The “Med-fusion” DPP had the lowest protein-to-fat ratio (p < 0.001). Infants of women following the “Dairy-focused” DPP had the highest birth height centiles (p = 0.007) and the lowest ponderal index (p = 0.003). The NMR-metabolomics approach was implemented on a subset of women that provided amniotic fluid (AF) specimens (n = 62) to elucidate distinct metabolic signatures associated with DPPs. PCA and OPLS-DA models verified the adherence to three DPPs, revealing that the levels of several amino acids (AAs) were the highest in “Dairy-focused”, reflecting its protein-rich nature. The “Traditional-inspired” DPP showed decreased AAs and glucose levels. This knowledge may contribute to optimizing maternal dietary recommendations. Further research is needed to validate these findings and better understand the relationships between maternal diet, AF metabolic signature, and neonatal anthropometrics.This study aimed to characterize dietary protein patterns (DPPs) in a sample pool of 298 well-nourished pregnant women and explore potential associations between DPPs and neonatal anthropometrics. Maternal dietary data were collected using a validated food frequency questionnaire. Neonatal anthropometrics were abstracted from health booklets. A hierarchical cluster analysis identified three DPPs: “Dairy-focused”, “Med-fusion”, and “Traditional-inspired”. The “Dairy-focused” DPP exhibited the highest protein intake ( It is well-established that maternal nutrition during pregnancy is a major environmental stimulus that can alter fetal morphology and physiology, resulting in different phenotypes that affect offspring survival and long-term health ,2,3. In A large body of epidemiological studies has indicated a positive association between maternal protein intake and neonatal anthropometric characteristics ,10,11,12New potentials in nutritional science are offered with the development of highly sensitive analytical platforms and (bio)informatics. Specifically, the implementation of metabolomics in various biological matrixes is an efficient tool for exploring the effects of perinatal nutrition on pregnancy evolution and outcome ,28,29,30As such, the primary objective of the current study was to explore potential associations between maternal DPPs in the second trimester of pregnancy and neonatal anthropometrics. The secondary objective pertained to the implementation of NMR metabolomics in a subset of participants that provided AF specimens to comprehensively evaluate metabolic signatures related to maternal DPPs.A. Materials and Methods Regarding the Primary ObjectiveThree hundred twenty-seven pregnant women who attended the 1st Department of Obstetrics and Gynecology, Papageorgiou General Hospital in Thessaloniki (Greece), for a scheduled prenatal visit were initially invited to participate in the present prospective study. To be eligible for participating in the survey, women had to meet the following criteria: a. be more than 18 years of age, b. be familiar with the Greek language, c. have a singleton pregnancy, d. be in the second trimester of pregnancy at the time of enrollment, and e. be, apparently, healthy . The study design, the flow of participants, and the key processes applied in the current study are schematically depicted in Initially, women were informed about the objectives of the current study. Those who agreed to participate in the research study gave their signed consent and provided information regarding sociodemographic characteristics. Maternal anthropometric measurements were also taken, and women completed a structured interview concerning maternal dietary habits. Further details as far as the data collection process is concerned are presented in The procedures followed were in accordance with ethical approval attained from the Bioethics Committee of the Medical School, Aristotle University, Thessaloniki, Greece (A19479—26/2/08), and in compliance with the declaration of Helsinki.2). Participants were categorized into BMI groups based on classification criteria provided by the World Health Organization (WHO)  [All women provided information on age, occupation, education, pre-pregnancy anthropometrics (height and weight), level of physical activity (PA), and smoking history . Educati0 kg/m2) . Smokers0 kg/m2) .Maternal dietary intake in the second trimester was assessed using a Mediterranean-oriented semi-quantitative food frequency questionnaire (FFQ), previously validated among 179 pregnant women in Greece . The FFQInformation regarding childbirth , neonate gender, and anthropometric characteristics (birth weight and height) were retrieved from the newborn’s health booklet during a structured telephone interview with a well-trained interviewer. In Greece, newborn health booklets are filled out by medical staff immediately after delivery and are given to all mothers upon leaving the hospital. The ponderal index was calculated by dividing the weight (g) by the third power of the height (cm) and multiplying it by 100 . Birth wn = 327) for one of the following reasons: a. were diagnosed with pregnancy complications, such as gestational diabetes mellitus, pregnancy-induced hypertension, preeclampsia, etc. (n = 4), b. had terminated pregnancy (n = 2), c. had preterm delivery (n = 5), d. had born infants with structural malformations, chromosomal abnormalities, and/or congenital conditions that could affect fetal growth or development (n = 7), or e. could not be located or dropped out (n = 3).As depicted inA Microsoft Excel database was used to transform information collected regarding dietary data into daily intakes . During A hierarchical cluster analysis (HCA) ,38 was aTo calculate protein intake (g/day) for each of the 298 participants, the amount (g) of each food consumed daily was multiplied by the protein content (g) of this specific food.To convert these intakes into the percentage (%) of energy derived from protein, the formula given below was applied:To facilitate the interpretation of HCA, foods were classified, according to their protein content as well as practices/preferences reflecting dietary habits, into 19 predefined and mutually exclusive food groups  40,41,4,43.10 (X + 1) transformed to remove the potential extraneous effect of variables with the largest variances as well as to achieve homogeneity of variance [The percentages of energy derived from protein for the 19 food groups were logvariance .To create the input variables, the procedure described below was followed:These 19 log-transformed values for the 298 participants were used as input variables in the HCA.a = 0.05 (p = 0.05)), (b) the size  and the interpretation of each DPP, and (c) the tree diagram resulting from the Ward method of HCA [The three-cluster solution was selected as the best-case scenario.The number of DPPs selected, after performing several runs of cluster formation, was based on the following criteria: (a) the number of input variables with statistically significant differences in means .All statistical analyses were performed with IBM SPSS Statistics software, Version 28.0. For all hypothesis testing procedures, the significance level was predetermined at n) and the corresponding percentages (%) for categorical data. Food group consumption and nutrient intake data were presented as mean ± SD.Demographic/anthropometric and selected lifestyle characteristics of women were presented as the mean ± standard deviation (SD) for quantitative data and as the number of subjects ; cluster membership was treated as the independent variable, while the consumption of food groups as the dependent variable. The R2 value indicates the percentage (%) of the variance in the investigated food group consumption explained by the differences among DPPs. In the methodological frame of ANOVA, R2 is computationally and conceptually equivalent to the “Eta-squared” (η2) index, a measure of the independent variable’s effect size  [η2 = R2 = , where SS is the corresponding sum of squares [The contribution of each of the 19 food groups in the cluster construction was evaluated by investigating the magnitude and the statistical significance level of the corresponding coefficients of determination, bership) . Eta-squ squares .χ2) for categorical variables. The significance level  for χ2 was calculated with the Monte Carlo simulation method using 10,000 random samples. This methodological approach leads to safe inductive conclusions even in cases where the methodological assumptions of χ2 are not fully met [The homogeneity of variance among the three DPPs was examined using Levene’s test. To compare differences across DPPs in food groups, nutrient intake, and maternal and birth outcome data, the AΝOVA method followed by the Tukey’s or the Games–Howell post hoc test (when the homogeneity of variance assumption was violated) was used. DPPs were compared using the chi-squared test  was established using the last menstruation date confirmed with ultrasound scanning. After the biochemical analysis for cytogenetic-based diagnostics was realized, aliquots from the residual AF were used for NMR metabolomics .The analysis of AF samples was carried out using nuclear magnetic resonance (NMR) spectroscopy  followin2O and 150 μL phosphate buffer in D2O were added to lyophilized AF samples. After centrifugation , 50 μL of sodium maleate was added as an internal standard to 500 μL of the supernatant, and the sample was transferred to 5 mm NMR tubes.For the AF sample preparation, 400 μL DSodium maleate was chosen as the reference standard since it is suitable for the CPMG pulse sequence and provides a distinct peak in the 1 H NMR spectrum. The samples were thawed at room temperature for 60 min before performing NMR experiments.All NMR spectra were acquired using a Varian-600MHz NMR spectrometer equipped with a triple resonance probe {HCN} at 25 °C. The Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence was applied to AF samples with 128 transients collected with 64 K data points. The relaxation delay was set to 6 s. The receiver gain was kept constant for all acquisitions. Proton spectra were referenced at the resonance peak of sodium maleate (5.95 ppm).1H-NMR spectra were preprocessed with the MestreNova (v.10.1) software . Manual phase correction, automatic baseline correction, and sinc apodization were applied to improve spectra resolution. Total area normalization and binning of 0.0001 ppm were selected. A superimposed spectrum was constructed, and the peaks were manually aligned. The water D2O region (4.68–5.00 ppm) was excluded.All Metaboneer, an in-house, fully automated metabolite identification platform , facilitA series of 2D experiments, i.e., gCOSY, zTOCSY, gHMBCad, and gHSQCad experiments, were recorded at 25 °C and permitted the unambiguous assignment of metabolites; their acquisition parameters are described in Postprocessing of Spectral Data: SIMCA-P  was facilitated. The spectral data were mean-centered Pareto-scaled (Par), and the principal component analysis (PCA) and orthogonal partial least squared discriminant analysis (OPLS-DA) models were extracted at a confidence level of 95%. The mathematical background and applications of these methods have been extensively discussed [iscussed .Identification of Important Features: S-line plots were used to pinpoint the metabolites that contributed to the samples’ discrimination.Model Validation: The quality of models (PCA/OPLS-DA) was described using the goodness-of-fit R2 (0 ≤ R2 ≤ 1) and the predictive ability Q2 (0 ≤ Q2 ≤1) values. The R2 explained the variation, thus constituting a quantitative measure of how well the data in the training set were mathematically reproduced. The overall predictive ability of the model was assessed using the cumulative Q2, representing the fraction of the variation in Y that could be predicted using the model, which was extracted according to the internal cross-validation default method of the software SIMCA-P. Q2 is considered a de facto default diagnostic parameter for validating OPLS-DA models in metabolomics. In particular, the difference between the goodness of fit and the predictive ability always remained lower than 0.3 (R2X(cum) − Q2 (cum) < 0.3), and the goodness of fit never equaled one (R2X(cum) ≠ 1). The extracted models abided by these rules; therefore, their robustness and predictive response were enhanced and over-fitting was effaced.p-value < 0.05. Permutation tests were used (999 permutations) to evaluate whether the specific classification of two classes in a model was significantly better than any other models obtained by randomly permuting the original group’s attribution. An additional measure of PLS-DA model validity included the extraction of receiver-operating characteristic (ROC) curves to assess the ability of the PLS latent variable T pred to correctly classify the test set. A marker explained a low, fair, and superior diagnostic accuracy when the area under the ROC (AUROC) curve reached values of 0.5 < AUC < 0.7, 0.7 < AUC < 0.9, and AUC > 0.9, respectively. The area under the ROC (AUROC) was calculated. A perfect discrimination corresponded to an AUROC equal to 1 [Regression models were validated using cross-validation analysis of variance (CV-ANOVA) with a ual to 1 ,52.Metabolic Pathways: The online platform MetaboAnalyst RRID:SCR_015539  [As already mentioned, from the 298 women who finally participated in the study, 62 women also provided AF samples for NMR metabolomics analysis . For thin = 74), “Med-fusion” (n = 104), and “Traditional-inspired” (n = 120)) (n = 9), “Med-fusion” (n = 23), and “Traditional-inspired” (n = 30)) were compared with the corresponding 95% bootstrap CIs for each of the three DPPs (“Dairy-focused” (n = 74), “Med-fusion” (n = 104) and “Traditional-inspired” (n = 120)).In more detail, initially, for each of the three extracted DPPs from the whole sample (“Dairy-focused” ( = 120)) , 95% booA. Results Regarding the Primary Objective2, and 65.1% were of normal weight. Less than 10% of the study population was obese.Selected characteristics of the 298 pregnant women participating in the current prospective study are summarized inn = 74), “Med-fusion” (n = 104), and “Traditional-inspired” (n = 120). Selected sociodemographic and anthropometric characteristics of the women in each DPP are listed in After applying HCA to the log-transformed percentage of energy derived from protein intake, three DPPs were identified. The patterns were named as follows: “Dairy-focused”  of the energy derived from protein.p ≤ 0.05) were observed. The most profound differences . The “Dairy-focused” DPP presented the highest intake of “low-fat dairy products” (2.92±1.41%), whereas the “Med-fusion” DPP consumed more “full-fat dairy products” (1.85±1.37%) compared with the two other DPPs. The “Traditional-inspired” DPP was characterized by an increased intake of “whole grain cereals” (1.50±0.58%) and intermediate consumption of both “low-fat dairy products” and “full-fat dairy products” .As listed inferences A–C, as sNotably, women in the “Dairy-focused” DPP also preferred consuming other dairy products, such as yellow cheese , while in the case of the “Med-fusion” DPP, the elevated intake of “Refined cereals” was accompanied with an increased preference for all starchy foods . Women in the “Traditional-inspired” DPP favored more nutritious plant-based products, such as legumes and nuts.p = 0.004) and as a percentage of total energy intake (%E) (p < 0.001) compared with the “Med-fusion” and “Traditional-inspired” DPPs. Differences were also reported regarding the quality of protein consumed. Individuals following the “Dairy-focused” DPP presented the highest animal protein intake (mean different: ~8 g), while those in the “Traditional-inspired” DPP consumed much more plant protein (6.12 ± 0.95%E) compared with those following the “Dairy-focused” and the “Med-fusion” DPP .Differences in food group consumption between DPPs were mirrored in the nutritional profile as well as the indices of dietary quality . IndividBeyond the variations in protein, the different dietary behaviors were also reflected in the “whole protein package” . ParticiIn No statistically significant differences were noted between the DPPs regarding gestational age or neonate gender.p = 0.601), and height (p = 0.279) (p = 0.007). This statistically significant difference was also accompanied by a slightly lower ponderal index.Women following the “Dairy-focused” DPP gave birth to infants with slightly lower ponderal index (2.39 ± 0.26 g/cm3) than those following the “Med-fusion” (2.49 ± 0.25 g/cm3) and “Traditional-inspired” DPPs (2.52 ± 0.23 g/cm3) (p = 0.003).No statistically significant differences were found among the three DPPs for birth weight (= 0.279) .HoweverB. Results Regarding the Secondary Objectiven = 62) to the full dimensions of the sample (n = 298) was to compare the dietary databetween the initial DPPs, derived from the whole sample , in response to a dietary pattern, were aminoacyl-tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, histidine metabolism, pantothenate and CoA biosynthesis, beta-alanine metabolism, alanine, aspartate and glutamate metabolism and valine, leucine and isoleucine degradation. Of these, the “histidine metabolism” and the “alanine, aspartate, and glutamate metabolism” pathways had the greatest impact.Our results revealed that the primary disturbed statistically significant pathways , the interpretation of their dominating presence in the “Dairy-focused” AF specimens seems to be supported by the existing literature ,74,75,76The assumption that the maternal diet contributes to the pool of free AAs in AF could also be sound in the case of histidine. The human organism, during intrauterine life, has not yet developed the ability to produce histidine . Indeed,Providing explanations regarding the presence of several dispensable AAs  in elevated levels in the “Dairy-focused” DPP compared with the other two DPPs is rather a laborious process. So far, several studies conducted among pregnant  and non-Glucose also appears to be a metabolite associated with the dietary habits of the population under study. A plausible explanation for this observation may be also associated with the specific protein sources consumed. As has already been discussed, women in the “Traditional-inspired” DPP compared with the other DPPs exhibited a higher preference for plant protein sources, such as whole grain cereals, legumes, and nuts; a dietary choice that is directly reflected in the intake of dietary fibers and the quotient of carbohydrate-to- fiber. As such, it can be postulated that women in the “Traditional-inspired” DPP may exhibit a more gradual rise in postprandial glucose levels than the other DPPs, resulting in differential glucose fluxes from the mother to the developing fetus through the placenta. Similar findings have also been reported in previous studies conducted both in humans  and animThe findings of the current study should be considered in the context of potential strengths and limitations. Notably, the implementation of a protein-centric approach to identify maternal dietary patterns is an innovative aspect of this work. This methodological framework enables a more comprehensive understanding of the potential associations between maternal protein intake during the second trimester of pregnancy and fetal growth and development. A noteworthy advantage of our study is the use of a validated FFQ for dietary data collection, which was indeed completed through personal interviews by a registered dietician or a well-trained interviewer. This approach ensured to a great extent the accuracy and reliability of the obtained information . Moreoven = 62) revealed unique metabolic signatures associated with each DPP, indicating that not only proteins but also the “whole protein package” might have a differential effect on metabolic processes. Moreover, these findings highlight the importance of investigating DPPs during pregnancy, as they may be related to the risk of non-communicable diseases in later life. However, further research with larger sample sizes and comprehensive metabolomic analyses is urgently warranted to validate these findings and better understand the associations between maternal DPPs, neonatal anthropometrics, and AF metabolic signatures. This knowledge may also contribute to optimizing maternal dietary recommendations regarding optimal protein intake and the proportions of proteins derived from different sources.In conclusion, the findings of the current prospective study suggest that maternal protein intake, when investigated from the whole-diet approach, may influence neonatal size, as depicted in birth height centiles and the ponderal index. The comparative analysis of AF specimens (


MYC, JUN and VEGFA as well as HCV IRES to increase their synthesis and promote self-renewal and tumor-initiation at the post-transcriptional level. MSI2 binds a lncRNA to interfere with processing of a miRNA that reduced MYC translation in basal conditions. Deregulation of this integrated MSI2-lncRNA-MYC regulatory loop drives self-renewal and tumorigenesis through increased IRES-dependent translation of MYC mRNA. Overexpression of MSI2 in TICs promoted their self-renewal and tumor-initiation properties. Inhibition of MSI2-RNA binding reduced HCV IRES activity, viral replication and liver hyperplasia in humanized mice predisposed by virus infection and alcohol high-cholesterol high-fat diet. Together MSI2, integrating the MYC oncogenic pathway, can be employed as a therapeutic target in the treatment of HCC patients.RNA-binding protein Musashi 2 (MSI2) is elevated in several cancers and is linked to poor prognosis. Here, we tested if MSI2 promotes MYC and viral mRNA translation to induce self-renewal via an internal ribosome entry sequence (IRES). We performed RIP-seq using anti-MSI2 antibody in tumor-initiating stem-like cells (TICs). MSI2 binds the internal ribosome entry site (IRES)-containing oncogene mRNAs including  MSI2 belongs to the Musashi family of RNA binding proteins . These MPrimary liver cancers include cholangiocarcinomas, hepatoblastomas, and hepatocellular carcinomas (HCC), of which the latter accounts for > 90% of primary liver cancers. The estimated incidence of new cases worldwide is about 500,000-1,000,000 each year, causing 600,000 deaths globally . HCC is MYC mRNA in non-transformed cells . Th. ThMYC onscripts . Two majMYC mRNA level  was highly expressed in primary hepatocytes but present at very low levels in HCC and TICs. By contrast, the longer form of MYC (MYC1) was higher in transformed cells (HCC and TICs) and lower in primary hepatocytes Fig. , inset. vel Fig. . This reTo elucidate the post-transcriptional effect of MSI2 on MYC protein turnover, we added cycloheximide to block protein synthesis in Huh7 cells stably transduced with either scrambled shRNA or specific shRNA to silence MSI2. Cell lysates were obtained at different time points and probed with anti-MYC antibody Fig. . While oMYC mRNA was modulated by MSI2, we generated MYC cDNA constructs that had only the MYC mRNA coding sequence (CDS) and CDS variants that included CDS with only the 3′UTR, CDS with only the5′UTR or CDS with both the 5′ and 3′UTRs  and MYC-S (p42) was stimulated by the 5′UTR although there was no evidence showing the longest-form of MYC (p67) was stimulated by the 5′UTR .To determine how the translational activity of MYC in HepG2 and Hep3B cells was examined by immunoblotting.  initiation site  IRES for comparison to the cap-dependent translation of MYC (without MYC IRES) (pRF). The reporter constructs containing IRES elements from APAF-1 (pRAF), or BAG-1 (pRBF) mRNAs (examples of cellular IRESs) and HCV (+)-strand RNA (pRHCV) were used as specificity controls as described earlier [MYC and 60% change in HCV, IRES-mediated translation activity in the pRMF- and pRHCV-transfected cells, respectively. In the presence of sh-MSI2 the luciferase activity was reduced to the basal level for both MYC and HCV reporters. The responsiveness of the HCV IRES to MSI2 level may suggest an alternative mechanism for translational initiation (not the same as for MYC) [APAF-1 and BAG-1 IRES elements did not show significant changes in luciferase activity, suggesting that MSI2 was specific for MYC IRES but not for other cellular IRES elements. However, addition of the MSI2 binding motif to APAF-1 and BAG-1 IRES reporters made them responsive to MSI2 expression  . APAF-1 ion Fig. . CD133 with purified MSI2-His6 was performed with both 32P-labeled MYC IRES or just the MSI2 binding motif in the presence or absence of cold probes, as previously described [32P-labeled RNA transcripts generated from pSK-ML (MYC IRES expression construct) or pSK-GAP-L (GAPDH vector for specificity control) were incubated with purified recombinant MSI2 as indicated, then subjected to EMSA. The EMSA results demonstrated that MSI2-bound RNA probe complexes increased in a MSI2 dose-dependent manner  RNA).To further investigate if MSI2 promotes HCV RNA replication via the HCV IRES, HCV IRES reporter assays were performed in the presence or absence of mutations within the presumptive MSI2-binding sites of the HCV IRES. HCV IRES reporter activity was increased by ectopic MSI2 expression, but was compromised by mutations in the MSI2-binding sites Fig. K, L. AntMIR22HG RNA was identified from our RIP-Seq analysis as a critical binding partner of MSI2  . Accordingly, mutation of the Target-2 site resulted in mitigation of miR-22 activity . Thus, these data indicated that MSI2 is capable of indirectly augmenting translation of mouse MYC by antagonizing MIR22HG processing resulting in decreased level of miR-22. Although human MYC 3′UTR has only a single miR-22 site, we strongly believe that it is functional based on the sum of experimental results we have presented thus far. This model is summarized in Fig. Northern blotting analysis in HCC cells showed that overexpression of MSI2 significantly reduced the level of mature lls Fig.  and mouslls Fig. . ReductiR) Fig. S. Accordiity Fig. . By contmiR-7014-3P among the MSI2-bound miRs may potentially regulate VEGFA expression. An online database (miRDB.org) predicted putative miR-7014-3P binding sites located in the 3′UTR of VEGFA mRNA ) than in normal cultured cells ). Furthermore, the ratio of MSI2+/MYC+ in the total MYC+ population ) was higher than that of MSI2+/NANOG+ in the total NANOG+ population ) of spheroid cells, demonstrating that upregulated MYC compared to NANOG was more likely subjected to IRES/MSI2 regulation . Pairwise comparisons showed that mice in the MSI2 knockdown group lived significantly longer than the control (scramble) group or the MSI2 overexpressing group. However, there was no significant difference in survival between the scrambled group and the MSI2 overexpressing group (p = 0.22)  Fig. . Immunoh22) Fig. . MSI2 ov22) Fig. . These d22) Fig. .p < 0.001)  and patients with high staged disease (p < 0.001) . Nonetheless for all patients, the IRS scores were always higher in the tumor tissues than in the non-tumor tissues.Based on the mouse tumorigenesis studies we assessed the clinical relevance of our findings in human HCC clinical samples. These data are presented in Supplementary Table 01) Fig. . AdditioMYC and MSI2 mRNA levels   between MSI2 and mRNA regions, local quality of binding was estimated Fig. . Human MThe potential interacting residues between MSI2 to RNA were selected based on polar, charge, hydrophobic or polar-hydrophobic interactions, within the range of 4 Å, these were Fig. , middle:Binding of inhibitor compounds to MSI2 was modeled by docking to Cavity 2. First, each inhibitor compound was energy-minimized by force field and then was individually docked to Cavity 2 Fig. . The shaWe identified GW7647 as a potential inhibitor of MSI2  that can form this type of C-shape were generally predicted as potential inhibitors. Potentially if their carbon chain at the C curve could have negative charged/polar residues (such as those in Gossypol) that would structurally complement those polar residues of the MSI2 loop would be predicted to be better inhibitors. These residues could perhaps mimic the phosphates of an RNA ligand molecule and improve binding  or mutant IRES RNA as probes for fluorescence polarization binding assays with recombinant MSI2. Inhibition of binding activity was tested using the selected, small molecule library assayed in 384-well plates  except a subgroup of chemicals that stood out with selective toxicity toward Huh7 cells alone  assay with GW7647 was performed using full-length, wild type MSI2 and single substitution mutants of MSI2: K184A, P187A and K188A. The MSI2 K184A and K188A mutations partially abrogated inhibitor binding activity but to a much lesser degree than for the P187A mutant. Additional microscale thermophoresis (MST) assays were performed in the presence of pharmacological inhibitor GW7647 and full-length wild type MSI2 or substitution mutants; included in this analysis were other RNA-binding proteins and non-RNA-binding protein (GST) Fig. , P. EMSAing Fig. , right. TM mice were transplanted with a small tissue piece (62.5 mm3) from a single patient HCC; this patient had prior etiology of alcoholism and/or HCV infection. These xenografts formed rapidly growing tumors after 16 to 35 days post-implantation and reached an average size of 2,000 mm3. Simvastatin treatment  almost completely suppressed further tumor growth, as compared to control vehicle treatment . GW7647 treatment reduced the HCV RNA levels Fig. , right. ice Fig. . These rmiR-22 and thus indirectly enhanced overall MYC expression In vivo animal studiesHuman patients’ tissue samplesEXPERIMENTAL MODEL AND SUBJECT DETAILSHuman patients’ tissue samples: The clinical and pathological characteristics of the patients are summarized in Supplementary Table Cells and cell lines.Plasmids, lentivirus and retrovirus vectors, and production of lentivirus & retrovirusesIsolation of mouse TICs using MACS7 TICs overexpressing Flag-MSI2 and stably transduced shRNA against MSI2 were subjected to RNA immunoprecipitation (RIP) using the MBL RIP kit . In brief, cells were washed with cold PBS and lysed with provided RIP lysis buffer. Anti-Flag M2 Ab (Sigma-Aldrich), anti-rabbit Ab, or anti-MSI2 Ab (EMD Millipore), pre-incubated with magnetic beads (5 μg), were used to immunoprecipitate Flag–MSI2–RNA complexes. Immunoprecipitated complexes were washed and treated with proteinase K. Finally, RNA was extracted with the reagents provided in the kit.RNA-Immunoprecipitation (RIP): 2 × 10Bioinformatics analysis of RNA sequencingBioinformatics analysis of binding motif from MSI2 RIP-seqReverse transcription and quantitative PCR (RT-qPCR)SDS-PAGE and immunoblot analysisSite-directed mutagenesisLuciferase reporter assaysUV cross-linking and EMSASucrose density gradient centrifugation and RT-qPCR analysisMicroRNA isolation, cDNA synthesis, and RT-qPCRmiR22hgNorthern blots for Spheroid FormationSerial Spheroid FormationIn silico 3D structure predictionFluorescence Polarization assayHistology & ImmunohistochemistryImmunofluorescence StainingStatistical AnalysisQUANTIFICATION AND STATISTICAL ANALYSISDATA AND CODE AVAILABILITYCover ImageCover ImageSupplementary Table 4Supplementary InformationUncropped films from each FigureSupplementary Table 1.Supplementary Table 2.Supplementary Table 3


The metastasis of tumor cells into vital organs is a major cause of death from diverse types of malignancies ,2,3,4. OTo address this issue, our group has recently conducted a transposon-based (Sleeping Beauty) mutagenesis screen on drug-naïve, retinoblastoma Rb-deficient genetic background  and idenOur genetic screen identified oncogenic drivers that promote primary-only mammary tumors, metastasis-only lung lesions, or both primary and metastatic growth (which we termed “shared” oncogenic drivers). Importantly, using DNA sequence data on the transposon integration sites, we were able to establish clonal relationships between metastasis drivers and primary drivers, thus excluding the possibility of unrelated primary mammary and primary lung tumors. The shared oncogenic drivers comprised a major MET-RAS hub  known to promote tumor growth and cell migration ,31,32, aTo ask whether similar organization occurs in breast cancer patients, we calculated the pathway activity of 26 different oncogenic signaling in several independent cohorts of paired primary and metastatic breast lesions ,35,36,37We envisage that primary-enriched oncogenic drivers evolve to promote clonal competition and expansion at the local site. Such drivers may not be compatible with, and may even oppose, the metastatic cascade; therefore, their activity is diminished in metastases ,2,3,4. FTwo major conclusions can be drawn from this study. First, in line with the title of this Editorial, the therapeutic importance of oncogenic drivers that promote primary cancer  can only be appreciated by understanding their impact on metastasis. Second, targeting shared- and metastasis-enriched or -specific but not primary-enriched drivers represents a rational strategy to prevent metastases at the time of primary tumor detection .Cognizant of the therapeutic importance of targeting metastasis-enriched pathways in conjunction with shared oncogenic alterations, there is an ever-growing interest in deepening our understanding of metastasis and its druggable targets.https://www.mdpi.com/journal/cancers/special_issues/Progression_Metastasis_cancer, accessed on 7 November 2023) [The Special Issue “From Progression to Metastasis of Solid Cancer” (er 2023) ; character 2023) ; therapeer 2023) ; the imper 2023) ; and finer 2023) .This Special Issue also contains an Editorial summation of metastasis  and insiCancers has dedicated multiple other past and ongoing Special Issues to metastatic cancer, including “Targeting Bone Metastasis in Cancers” [https://www.mdpi.com/journal/cancers/special_issues/Molecular_Regulation_Cancer_Progression_Metastasis, accessed on 7 November 2023); “Genomic Landscape of Breast Cancer: From Primary to Metastasis” ; “Targeting Cancer Metastasis” (volume II) ; and “New Insights into the Molecular Mechanism of Epithelial Plasticity in Cancer” .Cancers” ; “High-RCancers” ; “AdvancCancers” ; “MetastCancers” ; “UndersThis remarkable collection of articles uncovers intrinsic oncogenic drivers and external factors from the tumor microenvironment, including the immune surveillance system that contribute to metastatic progression. Such oncogenic drivers and factors are ideal targets for combination therapies comprising patient-tailored inhibitors for shared oncogenic drivers that promote both primary and metastatic growth, together with inhibitors for metastasis-specific or -enriched drivers. Future challenges include the further exploration of the novel mechanisms of metastasis and interventions for each cancer type and subtype, as well as the systematic evaluation of drug combinations against shared- and metastasis-enriched or specific pathways, initially in pre-clinical animal models and then in clinical settings.


Although human resource management (HRM) practices all seek to support and improve organizational functioning, the value ascribed to various HRM practices differs greatly among employees. Drawing on an exhaustive measure of HRM practices, this study proposed a new conceptualization and measure of HRM values, the HRM Values Scale (HRM-VS).To examine the psychometric properties of scores obtained on this new measure, we rely on a sample of 979 employees occupying a variety of jobs within various private and public organizations.Through the comparison of confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) solutions, our results supported a nine-factor structure of participants’ responses to the HRM-VS and the measurement invariance of this solution across male and female employees. Specifically, they support that the HRM-VS items adequately capture core HRM values underlying independent HRM practices. Criterion-related validity was evidenced with respect to employees’ ratings of intrinsic and extrinsic job satisfaction.The HRM-VS appears to represent a promising tool for research and intervention seeking to account for individual differences in the relative importance of various HRM practices, in order to devise more effective HRM systems.This new concise but complete measure could help better guide organizations in tailoring their strategic HRM.This study introduces HRM values as a valid concept that characterizes what employees desire or consider to be important in relation to HRM practices. Research in strategic human resource management (HRM) seeks to document the contribution of HRM systems, or of interrelated sets of HRM practices, to the performance and optimal functioning of employees and organizations . HRM prapractices initially developed by values. More precisely, rather than focusing on the presence or absence of each of these HRM practices as did a priori factor structure of employees’ ratings of the importance they attribute  to each of the HRM practices included in To address this issue, the present study seeks to broaden our understanding of HRM practices by offering a perspective focusing on the “relative importance” that employees place on individuals HRM practices, which we hereafter refer to as their HRM values. To achieve this objective, we first review how HRM practices have been conceptualized and operationalized in previous research in order to identify a core set of HRM practices. It is noteworthy that, despite growing empirical evidence regarding the importance of HRM systems and practices, a recent systematic review by Drawing on the research literature on work values e.g., , we broaArguably, the most comprehensive measure of HRM practices available to date has been developed by As part of their data collection, The present study seeks to contribute to research on HRM practices by proposing a concise but comprehensive measure of HRM values, allowing us to more systematically investigate the importance ascribed by individual employees to a series of HRM practices. More precisely, using a combination of Confirmatory Factor Analyses (CFA) and Exploratory Structural Equation Modeling ESEM; , we firsThe data used in this study comes from Work experience in a similar job); (2) Induction ; (3) Training and development ; (4) Leadership ; (5) Performance appraisal ; (6) Compensation ; (7) Benefits ; (8) Communication and participation ; (9) Work-life balance ; (10) Work design . Participants were asked to indicate on a 6-point scale ranging from 1 (very low importance) to 6 (very high importance) the degree of importance they ascribed to each HRM practice.Participants completed The feeling of having accomplished something through my job) and extrinsic job satisfaction . Participants indicated how they felt about each statement on a 6 point-scale ranging from 1 (very weak satisfaction) to 6 (very strong satisfaction). Previous studies support the scale score reliability and validity of these scales , this additional indicator will not be considered due to its known over sensitivity to sample size and minor misspecifications  version of the HRM-VS was investigated by comparing CFA and ESEM representations. In both representations, the ications . Model sications .“The parameter estimates from these two solutions will also inspected to select the subset of items to be retained for the development of the short version of the HRM-VS”. This selection of item followed current published state-of-the-art guidelines for the development of questionnaire short-forms . Thus, iω), which directly considers the relative contribution of each item  to the definition of the factors and the item specific uniquenesses (including random measurement error)  e.g., . We alsor) e.g., .Evidence of measurement invariance is of outmost importance to assess the ability to generalize, and compare, the results obtained using a measurement instrument to members of different groups. In the present study, we investigate the generalizability  of ratings obtained on the final version of the HRM-VS across samples of male and female participants. These tests were realized in the following sequence : (a) ConTests of criterion-related validity were finally conducted by incorporating latent CFA factors representing intrinsic and extrinsic job satisfaction as outcomes to the final retained measurement model .χ2 = 6958.800; df = 2,510; CFI = 0.799; TLI = 0.789; RMSEA = 0.043; with a 90% confidence interval of 0.041 to 0.044). However, most factors were well-defined by most of their items in this solution, with factor loadings ranging from 0.170 to 0.810 (M = 0.601). The exact parameter estimates from this solution are reported in χ2 = 4238.885; df = 1,943; CFI = 0.896; TLI = 0.856; RMSEA = 0.035; confidence interval = 0.033 to 0.036), it remained unsatisfactory according to the CFI and TLI. The exact parameter estimates from this solution are reported in M = 0.483). More precisely, more than half of the factors appeared to be weakly defined by a majority of their items: Leadership , selection , compensation , benefits , and work design . In addition, with only two indicators, the induction factor appeared to be particularly problematic . We decided not to retain this factor for the development of the short version. In addition, the ESEM solution also revealed some cross-loadings . However, only 18 of those (out of 657) were higher than 0.200. Of those, 10 items had strong cross-loading but retained their main loading on their a priori factor, whereas the remaining 8 items (two of which were associated with the problematic induction factor) had their highest loading on a non-target factor. This second set of items were automatically excluded from the short version. Finally, and supporting the value of this representation, factor correlations were much smaller in the ESEM solution  than in the CFA solution .The CFA solution estimated on the original version of the HRM-VS resulted in an unacceptable level of fit to the data according to the CFI and TLI  according to the TLI. In contrast, the ESEM solution resulted in a fully acceptable level of fit to the data . Parameter estimates associated with these solutions are reported in Following a detailed examination of the parameter estimates from the CFA and ESEM solution estimated on the complete version of the HRM-VS and the consultation of our panel of expert, a short version of the HRM-VS was constructed. In this version, a total of 36 items are used to define 9 correlated factors , with four items associated with each factor. As for the long version, the CFA solution estimated on this shorter version of the HRM-VS failed to achieve a satisfactory level of fit to the data  and ESEM  solutions. In addition, although cross-loadings were present in the ESEM solution, they remain generally small , smaller than the target loadings, and only 2 of them (out of 288) were higher than 0.200, while 35 were between 0.100 and 0.200. Finally, factor correlations where once again smaller in the ESEM  solution than in the CFA solution , lending further support to the superiority of the ESEM solution, which was retained for further analyses. In this solution, most factors had an acceptable to satisfactory level of composite and construct reliability : (1) work-life balance ω = 0.556 and H = 0.559; (2) leadership ω = 0.746 and H = 0.801; (3) communication and participation ω = 0.715 and H = 0.759; (4) performance appraisal ω = 0.866 and H = 0.860; (5) selection ω = 0.601 and H = 0.597; (6) training and development ω = 0.764 and H = 0.788; (7) compensation ω = 0.781 and H = 0.807; (8) benefits ω = 0.837 and H = 0.843; (9) work design ω = 0.749 and H = 0.687.These results reveal similarly well-defined factors for both the CFA , and revealed that both additional factors were also associated with satisfactory estimates of composite reliability . Interested readers can consult the latent correlations estimated as part of this model in Tests of criterion-related validity were realized by incorporating latent factors representing intrinsic and extrinsic job satisfaction to the final ESEM solution. The measurement model including all of these latent factors resulted in an excellent level of fit to the data  did not show any association with either extrinsic or intrinsic job satisfaction. Because these HRM practices have been consistently associated with employees’ job satisfaction , a questArguably, our core contribution lies in introducing HRM values as a valid concept that characterize what employees desire or consider to be important in relation to HRM practices. These values may reflect individual differences in how employees perceive, interpret, and react to the HRM practices prevailing, or lacking, in their work settings. By focusing on these HRM values, we depart from the traditional view that HRM practices will necessarily influence every employee in the same manner to HRM practices, to rather highlight the fact that each employee is likely to react differently to distinct HRM practices based on what they consider important to them. In doing so, this study adds to a new stream of research seeking to better understand the sources of the known variability in employees’ reactions to HRM practices. Whereas a previous study  showed tBeyond these more theoretical and practical implications, the current research also has methodological implications. Indeed, sound psychometric measurement goes hand in hand with latent variable models . Latent a priori indicators) useless to guide the development of a more psychometrically sound (and shorter) version. In contrast, the ESEM solution made it relatively easy to identify problematic items, thus adding to previous demonstrations of the value of this novel statistical approach  of scores obtained on the HRM-VS, certain limitations should be considered. First, although we were able to identify a relevant set of items allowing us to assess the value ascribed by employees to nine distinct HRM practices, the two items used to measure induction were found to be problematic. As induction practices are typically tied in the socialization process whereby the organization welcomes new employees, future studies should aim at improving the item content of this dimension at different stages of the organization life and of employees’ socialization. Second, this study relied exclusively on self-report measures, which increases the risk of bias in responses. Future studies should thus consider adopting more objective outcome measures  to obtain a more accurate assessment of the criterion-validity of scores obtained on this measure. Third, the present study relied on a cross-sectional design, which does not allow us to establish the directionality of the associations tested in relation to the criterion-related validity of scores obtained on our measure. Fourth, this study relied on a fairly limited set of outcomes. While the relations between several HRM values and intrinsic and extrinsic job satisfaction has been established, further research should enhance our understanding of behavioral  and other attitudinal  outcomes. Further studies are also needed to improve our understanding of the predictors of HRM values. For example, would the presence (or the absence) of particular HRM practices affect the core HRM values. Fifth, our sample is limited to French-Canadian employees working in the service, manufacturing or public sectors. With respect to the generalization, our results should thus be replicated among more diversified samples of employees from other cultures and types of industries.This HRM-VS holds practical implications for both researchers and organizations. This new concise but complete measure could help better guide organizations in their strategic human resource management. By its specific nature, the HRM-VS opens way to evaluate what employees really want out of their organizations’ HRM systems. When rethinking their HR policies, organizations using this scale would thus benefit from an enhanced understanding about the most beneficial practices to focus on, considering their employees’ values, instead of relying on a one-size-fits-all approach of “best practices.” From a motivation standpoint, HRM managers still frequently focus either on punitive measures or incentives to regulate employee motivation and performance . To delibruno.fabi@uqtr.ca.The data analyzed in this study is subject to the following licenses/restrictions: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved by Comité d’éthique de la recherche avec des êtres humains de l’Université du Québec à Trois-Rivières. The patients/participants provided their written informed consent to participate in this study.SD-R: conception of the study under the supervision of CF and SA, data collection under the supervision of BF, analysis and interpretation of the data under the supervision of AM. AM, CF, SA, and BF: critical review of the manuscript. All authors contributed to the article and approved the submitted version.Preparation of this manuscript was supported by grants from the Fonds de Recherche du Québec – Société et Culture (2019-SE1-252542) and the Social Science and Humanity Research Council of Canada (435-2018-0368).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Nucleic acids include two main classes: deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). In the last few decades, a cohort of oncogenes and tumor suppressor genes has been discovered at the DNA level. Those genes are abnormally activated or inactivated by the genetic mutations of DNA in cancer cells. As a result, it is known that genetic mutations in DNA play a pivotal role in cancer development . In addiRNA was considered to be an intermediate messenger between DNA and functional proteins in the central dogma of molecular biology. Therefore, protein-coding mRNAs were centered on RNA biology. Although such non-coding RNAs as ribosomal RNA (rRNA) and transfer RNA (tRNA) were actively studied for their visible functions in protein synthesis, other non-coding RNAs were thought to be byproducts in gene expression in spite of studies implicating non-coding RNAs, such as H19 ,5. SinceThis Special Issue mainly introduces the various findings and current trends of RNA research and their potential in cancer diagnosis and therapy. Fekete et al. show that microRNAs are prognostic markers of platinum-based therapy of squamous cell carcinomas (SCC), such as cervical, head and neck, and lung tumors . Zhang eWith the growing interest in RNA modification, non-coding RNA modifications are also covered in this Special Issue. For instance, microRNA isoforms generated by microRNA modifications, such as A-I RNA editing, bring about racial disparities in lung cancer . The advThis Special Issue deals with a broad spectrum of nucleic acids in cancer, especially focused on various RNA types, modifications, functions, and analytical methodologies with interesting discoveries and summaries of RNA and cancer research. Overall, the research and review articles in this Special Issue provide insight into the current and future directions of RNA research in cancer. Although not discussed in this Special Issue, the critical roles of extracellular RNA, delivered by microvesicles and exosomes, have also been elucidated in other works ,28,29. I


The present study evaluated the role of inhibition in paradoxical choice in pigeons. In a paradoxical choice procedure, pigeons receive a choice between two alternatives. Choosing the “suboptimal” alternative is followed 20% of the time by one cue (the S+) that is always reinforced, and 80% of the time by another cue (S-) that is never reinforced. Thus, this alternative leads to an overall reinforcement rate of 20%. Choosing the “optimal” alternative, however, is followed by one of two cues (S3 or S4), each reinforced 50% of the time. Thus, this alternative leads to an overall reinforcement rate of 50%. González and Blaisdell  reported When choosing between two different amounts of a reward, all else being identical, organisms prefer the larger amount , which should imbue it with some excitatory value and counteract its inhibitory properties (counterconditioning). Although the manipulation increases the overall reinforcement rate associated with the suboptimal alternative – in fact, it changes it into the richer option, we predict that preference for the suboptimal alternative should decrease.The other manipulation involved the extinction of S4 (from 50% reinforcement to 0%), which should produce inhibition of extinction Bouton, . With exFurthermore, extinction of S4 provides an interesting opportunity to observe the nuanced role of inhibition on suboptimal choice. If we allow a sufficiently long delay, say 2 weeks, following extinction of S4, we should observe spontaneous recovery from extinction Pavlov, . If extino effect on preference for the suboptimal alternative or perhaps strengthen preference for the suboptimal alternative.By contrast, if inhibition is not necessary to develop a preference for the suboptimal alternative, as suggested by Laude et al. , then paColumba livia) from Double-T Farms were used. Six of the pigeons had experience with an intelligence test battery . Two pigeons had experience with another version of the suboptimal task with different stimuli than those used in the current study 3 years prior to the present study. For all animals, stimuli were selected to minimize transfer from prior experience. Subjects were individually housed in steel home cages with metal wire mesh floors in a vivarium. They were maintained at 80% of their free-feeding weight, with free access to water and grit while in their home cages. Testing occurred during the light portion of the 12-h light-dark cycle.Eight adult homing pigeons (The experiment was conducted in a flat-black Plexiglas chamber (38 cm wide × 36 cm deep × 38 cm high). Stimulus events were controlled by computer connected to an LCD color monitor (NEC MultiSync LCD1550M). The bottom edge of the viewing window was 13 cm above the chamber floor. Pecks to the LCD monitor were detected by an infrared touchscreen  located on the front panel. A custom-built food hopper  was in the center of the front panel, its access hole flush with the floor. The hopper delivered 5-s to 6-s access to mixed grain as reward. A personal computer operating Windows 10 OS controlled the experimental events and recorded all data.Six stimuli were used, two initial-link and four terminal-link stimuli see Fig. . The iniThere were two types of trials in each acquisition session of 120 trials, free-choice  and forced-choice  and continued until the following stability criteria were met: (a) there was no strictly increasing or decreasing trend in the proportion of choices in the last three sessions, and (b) the difference between the highest and lowest preference in the last three sessions did not differ by more than 20%. Once stability was reached, half the pigeons  received (a) Extinction of S4, the Choice Test and, 2 weeks later, the Spontaneous Recovery Test, and then (b) Reacquisition Training, Partial Reinforcement of S-, and the Choice Test. The remaining pigeons  received (a) Partial reinforcement of S- followed by the Choice Test, and then (b) Reacquisition Training, Extinction of S4, the Choice Test, and, 2 weeks later, the Spontaneous Recovery Test. After the first manipulation of S4 or S-, all pigeons experienced the Pigeons received six sessions of 80 trials of IL2 only. As during acquisition, a single peck on IL2 caused it to be removed from the display and immediately followed on 20% of the trials by the presentation of S3, which ended in reinforcement half of the time, or on 80% of the trials by the presentation of S4, which never ended in reinforcement . S3 and S4 were presented for 30 s.Pigeons received six sessions consisting of 80 trials of only IL1. A single peck to IL1 was followed by its removal from the display and presentation of either the S+ or S-. On 80% of these trials, S- was presented for 30s and half of these trials ended in reinforcement . On the remaining trials IL1 was followed by the 30-s S+, which always ended with reinforcement.Following the six sessions of Extinction of S4 or Partial reinforcement of S-, pigeons received two sessions each containing ten free-choice trials, which were the same as acquisition trials, except none of these trials ended in reinforcement.Following the two Choice test sessions after Extinction of S4, pigeons remained in their home cages for 2 weeks without receiving any experimental manipulations. Following this 2-week period, pigeons received one session of ten free-choice trials as described for the Choice test.Following the first testing round (Choice test or Spontaneous recovery test), pigeons were put back in the original training contingencies for five sessions to re-establish the level of preference for the suboptimal alternative obtained at the end of Acquisition and to prepare for the second manipulation.Preference was defined as the number of choices to the suboptimal alternative divided by the total number of free-choice trials completed. The data were analyzed using a repeated-measures ANOVA (RM-ANOVA) in which Sessions was the within-subject factor. When a significant main effect was found, post hoc analysis using Holm corrections were implemented.F = 11.815, p < .001, η2 = .628 showing a successful acquisition of the suboptimal choice preference.Figure Preliminary analysis found no order effect, and thus we analyzed each manipulation separately pooling across all birds. Figure F = 19.255, p < .001, η2 = .733. Post hoc analyses with Holm's method revealed that preference for the suboptimal alternative decreased following the Extinction of S4 , but it increased after the Spontaneous Recovery period .Figure t(7)= 7.973, p < .001), in which the preference for the suboptimal alternative decreased after S- become partially reinforced  did not change preference  can influence paradoxical preference. The results presented here suggest that information about the presence or absence of outcomes is learned, and excitatory and inhibitory properties are acquired to post-choice cues. We claim that, rather than being ignored, an inhibitory cue can pull preference toward its antecedent initial-link stimulus on a choice trial. In consequence, the results of this experiment suggest that pigeons increase their choice of the optimal alternative when the information conveyed by S4 was increased through inhibition of extinction, or when the information conveyed by S- decreased through partial reinforcement.We couched our explanation of the results in terms of the information that the cues provided about the outcome. The informational approach is, of course, not the only explanation. Perhaps a simpler explanation, such as contrast, can also account for our results. Various approaches to contrast have been elaborated, especially as applied to paradoxical choice. For instance, the Delta-Sigma model  or .3 (if we assumed both contrasts are added). That is, S3 has a contrast of positive .4 , and S4 has a contrast of negative .1 (from .1 to 0), The suboptimal alternative still has a higher contrast of +.8. In both cases, the contrast still favors a suboptimal preference, but the difference in contrast between options is reduced, which could explain the decrement in preference for the suboptimal option after both manipulations.Overall, our results seem to challenge the generally accepted idea that non-reinforced cues have no impact on preference in the paradoxical choice paradigm. Further research is needed to resolve the discrepancies between our results and those from prior studies (but see González & Blaisdell,


Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported. The past decade has seen growing interest in the role of circular RNAs (circRNAs) in various diseases , highligcircRNAs are present in different subcellular locations , 13 and circANRIL has been linked to atherosclerosis [circMICRA was described as a predictive molecule potentially associated to left ventricular dysfunction [circSMARCA5 has been correlated with tumor differentiation and cancer diameter [circCDYL has been shown to act as a predictive and prognostic marker of breast cancer [Research has also pointed circRNAs as promising biomarkers for several diseases, including cardiovascular infirmities and cancers. For example, clerosis , and cirfunction . In cancdiameter , whereast cancer .in silico characterization and, consequently, obtain further insights related to their functional roles in the cells [There are a multitude of algorithms for identifying back splicing junctions (BSJ) and circRNA formation. These algorithms are implemented in computational tools with high capacity for predicting these molecules, usually by extracting information from RNA sequencing (RNA-seq) data . Predicthe cells , 24.In this review, we will first provide an overview of what circRNAs are and how they are originated at the cellular level. This information is crucial for the development of predictive tools for its identification by exploring RNA-seq data or even using the transcript nucleotide sequences features. We will explore the different tools available for circRNA prediction, discussing the positive and negative aspects of them. We will then review the approaches to identify splicing variants and to perform a quantification, normalization and differential expression analysis of these RNAs, as well as the various algorithms for predicting their interactions with other molecules and to obtain insights of their functional roles in the cells. Finally, we provide a brief overview of the databases and repositories containing relevant data and information for circRNAs.cis-acting elements in the pre-mRNA and trans-acting splicing factors  RNA-binding protein-driven circularization depends on trans elements guided by RBPs that interact with cis elements to conform the circularization. These proteins recognize and bind to specific motifs in the intronic flanking regions. In this way, these proteins can dimerize and achieve structures that allow the back-splicing conformation and, consequently, the RNA circularization. Liu et al. (2019) [cis elements in flanking regions of back-splicing sites of 21,472 human circRNAs, identifying RNA binding and protein binding sites as a main factor for circRNA biogenesis. Huang et al. (2020) [The circRNAs are formed when the cell spliceosome machinery joins the 3′ and 5′ ends of the canonical spliced RNA through a back splicing reaction, which results in the circularization of the RNA molecule . Aufiero. (2019)  used a n. (2020)  summarizDistinct categories of circRNAs are recognized based on the origin of the genomic region that conforms the final circRNA sequence. Normally, intron pairing-driven and RNA-binding-protein-driven circularizations can generate three main categories, which can include the combination of multiple exons or intronic regions in the final circRNAs . (i) TheThese characteristics are important features to be considered in the prediction of circRNAs, and different approaches are being developed based on evaluation of the mapping structure of RNA sequencing reads against a reference genome or even through machine learning approaches considering characteristics present in the sequence of a protein-coding or non-coding transcript.RNA-seq and bioinformatic analysis provide a comprehensive understanding of the eukaryotic transcriptome and a deep exploration of the various types of RNA molecules available in the cell. For circRNA analysis, RNA-seq of total RNA with ribosomal RNA depletion is widely used, often followed by an RNase R step - which efficiently removes linear RNAs - to increase the necessary depth of sequencing. Despite the effectiveness of this approach, results can vary depending on circRNA expression levels, sample quality and sequencing quality , 22.de novo tools, whereas pseudo-reference tools rely on gene annotation files to identify junction reads  and pseudo-reference-based (candidate-based) . Segmenton reads . To giveCIRCexplorer3  requiresde novo identification of circRNAs from unmapped reads, which are mapped to the reference genome using BWA [CIRIquant , the latsing BWA . Then, tde novo detection algorithm for circRNAs prediction. The input files need to be trimmed to the best possible Phred Quality Score. The first step involves alignment to the reference genome using Bowtie2 [Find_circ  is a de  Bowtie2 , with SA Bowtie2  publisheOver the last decade, the interest of the scientific community on circRNAs has grown, and it is reflecting in an increase in more than 90% in the number of publications related to this molecule in literature . This inSensitivity and precision are important parameters to evaluate bioinformatic tools performance. In these software, sensitivity refers to the number of reads assigned to each circRNA by the algorithm and is reflected in the total number of circRNAs predicted. On the other hand, precision refers to the accuracy of the algorithm and can be increased by filtering for circRNA candidates by implementing thresholds for read counts. In 2017, Chen and colleagues  performeLater, in 2018, Hansen  evaluateIn addition to the two primary methods for identifying circRNAs, Chen et al. (2021)  publishehttps://conda.io/), which is an open-source package management system to facilitate the installation and configuration of software packages written in any language that runs in multi operation systems such as Windows, macOS and Linux.Before start using and testing these tools, it is also important to consider those that are easy and less time-consuming to install and configure. To facilitate the configuration of the proper environment to run a circRNA predictive analysis we recommend to use tools available in Conda environment   presenteThe circNick-LRS and circ-Panel-LRS  approachAn alternative to explore circRNA splicing variants is to use their identifiers in public databases and look for the mature sequence of circRNA isoforms in repositories like the circAtlas , which whttp://rnacomposer.cs.put.poznan.pl/), as well as RNAfold from the Vienna package [The structural confirmation is also relevant when studying the impact of full-length circRNA isoforms. Secondary structure and thermodynamic parameters can be obtained using the ViennaRNA Package 2.0 , conside package  and 3dRN package . These tIn summary, transitioning from next-generation sequencing (NGS) to third-generation sequencing (TGS) is a viable and effective option to move forward circRNA characterization, as TGS can sequence full-length reads that provide more information in downstream analyses on the complete circRNA sequence, including the splicing variants, characterization of interaction sites, modifications and superior structures. However, TGS is currently more expensive and has a higher error rate than NGS, but novel improved tools are starting to be released to overcome these limitations.Once the repertoire of circRNAs available in a transcriptome assay is defined, the next step is to obtain their expression patterns. Quantifying, normalizing and differentiating the expression of circRNAs remains a challenge in transcriptome analysis. CircRNAs, being rare in nature and often displaying low read counts, pose difficulties in obtaining their expression patterns. This is because a small fraction of the total reads from RNA-seq samples support the detection of BSJ, leading to difficulties in differentiating reads from the inner circRNA sequence and the linear form of the transcript. As a result, the number of total sequenced reads can impact the number of identified circRNAs. To mitigate this challenge, researchers have used the previously mentioned RNase R treatment during library preparation, which enriches the sample with circRNAs and focuses the sequencing procedure on circRNA sequences.Before conducting differential expression analysis, many authors apply a filter to reduce false positives and increase precision of circRNAs identification. For instance, when generating an expression matrix containing each circRNA expression values to be used in a downstream differential expression comparison, studies normally consider only those circRNAs containing read counts greater than 2; or to normalize this expression matrix prior its filtering considering mapped back-splice junction reads per million (RPM) or counts per million (CPM) normalized values greater than 0.1 .U test to differentiate the expression values of circRNAs within different groups of samples.Different challenges, such as the number of total read sequences, low read counts for each circRNA, high variability between samples and a lack of coverage modeling, can affect differential expression analysis. Some commonly used software for mRNA differential expression, such as DESeq2  and edgeCircRNAs have been described to exert their effects through several mechanisms . One of  or R packages like VizNetwork (https://datastorm-open.github.io/visNetwork/) or networkD3 (http://christophergandrud.github.io/networkD3/), and integrating data from public protein–protein interaction databases such as STRING [Computational approaches can be used to predict the interaction between circRNAs and miRNAs or RNA-binding proteins. These predictions can be used to generate interaction networks, by visualizing the results using tools such as Cytoscape s STRING , IntAct s STRING  or BIND s STRING . Enrichms STRING , PANTHERs STRING  or DAVIDs STRING , to idenMultiple tools are used to analyze circRNA-miRNA interactions, contributing in the prediction of gene expression patterns on which this interaction could have some kind of effect, favoring its degradation, or changing expression patterns of miRNA and mRNAs through competing endogenous mechanisms. Among them, we can highlight RNAhybrid , GSTAr , miRTargMain circRNAs and RBP (RNA-binding protein) interactions are involved in the regulation of gene transcription and translation of peptides. Although RBPs can bind both linear RNAs and circRNAs, evidence suggests a preference for RBP–circRNA binding due to the tertiary structures of circRNAs . RBPs plhttp://service.tartaglialab.com/page/catrapid_omics2_group) [Protein-circRNA interactions are primarily studied through experimental techniques, and specific circRNA tools are limited. Most bioinformatics tools also do not consider the tertiary structure of circRNAs when determining protein-circRNA interactions . A notab2_group) , which e2_group)  provides2_group) , 111, in2_group) , 111 is 2_group) , uses de2_group) , anotherThe validation of circRNA presence and functionality is crucial in the study of these non-coding RNAs. RT-qPCR is a sensitive method for detecting the expression levels of circRNAs and comparing them to those of linear RNAs. It can be used to quantify the expression levels in different samples, such as normal and diseased tissues, and to monitor changes in response to various stimuli. RT-qPCR of RNase R-treated samples can also be used to validate the detection of circRNAs. The technique requires divergent primers that target the BSJ, a unique sequence within the human genome . RNase RcircRNAs are attracting increasing attention from the scientific community and, as a result, a plethora of databases have been established to store extensive information on these RNA molecules . These rIn our review, we will address some considerations one should consider when using a repository as a reference. For instance, not all databases use the same genomic coordinates (chr:start-end), which can result from differences in the input file formats used by the databases. BED format-based databases are 0-based, whereas SAM format-based databases are 1-based, thus requiring adjustments when comparing data between databases. Additionally, some repositories may not provide complete information on their data, such as RNA-seq library type , primary nucleotide sequence of the mature circRNA, strand of origin , validation methods . This information is crucial because the presence of an exon junction does not necessarily indicate RNA circularization. For example, RNA-seq poly (A) + decreases sensitivity to detect circRNAs as in general these molecules are not polyadenylated, increasing the quantity of linear RNAs. Nevertheless, studies have shown that even RNA-seq polyadenylated libraries can detect quantifiable amounts of exon junctions, suggesting that their presence alone is not enough to ensure circularization , 117. AnCaenorhabditis elegans. It has been widely used as a reference in annotation pipelines. CircAtlas [Circ2Traits  was the ircAtlas , anotherircAtlas , CIRCexpircAtlas , DCC [43ircAtlas ) to annoircAtlas  MiOncoCiircAtlas  and CircircAtlas  are repoircAtlas , circRNAircAtlas , Circ2TrircAtlas  and CircircAtlas .Each of the publicly available databases  has uniqIn summary, the various circRNA databases have unique characteristics that aid in the study of these RNA molecules. However, there are also challenges that need to be addressed, such as the lack of a universal naming convention, the incompleteness of data in some databases, and differences in genomic coordinates used by different databases. Efforts should be directed toward standardizing these databases to enable more comprehensive and accurate analysis of circRNAs.CircRNAs are a growing class of non-coding RNAs that are increasingly being recognized for their role in cellular processes. Despite the availability of various bioinformatic tools to identify and quantify circRNA expression, the lack of a uniform nomenclature system and the diverse expression patterns across different species, genes, tissues and developmental stages still present challenges in circRNA research. The mechanisms of circRNA action, including sponge effect, interaction and peptide coding, are the focus of ongoing studies. However, the limitations of current bioinformatic approaches hinder a comprehensive analysis of circRNAs, such as the inability to accurately quantify expression levels based on the full circRNA sequence and to consider the mature isoform sequences derived from the same BSJ. Direct RNA sequencing or TGS techniques can address these issues and improve the prediction of miRNA and RBP interactions. Most algorithms for predicting circRNA-protein interactions are based on general properties of non-coding RNAs and overlook the tertiary structures of circRNAs, which can greatly influence these interactions. Although some neural network-based algorithms consider these structures, they require a large amount of training data that is currently limited. To advance circRNA research, the development of specific algorithms and better integration of data is necessary. Additionally, a common nomenclature system is crucial for comparing circRNA expression across different conditions, diseases or tissues in standardized ways within different publicly available databases.It is worth noting that other publications in the literature have reviewed different aspects of the computational tools for circRNA identification and its in silico functional characterization (see , 71, 139Bioinformatic tools for circRNA prediction and databases should be implemented in an integrative way, to facilitate experimental circRNA-related data interpretation.circRNA identification experiments from RNAseq expression data can be evaluated by several detection methods and is case dependent on the implementation of more than one.circRNAs have a great potential to regulate other RNA species, and circRNA functions can be explored by interaction prediction pipelines with both miRNAs and RBPs, and should be corroborated by experimental procedures.