Epigenetic markers such as post-translational histone modification and altered levels of chromatin modifiers during acclimation and its decline suggest that dynamic epigenetic mechanisms controlling gene expression induce HACT and acclimation memory, to enable the rapid return of the protected phenotype. In this review the link between in vivo physiological evidence and the associated cellular and molecular mechanisms leading to HACT and its difference from short-acting cross-tolerance strategies will be discussed.The primary outcome of heat acclimation is increased thermotolerance, which stems from enhancement of innate cytoprotective pathways. These pathways produce “ON CALL” molecules that can combat stressors to which the body has never been exposed, via cross-tolerance mechanisms (heat acclimation-mediated cross-tolerance—HACT). The foundation of HACT lies in the sharing of generic stress signaling, combined with tissue/organ- specific protective responses. HACT becomes apparent when acclimatory homeostasis is achieved, lasts for several weeks, and has a memory. HACT differs from other forms of temporal protective mechanisms activated by exposure to lower “doses” of the stressor, which induce adaptation to higher “doses” of the same/different stressor; e.g., preconditioning, hormesis. These terms have been adopted by biochemists, toxicologists, and physiologists to describe the rapid cellular strategies ensuring homeostasis. HACT employs two major protective avenues: constitutive injury attenuation and abrupt post-insult release of help signals enhanced by acclimation. To date, the injury-attenuating features seen in all organs studied include fast-responding, enlarged cytoprotective reserves with HSPs, anti-oxidative, anti-apoptotic molecules, and HIF-1α nuclear and mitochondrial target gene products. Using cardiac ischemia and brain hypoxia models as a guide to the broader framework of phenotypic plasticity, HACT is enabled by a metabolic shift induced by HIF-1α and there are less injuries caused by Ca Research into the important topic of acclimation/acclimatization to an adverse environment began in the late Nineteenth century, in the wake of colonialization, and the necessity to adapt to harsh occupational environments in hot tropical countries. The concept was the subject of medical and scientific debates. Based on the experimental physiological evidence existing at the time, the interactions between stressor(s) and the corresponding integrative response(s) were termed, “cross-adaptation” and/or “cross-tolerance” Adolpf, , even tha rapidly evoked cellular strategy that ensures homeostasis, in contrast to acclimation-induced cross-tolerance, which, takes place after acclimation homeostasis has been achieved  is a reversible, within-lifetime phenotypic adaptation to high ambient temperatures. The primary physiological signature of HA is increased heat endurance via prolongation of the operating thermoregulatory span of physiological mechanisms, combined with improved thermotolerance associated with enhanced molecular cytoprotective mechanisms  differs from the cross-tolerance that occurs following classical preconditioning [analogous to the heat shock response (HSR)], a two-window transient effect Figure  evoked bIn contrast, HACT develops slowly during exposure to the acclimating stressors, and is shaped by the nature of the acclimation, as it occurs. HA is a biphasic process, with an initial, transient phase, during which intensive neural stimulation controls physiological processes to alleviate increased body temperature, while molecular pathways are recruited to maintain DNA integrity. This phase, known as “short-term HA,” takes place over ~5 days, and constitutes the “on” switch, which triggers the lengthier processes necessary to achieve acclimatory homeostasis  constitutive injury attenuation; and (ii) the abrupt, post-insult release of help signals that are enhanced in the acclimated phenotype. Moreover, recent experimental evidence regarding “injury attenuation” , are usually employed in studies of human HA. Aerobic exercise training boosts muscle performance; consequently, biochemical adaptations enhance aerobic and anaerobic energy metabolism to meet the need for greater ATP generation. These alterations seem to be in conflict with critical adaptive features of HA, such as a decreased basal metabolic rate. Proceeding with the example of HACT against ischemia, Figure 2+ sensitivity  seen in both the HA and HAEX, but not in the EX groups .The HA phenotype (passive heat exposure as a singular adaptagent), however, displays a variety of specific features that can be cardioprotective, in and of themselves. Thus, a deeper understanding of the roles played by environmental heat vs. metabolic heat load (achieved by training) in the protected phenotype may enable us to determine the origins of the protective features in the aforementioned experimental groups. Here, we are referring to decreased Cas Figure . These fIn a review by Powers et al. , the autPollak et al.  in a nov2 levels in the rat heart and mouse brain subjected to cardiac ischemia and TBI, respectively.Observations of the HA-protected rat cardiophenotype indicate: (i) enhanced glycolytic capacity, though at a slower rate; (ii) larger pre-ischemic endogenous glycogen stores; and (iii) upregulation of the transcript phosphofructokinase 2 (PFK2), a rate-limiting glycolytic enzyme in the normoxic HA rat heart .HIF-1α exerts its transcriptional activation as the heterodimer HIF-1, the product of HIF-1α with the constitutive HIF-1β. Its role in cardio-HACT and neuro-HACT was confirmed by instituting a HIF-1α dimerization blockade either prior to ischemia in the heart, or prior to recovery from traumatic brain injury. In the rat, a HIF-1α dimerization blockade was also conducted during the entire 1 month HA session. We have shown that a continuous HIF-1α dimerization blockade is needed to attenuate HACT, even though no changes in acclimatory levels of HSP72 were measured  to ischemic insult, and the downregulation of the bcl-2 death promotor (BAD) to heat stress, after heat acclimation  of de novo induction upon stress. However, HA also exerts a global protective effect that is implemented in the featured HACT. Here, the impact of (i) the HIF-1α metabolic switch, and (ii) how HA induces processes to cope with the deleterious effects of Ca2+, both studied extensively in our laboratory, will be discussed as prototypes.Unequivocal evidence underscores the important role played by HSP72 and HIF-1α in HACT against impairment in oxygen homeostasis, possibly only because of elevated constitutive levels, which mitigate the need for immediate 2-regulated subunit; however, HA upregulates HIF-1α under normoxic conditions. An important adaptive strategy of the HA phenotype to decreased oxygen availability stems from its reliance upon enhanced glycolysis at a slower rate  was required for the return of HACT , Tetievsky et al.  changes in the linker histones participating in nucleosome spacing, transcription factor accessibility and, in turn, gene expression control during (ii) de-acclimation, a molecular set-up whereby constitutive histone H4 acetylation by the histone-acetylase Tip60 enables HSF1 (heat shock transcription factor 1) binding to the heat shock element, thus predisposing to maintain HSP72 proteostasis, and a return of HACT (Tetievsky et al., Corroborating our postulation of the importance of epigenetics in cross-tolerance is the review by Khoury et al.  on the rGidday , in a reIn sum, the dynamic epigenetic phenomenon not only induces long-lasting HACT, but enables preservation of its physiological beneficial features in a dormant manner. A rapid, short acclimation stimulus re-establishes the physiological, protected HA phenotype. The transcriptional machinery, however, is a continuum. HACT has beneficial roles in health and disease and is already employed in thermotherapy. Yet, our knowledge of the epigenetic within-life dynamic mechanisms involved, as well as how long they persist, is still in its infancy.The author confirms being the sole contributor of this work and approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Endometrial Cancer (EC) is one of the most common female cancers. Genome-wide association studies (GWAS) have been investigated to identify genetic polymorphisms that are predictive of EC risks. Here we utilized a meta-dimensional integrative approach to seek genetically susceptible pathways that may be associated with tumorigenesis and progression of EC. We analyzed GWAS data obtained from Connecticut Endometrial Cancer Study (CECS) and identified the top 20 EC susceptible pathways. To further verify the significance of top 20 EC susceptible pathways, we conducted pathway-level multi-omics analyses using EC exome-Seq, RNA-Seq and survival data, all based on The Cancer Genome Atlas (TCGA) samples. We measured the overall consistent rankings of these pathways in all four data types. Some well-studied pathways, such as p53 signaling and cell cycle pathways, show consistently high rankings across different analyses. Additionally, other cell signaling pathways , genetic information processing pathway (e.g. homologous recombination) and metabolism pathway (e.g. sphingolipid metabolism) are also highly associated with EC risks, diagnosis and prognosis. In conclusion, the meta-dimensional integration of EC cohorts has suggested some common pathways that may be associated from predisposition, tumorigenesis to progression. Endometrial cancer (EC) arises from the endometrium, the inner lining of the uterus. It is the most prevalent gynecologic malignancy and one of the most common cause of deaths of women's cancers. In 2016, it is estimated that there will be 60,050 new cases of EC, and an estimated 10,470 people will die of this disease in the US. Patients with later stage EC have higher risks of tumor recurrence and lower 5-year survival rates  [Besides the lack of replicability exemplified by the EC studies above, there exist other issues of SNP-based GWAS approach. For example, highly significant SNPs usually can only explain a small proportion of heritability while moderately significant SNPs may also harbor predictive information –7. Set-lBeyond genetic predisposition, other alteration events including somatic mutation and gene expression changes also have consequences on the development of EC , 15. TheIn this study, we first conducted pathway-level GWAS analyses of EC with four different methods, and combined their results by Monte-Carlo simulations . Accordi−8  < 1e−8 . Seven S−8  with combined p-values and obtained no strong correlation  based clustering using the normalized SNP counts of the top 20 pathways. We identified two subgroups of EC patients Figure  based onWe next extracted the contributing SNPs of top 20 pathways based on PLINK results and mapped them to genes. Based on the pathway- and gene-level results , we creaWe were interested to see if any pathway with an increased susceptibility to EC also had preferable somatic mutations . To studPathifier algorithm to obtain pathway deregulation score (PDS) for each pathway within each patient, as reported before [Our next question was whether the pathways subjective to EC-susceptibility also have measurable changes in gene expression, such as those shown by eQTL studies . To answd before . We thenA more downstream potential impact of genetic predisposition pathways is cancer patient's relapse-free survival (RFS) . To explBefore integration, we first ranked all pathways based on the results from four different data types mentioned above. Then we calculated the average rankings followed by the permutation test. The permutation-based empirical p-value represents the overall consistency of pathway significance across different EC data types . AnchoriOur study here represents an exemplification of using systems-genomics approach to interrogate molecular mechanisms of diseases , 45. ComWe hypothesize that some pathway alterations that predispose the population to EC may be manifested at the somatic levels and impact the disease progression , 48, sinWe utilized average rankings with permutation-based empirical p-values to measure the overall significance of the top 20 pathways across different data types. Cell signaling and cell cycle regulation related pathways are ranked top four Figure . One patIGF-1/mTOR is significant in both GWAS and exome-Seq data. In this pathway, IGF1 and its downstream targets, EIF2S1 and EIF2B5 are significantly associated with EC Figure . IGF1 plOther pathways that may be of therapeutic targets are IL-5 pathway and sphingolipid metabolism. IL-5 pathway gives significant results in GWAS, survival data, and has FDR < 0.1 in exome-Seq data. The survival result shows its important value in EC relapse prognosis. The binding of IL-5 to IL-5Rα receptor induces the migration of cancer cells was reported , and corPerhaps most surprising finding of this study is that four blood coagulation pathways clumped together in GWAS network can significantly differentiate the two subgroups among EC patients. Blood coagulation disorders were observed in some of the cancer patients before –34, and Our meta-dimensional, pathway-level analysis offers a systematic way of studying the influence of genetic variants in EC, through multiple sources of information that include GWAS, exome-Seq, RNA-Seq, survival analysis. It has confirmed the importance of most well-studied cancer-associated pathways, such as p53 signaling pathway and cell cycle pathway. Moreover, it also sheds light on some less well-studied pathways in relation to EC, such as IGF-1/mTOR, sphingolipid metabolism, and IL-5 pathway. The impacts of these pathways may be associated with EC susceptibility, tumor development, and progression. It would be interesting to conduct follow-up population studies to investigate the association between these pathways and EC risks. Additionally, pathway level stratification in EC patients identified blood coagulation as the top functional super-group differentiating the subpopulations. It will be of great interest to confirm this in other cohorts, as well as associating it with other clinical outcomes. From the study design point of view, a comprehensive study in the future which obtains different types of data from the same population will likely yield more consistent pathway-level results. Some limitations of the pathway-based integration include the incomplete knowledge about pathways currently, as well as lacking topology information in the current pathway metrics. One alternative method is to derive functional units using the graph-based network approach . NeverthWe obtained data from Connecticut Endometrial Cancer Study (CECS) , a populWe downloaded TCGA public Illumina GA_DNASeq level 2 data sets from Broad Institute (BI). A total number of 194 patients with both primary solid tumor samples and normal samples  were used in the analysis. Only the variants unique to the tumor but not the normal sample of each patient were used for downstream analysis.We downloaded TCGA public IlluminaGA_RNASeqV2, and IlluminaHiSeq_RNASeqV2 data sets from University of North Carolina (UNC). We used 23 pairs of tumor/adjacent normal samples for pathway-level gene expression analysis. Additionally, we used 457 primary tumor samples with their clinical information for pathway-level survival analysis (downloaded on 03/24/2015).http://www.broadinstitute.org/gsea/msigdb/index.jsp) [http://www.genome.jp/kegg/brite.html) and BIOCARTA pathway category (http://www.biocarta.com/genes/index.asp).We downloaded pathways from Molecular Signature Database (MsigDB) (dex.jsp) . In thisWe used Cochran-Armitage trend test for the association analysis between the single locus and the disease, by taking different genotypes into consideration. To test the SNP-SNP interactions associated with EC, we used Multifactor Dimension Reduction (MDR) , 23, a 1We used PLINK set-based test for the GWAS data at the gene level. First, we created set files, which contain SNPs associated with a particular gene based on their locations (within +/−20 kb of the gene body). To minimize the bias from correlated SNPs in the same set, we employed linkage disequilibrium (LD) prune so that SNPs under consideration are relatively independent. We selected top 5 (default setting) SNPs with p-values ≤ 0.05 to represent the genes. We used the average chi-square statistics of selected SNPs to represent the set statistics. To obtain the empirical p-values for sets, we applied permutations  on the phenotype labels. After each permutation, pseudo statistics were re-calculated for the SNPs as well as the SNP set statistics. The empirical p-values were determined as the percentages of permuted set statistics exceeding the original statistics.Four pathway-level packages were used for this purpose: PLINK set-based test, GenGen, INRICH and GSEA Pre-ranked test. We elaborate the details of each method below:Pathway-level PLINK set-based test summarizes N SNPs per set (pathway). Given that a smaller N leads to a larger number of significant pathways, we examined the effect of a series of Ns on the pathway outcomes, and determined the optimum N = 20, as it is the elbow point (the point of changing slopes) based on pathways vs. N . Like geGenGen is a software which extends Gene Set Enrichment Analysis (GSEA)  on the Gi ⊠ R approximately matches the original interval Ii ⊠ I  for the number of SNPs and overlapping genes; we also ensured approximately similar SNP densities per kb.INRICH takes a set of independent genomic intervals created by PLINK. It calculates the primary enrichment statistic for each pathway, depending on the number of intervals that overlap at least one target gene . We used0.05 transformed empirical p-value. We conducted GSEA Pre-ranked test based on gene ID permutations.GSEA Pre-ranked test was adapted from the gene-level results of PLINK set-based test. We removed the genes contain no significant SNP , then sorted genes in the descending order of loghttp://comisef.wikidot.com/tutorial:correlateduniformvariates), which also allowed us to control the false positive rates. We first applied Pearson's correlation test on the results from these four methods to generate the correlation matrix A, and then performed Choleski decomposition (A = L′L) on the correlation matrix A. We then ranked all pathways by their p-values across methods and calculated their average rankings. Using rankings instead of p-values ensures all methods being treated equally [As solid evidence of methodological superiority is lacking , we calc equally . We simuTo check if our GWAS results were confounded by other factors, such as pathway size and gene size, we performed a series of evaluations on different factors. Pathway size is the number of genes in a pathway and gene size is the number of SNPs in a gene. SNP frequency is calculated as SNP counts per 1kb of gene length. Pathway annotation degree of a gene is the number of pathways this gene is assigned to. We also estimated the significance level of the average SNP frequency in top 20 pathways, by comparing to the simulated null-distribution of average SNP frequency in random 20 pathways .We used the R-package NMF  to identgarmiregroup.org/data. Other processed data can be found in Supplementary data.The raw genotype data are submitted to the Epidemiology of Endometrial Cancer Consortium (E2C2) under NCI , which has initiated the data submission to dbGaP (accession number assigned phs000893.v1). Additionally, the pre-computed p-values of SNPs are on the website: 0.05  are represented by the size of the nodes.We extracted contributing SNPs of top 20 pathways from PLINK results and mapped them to genes. Corresponding empirical p-values were also extracted from PLINK results. To visualize the relationship of genes and pathways, we used Cytoscape  to build0.05 transformation of the p-values of genes generated by MutSigCV, and removed the genes with log0.05 p-value equal to 0 , since they didn't contribute meaningful mutations. We then sorted the genes and used them for GSEA Pre-ranked test at the pathway level result.We applied MutSigCV  to the EPathifier [For pathway-level analysis on the paired TCGA RNA-Seq data, we first applied DESeq2  to normaathifier  algorithathifier . It is aPathifier to summarize the expression values from the gene level to pathway level. We then dichotomized the patients by median PDS, and assigned the patient with a higher PDS than median to a higher-risk group; otherwise, we assigned this patient to a lower-risk group. We used Kaplan-Meier curves to present the prognosis of the higher- and lower-risk groups. The Wilcoxon log-rank test was then conducted on the Kaplan-Meier curves to detect the survival difference between these two groups. All survival analysis was conducted using the R package Survival [Relapse-free survival analysis was used to study the prognostic associations of pathways based on EC TCGA RNA-Seq data set. A total of 537 samples had clinical information, and among them 457 samples had relapse free survival information (downloaded 03/24/2015). Patients without relapse events during the study were considered censored. To realize pathway-level survival analysis, we first applied Survival , 75. AllTo evaluate the overall significances of the top 20 pathways, we ranked them based on their results from four different data types and calculated the average rankings. We then conducted resampling for 10,000 times on the rankings from each data type, and calculated the average rankings. This generated a null-distribution of average rankings which can be used to calculate the empirical p-value of each pathway. Pathway empirical p-values represent the overall consistency across different data types.


Cancerous cells are characterized by enlarged nuclei and prominent nucleoli. However, the specific nuclear components involved, the underlying mechanisms, and the implications for cancer progression, are far from clear. The nuclear lamina, a scaffold-like network of protein filaments surrounding the nuclear periphery, is required in the regulation of most nuclear activities, including nuclear shape, DNA replication, RNA transcription, nuclear and chromatin organization, cell cycle regulation, cell development and differentiation, and apoptosis PRR14 has previously been identified as a novel component of the nuclear lamina, tethering heterochromatin to the nuclear lamina during the cell cycle.4 Our group confirmed this observation in myoblast C2C12 cells and demonstrated that this bridge is not only physical but functional as well; PRR14 directly binds to HP1α, a constitutive component of heterochromatin, and myogenesis is tightly regulated by this binding.5 Disruption of this interaction almost completely blocked myogenesis and the majority of cells underwent apoptosis. Could PRR14 be a missing link in the common mechanism mediating nuclear morphological change and its functional alteration during tumorigenesis?Oncogene, we have shown that the expression level of PRR14 is significantly increased in lung cancer with, or without, gene copy number variation.6 Indeed, PRR14 is commonly elevated in many cancer types besides lung cancer7 , we conclude that PRR14 expression is positively associated with both gene expression and generic transcription pathways. In addition, PRR14 overexpression results in markedly enlarged cell size. Thus, we hypothesized that PRR14 may be a novel oncogene activating the PI3-kinase/Akt/mTOR signal pathway, which is known to regulate mammalian cell size through generic gene expression9 and a commonly activated pathway in lung cancer.10 Consistent with this, the high expression of PRR14 significantly associates with a decreased 5-year survival rate in more than 1900 lung cancer patients. And the PI3K/Akt/mTOR pathway activation was confirmed through analysis of phosphorylation status of its downstream effectors. Furthermore, we demonstrated that the activation of the pathway was achieved by direct interaction of PRR14 with GRB2, a regulator of PI3K signaling pathway.12 Consequently, elevated PRR14 promoted, and reduced PRR14 impeded, lung cancer cell proliferation as well as tumor formation. As expected, elevated PRR14 was specifically associated with a heightened sensitivity to the inhibitors of PI3K (GDC-0941) and its downstream effector mTOR (Torin2 and LY294002), but showed no effect on the mitogen-activated protein kinase (MAPK) inhibitor (U0126; PRR14 variants with higher affinity for GRB2 in tumors and they demonstrated a higher capacity to activate PI3K/AKT/mTOR signaling pathway and form tumors, which further confirmed our hypothesis.From comparative analysis of differentially expressed transcripts, between high-r U0126; . Fortuna13 To exclude the possibility that PRR14 activates AKT/mTOR pathway through Ras instead of PI3K pathway, the phosphorylation status of ERK was used as a readout of the MAPK pathway activity, an alternate downstream pathway of Ras from the Akt/mTOR pathway. The phosphorylation status of ERK was not significantly altered.GRB2 is also involved in Ras signaling pathway, and AKT/mTOR and MAPK pathways are both its downstream signal pathways.14 Although the activity of the pathway has been extensively explored, previous research has mainly focused on the cytoplasmic component, whereas its functions and effector proteins within the nucleus are poorly understood. Noticeably, both previous4 and our5 research has demonstrated that PRR14, with nuclear localization signal in both its N- and C-terminals, is predominately localized within the cell nucleus including the nuclear lamina during mitosis. Our findings may therefore provide a novel and common nuclear target in the PI3K pathway.The PI3K pathway has been demonstrated to regulate diverse cellular processes including proliferation, adhesion, survival and motility, and its aberrant activation is frequently observed in various cancers conferring sensitivity and resistance to conventional therapies. Therefore, the PI3K pathway is considered a well-validated target for cancer treatment.


High quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation.We developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented.GROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.The online version of this article (10.1186/s12859-018-2126-1) contains supplementary material, which is available to authorized users. Million . Millio results .In order to automatically annotate protein sequences with a high degree of accuracy (i.e. by limiting annotation error propagation between homologous proteins), the UniProt consortium has developed the UniRule system . It contHere, we present an expert system, named GROOLS (Genomic Rule Object-Oriented Logic System), that uses paraconsistent logic to assist biocurators in the evaluation of the functional annotation of genes. The GROOLS method is inspired from a previous work about the HERBS software  that was presented in 2003 during a workshop of the HAMAP project . SimilarPrior Knowledge (PK) concepts represent biological processes and their components (e.g. a metabolic pathway and its reactions)Observation (O) concepts are information about PK concepts in a given organism.GROOLS uses a generic model to represent biological processes in a hierarchical structure Fig. . Indeed,‘part’ relations describe compositions (e.g. a metabolic pathway is composed of a set of reactions)‘subtype’ relations describe generalizations and specializations .The hierarchical organization of PK concepts is made by two types of edges in the conceptual graph: Edges are directed and labelled with the suffix “-of” (i.e. ‘part-of’ and ‘subtype-of’) when the relation is from the parent concept to the child concept and, conversely, with the prefix “has-” (i.e. ‘has-part’ and ‘has-subtype’).Computations are bioinformatics predictions Experimentations are empirical evidences (e.g. growth phenotypes with defined metabolites as sole carbon and energy source)Curations are human expertises supported by direct or indirect evidences To give clues about the existence of PK concepts in a given organism, three types of Observation are used in GROOLS: ‘prediction-of’ edges for Computation and Curation observations‘expectation-of’ edges for Experimentation and Curation observations.Finally, Observations are linked to PK concepts with two types of relations : This GROOLS conceptual graph model is generic enough to capture the overall background knowledge on biological processes represented by PK concepts  and organism specific knowledge represented by Observations that come from experimentations or its genome annotation. Each observation corresponds either to an assertion or a rejection of a PK concept. Such states are represented by the true (t) and false (f) values also named truth values in classical logic. Observations will be propagated through the conceptual graph to assign PK concepts prediction or expectation values (see the “Reactive reasoning” paragraph). These values answer the following questions: “Is the PK concept predicted in the studied genome?” and “Is it expected in the organism?”.∅} (no Observation). To rank Otvs according to their level of truth or falsehood, they are associated to the truth degrees 1, 0, ∅} sets, respectively.In biology, several experimentations or bioinformatics predictions may be related to a same PK concept. To capture facts from this observable space, Observations are grouped by their PK target and relation type (i.e. ‘prediction-of’ or ‘expectation-of’) resulting from an Observation truth value powerset of order 2  and child PK predictions. The algorithm propagates predictions from the leaves to the roots of the conceptual graph using two distinct rules: one for ‘has-part’ relations (Rule 1) and another for ‘has-subtype’ relations (Rule 2). For the Rule 1, PK predictions are the union of Otvs  and Ptvs  from child PK concepts. In Rule 2, PK predictions are the union of Otvs and Ptvs from child PK concepts having the greatest degree of belief .In a second step, PK expectations are propagated from the roots to the leaves of the graph by collecting truth value sets from direct or indirect Observations (Experimentation or Curation type). For the ‘has-part’ relations, PK expectations are the union of Otvs and Etvs  from parent PK concepts (Rule 3). For the ‘has-subtype’ relations, parent PK expectations are propagated to child PK that have the greatest degree of belief for their prediction with the exception of false-only expectations ({{f}}) that are propagated to all child PK (Rule 4). ∅}) and a true or false value, the approximated value is NONE meaning that some parts of the concept are unknown. For expectations, unknown values are not considered in the approximation as they do not represent missing part of a concept but lack of information about a higher concept among others having empirical evidences . Thus {{ ∅}{t}} and {{ ∅}{f}} expectation sets are approximated to TRUE and FALSE, respectively.At the end of the reasoning, to ease human interpretation of PK predictions and expectations, truth value sets are approximated in four values: TRUE, FALSE, BOTH, NONE Table . When boThe combination of these approximated values of PK predictions and expectations lead to 16 different states Table . They ex∅}}) are not considered when at least one other child PK has a true-only prediction ({{t}}). Using this Specific mode, reasoning is less strict as partial evidences on child PK are sufficient to predict a parent PK.Three additional reasoning modes have been developed to handle particular cases in the reasoning. In the Dispensable mode, PK concepts can be flagged as non-essential meaning that their presence is not required to realize a biological process. In this case, predictions and expectations are not propagated to PK concepts labelled as “Dispensable”. In the Falsehood mode, leaf PK concepts (i.e. concepts without ‘has-part’ and ‘has-subtype’ relations) with no direct Observations have their prediction assigned to false-only ({{f}}). This reasoning mode assumes that all leaf concepts of the graph are predictable, and thus, an absence of Observation corresponds to a false prediction. In the Specific mode, a PK is labelled as “Specific” when it is linked to only one parent PK by a ‘part-of’ relation. Moreover, in this mode, Rule 1 (propagation of child PK predictions to parent PK using ‘part’ relations) is adapted: child PK with unknown prediction ({{ Prior Knowledge (PK) concepts represent biological processes and their components (e.g. a metabolic pathway and its reactions). Genome Properties  and UniPFrom the Genome Properties description file , “Properties”, “Components” and “Evidences” were extracted and transformed in PK concepts. ‘Part’ relations were established between “Properties” and “Components”, and ‘subtype’ relations between “Components” and “Evidences”. In the Genome Properties model, “Evidences” correspond either to pathway variants or predictors (i.e. leaf concepts in the graph). Predictors are protein family Hidden Markov Models (HMMs) from PFAM  and TIGRFrom the UniPathway Open Biological Ontology (OBO) file, UPA (pathways), ULS (linear sub-pathways), UER (enzymatic reactions) and UCR  were extracted and transformed in PK concepts. For ULS linked to a same UPA, additional concepts, named Variants, were added when several paths of ULS, that share input/output compounds, exist. Other Variants were made for UER that are specializations of generic UER . According to “is_a” and “super_pathway” relations in the OBO file, UPA were linked together by ‘subtype’ relations in the conceptual graph. ‘Subtype’ relations were also used to link Variants to UPA or UER. Other relations between PK concepts were converted to ‘part’ relations.To populate Genome Properties conceptual graph, Observations of Computation type can be extracted from UniProtKB database  in whichGROOLS can integrate Phenotype MicroArrays (PMs) BIOLOG experiments as Observations of Experimentation type. This quantitative growth phenotype data should be first discretized using the omp R package  with “grTrue Positive (TP): ‘Confirmed Presence’True Negative (TN): ‘Confirmed Absence’ and ‘Absent’False Positive (FP): ‘Unexpected Presence’ and ‘Contradictory Presence’False Negative (FN): ‘Missing’, ‘Unexpected Absence’ and ‘Contradictory Absence’ concepts.Statistics were computed to evaluate predictions in confrontation to biological expectations. In this evaluation, only concepts with univocal expectations  are considered. Concepts are classified using their conclusion value as follow: Accuracy (ACC), Precision, Recall and F1 score are computed as follow: https://github.com/Grools/grools-drools-checker) was written using the DROOLS framework . In the present version of GROOLS software (https://github.com/Grools/grools-application), rules were directly implemented in Java with functional programming to tackle performance issues during the graph reasoning. The reasoner is available in a separate library (https://github.com/Grools/grools-reasoner).GROOLS is built on object-oriented logic programming. This paradigm facilitates the representation of biological knowledge with Prior Knowledge concepts and their relations. A first prototype of GROOLS (GROOLS contains plugins to construct the PK graph from two databases: UniPathway (in OBO format) and Genome Properties (in non-standard format). Using this plugin system, database file content can be improved by users. Moreover, other resources on biological processes can be integrated without changing the reasoning library.For Observations, an unified CSV file format is used to represent Computation, Curation and Experimentation data. Each row is composed of seven fields to describe an Observation: “Name”, “EvidenceFor” (name of the related Prior Knowledge), “Type” , “isPresent” , “Source” , “Label” and “Description”.Three shell scripts are provided as example to run GROOLS on Genome Properties using UniProtKB proteomes (“uniprot_genpropToGrools.sh” script) and on UniPathway using MicroScope or UniProtKB annotations .GROOLS results are provided in CSV format and in HTML pages containing graphical representations of the conceptual graph for each biological process. GROOLS execution time is about 1 to 2 minutes to completely analyze a genome. The reasoning part takes only few seconds while most of the execution time is spent in data extraction and graphical output generation.http://www.microme.eu) and linked to their corresponding degradation pathway PK concepts. Given that studied organisms are prototroph, additional Observations with true truth value were added on amino acid biosynthesis pathway concepts (i.e. these pathways are expected in the organisms). Detailed results on the 14 studied organisms are available at this Web address: https://www.genoscope.cns.fr/agc/grools. Statistics on GROOLS conclusions are reported in Table As a test case, GROOLS was executed on 14 organisms for which UniProtKB and MicroScope annotations are available. The reasoning was made on Genome Properties and UniPathway conceptual graphs, enabling or not the falsehood (for Genome Properties) and specific (for UniPathway) modes. In addition, the dispensable mode was enabled for Genome Properties as this resource contains information about non-essential components of biological processes. For Observations of Experimentation type, growth phenotypes (PM BIOLOG technology) were retrieved for these organisms on the Microme project website  gives high level of accuracy (79.14%), reflecting the exquisitely sensitive searches of TIGRFAM models. In the falsehood mode, pathway prediction accuracy decreases from 78.80 to 67.93%. As expected, we observe that contradictions on pathway predictions (i.e. conclusions with BOTH predictions) are only detected using the falsehood mode. In this mode, the prediction value of a leaf component  is set to false-only when any corresponding PFAM or TIGRFAM domain is detected among all organism proteins. Through the propagation of these false values, pathway predictions change then from NONE to FALSE or BOTH values. One advantage of the falsehood mode is thus to raise contradictions between predictions and expectations at the pathway level (i.e. ‘Contradictory Absence’ and ‘Contradictory Presence’ conclusions). Furthermore, using falsehood mode, absence of pathways and prediction contradictions can be detected even without expectations (i.e. ‘Unconfirmed Absence’ and ‘Unconfirmed Contradiction’ conclusions).Using UniPathway as a source of biological processes, prediction accuracies for pathways and functional units are higher for MicroScope than for UniProtKB: about twice as many functional units are correctly predicted using MicroScope annotations. Indeed, MicroScope contains further annotations on enzymes. In addition to EC numbers, reactions identifiers from MetaCyc or RHEA are generally provided in MicroScope, which facilitate the mapping of Observations on UniPathway chemical reactions (UCRs). Moreover, the UniRule annotation system is still limited to few UniProtKB entries and many entries contain original submitted annotations that are very heterogeneous in quality. Beside, the specific mode shows a positive impact on prediction accuracy. For MicroScope annotations, pathway and functional unit accuracy values increase from 69.35% to 73.70% and from 82.22% to 84.15%, respectively. Indeed, the number of ‘Missing’ conclusions  is reduced to half using the specific mode. In this mode, a pathway variant is assigned a true prediction if at least one specific reaction is predicted . Thus, the specific mode helps GROOLS reasoning to select the correct pathway variant (i.e. the one with the greatest degree of belief) for which pathway expectations are propagated. Similarly to accuracy, we observed that F1 scores, which do not use true negative data, are also slightly better in the specific mode. Furthermore, the number of ‘Unexplained’ conclusions on pathways decreases considerably in the specific mode. This led to many more predicted pathways that can be evaluated by users even without experimental data (i.e. ‘Unconfirmed Presence’ conclusions).For BIOLOG results, it should be noticed that 23% and 37% of Genome Properties and UniPathway pathways have contradictory expectations due to ambiguous discretizations or discordant experimental replicates. By taking into account contradictory experimental results, GROOLS may help biologists to deal with these inconsistencies by pointing out, for example, pathways with ‘Ambiguous Presence’ conclusion, which suggests that, since the pathway is predicted, the organism should be able to degrade the tested compound.Escherichia coli and Bacillus subtilis) or other well-studied organisms  have high prediction accuracies for functional units (accuracy > 85%) and pathways (accuracy > 70%). After manual inspection of some results, many discrepancies were found to be annotation mapping problems with UniPathway reactions (i.e. EC numbers or MetaCyc/RHEA reactions are not correctly cross-referenced) or incomplete pathway definition (i.e. missing variant) as illustrated in the second example below. Other organisms, like Chitinophaga pinensis and Kytococcus sedentarius, have low prediction accuracies (< 70%). These organisms are from phylum (i.e. Bacteroidetes and Actinobacteria) with limited metabolic knowledge. Potential new pathways and enzymes remain to be discovered or, more easily, predictors like TIGRFAM models could be improved to gain sensitivity as illustrated in the first example just below.Detailed statistics on prediction accuracy for the 14 studied organisms are represented in Fig. Kytococcus sedentarius is flagged by GROOLS as ‘Contradictory Absence’ conclusion meaning that, although the pathway is expected in this organism, some predictions are in contradiction. Two pathway variants for cysteine biosynthesis are described in the Genome Properties model: GenProp0304  and GenProp0218 (cysteine biosynthesis from serine). Among all functional units, only one is predicted in K. sedentarius (Evidence_61339 for TIGR01139 HMM) others are absent and have their prediction value set to false . Rules 1 and 2 of GROOLS reasoning were then applied to propagate predictions to parent concepts leading to ‘false-only’ (FALSE) and ‘true and false’ (BOTH) predictions for GenProp0304 and GenProp0218, respectively. Using rules 3 and 4, the Observation Exp_GenProp0305 (i.e. cysteine biosynthesis is expected in the organism) was propagated only through GenProp0218 as it has the greatest degree of belief for its prediction . This reasoning decision confirmed the presence of Evidence_61339 (TIGR01139) whereas Evidence_61340 (TIGR01172) is flagged as ‘Unexpected Absence’: any protein in K. sedentarius has significant alignment with TIGR01172 HMM. Such conclusion suggests that this missing function should be present in K. sedentarius. A quick investigation of TIGRFAM results showed that Ksed_26170 protein (C7NGP7 UniProtKB entry) shares an alignment with TIGR01172 HMM with a score of 200.3 that is just below the trusted cutoff (210.65). Thus, this protein is certainly a good candidate for the missing function and the TIGRFAM HMM should be updated accordingly to gain sensitivity in the detection of proteins from less-studied organisms like the actinobacteria K. sedentarius.As depicted in Fig. Acinetobacer baylyi UniProtKB annotations , GROOLS reasoning concludes that both variants and reactions could be missing (i.e. GROOLS is not able to choose between those two). Unfortunately, UniPathway is wrong because a third pathway variant is known and reported in other metabolic databases like MetaCyc [A. baylyi by an heterotrimer made of GatA, B, C proteins which cannot be assigned to the UER01035 as the corresponding EC number (6.3.5.6) is missing in their UniProtKB annotation.In UniPathway, two variants made of one enzymatic reaction are described for the biosynthesis of asparagine UPA00134). Using . Using A MetaCyc . It corrFollowing the initiative of Genome Properties  and IMG In terms of methodological limitations, GROOLS does not support quantitative values for observations : they must be discretized in true and false values before the reasoning. Beside, GROOLS deals only with biological processes that can be described with specialization (’subtype’) and composition (’part’) relations, like metabolic pathways or macromolecular systems (e.g. secretion systems). Thus, it is not designed to reason on processes containing regulation information . Nevertheless, a new type of relations, named ‘avoid’, could be implemented in GROOLS. Unlike other relations between PK, ‘avoid’ relation will indicate concept components that should not be present. This type of relation could be useful to model more complex phenotypes like, for example, ‘strict anaerobe’ for which anaerobic metabolic pathways should be predicted, but not aerobic ones.To increase the diversity of biological processes in GROOLS, we plan to integrate data from MetaCyc  that conTo conclude, GROOLS, by its reactive nature and its fast execution time, could be integrated in an annotation system (such as MicroScope) as a live annotation companion to guide biologists during the curation process of their genome: at each protein annotation update, a new observation should be created in the GROOLS working memory and, then, would be propagated locally through the graph to alert users about impacts on biological process conclusions.GROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It allows users to compare different sources of annotations for a genome. Furthermore, using conclusion values, biocurators can quickly focused on missing or contradictory predictions to improve protein functional annotation. With the aim of building a metabolic model of an organism, and before applying time-intensive gap filling methods, GROOLS could thus be used to improve the initial metabolic network reconstruction in regards to the genome annotation and growth phenotype data.Additional file 1Graphical illustration of GROOLS reasoning. (PDF 143 kb)Additional file 2Accuracy and F1 scores with detailed counts. (PDF 55 kb)Additional file 3Textual descriptions for both examples illustrated in Figs.


Flo2 gene is a member of a conserved gene family in plants. This gene has been found to be related to thousand grain weight (TGW) in rice. Its orthologs in hexaploid wheat were cloned, and the haplotype variation in TaFlo2-A1 was tested for association with TGW.The TaFlo2-A1, TaFlo2-B1 and TaFlo2-D1 contained 23, 23 and 24 exons, respectively. The deduced proteins of TaFlo2-A1 (1734 aa), TaFlo2-B1 (1698 aa) and TaFlo2-D1 (1682 aa) were highly similar (>94%) and exhibited >77% similarity with the rice FLO2 protein. Like the rice FLO2 protein, four tetratricopeptide repeat (TPR) motifs were observed in the deduced TaFLO2 protein. An 8-bp InDel (−10 to −17 bp) in the promoter region and five SNPs in first intron of TaFlo2-A1 together formed two haplotypes, TaFlo2-A1a and TaFlo2-A1b, in bread wheat. TaFlo2 was located on homeologous group 2 chromosomes. TaFlo2-A1 was inferred to be located on deletion bin ‘2AL1–0.85-1.00’. The TaFlo2-A1 haplotypes were characterized in the Chinese Micro Core Collection (MCC) and Pakistani wheat collection using the molecular marker TaFlo2-Indel8. TaFlo2-A1 was found to be associated with TGW but not with grain number per spike (GpS) in both the MCC and Pakistani wheat collections. The frequency of TaFlo2-A1b (positive haplotype) was low in commercial wheat cultivars; thus this haplotype can be selected to improve grain weight without negatively affecting GpS. The expression level of TaFlo2-A1 in developing grains at 5 DAF (days after flowering) was positively correlated with TGW in cultivars carrying the positive haplotype.The cloned sequences of Flo2 gene in hexaploid wheat. Furthermore, the newly developed molecular marker ‘TaFlo2-InDel8’ could be incorporated into the kit of wheat breeders for use in marker-assisted selection.This study will likely lead to additional investigations to understand the regulatory mechanism of the The online version of this article (10.1186/s12870-017-1114-3) contains supplementary material, which is available to authorized users. Enhancing the grain yield potential of wheat is a key focus of wheat breeders. Grain yield is the product of various yield components. Wheat grain yield per unit area is the product of grain yield per spike (GYS) and the number of spikes per unit area. The latter depends on sowing density and is highly affected by environmental factors. The GYS is determined by grain number per spike (GpS) and thousand grain weight (TGW), which are variably correlated in different wheat collections/populations. A significant negative correlation between these two traits has been reported in bi-parental populations –3, but n−1 in 1992 to ~5.4 T ha−1 today is mainly due to the genetic increase in TGW from ~20 g to ~45 g, respectively [−1 can be obtained by just a 1-g increase in TGW [TGW in wheat has been one of the target traits for selection during domestication and breeding , 8. For e in TGW . Howevere in TGW . For exae in TGW  discovere in TGW .TaTGW6 [TaCwi-A1 [TaSus2-2B [TaSus2-2A, TaSus1-7A [TaGW2-6A, 6B [TaCKX6-D1 [TaSAP1-A1 [TaGS1a [TaGS-D1 [TaGASR-A1 [Nevertheless, from this plethora of QTLs, few loci/genes have been cloned by map-based cloning approaches mainly because of the large and complex hexaploid genome ~17 Gb) that consists of three homeologous genomes  and an abundance of repeat sequences (80%) . StudiesTaTGW6 , 17, TaC Gb that TaCwi-A1 , TaSus2-aSus2-2B , TaSus2-aSus1-7A , TaGW2-62-6A, 6B , 12, 21,aCKX6-D1 , TaSAP1-aSAP1-A1 , TaGS1a  [TaGS1a , TaGS-D1[TaGS-D1 , and TaGaGASR-A1  were disFLOURY ENDOSPERM2 (Flo2) gene is a member of a conserved gene family in plants. In rice, this gene has been shown to have a tetratricopeptide repeat (TPR) motif consisting of 3–16 tandem repeats of 34 aa residues that mediate protein–protein interactions in the nucleus [OsFlo2 gene was cloned in the indica cultivar ‘Kasalath’; this gene was found to have 23 exons and 22 introns and coded for a protein consisting of 1720 amino acid residues that had three TPR motifs in the middle [Flo2 was constitutive in both vegetative tissues and developing seeds, and the expression was relatively high level in developing seeds. The flo2 mutants exhibit a significant reduction in amylose content and grain weight and exhibit altered expression of various starch synthesis-related genes, indicating its key role in regulating rice grain weight and starch quality [Flo2 orthologs in hexaploid wheat, the association of TaFlo2-A1 sequence polymorphisms with TGW and the comparison of temporal expression profiles of TaFlo2-A1 haplotypes in flag leaves and developing caryopses.The  nucleus , 28. Thee middle . The exp quality , 28. In TaFlo2 gene in hexaploid wheat, Chinese Spring (CS) and two sets of cultivars with lower and higher TGW were used; the set of cultivars with higher TGW included Dixiuzao (49.5 g), Enmai4 (49.2 g), Liying 5(49.4 g) and Laizhou 953 (52.2 g), and the set with lower TGW included Jinyang 60 (23.5 g), Baihuamai (24.1 g), Sanyuehuang (25.2 g) and Zipi (25.5 g). The Chinese Spring nulli-tetrasomic lines were used to assign TaFlo2 genes to wheat homeologous chromosomes. The Chinese Micro Core Collection  and Pakistani wheat collection (130 accessions) were used to confirm the association between TaFlo2-A1 haplotypes and TGW. To avoid the effect of population structure, normalized MCC subpopulations were used for association analysis [For cloning the analysis , 29. Theanalysis  considerOsFlo2 gene (NC_008397) was used as a query for BLAST searches against the wheat sequences database in the URGI (https://urgi.versailles.inra.fr/). All wheat scaffold sequences with high similarity to the rice OsFlo2 sequence were assembled to construct a putative TaFlo2 gene using DNAMAN (http://www.lynnon.com). Based on the scaffold sequences, six conserved primer pairs were used to specifically amplify TaFlo2 coding and promoter sequences from the three wheat sub-genomes: A, B and D (Table TaFlo2 mRNA of 4902 bp was cloned in Chinese Spring using three primer pairs designed from the predicted mRNA sequence (Table http://www.lynnon.com).The genomic sequence of the rice  D Table . The TaFce Table . GenomicTaFlo2-A1a or TaFlo2-A1b) for the TaFlo2-A1 gene. For MCC, the average values of TGW of the two haplotype groups were calculated using replicated data collected from 3 years  of plants in Beijing [The 262 MCC and 130 Pakistani varieties were genotyped with the primer pair TaFlo2-InDel8, and PCR product was run on 8% polyacrylamide gels. Based on TaFlo2-InDel8 scoring, the MCC and Pakistani accessions sorted into two groups according to their haplotypes  among different samples were made by ANOVA using SPSS 13.0.The Yangmai 19, Chinese Spring, Pubing3228, Shannong23 and Zhengmai9405 varieties were sown at the experimental station of the Institute of Genetics & Developmental Biology, CAS in Beijing, China in October 2014; three rows of each variety were planted. The length of each row was 2 m, and the row-to-row distance was 20 cm. The plants were managed in accordance with standard agronomic practices; irrigation and fertilizer were supplied for optimal growth. Twelve- day-old flag leaves of five plants from each variety were harvested and stored at −80 °C. Unfertilized grains were collected from each variety 1–2 days before flowering (DBF). Fertilized grains were collected from each variety at 5, 10, 15, 20 and 25 days after flowering (DAF). The flag leaf and developing grain samples were processed for the preparation of total RNA as described previously . Three beviously . Statisthttp://www.lynnon.com). Amino acid sequence alignment was accomplished using ClustalW2 in EMBLEBI . Potential signal peptide sequences in the deduced proteins of TaFLO2 and its homologs were predicted using Softberry software (http://www.softberry.com/berry.phtml). The predicted TaFLO2 protein was BLASTed both in the NCBI smart blast system (http://blast.st-va.ncbi.nlm.nih.gov/smartblast) to search for homologous proteins and in the NCBI CD system (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) to search for conserved domains.Nucleotide and protein identities among the compared sequences were calculated using DNAMAN  was used as a query against the wheat genome sequences database in the URGI (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi). Three bread wheat scaffolds  with high similarity  were identified as potential orthologs to the rice Flo2 gene. The sequences of these scaffolds were downloaded and assembled with DNASTAR (http://www.dnastar.com/) to construct a putative TaFlo2 sequence.To select potential candidate TaFlo2 homologs and predict their deduced protein sequence and structure, Softberry (http://www.softberry.com/berry.phtml) and NCBI (https://www.ncbi.nlm.nih.gov/) tools were used. The deduced proteins of TaFlo2-A1 (1734 aa), TaFlo2-B1 (1698 aa) and TaFlo2-D1 (1682 aa) were highly similar (>94% identity among themselves) and exhibited >77% similarity with the rice FLO2 protein. Like in the rice FLO2 protein [Aegilops tauschii, Brachypodium distachyon and long-grain rice proteins  at position 1417–1419 bp . Further analysis revealed that the TaFlo2-A1 gene was located on deletion bin ‘2AL1–0.85-1.00’.To assign chromosomal locations to  2D Fig. . The cloTaFlo2-A1 with TGW, two natural populations, the Chinese Micro Core Collection (MCC) and the Pakistani collection, were used. In the MCC, the homozygous TaFlo2-A1a haplotype was found in 219 (83.5%) accessions, whereas the TaFlo2-A1b haplotype was found in 43 (16.5%) accessions. In the Pakistani wheat collection, the number of accessions carrying TaFlo2-A1a and TaFlo2-A1b were 85 (64.6%) and 45 (35.4%), respectively. Both in the MCC and Pakistani collections, the positive haplotype TaFlo2A-A1b had a lower frequency, which showed the scope of improving grain weight.To associate TaFlo2-A1a and TaFlo2-A1b was statistically significant in both populations  across the 3 years of data. The mean differences in TGW between the two haplotypes in 2002, 2005 and 2006 were 7.00 ± 1.12 g, 7.80 ± 1.11 g and 8.40 ± 0.94 g, respectively. Consistent with the results of the MCC, the mean difference in TGW between groups of accessions having TaFlo2-A1a and TaFlo2-A1b was also significant (P ≤ 0.05) across both years of data in the Pakistani wheat collection. The values of the mean difference between the two haplotypes (TaFlo2-A1a and TaFlo2-A1b) in the Pakistani wheat population were 4.50 ± 0.71 g and 5.20 ± 0.72 g for 2009 and 2010, respectively. The phenotypic variance for TGW explained by TaFlo2-A1 haplotypes was 6.19% in 2002, 7.76% in 2005 and 8.37% in 2006 in the MCC. In the Pakistani collection, the phenotypic variance for TGW explained by TaFlo2-A1 haplotypes was 4.42% in 2009 and 5.11% in 2010 , an association analysis was performed for GpS in both populations. However, the differences in GpS between the haplotypes TaFlo2-A1a and TaFlo2-A1b were not significant in either population  for high TGW instead of loci for low number of kernels per spike and/or low grain yield.Collectively, our data demonstrated that TaFlo2-A1a and TaFlo2-A1b on TGW at the gene expression level in flag leaves and developing grains, two polymorphic accessions were used. The expression level of TaFLO2 was positively correlated with TGW, which is consistent with previous results in rice [TaFlo2-A1a, which exhibits low expression levels, represented the group of accessions that have low average TGW, and the haplotype TaFlo2-A1b, which exhibits high expression levels, represented the group of accessions that have high average TGW in both Chinese and Pakistani wheat populations. Quantitative RT-PCR assays showed that for both types of haplotypes, the expression level was maximum in 12-day-old flag leaves followed by expression in developing grains sampled at 5 DAF. However, the expression of both types of haplotypes decreased rapidly in the fertilized caryopses collected at 10, 15, 20 and 25 DAF. The expression level of TaFlo2-A1b was higher than that of TaFlo2-A1a at all tested stages but significantly differed only in flag leaves and developing grains at 5 DAF  to 8.37% (in the MCC) of phenotypic variation. The TGW-related QTL identified on 2AL includes ‘Xgwm339-Xbarc311’ in 139 RILs between two hard red spring wheat lines [QTgw.ipk-2A (Xgwm372) in 111 BC2F3 lines derived from the cross ‘Flair × XX86’ [QGwt.crc-2A (Xgwm558-Xgwm294) in a double-haploid population generated from the cross ‘RL4452 × AC Domain’ [QTkw.sdau-2A (Xwmc181a-Xubc840c) in 131 RILs derived from ‘Chuan 35050’ × ‘Shannong 483’ [QSZ.uaf-2A.1 (Xwmc455) in natural populations of 108 CIMMYT and Pakistani spring wheat accessions [QTkw.hwwgr-2AL (Xgwm312–IWA6090) in 127 RILs derived from ‘Ning7840’ × ‘Clark’ [In our study, at lines ; QTgw.ip × XX86’ ; QGwt.cr Domain’ ; QTkw.sdcessions ; and QTkXgwm71.2/Xgwm558 and Xgwm294, with an interval of 22 cM according to the consensus map of Somers [http://wheat.pw.usda.gov/ggpages/SSRclub/GeneticPhysical/). From this TGW-QTL-rich region, only one gene, TaCwi-A1, has been isolated thus far between the Xgwm 71.2 and Xbarc15 deletion bin ‘C-2AL1–0.85’, which is adjacent to the centromere [http://wheat.pw.usda.gov/ggpages/SSRclub/GeneticPhysical/) and ‘Yu 8679 × Jing 411’ SSR + SNP [TaFlo2-A1 was inferred on deletion bin ‘2AL1–0.85-1.00’. Hence, the TaFlo2-A1 is a TGW-related gene located on the distal deletion bin of chromosome 2AL, and the molecular marker ‘TaFlo2-InDel8’ is an addition to the kit of wheat breeders for marker-assisted selection.These QTL on 2AL are located between f Somers  or 16.1 ntromere . By inteSR + SNP  maps, thThe relationship between the number of grains per spike (GpS) and the TGW was traditionally found as being negatively correlated –3. HowevTaFlo2-A1b) identified in this study would help to increase TGW without reducing the average GpS in wheat.In many genome-wide association studies (GWAS) for TGW and GpS, many loci were found to be associated with only one of the traits , 29, 50.TaFlo2-A1, which is represented by two haplotypes in our study, was found to be significantly associated with TGW. Polymorphisms of an 8-bp InDel in the promoter and of five SNPs in the first intron were observed in TaFlo2-A1. The orthologs TaFlo2-B1 and TaFlo2-D1 lacked sequence variations associated with TGW  was higher than that of the negative haplotype (TaFlo2-A1a). The phenomenon of higher expression of the positive TaFlo2 haplotype is consistent with the results for rice OsFlo2, in which the overexpression of the positive haplotype significantly increases grain size [flo2 mutation in the promoter and in the open reading frame hinders the expression of genes involved in the synthesis of starch and protein [flo2 mutations negatively affect grain quality attributes such as amylose content, grain appearance and physiochemical properties despite maintaining or increasing grain size [OsFlo2 and TaFlo2-A1 at the sequence, structure and expression levels, the 8-bp InDel mutation in the TaFlo2-A1 promoter likely regulates grain size by affecting the expression of genes involved in the synthesis of starch and protein in wheat grains. Therefore, the increased expression of TaFlo2-A1 has a positive effect on grain yield but may have a negative effect on some grain quality attributes in wheat, which shall need further investigations.Flo2 is considered to be a member of a conserved gene family in plants . TaFlo2-ain size . In rice protein , 28. In ain size , 28. BasFlo2 gene in wheat.The newly developed molecular marker ‘TaFlo2-InDel8’ is an addition to the kit of wheat breeder for marker-assisted selection. This study likely lead to additional investigations to unveil the exact regulatory mechanism of the Flo2 orthologs in hexaploid wheat were cloned, and TaFlo2-A1 was found to be associated with TGW but not with grain number per spike (GpS) in both the MCC and Pakistani wheat collections. The frequency of TaFlo2-A1b (positive haplotype) was low in commercial wheat cultivars; thus this haplotype can be selected to improve grain weight. This study likely lead to additional investigations to understand the regulatory mechanism of the Flo2 gene in hexaploid wheat. The newly developed molecular marker ‘TaFlo2-InDel8’ could be incorporated into the kit of wheat breeders for use in marker-assisted selection.The


These findings provide significant insight into lncRNA regulatory network in T2DM.Increasing epidemic of type 2 diabetes mellitus (T2DM) and its comorbidities makes it urgent to understand the pathogenesis and regulatory mechanism. However, little is known about the regulatory role of lncRNAs in diabetes. Here, we constructed a T2DM‐related competitive endogenous RNA (ceRNA) network (DMCN) to explore biological function of lncRNAs during the development of diabetes mellitus. This network contained 351 nodes including 98 mRNAs, 86 microRNAs and 167 lncRNAs. Functional analysis showed that the mRNAs in DMCN were annotated into some diabetes‐related pathways. Furthermore, mTOR‐centred subnetwork was extracted and ncRNA‐involved mTOR pathway was established. Finally, we validated that NEAT1 was potentially communicated with mTOR signalling target protein mLST8  The increasing epidemic of T2DM and its comorbidities urge our understanding of the pathogenesis and regulatory mechanisms of T2DM et al. developed Starbase v 2.0 to systemically identify the RNA–RNA and protein–RNA interaction networks from 108 CLIP‐Seq data sets generated by tumour samples Recently, various ceRNA networks have been constructed to gain a global view of regulatory interaction networks of ceRNAs in certain diseases. Yang via the association with miR‐181b.In this study, we constructed a T2DM‐related ceRNA network (DMCN) to explore the biological functions of lncRNAs in the development of diabetes mellitus. After functional analysis, we found that the mRNAs in DMCN were annotated into some diabetes‐related pathways. Then, mTOR‐centred subnetwork was extracted and ncRNA‐involved mTOR pathway was established. Finally, we validated that NEAT1 was potentially communicated with mTOR signalling target protein mLST8 We downloaded human T2DM‐related genes from The Text‐mined Hypertension, Obesity and Diabetes candidate gene database (T‐HOD) The DIANA‐TarBase (V6.0) The human microRNA–lncRNA interaction pairs were identified from starBase (V2.0) The microRNA–lncRNA–gene interactions were integrated if microRNA–mRNA and microRNA–lncRNA pairs shared common microRNAs. By combining the collected T2DM‐related microRNA–mRNA pairs with T2DM‐related microRNA–lncRNA pairs, we identified 543 pairs of T2DM‐related microRNA–lncRNA–gene interactions and then constructed the human T2DM‐related ceRNA network.K is the number of edges that connect to node i. It can be defined as follows: Deg (i) = KThe degree is the number of edges that connect to a certain node. n nodes, the betweenness Bi stands for a node i asFor a graph G: =  with Sst is the number of shortest paths from s to t, and Sst(i) represents the number of shortest paths from s to t that pass through a node i. The measure is normalized by the number of pairs of nodes except i, which is (n‐1)(n‐2).where http://www.genome.jp/kegg/) is a collection of online databases. T2DM genes were subjected to KEGG pathway analysis, and FDR values <0.1 were considered as statistically significant pathways.To investigate biological functions of T2DM‐related genes in the network, we used DAVID 6.7 for gene ontology (GO) functional annotation of T2DM genes and KEGG pathway for pathway enrichment http://mirtarbase.mbc.nctu.edu.tw), which is an experimentally validated microRNA–target interactions database We manually draw figure of mTOR signalling according to KEGG pathway database and annotated mirRNAs and lncRNAs to this pathway according to their interactions with genes involved in mTOR signalling pathway. The mirRNAs and their target genes were downloaded from miRTarBase . Cells were grown in DMEM or RPMI‐1640 supplemented with 10% foetal bovine serum, 100 U/ml penicillin and 50 mg/ml streptomycin at 37°C with 5% COTotal RNA was isolated, and complementary DNA (cDNA) was generated with the use of a High Capacity cDNA Reverse Transcription Kit . cDNA from miRNAs was then generated with the use of TaqMan miRNA Reverse Transcription Kit . Quantitative reverse transcriptase PCR (RT‐PCR) was conducted using TaqMan gene expression assays for NEAT1 MALAT1 and GAPDH or TaqMan Universal Master Mix II with TaqMan microRNA assay for miR‐181b, miR‐144‐3p and U6.Two Luc‐NEAT1 constructs were generated including potential binding site (NEAT1‐WT) or mutant binding site (NEAT1‐Mut). HEK293 cells were seeded in 12‐well plates, cultured for 24 hrs and then cotransfected with NEAT1‐WT or NEAT1‐Mut plasmid and miR‐181b mimics or negative control sequences using Lipofectamine 2000 . The luciferase activity was measured 48 hrs after transfection using Dual‐Luciferase reporter assay system .Proteins from cells were separated by sodium dodecyl sulphate‐polyacrylamide gel electrophoresis (10–12%). After blocking, the membranes were probed with antibody for mLST8 (ab25974) and β‐actin (ab8227)  overnight at 4°C. The membranes were then incubated with infrared (IR) fluorescent dye‐labelled secondary antibody  for 1 hr. The bands were analysed using an IR Imaging System , and band density was quantified using Odyssey 3.0 software  and normalized to β‐actin.RNA‐binding protein immunoprecipitation (RIP) assay was performed using EZ‐Magna RIP Kit  according to the manufacturer's instruction. The enrichment of miRNAs binding AGO2, a key component of the microRNA‐containing RISC complex, was used as the positive control, and U6 as the negative control. Briefly, cells were lysed with RIP lysis buffer, followed by incubated with RIP buffer containing magnetic beads conjugated with human anti‐Ago2 antibody (Millipore). Proteinase K was used to digest the protein, and then immunoprecipitated RNA was isolated. Purified RNA was subjected to qRT‐PCR analysis.t‐test. P ≤ 0.05 was considered statistically significant.All experiments were repeated three times. Data were shown as mean ± S.D. and analysed with a two‐tailed Student's We constructed DMCN that included 98 genes, 86 microRNAs and 167 lncRNAs Fig. . All 351We also calculated a network feature‐node betweenness, defined as the fraction of shortest paths between node pairs that pass through a given node. The betweenness of a microRNA represents its role in connecting mRNAs and lncRNAs to each other Fig. . In our To explore biological functions of lncRNAs during the development of diabetes mellitus, we annotated DMCN into GO and KEGG pathways. The GO BP  analysis showed that the most significant enriched terms were bidirectional regulation of apoptosis and cell death, and others were related to response to external and internal stimulus and cellular metabolism Fig. A. The toIn global diabetes‐related ce‐network, we focused on mTOR subnetwork due to the central role of mTOR in energy metabolism. The components of mTOR subnetwork were shown in Figure in vivoThe mTOR subnetwork extracted from DMCN consisted of mTOR gene, three microRNAs and 15 lncRNAs Fig. A. We focAfter transfection of miR181b mimics into HL7720 cells, we found that miR‐181b level increased Fig. A left whTo investigate whether mTOR signalling pathway plays an important role in ceRNA of NEAT1 and miR‐181b, we searched the TargetScan database 7.1 and found that mLST8, a subunit of both mTOR complex 1 (mTORC1) and complex 2 (mTORC2), was a potential target of miR‐181b. As expected, miR‐181b level increased after transfection with miR‐181b mimics . miR‐21 was reported to be positively correlated with BMI To investigate the functions of T2DM‐related genes, we annotated these genes into GO and KEGG analysis. We found that the most significant terms were involved in the regulation of cell death. Apoptosis of β‐cells has been demonstrated to be involved in autoimmune T1DM and T2DM Due to its central role in energy metabolism, we extracted mTOR subnetwork from the global DMCN and aimed to investigate the regulatory role of ncRNAs in mTOR pathway. As shown in Figure In silico analysis has revealed two miR‐144 binding sites in the mTOR 3′ untranslated region, and in vitro assays showed that mTOR is a direct target of mir‐144 Recently, various studies showed that microRNAs affect mTOR pathway by targeting upstream regulators including IGF‐R, PI3K and Akt Then, we annotated these ncRNAs to mTOR signalling pathway. We further validated two ceRNA pairs (MALAT1‐miR‐144‐mTOR and NEAT1‐miR‐181b‐mTOR) involved in mTOR pathway and explored their significance in diabetes mellitus. NEAT1 is positively co‐expressed with mLST8, and mLST8 expression was decreased by mir‐181b mimics, indicating potential communication between NEAT1 and mTOR signaling pathway medicated by microRNA‐target sites. These findings establish a novel connection between lncRNA and microRNA in diabetes mellitus.via the association with miR‐181b. These findings provide significant insight into lncRNA regulatory network in T2DM.In summary, we constructed a T2DM‐related ceRNA network (DMCN) to explore the biological functions of lncRNAs during the development of diabetes mellitus. A mTOR‐centred ceRNA subnetwork was extracted, and we experimentally validated that NEAT1 was potentially communicated with mTOR signalling pathway target protein mLST8 None.Figure S1 Schematic figure of construction of T2DM related ceRNA network (DMCN).Figure S2 Example network with five nodes and five edges.Click here for additional data file.


Recently, with the rapid progress of high-throughput sequencing technology, diverse genomic data are easy to be obtained. To effectively exploit the value of those data, integrative methods are urgently needed. In this paper, based on SNF (Similarity Network Diffusion) , we prop Cancer is believed to be a complicated and heterogeneous disease since that it is driven by different combinations of mutated genes rather than the individual gene, and those mutations vary among tumor samples. Great efforts have been made by several large-scale projects such as The Cancer Genome Atlas (TCGA) , InternaUntil now, many successful researches on such integrative framework for cancer subtype identification have been published. For instance, Liu et al.  brought There are intrinsic differences between mutation data and other data types with quantitative value: (i) mutation data has binary value so it is not suitable for Euclidean measurement; (ii) high-dimensionality makes typical binary similarity measures hard to be used; (iii) its sparseness (fewer than 100 genes mutated in nearly ten thousand genes) makes it heterogeneous such that clinically identical patients rare to share more than a single mutation. So it makes traditional distance-based similarity measurement impossible to be used. Actually, somatic mutation data has important value since it provides information about relationships between genes and biochemical pathways and comprehensive insight into tumor progress  brought P value and silhouette value compared to SNF. The clustering result broadly met the PAM50 classification indicated clinical value for treatment.In this paper, we proposed a method named ndmaSNF (network diffusion model assisted SNF) based on the integrative framework of SNF  for cancMoreover, existing methods generally identify network modules common to all tumors which may ignore the heterogeneity between various subtypes. In this study, we first use ndmaSNF on various data sources to gain cancer subtypes, and for each cancer subtype, we use DriverNet  to get pP value as metrics on four cancer datasets . The experimental results are listed in Tables P value, the lower the better; for silhouette value, the higher the better).We evaluated the performance of our method ndmaSNF by comparing it with two state-of-the-art methods, i.e. SNF  and LRAcIn Table In terms of silhouette value, the promotion of our method compared with other methods was slight Table . And forTo further validate that our ndmaSNF can identify subtypes with biological and clinical differences, we then did in-depth research on breast invasive carcinoma. Breast invasive carcinoma (BIC) is a common breast cancer, growing into normal and healthy tissues.P value of 2.46E-08. To show the extent of those subtypes discovered by our method corresponded to the established PAM50 classification, we gathered statistics of the distribution of PAM50 per subtype. C1-C5 in Figure P = 0.036) which can be seen in Figure We totally identified 5 subtypes of BIC with log-rank In Figure ZNF670, SNMYD3, CNST, and TFB2M for cluster 1; CCND1, MAP3K1 and ERBB2 for cluster2; TP53, CTSS, NLRP3, SH3BP5L for cluster 3; PIK3CA, GATA3 for cluster 4 and TP53, PIK3CA, ERBB2 and CCND1 for cluster 5. It shows that each subtype identified has a different combination of genes highly mutated and corresponded to various biological processes.Furthermore, we turned to mutation frequencies for more validation. Thus, we focus on the genes with high mutation frequency and can find evident differences between each cluster Figure : ZNF670,To further study by what gene combination each subtype is driven, and whether those driver genes combination are different corresponded to different biological pathway, we applied DriverNet  to find TP53 showed great importance in all subtypes, however, a total combination of top driver genes is distinct in each subtype. To clearly show the difference, we used the top 60 driver genes identified from each subtype to do further study including pathway enrichment analysis and network module identification. The aim is to find out what biological process and important pathway those driver genes from different subtypes participated in.In table We did a KEGG pathway enrichment analysis per subtype and selected pathways related to breast cancer. From Figure C1 is typically enriched in Wnt and ErbB signaling pathway. The Wnt signaling pathway is one of a group of signal transduction pathways made of proteins that pass signals into a cell through cell surface receptors. Wnt signaling is identified for its role in carcinogenesis. This pathway's clinical importance is demonstrated by mutations that lead to various diseases, including breast cancer . FurtherC2 is typically enriched in MAPK signaling pathway. The MAPK is a chain of proteins in the cell that communicates a signal from a receptor on the surface of the cell to the DNA in the nucleus of the cell. When one of the proteins in the pathway is mutated, it can become stuck in the “on” or “off” position, which is a necessary step in the development of many cancers. Components of the MAPK/ERK pathway were discovered when they were found in cancer cells. Drugs that reverse the “on” or “off” switch are being investigated as cancer treatments .C3 is typically enriched in p53 signaling pathway. In breast cancer, p53 mutation is associated with more aggressive disease and worse overall survival. Molecular pathological analysis of the structure and expression of constituents of the p53 pathway is likely to have value in diagnosis, in prognostic assessment and in treatment of breast cancer .C4 is typically enriched in Cell cycle and Adherens junction. The cell cycle is the series of events that takes place in a cell leading to its division and duplication. Regulation of the cell cycle involves processes crucial to the survival of a cell, including the detection and repair of genetic damage as well as the prevention of uncontrolled cell division. Adherens junctions, the most common type of intercellular adhesions, are important for maintaining tissue architecture and cell polarity and can limit cell movement and proliferation.C5 is typically enriched in many pathways represented in C1-C4 such as Cell cycle, p53 signaling pathway, Adherens junction, ErbB signaling pathway and Apoptosis. This also can be reflected in Figure To get a more clear understanding of the combination of different driver genes, we seek for their significance at network module level. Therefore, we used them as seed genes to find network module per subtype.TP53 is a well-known tumor suppressor gene associated with various cancers including breast invasive carcinoma. Its mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables . Furthermore, i, jε is defined as follows:Here iX and each of its neighbors. Obviously, the Euclidean distance measure is suitable for continuous variables. For discrete data, the chi-square distance is proposed (Supplementary Note-Chi-squared distance). There are two derivatives of matrix W, namely, matrix P and matrix S. Matrix P carries the full information about the similarity of each patient to all others obtained by performing normalization on W:Here S only encodes the similarity to the K most similar patients for each patient via K nearest neighbors (KNN):iN represents a set of iX’s neighbors including iX. By using P as the global structure and S capturing local structure, a nonlinear iterative procedure is proposed:PV)(.Matrix We proposed to apply network diffusion model  incorporWe first mapped patient mutation profile onto a gene interaction network. Then network diffusion model was applied to diffuse the effect of each mutated gene over this network for each patient according to the function:F0is the binary patient-by-gene mutation data (Figure A is a degree-normalized adjacency matrix of the gene interaction network (Figure Ft+1 converges Ft+1 obtained is a “smoothed” mutation profile with quantitative value indicating the influence of each mutation per patient through network diffusion (Figure a Figure , and A ik Figure .α is usen Figure . In thisP value for log-rank test of survival analysis by using survival time. P value measures the degree of significant difference between survival data of different subtypes. The lower the P value is, the more obvious the difference between subtypes is. For survival analysis, we took the same method used in SNF [To compare the performance of our method with established methods, we chose two metrics as evaluation index. First, we used d in SNF , thus weWe also used silhouette value  to measu