Crystal structure of {2-[4-(4-chlorophenyl)-4-hydroxy-l-piperidinylmethyl] cyclopentyl}-(4-fluorophenyl)-methanone, C24H27CIFNO2

Abstract C24H27ClFNO2, triclinic, P1̅̅̅̅ (No. 2), a = 9.494(2) Å, b = 10.769(2) Å, c = 11.377(3)Å, α = 87.18(3)°, β = 67.27(3)°, γ = 88.01(3)°, V = 1071.4 Å3, Z = 2, Rgt(F) = 0.076, wRobs(F2) = 0.302, T = 293 K

Validation of age determination using otoliths of the European anchovy (Engraulis encrasicolus L.) in the Bay of Biscay

Validation of the age determination procedure using otoliths of European anchovy in the Bay of Biscay was achieved by monitoring very strong year-classes in successive spring catches and surveys, as well as the seasonal occurrence of edge types. Historical corroboration of the ageing method was obtained by cross-correlation between successive age groups by year-classes in catches and surveys (1987–2013). Summary annual growth in length is also presented. Yearly annuli consist of a hyaline zone (either single or composite) and a wide opaque zone, disrupted occasionally by some typical checks (mainly at age-0 and age-1 at peak spawning time). Age determination, given a date of capture, requires knowledge of the typical annual growth pattern of otoliths, their seasonal edge formation by ages and the most typical checks. Most opaque growth occurs in summer and is minimal (translucent) in winter. Opaque zone formation begins earlier in younger fish (in spring), and this helps distinguish age-1 from age-2þ.Versión del edito

Validation of age determination from Otoliths for Bay of Biscay anchovy

Comprehension of the annual pattern of annulus formation throughout the anchovy life span was first achieved from the observations of the strong 1982 year class which showed a neat annual progression of modal lengths passing through the fishery until the exceptional age of 5. Validation of the proposed method was subsequently obtained through monitoring of the progression of the strong 1987, 1989 and 1991 year-classes, both by spring annual surveys and by continuous sampling of the commercial catches, coupled to the monitoring of the seasonal marginal edge formation of the otoliths. Since then Age validation has been confirmed by the correlation between the pulses of recruitments (at age 1), as reflected in their relative occurrence in the population in Spring, and the abundance of those recruitments according to surveys. Typically, annual growth of anchovy otoliths of the one and two years old diminish to about 2/3-1/2 and 1/3 of that occurring in their previous ages respectively. Growth of older ages (three and four) are rather similar as, or slightly lesser than, at age 2. Maximum growth (white band formation) occurs in summer and growth detentions (with translucent annulus formation) in winter time. However the opaque edge formation begins sooner at the age of 1 (around February-March) than at older ages (May or June). During the first winter several translucent rings are occasionally formed resulting in a composite annulus formation. In addition during June/July, at peak spawning, a check is formed in many of the one year old anchovies. However, not all year classes, neither all anchovies lay down the same amount of checks and many of them may not show any. As such age determination requires the knowledge of the typical annual growth pattern of otoliths, of their seasonal edge formation by ages and of the most typical checks

Splicing Factor SLU7 Prevents Oxidative Stress-Mediated Hepatocyte Nuclear Factor 4α Degradation, Preserving Hepatic Differentiation and Protecting From Liver Damage

Background and Aims: Hepatocellular dedifferentiation is emerging as an important determinant in liver disease progression. Preservation of mature hepatocyte identity relies on a set of key genes, predominantly the transcription factor hepatocyte nuclear factor 4α (HNF4α) but also splicing factors like SLU7. How these factors interact and become dysregulated and the impact of their impairment in driving liver disease are not fully understood. Approach and Results: Expression of SLU7 and that of the adult and oncofetal isoforms of HNF4α, driven by its promoter 1 (P1) and P2, respectively, was studied in diseased human and mouse livers. Hepatic function and damage response were analyzed in wild-type and Slu7-haploinsufficient/heterozygous (Slu7) mice undergoing chronic (CCl) and acute (acetaminophen) injury. SLU7 expression was restored in CCl-injured mice using SLU7-expressing adeno-associated viruses (AAV-SLU7). The hepatocellular SLU7 interactome was characterized by mass spectrometry. Reduced SLU7 expression in human and mouse diseased livers correlated with a switch in HNF4α P1 to P2 usage. This response was reproduced in Slu7 mice, which displayed increased sensitivity to chronic and acute liver injury, enhanced oxidative stress, and marked impairment of hepatic functions. AAV-SLU7 infection prevented liver injury and hepatocellular dedifferentiation. Mechanistically we demonstrate a unique role for SLU7 in the preservation of HNF4α1 protein stability through its capacity to protect the liver against oxidative stress. SLU7 is herein identified as a key component of the stress granule proteome, an essential part of the cell’s antioxidant machinery. Conclusions: Our results place SLU7 at the highest level of hepatocellular identity control, identifying SLU7 as a link between stress-protective mechanisms and liver differentiation. These findings emphasize the importance of the preservation of hepatic functions in the protection from liver injury.Supported by MINECO/AEI/FEDER (UE SAF2016-75972-R, PID2019-104265RB-I00/AEI/10.13039/501100011033, and PID2019-104878RB-100/AEI/10.13039/501100011033), CIBERehd, Fundación La Caixa (HEPACARE), an AECC postdoctoral fellowship (POSTD18014AREC, to M.A.), a Ministerio de Educación FPU fellowship (FPU18/01461, to M.G.R.), a Ministerio de Educación FPI fellowship (BES-2017-079883, to M.R.); a Ramón y Cajal Program contract (RYC2018-024475-1, to M.G.F.B.), the Fundación Eugenio Rodríguez Pascual, the Fundación Mario Losantos, the Fundación M. Torres, and a generous donation from Mr. Eduardo Avila

Deficient maternal care resulting from immunological stress during pregnancy is associated with a sex-dependent enhancement of conditioned fear in the offspring

Activation of maternal stress response systems during pregnancy has been associated with altered postpartum maternal care and subsequent abnormalities in the offspring’s brain and behavioral development. It remains unknown, however, whether similar effects may be induced by exposure to immunological stress during pregnancy. The present study was designed to address this issue in a mouse model of prenatal immune activation by the viral mimic polyriboinosinic–polyribocytidilic acid (PolyI:C). Pregnant mice were exposed to PolyI:C-induced immune challenge or sham treatment, and offspring born to PolyI:C- and sham-treated dams were simultaneously cross-fostered to surrogate rearing mothers, which had either experienced inflammatory or vehicle treatment during pregnancy. We evaluated the effects of the maternal immunological manipulation on postpartum maternal behavior, and we assessed the prenatal and postnatal maternal influences on anxiety- and fear-related behavior in the offspring at the peri-adolescent and adult stage of development. We found that PolyI:C treatment during pregnancy led to changes in postpartum maternal behavior in the form of reduced pup licking/grooming and increased nest building activity. Furthermore, the adoption of neonates by surrogate rearing mothers, which had experienced PolyI:C-induced immunological stress during pregnancy, led to enhanced conditioned fear in the peri-adolescent and adult offspring, an effect that was exclusively seen in female but not male subjects. Unconditioned (innate) anxiety-related behavior as assessed in the elevated plus maze and open field explorations tests were not affected by the prenatal and postnatal manipulations. Our results thus highlight that being raised by gestationally immune-challenged surrogate mothers increases the vulnerability for specific forms of fear-related behavioral pathology in later life, and that this association may be mediated by deficits in postpartum maternal care. This may have important implications for the identification and characterization of early-life risk factors involved in the developmental etiology of fear-related neuropsychiatric disorders

Gene selection for classification of microarray data based on the Bayes error

<p>Abstract</p> <p>Background</p> <p>With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.</p> <p>Results</p> <p>In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes.</p> <p>Conclusion</p> <p>The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.</p

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: a machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.This research was funded by: Instituto de Salud Carlos III (ISCIII) co-financed by Fondo Europeo de Desarrollo Regional (FEDER) Una manera de hacer Europa, grant numbers: PI16/01126 (M.A.A.), PI19/00819 (M.J.M. and J.J.G.M.), PI15/01132, PI18/01075 and Miguel Servet Program CON14/00129 (J.M.B.); Fundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation), grant name: Rare Cancers 2017 (J.M.U., M.L.M., J.M.B., M.J.M., R.I.R.M., M.G.F.-B., C.B., M.A.A.); Gobierno de Navarra Salud, grant number 58/17 (J.M.U., M.A.A.); La Caixa Foundation, grant name: HEPACARE (C.B., M.A.A.); AMMF The Cholangiocarcinoma Charity, UK, grant number: 2018/117 (F.J.C. and M.A.A.); PSC Partners US, PSC Supports UK, grant number 06119JB (J.M.B.); Horizon 2020 (H2020) ESCALON project, grant number H2020-SC1-BHC-2018–2020 (J.M.B.); BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia, grant numbers BIO15/CA/016/BD (J.M.B.) and BIO15/CA/011 (M.A.A.). Department of Health of the Basque Country, grant number 2017111010 (J.M.B.). La Caixa Foundation, grant number: LCF/PR/HP17/52190004 (M.L.M.), Mineco-Feder, grant number SAF2017-87301-R (M.L.M.), Fundación BBVA grant name: Ayudas a Equipos de Investigación Científica Umbrella 2018 (M.L.M.). MCIU, grant number: Severo Ochoa Excellence Accreditation SEV-2016-0644 (M.L.M.). Part of the equipment used in this work was co-funded by the Generalitat Valenciana and European Regional Development Fund (FEDER) funds (PO FEDER of Comunitat Valenciana 2014–2020). Gobierno de Navarra fellowship to L.C. (Leticia Colyn); AECC post-doctoral fellowship to M.A.; Ramón y Cajal Program contracts RYC-2014-15242 and RYC2018-024475-1 to F.J.C. and M.G.F.-B., respectively. The generous support from: Fundación Eugenio Rodríguez Pascual, Fundación Echébano, Fundación Mario Losantos, Fundación M Torres and Mr. Eduardo Avila are acknowledged. The CNB-CSIC Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0001 (F.J.C.). Comunidad de Madrid Grant B2017/BMD-3817 (F.J.C.).Peer reviewe

Very Important Pool (VIP) genes – an application for microarray-based molecular signatures

<p>Abstract</p> <p>Background</p> <p>Advances in DNA microarray technology portend that molecular signatures from which microarray will eventually be used in clinical environments and personalized medicine. Derivation of biomarkers is a large step beyond hypothesis generation and imposes considerably more stringency for accuracy in identifying informative gene subsets to differentiate phenotypes. The inherent nature of microarray data, with fewer samples and replicates compared to the large number of genes, requires identifying informative genes prior to classifier construction. However, improving the ability to identify differentiating genes remains a challenge in bioinformatics.</p> <p>Results</p> <p>A new hybrid gene selection approach was investigated and tested with nine publicly available microarray datasets. The new method identifies a Very Important Pool (VIP) of genes from the broad patterns of gene expression data. The method uses a bagging sampling principle, where the re-sampled arrays are used to identify the most informative genes. Frequency of selection is used in a repetitive process to identify the VIP genes. The putative informative genes are selected using two methods, t-statistic and discriminatory analysis. In the t-statistic, the informative genes are identified based on p-values. In the discriminatory analysis, disjoint Principal Component Analyses (PCAs) are conducted for each class of samples, and genes with high discrimination power (DP) are identified. The VIP gene selection approach was compared with the p-value ranking approach. The genes identified by the VIP method but not by the p-value ranking approach are also related to the disease investigated. More importantly, these genes are part of the pathways derived from the common genes shared by both the VIP and p-ranking methods. Moreover, the binary classifiers built from these genes are statistically equivalent to those built from the top 50 p-value ranked genes in distinguishing different types of samples.</p> <p>Conclusion</p> <p>The VIP gene selection approach could identify additional subsets of informative genes that would not always be selected by the p-value ranking method. These genes are likely to be additional true positives since they are a part of pathways identified by the p-value ranking method and expected to be related to the relevant biology. Therefore, these additional genes derived from the VIP method potentially provide valuable biological insights.</p

Identification of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests

The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes