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The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design.

Abstract Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for molecular competition networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their noncompetitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we highlighted the nonnegligible role of weak interactions and limited amounts of resources and examined how biological constraints influence the computational capacity of MCNs. The study suggested the potential of MCNs as efficient computational structures and provided new insights into cellular information processing.

Abstract 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.

Summary: The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We developed a unified informatics framework for seamless cell-centric data assembly and built the human Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled human cells from 116 published datasets, covering 38 organs and 11 systems. We invented three new methods of atlas applications based on the cell-centric assembly: “in data” cell sorting for targeted data retrieval with customizable logic expressions, “quantitative portraiture” for multi-view representations of biological entities, and customizable reference creation for generating references for automatic annotations. Case studies on agile construction of user-defined sub-atlases and “in data” investigation of CAR-T off-targets in multiple organs showed the great potential enabled by the cell-centric ensemble atlas.

Abstract 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.

Summary: Characterizing the noise modulation pattern of microRNA is valuable for both microRNA function analysis and synthetic biology applications. Here we propose a coarse-grained model to simulate how the properties of microRNAs, competing RNAs, and microRNA response elements affect gene expression noise. We also detail an experimental protocol based on synthetic gene circuits and flow cytometry to quantify the noise. This framework is easy-to-use for the study and application of both microRNA and gene expression noise.For complete details on the use and execution of this protocol, please refer to Wei et al. (2021).

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.

Abstract Background Chemoresistance is one of the main causes of poor prognosis in pancreatic cancer patients. Understanding the mechanisms implicated in chemoresistance of pancreatic cancer is critical to improving patient outcomes. Recent evidences indicate that the long noncoding RNAs (lncRNAs) are involving in chemoresistance of pancreatic cancer. However, the mechanisms of lncRNAs contribute to resistance in pancreatic cancer and remain largely unknown. The objective of this study is to construct a chemoresistance-related lncRNA-associated competing endogenous RNA (ceRNA) network of pancreatic cancer and identify the key lncRNAs in regulating chemoresistance of the network. Methods Firstly, lncRNA expression profiling of gemcitabine-resistant pancreatic cancer cells was performed to identify lncRNAs related to chemoresistance by microarray analysis. Secondly, with insights into the mechanism of ceRNA, we used a bioinformatics approach to construct a chemoresistance-related lncRNAs-associated ceRNA network. We then identified the topological key lncRNAs in the ceRNA network and demonstrated its function or mechanism in chemoresistance of pancreatic cancer using molecular biological methods. Further studies evaluated its expression to assess its potential association with survival in patients with pancreatic cancer. Results Firstly, we demonstrated that lncRNAs were dysregulated in gemcitabine-resistant pancreatic cancer cells. We then constructed a chemoresistance-related lncRNA-associated ceRNA network and proposed that lncRNA Homo sapiens glutathione S-transferase mu 3, transcript variant 2 and noncoding RNA (GSTM3TV2; NCBI Reference Sequence: NR_024537.1) might act as a key ceRNA to enhance chemoresistance by upregulating L-type amino acid transporter 2 (LAT2) and oxidized low-density lipoprotein receptor 1(OLR1) in pancreatic cancer. Further studies demonstrated that GSTM3TV2, overexpressed in gemcitabine-resistant cells, enhanced the gemcitabine resistance of pancreatic cancer cells in vitro and in vivo. Mechanistically, we identified that GSTM3TV2 upregulated LAT2 and OLR1 by competitively sponging let-7 to promote gemcitabine resistance. In addition, we revealed that the expression levels of GSTM3TV2 were significantly increased in pancreatic cancer tissues and were associated with poor prognosis. Conclusion Our results suggest that GSTM3TV2 is a crucial oncogenic regulator involved in chemoresistance and could be a new therapeutic target or prognostic marker in pancreatic cancer.

Abstract Background Previous studies showed that the majority of developmental genes are devoid of DNA methylation at promoters even when they are repressed. Such hypomethylated regions at developmental genes are unusually large and extend well beyond proximal promoters, forming DNA methylation valleys (DMVs) or DNA methylation canyons. However, it remains elusive how most developmental genes can evade DNA methylation regardless of their transcriptional states. Results We show that DMVs are hypomethylated in development and are highly conserved across vertebrates. Importantly, DMVs are hotspots of regulatory regions for key developmental genes and show low levels of deamination mutation rates. By analyzing a panel of DNA methylomes from mouse tissues, we identify a subset of DMVs that are dynamically methylated. These DMVs are strongly enriched for Polycomb-deposited H3K27me3 when the associated genes are silenced, and surprisingly show elevated DNA methylation upon gene activation. 4C-seq analyses indicates that Polycomb-bound DMVs form insulated and self-interacting chromatin domains. Further investigations show that DNA hypomethylation is better correlated with the binding of Polycomb than with H3K27me3. In support of a role of Polycomb in DMV hypomethylation, we observe aberrant methylation in DMVs in mouse embryonic stem cells deficient in the EED protein. Finally, we show that Polycomb regulates hypomethylation of DMVs likely through ten-eleven translocation (TET) proteins. Conclusions We show that Polycomb promotes the hypomethylation of DMVs near key developmental genes. These data reveal a delicate interplay between histone modifiers and DNA methylation, which contributes to their division at distinct gene targets, allowing lineage-specifying genes to largely maintain DNA methylation-free at regulatory elements.

The CRISPR-Cas9 system is emerging as a powerful technology for gene editing (modifying the genome sequence) and gene regulation (without modifying the genome sequence). Designing sgRNAs for specific genes or regions of interest is indispensable to CRISPR-based applications. CRISPR-ERA (http://crispr-era.stanford.edu/) is one of the state-of-the-art designer webserver tools, which has been developed both for gene editing and gene regulation sgRNA design. This protocol discusses how to design sgRNA sequences and genome-wide sgRNA library using CRISPR-ERA.

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. [ABSTRACT FROM AUTHOR]

Cellular senescence is a tumor-suppressive program that involves chromatin reorganization and specific changes in gene expression that trigger an irreversible cell-cycle arrest. Here we combine quantitative mass spectrometry, ChIP deep-sequencing, and functional studies to determine the role of histone modifications on chromatin structure and gene-expression alterations associated with senescence in primary human cells. We uncover distinct senescence-associated changes in histone-modification patterns consistent with a repressive chromatin environment and link the establishment of one of these patterns-loss of H3K4 methylation- to the retinoblastoma tumor suppressor and the H3K4 demethylases Jaridia and Jaridib. Our results show that Jaridia/ b-mediated H3K4 demethylation contributes to silencing of retinoblastoma target genes in senescent cells, suggesting a mechanism by which retinoblastoma triggers gene silencing. Therefore, we link the Jaridia and Jaridib demethylases to a tumor-suppressor network controlling cellular senescence. [ABSTRACT FROM AUTHOR]

ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChI-APET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate. [ABSTRACT FROM AUTHOR]

Summary: The CRISPR/Cas9 system was recently developed as a powerful and flexible technology for targeted genome engineering, including genome editing (altering the genetic sequence) and gene regulation (without altering the genetic sequence). These applications require the design of single guide RNAs (sgRNAs) that are efficient and specific. However, this remains challenging, as it requires the consideration of many criteria. Several sgRNA design tools have been developed for gene editing, but currently there is no tool for the design of sgRNAs for gene regulation. With accumulating experimental data on the use of CRISPR/Cas9 for gene editing and regulation, we implement a comprehensive computational tool based on a set of sgRNA design rules summarized from these published reports. We report a genome-wide sgRNA design tool and provide an online website for predicting sgRNAs that are efficient and specific. We name the tool CRISPR-ERA, for clustered regularly interspaced short palindromic repeat-mediated editing, repression, and activation (ERA). Availability and implementation: http://CRISPR-ERA.stanford.edu. [ABSTRACT FROM AUTHOR]

Background: Alternative splicing expands transcriptome diversity and plays an important role in regulation of gene expression. Previous studies focus on the regulation of a single cassette exon, but recent experiments indicate that multiple cassette exons within a gene may interact with each other. This interaction can increase the potential to generate various transcripts and adds an extra layer of complexity to gene regulation. Several cases of exon interaction have been discovered. However, the extent to which the cassette exons coordinate with each other remains unknown. Results: Based on EST data, we employed a metric of correlation coefficients to describe the interaction between two adjacent cassette exons and then categorized these exon pairs into three different groups by their interaction (correlation) patterns. Sequence analysis demonstrates that strongly-correlated groups are more conserved and contain a higher proportion of pairs with reading frame preservation in a combinatorial manner. Multiple genome comparison further indicates that different groups of correlated pairs have different evolutionary courses: (1) The vast majority of positively-correlated pairs are old, (2) most of the weakly-correlated pairs are relatively young, and (3) negatively-correlated pairs are a mixture of old and young events. Conclusion: We performed a large-scale analysis of interactions between adjacent cassette exons. Compared with weakly-correlated pairs, the strongly-correlated pairs, including both the positively and negatively correlated ones, show more evidence that they are under delicate splicing control and tend to be functionally important. Additionally, the positively-correlated pairs bear strong resemblance to constitutive exons, which suggests that they may evolve from ancient constitutive exons, while negatively and weakly correlated pairs are more likely to contain newly emerging exons. [ABSTRACT FROM AUTHOR]

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Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. [ABSTRACT FROM PUBLISHER]

Motivation: MicroRNAs are a class of endogenous small RNAs that play regulatory roles. Intergenic miRNAs are believed to be transcribed independently, but the transcriptional control of these crucial regulators is still poorly understood. Results: In this work, phylogenetic footprinting is used to identify conserved cis-regulatory elements (CCEs) surrounding intergenic miRNAs in Drosophila. With a two-step strategy that takes advantage of both alignment-based and motif-based methods, we identified CCEs that are conserved across the 12 fly species. When compared with TRANSFAC database, these CCEs are significantly enriched in known transcription factor binding sites (TFBSs). Moreover, several TFs that play essential roles in Drosophila development (e.g. Adf-1, Abd-B, Sd, Prd, Ubx, Zen and En) are found to be preferentially regulating the miRNA genes. Further analysis revealed many over-represented cis-regulatory modules (CRMs) composed of multiple known TFBSs, motif pairs with significant distance constraints and a number of novel motifs, many of which preferentially occur near the transcription start site of protein-coding genes. Additionally, a number of putative miRNA-TF regulatory feedback loops were also detected. Availability: Supplementary Material and the Perl scripts performing two-step phylogenetic footprinting are available at http://bioinfo.au.tsinghua.edu.cn/member/xwwang/mircisreg Contact: daulyd@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Cellular senescence acts as a potent barrier to tumorigenesis and contributes to the anti-tumor activity of certain chemotherapeutic agents. Senescent cells undergo a stable cell cycle arrest controlled by RB and p53 and, in addition, display a senescence-associated secretory phenotype (SASP) involving the production of factors that reinforce the senescence arrest, alter the microenvironment, and trigger immune surveillance of the senescent cells. Through a proteomics analysis of senescent chromatin, we identified the nuclear factor-κB (NF-κB) subunit p65 as a major transcription factor that accumulates on chromatin of senescent cells. We found that NF-κB acts as a master regulator of the SASP, influencing the expression of more genes than RB and p53 combined. In cultured fibroblasts, NF-κB suppression causes escape from immune recognition by natural killer (NK) cells and cooperates with p53 inactivation to bypass senescent cells . In a mouse lymphoma model , NF -κB inhibition bypasses treatment-induced senescence, producing drug resistance, early relapse, and reduced survival. Our results demonstrate that NF-κB controls both cell-autonomous and non-cell-autonomous aspects of the senescence program and identify a tumor-suppressive function of NF-κB that contributes to the outcome of cancer therapy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]