2-5, Sox18, Myod1, and Runx1. Our very large-scale whole-transcriptome profile of early mouse embryos yielded a novel and valuable resource for studies in developmental biology and stem cell research. The database is available at http://dbtmee.hgc.jp.

Correction to: RNA-sequencing reveals positional memory of multipotent mesenchymal stromal cells from oral and maxillofacial tissue transcriptomes.

An amendment to this paper has been published and can be accessed via the original article.

RNA-sequencing reveals positional memory of multipotent mesenchymal stromal cells from oral and maxillofacial tissue transcriptomes.

BACKGROUND: Multipotent mesenchymal stromal cells (MSCs) can be isolated from numerous tissues and are attractive candidates for therapeutic clinical applications due to their immunomodulatory and pro-regenerative capacity. Although the minimum criteria for defining MSCs have been defined, their characteristics are known to vary depending on their tissue of origin. RESULTS: We isolated and characterized human MSCs from three different bones (ilium (I-MSCs), maxilla (Mx-MSCs) and mandible (Md-MSCs)) and proceeded with next generation RNA-sequencing. Furthermore, to investigate the gene expression profiles among other cell types, we obtained RNA-seq data of human embryonic stem cells (ESCs) and several types of MSCs (periodontal ligament-derived MSCs, bone marrow-derived MSCs, and ESCs-derived MSCs) from the Sequence Reads Archive and analyzed the transcriptome profile. We found that MSCs derived from tissues of the maxillofacial region, such as the jaw bone and periodontal ligament, were HOX-negative, while those derived from other tissues were HOX-positive. We also identified that MSX1, LHX8, and BARX1, an essential regulator of craniofacial development, were strongly expressed in maxillofacial tissue-derived MSCs. Although MSCs may be divided into two distinct groups, the cells originated from over the neck or not, on the basis of differences in gene expression profile, the expression patterns of all CD antigen genes were similar among different type of MSCs, except for ESCs. CONCLUSIONS: Our findings suggest that MSCs from different anatomical locations, despite meeting general characterization criteria, have remarkable differences in gene expression and positional memory. Although stromal cells from different anatomical sources are generally categorized as MSCs, their differentiation potential and biological functions vary. We suggested that MSCs may retain an original tissue memory about the developmental process, including gene expression profiles. This could have an important impact when choosing an appropriate cell source for regenerative therapy using MSCs.

Mutational diversity in mutY deficient Helicobacter pylori and its effect on adaptation to the gastric environment.

Helicobacter pylori, a pathogenic bacterium that colonizes in the human stomach, harbors DNA repair genes to counter the gastric environment during chronic infection. In addition, H. pylori adapts to the host environment by undergoing antigenic phase variation caused by genomic mutations. The emergence of mutations in nucleotide sequences is one of the major factors underlying drug resistance and genetic diversity in bacteria. However, it is not clear how DNA repair genes contribute to driving the genetic change of H. pylori during chronic infection. To elucidate the physiological roles of DNA repair genes, we generated DNA repair-deficient strains of H. pylori (ΔuvrA, ΔuvrB, ΔruvA, Δnth, ΔmutY, ΔmutS, and Δung). We performed susceptibility testing to rifampicin in vitro and found that ΔmutY exhibited the highest mutation frequency among the mutants. The number of bacteria colonizing the stomach was significantly lower with ΔmutY strain compared with wild-type strains in a Mongolian gerbil model of H. pylori infection. Furthermore, we performed a genomic sequence analysis of the strains isolated from the Mongolian gerbil stomachs eight weeks after infection. We found that the isolated ΔmutY strains exhibited a high frequency of spontaneous G:C to T:A mutations. However, the frequency of phase variations in the ΔmutY strain was almost similar to the wild-type strain. These results suggest that MutY may play a role in modes of gastric environmental adaptation distinct from phase variation.

DBTSS/DBKERO for integrated analysis of transcriptional regulation.

DBTSS (Database of Transcriptional Start Sites)/DBKERO (Database of Kashiwa Encyclopedia for human genome mutations in Regulatory regions and their Omics contexts) is the database originally initiated with the information of transcriptional start sites and their upstream transcriptional regulatory regions. In recent years, we updated the database to assist users to elucidate biological relevance of the human genome variations or somatic mutations in cancers which may affect the transcriptional regulation. In this update, we facilitate interpretations of disease associated genomic variation, using the Japanese population as a model case. We enriched the genomic variation dataset consisting of the 13,368 individuals collected for various genome-wide association studies and the reference epigenome information in the surrounding regions using a total of 455 epigenome datasets (four tissue types from 67 healthy individuals) collected for the International Human Epigenome Consortium (IHEC). The data directly obtained from the clinical samples was associated with that obtained from various model systems, such as the drug perturbation datasets using cultured cancer cells. Furthermore, we incorporated the results obtained using the newly developed analytical methods, Nanopore/10x Genomics long-read sequencing of the human genome and single cell analyses. The database is made publicly accessible at the URL (http://dbtss.hgc.jp/).

ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets.

ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates.