A global transcriptional network connecting noncoding mutations to changes in tumor gene expression

HYENA detects oncogenes activated by distal enhancers in cancer

Somatic structural variations (SVs) in cancer can shuffle DNA content in the genome, relocate regulatory elements, and alter genome organization. Enhancer hijacking occurs when SVs relocate distal enhancers to activate proto-oncogenes. However, most enhancer hijacking studies have only focused on protein-coding genes. Here, we develop a computational algorithm 'HYENA' to identify candidate oncogenes (both protein-coding and non-coding) activated by enhancer hijacking based on tumor whole-genome and transcriptome sequencing data. HYENA detects genes whose elevated expression is associated with somatic SVs by using a rank-based regression model. We systematically analyze 1146 tumors across 25 types of adult tumors and identify a total of 108 candidate oncogenes including many non-coding genes. A long non-coding RNA TOB1-AS1 is activated by various types of SVs in 10% of pancreatic cancers through altered 3-dimensional genome structure. We find that high expression of TOB1-AS1 can promote cell invasion and metastasis. Our study highlights the contribution of genetic alterations in non-coding regions to tumorigenesis and tumor progression.

Interferon regulatory factor 4 modulates epigenetic silencing and cancer-critical pathways in melanoma cells

Interferon regulatory factor 4 (IRF4) was initially identified as a key controller in lymphocyte differentiation and function, and subsequently as a dependency factor and therapy target in lymphocyte-derived cancers. In melanocytes, IRF4 takes part in pigmentation. Although genetic studies have implicated IRF4 in melanoma, how IRF4 functions in melanoma cells has remained largely elusive. Here, we confirmed prevalent IRF4 expression in melanoma and showed that high expression is linked to dependency in cells and mortality in patients. Analysis of genes activated by IRF4 uncovered, as a novel target category, epigenetic silencing factors involved in DNA methylation (DNMT1, DNMT3B, UHRF1) and histone H3K27 methylation (EZH2). Consequently, we show that IRF4 controls the expression of tumour suppressor genes known to be silenced by these epigenetic modifications, for instance cyclin-dependent kinase inhibitors CDKN1A and CDKN1B, the PI3-AKT pathway regulator PTEN, and primary cilium components. Furthermore, IRF4 modulates activity of key downstream oncogenic pathways, such as WNT/β-catenin and AKT, impacting cell proliferation and survival. Accordingly, IRF4 modifies the effectiveness of pertinent epigenetic drugs on melanoma cells, a finding that encourages further studies towards therapeutic targeting of IRF4 in melanoma.

HYENA detects oncogenes activated by distal enhancers in cancer

Somatic structural variations (SVs) in cancer can shuffle DNA content in the genome, relocate regulatory elements, and alter genome organization. Enhancer hijacking occurs when SVs relocate distal enhancers to activate proto-oncogenes. However, most enhancer hijacking studies have only focused on protein-coding genes. Here, we develop a computational algorithm "HYENA" to identify candidate oncogenes (both protein-coding and non-coding) activated by enhancer hijacking based on tumor whole-genome and transcriptome sequencing data. HYENA detects genes whose elevated expression is associated with somatic SVs by using a rank-based regression model. We systematically analyze 1,146 tumors across 25 types of adult tumors and identify a total of 108 candidate oncogenes including many non-coding genes. A long non-coding RNA TOB1-AS1 is activated by various types of SVs in 10% of pancreatic cancers through altered 3-dimensional genome structure. We find that high expression of TOB1-AS1 can promote cell invasion and metastasis. Our study highlights the contribution of genetic alterations in non-coding regions to tumorigenesis and tumor progression.

Danggui Jixueteng decoction for the treatment of myelosuppression after chemotherapy: A combined metabolomics and network pharmacology analysis

Objective

This study aimed to explore the mechanism of the Danggui Jixueteng decoction (DJD) in treating Myelosuppression after chemotherapy (MAC) through network pharmacology and metabolomics.

Methods

We obtained the chemical structures of DJD compounds from TCMSP and PubMed. SwissTargetPrediction, STITCH, CTD, GeneCards, and OMIM were utilized to acquire component targets and MAC-related targets. We identified the key compounds, core targets, main biological processes, and signaling pathways related to DJD by constructing and analyzing related networks. The main active compounds and key proteins of DJD in treating AA were confirmed by molecular docking. A MAC rat model was established through intraperitoneal injection of cyclophosphamide to confirm DJD's effect on the bone marrow hematopoietic system. Untargeted metabolomics analyzed serum metabolite differences between MAC rats and the control group, and before and after DJD treatment, to explore DJD's mechanism in treating MAC.

Results

Of the 93 active compounds identified under screening conditions, 275 compound targets and 3113 MAC-related targets were obtained, including 95 intersecting targets; AKT1, STAT3, CASP3, and JUN were key proteins in MAC treatment. The phosphatidylinositol-3-kinase/RAC-alpha serine/threonine-protein kinase (PI3K/AKT) signaling pathway may play a crucial role in MAC treatment with DJD. Molecular docking results showed good docking effects of key protein AKT1 with luteolin, β-sitosterol, kaempferol, and glycyrrhizal chalcone A. In vivo experiments indicated that, compared to the model group, in the DJD group, levels of WBCs, RBCs, HGB, and PLTs in peripheral blood cells, thymus index increased, spleen index decreased, serum IL-3, GM-CSF levels increased, and IL-6, TNF-α, and VEGF levels decreased (p < 0.01); the pathological morphology of femoral bone marrow improved. Eleven differential metabolites were identified as differential serum metabolites, mainly concentrated in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and arachidonic acid metabolism.

Conclusion

This study revealed that DJD's therapeutic effects are due to multiple ingredients, targets, and pathways. DJD may activate the PI3K/AKT signaling pathway, promote hematopoietic-related cytokine production, regulate related metabolic pathways, and effectively alleviate cyclophosphamide-induced myelosuppression after chemotherapy in rats.

Exploring the potential mechanism of Simiao Yongan decoction in the treatment of diabetic peripheral vascular disease based on network pharmacology and molecular docking technology

The study aims to investigate the potential action targets and molecular mechanisms of Simiao Yongan decoction (SMYAD) in treating diabetic peripheral vascular disease (DPVD) by utilizing network pharmacology analysis and molecular docking technology. The components and targets of SMYAD were screened using the TCMSP database, while DPVD-related genes were obtained from the GeneCards, OMIM, and Disgenet databases. After intersecting the gene sets, a Protein-Protein Interaction (PPI) network was established, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out. The practical chemical components and core targets identified were molecularly docked using AutoDock software. A total of 126 active compounds were screened from which 25 main components included quercetin, rutoside, hesperidin, naringin, and β-sitosterol were determined to be the active components most associated with the core targets. A total of 224 common target genes were obtained. Among them, JUN, AKT1, MAPK3, TP53, STAT3, RELA, MAPK1, FOS, and others are the expected core targets of traditional Chinese medicine. The top-ranked GO enrichment analysis results included 727 biological processes (BP), 153 molecular functions (MF), and 102 cellular components (CC). KEGG pathway enrichment analysis involved mainly 178 signaling pathways, such as cancer signaling pathway, AGE-RAGE signaling pathway, interleukin-17 signaling pathway, tumor necrosis factor signaling pathway, endocrine resistance signaling pathway, cell aging signaling pathway, and so on. The molecular docking results demonstrate that the principal chemical components of SMYAD exhibit considerable potential for binding to the core targets. SMYAD has the potential to treat DPVD through various components, targets, and pathways. Its mechanism of action requires further experimental investigation.

Associations of differentially expressed genes with psychoneurological symptoms in patients with head and neck cancer: A longitudinal study

OBJECTIVE

Patients with head and neck cancer (HNC) experience psychoneurological symptoms (PNS, i.e., depression, fatigue, sleep disturbance, pain, and cognitive dysfunction) during intensity-modulated radiotherapy (IMRT) that negatively impact their functional status, quality of life, and overall survival. The underlying mechanisms for PNS are still not fully understood. This study aimed to examine differentially expressed genes and pathways related to PNS for patients undergoing IMRT (i.e., before, end of, 6 months, and 12 months after IMRT).

METHODS

Participants included 142 patients with HNC (mean age 58.9 ± 10.3 years, 72.5% male, 83.1% White). Total RNA extracted from blood leukocytes were used for genome-wide gene expression assays. Linear mixed effects model was used to examine the association between PNS and gene expression across time. Gene Ontology (GO) enrichment analysis was employed to identify pathways related to PNS.

RESULTS

A total of 1352 genes (162 upregulated, 1190 downregulated) were significantly associated with PNS across time (false discovery rate (FDR) < 0.05). Among these genes, 112 GO terms were identified (FDR < 0.05). The top 20 GO terms among the significant upregulated genes were related to immune and inflammatory responses, while the top 20 GO terms among the significant downregulated genes were associated with telomere maintenance.

CONCLUSION

This study is the first to identify genes and pathways linked to immune and inflammatory responses and telomere maintenance that are associated with PNS in patients with HNC receiving IMRT. Inflammation and aging markers may be candidate biomarkers for PNS. Understanding biological markers may produce targets for novel interventions.

Functional analysis of recurrent CDC20 promoter variants in human melanoma

Small nucleotide variants in non-coding regions of the genome can alter transcriptional regulation, leading to changes in gene expression which can activate oncogenic gene regulatory networks. Melanoma is heavily burdened by non-coding variants, representing over 99% of total genetic variation, including the well-characterized TERT promoter mutation. However, the compendium of regulatory non-coding variants is likely still functionally under-characterized. We developed a pipeline to identify hotspots, i.e. recurrently mutated regions, in melanoma containing putatively functional non-coding somatic variants that are located within predicted melanoma-specific regulatory regions. We identified hundreds of statistically significant hotspots, including the hotspot containing the TERT promoter variants, and focused on a hotspot in the promoter of CDC20. We found that variants in the promoter of CDC20, which putatively disrupt an ETS motif, lead to lower transcriptional activity in reporter assays. Using CRISPR/Cas9, we generated an indel in the CDC20 promoter in human A375 melanoma cell lines and observed decreased expression of CDC20, changes in migration capabilities, increased growth of xenografts, and an altered transcriptional state previously associated with a more proliferative and less migratory state. Overall, our analysis prioritized several recurrent functional non-coding variants that, through downregulation of CDC20, led to perturbation of key melanoma phenotypes.

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.

CRISPR-Cas System Is an Effective Tool for Identifying Drug Combinations That Provide Synergistic Therapeutic Potential in Cancers

Despite numerous efforts, the therapeutic advancement for neuroblastoma and other cancer treatments is still ongoing due to multiple challenges, such as the increasing prevalence of cancers and therapy resistance development in tumors. To overcome such obstacles, drug combinations are one of the promising applications. However, identifying and implementing effective drug combinations are critical for achieving favorable treatment outcomes. Given the enormous possibilities of combinations, a rational approach is required to predict the impact of drug combinations. Thus, CRISPR-Cas-based and other approaches, such as high-throughput pharmacological and genetic screening approaches, have been used to identify possible drug combinations. In particular, the CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats) is a powerful tool that enables us to efficiently identify possible drug combinations that can improve treatment outcomes by reducing the total search space. In this review, we discuss the rational approaches to identifying, examining, and predicting drug combinations and their impact.

Gene editing of non-coding regulatory DNA and its application in crop improvement

The development of the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas) system has provided precise and efficient strategies to edit target genes and generate transgene-free crops. Significant progress has been made in the editing of protein-coding genes; however, studies on the editing of non-coding DNA with regulatory roles lags far behind. Non-coding regulatory DNAs, including those which can be transcribed into long non-coding RNAs (lncRNAs), and miRNAs, together with cis-regulatory elements (CREs), play crucial roles in regulating plant growth and development. Therefore, the combination of CRISPR/Cas technology and non-coding regulatory DNA has great potential to generate novel alleles that affect various agronomic traits of crops, thus providing valuable genetic resources for crop breeding. Herein, we review recent advances in the roles of non-coding regulatory DNA, attempts to edit non-coding regulatory DNA for crop improvement, and potential application of novel editing tools in modulating non-coding regulatory DNA. Finally, the existing problems, possible solutions, and future applications of gene editing of non-coding regulatory DNA in modern crop breeding practice are also discussed.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.

Artificial intelligence reveals dysregulation of osteosarcoma and cuproptosis-related biomarkers, PDHA1, CDKN2A and neutrophils

At present, the impact of cuproptosis-related genes in the study of osteosarcoma is largely unknown. Genome-wide data of osteosarcoma and controls were downloaded from 3 different databases, and specific diagnostic models associated with cuproptosis in osteosarcoma were constructed by support vector machines with artificial intelligence, random forest trees and LASSO regression. Differential analysis of immune cell infiltration was examined using routine blood data from 25,665 cases. Differential expression was examined using immunohistochemistry and PCR. PDHA1 and CDKN2A were obtained as specific cuproptosis-related biomarkers for osteosarcoma after artificial intelligence analysis. PDHA1, CDKN2A and neutrophils were differentially expressed in OS and control groups. PDHA1 and CDKN2A are significantly dysregulated in OS and are able to serve as biomarkers of OS.

Roles of mitochondrial genetics in cancer metastasis

The contributions of mitochondria to cancer have been recognized for decades. However, the focus on the metabolic role of mitochondria and the diminutive size of the mitochondrial genome compared to the nuclear genome have hindered discovery of the roles of mitochondrial genetics in cancer. This review summarizes recent data demonstrating the contributions of mitochondrial DNA (mtDNA) copy-number variants (CNVs), somatic mutations, and germline polymorphisms to cancer initiation, progression, and metastasis. The goal is to summarize accumulating data to establish a framework for exploring the contributions of mtDNA to neoplasia and metastasis.

Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

Network Pharmacology and Experimental Validation to Reveal Effects and Mechanisms of Icariin Combined with Nobiletin against Chronic Obstructive Pulmonary Diseases

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a long-term respiratory disorder marked by restricted airflow and persistent respiratory symptoms. According to previous studies, icariin combined with nobiletin (I&N) significantly ameliorates COPD, but the therapeutic mechanisms remain unclear.

PURPOSE

The aim of the study is to investigate the therapeutic mechanisms of I&N against COPD using network pharmacology and experimental validation.

METHODS

The targets of I&N and related genes of COPD were screened and their intersection was selected. Next, the protein-protein interaction (PPI) networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Further, a COPD rat model was established to validate the effect and mechanisms of I&N.

RESULTS

445 potential targets I&N were obtained from SwissTargetPrediction, STITCH 5.0, and PharmMapper databases. 1831 related genes of COPD were obtained from GeneCards, DrugBank, and DisGeNet databases. 189 related genes were screened via matching COPD targets with I&N. 16 highest score targets among 189 targets were obtained according to PPI networks. GO and KEGG pathway enrichment analyses of 16 highest score targets suggested that these key genes of I&N were mostly enriched in the tumor necrosis factor (TNF) pathway, mitogen-activated protein kinase (MAPK) pathway, and phosphatidyl inositol 3-kinase (PI3K)-protein kinase B (AKT) pathway. Therefore, the treatments of I&N for COPD were connected with inflammation-related pathways. In in vivo experiments, the studies indicated that I&N improved the lung function and alleviated the damage of pulmonary histopathology. Moreover, I&N reduced levels of interleukin (IL)-6, IL-1β, and TNF-α in lung tissues of COPD rats and inhibited the activation of the MAPK pathway and PI3K-Akt pathway.

CONCLUSIONS

Icariin combined with nobiletin has therapeutic effects on COPD by inhibiting inflammation. The potential mechanisms of I&N may relate to the MAPK pathway and PI3K-Akt pathway.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

The mechanism of Lingze tablets in the treatment of benign prostatic hyperplasia based on network pharmacology and molecular docking technology

Lingze tablets has been used as a drug in the treatment of kidney deficiency-dampnes shea-stasis type benign prostatic hyperplasia (BPH) in Traditional Chinese Medicine, and it was found effective for BPH treatment. We aimed to investigate the mechanism of the Lingze tablets in the treatment of BPH through the network pharmacology and molecular docking technology. The active compounds of Lingze tablets were retrieved from the TCMSP, BATMAN-TCM and ETCM databases. The ADME of active compounds was screened for Swiss target prediction, and the targets of the active compounds were predicted. DisGeNET, Genecards and OMIM were used to obtain the disease targets of BPH, and the targets of Lingze tablets in the treatment of BPH were obtained by venny2.1. String platform and cytoscape softwares were used to construct the PPI network. Go enrichment analysis and KEGG signal pathway analysis were analysed by mediascape. The 'component-target-pathway' networks diagram was constructed by the cytoscape software. Molecular docking was carried out by autodock software. Lingze tablets could serve as a drug for BPH treatment by regulating SRC, MAPK1, PIK3CA, JAK2 and other disease targets, intervening in biological processes such as cell migration, cell activity, cytokine secretion, protein phosphorylation, MAPK, transferase activity and PI3K/AKT signalling pathways.

PROBER identifies proteins associated with programmable sequence-specific DNA in living cells

DNA-protein interactions mediate physiologic gene regulation and may be altered by DNA variants linked to polygenic disease. To enhance the speed and signal-to-noise ratio (SNR) in the identification and quantification of proteins associated with specific DNA sequences in living cells, we developed proximal biotinylation by episomal recruitment (PROBER). PROBER uses high-copy episomes to amplify SNR, and proximity proteomics (BioID) to identify the transcription factors and additional gene regulators associated with short DNA sequences of interest. PROBER quantified both constitutive and inducible association of transcription factors and corresponding chromatin regulators to target DNA sequences and binding quantitative trait loci due to single-nucleotide variants. PROBER identified alterations in regulator associations due to cancer hotspot mutations in the hTERT promoter, indicating that these mutations increase promoter association with specific gene activators. PROBER provides an approach to rapidly identify proteins associated with specific DNA sequences and their variants in living cells.

Exploring Quercetin Anti-Osteoporosis Pharmacological Mechanisms with In Silico and In Vivo Models

Since osteoporosis critically influences the lives of patients with a high incidence, effective therapeutic treatments are important. Quercetin has been well recognized as a bone-sparing agent and thus the underlying mechanisms warrant further investigation. In the current study, the network pharmacology strategy and zebrafish model were utilized to explain the potential pharmacological effects of quercetin on osteoporosis. The potential targets and related signaling pathways were explored through overlapping target prediction, protein–protein interaction network construction, and functional enrichment analysis. Furthermore, we performed docking studies to verify the specific interactions between quercetin and crucial targets. Consequently, 55 targets were related to osteoporosis disease among the 159 targets of quercetin obtained by three database sources. Thirty hub targets were filtered through the cytoNCA plugin. Additionally, the Gene Ontology functions in the top 10 respective biological processes, molecular functions, and cell components as well as the top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were depicted. The most significance difference in the KEGG pathways was the TNF signaling pathway, consisting of the Nuclear Factor Kappa B Subunit (NF-κB), Extracellular Regulated Protein Kinases (ERK) 1/2, Activator Protein 1 (AP-1), Interleukin 6 (IL6), Transcription factor AP-1 (Jun), and Phosphatidylinositol 3 Kinase (PI3K), which were probably involved in the pharmacological effects. Moreover, molecular docking studies revealed that the top three entries were Interleukin 1 Beta (IL1B), the Nuclear Factor NF-Kappa-B p65 Subunit (RelA), and the Nuclear Factor Kappa B Subunit 1 (NFKB1), respectively. Finally, these results were verified by alizarin red-stained mineralized bone in zebrafish and related qPCR experiments. The findings probably facilitate the mechanism elucidation related to quercetin anti-osteoporosis action.

Mechanisms of Quercetin against atrial fibrillation explored by network pharmacology combined with molecular docking and experimental validation

Atrial fibrillation (AF) is a common atrial arrhythmia for which there is no specific therapeutic drug. Quercetin (Que) has been used to treat cardiovascular diseases such as arrhythmias. In this study, we explored the mechanism of action of Que in AF using network pharmacology and molecular docking. The chemical structure of Que was obtained from Pubchem. TCMSP, Swiss Target Prediction, Drugbank, STITCH, Pharmmapper, CTD, GeneCards, DISGENET and TTD were used to obtain drug component targets and AF-related genes, and extract AF and normal tissue by GEO database differentially expressed genes by GEO database. The top targets were IL6, VEGFA, JUN, MMP9 and EGFR, and Que for AF treatment might involve the role of AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and IL-17 signaling pathway. Molecular docking showed that Que binds strongly to key targets and is differentially expressed in AF. In vivo results showed that Que significantly reduced the duration of AF fibrillation and improved atrial remodeling, reduced p-MAPK protein expression, and inhibited the progression of AF. Combining network pharmacology and molecular docking approaches with in vivo studies advance our understanding of the intensive mechanisms of Quercetin, and provide the targeted basis for clinical Atrial fibrillation treatment.

Therapeutic Potential of Berberine for Osteoporosis and its Underlying Mechanisms: A Bioinformatics, Network Pharmacology, Molecular Dynamics Simulation Study

Osteoporosis is a systemic skeletal disease that can easily lead to bone fractures. Berberine has been shown to be effective in treating osteoporosis. This study was conducted to identify the potential mechanism of berberine in treating this complaint. We screened potential targets of berberine and identified the osteoporosis-related differentially expressed genes (DEGs) in the microarray dataset GSE56815. Protein–protein interaction (PPI) network construction, hub targets identification, and pathway enrichment were carried out to find the potential targets. Molecular docking and molecular dynamics studies were performed to verify the combination of berberine with its treatment-related central targets. In addition, SwissADME preliminarily evaluated the physicochemical properties of berberine. Through data mining, 23 osteoporosis-related targets of berberine were selected. PPI and module analyses suggested that AKT1, MAPK1, ESR1, AR, TP53, and PTGS2 are the core targets of berberine. Docking and molecular dynamics studies showed that berberine could stably bind to core proteins to form a protein–ligand complex. The enrichment analysis showed that the estrogen signaling pathway and thyroid hormone signaling pathway play important roles in curing osteoporosis. To sum up, berberine primarily acts on AKT1, MAPK1, ESR1, AR, TP53, and PTGS2, mainly regulating the estrogen and thyroid hormone signaling pathways to treat osteoporosis in a multi-target, multi-pathway, and multi-system manner.

Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer

These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied "the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer". It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems-biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).

Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases

BACKGROUND

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known.

METHODS

We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS ( n =765,348) and UK Biobank GWAS (90% of the cohort; n =451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data ( n =45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study ( n =8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR.

RESULTS

The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α -1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR.

CONCLUSIONS

A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.

High-throughput and multi-phases identification of autoantibodies in diagnosing early-stage breast cancer and subtypes

Autoantibodies (AAbs) targeted tumor‐associated antigens (TAAs) have the potential for early detection of breast cancer. Here, 574 early‐stage breast cancer (ES‐BC) patients containing 4 subtypes (Luminal A, Luminal B, HER2+, TN), 126 benign breast disease (BBD) patients, and 199 normal healthy controls (NHC) were separated into three‐phases to discover, verify, and validate AAbs. In discovery phase using high‐throughput protein microarray, 37 AAbs with sensitivity of 31.25%‐86.25% and specificity over 73% in ES‐BC, and 40 AAbs with different positive rates between subtypes were identified as candidates. In verification phase, 18 AAbs were significantly increased compared with the Control (BBD and NHC) in focused array. Ten out of 18 AAbs exhibited a significant difference between subtypes (P < .05). In ELISA validation phase, 5 novel AAbs (anti‐KJ901215, ‐FAM49B, ‐HYI, ‐GARS, ‐CRLF3) exhibited significantly higher levels in ES‐BC compared with BBD/NHC (P < .05). The sensitivities of individual AAb and a 5‐AAbs panel were 20.41%‐28.57% and 38.78%, whereas the specificities were over 90% and 85.94%. Simultaneously, 4 AAbs except anti‐GARS differed significantly between TN and non‐TN subtype (P < .05). We constructed 3 random forest classifier models based on AAbs to discriminant ES‐BC from Control or BBD, and to discern TN subtype, which yielded an area under the curve of 0.870, 0.860, and 0.875, respectively. Biological interaction analysis revealed 4 TAAs, except for KJ901215, that were associated with well known proteins of BC. This study discovered and stepwise validated 5 novel AAbs with the potential to diagnose ES‐BC and discern TN subtype, indicating easy‐to‐detect and minimally invasive diagnostic value of serum AAbs ahead of biopsy for future application.

Identifying the Mechanisms of Rosa Roxburghii Tratt on Treating Gastric Cancer: Combining the Targetable Screening From the Cancer Genome Atlas With Network Pharmacology

The Chinese herbal medicine Rosa roxburghii Tratt (RRT) is widely used in the treatment of malignant tumors, including gastric cancer (GC), but its pharmacological mechanism remains unclear. The purpose of this research was to identify the mechanisms of RRT on treating GC by using network pharmacology and molecular docking, combined with the analysis of differential expressed genes in GEO gene chips and TCGA database. We first defined the effective components of RRT and their potential targets for the treatment of GC, and identified core targets according to the topology analysis by constructing a protein-protein interaction network. Furthermore, molecular docking was used to verify the docking between the core active ingredients and the key targets. The results showed that the effect of RRT may be closely associated with multiple signal pathways, including pathways in cancer, phosphatidylinositol 3-kinase-AKT serine/threonine kinase (PI3K-Akt), tumor necrosis factor (TNF), hypoxia-inducible factor 1 (HIF-1), and mitogen-activated protein kinase (MAPK). It is suggested that RRT may play an effect by regulating hypoxia, improving the tumor microenvironment, inhibiting inflammatory reactions and promoting apoptosis. The mechanism of RRT in the treatment of GC is revealed here for the first time based on network pharmacology analysis, which may provide a new direction for further exploration of the mechanisms of RRT in the treatment of GC and a new perspective for research on anti-tumor drugs.

Network Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of the Drug Pair Astragalus-Angelica for Treatment of Male Infertility

Background

The traditional Chinese medicines Astragalus and Angelica are often combined to treat male infertility, but the specific therapeutic mechanism is not clear. Therefore, this study applies a network pharmacology approach to investigate the possible mechanism of action of the drug pair Astragalus-Angelica (PAA) in the treatment of male infertility.

Methods

Relevant targets for PAA treatment of male infertility are obtained through databases. Protein-protein interactions (PPIs) are constructed through STRING database and screen core targets, and an enrichment analysis is conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target protein and the ligand of PAA.

Results

The active ingredients of 112 PAA, 980 corresponding targets, and 374 effective targets of PAA for the treatment of male infertility were obtained, which are related to PI3K-Akt signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway, IL-17 signaling pathway, and thyroid hormone signaling pathway.

Conclusion

In this study, using a network pharmacology method, we preliminarily analyzed the effective components and action targets of the PAA. We also explored the possible mechanism of action of PAA in treating male infertility. They also lay a foundation for expanding the clinical application of PAA and provide new ideas and directions for further research on the mechanisms of action of the PAA and its components for male infertility treatment.

Shiquan Yuzhen Decoction inhibits angiogenesis and tumor apoptosis caused by non-small cell lung cancer and promotes immune response

BACKGROUND

TCM treatment for lung carcinoma has been reported by many researches. Shiquan Yuzhen Decoction can be used in the clinical treatment of lung carcinoma, but its specific mechanism is still under exploration at present.

METHODS

The active ingredients and mechanism of Shiquan Yuzhen Decoction on non-small cell lung carcinoma were discussed by network pharmacology. The main active ingredients, targets and disease genes of non-small cell lung carcinoma of Shiquan Yuzhen Decoction were screened through relevant databases. Lewis lung carcinoma bearing mice model was established by inoculating Lewis lung carcinoma cells to C57BL/6 mice under the right armpit. Different doses of Shiquan Yuzhen Decoction were used to observe the apoptosis and angiogenesis changes of tumor tissues in mice.

RESULTS

A total of 26 key active compounds meeting the evaluation of generic properties and 182 main targets were screened out. The multi-level network model shows that Shiquan Yuzhen Decoction can regulate the target gene network of non-small cell lung carcinoma. And it can inhibit tumor growth in tumor-bearing mice, induce apoptosis of tumor cells, and evidently increase the activities of Caspase-3, 8 and 9. The dose of 17.4 g/kg can evidently inhibit the formation of microvessels in transplanted tumor tissues, improve the sensitivity of mice's diet and activities, increase the spleen index of tumor-bearing mice, and inhibit inflammatory factors.

CONCLUSION

Shiquan Yuzhen Decoction can evidently improve the quality of life of Lewis lung carcinoma-bearing mice and inhibit tumor growth in mice, which is a potential clinical treatment plan.

Enhancer release and retargeting activates disease-susceptibility genes

The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as 'enhancer release and retargeting'. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner-consistent with a model of 'enhancer scanning' inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson's disease (NUCKS1-RAB7L1) and three loci associated with cancer (CLPTM1L-TERT, ZCCHC7-PAX5 and PVT1-MYC).

Mechanisms of breast cancer metastasis

Invasive breast cancer tends to metastasize to lymph nodes and systemic sites. The management of metastasis has evolved by focusing on controlling the growth of the disease in the breast/chest wall, and at metastatic sites, initially by surgery alone, then by a combination of surgery with radiation, and later by adding systemic treatments in the form of chemotherapy, hormone manipulation, targeted therapy, immunotherapy and other treatments aimed at inhibiting the proliferation of cancer cells. It would be valuable for us to know how breast cancer metastasizes; such knowledge would likely encourage the development of therapies that focus on mechanisms of metastasis and might even allow us to avoid toxic therapies that are currently used for this disease. For example, if we had a drug that targeted a gene that is critical for metastasis, we might even be able to cure a vast majority of patients with breast cancer. By bringing together scientists with expertise in molecular aspects of breast cancer metastasis, and those with expertise in the mechanical aspects of metastasis, this paper probes interesting aspects of the metastasis cascade, further enlightening us in our efforts to improve the outcome from breast cancer treatments.

Prospects Of Non-Coding Elements In Genomic Dna Based Gene Therapy

Gene therapy has made significant development since the commencement of the first clinical trials a few decades ago and has remained a dynamic area of research regardless of obstacles such as immune response and insertional mutagenesis. Progression in various technologies like next-generation sequencing (NGS) and nanotechnology has established the importance of non-coding segments of a genome, thereby taking gene therapy to the next level. In this review, we have summarized the importance of non-coding elements, highlighting the advantages of using full-length genomic DNA loci (gDNA) compared to complementary DNA (cDNA) or minigene, currently used in gene therapy. The focus of this review is to provide an overview of the advances and the future of potential use of gDNA loci in gene therapy, expanding the therapeutic repertoire in molecular medicine.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Pan-cancer analysis of non-coding recurrent mutations and their possible involvement in cancer pathogenesis

Cancer-related mutations have been mainly identified in protein-coding regions. Recent studies have demonstrated that mutations in non-coding regions of the genome could also be a risk factor for cancer. However, the non-coding regions comprise 98% of the total length of the human genome and contain a huge number of mutations, making it difficult to interpret their impacts on pathogenesis of cancer. To comprehensively identify cancer-related non-coding mutations, we focused on recurrent mutations in non-coding regions using somatic mutation data from COSMIC and whole-genome sequencing data from The Cancer Genome Atlas (TCGA). We identified 21 574 recurrent mutations in non-coding regions that were shared by at least two different samples from both COSMIC and TCGA databases. Among them, 580 candidate cancer-related non-coding recurrent mutations were identified based on epigenomic and chromatin structure datasets. One of such mutation was located in RREB1 binding site that is thought to interact with TEAD1 promoter. Our results suggest that mutations may disrupt the binding of RREB1 to the candidate enhancer region and increase TEAD1 expression levels. Our findings demonstrate that non-coding recurrent mutations and coding mutations may contribute to the pathogenesis of cancer.

Cis-regulatory mutations with driver hallmarks in major cancers

Despite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants remains challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, genetic data, and allele-specific gene expression from TCGA, we identified 320 somatic non-coding mutations that affect gene expression in cis (FDR<0.25). These mutations cluster into 47 cis-regulatory elements that modulate expression of their subject genes through diverse molecular mechanisms. We further show that these mutations have hallmark features of non-coding drivers; namely, that they preferentially disrupt transcription factor binding motifs, are associated with a selective advantage, increased oncogene expression and decreased tumor suppressor expression.

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Enrichment of functional non-coding somatic mutations predicts drivers

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Elevated variant allele frequencies are consistent with roles in tumorigenesis

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Putative non-coding drivers disrupt transcription factor binding motifs

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Predicted drivers associate with increased oncogene and decreased TSG expression

Pan-cancer noncoding genomic analysis identifies functional CDC20 promoter mutation hotspots

Noncoding DNA sequences occupy more than 98% of the human genome; however, few cancer noncoding drivers have been identified compared with cancer coding drivers, probably because cancer noncoding drivers have a distinct mutation pattern due to the distinct function of noncoding DNA. Here we performed pan-cancer whole genome mutation analysis to screen for functional noncoding mutations that influence protein factor binding. Recurrent mutations were identified in the promoter of CDC20 gene. These CDC20 promoter hotspot mutations disrupt the binding of ELK4 transcription repressor, lead to the up-regulation of CDC20 transcription. Physiologically ELK4 binds to the unmutated hotspot sites and is involved in DNA damage-induced CDC20 transcriptional repression. Overall, our study not only identifies a detailed mechanism for CDC20 gene deregulation in human cancers but also finds functional noncoding genetic alterations, with implications for the further development of function-based noncoding driver discovery pipelines.

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Pan-cancer noncoding analysis for mutations that influence protein factor binding

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Recurrent mutations were identified in the promoter of CDC20 gene

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Promoter hotspot mutations disrupt ELK4 binding, up-regulate CDC20 transcription

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Promoter hotspot mutation site is involved in DNA damage-induced CDC20 repression

Developing a gene panel for pharmacoresistant epilepsy: a review of epilepsy pharmacogenetics

Evaluating genes involved in the pharmacodynamics and pharmacokinetics of epilepsy drugs is critical to better understand pharmacoresistant epilepsy. We reviewed the pharmacogenetics literature on six antiseizure medicines (carbamazepine, perampanel, lamotrigine, levetiracetam, sodium valproate and zonisamide) and compared the genes found with those present on epilepsy gene panels using a functional annotation pathway analysis. Little overlap was found between the two gene lists; pharmacogenetic genes are mainly involved in detoxification processes, while epilepsy panel genes are involved in cell signaling and gene expression. Our work provides support for a specific pharmacoresistant epilepsy gene panel to assist antiseizure medicine selection, enabling personalized approaches to treatment. Future efforts will seek to include this panel in genomic analyses of pharmacoresistant patients, to determine clinical utility and patient treatment responses.

Alternatively Splicing Interactomes Identify Novel Isoform-Specific Partners for NSD2

Nuclear receptor SET domain protein (NSD2) plays a fundamental role in the pathogenesis of Wolf-Hirschhorn Syndrome (WHS) and is overexpressed in multiple human myelomas, but its protein-protein interaction (PPI) patterns, particularly at the isoform/exon levels, are poorly understood. We explored the subcellular localizations of four representative NSD2 transcripts with immunofluorescence microscopy. Next, we used label-free quantification to perform immunoprecipitation mass spectrometry (IP-MS) analyses of the transcripts. Using the interaction partners for each transcript detected in the IP-MS results, we identified 890 isoform-specific PPI partners (83% are novel). These PPI networks were further divided into four categories of the exon-specific interactome. In these exon-specific PPI partners, two genes, RPL10 and HSPA8, were successfully confirmed by co-immunoprecipitation and Western blotting. RPL10 primarily interacted with Isoforms 1, 3, and 5, and HSPA8 interacted with all four isoforms, respectively. Using our extended NSD2 protein interactions, we constructed an isoform-level PPI landscape for NSD2 to serve as reference interactome data for NSD2 spliceosome-level studies. Furthermore, the RNA splicing processes supported by these isoform partners shed light on the diverse roles NSD2 plays in WHS and myeloma development. We also validated the interactions using Western blotting, RPL10, and the three NSD2 (Isoform 1, 3, and 5). Our results expand gene-level NSD2 PPI networks and provide a basis for the treatment of NSD2-related developmental diseases.

Exploiting XG Boost for Predicting Enhancer-promoter Interactions

Background:

Gene expression and disease control are regulated by the interaction between distal enhancers and proximal promoters, and the study of enhancer promoter interactions (EPIs) provides insight into the genetic basis of diseases.

Objective:

Although the recent emergence of high-throughput sequencing methods have a deepened understanding of EPIs, accurate prediction of EPIs still limitations.

Methods:

We have implemented a XGBoost-based approach and introduced two sets of features (epigenomic and sequence) to predict the interactions between enhancers and promoters in different cell lines.

Results:

Extensive experimental results show that XGBoost effectively predicts EPIs across three cell lines, especially when using epigenomic and sequence features.

Conclusion:

XGBoost outperforms other methods, such as random forest, Adadboost, GBDT, and TargetFinder.

Pathogenic Mechanisms of Somatic Mutation and Genome Mosaicism in Aging

Age-related accumulation of postzygotic DNA mutations results in tissue genetic heterogeneity known as somatic mosaicism. Although implicated in aging as early as the 1950s, somatic mutations in normal tissue have been difficult to study because of their low allele fractions. With the recent emergence of cost-effective high-throughput sequencing down to the single-cell level, enormous progress has been made in our capability to quantitatively analyze somatic mutations in human tissue in relation to aging and disease. Here we first review how recent technological progress has opened up this field, providing the first broad sets of quantitative information on somatic mutations in vivo necessary to gain insight into their possible causal role in human aging and disease. We then propose three major mechanisms that can lead from accumulated de novo mutations across tissues to cell functional loss and human disease.

Gene expression pattern analysis using dual-color RT-MLPA and integrative genome-wide association studies of eQTL for tuberculosis suscepitibility

Background

When infected with Mycobacterium tuberculosis, only a small proportion of the population will develop active TB, and the role of host genetic factors in different TB infection status was not fully understood.

Methods

Forty-three patients with active tuberculosis and 49 with latent tuberculosis were enrolled in the prospective cohort. Expressing levels of 27 candidate mRNAs, which were previously demonstrated to differentially expressed in latent and active TB, were measured by dual color reverse transcription multiplex ligation dependent probe amplification assay (dcRT-MLPA). Using expression levels of these mRNAs as quantitative traits, associations between expression abundance and genome-wild single nucleotide polymorphisms (SNPs) were calculated. Finally, identified candidate SNPs were further assessed for their associations with TB infection status in a validation cohort with 313 Chinese Han cases.

Results

We identified 9 differentially expressed mRNAs including il7r, il4, il8, tnfrsf1b, pgm5, ccl19, il2ra, marco and fpr1 in the prospective cohort. Through expression quantitative trait loci mapping, we screened out 8 SNPs associated with these mRNAs. Then, CG genotype of the SNP rs62292160 was finally verified to be significantly associated with higher transcription levels of IL4 in LTBI than in TB patients.

Conclusion

We reported that the SNP rs62292160 in Chinese Han population may link to higher expression of il4 in latent tuberculosis. Our findings provided a new genetic variation locus for further exploration of the mechanisms of TB and a possible target for TB genetic susceptibility studies, which might aid the clinical decision to precision treatment of TB.

Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity

To better understand cellular communication driving diverse behaviors, we need to uncover the molecular mechanisms of within-cell-type functional heterogeneity. While single-cell RNA sequencing (scRNAseq) has advanced our understanding of cell heterogeneity, linking individual cell phenotypes to transcriptomic data remains challenging. Here, we used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into heterogeneous cell behaviors than unsupervised scRNAseq. Using a simple 3D in vitro breast cancer (BRCA) model, we conducted pheno-scRNAseq on invasive and non-invasive cells and compared the results to phenotype-agnostic scRNAseq analysis. Pheno-scRNAseq identified unique and more selective differentially expressed genes than unsupervised scRNAseq analysis. Functional studies validated the utility of pheno-scRNAseq in understanding within-cell-type functional heterogeneity and revealed that migration phenotypes were coordinated with specific metabolic, proliferation, stress, and immune phenotypes. This approach lends new insight into the molecular systems underlying BRCA cell phenotypic heterogeneity.

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes

Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic noncoding mutations from personal cancer genomes. Therefore, although most somatic mutations are localized in the noncoding cancer genome, their effects on driving tumorigenesis and progression have not been systematically explored and noncoding somatic alleles have not been leveraged in current clinical practice to guide personalized screening, diagnosis, and treatment. Here, we present a deep learning framework to capture pathogenic noncoding mutations in personal cancer genomes, which perturb gene regulation by altering chromatin architecture. We deployed the system specifically for localized prostate cancer by integrating large-scale prostate cancer genomes and the prostate-specific epigenome. We exhaustively evaluated somatic mutations in each patient's genome and agnostically identified thousands of somatic alleles altering the prostate epigenome. Functional genomic analyses subsequently demonstrated that affected genes displayed differential expression in prostate tumor samples, were vulnerable to expression alterations, and were convergent onto androgen receptor-mediated signaling pathways. Accumulation of pathogenic regulatory mutations in these affected genes was predictive of clinical observations, suggesting potential clinical utility of this approach. Overall, the deep learning framework has significantly expanded our view of somatic mutations in the vast noncoding genome, uncovered novel genes in localized prostate cancer, and will foster the development of personalized screening and therapeutic strategies for prostate cancer. SIGNIFICANCE: This study's characterization of the noncoding genome in prostate cancer reveals mutational signatures predictive of clinical observations, which may serve as a powerful prognostic tool in this disease.

Multi-omics analysis reveals the functional transcription and potential translation of enhancers

Enhancer can transcribe RNAs, however, most of them were neglected in traditional RNA-seq analysis workflow. Here, we developed a Pipeline for Enhancer Transcription (PET, http://fun-science.club/PET) for quantifying enhancer RNAs (eRNAs) from RNA-seq. By applying this pipeline on lung cancer samples and cell lines, we showed that the transcribed enhancers are enriched with histone marks and transcription factor motifs (JUNB, Hand1-Tcf3 and GATA4). By training a machine learning model, we demonstrate that enhancers can predict prognosis better than their nearby genes. Integrating the Hi-C, ChIP-seq and RNA-seq data, we observe that transcribed enhancers associate with cancer hallmarks or oncogenes, among which LcsMYC-1 (Lung cancer-specific MYC eRNA-1) potentially supports MYC expression. Surprisingly, a significant proportion of transcribed enhancers contain small protein-coding open reading frames (sORFs) and can be translated into microproteins. Our study provides a computational method for eRNA quantification and deepens our understandings of the DNA, RNA and protein nature of enhancers.

Recapitulation of Human Embryonic Heartbeat to Promote Differentiation of Hepatic Endoderm to Hepatoblasts

Hepatic development requires multiple sequential physicochemical environmental changes in an embryo, and human pluripotent stem cells (hPSCs) allow for the elucidation of this embryonic developmental process. However, the current in vitro methods for hPSC-hepatic differentiation, which employ various biochemical substances, produce hPSC-derived hepatocytes with less functionality than primary hepatocytes, due to a lack of physical stimuli, such as heart beating. Here, we developed a microfluidic platform that recapitulates the beating of a human embryonic heart to improve the functionality of hepatoblasts derived from hepatic endoderm (HE) in vitro. This microfluidic platform facilitates the application of multiple mechanical stretching forces, to mimic heart beating, to cultured hepatic endoderm cells to identify the optimal stimuli. Results show that stimulated HE-derived hepatoblasts increased cytochrome P450 3A (CYP3A) metabolic activity, as well as the expression of hepatoblast functional markers (albumin, cytokeratin 19 and CYP3A7), compared to unstimulated hepatoblasts. This approach of hepatic differentiation from hPSCs with the application of mechanical stimuli will facilitate improved methods for studying human embryonic liver development, as well as accurate pharmacological testing with functional liver cells.

Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group

PURPOSE

Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival.

METHODS

We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data.

RESULTS

We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status.

CONCLUSION

This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers

Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here, we developed a computational framework to identify putative causal noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as putative causal mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation profiles can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.

Beyond the coding genome: non-coding mutations and cancer

Latest advancements in genomics involving individuals from different races and geographical locations has led to the identification of thousands of common as well as rare genetic variants and copy number variations (CNVs). These studies have surprisingly revealed that the majority of genetic variation is not present within the coding region but rather in the non-coding region of the genome, which is also termed as "Medical Genome". This short review describes how mutations/variations within; regulatory sequences, architectural proteins and transcriptional regulators give rise to the aberrant gene expression profiles that drives cellular transformations and malignancies.

OncoBase: a platform for decoding regulatory somatic mutations in human cancers

Whole-exome and whole-genome sequencing have revealed millions of somatic mutations associated with different human cancers, and the vast majority of them are located outside of coding sequences, making it challenging to directly interpret their functional effects. With the rapid advances in high-throughput sequencing technologies, genome-scale long-range chromatin interactions were detected, and distal target genes of regulatory elements were determined using three-dimensional (3D) chromatin looping. Herein, we present OncoBase (http://www.oncobase.biols.ac.cn/), an integrated database for annotating 81 385 242 somatic mutations in 68 cancer types from more than 120 cancer projects by exploring their roles in distal interactions between target genes and regulatory elements. OncoBase integrates local chromatin signatures, 3D chromatin interactions in different cell types and reconstruction of enhancer-target networks using state-of-the-art algorithms. It employs informative visualization tools to display the integrated local and 3D chromatin signatures and effects of somatic mutations on regulatory elements. Enhancer-promoter interactions estimated from chromatin interactions are integrated into a network diffusion system that quantitatively prioritizes somatic mutations and target genes from a large pool. Thus, OncoBase is a useful resource for the functional annotation of regulatory noncoding regions and systematically benchmarking the regulatory effects of embedded noncoding somatic mutations in human carcinogenesis.

regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

dbInDel: a database of enhancer-associated insertion and deletion variants by analysis of H3K27ac ChIP-Seq

Summary

Cancer hallmarks rely on its specific transcriptional programs, which are dysregulated by multiple mechanisms, including genomic aberrations in the DNA regulatory regions. Genome-wide association studies have shown many variants are found within putative enhancer elements. To provide insights into the regulatory role of enhancer-associated non-coding variants in cancer epigenome, and to facilitate the identification of functional non-coding mutations, we present dbInDel, a database where we have comprehensively analyzed enhancer-associated insertion and deletion variants for both human and murine samples using ChIP-Seq data. Moreover, we provide the identification and visualization of upstream TF binding motifs in InDel-containing enhancers. Downstream target genes are also predicted and analyzed in the context of cancer biology. The dbInDel database promotes the investigation of functional contributions of non-coding variants in cancer epigenome.

Availability and implementation

The database, dbInDel, can be accessed from http://enhancer-indel.cam-su.org/.

Supplementary information

Supplementary data are available at Bioinformatics online.