Circular Silhouette and a Fast Algorithm.

Circular data clustering has recently been solved exactly in sub-quadratic time. However, the solution requires a given number of clusters; methods for choosing this number on linear data are inapplicable to circular data. To fill this gap, we introduce the circular silhouette to measure cluster quality and a fast algorithm to calculate the average silhouette width. The algorithm runs in linear time to the number of points on sorted data, instead of quadratic time by the silhouette definition. Empirically, it is over 3000 times faster than by silhouette definition on 1,000,000 circular data points in five clusters. On simulated datasets, the algorithm returned correct numbers of clusters. We identified clusters on round genomes of human mitochondria and bacteria. On sunspot activity data, we found changed solar-cycle patterns over the past two centuries. Using the circular silhouette not only eliminates the subjective selection of number of clusters, but is also scalable to big circular and periodic data abundant in science, engineering, and medicine.

Comparative transcriptome analyses reveal insights into catkin bloom patterns in pecan protogynous and protandrous cultivars.

In perennial plants such as pecan, once reproductive maturity is attained, there are genetic switches that are regulated and required for flower development year after year. Pecan trees are heterodichogamous with both pistillate and staminate flowers produced on the same tree. Therefore, defining genes exclusively responsible for pistillate inflorescence and staminate inflorescence (catkin) initiation is challenging at best. To understand these genetic switches and their timing, this study analyzed catkin bloom and gene expression of lateral buds collected from a protogynous (Wichita) and a protandrous (Western) pecan cultivar in summer, autumn and spring. Our data showed that pistillate flowers in the current season on the same shoot negatively impacted catkin production on the protogynous 'Wichita' cultivar. Whereas fruit production the previous year on 'Wichita' had a positive effect on catkin production on the same shoot the following year. However, fruiting the previous year nor current year pistillate flower production had no significant effect on catkin production on 'Western' (protandrous cultivar) cultivar. The RNA-Seq results present more significant differences between the fruiting and non-fruiting shoots of the 'Wichita' cultivar compared to the 'Western' cultivar, revealing the genetic signals likely responsible for catkin production. Our data presented here, indicates the genes showing expression for the initiation of both types of flowers the season before bloom.

Statistical evidence for the presence of trajectory in single-cell data.

BACKGROUND: Cells progressing from an early state to a developed state give rise to lineages in cell differentiation. Knowledge of these lineages is central to developmental biology. Each biological lineage corresponds to a trajectory in a dynamical system. Emerging single-cell technologies such as single-cell RNA sequencing can capture molecular abundance in diverse cell types in a developing tissue. Many computational methods have been developed to infer trajectories from single-cell data. However, to our knowledge, none of the existing methods address the problem of determining the existence of a trajectory in observed data before attempting trajectory inference. RESULTS: We introduce a method to identify the existence of a trajectory using three graph-based statistics. A permutation test is utilized to calculate the empirical distribution of the test statistic under the null hypothesis that a trajectory does not exist. Finally, a p-value is calculated to quantify the statistical significance for the presence of trajectory in the data. CONCLUSIONS: Our work contributes new statistics to assess the level of uncertainty in trajectory inference to increase the understanding of biological system dynamics.

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.

Detecting genetic epistasis by differential departure from independence.

Countering prior beliefs that epistasis is rare, genomics advancements suggest the other way. Current practice often filters out genomic loci with low variant counts before detecting epistasis. We argue that this practice is far from optimal because it can throw away strong epistatic patterns. Instead, we present the compensated Sharma-Song test to infer genetic epistasis in genome-wide association studies by differential departure from independence. The test does not require a minimum number of replicates for each variant. We also introduce algorithms to simulate epistatic patterns that differentially depart from independence. Using two simulators, the test performed comparably to the original Sharma-Song test when variant frequencies at a locus are marginally uniform; encouragingly, it has a marked advantage over alternatives when variant frequencies are marginally nonuniform. The test further revealed uniquely clean epistatic variants associated with chicken abdominal fat content that are not prioritized by other methods. Genes involved in most numbers of inferred epistasis between single nucleotide polymorphisms (SNPs) belong to pathways known for obesity regulation; many top SNPs are located on chromosome 20 and in intergenic regions. Measuring differential departure from independence, the compensated Sharma-Song test offers a practical choice for studying epistasis robust to nonuniform genetic variant frequencies.

Inference of trajectory presence by tree dimension and subset specificity by subtree cover.

The complexity of biological processes such as cell differentiation is reflected in dynamic transitions between cellular states. Trajectory inference arranges the states into a progression using methodologies propelled by single-cell biology. However, current methods, all returning a best trajectory, do not adequately assess statistical significance of noisy patterns, leading to uncertainty in inferred trajectories. We introduce a tree dimension test for trajectory presence in multivariate data by a dimension measure of Euclidean minimum spanning tree, a test statistic, and a null distribution. Computable in linear time to tree size, the tree dimension measure summarizes the extent of branching more effectively than globally insensitive number of leaves or tree diameter indifferent to secondary branches. The test statistic quantifies trajectory presence and its null distribution is estimated under the null hypothesis of no trajectory in data. On simulated and real single-cell datasets, the test outperformed the intuitive number of leaves and tree diameter statistics. Next, we developed a measure for the tissue specificity of the dynamics of a subset, based on the minimum subtree cover of the subset in a minimum spanning tree. We found that tissue specificity of pathway gene expression dynamics is conserved in human and mouse development: several signal transduction pathways including calcium and Wnt signaling are most tissue specific, while genetic information processing pathways such as ribosome and mismatch repair are least so. Neither the tree dimension test nor the subset specificity measure has any user parameter to tune. Our work opens a window to prioritize cellular dynamics and pathways in development and other multivariate dynamical systems.

Four chromosome scale genomes and a pan-genome annotation to accelerate pecan tree breeding.

Genome-enabled biotechnologies have the potential to accelerate breeding efforts in long-lived perennial crop species. Despite the transformative potential of molecular tools in pecan and other outcrossing tree species, highly heterozygous genomes, significant presence-absence gene content variation, and histories of interspecific hybridization have constrained breeding efforts. To overcome these challenges, here, we present diploid genome assemblies and annotations of four outbred pecan genotypes, including a PacBio HiFi chromosome-scale assembly of both haplotypes of the 'Pawnee' cultivar. Comparative analysis and pan-genome integration reveal substantial and likely adaptive interspecific genomic introgressions, including an over-retained haplotype introgressed from bitternut hickory into pecan breeding pedigrees. Further, by leveraging our pan-genome presence-absence and functional annotation database among genomes and within the two outbred haplotypes of the 'Lakota' genome, we identify candidate genes for pest and pathogen resistance. Combined, these analyses and resources highlight significant progress towards functional and quantitative genomics in highly diverse and outbred crops.

Fast Optimal Circular Clustering and Applications on Round Genomes.

Round genomes are found in bacteria, plant chloroplasts, and mitochondria. Genetic or epigenetic marks can present biologically interesting clusters along a circular genome. The circular data clustering problem groups N points on a circle into K clusters to minimize the within-cluster sum of squared distances. Repeatedly applying the K-means algorithm takes quadratic time, impractical for large circular datasets. To overcome this issue, we developed a reproducible fast optimal circular clustering (FOCC) algorithm of worst-case O(KN log2 N) time. The core is a fast optimal framed clustering algorithm, which we designed by integrating two divide-and-conquer and one bracket dynamic programming strategies. The algorithm is optimal based on a property of monotonic increasing cluster borders over frames on linearized data. On clustering 50,000 circular data points, FOCC outruns brute-force or heuristic circular clustering by three orders of magnitude in time. We produced clusters of CpG sites and genes along three round genomes, exhibiting higher quality than heuristic clustering. More broadly, the presented subquadratic-time algorithms offer the fastest known solution to not only framed and circular clustering, but also angular, periodical, and looped clustering. We implemented these algorithms in the R package 'OptCirClust' (https://CRAN.R-project.org/package=OptCirClust).

Fundamental gene network rewiring at the second order within and across mammalian systems.

MOTIVATION: Genetic or epigenetic events can rewire molecular networks to induce extraordinary phenotypical divergences. Among the many network rewiring approaches, no model-free statistical methods can differentiate gene-gene pattern changes not attributed to marginal changes. This may obscure fundamental rewiring from superficial changes. RESULTS: Here we introduce a model-free Sharma-Song test to determine if patterns differ in the second order, meaning that the deviation of the joint distribution from the product of marginal distributions is unequal across conditions. We prove an asymptotic chi-squared null distribution for the test statistic. Simulation studies demonstrate its advantage over alternative methods in detecting second-order differential patterns. Applying the test on three independent mammalian developmental transcriptome datasets, we report a lower frequency of co-expression network rewiring between human and mouse for the same tissue group than the frequency of rewiring between tissue groups within the same species. We also find second-order differential patterns between microRNA promoters and genes contrasting cerebellum and liver development in mice. These patterns are enriched in the spliceosome pathway regulating tissue specificity. Complementary to previous mammalian comparative studies mostly driven by first-order effects, our findings contribute an understanding of system-wide second-order gene network rewiring within and across mammalian systems. Second-order differential patterns constitute evidence for fundamentally rewired biological circuitry due to evolution, environment or disease. AVAILABILITY AND IMPLEMENTATION: The generic Sharma-Song test is available from the R package 'DiffXTables' at https://cran.r-project.org/package=DiffXTables. Other code and data are described in Section 2. SUPPLEMENTARY INFORMATION: Supplementary data 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.

A genome-wide analysis of pentatricopeptide repeat (PPR) protein-encoding genes in four Gossypium species with an emphasis on their expression in floral buds, ovules, and fibers in upland cotton.

Cotton is the most important natural fiber used in textiles. Breeding for "three-lines", i.e., cytoplasmic male sterility (CMS)-based sterile (A), maintainer (B), and restorer (R) line, is a promising approach to harness hybrid vigor in cotton. Pentatricopeptide repeat (PPR) protein-encoding genes play an important role in plant growth and development including restoration of CMS plants to male fertility. However, PPRs, especially those contributing to CMS and fiber development, remain largely unknown in cotton. In this study, a genome-wide identification and characterization of PPR gene family in four Gossypium species with genome sequences (G. arboreum, G. raimondii, G. hirsutum, and G. barbadense) were performed, and expressed PPR genes in developing floral buds, ovules, and fibers were compared to identify possible PPRs related to CMS restoration and fiber development. A total of 539, 558, 1032, and 1055 PPRs were predicted in the above four species, respectively, which were further mapped to chromosomes for a synteny analysis. Through an RNA-seq analysis, 86% (882) PPRs were expressed in flowering buds of upland cotton (G. hirsutum); however, only 11 and 6 were differentially expressed (DE) between restorer R and its near-isogenic (NI) B and between R and its NI A line, respectively. Another RNA-seq analysis identified the expression of only 54% (556) PPRs in 0 and 3 day(s) post-anthesis (DPA) ovules and 24% (247) PPRs in 10 DPA fibers; however, only 59, 6, and 27 PPRs were DE in 0 and 3 DPA ovules, and 10 DPA fibers between two backcross inbred lines (BILs) with differing fiber length, respectively. Only 2 PPRs were DE between Xuzhou 142 and its fiberless and fuzzless mutant. Quantitative RT-PCR analysis confirmed the validity of the RNA-seq results for the gene expression pattern. Therefore, only a very small number of PPRs may be associated with fertility restoration of CMS and genetic differences in fiber initiation and elongation. These results lay a foundation for understanding the roles of PPR genes in cotton, and will be useful in the prioritization of candidate PPR gene functional validation for cotton CMS restoration and fiber development.

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.

A meta-analysis of quantitative trait loci for abiotic and biotic stress resistance in tetraploid cotton.

The number and location of mapped quantitative trait loci (QTL) depend on genetic populations and testing environments. The identification of consistent QTL across genetic backgrounds and environments is a pre-requisite to marker-assisted selection. This study analyzed a total of 661 abiotic and biotic stress resistance QTL based on our previous work and other publications using the meta-analysis software Biomercator. It identified chromosomal regions containing QTL clusters for different resistance traits and hotspots for a particular resistance trait in cotton from 98 QTL for drought tolerance under greenhouse (DT) and 150 QTL in field conditions (FDT), 80 QTL for salt tolerance in the greenhouse conditions (ST), 201 QTL for resistance to Verticillium wilt (VW, Verticillium dahliae), 47 QTL for resistance to Fusarium wilt (FW, Fusarium oxysporum f. sp. vasinfectum), and 85 QTL for resistance to root-knot nematodes (RKN, Meloiodogyne incognita) and reniform nematodes (RN, Rotylenchulus reniformis). The traits used in QTL mapping for abiotic stress tolerance included morphological traits-plant height and fresh and dry shoot and root weights, physiological traits-chlorophyll content, osmotic potential, carbon isotope ratio, stomatal conductance, photosynthetic rate, transpiration, canopy temperature, and leaf area index, agronomic traits-seedcotton yield, lint yield, boll weight, and lint percent, and fiber quality traits-fiber length, uniformity, strength, elongation, and micronaire. The results showed that resistance QTL are not uniformly distributed across the cotton genome; some chromosomes carried disproportionally more QTL, QTL clusters, or hotspots. Twenty-three QTL clusters were found on 15 chromosomes (c3, c4, c5, c6, c7, c11, c14, c15, c16, c19, c20, c23, c24, c25, and c26). Moreover, 28 QTL hotshots were associated with different resistance traits including one hotspot on c4 for Verticillium wilt resistance, two QTL hotspots on c24 for chlorophyll content measured under both drought and salt stress conditions, and three other hotspots on c19 for the resistance to Verticillium wilt and Fusarium wilt, and micronaire under drought stress conditions. This meta-analysis of stress tolerance QTL provides an important foundation for cotton breeding and further studies on the genetic mechanisms of abiotic and biotic stress resistance in cotton.

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.

Transcriptome responses in alfalfa associated with tolerance to intensive animal grazing.

Tolerance of alfalfa (Medicago sativa L.) to animal grazing varies widely within the species. However, the molecular mechanisms influencing the grazing tolerant phenotype remain uncharacterized. The objective of this study was to identify genes and pathways that control grazing response in alfalfa. We analyzed whole-plant de novo transcriptomes from grazing tolerant and intolerant populations of M. sativa ssp. falcata subjected to grazing by sheep. Among the Gene Ontology terms which were identified as grazing responsive in the tolerant plants and differentially enriched between the tolerant and intolerant populations (both grazed), most were associated with the ribosome and translation-related activities, cell wall processes, and response to oxygen levels. Twenty-one grazing responsive pathways were identified that also exhibited differential expression between the tolerant and intolerant populations. These pathways were associated with secondary metabolite production, primary carbohydrate metabolic pathways, shikimate derivative dependent pathways, ribosomal subunit composition, hormone signaling, wound response, cell wall formation, and anti-oxidant defense. Sequence polymorphisms were detected among several differentially expressed homologous transcripts between the tolerant and intolerant populations. These differentially responsive genes and pathways constitute potential response mechanisms for grazing tolerance in alfalfa. They also provide potential targets for molecular breeding efforts to develop grazing-tolerant cultivars of alfalfa.