scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse.

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse.

Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species.

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis.

Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.

SEdb 2.0: a comprehensive super-enhancer database of human and mouse.

Super-enhancers (SEs) are cell-specific DNA cis-regulatory elements that can supervise the transcriptional regulation processes of downstream genes. SEdb 2.0 (http://www.licpathway.net/sedb) aims to provide a comprehensive SE resource and annotate their potential roles in gene transcriptions. Compared with SEdb 1.0, we have made the following improvements: (i) Newly added the mouse SEs and expanded the scale of human SEs. SEdb 2.0 contained 1 167 518 SEs from 1739 human H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) samples and 550 226 SEs from 931 mouse H3K27ac ChIP-seq samples, which was five times that of SEdb 1.0. (ii) Newly added transcription factor binding sites (TFBSs) in SEs identified by TF motifs and TF ChIP-seq data. (iii) Added comprehensive (epi)genetic annotations of SEs, including chromatin accessibility regions, methylation sites, chromatin interaction regions and topologically associating domains (TADs). (iv) Newly embedded and updated search and analysis tools, including 'Search SE by TF-based', 'Differential-Overlapping-SE analysis' and 'SE-based TF-Gene analysis'. (v) Newly provided quality control (QC) metrics for ChIP-seq processing. In summary, SEdb 2.0 is a comprehensive update of SEdb 1.0, which curates more SEs and annotation information than SEdb 1.0. SEdb 2.0 provides a friendly platform for researchers to more comprehensively clarify the important role of SEs in the biological process.

Cross-modal integration of bulk RNA-seq and single-cell RNA sequencing data to reveal T-cell exhaustion in colorectal cancer.

Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer-related deaths. Recent studies have identified T-cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long-standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi-omics deep learning approach that integrates cross-model data to investigate the heterogeneity of T-cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross-modal knowledge distillation model to predict the heterogeneity of T-cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi-omics, providing crucial data for exploring the stages of T-cell exhaustion associated with CRC and relevant therapeutic targets.

Safety and efficacy of sirolimus in recurrent intravenous leiomyomatosis, pulmonary benign metastatic leiomyomatosis, and leiomyomatosis peritonealis disseminata: a pilot study.

BACKGROUND: Intravenous leiomyomatosis (IVL), pulmonary benign metastatic leiomyomatosis (PBML), and leiomyomatosis peritonealis disseminata (LPD) are leiomyomas with special growth patterns and high postoperative recurrence rates. We report the safety and efficacy of a pilot study of sirolimus in the treatment of recurrent IVL, PBML, and recurrent LPD. METHODS: This was a pilot study to evaluate the safety and efficacy of sirolimus in the treatment of leiomyomatosis (ClinicalTrials.gov identifier NCT03500367) conducted in China. Patients received oral sirolimus 2 mg once a day for a maximum of 60 months or until disease progression, intolerable toxicity, withdrawal of consent, or investigator decision to stop. The primary end point of this study was the objective response rate. Secondary end points included safety and tolerability, disease control rate, and progression-free survival. RESULTS: A total of 15 patients with leiomyomatosis were included in the study, including five with recurrent IVL, eight with PBML and two with recurrent LPD. The median follow-up time was 15 months (range 6-54 months), nine patients (60%) had treatment-related adverse events (including all levels), and two patients had treatment-related grade 3 or 4 adverse events. The objective response rate was 20.0% (95% CI, 7.1-45.2%), and the disease control rate was 86.7% (95% CI, 62.1-96.3%). Partial response was achieved in three patients. The median response time in the three partial response patients was 33 months (range 29-36 months), and the sustained remission time of these three patients reached 0, 18, and 25 months, respectively. CONCLUSIONS: Sirolimus was safe and effective in the treatment of recurrent IVL, PBML, and recurrent LPD. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT03500367. Registered on 18 April 2018.

Electrochemically Mediated Surface-Initiated Atom Transfer Radical Polymerization by ppm of CuII/Tris(2-pyridylmethyl)amine.

Atom transfer radical polymerization (ATRP) is one of the most widely used methods for modifying surfaces with functional polymer films and has received considerable attention in recent years. Here, we report an electrochemically mediated surface-initiated ATRP to graft polymer brushes onto solid substrates catalyzed by ppm amounts of CuII/TPMA in water/MeOH solution. We systematically investigated the type and concentrations of copper/ligand and applied potentials correlated to the polymerization kinetics and polymer brush thickness. Gradient polymer brushes and various types of polymer brushes are prepared. Block copolymerization of 2-hydroxyethyl methacrylate (HEMA) and 3-sulfopropyl methacrylate potassium salt (PSPMA) (poly(HEMA-b-SPMA)) with ultralow ppm eATRP indicates the remarkable preservation of chain end functionality and a pronounced "living" characteristic feature of ppm-level eATRP in aqueous solution for surface polymerization.

Analysis of risk factors for post-operative recurrence or progression of intravenous leiomyomatosis.

OBJECTIVE: To analyse the risk factors for post-operative recurrence or progression of intravenous leiomyomatosis and explore the impact of different treatment strategies on patient prognosis. METHODS: Patients with intravenous leiomyomatosis who underwent surgery from January 2011 to December 2020 and who were followed for ≥3 months were included. The primary endpoint was recurrence (for patients with complete resection) or progression (for patients with incomplete resection). Kaplan-Meier survival analysis was used to analyse the factors affecting recurrence. RESULTS: A total of 114 patients were included. The median age was 45 years old (range 24-58). The tumors were confined to the uterus and para-uterine vessels in 48 cases (42.1%), while in 66 cases (57.9%) it involved large vessels (iliac vein or genital vein and/or proximal large veins). The median follow-up time was 24 months (range 3-132). Twenty-nine patients (25.4%) had recurrence or progression. The median recurrence or progression time was 16 months (range 3-60). Incomplete tumor resection (p=0.019), involvement of the iliac vein or genital vein (p=0.042), involvement of the inferior vena cava (p=0.025), and size of the pelvic tumor ≥15 cm (p=0.034) were risk factors for recurrence and progression. For intravenous leiomyomatosis confined to the uterus or para-uterine vessels, no post-operative recurrence after hysterectomy and bilateral oophorectomy occurred in this cohort. Compared with hysterectomy and bilateral oophorectomy, the risk of recurrence after tumorectomy (with the uterus and ovaries retained) was significantly greater (p=0.009), while the risk of recurrence after hysterectomy was not significantly increased (p=0.058). For intravenous leiomyomatosis involving the iliac vein/genital vein and the proximal veins, post-operative aromatase inhibitor treatment (p=0.89) and two-stage surgery (p=0.86) were not related to recurrence in patients with complete tumor resection. CONCLUSION: Incomplete tumor resection, extent of tumor lesions and size of the pelvic tumor were risk factors for post-operative recurrence and progression of intravenous leiomyomatosis.

TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.

Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.