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We studied the pattern of personality development in a longitudinal population-based sample of 752 American adolescents. Personality was assessed reliably with the Junior Temperament and Character Inventory at 12, 14, and 16 years of age. The rank-order stability of Junior Temperament and Character Inventory traits from age 12 to 16 was moderate (r =.35). Hierarchical linear modeling of between-group variance due to gender and within-group variance due to age indicated that harm avoidance and persistence decreased whereas self-directedness and cooperativeness increased from age 12 to 16. Novelty seeking, reward dependence, and self-transcendence increased from age 12 to 14 and then decreased. This biphasic pattern suggests that prior to age 14 teens became more emancipated from adult authorities while identifying more with the emergent norms of their peers, and after age 14 their created identity was internalized. Girls were more self-directed and cooperative than boys and maintained this advantage from age 12 to 16. Dependability of temperament at age 16 was mainly predicted by the same traits at earlier ages. In contrast, maturity of character at age 16 was predicted by both temperament and character at earlier ages. We conclude that character develops rapidly in adolescence to self-regulate temperament in accord with personally valued goals shaped by peers. [ABSTRACT FROM AUTHOR]

There is fundamental doubt about whether the natural unit of measurement for temperament and personality corresponds to single traits or to multitrait profiles that describe the functioning of a whole person. Biogenetic researchers of temperament usually assume they need to focus on individual traits that differ between individuals. Recent research indicates that a shift of emphasis to understand processes within the individual is crucial for identifying the natural building blocks of temperament. Evolution and development operate on adaptation of whole organisms or persons, not on individual traits or categories. Adaptive functioning generally depends on feedback among many variable processes in ways that are characteristic of complex adaptive systems, not machines with separate parts. Advanced methods of unsupervised machine learning can now be applied to genome-wide association studies and brain imaging in order to uncover the genotypic--phenotypic architecture of traits like temperament, which are strongly influenced by complex interactions, such as genetic epistasis, pleiotropy and gene--environment interactions. We have found that the heritability of temperament can be nearly fully explained by a large number of genetic variants that are unique for multi-trait profiles, not single traits. The implications of this finding for research design and precision medicine are discussed. This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'. [ABSTRACT FROM AUTHOR]

The DNA-binding protein PhoP controls virulence and Mg2+ homeostasis in the Gram-negative pathogen Salmonella enterica serovar Typhimurium. PhoP regulates expression of a large number of genes that differ both in their ancestry and in the biochemical functions and physiological roles of the encoded products. This suggests that PhoP-regulated genes are differentially expressed. To understand how a bacterial activator might generate varied gene expression behaviour, we investigated the cis-acting promoter features (i.e. the number of PhoP binding sites, as well as their orientation and location with respect to the sites bound by RNA polymerase and the sequences that constitute the PhoP binding sites) in 23 PhoP-activated promoters. Our results show that natural PhoP-activated promoters utilize only a limited number of combinations of cis-acting features - or promoter architectures. We determine that PhoP activates transcription by different mechanisms, and that ancestral and horizontally acquired PhoP-activated genes have distinct promoter architectures. [ABSTRACT FROM AUTHOR]

Summary: PhoP and PhoQ comprise a two-component system in the bacterium Salmonella enterica. PhoQ is the sensor kinase/phosphatase that modifies the phosphorylation state of the regulator PhoP in response to stimuli. The amount of phosphorylated PhoP surges after activation, then declines to reach a steady-state level. We now recapitulate this surge in vitro by incubating PhoP and PhoQ with ATP and ADP. Mathematical modeling identified PhoQ''s affinity for ADP as the key parameter dictating phosphorylated PhoP levels, as ADP promotes PhoQ''s phosphatase activity toward phosphorylated PhoP. The lid covering the nucleotide-binding pocket of PhoQ governs the kinase to phosphatase switch because a lid mutation that decreased ADP binding compromised PhoQ''s phosphatase activity in vitro and resulted in sustained expression of PhoP-dependent mRNAs in vivo. This feedback mechanism may curtail futile ATP consumption because ADP not only stimulates PhoQ''s phosphatase activity but also inhibits ATP binding necessary for the kinase reaction. [ABSTRACT FROM AUTHOR]

Genetic and genomic approaches have been successfully used to assign genes to distinct regulatory networks. However, the present challenge of distinguishing differentially regulated genes within a network is particularly hard because members of a given network tend to have similar regulatory features. We have addressed this challenge by developing a method, termed Gene Promoter Scan, that discriminates coregulated promoters by simultaneously considering both multiple cis promoter features and gene expression. Here, we apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Escherkhia coli and salmonella enterica. Our analysis uncovered members of the PhoP regulon and interactions with other regulatory systems that were not discovered in previous approaches. The predictions made by Gene Promoter Scan were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription, regulates acid resistance determinants, and is a central element in a highly connected network. [ABSTRACT FROM AUTHOR]

In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired differential gene expression analysis for biological feature selection in machine learning models, enhancing robustness and interpretability while accounting for patient variability. This method compares primary tumor tissue with the same patient's healthy tissue, improving gene selection by eliminating individual-specific artifacts. A focus on carcinoma was selected due to its prevalence and the availability of the data; we aim to identify biomarkers involved in general carcinoma progression, including less-researched types. Our findings identified 27 pivotal genes that can distinguish between healthy and carcinoma tissue, even in unseen carcinoma types. Additionally, the panel could precisely identify the tissue-of-origin in the eight carcinoma types used in the discovery phase. Notably, in a proof of concept, the model accurately identified the primary tissue origin in metastatic samples despite limited sample availability. Functional annotation reveals these genes' involvement in cancer hallmarks, detecting subtle variations across carcinoma types. We propose paired differential gene expression analysis as a reference method for the discovering of robust biomarkers. [ABSTRACT FROM AUTHOR]

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30–45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research. [ABSTRACT FROM AUTHOR]

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.esSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods’ aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further. [ABSTRACT FROM AUTHOR]

Related organisms typically rely on orthologous regulatory proteins to respond to a given signal. However, the extent to which (or even if) the targets of shared regulatory proteins are maintained across species has remainedlargely unknown. This question is of particular significance in bacteria due to the widespread effects of horizontal gene transfer. Here, we address this question by investigating the regulons controlled by the DNA binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. We establish that the ancestral PhoP protein directs largely different gene sets in ten analyzed species of the family Enterobacteriaceae, reflecting both regulation of species-specific targets and transcriptional rewiring of shared genes. The two targets directly activated by PhoP in all ten species (the most distant of which diverged .200 million years ago), and coding for the most conserved proteins are the phoPQ operon itself and the lipoprotein encoding slyB gene, which decreases PhoP protein activity. The Mg2+-responsive PhoP protein dictates expression of Mg2+ transporters and of enzymes that modify Mg2+-binding sites in the cell envelope in most analyzed species. In contrast to the core PhoP regulon, which determines the amount of active PhoP and copes with the low Mg2+ stress, the variable members of the regulon contribute species-specific traits, a property shared with regulons controlled by dissimilar regulatory proteins and responding to different signals. [ABSTRACT FROM AUTHOR]

Background: The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue-specific methylation pattern. Results: We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation. Conclusion: Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods. [ABSTRACT FROM AUTHOR]

This is the first report of the psychobiology of stress in BASE jumpers, one of the most dangerous forms of extreme sport. We tested the hypotheses that indicators of emotional style (temperament) predict salivary cortisol reactivity, whereas indicators of intentional goal-setting (persistence and character) predict salivary alpha-amylase reactivity during BASE jumping. Ninety-eight subjects completed the Temperament and Character Inventory (TCI) the day before the jump, and 77 also gave salivary samples at baseline, pre-jump on the bridge over the New River Gorge, and post-jump upon landing. Overall BASE jumpers are highly resilient individuals who are highly self-directed, persistent, and risk-taking, but they are heterogeneous in their motives and stress reactivity in the Hypothalamic-Pituitary-Adrenal (HPA) stress system (cortisol reactivity) and the sympathetic arousal system (alpha-amylase reactivity). Three classes of jumpers were identified using latent class analysis based on their personality profiles, prior jumping experience, and levels of cortisol and alpha-amylase at all three time points. “Masterful” jumpers (class 1) had a strong sense of self-directedness and mastery, extensive prior experience, and had little alpha-amylase reactivity and average cortisol reactivity. “Trustful” jumpers (class 2) were highly cooperative and trustful individuals who had little cortisol reactivity coincident with the social support they experienced prior to jumping. “Courageous” jumpers (class 3) were determined despite anxiety and inexperience, and they had high sympathetic reactivity but average cortisol activation. We conclude that trusting social attachment (Reward Dependence) and not jumping experience predicted low cortisol reactivity, whereas persistence (determination) and not jumping experience predicted high alpha-amylase reactivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An abstract of the article "Intramolecular arrangement of sensor and regulator overcomes relaxed specificity in hybrid two-component systems," by Guy E. Townsend II and colleagues is presented.

Transcriptional regulators recognize specific DNA sequences. Because these sequences are embedded in the background of genomic DNA, it is hard to identify the key cis-regulatory elements that determine disparate patterns of gene expression. The detection of the intra- and inter-species differences among these sequences is crucial for understanding the molecular basis of both differential gene expression and evolution. Here, we address this problem by investigating the target promoters controlled by the DNA-binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. PhoP is particularly interesting; it is highly conserved in different gamma/enterobacteria, regulating not only ancestral genes but also governing the expression of dozens of horizontally acquired genes that differ from species to species. Our approach consists of decomposing the DNA binding site sequences for a given regulator into families of motifs (i.e., termed submotifs) using a machine learning method inspired by the ''Divide & Conquer'' strategy. By partitioning a motif into sub-patterns, computational advantages for classification were produced, resulting in the discovery of new members of a regulon, and alleviating the problem of distinguishing functional sites in chromatin immunoprecipitation and DNA microarray genome-wide analysis. Moreover, we found that certain partitions were useful in revealing biological properties of binding site sequences, including modular gains and losses of PhoP binding sites through evolutionary turnover events, as well as conservation in distant species. The high conservation of PhoP submotifs within gamma/ enterobacteria, as well as the regulatory protein that recognizes them, suggests that the major cause of divergence between related species is not due to the binding sites, as was previously suggested for other regulators. Instead, the divergence may be attributed to the fast evolution of orthologous target genes and/or the promoter architectures resulting from the interaction of those binding sites with the RNA polymerase. [ABSTRACT FROM AUTHOR]

Background: A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined. Results: We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same network motif. Conclusion: Global transcriptional regulators control multiple promoters by a variety of network motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or network motif, but also the cis-acting elements participating in each of these connexions. [ABSTRACT FROM AUTHOR]

Colorectal cancer treatment has advanced over the past decade. The drug 5-fluorouracil is still used with a wide percentage of patients who do not respond. Therefore, a challenge is the identification of predictive biomarkers. The protein kinase R (PKR also called EIF2AK2) and its regulator, the non-coding pre-mir-nc886, have multiple effects on cells in response to numerous types of stress, including chemotherapy. In this work, we performed an ambispective study with 197 metastatic colon cancer patients with unresectable metastases to determine the relative expression levels of both nc886 and PKR by qPCR, as well as the location of PKR by immunohistochemistry in tumour samples and healthy tissues (plasma and colon epithelium). As primary end point, the expression levels were related to the objective response to first-line chemotherapy following the response evaluation criteria in solid tumours (RECIST) and, as the second end point, with survival at 18 and 36 months. Hierarchical agglomerative clustering was performed to accommodate the heterogeneity and complexity of oncological patients' data. High expression levels of nc886 were related to the response to treatment and allowed to identify clusters of patients. Although the PKR mRNA expression was not associated with chemotherapy response, the absence of PKR location in the nucleolus was correlated with first-line chemotherapy response. Moreover, a relationship between survival and the expression of both PKR and nc886 in healthy tissues was found. Therefore, this work evaluated the best way to analyse the potential biomarkers PKR and nc886 in order to establish clusters of patients depending on the cancer outcomes using algorithms for complex and heterogeneous data. [ABSTRACT FROM AUTHOR]