Agomelatine, a Melatonin-Derived Drug, as a New Strategy for the Treatment of Colorectal Cancer.

The potential use of agomelatine as an alternative treatment for colorectal cancer is evaluated in this work. The effect of agomelatine was studied in an in vitro model using two cell lines with different p53 statuses (HCT-116, wild-type p53, and HCT-116 p53 null) and an in vivo xenograft model. The inhibitory effects of agomelatine and melatonin were stronger in the cells harboring the wild-type p53, although in both cell lines, the effect of agomelatine was greater than that of the melatonin. In vivo, only agomelatine was able to reduce the volumes of tumors generated by the HCT-116-p53-null cells. Both treatments induced changes in the rhythmicity of the circadian-clock genes in vitro, albeit with some differences. Agomelatine and melatonin regulated the rhythmicity of Per1-3, Cry1, Sirt1, and Prx1 in the HCT-116 cells. In these cells, agomelatine also regulated Bmal1 and Nr1d2, while melatonin changed the rhythmicity of Clock. In the HCT-116-p53-null cells, agomelatine regulated Per1-3, Cry1, Clock, Nr1d2, Sirt1, and Prx1; however, melatonin only induced changes in Clock, Bmal1, and Sirt1. The differences found in the regulation of the clock genes may explain the greater oncostatic effect of agomelatine in CRC.

Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis.

The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).

Core Circadian Clock Proteins as Biomarkers of Progression in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common tumours in developed countries. Although its incidence and mortality rates have decreased, its prognosis has not changed, and a high percentage of patients with CRC develop relapse (metachronous metastasis, MM, or local recurrence, LR) during their disease. The identification of these patients is very important for their correct management, but the lack of prognostic markers makes it difficult. Given the connection between circadian disruption and cancer development and progression, we aimed to analyse the prognostic significance of core circadian proteins in CRC. We measured the expression of PER1-3, CRY1-2, BMAL1 and NR1D2 in a cohort of CRC patients by immunohistochemistry (IHC) and analysed their prognostic potential in this disease. A low expression of PER2 and BMAL1 was significantly associated with metastasis at the moment of disease diagnosis, whereas a high expression of CRY1 appeared as an independent prognostic factor of MM development. A high expression of NR1D2 appeared as an independent prognostic factor of LR development after disease diagnosis. Moreover, patients with a low expression of BMAL1 and a high expression of CRY1 showed lower OS and DFS at five years. Although these markers need to be validated in larger and different ethnic cohorts, the simplicity of IHC makes these proteins candidates for personalizing CRC treatment.

Three genetic-environmental networks for human personality.

Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.

Uncovering the complex genetics of human temperament.

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic-phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37-53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

Uncovering the complex genetics of human character.

Human personality is 30-60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic-phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Decomposition of brain diffusion imaging data uncovers latent schizophrenias with distinct patterns of white matter anisotropy.

Fractional anisotropy (FA) analysis of diffusion tensor-images (DTI) has yielded inconsistent abnormalities in schizophrenia (SZ). Inconsistencies may arise from averaging heterogeneous groups of patients. Here we investigate whether SZ is a heterogeneous group of disorders distinguished by distinct patterns of FA reductions. We developed a Generalized Factorization Method (GFM) to identify biclusters (i.e., subsets of subjects associated with a subset of particular characteristics, such as low FA in specific regions). GFM appropriately assembles a collection of unsupervised techniques with Non-negative Matrix Factorization to generate biclusters, rather than averaging across all subjects and all their characteristics. DTI tract-based spatial statistics images, which output is the locally maximal FA projected onto the group white matter skeleton, were analyzed in 47 SZ and 36 healthy subjects, identifying 8 biclusters. The mean FA of the voxels of each bicluster was significantly different from those of other SZ subjects or 36 healthy controls. The eight biclusters were organized into four more general patterns of low FA in specific regions: 1) genu of corpus callosum (GCC), 2) fornix (FX)+external capsule (EC), 3) splenium of CC (SCC)+retrolenticular limb (RLIC)+posterior limb (PLIC) of the internal capsule, and 4) anterior limb of the internal capsule. These patterns were significantly associated with particular clinical features: Pattern 1 (GCC) with bizarre behavior, pattern 2 (FX+EC) with prominent delusions, and pattern 3 (SCC+RLIC+PLIC) with negative symptoms including disorganized speech. The uncovered patterns suggest that SZ is a heterogeneous group of disorders that can be distinguished by different patterns of FA reductions associated with distinct clinical features.

Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies.

OBJECTIVE: The authors sought to demonstrate that schizophrenia is a heterogeneous group of heritable disorders caused by different genotypic networks that cause distinct clinical syndromes. METHOD: In a large genome-wide association study of cases with schizophrenia and controls, the authors first identified sets of interacting single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (SNP sets) regardless of clinical status. Second, they examined the risk of schizophrenia for each SNP set and tested replicability in two independent samples. Third, they identified genotypic networks composed of SNP sets sharing SNPs or subjects. Fourth, they identified sets of distinct clinical features that cluster in particular cases (phenotypic sets or clinical syndromes) without regard for their genetic background. Fifth, they tested whether SNP sets were associated with distinct phenotypic sets in a replicable manner across the three studies. RESULTS: The authors identified 42 SNP sets associated with a 70% or greater risk of schizophrenia, and confirmed 34 (81%) or more with similar high risk of schizophrenia in two independent samples. Seventeen networks of SNP sets did not share any SNP or subject. These disjoint genotypic networks were associated with distinct gene products and clinical syndromes (i.e., the schizophrenias) varying in symptoms and severity. Associations between genotypic networks and clinical syndromes were complex, showing multifinality and equifinality. The interactive networks explained the risk of schizophrenia more than the average effects of all SNPs (24%). CONCLUSIONS: Schizophrenia is a group of heritable disorders caused by a moderate number of separate genotypic networks associated with several distinct clinical syndromes.

A multiobjective method for robust identification of bacterial small non-coding RNAs.

MOTIVATION: Small non-coding RNAs (sRNAs) have major roles in the post-transcriptional regulation in prokaryotes. The experimental validation of a relatively small number of sRNAs in few species requires developing computational algorithms capable of robustly encoding the available knowledge and using this knowledge to predict sRNAs within and across species. RESULTS: We present a novel methodology designed to identify bacterial sRNAs by incorporating the knowledge encoded by different sRNA prediction methods and optimally aggregating them as potential predictors. Because some of these methods emphasize specificity, whereas others emphasize sensitivity while detecting sRNAs, their optimal aggregation constitutes trade-off solutions between these two contradictory objectives that enhance their individual merits. Many non-redundant optimal aggregations uncovered by using multiobjective optimization techniques are then combined into a multiclassifier, which ensures robustness during detection and prediction even in genomes with distinct nucleotide composition. By training with sRNAs in Salmonella enterica Typhimurium, we were able to successfully predict sRNAs in Sinorhizobium meliloti, as well as in multiple and poorly annotated species. The proposed methodology, like a meta-analysis approach, may begin to lay a possible foundation for developing robust predictive methods across a wide spectrum of genomic variability. AVAILABILITY AND IMPLEMENTATION: Scripts created for the experimentation are available at http://m4m.ugr.es/SupInfo/sRNAOS/sRNAOSscripts.zip. CONTACT: delval@decsai.ugr.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS.

It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population. The remaining unexplained variance or missing heritability is thought to be due to marginal effects of many loci with small effects and has eluded attempts to identify its sources. Combination of different studies appears to resolve in part this problem. However, neither individual GWAS nor meta-analytic combinations thereof are helpful for disclosing which genetic variants contribute to explain a particular phenotype. Here, we propose that most of the missing heritability is latent in the GWAS data, which conceals intermediate phenotypes. To uncover such latent information, we propose the PGMRA server that introduces phenomics--the full set of phenotype features of an individual--to identify SNP-set structures in a broader sense, i.e. causally cohesive genotype-phenotype relations. These relations are agnostically identified (without considering disease status of the subjects) and organized in an interpretable fashion. Then, by incorporating a posteriori the subject status within each relation, we can establish the risk surface of a disease in an unbiased mode. This approach complements-instead of replaces-current analysis methods. The server is publically available at http://phop.ugr.es/fenogeno.