The person-to-person transmission landscape of the gut and oral microbiomes.

The human microbiome is an integral component of the human body and a co-determinant of several health conditions1,2. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown3,4. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies5, especially those on non-infectious, microbiome-associated diseases.

A novel logical model of COVID-19 intracellular infection to support therapies development.

In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.

Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS.

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

Differential activity of transcribed enhancers in the prefrontal cortex of 537 cases with schizophrenia and controls.

Transcription at enhancers is a widespread phenomenon which produces so-called enhancer RNA (eRNA) and occurs in an activity-dependent manner. However, the role of eRNA and its utility in exploring disease-associated changes in enhancer function, and the downstream coding transcripts that they regulate, is not well established. We used transcriptomic and epigenomic data to interrogate the relationship of eRNA transcription to disease status and how genetic variants alter enhancer transcriptional activity in the human brain. We combined RNA-seq data from 537 postmortem brain samples from the CommonMind Consortium with cap analysis of gene expression and enhancer identification, using the assay for transposase-accessible chromatin followed by sequencing (ATACseq). We find 118 differentially transcribed eRNAs in schizophrenia and identify schizophrenia-associated gene/eRNA co-expression modules. Perturbations of a key module are associated with the polygenic risk scores. Furthermore, we identify genetic variants affecting expression of 927 enhancers, which we refer to as enhancer expression quantitative loci or eeQTLs. Enhancer expression patterns are consistent across studies, including differentially expressed eRNAs and eeQTLs. Combining eeQTLs with a genome-wide association study of schizophrenia identifies a genetic variant that alters enhancer function and expression of its target gene, GOLPH3L. Our novel approach to analyzing enhancer transcription is adaptable to other large-scale, non-poly-A depleted, RNA-seq studies.

The Italian autism network (ITAN): a resource for molecular genetics and biomarker investigations.

BACKGROUND: A substantial genetic component accounts for Autism Spectrum Disorders (ASD) aetiology, with some rare and common genetic risk factors recently identified. Large collections of DNAs from thoroughly characterized ASD families are an essential step to confirm genetic risk factors, identify new variants and investigate genotype-phenotype correlations. The Italian Autism Network aimed at constituting a clinical database and a biorepository of samples derived from ASD subjects and first-degree relatives extensively and consistently characterized by child psychiatry centers in Italy. METHODS: The study was approved by the ethical committee of the University of Verona, the coordinating site, and by the local ethical committees of each recruiting site. Certified staff was specifically trained at each site for the overall study conduct, for clinical protocol administration and handling of biological material. A centralized database was developed to collect clinical assessment and medical records from each recruiting site. Children were eligible for recruitment based on the following inclusion criteria: age 4-18 years, at least one parent or legal guardian giving voluntary written consent, meeting DSM-IV criteria for Autistic Disorder or Asperger's Disorder or Pervasive Developmental Disorder NOS. Affected individuals were assessed by full psychiatric, neurological and physical examination, evaluation with ADI-R and ADOS scales, cognitive assessment with Wechsler Intelligence Scale for Children or Preschool and Primary, Leiter International Performance Scale or Griffiths Mental Developmental Scale. Additional evaluations included language assessment, the Krug Asperger's Disorder Index, and instrumental examination such as EEG and structural MRI. DNA, RNA and plasma were collected from eligible individuals and relatives. A central laboratory was established to host the biorepository, perform DNA and RNA extraction and lymphocytes immortalisation. DISCUSSION: The study has led to an extensive collection of biological samples associated with standardised clinical assessments from a network of expert clinicians and psychologists. Eighteen sites have received ADI/ADOS training, thirteen of which have been actively recruiting. The clinical database currently includes information on 812 individuals from 249 families, and the biorepository has samples for 98% of the subjects. This effort has generated a highly valuable resource for conducting clinical and genetic research of ASD, amenable to further expansion.

The endogenous and reactive depression subtypes revisited: integrative animal and human studies implicate multiple distinct molecular mechanisms underlying major depressive disorder.

BACKGROUND: Traditional diagnoses of major depressive disorder (MDD) suggested that the presence or absence of stress prior to onset results in either 'reactive' or 'endogenous' subtypes of the disorder, respectively. Several lines of research suggest that the biological underpinnings of 'reactive' or 'endogenous' subtypes may also differ, resulting in differential response to treatment. We investigated this hypothesis by comparing the gene-expression profiles of three animal models of 'reactive' and 'endogenous' depression. We then translated these findings to clinical samples using a human post-mortem mRNA study. METHODS: Affymetrix mouse whole-genome oligonucleotide arrays were used to measure gene expression from hippocampal tissues of 144 mice from the Genome-based Therapeutic Drugs for Depression (GENDEP) project. The study used four inbred mouse strains and two depressogenic 'stress' protocols (maternal separation and Unpredictable Chronic Mild Stress) to model 'reactive' depression. Stress-related mRNA differences in mouse were compared with a parallel mRNA study using Flinders Sensitive and Resistant rat lines as a model of 'endogenous' depression. Convergent genes differentially expressed across the animal studies were used to inform candidate gene selection in a human mRNA post-mortem case control study from the Stanley Brain Consortium. RESULTS: In the mouse 'reactive' model, the expression of 350 genes changed in response to early stresses and 370 in response to late stresses. A minimal genetic overlap (less than 8.8%) was detected in response to both stress protocols, but 30% of these genes (21) were also differentially regulated in the 'endogenous' rat study. This overlap is significantly greater than expected by chance. The VAMP-2 gene, differentially expressed across the rodent studies, was also significantly altered in the human study after correcting for multiple testing. CONCLUSIONS: Our results suggest that 'endogenous' and 'reactive' subtypes of depression are associated with largely distinct changes in gene-expression. However, they also suggest that the molecular signature of 'reactive' depression caused by early stressors differs considerably from that of 'reactive' depression caused by late stressors. A small set of genes was consistently dysregulated across each paradigm and in post-mortem brain tissue of depressed patients suggesting a final common pathway to the disorder. These genes included the VAMP-2 gene, which has previously been associated with Axis-I disorders including MDD, bipolar depression, schizophrenia and with antidepressant treatment response. We also discuss the implications of our findings for disease classification, personalized medicine and case-control studies of MDD.

Genetic susceptibility for bipolar disorder and response to antidepressants in major depressive disorder.

The high heterogeneity of response to antidepressant treatment in major depressive disorder (MDD) makes individual treatment outcomes currently unpredictable. It has been suggested that resistance to antidepressant treatment might be due to undiagnosed bipolar disorder or bipolar spectrum features. Here, we investigate the relationship between genetic susceptibility for bipolar disorder and response to treatment with antidepressants in MDD. Polygenic scores indexing risk for bipolar disorder were derived from the Psychiatric Genomics Consortium Bipolar Disorder whole genome association study. Linear regressions tested the effect of polygenic risk scores for bipolar disorder on proportional reduction in depression severity in two large samples of individuals with MDD, treated with antidepressants, NEWMEDS (n=1,791) and STAR*D (n=1,107). There was no significant association between polygenic scores for bipolar disorder and response to treatment with antidepressants. Our data indicate that molecular measure of genetic susceptibility to bipolar disorder does not aid in understanding non-response to antidepressants.

A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing.

BACKGROUND: miR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-wide association studies have implicated miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing. METHODS: Using RNA sequencing data from postmortem prefrontal cortex (N = 522), we identified a coexpression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of coexpression prediction and associated them with functional magnetic resonance imaging activation in healthy volunteers (n1 = 214; n2 = 136; n3 = 2075; n4 = 1800) and with short-term treatment response in patients with schizophrenia (N = 427). RESULTS: In 4652 human participants, we found that 1) schizophrenia risk genes were coexpressed in a biologically validated set enriched for miR-137 targets; 2) increased expression of miR-137 target risk genes was mediated by low prefrontal miR-137 expression; 3) alleles that predict greater gene set coexpression were associated with greater prefrontal activation during emotion processing in 3 independent healthy cohorts (n1, n2, n3) in interaction with age (n4); and 4) these alleles predicted less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia. CONCLUSIONS: The functional translation of miR-137 target gene expression linked with schizophrenia involves the neural substrates of emotion processing.

Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs.

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues.

Exploratory analysis of L1 retrotransposons expression in autism.

BACKGROUND: Autism spectrum disorder (ASD) is a set of highly heterogeneous neurodevelopmental diseases whose genetic etiology is not completely understood. Several investigations have relied on transcriptome analysis from peripheral tissues to dissect ASD into homogenous molecular phenotypes. Recently, analysis of changes in gene expression from postmortem brain tissues has identified sets of genes that are involved in pathways previously associated with ASD etiology. In addition to protein-coding transcripts, the human transcriptome is composed by a large set of non-coding RNAs and transposable elements (TEs). Advancements in sequencing technologies have proven that TEs can be transcribed in a regulated fashion, and their dysregulation might have a role in brain diseases. METHODS: We exploited published datasets comprising RNA-seq data from (1) postmortem brain of ASD subjects, (2) in vitro cell cultures where ten different ASD-relevant genes were knocked out and (3) blood of discordant siblings. We measured the expression levels of evolutionarily young full-length transposable L1 elements and characterized the genomic location of deregulated L1s assessing their potential impact on the transcription of ASD-relevant genes. We analyzed every sample independently, avoiding to pool together the disease subjects to unmask the heterogeneity of the molecular phenotypes. RESULTS: We detected a strong upregulation of intronic full-length L1s in a subset of postmortem brain samples and in in vitro differentiated neurons from iPSC knocked out for ATRX. L1 upregulation correlated with an high number of deregulated genes and retained introns. In the anterior cingulate cortex of one subject, a small number of significantly upregulated L1s overlapped with ASD-relevant genes that were significantly downregulated, suggesting the possible existence of a negative effect of L1 transcription on host transcripts. LIMITATIONS: Our analyses must be considered exploratory and will need to be validated in bigger cohorts. The main limitation is given by the small sample size and by the lack of replicates for postmortem brain samples. Measuring the transcription of locus-specific TEs is complicated by the repetitive nature of their sequence, which reduces the accuracy in mapping sequencing reads to the correct genomic locus. CONCLUSIONS: L1 upregulation in ASD appears to be limited to a subset of subjects that are also characterized by a general deregulation of the expression of canonical genes and an increase in intron retention. In some samples from the anterior cingulate cortex, L1s upregulation seems to directly impair the expression of some ASD-relevant genes by a still unknown mechanism. L1s upregulation may therefore identify a group of ASD subjects with common molecular features and helps stratifying individuals for novel strategies of therapeutic intervention.

Single-cell-led drug repurposing for Alzheimer's disease.

Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy.

Lysosomal cystine export regulates mTORC1 signaling to guide kidney epithelial cell fate specialization.

Differentiation is critical for cell fate decisions, but the signals involved remain unclear. The kidney proximal tubule (PT) cells reabsorb disulphide-rich proteins through endocytosis, generating cystine via lysosomal proteolysis. Here we report that defective cystine mobilization from lysosomes through cystinosin (CTNS), which is mutated in cystinosis, diverts PT cells towards growth and proliferation, disrupting their functions. Mechanistically, cystine storage stimulates Ragulator-Rag GTPase-dependent recruitment of mechanistic target of rapamycin complex 1 (mTORC1) and its constitutive activation. Re-introduction of CTNS restores nutrient-dependent regulation of mTORC1 in knockout cells, whereas cell-permeant analogues of L-cystine, accumulating within lysosomes, render wild-type cells resistant to nutrient withdrawal. Therapeutic mTORC1 inhibition corrects lysosome and differentiation downstream of cystine storage, and phenotypes in preclinical models of cystinosis. Thus, cystine serves as a lysosomal signal that tailors mTORC1 and metabolism to direct epithelial cell fate decisions. These results identify mechanisms and therapeutic targets for dysregulated homeostasis in cystinosis.

A pediatric quantitative systems pharmacology model of neurofilament trafficking in spinal muscular atrophy treated with the antisense oligonucleotide nusinersen.

Phosphorylated neurofilament heavy subunit (pNfH) has been recently identified as a promising biomarker of disease onset and treatment efficacy in spinal muscular atrophy (SMA). This study introduces a quantitative systems pharmacology model representing the SMA pediatric scenario in the age range of 0-20 years with and without treatment with the antisense oligonucleotide nusinersen. Physiological changes typical of the pediatric age and the contribution of SMA and its treatment to the peripheral pNfH levels were included in the model by extending the equations of a previously developed mathematical model describing the neurofilament trafficking in healthy adults. All model parameters were estimated by fitting data from clinical trials that enrolled SMA patients treated with nusinersen. The data from the control group of the study was employed to build an in silico population of untreated subjects, and the parameters related to the treatment were estimated by fitting individual pNfH time series of SMA patients followed during the treatment. The final model reproduces well the pNfH levels in the presence of SMA in both the treated and untreated conditions. The results were validated by comparing model predictions with the data obtained from an additional cohort of SMA patients. The reported good predictive model performance makes it a valuable tool for investigating pNfH as a biomarker of disease progression and treatment response in SMA and for the in silico evaluation of novel treatment protocols.

Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations.

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study-and most other large-scale human genetics studies-was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis.

BACKGROUND: It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way. METHODS AND FINDINGS: The NEWMEDS consortium, an academia-industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10(-8)). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10(-8)) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D. CONCLUSIONS: No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study. Please see later in the article for the Editors' Summary.

Biomarkers for antipsychotic therapies.

Molecular biomarkers for antipsychotic treatments have been conceptually linked to the measurements of dopamine functions, mostly D(2) receptor occupancy, either by imaging using selective PET/SPECT radioactive tracers or by assessing plasma prolactin levels. A quest for novel biomarkers was recently proposed by various academic, health service, and industrial institutions driven by the need for better treatments of psychoses. In this review we conceptualize biomarkers within the Translational Medicine paradigm whose goal was to provide support to critical decision-making in drug discovery. At first we focused on biomarkers as outcome measure of clinical studies by searching into the database clinicaltrial.gov. The results were somewhat disappointing, showing that out of 1,659 antipsychotic trials only 18 used a biomarker as an outcome measure. Several of these trials targeted plasma lipids as sentinel marker for metabolic adverse effects associated with the use of atypical antipsychotics, while only few studies were aimed to new disease specific biological markers. As an example of a mechanistic biomarker, we described the work done to progress the novel class of glycine transporter inhibitors as putative treatment for negative symptoms of schizophrenia. We also review how large-scale multiplex biological assays were applied to samples from tissues of psychiatric patients, so to learn from changes of numerous analytes (metabolic products, lipids, proteins, RNA transcripts) about the substrates involved in the disease. We concluded that a stringent implementation of these techniques could contribute to the endophenotypic characterization of patients, helping in the identification of key biomarkers to drive personalized medicine and new treatment development.

An age-dependent mathematical model of neurofilament trafficking in healthy conditions.

Neurofilaments (Nfs) are the major structural component of neurons. Their role as a potential biomarker of several neurodegenerative diseases has been investigated in past years with promising results. However, even under physiological conditions, little is known about the leaking of Nfs from the neuronal system and their detection in the cerebrospinal fluid (CSF) and blood. This study aimed at developing a mathematical model of Nf transport in healthy subjects in the 20-90 age range. The model was implemented as a set of ordinary differential equations describing the trafficking of Nfs from the nervous system to the periphery. Model parameters were calibrated on typical Nf levels obtained from the literature. An age-dependent function modeled on CSF data was also included and validated on data measured in serum. We computed a global sensitivity analysis of model rates and volumes to identify the most sensitive parameters affecting the model's steady state. Age, Nf synthesis, and degradation rates proved to be relevant for all model variables. Nf levels in the CSF and in blood were observed to be sensitive to the Nf leakage rates from neurons and to the blood clearance rate, and CSF levels were also sensitive to rates representing CSF turnover. An additional parameter perturbation analysis was also performed to investigate possible transient effects on the model variables not captured by the sensitivity analysis. The model provides useful insights into Nf transport and constitutes the basis for implementing quantitative system pharmacology extensions to investigate Nf trafficking in neurodegenerative diseases.

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism.

Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.

Escitalopram affects cytoskeleton and synaptic plasticity pathways in a rat gene-environment interaction model of depression as revealed by proteomics. Part II: environmental challenge.

Large-scale investigations aimed at elucidating the molecular mechanism of action of antidepressant treatment are achievable through the application of proteomic technologies. We performed a proteomic study on the Flinders Sensitive Line (FSL), a genetically selected rat model of depression, and the control Flinders Resistant Line (FRL). To evaluate gene-environment interactions, FSL and FRL animals were separated from their mothers for 3 h from postnatal days 2 to 14 (maternal separation; MS), since early-life trauma is considered an important antecedent of depression. All groups received either escitalopram (Esc) admixed to food pellets (25 mg/kg.d) or vehicle for 1 month. Protein extracts from prefrontal/frontal cortex and hippocampus were separated by 2D electrophoresis. Proteins differentially modulated were identified by mass spectrometry. Bioinformatics analyses were performed to discover gene ontology terms associated with the modulated proteins. This paper was focused on the modifications induced by the environmental challenge of MS, both on the predisposed genetic background and on the resistant phenotype. The combination between Esc treatment and MS was investigated by comparing the MS, Esc-treated rats with rats subjected to each single procedure. In MS rats, antidepressant treatment influenced mainly proteins involved in carbohydrate metabolism in FSL rats and in vesicle-mediated transport in FRL rats. When studying the interaction between Esc and MS vs. non-separated rats, proteins playing a role in cytoskeleton organization, neuronal development, vesicle-mediated transport and synaptic plasticity were identified. The results provide further support to the available reports that antidepressant treatment affects intracellular pathways and also suggest new potential targets for future therapeutic intervention.

Escitalopram modulates neuron-remodelling proteins in a rat gene-environment interaction model of depression as revealed by proteomics. Part I: genetic background.

The wide-scale analysis of protein expression provides a powerful strategy for the molecular exploration of complex pathophysiological mechanisms, such as the response to antidepressants. Using a 2D proteomic approach we investigated the Flinders Sensitive Line (FSL), a genetically selected rat model of depression, and the control Flinders Resistant Line (FRL). To evaluate gene-environment interactions, FSL and FRL pups were separated from their mothers for 3 h (maternal separation, MS), as early-life trauma is considered an important antecedent of depression. All groups were treated with either escitalopram (Esc) admixed to food (25 mg/kg.d) or vehicle for 1 month. At the week 3, forced swim tests were performed. Protein extracts from prefrontal/frontal cortex and hippocampus were separated by 2D electrophoresis. Proteins displaying statistically significant differences in expression levels were identified by mass spectrometry. Immobility time values in the forced swim test were higher in FSL rats and reduced by antidepressant treatment. Moreover, the Esc-induced reduction in immobility time was not detected in MS rats. The impact of genetic background in response to Esc was specifically investigated here. Bioinformatics analyses highlighted gene ontology terms showing tighter associations with the modulated proteins. Esc modulated protein belonging to cytoskeleton organization in FSL; carbohydrate metabolism and intracellular transport in FRL. Proteins differently modulated in the two strains after MS and Esc play a role in cytoskeleton organization, vesicle-mediated transport, apoptosis regulation and macromolecule catabolism. These findings suggest pathways involved in neuronal remodelling as molecular correlates of response to antidepressants in a model of vulnerability.