Norcantharidin overcomes vemurafenib resistance in melanoma by inhibiting pentose phosphate pathway and lipogenesis via downregulating the mTOR pathway.

Melanoma is the most aggressive type of skin cancer with a high incidence and low survival rate. More than half of melanomas present the activating BRAF mutations, along which V600E mutant represents 70%-90%. Vemurafenib (Vem) is an FDA-approved small-molecule kinase inhibitor that selectively targets activated BRAF V600E and inhibits its activity. However, the majority of patients treated with Vem develop acquired resistance. Hence, this study aims to explore a new treatment strategy to overcome the Vem resistance. Here, we found that a potential anticancer drug norcantharidin (NCTD) displayed a more significant proliferation inhibitory effect against Vem-resistant melanoma cells (A375R) than the parental melanoma cells (A375), which promised to be a therapeutic agent against BRAF V600E-mutated and acquired Vem-resistant melanoma. The metabolomics analysis showed that NCTD could, especially reverse the upregulation of pentose phosphate pathway and lipogenesis resulting from the Vem resistance. In addition, the transcriptomic analysis showed a dramatical downregulation in genes related to lipid metabolism and mammalian target of the rapamycin (mTOR) signaling pathway in A375R cells, but not in A375 cells, upon NCTD treatment. Moreover, NCTD upregulated butyrophilin (BTN) family genes, which played important roles in modulating T-cell response. Consistently, we found that Vem resistance led to an obvious elevation of the p-mTOR expression, which could be remarkably reduced by NCTD treatment. Taken together, NCTD may serve as a promising therapeutic option to resolve the problem of Vem resistance and to improve patient outcomes by combining with immunomodulatory therapy.

Overcoming biases in causal inference of molecular interactions.

MOTIVATION: Computer inference of biological mechanisms is increasingly approachable due to dynamically rich data sources such as single-cell genomics. Inferred molecular interactions can prioritize hypotheses for wet-lab experiments to expedite biological discovery. However, complex data often come with unwanted biological or technical variations, exposing biases over marginal distribution and sample size in current methods to favor spurious causal relationships. RESULTS: Considering function direction and strength as evidence for causality, we present an adapted functional chi-squared test (AdpFunChisq) that rewards functional patterns over non-functional or independent patterns. On synthetic and three biology datasets, we demonstrate the advantages of AdpFunChisq over 10 methods on overcoming biases that give rise to wide fluctuations in the performance of alternative approaches. On single-cell multiomics data of multiple phenotype acute leukemia, we found that the T-cell surface glycoprotein CD3 delta chain may causally mediate specific genes in the viral carcinogenesis pathway. Using the causality-by-functionality principle, AdpFunChisq offers a viable option for robust causal inference in dynamical systems. AVAILABILITY AND IMPLEMENTATION: The AdpFunChisq test is implemented in the R package 'FunChisq' (2.5.2 or above) at https://cran.r-project.org/package=FunChisq. All other source code along with pre-processed data is available at Code Ocean https://doi.org/10.24433/CO.2907738.v1. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

The cellulose synthase (CesA) gene family in four Gossypium species: phylogenetics, sequence variation and gene expression in relation to fiber quality in Upland cotton.

Cellulose synthases (CesAs) are multi-subunit enzymes found on the plasma membrane of plant cells and play a pivotal role in cellulose production. The cotton fiber is mainly composed of cellulose, and the genetic relationships between CesA genes and cotton fiber yield and quality are not fully understood. Through a phylogenetic analysis, the CesA gene family in diploid Gossypium arboreum and Gossypium raimondii, as well as tetraploid Gossypium hirsutum ('TM-1') and Gossypium barbadense ('Hai-7124' and '3-79'), was divided into 6 groups and 15 sub-groups, with each group containing two to five homologous genes. Most CesA genes in the four species are highly collinear. Among the five cotton genomes, 440 and 1929 single nucleotide polymorphisms (SNPs) in the CesA gene family were identified in exons and introns, respectively, including 174 SNPs resulting in amino acid changes. In total, 484 homeologous SNPs between the A and D genomes were identified in diploids, while 142 SNPs were detected between the two tetraploids, with 32 and 82 SNPs existing within G. hirsutum and G. barbadense, respectively. Additionally, 74 quantitative trait loci near 18 GhCesA genes were associated with fiber quality. One to four GhCesA genes were differentially expressed (DE) in ovules at 0 and 3 days post anthesis (DPA) between two backcross inbred lines having different fiber lengths, but no DE genes were identified between these lines in developing fibers at 10 DPA. Twenty-seven SNPs in above DE CesA genes were detected among seven cotton lines, including one SNP in Ghi_A08G03061 that was detected in four G. hirsutum genotypes. This study provides the first comprehensive characterization of the cotton CesA gene family, which may play important roles in determining cotton fiber quality.

Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers.

MOTIVATION: Chromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality and reproducibility. RESULTS: We present the chromosome clustering method, establish its optimality and runtime and evaluate its performance. It uses dynamic programming enhanced with an algorithm to reduce search-space in-place to decrease runtime overhead. Using the method, we delineated outstanding genomic zones in 17 human cancer types. We identified strong continuity in dysregulation polarity-dominance by either up- or downregulated genes in a zone-along chromosomes in all cancer types. Significantly polarized dysregulation zones specific to cancer types are found, offering potential diagnostic biomarkers. Unreported previously, a total of 109 loci with conserved dysregulation polarity across cancer types give insights into pan-cancer mechanisms. Efficient chromosomal clustering opens a window to characterize molecular patterns in cancer genome and beyond. AVAILABILITY AND IMPLEMENTATION: Weighted univariate clustering algorithms are implemented within the R package 'Ckmeans.1d.dp' (4.0.0 or above), freely available at https://cran.r-project.org/package=Ckmeans.1d.dp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs.

BACKGROUND: Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene's directional effect on human cancer. RESULTS: We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia. CONCLUSIONS: This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers.

A fast exact functional test for directional association and cancer biology applications.

Directional association measured by functional dependency can answer important questions on relationships between variables, for example, in discovery of molecular interactions in biological systems. However, when one has no prior information about the functional form of a directional association, there is not a widely established statistical procedure to detect such an association. To address this issue, here we introduce an exact functional test for directional association by examining the strength of functional dependency. It is effective in promoting functional patterns by reducing statistical power on non-functional patterns. We designed an algorithm to carry out the test using a fast branch-and-bound strategy, which achieved a substantial speedup over brute-force enumeration. On data from an epidemiological study of liver cancer, the test identified the hepatitis status of a subject as the most influential risk factor among others for the cancer phenotype. On human lung cancer transcriptome data, the test selected 1049 transcription start sites of putative noncoding RNAs directionally associated with lung cancers, stronger than 95% of 589 curated cancer genes. These predictions include non-monotonic interaction patterns, to which other routine tests were insensitive. Complementing symmetric (non-directional) association methods such as Fisher's exact test, the exact functional test is a unique exact statistical test for evaluating evidence for causal relationships.

Nonmonotonic Pathway Gene Expression Analysis Reveals Oncogenic Role of p27/Kip1 at Intermediate Dose.

The mechanistic basis by which the level of p27Kip1 expression influences tumor aggressiveness and patient mortality remains unclear. To elucidate the competing tumor-suppressing and oncogenic effects of p27Kip1 on gene expression in tumors, we analyzed the transcriptomes of squamous cell papilloma derived from Cdkn1b nullizygous, heterozygous, and wild-type mice. We developed a novel functional pathway analysis method capable of testing directional and nonmonotonic dose response. This analysis can reveal potential causal relationships that might have been missed by other nondirectional pathway analysis methods. Applying this method to capture dose-response curves in papilloma gene expression data, we show that several known cancer pathways are dominated by low-high-low gene expression responses to increasing p27 gene doses. The oncogene cyclin D1, whose expression is elevated at an intermediate p27 dose, is the most responsive gene shared by these cancer pathways. Therefore, intermediate levels of p27 may promote cellular processes favoring tumorigenesis-strikingly consistent with the dominance of heterozygous mutations in CDKN1B seen in human cancers. Our findings shed new light on regulatory mechanisms for both pro- and anti-tumorigenic roles of p27Kip1. Functional pathway dose-response analysis provides a unique opportunity to uncover nonmonotonic patterns in biological systems.

Constrained inference of protein interaction networks for invadopodium formation in cancer.

Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. They benchmarked CGLN on randomly generated networks, and CGLN outperformed its alternatives when 50% or more interactions in a network are given as local constraints. CGLN is then applied to infer dynamic protein interaction networks regulating invadopodium formation in motile cancer cells. CGLN predicted 134 novel protein interactions for their involvement in invadopodium formation. The most frequently predicted interactions centre around focal adhesion kinase and tyrosine kinase substrate TKS4, and 14 interactions are supported by the literature in molecular contexts related to invadopodium formation. As an alternative to the Bayesian paradigm, the CGLN method offers constrained network inference without requiring prior probabilities and thus can promote novel interactions, consistent with the discovery process of scientific facts that are not yet in common beliefs.

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Integrated cellular and gene interaction modelling of pattern formation.

Cellular behaviour depends on and also modifies protein concentration and activity. An integrated cellular and gene interaction model is proposed to reveal this relationship. In this model, protein activity varies spatiotemporally with cellular location, gene interaction, and diffusion. In the meanwhile, cellular behaviour can vary spatially, driven by cell-cell signalling and inhomogeneous protein distribution across cells. This model integrates two components. The first component adopts a variation of the reaction-diffusion mechanism at the gene expression level. The second component is a lattice cellular model based on the Differential Adhesion Hypothesis (DAH) for cell sorting at the cellular level. Cell sorting and tumour invasion were simulated to illustrate the model. This model approximates cellular pattern formation more closely than existing models based on cell density.