Search

Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.

Hepatocellular carcinoma (HCC) has emerged as the third cause of cancer-related death owing to lacking effective systemic therapies. Genomic DNA sequencing revealed the high frequency of loss-of-function mutations in ARID2, which encodes a subunit of SWI/SNF chromatin remodeling complex, however, the therapeutic strategy for the HCC patients with ARID2 mutations is still completely unclear. In this study, we first performed a high-throughput screening approach using a compound library consisting of 2 180 FDA-approved drugs and other compounds, to elicit the potential drugs for synthetic lethality to target ARID2-deficient HCC cells. Interestingly, JQ1, a selective inhibitor of bromodomain protein BRD4, uniquely suppressed the growth of ARID2- deficient HCC cells. Next JQ1 is further confirmed to predominantly induce cell lethality upon ARID2 depletion through exacerbating DNA damage, especially double strand breaks (DSBs). Functional assays demonstrated that both BRD4 inhibition and ARID2 deficiency synergistically impede two main DNA damage repair pathways, homologous recombination (HR) and non-homologous end-joining (NHEJ), through attenuating the transcription of BRCA1, RAD51, and 53BP1, which encode the core molecules responsible for DSB repair. Mechanistically, both ARID2 and BRD4 exert a synergistic effect for maintaining transcriptional enhancer-promoter loops of these genes within chromatin conformation. However, as both ARID2 and BRD4 are disrupted, the expression of these DNA repair-related genes in response to DNA damage are hindered, resulting in DSB accumulation and cell apoptosis. Taken together, this study discloses that BRD4 inhibition may induce synthetic lethality in ARID2-deficient HCC cells, which might provide a potential therapeutic strategy for HCC patients with ARID2 mutations.

IntroductionTumor clonal structure is closely related to future progression, which has been mainly investigated via mutation abundance clustering in bulk sample. With limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking.MethodsHere, using bulk and single-cell mutational data from liver and colorectal cancers, we would like to check the possibility of obtaining accurate tumor clonal structures from bulk-level analysis. We checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. We examined VAF ranges for different groups of co-mutations, and also the possibility of discriminating them.ResultsWhile bulk analysis suggested absence of subclonal peaks and possibly neutral evolution in some cases, single-cell analysis identified co-existing subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them, even with other parameter introduced. The difference between mutation cluster and tumor subclone is accountable for the challenge in bulk clonal deconvolution, especially in case of branched evolution as shown in colorectal cancer.ConclusionComplex subclonal structures and dynamic evolution are hidden under the seemingly clonal neutral pattern at bulk level, suggesting single-cell analysis will be needed to avoid under-estimation of tumor heterogeneity.Research HighlightsBulk-level mutation abundance clusters are not equal to tumor subclones.Different groups of co-mutations could not be discriminated at bulk-level.Single-cell mutational analysis can identify rather than infer tumor subclones.Co-existing tumor subclones may have clonal neutral appearance at bulk-level.Lay summarySystematic comparison of tumor clonal structure differences between bulk and single-cell mutational analysis is lacking. Here we performed such as study and found that complex subclonal structures and dynamic evolution are hidden under clonal neutral appearance at bulk level in liver and colorectal cancers, suggesting single-cell analysis will be needed to avoid under-estimation of tumor heterogeneity.

Background As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. Methods Here, we applied single-cell genomic amplification and RNA-Seq to a specimen of human prostate BCC (CK34βE12+ /P63+ /PAP- /PSA- ). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the transcriptomes of 69 single cells were also obtained. Results The five putative driver genes mutated in BCC are CASC5, NUTM1, PTPRC, KMT2C, and TBX3, and the top three nucleotide substitutions are C>T, T>C, and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated that these single cells are from the same tumor clone. The 69 single cells were clustered into tumor, stromal, and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers like KRT5, KRT14, and KRT23 and epithelial markers EPCAM, CDH1, and CD24. The transcription factor covariance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal cell signatures. Interestingly, at single-cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer-derived circulating tumor cells. Conclusions This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.

Genetic heterogeneity of tumor is closely related to clonal evolution, phenotypic diversity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.

Large-conductance and Ca2+-activated K+ (BK) channels are expressed in human hepatic stellate cells (HSCs), where they have roles in normal hepatic microcirculation, as well as in portal hypertension in liver cirrhosis through the regulation of contractility in activated HSCs. Nevertheless, whether BK channel activity exerts protective effects against aberrant HSC activation and hepatic fibrosis is unknown. Here, we report that BK channels are expressed in activated primary rat HSCs as well as in a human HSC line. Moreover, whole-cell K+ currents recorded from activated HSCs were markedly increased by exposure to rottlerin, a BK channel-specific activator, but were inhibited by treatment with the BK channel-specific inhibitor, paxilline, suggesting that BK channels are functional in activated HSCs. Overexpression but not downregulation of the BK channel pore-forming alpha subunit, KCNMA1, led to reduced migration and collagen expression in activated HSCs. Consistently, rottlerin treatment suppressed the fibrogenic cell function both in vitro and in CCl4-induced liver fibrosis in vivo. Microarray and pathway analysis, combined with a luciferase reporter assay and western blotting, further showed that rottlerin treatment led to a significant downregulation of the profibrotic TGFβ1/SMAD3 and JAK/STAT3 signaling pathways, both in vitro and in vivo. Our findings not only link BK channel function to profibrotic signaling pathways, but also provide evidence that BK channel activation represents a promising therapeutic strategy for the treatment of liver fibrosis.

As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. Here we applied single-cell genomic amplification and RNA-Seq to a specimen of human prostate BCC (CK34βE12+/P63+/PAP−/PSA−). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the 5 putative driver genes mutated areCASC5,NUTM1,PTPRC,KMT2CandTBX3. The top 3 nucleotide substitutions are C>T, T>C and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated these single cells are from the same tumor clone. The transcriptomes of 69 single cells were obtained, and they were clustered into tumor, stromal and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers likeKRT5,KRT14andKRT23, and epithelial markersEPCAM,CDH1andCD24. The transcription factor (TF) co-variance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal-cell signatures. Interestingly, at single-cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer-derived circulating tumor cells. This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

The high-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate immune response of the T cells. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response. Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Here we retrospect the DNA origins of the immune-positive and non-negative neoantigens in the context of 3D genome and discovered that 1) DNA loci of the immuno-positive neoantigens tend to cluster genome-wise. 2) DNA loci of the immuno-positive neoantigens tend to belong to active chromosomal compartment (compartment A) in some chromosomes. 3). DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We believe that the 3D genome information will help more precise neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Background The differentiation and maturation trajectories of fetal liver stem/progenitor cells (LSPCs) are not fully understood at single-cell resolution, and a priori knowledge of limited biomarkers could restrict trajectory tracking. Results We employed marker-free single-cell RNA-Seq to characterize comprehensive transcriptional profiles of 507 cells randomly selected from seven stages between embryonic day 11.5 and postnatal day 2.5 during mouse liver development, and also 52 Epcam-positive cholangiocytes from postnatal day 3.25 mouse livers. LSPCs in developing mouse livers were identified via marker-free transcriptomic profiling. Single-cell resolution dynamic developmental trajectories of LSPCs exhibited contiguous but discrete genetic control through transcription factors and signaling pathways. The gene expression profiles of cholangiocytes were more close to that of embryonic day 11.5 rather than other later staged LSPCs, cuing the fate decision stage of LSPCs. Our marker-free approach also allows systematic assessment and prediction of isolation biomarkers for LSPCs. Conclusions Our data provide not only a valuable resource but also novel insights into the fate decision and transcriptional control of self-renewal, differentiation and maturation of LSPCs. Electronic supplementary material The online version of this article (10.1186/s12864-017-4342-x) contains supplementary material, which is available to authorized users.

Polycyclic tetramate macrolactams (PTMs) were identified as distinct secondary metabolites of the mangrove-derived Streptomyces xiamenensis 318. Together with three known compounds—ikarugamycin (1), capsimycin (2) and capsimycin B (3)—two new compounds, capsimycin C (4) with trans-diols and capsimycin D (5) with trans-configurations at C-13/C-14, have been identified. The absolute configurations of the tert/tert-diols moiety was determined in 4 by NMR spectroscopic analysis, CD spectral comparisons and semi-synthetic method. The post-modification mechanism of the carbocyclic ring at C-14/C-13 of compound 1 in the biosynthesis of an important intermediate 3 was investigated. A putative cytochrome P450 superfamily gene, SXIM_40690 (ikaD), which was proximally localized to the ikarugamycin biosynthetic pathway, was characterized. In vivo gene inactivation and complementation experiment confirmed that IkaD catalysed the epoxide-ring formation reaction and further hydroxylation of ethyl side chain to form capsimycin G (3′). Binding affinities and kinetic parameters for the interactions between ikarugamycin (1) and capsimycin B (3) with IkaD were measured with Surface Plasmon Resonance. The intermediate compound 3′ was isolated and identified as 30-hydroxyl-capsimycin B. The caspimycins 2 and 3, were transferred to methoxyl derivatives, 6 and 7, under acidic and heating conditions. Compounds 1–3 exhibited anti-proliferative activities against pancreatic carcinoma with IC50 values of 1.30–3.37 μM.

Insulin signaling is mediated by a highly integrated network that controls glucose metabolism, protein synthesis, cell growth, and differentiation. Our previous work indicates that the insulin receptor tyrosine kinase substrate (IRTKS), also known as BAI1-associated protein 2-like 1 (BAIAP2L1), is a novel regulator of insulin network, but the mechanism has not been fully studied. In this work we reveal that IRTKS co-localizes with Src homology (SH2) containing inositol polyphosphate 5-phosphatase-2 (SHIP2), and the SH3 domain of IRTKS directly binds to SHIP2&rsquo, s catalytic domain INPP5c. IRTKS suppresses SHIP2 phosphatase to convert phosphatidylinositol 3,4,5-triphosphate (PI(3,4,5)P3, PIP3) to phosphatidylinositol (3,4) bisphosphate (PI(3,4)P2). IRTKS-knockout significantly increases PI(3,4)P2 level and decreases cellular PI(3,4,5)P3 content. Interestingly, the interaction between IRTKS and SHIP2 is dynamically regulated by insulin, which feeds back and affects the tyrosine phosphorylation of IRTKS. Furthermore, IRTKS overexpression elevates PIP3, activates the AKT&ndash, mTOR signaling pathway, and increases cell proliferation. Thereby, IRTKS not only associates with insulin receptors to activate PI3K but also interacts with SHIP2 to suppress its activity, leading to PIP3 accumulation and the activation of the AKT&ndash, mTOR signaling pathway to modulate cell proliferation.

Somatic mutations of many cancer genes tend to co-occur (termed co-mutations) in certain patterns during tumor initiation and progression. However, the genetic and epigenetic mechanisms that contribute to the co-mutations of these cancer genes have yet to be explored. Here, we systematically investigated the association between the somatic co-mutations of cancer genes and high-order chromatin conformation. Significantly, somatic point co-mutations in protein-coding genes were closely associated with high-order spatial chromatin folding. We propose that these regions be termed Spatial Co-mutation Hotspots (SCHs) and report their occurrence in different cancer types. The conserved mutational signatures and DNA sequences flanking these point co-mutations, as well as CTCF-binding sites, are also enriched within the SCH regions. The genetic alterations that are harboured in the same SCHs tend to disrupt cancer driver genes involved in multiple signalling pathways. The present work demonstrates that high-order spatial chromatin organisation may contribute to the somatic co-mutations of certain cancer genes during tumor development.

Voltage-gated ether à go-go (EAG) K+ channels are expressed in various types of cancer cells and also in the central nervous system. Aberrant overactivation of human EAG1 (hEAG1) channels is associated with cancer and neuronal disorders such as Zimmermann-Laband and Temple-Baraitser syndromes. Although hEAG1 channels are recognized as potential therapeutic targets, regulation of their functional properties is only poorly understood. Here, we show that the membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2) is a potent inhibitory gating modifier of hEAG1 channels. PIP2 inhibits the channel activity by directly binding to a short N-terminal segment of the channel important for Ca2+/calmodulin (CaM) binding as evidenced by bio-layer interferometry measurements. Conversely, depletion of endogenous PIP2 either by serotonin-induced phospholipase C (PLC) activation or by a rapamycin-induced translocation system enhances the channel activity at physiological membrane potentials, suggesting that PIP2 exerts a tonic inhibitory influence. Our study, combining electrophysiological and direct binding assays, demonstrates that hEAG1 channels are subject to potent inhibitory modulation by multiple phospholipids and suggests that manipulations of the PIP2 signaling pathway may represent a strategy to treat hEAG1 channel-associated diseases.

Transcription factors (TFs) play key roles in controlling gene expression. Systematic identification and annotation of TFs, followed by construction of TF databases may serve as useful resources for studying the function and evolution of transcription factors. We developed a comprehensive plant transcription factor database PlantTFDB (http://planttfdb.cbi.pku.edu.cn), which contains 26,402 TFs predicted from 22 species, including five model organisms with available whole genome sequence and 17 plants with available EST sequences. To provide comprehensive information for those putative TFs, we made extensive annotation at both family and gene levels. A brief introduction and key references were presented for each family. Functional domain information and cross-references to various well-known public databases were available for each identified TF. In addition, we predicted putative orthologs of those TFs among the 22 species. PlantTFDB has a simple interface to allow users to search the database by IDs or free texts, to make sequence similarity search against TFs of all or individual species, and to download TF sequences for local analysis.

Supplemental Digital Content is available in the text, The mutation status of cancer driver genes may correlate with different degrees of malignancy of cancers. The doubling time and multidrug resistance are 2 phenotypes that reflect the degree of malignancy of cancer cells. Because most of cancer driver genes are hard to target, identification of their synthetic lethal partners might be a viable approach to treatment of the cancers with the relevant mutations. The genome-wide screening for synthetic lethal partners is costly and labor intensive. Thus, a computational approach facilitating identification of candidate genes for a focus synthetic lethal RNAi screening will accelerate novel anticancer drug discovery. We used several publicly available cancer cell lines and tumor tissue genomic data in this study. We compared the doubling time and multidrug resistance between the NCI-60 cell lines with mutations in some cancer driver genes and those without the mutations. We identified some candidate synthetic lethal genes to the cancer driver genes APC, KRAS, BRAF, PIK3CA, and TP53 by comparison of their gene phenotype values in cancer cell lines with the relevant mutations and wild-type background. Further, we experimentally validated some of the synthetic lethal relationships we predicted. We reported that mutations in some cancer driver genes mutations in some cancer driver genes such as APC, KRAS, or PIK3CA might correlate with cancer proliferation or drug resistance. We identified 40, 21, 5, 43, and 18 potential synthetic lethal genes to APC, KRAS, BRAF, PIK3CA, and TP53, respectively. We found that some of the potential synthetic lethal genes show significantly higher expression in the cancers with mutations of their synthetic lethal partners and the wild-type counterparts. Further, our experiments confirmed several synthetic lethal relationships that are novel findings by our methods. We experimentally validated a part of the synthetic lethal relationships we predicted. We plan to perform further experiments to validate the other synthetic lethal relationships predicted by this study. Our computational methods achieve to identify candidate synthetic lethal partners to cancer driver genes for further experimental screening with multiple lines of evidences, and therefore contribute to development of anticancer drugs.

Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs. National Science Foundation [MCB 0520140, 0820823, DBI-0640769] National Science Foundation Major Research Instrumentation grant [DBI 0521587] National Science Foundation Arabidopsis [2010 DBI0520267] Samuel Roberts Noble Foundation This work was supported by the National Science Foundation (grants no. MCB 0520140 and 0820823). Additional support included: funding from the National Science Foundation Major Research Instrumentation grant no. DBI 0521587 (Ruth Welti); National Science Foundation Arabidopsis 2010 DBI0520267 (Eve S. Wurtele); The Samuel Roberts Noble Foundation for personnel support (Lloyd W. Sumner and David V. Hultman) and instrumentation purchase; Carnegie Institution for Science (Kun He, Seung Y. Rhee) and National Science Foundation grant DBI-0640769 (Seung Y. Rhee); Yun Lu for performing GC TOEMS in the Fiehn laboratory; Agricultural Research Center at Washington State University (B. Markus Lange). The authors would also like to acknowledge the W. M. Keck Foundation for support at Iowa State University. We acknowledge the very kind support of all the collaborators listed at www.plantmetabolomics.org, who contributed Arabidopsis T-DNA tagged mutant seed stocks, in particular the late Dr. Christian R. H. Raetz (Duke University) for the seed stock carrying the lpxA-1 allele, and Dr. David J. Oliver (Iowa State University) for the seed stock carrying the oxp1 allele.