Effects Of Individual Genes Research Articles

Assessment of TGx-DDI genes for genotoxicity in a comprehensive panel of chemicals

Background The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. Methods Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. Results Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. Discussion Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening.

Natural variation in the SVP contributes to the pleiotropic adaption of Arabidopsis thaliana across contrasted habitats

Coordinated plant adaptation involves the interplay of multiple traits driven by habitat-specific selection pressures. Pleiotropic effects, wherein genetic variants of a single gene control multiple traits, can expedite such adaptations. Until present, only a limited number of genes have been reported to exhibit pleiotropy. Here, we create a recombinant inbred line (RIL) population derived from two Arabidopsis thaliana (A. thaliana) ecotypes originating from divergent habitats. Using this RIL population, we identify an allelic variation in a MADS-box transcription factor, SHORT VEGETATIVE PHASE (SVP), which exerts a pleiotropic effect on leaf size and drought-versus-humidity tolerance. Further investigation reveals that a natural null variant of the SVP protein disrupts its normal regulatory interactions with target genes, including GRF3, CYP707A1/3, and AtBG1, leading to increased leaf size, enhanced tolerance to humid conditions, and changes in flowering time of humid conditions in A. thaliana. Remarkably, polymorphic variations in this gene have been traced back to early A. thaliana populations, providing a genetic foundation and plasticity for subsequent colonization of diverse habitats by influencing multiple traits. These findings advance our understanding of how plants rapidly adapt to changing environments by virtue of the pleiotropic effects of individual genes on multiple trait alterations.

Development and validation of a mutation-annotated prognostic score for intrahepatic cholangiocarcinoma after resection: a retrospective cohort study.

The value of existing prognostic models for intrahepatic cholangiocarcinoma is limited. The inclusion of prognostic gene mutations would enhance the predictive efficacy. In the screening cohorts, univariable Cox regression analysis was applied to investigate the effect of individual mutant genes on overall survival (OS). In the training set, multivariable analysis was performed to evaluate the independent prognostic roles of the clinicopathological and mutational parameters, and a prognostic model was constructed. Internal and external validations were conducted to evaluate the performance of this model. Among the recurrent mutations, only TP53 and KRASG12 were significantly associated with OS across all three screening cohorts. In the training cohort, TP53 and KRASG12 mutations in combination with seven other clinical parameters (tumor size, tumor number, vascular invasion, lymph node metastasis, adjacent invasion, CA19-9, and CEA), were independent prognostic factors for OS. A mutation-annotated prognostic score (MAPS) was established based on the nine prognosticators. The C-indices of MAPS (0.782 and 0.731 in the internal and external validation cohorts, respectively) were statistically higher than those of other existing models ( P <0.05). Furthermore, the MAPS model also demonstrated significant value in predicting the possible benefits of upfront surgery and adjuvant therapy. The MAPS model demonstrated good performance in predicting the OS of intrahepatic cholangiocarcinoma patients. It may also help predict the possible benefits of upfront surgery and adjuvant therapy.

Research Progress on Animal Models of Diabetes Mellitus

Diabetes Mellitus (DM) is a complicated metabolic illness defined by hyperglycemia due to faulty insulin production or activity, and it is one of the world’s fastest-growing public health concerns. The construction of a good diabetes mellitus model is crucial for diabetes mellitus pathophysiology research, diabetic mellitus prevention, and the development of effective therapeutic therapy. Diabetes mellitus models are now chemically induced, spontaneous, and genetically created. The models were created using the chemical induction of streptozotocin and alloxan, and the spontaneous type of diabetes mellitus animal models are appropriate for T1DM, T2DM, and related phenotypes, including obesity and insulin resistance. Genetically engineered animal models are much more closely related to human diabetes mellitus, including gene knockout animal models. They can be used to explain the cause of diabetes mellitus in terms of molecular mechanisms and to study the effects of individual genes on insulin secretion and insulin resistance faultlessly. This research provides a detailed description of T1DM and T2DM animal models, illustrating the importance of diabetes mellitus.

PADI1 and Its Co-Expressed Gene Signature Unveil Colorectal Cancer Prognosis and Immunotherapy Efficacy.

Peptidyl arginine deiminase 1 (PADI1) catalyzes protein citrullination and has a role in regulating immune responses. The tumor immune microenvironment has been reported to be important in colorectal cancer (CRC), which was correlated with the ability of CRC patients to benefit from immunotherapy. However, there is a lack of molecular markers for matching CRC immunotherapy. Previously, single-gene risk models have only considered the effect of individual genes on intrinsic tumor properties, ignoring the role of genes and their co-expressed genes as a whole. In this study, we analyzed the differential expression of PADI1 in colorectal cancer (CRC). We found that PADI1 was highly expressed in CRC. Subgroup survival analysis revealed a prognostic survival difference for PADI1 in CRC patients aged less than 65 years, male, T stage, N0, M0, and stage I-II (p < 0.05). In addition, we analyzed the functions and signaling pathways associated with PADI1 in CRC and found that it was highly enriched in several immune-related functions and pathways. Then, a set of PADI1 co-expressed genes (PCGs) risk-prognosis scores was developed with PADI1 as the core, which could accurately predict the prognosis of CRC (p < 0.05). PCGs risk score can be an independent prognostic factor for CRC. A new set of Norman plot models were developed for clinical characteristics with age, sex, and TNM stage, which can accurately predict CRC 1, 3, and 5 years survival, and calibration curves and decision curve analysis (DCA) validated the accuracy of the models. The risk score assessed the immune microenvironment of CRC and found that the immune score was higher in the low-risk group, and CD4+ T cells, helper T cells, and eosinophils were more infiltrated in the low-risk group (p < 0.05). Immunotherapy efficacy was better in the low-risk group (p < 0.05). The underlying mechanism may be that the high-risk group of PCGs was enriched in some pathways that promote immune escape and immune dysfunction. In conclusion, PCGs may better predict CRC prognosis and immunotherapeutic response.

The Role of the Environment in Horizontal Gene Transfer.

Gene-by-environment interactions play a crucial role in horizontal gene transfer by affecting how the transferred genes alter host fitness. However, how the environment modulates the fitness effect of transferred genes has not been tested systematically in an experimental study. We adapted a high-throughput technique for obtaining very precise estimates of bacterial fitness, in order to measure the fitness effects of 44 orthologs transferred from Salmonella Typhimurium to Escherichia coli in six physiologically relevant environments. We found that the fitness effects of individual genes were highly dependent on the environment, while the distributions of fitness effects across genes were not, with all tested environments resulting in distributions of same shape and spread. Furthermore, the extent to which the fitness effects of a gene varied between environments depended on the average fitness effect of that gene across all environments, with nearly neutral and nearly lethal genes having more consistent fitness effects across all environments compared to deleterious genes. Put together, our results reveal the unpredictable nature of how environmental conditions impact the fitness effects of each individual gene. At the same time, distributions of fitness effects across environments exhibit consistent features, pointing to the generalizability of factors that shape horizontal gene transfer of orthologous genes.

Pneumococcal genetic variability in age-dependent bacterial carriage.

The characteristics of pneumococcal carriage vary between infants and adults. Host immune factors have been shown to contribute to these age-specific differences, but the role of pathogen sequence variation is currently less well-known. Identification of age-associated pathogen genetic factors could leadto improved vaccine formulations. We therefore performed genome sequencing in a large carriage cohort of children and adults and combined this with data from an existing age-stratified carriage study. We compiled a dictionary of pathogen genetic variation, including serotype, strain, sequence elements, single-nucleotide polymorphisms (SNPs), and clusters of orthologous genes (COGs) for each cohort - all of which were used in a genome-wide association with host age. Age-dependent colonization showed weak evidence of being heritable in the first cohort (h2 = 0.10, 95% CI 0.00-0.69) and stronger evidence in the second cohort (h2 = 0.56, 95% CI 0.23-0.87). We found that serotypes and genetic background (strain) explained a proportion of the heritability in the first cohort (h2serotype = 0.07, 95% CI 0.04-0.14 and h2GPSC = 0.06, 95% CI 0.03-0.13) and the second cohort (h2serotype = 0.11, 95% CI 0.05-0.21 and h2GPSC = 0.20, 95% CI 0.12-0.31). In a meta-analysis of these cohorts, we found one candidate association (p=1.2 × 10-9) upstream of an accessory Sec-dependent serine-rich glycoprotein adhesin. Overall, while we did find a small effect of pathogen genome variation on pneumococcal carriage between child and adult hosts, this was variable between populations and does not appear to be caused by strong effects of individual genes. This supports proposals for adaptive future vaccination strategies that are primarily targeted at dominant circulating serotypes and tailored to the composition of the pathogen populations.

Cancer genetic alterations and risk of venous thromboembolism

Cancer has long been known to incur an increased risk of venous thromboembolism (VTE). Multiple risk factors for cancer-associated thrombosis (CAT) have been identified, and several pathophysiological mechanisms elucidated. However, until recently there was scant data available about the influence of cancer-specific somatic genetic alterations on the risk of venous thromboembolism. In the last few years, several gene loci were found to modulate the risk of CAT, usually causing an increase in risk but sometimes found to have a protective effect. Notably, cancer-specific somatic genetic alterations in KRAS, IDH1, ALK and ROS1 have been found to alter the risk of CAT by independent groups. Work in this field is limited by the high-dimensionality and often sparse nature of genomic datasets. Also, early data suggest that for certain genes the effect on VTE risk can be tumor type-specific, which suggests that predictive models must factor such interactions in order to optimally leverage genetic information. Notably, individual gene effects appear to be often small and no one gene explains most of the variability of CAT risk. Ultimately, improved knowledge of the genetic determinants of CAT will help ameliorate risk stratification for this complication and hopefully provide mechanistic insights. Better risk stratification could lead to enhanced pharmacological VTE prophylaxis, while advancements in the understanding of the biology of CAT could conceivably lead to non-anticoagulant targeted therapies for thrombosis.

Childhood parenting and adolescent internalizing and externalizing symptoms: Moderation by multilocus hypothalamic-pituitary-adrenal axis-related genetic variation.

Genetic variants that regulate hypothalamic-pituitary-adrenal (HPA) axis function have been demonstrated to moderate the association between parenting and mental health. However, extant research has focused primarily on (i) effects of individual genes or (ii) maternal as opposed to paternal parenting. Using a multilocus genetic profile score (MGPS) approach, the current study is the first to examine the moderation effect of multilocus HPA-axis related genetic variants on the association of both maternal and paternal parenting with adolescent internalizing and externalizing symptoms. In a sample of 772 Chinese Han adolescents (Mage = 16.48 ± 1.40 years; 50.1% girls), a theory-driven MGPS was calculated using six polymorphisms within HPA-axis related genes (CRHR1, NR3C1, NR3C2, FKBP5, COMT, and HT1RA). Results showed that the MGPS interacted with both maternal and paternal parenting in the association with adolescent internalizing symptoms, but not externalizing symptoms. Consistent with the differential susceptibility model, adolescents with high versus low MGPS exhibited not only more internalizing symptoms when exposed to low quality of parenting but also less internalizing symptoms when exposed to high quality of parenting. The current findings highlight the potential value of using a multilocus approach to understanding gene-by-environment interaction (G×E) effects underlying mental health. Within such G×E effects, not only maternal but also paternal parenting should be addressed.

Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape.

Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait - craniofacial shape - in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.

3D Attention M-net for Short-axis Left Ventricular Myocardium Segmentation in Mice MR cardiac Images.

Small rodent cardiac magnetic resonance imaging (MRI) plays an important role in preclinical models of cardiac disease. Accurate myocardial boundaries delineation is crucial to most morphological and functional analysis in rodent cardiac MRIs. However, rodent cardiac MRIs, due to animal's small cardiac volume and high heart rate, are usually acquired with sub-optimal resolution and low signal-to-noise ratio (SNR). These rodent cardiac MRIs can also suffer from signal loss due to the intra-voxel dephasing. These factors make automatic myocardial segmentation challenging. Manual contouring could be applied to label myocardial boundaries but it is usually laborious, time consuming, and not systematically objective. In this study, we present a deep learning approach based on 3D attention M-net to perform automatic segmentation of left ventricular myocardium. In the deep learning architecture, we use dual spatial-channel attention gates between encoder and decoder along with multi-scale feature fusion path after decoder. Attention gates enable networks to focus on relevant spatial information and channel features to improve segmentation performance. A distance derived loss term, besides general dice loss and binary cross entropy loss, was also introduced to our hybrid loss functions to refine segmentation contours. The proposed model outperforms other generic models, like U-Net and FCN, in major segmentation metrics including the dice score (0.9072), Jaccard index (0.8307) and Hausdorff distance (3.1754 pixels), which are comparable to the results achieved by state-of-the-art models on human cardiac ACDC17 datasets.Clinical relevance Small rodent cardiac MRI is routinely used to probe the effect of individual genes or groups of genes on the etiology of a large number of cardiovascular diseases. An automatic myocardium segmentation algorithm specifically designed for these data can enhance accuracy and reproducibility of cardiac structure and function analysis.

Mesenchymal stem cells: a brief review of classis concepts and new factors of osteogenic differentiation

Molecular genetic mechanisms, signaling pathways, cultural conditions, factors, and markers of osteogenic differentiation of mesenchymal stem cells (MSC) are actively studied despite numerous works in this area of cellular technologies. This is largely due to the accumulating contradictions in seemingly classical knowledge, as well as permanent updating of the results in the field. In this regard, we focused on the main classical concepts and some new factors and mechanisms that have a noticeable regulatory effect on the differentiation potential of postnatal MSCs. The present review considers the significance of MSC sources for their differentiation capacity, as well as the role of the cellular microenvironment. The issues of classification, terminology, and functional activity of MSCs from various sources are discussed. The paracrine potential of MSCs in tissue regeneration has been considered; sufficient importance of inflammation in osteogenesis is noted, in particular, the presence of inflammatory cytokines and chemokines in the lesion focus, produced not only by microenvironmental cells but also by blood cells, including mononuclear leukocytes, migrating to the affected site. An important role in this review is given to biomechanical signals and to influence of conformational changes in cell cytoskeleton (cell shape) upon MSC differentiation, since the morphological features of cells and the structure of cytoskeleton are modulated by interactions of the cell surface with environmental factors, including hydrostatic pressure, fluid flow, compression/stretching loads. The data are presented concerning elasticity of extracellular matrix being a determining factor of cell differentiation. We conclude that one should switch from point studies of individual gene effects to multiple measurements of the gene-regulatory profile and biomolecules responsible for multiple, still poorly studied osteogenic factors of endogenous and exogenous origin. Among cornerstones in future (epi)genetic studies will be to decide if osteomodulatory effects are realized through specific signaling pathways and/or via cross-signaling with known genes controlling osteogenic differentiation of MSCs.

Differential Effect of Polymorphisms on Body Mass Index Across the Life Course of Japanese: The Japan Multi-Institutional Collaborative Cohort Study.

BackgroundObesity is a reported risk factor for various health problems. Genome-wide association studies (GWASs) have identified numerous independent loci associated with body mass index (BMI). However, most of these have been focused on Europeans, and little evidence is available on the genetic effects across the life course of other ethnicities.MethodsWe conducted a cross-sectional study to examine the associations of 282 GWAS-identified single nucleotide polymorphisms with three BMI-related traits, current BMI, BMI at 20 years old (BMI at 20), and change in BMI (BMI change), among 11,586 Japanese individuals enrolled in the Japan Multi-Institutional Collaborative Cohort study. Associations were examined using multivariable linear regression models.ResultsWe found a significant association (P < 0.05/282 = 1.77 × 10−4) between BMI and 11 polymorphisms in or near FTO, BDNF, TMEM18, HS6ST3, and BORCS7. The trend was similar between current BMI and BMI change, but differed from that of the BMI at 20. Among the significant variants, those on FTO were associated with all BMI traits, whereas those on TMEM18 and HS6SR3 were only associated with BMI at 20. The association of FTO loci with BMI remained, even after additional adjustment for dietary energy intake.ConclusionsPreviously reported BMI-associated loci discovered in Europeans were also identified in the Japanese population. Additionally, our results suggest that the effects of each loci on BMI may vary across the life course and that this variation may be caused by the differential effects of individual genes on BMI via different pathways.

Three phases of Gene × Environment interaction research: Theoretical assumptions underlying gene selection.

Some Gene × Environment interaction (G×E) research has focused upon single candidate genes, whereas other related work has targeted multiple genes (e.g., polygenic scores). Each approach has informed efforts to identify individuals who are either especially vulnerable to the negative effects of contextual adversity (diathesis stress) or especially susceptible to both positive and negative contextual conditions (differential susceptibility). A critical step in all such molecular G×E research is the selection of genetic variants thought to moderate environmental influences, a subject that has not received a great deal of attention in critiques of G×E research (beyond the observation of small effects of individual genes). Here we conceptually distinguish three phases of G×E work based on the selection of genes presumed to moderate environmental effects and the theoretical basis of such decisions: (a) single candidate genes, (b) composited (multiple) candidate genes, and (c) GWAS-derived polygenic scores. This illustrative, not exhaustive, review makes it clear that implicit or explicit theoretical assumptions inform gene selection in ways that have not been clearly articulated or fully appreciated.

Precision Health and Nursing: Seeing the Familiar in the Foreign.

Precision health is the integration of personal genomic data with biological, environmental, behavioral, and other information relevant to the care of a patient. Genetics and genomics are essential components of precision health. Genetics is the study of the effects of individual genes, and genomics is the study of all the components of the genome and interactions between genes, environmental factors, and other psychosocial and cultural factors. Knowledge about the role of genetics and genomics on health outcomes has increased substantially since the completion of the human genome project in 2003. Insights about genetics and genomics obtained from bench science are now having positive clinical implications on patient health outcomes. Nurses have the potential to make distinct contributions to precision health due to their unique role in the health care system. In this article, we discuss gaps in the development of precision health in nursing and how nursing can expand the definition of precision health to actualize its potential. Precision health plays a role in nursing practice. Understanding this connection positions nurses to incorporate genetic and genomic knowledge into their nursing practice.

VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data.

BackgroundMany traits and diseases are thought to be driven by >1 gene (polygenic). Polygenic risk scores (PRS) hence expand on genome-wide association studies by taking multiple genes into account when risk models are built. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions ais found in small datasets, large datasets have not been processed yet owing to the high computational complexity of the search for epistatic interactions.FindingsWe have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to the whole genome of population-scale datasets with 100,000,000 genomic variants and 100,000 samples.ConclusionsCompared with traditional monogenic genome-wide association studies, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high-dimensional genomic data in a manageable time.

Genetic interaction profiles of regulatory kinases differ between environmental conditions and cellular states

Cell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing (Bar‐seq) to test the role of ~4,000 gene deletion mutants and ~12,000 pairwise interactions between all non‐essential genes and the protein kinase genes TOR1, RIM15, and PHO85 in three different nutrient‐restricted conditions in both proliferative and quiescent cells. We detect up to 10‐fold more genetic interactions in quiescent cells than proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro‐quiescence signal. The master regulator of quiescence, RIM15, shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole‐related functions show consistent genetic interactions with RIM15 in response to different starvation signals, suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome‐wide genetic interaction profiling to additional conditions, and phenotypes, and highlights the conditional dependence of epistasis.

KIR3DL1 and HLA-Bw4 Interaction Showed a Favorable Role in Patients with Myelodysplastic Syndromes in Chinese Southern Han.

Background The association studies of killer cell immunoglobulin-like receptors (KIRs) with the occurrence of myelodysplastic syndromes (MDS) are limited worldwide; this study investigated the genetic risk/protective factors of MDS in KIR and human leucocyte antigen (HLA) systems to gain a better understanding of the role played by KIR and their cognate HLA ligands in MDS pathogenesis. Methods We genotyped a total number of 77 patients with MDS from Chinese Southern Han and 745 healthy controls for the KIR loci and HLA class I. The carrier frequencies of KIR genes, KIR genotypes, class I HLA ligands, and KIR-HLA combinations were calculated by direct counting. The effect of individual KIR genes and HLA ligands on MDS risk was evaluated by logistic regression analyses using SAS 9.2 software. Results We found that neither the KIR genes nor the KIR genotypes were associated with the occurrence of MDS. However, we observed that the frequencies for the strong inhibitory ligand HLA-Bw4 as well as KIR3DL1-HLA-Bw4 combination were significantly higher in healthy controls than those in the MDS patient group, respectively (73.42% vs. 62.34%, P = 0.038; 70.87% vs. 59.74%, P = 0.043). Conclusion Our results showed that HLA-Bw4 ligand and KIR3DL1-HLA-Bw4 combination could confer a protective effect against MDS in Chinese Southern Han.

Analysis of genes within the schizophrenia-linked 22q11.2 deletion identifies interaction of night owl/LZTR1 and NF1 in GABAergic sleep control.

The human 22q11.2 chromosomal deletion is one of the strongest identified genetic risk factors for schizophrenia. Although the deletion spans a number of known genes, the contribution of each of these to the 22q11.2 deletion syndrome (DS) is not known. To investigate the effect of individual genes within this interval on the pathophysiology associated with the deletion, we analyzed their role in sleep, a behavior affected in virtually all psychiatric disorders, including the 22q11.2 DS. We identified the gene LZTR1 (night owl, nowl) as a regulator of night-time sleep in Drosophila. In humans, LZTR1 has been associated with Ras-dependent neurological diseases also caused by Neurofibromin-1 (Nf1) deficiency. We show that Nf1 loss leads to a night-time sleep phenotype nearly identical to that of nowl loss and that nowl negatively regulates Ras and interacts with Nf1 in sleep regulation. Furthermore, nowl is required for metabolic homeostasis, suggesting that LZTR1 may contribute to the genetic susceptibility to obesity associated with the 22q11.2 DS. Knockdown of nowl or Nf1 in GABA-responsive sleep-promoting neurons elicits the sleep phenotype, and this defect can be rescued by increased GABAA receptor signaling, indicating that Nowl regulates sleep through modulation of GABA signaling. Our results suggest that nowl/LZTR1 may be a conserved regulator of GABA signaling important for normal sleep that contributes to the 22q11.2 DS.

Abstract P2-11-15: A novel scoring system based on the clinical and genomic variables to predict the early relapse in estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer

Abstract Background: Despite low invasiveness in tumor biology and high sensitivity to endocrine therapy (ET), about 5% to 10% patients with ER+ breast cancer will relapse early in the first two years (yrs) after initiation of ET. Resistance to ET remains one of the leading causes of treatment failure. Methods: Patients with ER+/HER2- breast cancer were retrospectively identified from Shanghai Ruijin Hospital. Expression of 16 cancer-related genes were measured using RT-PCR based on 21-gene assay. Cox proportional hazard model was developed to identify the clinical and genomic variables associated with pre-2-yr relapse with P value of less than .10. Both the landmark analyses and tests for the interaction between genes expression and time were performed to identify the inconsistent effects of individual genes on relapse in two time periods. Finally, a risk score was established based on the clinical and genomic variables with the shrinkage correction and validated using a bootstrap method. Relationship between the risk score and log-hazard ratio of early relapse was presented by the cubic smoothing spline method. The primary endpoint was invasive disease-free survival (IDFS) and secondary endpoints were distant relapse-free survival (DRFS) and overall survival (OS). Results: A total of 1,227 patients were identified. In univariate Cox regression, both the age over 50 yrs (HR 0.55, 95% CI 0.27 - 1.10) and tumor size greater than 2cm (HR 3.26, 95% CI 1.61 - 6.60) were independent prognostic factors for early relapse. As for genomic variables, higher expression of ER (HR 0.79, 95% CI 0.64 - 0.97), PGR (HR 0.83, 95% CI 0.71 - 0.96), BCL2 (HR 0.75, 95% CI 0.55 - 1.03), CD68 (HR 0.66, 95% CI 0.47 - 0.91), GSTM1 (HR 0.78, 95% CI 0.58 - 1.04), and BAG1 (HR 0.69, 95% CI 0.49 - 0.97) were associated with increased early relapse. Of the genomic variables, the HR of PGR, CD68, and BAG1 were in the opposite direction for 2 time periods (interaction P .026, .003, and .011, respectively; Table 1). A scoring system was established based on 2 clinical variables and 6 genomic variables with the shrinkage adjustment (C-index, 0.68; bootstrap validated C-index, 0.69; Table 2). The risk score tended to be associated with early relapse linearly using cubic spline method. When used as a categorical variable with the cutoff point decided by X-tile, risk score higher than 2.5 was also associated with increased early relapse (HR 3.23, 95% CI 1.56 - 6.69, P&amp;lt; .001). Likewise, the score remained an independent predictor for 2-yr DRFS (HR 6.18, 95% CI 1.79 - 21.34, P = .001) and OS (HR 4.57, 95% CI 1.02 - 20.43, P = .029). Conclusion: The scoring system reported herein, taking into account both the clinical and genomic variables, may inform prognosis and endocrine responsiveness. For patients with high risk of early relapse, treatment escalation may be considered. Table 1. Univariate analysis for individual genes by time periods.0-5 yrs0-2 yrs2-5 yrsInteraction PHR (95% CI)PHR (95% CI)PHR (95% CI)PEstrogen moduleER0.92 (0.79-1.07).2730.79 (0.64-0.97).0241.05 (0.86-1.28).651.053PGR0.93 (0.84-1.04).2030.83 (0.71-0.96).0131.05 (0.91-1.21).545.026BCL20.84 (0.68-1.04).1050.75 (0.55-1.03).0740.91 (0.68-1.22).540.374SCUBE20.92 (0.81-1.04).1990.93 (0.77-1.12).4570.91 (0.77-1.08).290.878Proliferation moduleKi671.25 (1.01-1.55).0391.31 (0.94-1.83).1081.21 (0.92-1.60).178.710STK151.12 (0.94-1.33).2221.03 (0.77-1.37).8421.18 (0.94-1.47).152.467Survivin1.09 (0.91-1.30).3691.07 (0.80-1.43).6551.10 (0.87-1.38).428.888CCNB11.07 (0.87-1.32).5260.88 (0.64-1.21).4231.23 (0.94-1.61).126.110MYBL21.16 (0.97-1.40).1071.13 (0.84-1.52).4321.19 (0.94-1.50).151.783Invasion moduleMMP111.11 (0.95-1.29).2051.03 (0.82-1.31).7901.16 (0.95-1.43).154.460CTSL21.07 (0.89-1.29).4491.16 (0.87-1.54).3221.02 (0.80-1.30).863.516HER2 moduleGRB71.11 (0.88-1.41).3700.89 (0.62-1.26).5041.30 (0.96-1.75).085.105HER20.91 (0.75-1.11).3510.89 (0.66-1.21).4620.92 (0.71-1.20).550.865GSTM10.81 (0.67-0.99).0360.78 (0.58-1.04).0880.84 (0.65-1.09).192.673CD680.96 (0.76-1.21).7040.66 (0.47-0.91).0111.28 (0.95-1.73).106.003BAG10.97 (0.76-1.22).7690.69 (0.49-0.97).0311.24 (0.92-1.68).158.011 Table 2. Univariate β coefficients and shrinkage factor for clinical and genomic variables.BetaSEHR95% CIPShrinkage factorClinical variables0.857Age, vs ≤ 50 yrs-0.6000.3560.550.27-1.10.093Tumor size, vs ≤ 2cm1.1810.3603.261.61-6.60.001Genomic variables0.313ER-0.2410.1070.790.64-0.97.024PGR-0.1900.0760.830.71-0.96.013BCL2-0.2860.1600.750.55-1.03.074CD68-0.4210.1650.660.47-0.91.011GSTM1-0.2540.1490.780.58-1.04.088BAG1-0.3780.1750.690.49-0.97.031Abbreviation: SE, standard error; HR, hazard ratio; CI, confidence interval. Citation Format: Caijin Lin, Jiayi Wu, Shuning Ding, Lisa Andriani, Weilin Chen, Deyue Liu, Li Zhu. A novel scoring system based on the clinical and genomic variables to predict the early relapse in estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-11-15.