NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

GPro: generative AI-empowered toolkit for promoter design.

MOTIVATION: Promoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks. RESULTS: Here, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines. AVAILABILITY AND IMPLEMENTATION: GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network.

SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translation process, which supports the development of mRNA-based medicine or vaccines. Deep neural networks (DNN) can learn complex cis-regulatory codes from RNA sequences. However, extracting these cis-regulatory codes efficiently from DNN remains a significant challenge. Here, we propose a method based on our toolkit NeuronMotif and motif mutagenesis, which not only enables the discovery of diverse and high-quality motifs but also efficiently reveals motif interactions. By interpreting deep-learning models, we have discovered several crucial motifs that impact mRNA translation efficiency and stability, as well as some unknown motifs or motif syntax, offering novel insights for biologists. Furthermore, we note that it is challenging to enrich motif syntax in datasets composed of randomly generated sequences, and they may not contain sufficient biological signals. AVAILABILITY AND IMPLEMENTATION: The source code and data used to produce the results and analyses presented in this manuscript are available from GitHub (https://github.com/WangLabTHU/combmotif).

The loss of heterochromatin is associated with multiscale three-dimensional genome reorganization and aberrant transcription during cellular senescence.

Heterochromatin remodeling is critical for various cell processes. In particular, the "loss of heterochromatin" phenotype in cellular senescence is associated with the process of aging and age-related disorders. Although biological processes of senescent cells, including senescence-associated heterochromatin foci (SAHF) formation, chromosome compaction, and redistribution of key proteins, have been closely associated with high-order chromatin structure, the relationship between the high-order chromatin reorganization and the loss of heterochromatin phenotype during senescence has not been fully understood. By using senescent and deep senescent fibroblasts induced by DNA damage harboring the "loss of heterochromatin" phenotype, we observed progressive 3D reorganization of heterochromatin during senescence. Facultative and constitutive heterochromatin marked by H3K27me3 and H3K9me3, respectively, show different alterations. Facultative heterochromatin tends to switch from the repressive B-compartment to the active A-compartment, whereas constitutive heterochromatin shows no significant changes at the compartment level but enhanced interactions between themselves. Both types of heterochromatin show increased chromatin accessibility and gene expression leakage during senescence. Furthermore, increased chromatin accessibility in potential CTCF binding sites accompanies the establishment of novel loops in constitutive heterochromatin. Finally, we also observed aberrant expression of repetitive elements, including LTR (long terminal repeat) and satellite classes. Overall, facultative and constitutive heterochromatin show both similar and distinct multiscale alterations in the 3D map, chromatin accessibility, and gene expression leakage. This study provides an epigenomic map of heterochromatin reorganization during senescence.

ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data.

Quantifying cell proportions, especially for rare cell types in some scenarios, is of great value in tracking signals associated with certain phenotypes or diseases. Although some methods have been proposed to infer cell proportions from multicomponent bulk data, they are substantially less effective for estimating the proportions of rare cell types which are highly sensitive to feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from gene expression or DNA methylation data. ARIC employs a novel two-step marker selection strategy, including collinear feature elimination based on the component-wise condition number and adaptive removal of outlier markers. This strategy can systematically obtain effective markers for weighted $\upsilon$-support vector regression to ensure a robust and precise rare proportion prediction. We showed that ARIC can accurately estimate fractions in both DNA methylation and gene expression data from different experiments. We further applied ARIC to the survival prediction of ovarian cancer and the condition monitoring of chronic kidney disease, and the results demonstrate the high accuracy and robustness as well as clinical potentials of ARIC. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.

Evaluating methylation of human ribosomal DNA at each CpG site reveals its utility for cancer detection using cell-free DNA.

Ribosomal deoxyribonucleic acid (DNA) (rDNA) repeats are tandemly located on five acrocentric chromosomes with up to hundreds of copies in the human genome. DNA methylation, the most well-studied epigenetic mechanism, has been characterized for most genomic regions across various biological contexts. However, rDNA methylation patterns remain largely unexplored due to the repetitive structure. In this study, we designed a specific mapping strategy to investigate rDNA methylation patterns at each CpG site across various physiological and pathological processes. We found that CpG sites on rDNA could be categorized into two types. One is within or adjacent to transcribed regions; the other is distal to transcribed regions. The former shows highly variable methylation levels across samples, while the latter shows stable high methylation levels in normal tissues but severe hypomethylation in tumors. We further showed that rDNA methylation profiles in plasma cell-free DNA could be used as a biomarker for cancer detection. It shows good performances on public datasets, including colorectal cancer [area under the curve (AUC) = 0.85], lung cancer (AUC = 0.84), hepatocellular carcinoma (AUC = 0.91) and in-house generated hepatocellular carcinoma dataset (AUC = 0.96) even at low genome coverage (<1×). Taken together, these findings broaden our understanding of rDNA regulation and suggest the potential utility of rDNA methylation features as disease biomarkers.

DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.

Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole-genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as 'switching region' to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultralow sequencing depths. Further analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites' methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites.

MOTIVATION: Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns. RESULTS: We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns ('identical', 'uniform' and 'disordered') compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest. AVAILABILITY AND IMPLEMENTATION: MeConcord is available at https://github.com/WangLabTHU/MeConcord. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Allelic reprogramming of 3D chromatin architecture during early mammalian development.

In mammals, chromatin organization undergoes drastic reprogramming after fertilization. However, the three-dimensional structure of chromatin and its reprogramming in preimplantation development remain poorly understood. Here, by developing a low-input Hi-C (genome-wide chromosome conformation capture) approach, we examined the reprogramming of chromatin organization during early development in mice. We found that oocytes in metaphase II show homogeneous chromatin folding that lacks detectable topologically associating domains (TADs) and chromatin compartments. Strikingly, chromatin shows greatly diminished higher-order structure after fertilization. Unexpectedly, the subsequent establishment of chromatin organization is a prolonged process that extends through preimplantation development, as characterized by slow consolidation of TADs and segregation of chromatin compartments. The two sets of parental chromosomes are spatially separated from each other and display distinct compartmentalization in zygotes. Such allele separation and allelic compartmentalization can be found as late as the 8-cell stage. Finally, we show that chromatin compaction in preimplantation embryos can partially proceed in the absence of zygotic transcription and is a multi-level hierarchical process. Taken together, our data suggest that chromatin may exist in a markedly relaxed state after fertilization, followed by progressive maturation of higher-order chromatin architecture during early development.

OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions.

Chromatin accessibility, as a powerful marker of active DNA regulatory elements, provides valuable information for understanding regulatory mechanisms. The revolution in high-throughput methods has accumulated massive chromatin accessibility profiles in public repositories. Nevertheless, utilization of these data is hampered by cumbersome collection, time-consuming processing, and manual chromatin accessibility (openness) annotation of genomic regions. To fill this gap, we developed OpenAnnotate (http://health.tsinghua.edu.cn/openannotate/) as the first web server for efficiently annotating openness of massive genomic regions across various biosample types, tissues, and biological systems. In addition to the annotation resource from 2729 comprehensive profiles of 614 biosample types of human and mouse, OpenAnnotate provides user-friendly functionalities, ultra-efficient calculation, real-time browsing, intuitive visualization, and elaborate application notebooks. We show its unique advantages compared to existing databases and toolkits by effectively revealing cell type-specificity, identifying regulatory elements and 3D chromatin contacts, deciphering gene functional relationships, inferring functions of transcription factors, and unprecedentedly promoting single-cell data analyses. We anticipate OpenAnnotate will provide a promising avenue for researchers to construct a more holistic perspective to understand regulatory mechanisms.

DeSP: a systematic DNA storage error simulation pipeline.

BACKGROUND: Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. RESULTS: Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. CONCLUSIONS: DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.

CellTracker: an automated toolbox for single-cell segmentation and tracking of time-lapse microscopy images.

SUMMARY: Recent advances of long-term time-lapse microscopy have made it easy for researchers to quantify cell behavior and molecular dynamics at single-cell resolution. However, the lack of easy-to-use software tools optimized for customized research is still a major challenge for quantitatively understanding biological processes through microscopy images. Here, we present CellTracker, a highly integrated graphical user interface software, for automated cell segmentation and tracking of time-lapse microscopy images. It covers essential steps in image analysis including project management, image pre-processing, cell segmentation, cell tracking, manually correction and statistical analysis such as the quantification of cell size and fluorescence intensity, etc. Furthermore, CellTracker provides an annotation tool and supports model training from scratch, thus proposing a flexible and scalable solution for customized dataset analysis. AVAILABILITY AND IMPLEMENTATION: CellTracker is an open-source software under the GPL-3.0 license. It is implemented in Python and provides an easy-to-use graphical user interface. The source code, instruction manual and demos can be found at https://github.com/WangLabTHU/CellTracker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

cfDNApipe: a comprehensive quality control and analysis pipeline for cell-free DNA high-throughput sequencing data.

MOTIVATION: Cell-free DNA (cfDNA) is gaining substantial attention from both biological and clinical fields as a promising marker for liquid biopsy. Many aspects of disease-related features have been discovered from cfDNA high-throughput sequencing (HTS) data. However, there is still a lack of integrative and systematic tools for cfDNA HTS data analysis and quality control (QC). RESULTS: Here, we propose cfDNApipe, an easy-to-use and systematic python package for cfDNA whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data analysis. It covers the entire analysis pipeline for the cfDNA data, including raw sequencing data processing, QC and sophisticated statistical analysis such as detecting copy number variations (CNVs), differentially methylated regions and DNA fragment size alterations. cfDNApipe provides one-command-line-execution pipelines and flexible application programming interfaces for customized analysis. AVAILABILITY AND IMPLEMENTATION: https://xwanglabthu.github.io/cfDNApipe/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aging-induced IL27Ra signaling impairs hematopoietic stem cells.

Hematopoietic stem cell (HSC) aging correlates with an increasing risk of myeloproliferative disease and immunosenescence. In this study, we show that aging-related inflammation promotes HSC aging through tumor necrosis factor-α (TNF-α)→ERK→ETS1→interleukin27Ra (IL27Ra) pathway. TNF-α, a well-known biomarker of inflammation, increases during aging and induces the expression of IL27Ra on HSCs via ERK-ETS1 signaling. Deletion of IL27Ra rescues the functional decline and myeloid bias of HSCs and also reverses the inhibitory effect of TNF-α on HSCs. Aged IL27Ra-/- mice had a reduced proportion of myeloid-biased HSCs and did not display the biased myeloid differentiation that occurs in aged wild-type mice. IL27Ra+ HSCs exhibit impaired reconstitution capacity and myeloid-bias compared with IL27Ra- HSCs and serve as a myeloid-recovery pool upon inflammatory insult. Inflammation-related genes were enriched in IL27Ra+ HSCs and this enrichment increases with aging. Our study demonstrates that age-induced IL27Ra signaling impairs HSCs and raises the possibility that interfering with IL27Ra signaling can counter the physiologically deleterious effect of aging on hematopoietic capacity.

YTHDF3 modulates hematopoietic stem cells by recognizing RNA m6A modification on Ccnd1.

Hematopoietic stem cells (HSC) give rise to the cells of the blood system over the whole lifespan. N6-methyladenosine (m6A), the most prevalent RNA modification, modulates gene expression via the processes of "writing" and "reading". Recent studies showed that m6A "writer" genes (Mettl3 and Mettl14) play an essential role in HSC. However, which reader deciphers the m6A modification to modulate HSC remains unknown. In this study, we observed that dysfunction of Ythdf3 and Ccnd1 severely impaired the reconstitution capacity of HSC, which phenocopies Mettl3-deficient HSC. Dysfunction of Ythdf3 and Mettl3 results in a translational defect of Ccnd1. Ythdf3 and Mettl3 regulate HSC by transmitting m6A RNA methylation on the 5' untranslated region of Ccnd1. Enforced Ccnd1 expression completely rescued the defect of Ythdf3-/- HSC and partially rescued Mettl3-compromised HSC. Taken together, this study identified, for the first time, that Ccnd1 is the target of METTL3 and YTHDF3 to transmit the m6A RNA methylation signal and thereby regulate the reconstitution capacity of HSC.

Synthetic promoter design in Escherichia coli based on a deep generative network.

Promoter design remains one of the most important considerations in metabolic engineering and synthetic biology applications. Theoretically, there are 450 possible sequences for a 50-nt promoter, of which naturally occurring promoters make up only a small subset. To explore the vast number of potential sequences, we report a novel AI-based framework for de novo promoter design in Escherichia coli. The model, which was guided by sequence features learned from natural promoters, could capture interactions between nucleotides at different positions and design novel synthetic promoters in silico. We combined a deep generative model that guides the search for artificial sequences with a predictive model to preselect the most promising promoters. The AI-designed promoters were optimized based on the promoter activity in E. coli and the predictive model. After two rounds of optimization, up to 70.8% of the AI-designed promoters were experimentally demonstrated to be functional, and few of them shared significant sequence similarity with the E. coli genome. Our work provided an end-to-end approach to the de novo design of novel promoter elements, indicating the potential to apply deep learning methods to de novo genetic element design.

Genome-wide cell-free DNA methylation analyses improve accuracy of non-invasive diagnostic imaging for early-stage breast cancer.

Early detection is crucial to improve breast cancer (BC) patients' outcomes and survival. Mammogram and ultrasound adopting the Breast Imaging Reporting and Data System (BI-RADS) categorization are widely used for BC early detection, while suffering high false-positive rate leading to unnecessary biopsy, especially in BI-RADS category-4 patients. Plasma cell-free DNA (cfDNA) carrying on DNA methylation information has emerged as a non-invasive approach for cancer detection. Here we present a prospective multi-center study with whole-genome bisulfite sequencing data to address the clinical utility of cfDNA methylation markers from 203 female patients with breast lesions suspected for malignancy. The cfDNA is enriched with hypo-methylated genomic regions. A practical computational framework was devised to excavate optimal cfDNA-rich DNA methylation markers, which significantly improved the early diagnosis of BI-RADS category-4 patients (AUC from 0.78-0.79 to 0.93-0.94). As a proof-of-concept study, we performed the first blood-based whole-genome DNA methylation study for detecting early-stage breast cancer from benign tumors at single-base resolution, which suggests that combining the liquid biopsy with the traditional diagnostic imaging can improve the current clinical practice, by reducing the false-positive rate and avoiding unnecessary harms.

Dynamic transcriptome profiling in DNA damage-induced cellular senescence and transient cell-cycle arrest.

Cellular senescence is an irreversible cell cycle arrest process associated with aging and senescence-related diseases. DNA damage is an extensive feature of cellular senescence and aging. Different levels of DNA damage could lead to cellular senescence or transient cell-cycle arrest, but the genetic regulatory mechanisms determining cell fate are still not clear. In this work, high-resolution time course analysis of gene expression in DNA damage-induced cellular senescence and transient cell-cycle arrest was used to explore the transcriptomic differences between different cell fates after DNA damage response and to investigate the key regulatory factors affecting senescent cell fates. Pathways such as the cell cycle, DNA repair and cholesterol metabolism showed characteristic differential response. A number of key transcription factors were predicted to regulating cell cycle and DNA repair. Our study provides genome-wide insights into the molecular-level mechanisms of senescent cell fate decisions after DNA damage response.

The atypical E2F family member E2F7 couples the p53 and RB pathways during cellular senescence.

Oncogene-induced senescence is an anti-proliferative stress response program that acts as a fail-safe mechanism to limit oncogenic transformation and is regulated by the retinoblastoma protein (RB) and p53 tumor suppressor pathways. We identify the atypical E2F family member E2F7 as the only E2F transcription factor potently up-regulated during oncogene-induced senescence, a setting where it acts in response to p53 as a direct transcriptional target. Once induced, E2F7 binds and represses a series of E2F target genes and cooperates with RB to efficiently promote cell cycle arrest and limit oncogenic transformation. Disruption of RB triggers a further increase in E2F7, which induces a second cell cycle checkpoint that prevents unconstrained cell division despite aberrant DNA replication. Mechanistically, E2F7 compensates for the loss of RB in repressing mitotic E2F target genes. Together, our results identify a causal role for E2F7 in cellular senescence and uncover a novel link between the RB and p53 pathways.

ATRX-mediated chromatin association of histone variant macroH2A1 regulates α-globin expression.

The histone variant macroH2A generally associates with transcriptionally inert chromatin; however, the factors that regulate its chromatin incorporation remain elusive. Here, we identify the SWI/SNF helicase ATRX (α-thalassemia/MR, X-linked) as a novel macroH2A-interacting protein. Unlike its role in assisting H3.3 chromatin deposition, ATRX acts as a negative regulator of macroH2A's chromatin association. In human erythroleukemic cells deficient for ATRX, macroH2A accumulates at the HBA gene cluster on the subtelomere of chromosome 16, coinciding with the loss of α-globin expression. Collectively, our results implicate deregulation of macroH2A's distribution as a contributing factor to the α-thalassemia phenotype of ATRX syndrome.