Identification and Characterization of Metastasis-Initiating Cells in ESCC in a Multi-Timepoint Pulmonary Metastasis Mouse Model.

Metastasis is the biggest obstacle to esophageal squamous cell carcinoma (ESCC) treatment. Single-cell RNA sequencing analyses are applied to investigate lung metastatic ESCC cells isolated from pulmonary metastasis mouse model at multiple timepoints to characterize early metastatic microenvironment. A small population of parental KYSE30 cell line (Cluster S) resembling metastasis-initiating cells (MICs) is identified because they survive and colonize at lung metastatic sites. Differential expression profile comparisons between Cluster S and other subpopulations identified a panel of 7 metastasis-initiating signature genes (MIS), including CD44 and TACSTD2, to represent MICs in ESCC. Functional studies demonstrated MICs (CD44high) exhibited significantly enhanced cell survival (resistances to oxidative stress and apoptosis), migration, invasion, stemness, and in vivo lung metastasis capabilities, while bioinformatics analyses revealed enhanced organ development, stress responses, and neuron development, potentially remodel early metastasis microenvironment. Meanwhile, early metastasizing cells demonstrate quasi-epithelial-mesenchymal phenotype to support both invasion and anchorage. Multiplex immunohistochemistry (mIHC) staining of 4 MISs (CD44, S100A14, RHOD, and TACSTD2) in ESCC clinical samples demonstrated differential MIS expression scores (dMISs) predict lymph node metastasis, overall survival, and risk of carcinothrombosis.

Estimation of cell lineages in tumors from spatial transcriptomics data.

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

Discover immunotherapy biomarkers from single-cell cytometry data.

Currently, identifying novel biomarkers remains a crucial need for cancer immunotherapy. By leveraging single-cell cytometry data, Greene et al. developed an interpretable machine learning method, FAUST, to discover cell populations associated with clinical outcomes.

Systematic investigation of cytokine signaling activity at the tissue and single-cell levels.

Cytokines are critical for intercellular communication in human health and disease, but the investigation of cytokine signaling activity has remained challenging due to the short half-lives of cytokines and the complexity/redundancy of cytokine functions. To address these challenges, we developed the Cytokine Signaling Analyzer (CytoSig; https://cytosig.ccr.cancer.gov/ ), providing both a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor, and identified candidate therapeutic targets in human inflammatory diseases, such as CXCL8 for severe coronavirus disease 2019.

Sertoli cell­specific coxsackievirus and adenovirus receptor regulates cell adhesion and gene transcription via ß-catenin inactivation and Cdc42 activation.

Blood-testis barrier (BTB) and apical ectoplasmic specialization (ES) serve as structural supports for germ cell (GC) development. We demonstrated that the Sertoli cell (SC)-specific coxsackievirus and adenovirus receptor (CXADR) knockout (SC-CXADR-/-), but not the GC-specific knockout, impaired spermatogenesis. An increase in GC apoptosis and premature loss of elongated spermatids were observed in SC-CXADR-/- testes. The BTB function was compromised in SC-CXADR-/- testes with dysregulation of oocludin and zonula occludens-1 expression at the basal compartment of the seminiferous epithelium. An integrated omics analyses confirmed that altered gene ontology terms identified in SC-CXADR-/- testes are highly associated with spermatid development and differentiation, spermatogenesis, and sperm motility and are considered as unique testicular function terms. Leptin, Nasp, Tektin3, Larp 7, and acrosin, which are highly associated with male fertility, were found to be down-regulated in SC-CXADR-/- testes. Based on the data from the omics analyses, we employed the CXADR-deficient SC model to further investigate the molecular mechanisms involved. We unraveled that SC-CXADRs are required for ß-catenin inactivation and cell division cycle protein 42 (Cdc42) activation, resulting in maintaining the integrity and function of the BTB and apical ES as well as inhibiting gene transcription, such as the Myc gene, in the testes. We demonstrated for the first time that CXADR is an important mediator governing ß-catenin and Cdc42 signaling that is essential for spermatogenesis. The molecular mechanisms identified herein may provide new insights to unravel the novel functions and signaling cascades of CXADR in other key CXADR-expressing tissues.-Huang, K., Ru, B., Zhang, Y., Chan, W.-L., Chow, S.-C., Zhang, J., Lo, C., Lui, W.-Y. Sertoli cell-specific coxsackievirus and adenovirus receptor knockout regulates cell adhesion and gene transcription via ß-catenin inactivation and Cdc42 activation.

TISIDB: an integrated repository portal for tumor-immune system interactions.

SUMMARY: The interaction between tumor and immune system plays a crucial role in both cancer development and treatment response. To facilitate comprehensive investigation of tumor-immune interactions, we have designed a user-friendly web portal TISIDB, which integrated multiple types of data resources in oncoimmunology. First, we manually curated 4176 records from 2530 publications, which reported 988 genes related to anti-tumor immunity. Second, genes associated with the resistance or sensitivity of tumor cells to T cell-mediated killing and immunotherapy were identified by analyzing high-throughput screening and genomic profiling data. Third, associations between any gene and immune features, such as lymphocytes, immunomodulators and chemokines, were pre-calculated for 30 TCGA cancer types. In TISIDB, biologists can cross-check a gene of interest about its role in tumor-immune interactions through literature mining and high-throughput data analysis, and generate testable hypotheses and high quality figures for publication. AVAILABILITY AND IMPLEMENTATION: http://cis.hku.hk/TISIDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MR4Cancer: a web server prioritizing master regulators for cancer.

MOTIVATION: During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community. RESULTS: First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention. AVAILABILITY AND IMPLEMENTATION: http://cis.hku.hk/MR4Cancer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A framework for identifying dysregulated chromatin regulators as master regulators in human cancer.

MOTIVATION: Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated. RESULTS: Here, we designed an integrated framework based on multi-omics data to identify candidate master regulatory CRs affected by genomic alterations across eight cancer types in The Cancer Genome Atlas. Most of them showed consistent activated or repressed (i.e. oncogenic or tumor-suppressive) roles in cancer initiation and progression. In order to further explore the insight mechanism of the dysregulated CRs, we developed an R package ModReg based on differential connectivity to identify CRs as modulators of transcription factors (TFs) involved in tumorigenesis. Our analysis revealed that the connectivity between TFs and their target genes (TGs) tended to be disrupted in the patients who had a high expression of oncogenic CRs or low-expression of tumor-suppressive CRs. As a proof-of-principle study, 14 (82.4%) of the top-ranked 17 driver CRs in liver cancer were able to be validated by literature mining or experiments including shRNA knockdown and dCas9-based epigenetic editing. Moreover, we confirmed that CR SIRT7 physically interacted with TF NFE2L2, and positively modulated the transcriptional program of NFE2L2 by affecting ∼64% of its TGs. AVAILABILITY AND IMPLEMENTATION: ModReg is freely accessible at http://cis.hku.hk/software/ModReg.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recent advances in research on aspartate ß-hydroxylase (ASPH) in pancreatic cancer: A brief update.

Pancreatic cancer (PC) is a highly aggressive tumor, often difficult to diagnose and treat. Aspartate ß-hydroxylase (ASPH) is a type II transmembrane protein and the member of α-ketoglutarate-dependent dioxygenase family, found to be overexpressed in different cancer types, including PC. ASPH appears to be involved in the regulation of proliferation, invasion and metastasis of PC cells through multiple signaling pathways, suggesting its role as a tumor biomarker and therapeutic target. In this review, we briefly summarize the possible mechanisms of action of ASPH in PC and recent progress in the therapeutic approaches targeting ASPH.

miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer.

Summary: MicroRNAs play critical roles in oncogenesis by targeting a few key regulators or a large cohort of genes impinging on downstream signaling pathways. Conversely, miRNA activity is also titrated by competitive endogenous RNA such as lncRNA with sponge effect. Web-based server, miRNACancerMap, aims to unravel lncRNA-miRNA-mRNA tripartite complexity to predict the function and clinical relevance of miRNA with network perspective. In conjunction with large-scale data and information integration, miRNACancerMap implements various algorithms and pipelines to construct dynamic miRNA-centered network with rigorous Systems Biology approaches and the state-of-the-art visualization tool. The capability of the server to generate testable hypotheses was exemplified with cases to identify hub miRNAs regulating most of the differentially-expressed genes involved in cancer stage transition, miRNA-TF pairs shared by pan-cancers and lncRNA sponges validated by multiple datasets. LncRNAs sharing the same miRNAs binding sites as mRNAs can sequester miRNAs and indirectly regulate the activity of the related mRNAs. We have re-annotated traditional microarray chips, and included these datasets in the server to enable validation of the predicted lncRNA-miRNA-mRNA regulations derived from TCGA RNA-seq data. Of note, our server enables identifying miRNAs associated with cancer signaling pathways, and related lncRNA sponges from pan-cancers with only a few mouse clicks. Availability and implementation: http://cis.hku.hk/miRNACancerMAP. Supplementary information: Supplementary data are available at Bioinformatics online.

MICMIC: identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis.

Aberrant promoter methylation is a common mechanism for tumor suppressor inactivation in cancer. We develop a set of tools to identify genome-wide DNA methylation in distal regions with causal effect on tumorigenesis called MICMIC. Many predictions are directly validated by dCas9-based epigenetic editing to support the accuracy and efficiency of our tool. Oncogenic and lineage-specific transcription factors are shown to aberrantly shape the methylation landscape by modifying tumor-subtype core regulatory circuitry. Notably, the gene regulatory networks orchestrated by enhancer methylation across different cancer types are seen to converge on a common architecture. MICMIC is available on https://github.com/ZhangJlab/MICMIC .

CR2Cancer: a database for chromatin regulators in human cancer.

Chromatin regulators (CRs) can dynamically modulate chromatin architecture to epigenetically regulate gene expression in response to intrinsic and extrinsic signalling cues. Somatic alterations or misexpression of CRs might reprogram the epigenomic landscape of chromatin, which in turn lead to a wide range of common diseases, notably cancer. Here, we present CR2Cancer, a comprehensive annotation and visualization database for CRs in human cancer constructed by high throughput data analysis and literature mining. We collected and integrated genomic, transcriptomic, proteomic, clinical and functional information for over 400 CRs across multiple cancer types. We also built diverse types of CR-associated relations, including cancer type dependent (CR-target and miRNA-CR) and independent (protein-protein interaction and drug-target) ones. Furthermore, we manually curated around 6000 items of aberrant molecular alterations and interactions of CRs in cancer development from 5007 publications. CR2Cancer provides a user-friendly web interface to conveniently browse, search and download data of interest. We believe that this database would become a valuable resource for cancer epigenetics investigation and potential clinical application. CR2Cancer is freely available at http://cis.hku.hk/CR2Cancer.

6-C-(E-phenylethenyl)naringenin induces cell growth inhibition and cytoprotective autophagy in colon cancer cells.

6-C-(E-phenylethenyl)naringenin (6-CEPN) is a small molecule found in naringenin fortified fried beef. It has been shown to suppress colon cancer cell proliferation, but the underlying mechanisms are not fully understood. Here we demonstrate that 6-CEPN suppresses tumour cell proliferation through cell cycle arrest in G1 phase, induces necrotic cell death and autophagy in colon cancer cells. Blockade of autophagy by knockdown of the essential autophagy proteins, Atg7 or beclin-1, resulted in aggravated cell death in response to 6-CEPN treatment. In addition, genome-wide transcriptome expression profiling by RNA-sequencing revealed that 6-CEPN-mediated gene expression pattern was extremely similar to the transcriptome response induced by a RAS inhibitor salirasib (farnesylthiosalicylic acid [FTS; salirasib]). Subsequent molecular biological and biochemical experiments demonstrated that 6-CEPN indeed strongly inhibited RAS activation, leading to the inhibition of the downstream effector pathways c-Raf/mitogen-activated protein kinase (MEK)/extracellular signal-regulated kinase kinase and phosphoinositide 3-kinase/AKT/mammalian target of rapamycin. More importantly, our computational molecular docking data showed that 6-CEPN could bind to the active site of isoprenylcysteine carboxyl methyltransferase (Icmt), a critical enzyme for the activation of RAS. Icmt activity assay showed that 6-CEPN inhibited its activity significantly. Knockdown of Icmt by siRNA attenuated 6-CEPN-mediated autophagy and cell death. The present study demonstrates that 6-CEPN induces cell growth inhibition and cytoprotective autophagy in colon cancer cells, at least in part, though inhibition of the Icmt/RAS signalling pathways.

SABinder: A Web Service for Predicting Streptavidin-Binding Peptides.

Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community.

BDB: biopanning data bank.

The BDB database (http://immunet.cn/bdb) is an update of the MimoDB database, which was previously described in the 2012 Nucleic Acids Research Database issue. The rebranded name BDB is short for Biopanning Data Bank, which aims to be a portal for biopanning results of the combinatorial peptide library. Last updated in July 2015, BDB contains 2904 sets of biopanning data collected from 1322 peer-reviewed papers. It contains 25,786 peptide sequences, 1704 targets, 492 known templates, 447 peptide libraries and 310 crystal structures of target-template or target-peptide complexes. All data stored in BDB were revisited, and information on peptide affinity, measurement method and procedures was added for 2298 peptides from 411 sets of biopanning data from 246 published papers. In addition, a more professional and user-friendly web interface was implemented, a more detailed help system was designed, and a new on-the-fly data visualization tool and a series of tools for data analysis were integrated. With these new data and tools made available, we expect that the BDB database would become a major resource for scholars using phage display, with improved utility for biopanning and related scientific communities.

MDC-Analyzer: a novel degenerate primer design tool for the construction of intelligent mutagenesis libraries with contiguous sites.

Recent computational and bioinformatics advances have enabled the efficient creation of novel biocatalysts by reducing amino acid variability at hot spot regions. To further expand the utility of this strategy, we present here a tool called Multi-site Degenerate Codon Analyzer (MDC-Analyzer) for the automated design of intelligent mutagenesis libraries that can completely cover user-defined randomized sequences, especially when multiple contiguous and/or adjacent sites are targeted. By initially defining an objective function, the possible optimal degenerate PCR primer profiles could be automatically explored using the heuristic approach of Greedy Best-First-Search. Compared to the previously developed DC-Analyzer, MDC-Analyzer allows for the existence of a small amount of undesired sequences as a tradeoff between the number of degenerate primers and the encoded library size while still providing all the benefits of DC-Analyzer with the ability to randomize multiple contiguous sites. MDC-Analyzer was validated using a series of randomly generated mutation schemes and experimental case studies on the evolution of halohydrin dehalogenase, which proved that the MDC methodology is more efficient than other methods and is particularly well-suited to exploring the sequence space of proteins using data-driven protein engineering strategies.

PhD7Faster: predicting clones propagating faster from the Ph.D.-7 phage display peptide library.

Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl.

Epitope mapping of metuximab on CD147 using phage display and molecular docking.

Metuximab is the generic name of Licartin, a new drug for radioimmunotherapy of hepatocellular carcinoma. Although it is known to be a mouse monoclonal antibody against CD147, the complete epitope mediating the binding of metuximab to CD147 remains unknown. We panned the Ph.D.-12 phage display peptide library against metuximab and got six mimotopes. The following bioinformatics analysis based on mimotopes suggested that metuximab recognizes a conformational epitope composed of more than 20 residues. The residues of its epitope may include T28, V30, K36, L38, K57, F74, D77, S78, D79, D80, Q81, G83, S86, N98, Q100, L101, H102, G103, P104, V131, P132, and K191. The homology modeling of metuximab and the docking of CD147 to metuximab were also performed. Based on the top one docking model, the epitope was predicted to contain 28 residues: AGTVFTTV (23-30), I37, D45, E84, V88, EPMGTANIQLH (92-102), VPP (131-133), Q164, and K191. Almost half of the residues predicted on the basis of mimotope analysis also appear in the docking result, indicating that both results are reliable. As the predicted epitopes of metuximab largely overlap with interfaces of CD147-CD147 interactions, a structural mechanism of metuximab is proposed as blocking the formation of CD147 dimer.

MimoDB 2.0: a mimotope database and beyond.

Mimotopes are peptides with affinities to given targets. They are readily obtained through biopanning against combinatorial peptide libraries constructed by phage display and other display technologies such as mRNA display, ribosome display, bacterial display and yeast display. Mimotopes have been used to infer the protein interaction sites and networks; they are also ideal candidates for developing new diagnostics, therapeutics and vaccines. However, such valuable peptides are not collected in the central data resources such as UniProt and NCBI GenPept due to their 'unnatural' short sequences. The MimoDB database is an information portal to biopanning results of random libraries. In version 2.0, it has 15,633 peptides collected from 849 papers and grouped into 1818 sets. Besides the core data on panning experiments and their results, broad background information on target, template, library and structure is included. An accompanied benchmark has also been compiled for bioinformaticians to develop and evaluate their new models, algorithms and programs. In addition, the MimoDB database provides tools for simple and advanced searches, structure visualization, BLAST and alignment view on the fly. The experimental biologists can easily use the database as a virtual control to exclude possible target-unrelated peptides. The MimoDB database is freely available at http://immunet.cn/mimodb.