Energy Trade-off and 4 Extreme Human Body Types.

BACKGROUND: Resource trade-off theory suggests that increased performance on a given trait comes at the cost of decreased performance on other traits. METHODS: Growth data from 1889 subjects (996 girls) were used from the GrowUp1974 Gothenburg study. Energy Trade-Off (ETO) between height and weight for individuals with extreme body types was characterized using a novel ETO-Score (ETOS). Four extreme body types were defined based on height and ETOI at early adulthood: tall-slender, short-stout, short-slender, and tall-stout; their growth trajectories assessed from ages 0.5-17.5 years.A GWAS using UK BioBank data was conducted to identify gene variants associated with height, BMI, and for the first time with ETOS. RESULTS: Height and ETOS trajectories show a two-hit pattern with profound changes during early infancy and at puberty for tall-slender and short-stout body types. Several loci (including FTO, ADCY3, GDF5, ) and pathways were identified by GWAS as being highly associated with ETOS. The most strongly associated pathways were related to "extracellular matrix," "signal transduction," "chromatin organization," and "energy metabolism." CONCLUSIONS: ETOS represents a novel anthropometric trait with utility in describing body types. We discovered the multiple genomic loci and pathways probably involved in energy trade-off.

Personalized approach to childhood obesity: Lessons from gut microbiota and omics studies. Narrative review and insights from the 29th European childhood obesity congress.

The traditional approach to childhood obesity prevention and treatment should fit most patients, but misdiagnosis and treatment failure could be observed in some cases that lie away from average as part of individual variation or misclassification. Here, we reflect on the contributions that high-throughput technologies such as next-generation sequencing, mass spectrometry-based metabolomics and microbiome analysis make towards a personalized medicine approach to childhood obesity. We hypothesize that diagnosing a child as someone with obesity captures only part of the phenotype; and that metabolomics, genomics, transcriptomics and analyses of the gut microbiome, could add precision to the term "obese," providing novel corresponding biomarkers. Identifying a cluster -omic signature in a given child can thus facilitate the development of personalized prognostic, diagnostic, and therapeutic approaches. It can also be applied to the monitoring of symptoms/signs evolution, treatment choices and efficacy, predisposition to drug-related side effects and potential relapse. This article is a narrative review of the literature and summary of the main observations, conclusions and perspectives raised during the annual meeting of the European Childhood Obesity Group. Authors discuss some recent advances and future perspectives on utilizing a systems approach to understanding and managing childhood obesity in the context of the existing omics data.

Prediction of Adult Height by Machine Learning Technique.

CONTEXT: Prediction of AH is frequently undertaken in the clinical setting. The commonly used methods are based on the assessment of skeletal maturation. Predictive algorithms generated by machine learning, which can already automatically drive cars and recognize spoken language, are the keys to unlocking data that can precisely inform the pediatrician for real-time decision making. OBJECTIVE: To use machine learning (ML) to predict adult height (AH) based on growth measurements until age 6 years. METHODS: Growth data from 1596 subjects (798 boys) aged 0-20 years from the longitudinal GrowUp 1974 Gothenburg cohort were utilized to train multiple ML regressors. Of these, 100 were used for model comparison, the rest was used for 5-fold cross-validation. The winning model, random forest (RF), was first validated on 684 additional subjects from the 1974 cohort. It was additionally validated using 1890 subjects from the GrowUp 1990 Gothenburg cohort and 145 subjects from the Edinburgh Longitudinal Growth Study cohort. RESULTS: RF with 51 regression trees produced the most accurate predictions. The best predicting features were sex and height at age 3.4-6.0 years. Observed and predicted AHs were 173.9 ±â€…8.9 cm and 173.9 ±â€…7.7 cm, respectively, with prediction average error of -0.4 ±â€…4.0 cm. Validation of prediction for 684 GrowUp 1974 children showed prediction accuracy r = 0.87 between predicted and observed AH (R2 = 0.75). When validated on the 1990 Gothenburg and Edinburgh cohorts (completely unseen by the learned RF model), the prediction accuracy was r = 0.88 in both cases (R2 = 0.77). AH in short children was overpredicted and AH in tall children was underpredicted. Prediction absolute error correlated negatively with AH (P < .0001). CONCLUSION: We show successful, validated ML of AH using growth measurements before age 6 years. The most important features for prediction were sex, and height at age 3.4-6.0. Prediction errors result in over- or underestimates of AH for short and tall subjects, respectively. Prediction by ML can be generalized to other cohorts.

Steroid Metabolomic Signature of Insulin Resistance in Childhood Obesity.

OBJECTIVE: On the basis of urinary steroidal gas chromatography-mass spectrometry (GC-MS), we previously defined a novel concept of a disease-specific "steroid metabolomic signature" and reclassified childhood obesity into five groups with distinctive signatures. The objective of the current study was to delineate the steroidal signature of insulin resistance (IR) in obese children. RESEARCH DESIGN AND METHODS: Urinary samples of 87 children (44 girls) aged 8.5-17.9 years with obesity (BMI >97th percentile) were quantified for 31 steroid metabolites by GC-MS. Defined as HOMA-IR >95th percentile and fasting glucose-to-insulin ratio >0.3, IR was diagnosed in 20 (of 87 [23%]) of the examined patients. The steroidal fingerprints of subjects with IR were compared with those of obese children without IR (non-IR). The steroidal signature of IR was created from the product of IR - non-IR for each of the 31 steroids. RESULTS: IR and non-IR groups of children had comparable mean age (13.7 ± 1.9 and 14.6 ± 2.4 years, respectively) and z score BMI (2.7 ± 0.5 and 2.7 ± 0.5, respectively). The steroidal signature of IR was characterized by high adrenal androgens, glucocorticoids, and mineralocorticoid metabolites; higher 5α-reductase (An/Et) (P = 0.007) and 21-hydroxylase [(THE + THF + αTHF)/PT] activity (P = 0.006); and lower 11ßHSD1 [(THF + αTHF)/THE] activity (P = 0.012). CONCLUSIONS: The steroidal metabolomic signature of IR in obese children is characterized by enhanced secretion of steroids from all three adrenal pathways. As only the fasciculata and reticularis are stimulated by ACTH, these findings suggest that IR directly affects the adrenals. We suggest a vicious cycle model, whereby glucocorticoids induce IR, which could further stimulate steroidogenesis, even directly. We do not know whether obese children with IR and the new signature may benefit from amelioration of their hyperadrenalism.

Steroid metabolomic signature of liver disease in nonsyndromic childhood obesity.

OBJECTIVE: Analysis of steroids by gas chromatography-mass spectrometry (GC-MS) defines a subject's steroidal fingerprint. Here, we compare the steroidal fingerprints of obese children with or without liver disease to identify the 'steroid metabolomic signature' of childhood nonalcoholic fatty liver disease. METHODS: Urinary samples of 85 children aged 8.5-18.0 years with BMI >97% were quantified for 31 steroid metabolites by GC-MS. The fingerprints of 21 children with liver disease (L1) as assessed by sonographic steatosis (L1L), elevated alanine aminotransferases (L1A) or both (L1AL), were compared to 64 children without markers of liver disease (L0). The steroidal signature of the liver disease was generated as the difference in profiles of L1 against L0 groups. RESULTS: L1 comparing to L0 presented higher fasting triglycerides (P = 0.004), insulin (P = 0.002), INS/GLU (P = 0.003), HOMA-IR (P = 0.002), GGTP (P = 0.006), AST/SGOT (P = 0.002), postprandial glucose (P = 0.001) and insulin (P = 0.011). L1AL showed highest level of T-cholesterol and triglycerides (P = 0.029; P = 0.044). Fasting insulin, postprandial glucose, INS/GLU and HOMA-IR were highest in L1L and L1AL (P = 0.001; P = 0.017; P = 0.001; P = 0.001). The liver disease steroidal signature was marked by lower DHEA and its metabolites, higher glucocorticoids (mostly tetrahydrocortisone) and lower mineralocorticoid metabolites than L0. L1 patients showed higher 5α-reductase and 21-hydroxylase activity (the highest in L1A and L1AL) and lower activity of 11ßHSD1 than L0 (P = 0.041, P = 0.009, P = 0.019). CONCLUSIONS: The 'steroid metabolomic signature' of liver disease in childhood obesity provides a new approach to the diagnosis and further understanding of its metabolic consequences. It reflects the derangements of steroid metabolism in NAFLD that includes enhanced glucocorticoids and deranged androgens and mineralocorticoids.

High abundance of ArfGAP1 found in the mossy fibers in hilus of the dentate gyrus region of the mouse brain.

The Arf GTPase-activating protein ArfGAP1 and its brain-specific isoform ArfGAP1B play an important role in neurotransmission. Here we analyzed the distribution of ArfGAP1 in the mouse brain. We found high levels of ArfGAP1 in the mouse dentate gyrus where it displayed especially elevated level in the polymorph layer (hilus). Importantly, the ArfGAP1 signal follows the pathway of the granular cell axons so-called mossy fibers which extend from the dentate gyrus to CA3 via stratum lucidum and partially stratum oriens. Additionally, we identified differential expression of ArfGAP1 in the isocortex. Thus, staining with anti-ArfGAP1 antibodies allows distinction between cortical cell layers 1, 2, 3 and 5 from 4 and 6. Taken together, our data suggest that ArfGAP1 can be used as a specific marker of the dentate mossy fibers and as for visualization of cortical layers in immunohistochemical studies.

Steroid Metabolomic Disease Signature of Nonsyndromic Childhood Obesity.

CONTEXT: The profile of urinary steroids as measured by gas chromatography-mass spectrometry defines a subject's "steroidal fingerprint." OBJECTIVE: Here, we clustered steroidal fingerprints to characterize patients with nonsyndromic childhood obesity by "steroid metabolomic signatures." HYPOTHESIS: Nonsyndromic obesity is a symptom of different diseases and conditions, some of them will have their own signature. DESIGN: A total of 31 steroid metabolites were quantified by gas chromatography-mass spectrometry, and their excretion rates were z-transformed. Using MetaboAnalyst 3.0, we divided the subjects into 5 distinctive groups by k-means clustering. Steroidal fingerprints and clinical/biochemical data of patients in each cluster were analyzed. PATIENTS: A total of 87 obese children (44 females), aged 8.5-17.9 years, were clinically characterized, and their 24-hour urine was collected. RESULTS: Cluster 1 (n = 39, 21 females) had normal steroid profile. Cluster 2 (n = 20, 11 females) showed mild, nonspecific elevation of C19 and C21 steroids, females' resistance to polycystic ovary morphology, and hirsutism. Cluster 3 (n = 7 female), with relative 21-hydroxylase insufficiency, was characterized by partial or full polycystic ovary syndrome. Cluster 4 (n = 4 males), showed markedly elevated C21 steroids and imbalance in the 11ß-hydroxysteroid dehydrogenase system, higher insulin, increased frequency of glucose/insulin index more than 0.3, γ-glutamyl transpeptidase activity, systolic blood pressure, and tendency to liver steatosis. Cluster 5 (n = 17, 5 females) had elevated dehydroepiandrosterone and 17-OH-pregnenolone metabolites, suggesting 3ß-hydroxysteroid dehydrogenase insufficiency but no clinically unique phenotype. Z-score body mass index values were not significantly different between the clusters. CONCLUSIONS: We defined a novel concept of disease-specific steroid metabolomic signature based on urinary steroidal gas chromatography-mass spectrometry. Clustering by software designed for metabolic data analysis reclassified childhood obesity into 5 groups with distinctive signatures; groups require further definition and may require cluster-specific therapeutic strategies.

Predicting pubertal development by infantile and childhood height, BMI, and adiposity rebound.

BACKGROUND: Despite substantial heritability in pubertal development, children differ in maturational tempo. HYPOTHESES: (i) puberty and its duration are influenced by early changes in height and adiposity. (ii) Adiposity rebound (AR) is a marker for pubertal tempo. METHODS: We utilized published prospective data from 659 girls and 706 boys of the Study of Early Child Care and Youth Development. We investigated the age of pubarche-thelarche-gonadarche-menarche as a function of early height, BMI, and AR. RESULTS: In girls, height standard deviation scores correlated negatively with thelarche and pubarche from 15 mo of age and with menarche from 54 mo. BMI correlated negatively with thelarche from 36 mo of age and menarche from 54 mo. In boys, age at gonadarche correlated negatively with height from 36 mo of age. An AR was detected in 47% of girls and 55% of boys, who became heavier and had earlier and faster puberty than those with no AR. CONCLUSION: The onset and tempo of puberty are influenced by a two-hit program. The first is exerted during the infancy-childhood transition (ICT; 6-12 mo) and includes height, as an early predictor of maturational tempo. The second hit occurs at the childhood-juvenility transition (5-7 y) and is based on adiposity and its rebound.

Pheromone-encoding mRNA is transported to the yeast mating projection by specific RNP granules.

Association of messenger RNAs with large complexes such as processing bodies (PBs) plays a pivotal role in regulating their translation and decay. Little is known about other possible functions of these assemblies. Exposure of haploid yeast cells, carrying mating type "a," to "α pheromone" stimulates polarized growth resulting in a "shmoo" projection; it also induces synthesis of "a pheromone," encoded by MFA2. In this paper, we show that, in response to α pheromone, MFA2 mRNA is assembled with two types of granules; both contain some canonical PB proteins, yet they differ in size, localization, motility, and sensitivity to cycloheximide. Remarkably, one type is involved in mRNA transport to the tip of the shmoo, whereas the other-in local translation in the shmoo. Normal assembly of these granules is critical for their movement, localization, and for mating. Thus, MFA2 mRNAs are transported to the shmoo tip, in complex with PB-like particles, where they are locally translated.

Environmental rather than genetic factors determine the variation in the age of the infancy to childhood transition: a twins study.

OBJECTIVE: Using a twins study, we sought to assess the contribution of genetic against environmental factor as they affect the age at transition from infancy to childhood (ICT). STUDY DESIGN: The subjects were 56 pairs of monozygotic twins, 106 pairs of dizygotic twins, and 106 pairs of regular siblings (SBs), for a total of 536 children. Their ICT was determined, and a variance component analysis was implemented to estimate components of the familial variance, with simultaneous adjustment for potential covariates. RESULTS: We found substantial contribution of the common environment shared by all types of SBs that explained 27.7% of the total variance in ICT, whereas the common twin environment explained 9.2% of the variance, gestational age 3.5%, and birth weight 1.8%. In addition, 8.7% was attributable to sex difference, but we found no detectable contribution of genetic factors to inter-individual variation in ICT age. CONCLUSIONS: Developmental plasticity impacts much of human growth. Here we show that of the ∼50% of the variance provided to adult height by the ICT, 42.2% is attributable to adaptive cues represented by shared twin and SB environment, with no detectable genetic involvement.

Genome-wide SNP-genotyping array to study the evolution of the human pathogen Vibrio vulnificus biotype 3.

Vibrio vulnificus is an aquatic bacterium and an important human pathogen. Strains of V. vulnificus are classified into three different biotypes. The newly emerged biotype 3 has been found to be clonal and restricted to Israel. In the family Vibrionaceae, horizontal gene transfer is the main mechanism responsible for the emergence of new pathogen groups. To better understand the evolution of the bacterium, and in particular to trace the evolution of biotype 3, we performed genome-wide SNP genotyping of 254 clinical and environmental V. vulnificus isolates with worldwide distribution recovered over a 30-year period, representing all phylogeny groups. A custom single-nucleotide polymorphism (SNP) array implemented on the Illumina GoldenGate platform was developed based on 570 SNPs randomly distributed throughout the genome. In general, the genotyping results divided the V. vulnificus species into three main phylogenetic lineages and an additional subgroup, clade B, consisting of environmental and clinical isolates from Israel. Data analysis suggested that 69% of biotype 3 SNPs are similar to SNPs from clade B, indicating that biotype 3 and clade B have a common ancestor. The rest of the biotype 3 SNPs were scattered along the biotype 3 genome, probably representing multiple chromosomal segments that may have been horizontally inserted into the clade B recipient core genome from other phylogroups or bacterial species sharing the same ecological niche. Results emphasize the continuous evolution of V. vulnificus and support the emergence of new pathogenic groups within this species as a recurrent phenomenon. Our findings contribute to a broader understanding of the evolution of this human pathogen.

Peer group normalization and urine to blood context in steroid metabolomics: the case of CAH and obesity.

Traditional interpretation of GC-MS output involved the semi-quantitative estimation of outstanding low or high specific metabolites and the ratio between metabolites. Here, we utilize a systems biology approach to steroid metabolomics of a complex steroid-related disorder, using an all-inclusive analysis of the steroidal pathway in the form of a subject steroidal fingerprint and disease signature, providing novel methods of normalization and visualization. The study compares 324 normal children to pure enzymatic deficiency in 27 untreated 21-hydroxylase CAH patients and to complex disease in 70 children with obesity. Steroid profiles were created by quantitative data generated by GC-MS analyses. A novel peer-group normalization method defined each individual subject's control group in a multi-dimensional space of metadata parameters. Classical steroid pathway visualization was enhanced by adding urinary end-product sub-nodes and by color coding of semi-quantitative metabolic concentrations and enzymatic activities. Unbiased automated data analysis confirmed the common knowledge for CAH - the inferred 17-hydroxyprogesterone was up-regulated and the inferred 21-hydroxylase enzyme activity was down-regulated. In childhood obesity, we observe a general decrease of both glucocorticoid and mineralocorticoid metabolites, increased androgens, up-regulation of 17,20-lyase, 17-OHase and 11ß-HSD1 activity and down-regulation of 21-OHase enzymatic activity. Our study proved novel normalization and visualization techniques are to be useful in identifying subject fingerprint and disease signature in enzymatic deficiency and insufficiency, while demonstrating hypothesis generation in a complex disease such as childhood obesity.

GeneCards Version 3: the human gene integrator.

GeneCards (www.genecards.org) is a comprehensive, authoritative compendium of annotative information about human genes, widely used for nearly 15 years. Its gene-centric content is automatically mined and integrated from over 80 digital sources, resulting in a web-based deep-linked card for each of >73,000 human gene entries, encompassing the following categories: protein coding, pseudogene, RNA gene, genetic locus, cluster and uncategorized. We now introduce GeneCards Version 3, featuring a speedy and sophisticated search engine and a revamped, technologically enabling infrastructure, catering to the expanding needs of biomedical researchers. A key focus is on gene-set analyses, which leverage GeneCards' unique wealth of combinatorial annotations. These include the GeneALaCart batch query facility, which tabulates user-selected annotations for multiple genes and GeneDecks, which identifies similar genes with shared annotations, and finds set-shared annotations by descriptor enrichment analysis. Such set-centric features address a host of applications, including microarray data analysis, cross-database annotation mapping and gene-disorder associations for drug targeting. We highlight the new Version 3 database architecture, its multi-faceted search engine, and its semi-automated quality assurance system. Data enhancements include an expanded visualization of gene expression patterns in normal and cancer tissues, an integrated alternative splicing pattern display, and augmented multi-source SNPs and pathways sections. GeneCards now provides direct links to gene-related research reagents such as antibodies, recombinant proteins, DNA clones and inhibitory RNAs and features gene-related drugs and compounds lists. We also portray the GeneCards Inferred Functionality Score annotation landscape tool for scoring a gene's functional information status. Finally, we delineate examples of applications and collaborations that have benefited from the GeneCards suite. Database URL: www.genecards.org.

Microbial community genomics in eastern Mediterranean Sea surface waters.

Offshore waters of the eastern Mediterranean Sea are one of the most oligotrophic regions on Earth in which the primary productivity is phosphorus limited. To study the unexplored function and physiology of microbes inhabiting this system, we have analyzed a genomic library from the eastern Mediterranean Sea surface waters by sequencing both termini of nearly 5000 clones. Genome recruitment strategies showed that the majority of high-scoring pairs corresponded to genomes from the Alphaproteobacteria (SAR11-like and Rhodobacterales), Cyanobacteria (Synechococcus and high-light adapted Prochlorococcus) and diverse uncultured Gammaproteobacteria. The community structure observed, as evaluated by both protein similarity scores or metabolic potential, was similar to that found in the euphotic zone of the ALOHA station off Hawaii but very different from that of deep aphotic zones in both the Mediterranean Sea and the Pacific Ocean. In addition, a strong enrichment toward phosphate and phosphonate uptake and utilization metabolism was also observed.

Comparative community genomics in the Dead Sea: an increasingly extreme environment.

Owing to the extreme salinity ( approximately 10 times saltier than the oceans), near toxic magnesium levels (approximately 2.0 M Mg(2+)), the dominance of divalent cations, acidic pH (6.0) and high-absorbed radiation flux rates, the Dead Sea represents a unique and harsh ecosystem. Measures of microbial presence (microscopy, pigments and lipids) indicate that during rare bloom events after exceptionally rainy seasons, the microbial communities can reach high densities. However, most of the time, when the Dead Sea level is declining and halite is precipitating from the water column, it is difficult to reliably measure the presence of microorganisms and their activities. Although a number of halophilic Archaea have been previously isolated from the Dead Sea, polar lipid analyses of biomass collected during Dead Sea blooms suggested that these isolates were not the major components of the microbial community of these blooms. In this study, in an effort to characterize the perennial microbial community of the Dead Sea and compare it with bloom assemblages, we performed metagenomic analyses of concentrated biomass from hundreds of liters of brine and of microbial material from the last massive Dead Sea bloom. The difference between the two conditions was reflected in community composition and diversity, in which the bloom was different and less diverse from the residual brine population. The distributional patterns of microbial genes suggested Dead Sea community trends in mono- and divalent cation metabolisms as well as in transposable elements. This may indicate possible mechanisms and pathways enabling these microbes to survive in such a harsh environment.

Interactions between trabecular meshwork cells and lens epithelial cells: a possible mechanism in infantile aphakic glaucoma.

PURPOSE: Infantile aphakic glaucoma may develop as a postoperative complication of early childhood cataract surgery. It has been associated with risk factors including surgery in early life and retained lens material; however, its cause and mechanism are poorly understood. This study focused on the potential role of retained lens material (specifically, exposed lens epithelial cells [LECs]) in undesired changes of the trabecular meshwork (TM) structure and function. METHODS: Interactions between LECs and TM cells were studied by analyzing structural changes and differential gene and protein expression in TM cells cocultured with LECs. RESULTS: Subjecting normal TM cells to the presence of LECs resulted in changes in their structural features (such as increase in volume and size, and decrease in cell-cell interactions), as well as in their protein expression (mainly cytoskeletal) and gene expression (such as genes related to organ and cell morphogenesis, inflammatory response, response to stimulus, ion homeostasis, and several signaling pathways). CONCLUSIONS: Many of the changes observed in TM cells after exposure to LECs resemble alternations seen in primary open-angle glaucoma. This strengthens the suspected role of LECs in the development of aphakic glaucoma.

Analysis of cognitive performance in schizophrenia patients and healthy individuals with unsupervised clustering models.

Currently, assignment of cognitive test results to particular cognitive domains is guided by theoretical considerations and expert judgments which may vary. More objective means of classification may advance understanding of the relationships between test performance and the cognitive functions probed. We examined whether "atheoretical" analyses of cognitive test data can help identify potential hidden structures in cognitive performance. Novel data-mining methods which "let the data talk" without a priori theoretically bound constraints were used to analyze neuropsychological test results of 75 schizophrenia patients and 57 healthy individuals. The analyses were performed on the combined sample to maximize the "atheoretical" approach and allow it to reveal different structures of cognition in patients and controls. Analyses used unsupervised clustering methods, including hierarchical clustering, self-organizing maps (SOM), k-means and supermagnetic clustering (SPC). The model revealed two major clusters containing accuracy and reaction time measures respectively. The sensitivity (75% versus 52%) and specificity (95% versus 77% ) of these clusters for diagnosing schizophrenia differed. Downstream branching was influenced by stimulus domain. Predictions arising from this "atheoretical" model are supported by evidence from published studies. This preliminary study suggests that appropriate application of data-mining methods may contribute to investigation of cognitive functions.

Alterations in micro-ribonucleic acid expression profiles reveal a novel pathway for estrogen regulation.

Estrogens are steroid hormones that have been implicated in a variety of cellular and physiological processes in the development of diseases such as cancer and are also known to be associated with the effects of endocrine disrupting chemicals. Here we show that 17beta-estradiol (E(2)) alters microRNA (miRNA) expression profiles in the adult zebrafish (Danio rerio). An association between E(2) and the expression of 25 miRNAs was found 12 h after treatment. Among the most up-regulated miRNAs were miR-196b and let-7h, and the most down-regulated miRNAs included miR-130c and miR-101a. Tissue-specific changes in the transcripts levels of estrogen receptors (Esr1, Esr2a, and Esr2b) and miRNAs were found after hormone treatment. The most up-regulated miR-196b and its precursors are highly expressed in the skin and showed similar tissue-specific expression patterns after treatment, indicating a common pattern of regulation by E(2). MiR-196b was shown to fine-tune the expression of its target gene Hoxb8a after treatment in whole-body homogenates. Taken together, our results suggest a novel pathway for the multifunctional and pleiotropic effects of estrogens and open new directions for future investigations of their association with miRNAs involved in estrogen-regulated physiological processes and diseases.

Viral photosynthetic reaction center genes and transcripts in the marine environment.

Cyanobacteria of the genera Synechococcus and Prochlorococcus are important contributors to photosynthetic productivity in the open ocean. The discovery of genes (psbA, psbD) that encode key photosystem II proteins (D1, D2) in the genomes of phages that infect these cyanobacteria suggests new paradigms for the regulation, function and evolution of photosynthesis in the vast pelagic ecosystem. Reports on the prevalence and expression of phage photosynthesis genes, and evolutionary data showing a potential recombination of phage and host genes, suggest a model in which phage photosynthesis genes help support photosynthetic activity in their hosts during the infection process. Here, using metagenomic data in natural ocean samples, we show that about 60% of the psbA genes in surface water along the global ocean sampling transect are of phage origin, and that the phage genes are undergoing an independent selection for distinct D1 proteins. Furthermore, we show that different viral psbA genes are expressed in the environment.

Novel definition files for human GeneChips based on GeneAnnot.

BACKGROUND: Improvements in genome sequence annotation revealed discrepancies in the original probeset/gene assignment in Affymetrix microarray and the existence of differences between annotations and effective alignments of probes and transcription products. In the current generation of Affymetrix human GeneChips, most probesets include probes matching transcripts from more than one gene and probes which do not match any transcribed sequence. RESULTS: We developed a novel set of custom Chip Definition Files (CDF) and the corresponding Bioconductor libraries for Affymetrix human GeneChips, based on the information contained in the GeneAnnot database. GeneAnnot-based CDFs are composed of unique custom-probesets, including only probes matching a single gene. CONCLUSION: GeneAnnot-based custom CDFs solve the problem of a reliable reconstruction of expression levels and eliminate the existence of more than one probeset per gene, which often leads to discordant expression signals for the same transcript when gene differential expression is the focus of the analysis. GeneAnnot CDFs are freely distributed and fully compliant with Affymetrix standards and all available software for gene expression analysis. The CDF libraries are available from http://www.xlab.unimo.it/GA_CDF, along with supplementary information (CDF libraries, installation guidelines and R code, CDF statistics, and analysis results).