High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias.

Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

Network inference with Granger causality ensembles on single-cell transcriptomics.

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants.

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

Zac1 and the Imprinted Gene Network program juvenile NAFLD in response to maternal metabolic syndrome.

BACKGROUND AND AIMS: Within the next decade, NAFLD is predicted to become the most prevalent cause of childhood liver failure in developed countries. Predisposition to juvenile NAFLD can be programmed during early life in response to maternal metabolic syndrome (MetS), but the underlying mechanisms are poorly understood. We hypothesized that imprinted genes, defined by expression from a single parental allele, play a key role in maternal MetS-induced NAFLD, due to their susceptibility to environmental stressors and their functions in liver homeostasis. We aimed to test this hypothesis and determine the critical periods of susceptibility to maternal MetS. APPROACH AND RESULTS: We established a mouse model to compare the effects of MetS during prenatal and postnatal development on NAFLD. Postnatal but not prenatal MetS exposure is associated with histological, biochemical, and molecular signatures of hepatic steatosis and fibrosis in juvenile mice. Using RNA sequencing, we show that the Imprinted Gene Network (IGN), including its regulator Zac1, is up-regulated and overrepresented among differentially expressed genes, consistent with a role in maternal MetS-induced NAFLD. In support of this, activation of the IGN in cultured hepatoma cells by overexpressing Zac1 is sufficient to induce signatures of profibrogenic transformation. Using chromatin immunoprecipitation, we demonstrate that Zac1 binds the TGF-ß1 and COL6A2 promoters, forming a direct pathway between imprinted genes and well-characterized pathophysiological mechanisms of NAFLD. Finally, we show that hepatocyte-specific overexpression of Zac1 is sufficient to drive fibrosis in vivo. CONCLUSIONS: Our findings identify a pathway linking maternal MetS exposure during postnatal development to the programming of juvenile NAFLD, and provide support for the hypothesis that imprinted genes play a central role in metabolic disease programming.

Pan-cancer analysis, cell and animal experiments revealing TEAD4 as a tumor promoter in ccRCC.

AIMS: Transcriptional enhanced associate domain (TEAD) transcription factor family, a very important family in the hippo signaling pathway, has been found to play oncogenic functions in the occurrence of various malignant tumors. However, the expression of TEADs in pan-cancer and the important role of TEAD4 in clear cell renal cell carcinoma (ccRCC) have not been analyzed. Herein, we aim to evaluate the expression of TEADs in pan-cancer, and focus on analyzing the role of TEAD4 in the progression of ccRCC. MAIN METHODS: Data from the Cancer Genome Atlas (TCGA) was used to analyze the expression of TEADs in pan-cancer and its clinical correlation. TEAD4 expression in ccRCC tissues, biological functions in vitro and in vivo were analyzed by immunohistochemistry (IHC), western blotting, RNAi and Xenograft assay. Mircode, BioGRID and g: Profiler website were used to build a ceRNA network and downstream pathway prediction. KEY FINDINGS: TEAD1, TEAD2, TEAD3 and TEAD4 were highly expressed in 3, 6, 5, and 12 types of cancer tissues, respectively, indicating that TEAD4 is most closely related to tumor progression. Among the cancers with high TEAD4 expression, the expression of TEAD4 has the greatest correlation with the poor prognosis of ccRCC. We also found the malignant phenotypes of ccRCC cells in vitro and vivo have been significantly suppressed by silencing TEAD4. SIGNIFICANCE: TEADs, especially TEAD4, were overexpressed in many human tumors. This study is the first to show that TEAD4 acts as an oncogene in ccRCC and may be an important factor in progress of ccRCC.

A PtrLBD39-mediated transcriptional network regulates tension wood formation in Populus trichocarpa.

Tension wood (TW) is a specialized xylem tissue formed in angiosperm trees under gravitational stimulus or mechanical stresses (e.g., bending). The genetic regulation that underlies this important mechanism remains poorly understood. Here, we used laser capture microdissection of stem xylem cells coupled with full transcriptome RNA-sequencing to analyze TW formation in Populus trichocarpa. After tree bending, PtrLBD39 was the most significantly induced transcription factor gene; it has a phylogenetically paired homolog, PtrLBD22. CRISPR-based knockout of PtrLBD39/22 severely inhibited TW formation, reducing cellulose and increasing lignin content. Transcriptomic analyses of CRISPR-based PtrLBD39/22 double mutants showed that these two genes regulate a set of TW-related genes. Chromatin immunoprecipitation sequencing (ChIP-seq) was used to identify direct targets of PtrLBD39. We integrated transcriptomic analyses and ChIP-seq assays to construct a transcriptional regulatory network (TRN) mediated by PtrLBD39. In this TRN, PtrLBD39 directly regulates 26 novel TW-responsive transcription factor genes. Our work suggests that PtrLBD39 and PtrLBD22 specifically control TW formation by mediating a TW-specific TRN in Populus.

Constructing local cell-specific networks from single-cell data.

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

Bone Phenotyping Approaches in Human, Mice and Zebrafish - Expert Overview of the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal traits TranslatiOnal NEtwork").

A synoptic overview of scientific methods applied in bone and associated research fields across species has yet to be published. Experts from the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal Traits translational Network") Working Group 2 present an overview of the routine techniques as well as clinical and research approaches employed to characterize bone phenotypes in humans and selected animal models (mice and zebrafish) of health and disease. The goal is consolidation of knowledge and a map for future research. This expert paper provides a comprehensive overview of state-of-the-art technologies to investigate bone properties in humans and animals - including their strengths and weaknesses. New research methodologies are outlined and future strategies are discussed to combine phenotypic with rapidly developing -omics data in order to advance musculoskeletal research and move towards "personalised medicine".

Quantification of FAM20A in human milk and identification of calcium metabolism proteins.

BACKGROUND: FAM20A, a recently discovered protein, is thought to have a fundamental role in inhibiting ectopic calcification. Several studies have demonstrated that variants of FAM20A are causative for the rare autosomal recessive disorder, enamel-renal syndrome (ERS). ERS is characterized by defective mineralization of dental enamel and nephrocalcinosis suggesting that FAM20A is an extracellular matrix protein, dysfunction of which causes calcification of the secretory epithelial tissues. FAM20A is a low-abundant protein that is difficult to detect in biofluids such as blood, saliva, and urine. Thus, we speculated the abundance of FAM20A to be high in human milk, since the secretory epithelium of lactating mammary tissue is involved in the secretion of highly concentrated calcium. Therefore, the primary aim of this research is to describe the processes/methodology taken to quantify FAM20A in human milk and identify other proteins involved in calcium metabolism. METHOD: This study used mass spectrometry-driven quantitative proteomics: (1) to quantify FAM20A in human milk of three women and (2) to identify proteins associated with calcium regulation by bioinformatic analyses on whole and milk fat globule membrane fractions. RESULTS: Shotgun MS/MS driven proteomics identified FAM20A in whole milk, and subsequent analysis using targeted proteomics also successfully quantified FAM20A in all samples. Combination of sample preparation, fractionation, and LC-MS/MS proteomics analysis generated 136 proteins previously undiscovered in human milk; 21 of these appear to be associated with calcium metabolism. CONCLUSION: Using mass spectrometry-driven proteomics, we successfully quantified FAM20A from transitional to mature milk and obtained a list of proteins involved in calcium metabolism. Furthermore, we show the value of using a combination of both shotgun and targeted driven proteomics for the identification of this low abundant protein in human milk.

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning.

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA-protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA-protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA-protein interactions. Compared with other state-of-the-art methods on three lncRNA-protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA-protein interactions by combining raw sequence composition features, hand-designed features and structure features.

The neurobiological basis of divergent thinking: Insight from gene co-expression network-based analysis.

Although many efforts have been made to explore the genetic basis of divergent thinking (DT), there is still a gap in the understanding of how these findings relate to the neurobiology of DT. In a combined sample of 1,682 Chinese participants, by integrating GWAS with previously identified brain-specific gene co-expression network modules, this study explored for the first time the functional brain-specific gene co-expression networks underlying DT. The results showed that gene co-expression network modules in anterior cingulate cortex, caudate, amygdala and substantia nigra were enriched with DT association signals. Further functional enrichment analysis showed that these DT-related gene co-expression network modules were enriched for key biological process and cellular component related to myelination, suggesting that cortical and sub-cortical grey matter myelination may serve as important neurobiological basis of DT. Although the underlying mechanisms need to be further refined, this exploratory study may provide new insight into the neurobiology of DT.

The multicellular signalling network of ovarian cancer metastases.

BACKGROUND: Transcoelomic spread is the major route of metastasis of ovarian high-grade serous carcinoma (HGSC) with the omentum as the major metastatic site. Its unique tumour microenvironment with its large populations of adipocytes, mesothelial cells and immune cells establishes an intercellular signaling network that is instrumental for metastatic growth yet poorly understood. METHODS: Based on transcriptomic analysis of tumour cells, tumour-associated immune and stroma cells we defined intercellular signaling pathways for 284 cytokines and growth factors and their cognate receptors after bioinformatic adjustment for contaminating cell types. The significance of individual components of this network was validated by analysing clinical correlations and potentially pro-metastatic functions, including tumour cell migration, pro-inflammatory signal transduction and TAM expansion. RESULTS: The data show an unexpected prominent role of host cells, and in particular of omental adipocytes, mesothelial cells and fibroblasts (CAF), in sustaining this signaling network. These cells, rather than tumour cells, are the major source of most cytokines and growth factors in the omental microenvironment (n = 176 vs. n = 13). Many of these factors target tumour cells, are linked to metastasis and are associated with a short survival. Likewise, tumour stroma cells play a major role in extracellular-matrix-triggered signaling. We have verified the functional significance of our observations for three exemplary instances. We show that the omental microenvironment (i) stimulates tumour cell migration and adhesion via WNT4 which is highly expressed by CAF; (ii) induces pro-tumourigenic TAM proliferation in conjunction with high CSF1 expression by omental stroma cells and (iii) triggers pro-inflammatory signaling, at least in part via a HSP70-NF-κB pathway. CONCLUSIONS: The intercellular signaling network of omental metastases is majorly dependent on factors secreted by immune and stroma cells to provide an environment that supports ovarian HGSC progression. Clinically relevant pathways within this network represent novel options for therapeutic intervention.

Emergent Bistable Switches from the Incoherent Feed-Forward Signaling of a Positive Feedback Loop.

Bistability is intrinsically connected to various decision making processes in living systems. The operating principles of a bistable switch, generated from a positive feedback loop, are well understood both in natural and synthetic settings. However, the fate of dynamic modularity of a positive feedback loop is unknown when it is connected to another dynamically modular signaling motif. In order to address this, here we investigate feed-forward signaling of a positive feedback loop to determine the fate of a bistable switch under such signaling. Using the potential energy based high-throughput bifurcation analysis method, we uncover that in addition to the conventional bistability the hybrid motifs generate various emergent bistable switches, namely mushroom and isola switches, which are not produced by the individual motifs. Using random parameter sampling, network perturbation, and phase plane analysis, we establish the design principles of such emergent behaviors. Incoherent feed-forward signaling of a positive feedback loop with distinct regulatory thresholds of the two arms of the feed-forward loop are the key requirements for such emergent behaviors. Our calculations show that the specific types of atypical bistable responses depend on the logic gate configuration of the signals. However, the emergent bistable behaviors of the hybrid networks do not depend on the nature of the positive feedback loop.

Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes.

Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein-protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.

NRXN1α+/- is associated with increased excitability in ASD iPSC-derived neurons.

BACKGROUND: NRXN1 deletions are identified as one of major rare risk factors for autism spectrum disorder (ASD) and other neurodevelopmental disorders. ASD has 30% co-morbidity with epilepsy, and the latter is associated with excessive neuronal firing. NRXN1 encodes hundreds of presynaptic neuro-adhesion proteins categorized as NRXN1α/ß/γ. Previous studies on cultured cells show that the short NRXN1ß primarily exerts excitation effect, whereas the long NRXN1α which is more commonly deleted in patients involves in both excitation and inhibition. However, patient-derived models are essential for understanding functional consequences of NRXN1α deletions in human neurons. We recently derived induced pluripotent stem cells (iPSCs) from five controls and three ASD patients carrying NRXN1α+/- and showed increased calcium transients in patient neurons. METHODS: In this study we investigated the electrophysiological properties of iPSC-derived cortical neurons in control and ASD patients carrying NRXN1α+/- using patch clamping. Whole genome RNA sequencing was carried out to further understand the potential underlying molecular mechanism. RESULTS: NRXN1α+/- cortical neurons were shown to display larger sodium currents, higher AP amplitude and accelerated depolarization time. RNASeq analyses revealed transcriptomic changes with significant upregulation glutamatergic synapse and ion channels/transporter activity including voltage-gated potassium channels (GRIN1, GRIN3B, SLC17A6, CACNG3, CACNA1A, SHANK1), which are likely to couple with the increased excitability in NRXN1α+/- cortical neurons. CONCLUSIONS: Together with recent evidence of increased calcium transients, our results showed that human NRXN1α+/- isoform deletions altered neuronal excitability and non-synaptic function, and NRXN1α+/- patient iPSCs may be used as an ASD model for therapeutic development with calcium transients and excitability as readouts.

An androgen receptor switch underlies lineage infidelity in treatment-resistant prostate cancer.

Cancers adapt to increasingly potent targeted therapies by reprogramming their phenotype. Here we investigated such a phenomenon in prostate cancer, in which tumours can escape epithelial lineage confinement and transition to a high-plasticity state as an adaptive response to potent androgen receptor (AR) antagonism. We found that AR activity can be maintained as tumours adopt alternative lineage identities, with changes in chromatin architecture guiding AR transcriptional rerouting. The epigenetic regulator enhancer of zeste homologue 2 (EZH2) co-occupies the reprogrammed AR cistrome to transcriptionally modulate stem cell and neuronal gene networks-granting privileges associated with both fates. This function of EZH2 was associated with T350 phosphorylation and establishment of a non-canonical polycomb subcomplex. Our study provides mechanistic insights into the plasticity of the lineage-infidelity state governed by AR reprogramming that enabled us to redirect cell fate by modulating EZH2 and AR, highlighting the clinical potential of reversing resistance phenotypes.

1α,25-dihydroxyvitamin D3 protects retinal ganglion cells in glaucomatous mice.

BACKGROUND: Glaucoma is an optic neuropathy characterized by loss of function and death of retinal ganglion cells (RGCs), leading to irreversible vision loss. Neuroinflammation is recognized as one of the causes of glaucoma, and currently no treatment is addressing this mechanism. We aimed to investigate the anti-inflammatory and neuroprotective effects of 1,25(OH)2D3 (1α,25-dihydroxyvitamin D3, calcitriol), in a genetic model of age-related glaucomatous neurodegeneration (DBA/2J mice). METHODS: DBA/2J mice were randomized to 1,25(OH)2D3 or vehicle treatment groups. Pattern electroretinogram, flash electroretinogram, and intraocular pressure were recorded weekly. Immunostaining for RBPMS, Iba-1, and GFAP was carried out on retinal flat mounts to assess retinal ganglion cell density and quantify microglial and astrocyte activation, respectively. Molecular biology analyses were carried out to evaluate retinal expression of pro-inflammatory cytokines, pNFκB-p65, and neuroprotective factors. Investigators that analysed the data were blind to experimental groups, which were unveiled after graph design and statistical analysis, that were carried out with GraphPad Prism. Several statistical tests and approaches were used: the generalized estimated equations (GEE) analysis, t-test, and one-way ANOVA. RESULTS: DBA/2J mice treated with 1,25(OH)2D3 for 5 weeks showed improved PERG and FERG amplitudes and reduced RGCs death, compared to vehicle-treated age-matched controls. 1,25(OH)2D3 treatment decreased microglial and astrocyte activation, as well as expression of inflammatory cytokines and pNF-κB-p65 (p < 0.05). Moreover, 1,25(OH)2D3-treated DBA/2J mice displayed increased mRNA levels of neuroprotective factors (p < 0.05), such as BDNF. CONCLUSIONS: 1,25(OH)2D3 protected RGCs preserving retinal function, reducing inflammatory cytokines, and increasing expression of neuroprotective factors. Therefore, 1,25(OH)2D3 could attenuate the retinal damage in glaucomatous patients and warrants further clinical evaluation for the treatment of optic neuropathies.

Prefrontal cortex VAMP1 gene network moderates the effect of the early environment on cognitive flexibility in children.

During development, genetic and environmental factors interact to modify specific phenotypes. Both in humans and in animal models, early adversities influence cognitive flexibility, an important brain function related to behavioral adaptation to variations in the environment. Abnormalities in cognitive functions are related to changes in synaptic connectivity in the prefrontal cortex (PFC), and altered levels of synaptic proteins. We investigated if individual variations in the expression of a network of genes co-expressed with the synaptic protein VAMP1 in the prefrontal cortex moderate the effect of early environmental quality on the performance of children in cognitive flexibility tasks. Genes overexpressed in early childhood and co-expressed with the VAMP1 gene in the PFC were selected for study. SNPs from these genes (post-clumping) were compiled in an expression-based polygenic score (PFC-ePRS-VAMP1). We evaluated cognitive performance of the 4 years-old children in two cohorts using similar cognitive flexibility tasks. In the first cohort (MAVAN) we utilized two CANTAB tasks: (a) the Intra-/Extra-dimensional Set Shift (IED) task, and (b) the Spatial Working Memory (SWM) task. In the second cohort, GUSTO, we used the Dimensional Change Card Sort (DCCS) task. The results show that in 4 years-old children, the PFC-ePRS-VAMP1 network moderates responsiveness to the effects of early adversities on the performance in attentional flexibility tests. The same result was observed for a spatial working memory task. Compared to attentional flexibility, reversal learning showed opposite effects of the environment, as moderated by the ePRS. A parallel ICA analysis was performed to identify relationships between whole-brain voxel based gray matter density and SNPs that comprise the PFC-ePRS-VAMP1. The early environment predicts differences in gray matter content in regions such as prefrontal and temporal cortices, significantly associated with a genetic component related to Wnt signaling pathways. Our data suggest that a network of genes co-expressed with VAMP1 in the PFC moderates the influence of early environment on cognitive function in children.