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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

Natural SINEUP RNAs in Autism Spectrum Disorders: RAB11B-AS1 Dysregulation in a Neuronal CHD8 Suppression Model Leads to RAB11B Protein Increase.

CHD8 represents one of the highest confidence genetic risk factors implied in Autism Spectrum Disorders, with most mutations leading to CHD8 haploinsufficiency and the insurgence of specific phenotypes, such as macrocephaly, facial dysmorphisms, intellectual disability, and gastrointestinal complaints. While extensive studies have been conducted on the possible consequences of CHD8 suppression and protein coding RNAs dysregulation during neuronal development, the effects of transcriptional changes of long non-coding RNAs (lncRNAs) remain unclear. In this study, we focused on a peculiar class of natural antisense lncRNAs, SINEUPs, that enhance translation of a target mRNA through the activity of two RNA domains, an embedded transposable element sequence and an antisense region. By looking at dysregulated transcripts following CHD8 knock down (KD), we first identified RAB11B-AS1 as a potential SINEUP RNA for its domain configuration. Then we demonstrated that such lncRNA is able to increase endogenous RAB11B protein amounts without affecting its transcriptional levels. RAB11B has a pivotal role in vesicular trafficking, and mutations on this gene correlate with intellectual disability and microcephaly. Thus, our study discloses an additional layer of molecular regulation which is altered by CHD8 suppression. This represents the first experimental confirmation that naturally occurring SINEUP could be involved in ASD pathogenesis and underscores the importance of dysregulation of functional lncRNAs in neurodevelopment.

Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms.

RNA vaccines represent a milestone in the history of vaccinology. They provide several advantages over more traditional approaches to vaccine development, showing strong immunogenicity and an overall favorable safety profile. While preclinical testing has provided some key insights on how RNA vaccines interact with the innate immune system, their mechanism of action appears to be fragmented amid the literature, making it difficult to formulate new hypotheses to be tested in clinical settings and ultimately improve this technology platform. Here, we propose a systems biology approach, based on the combination of literature mining and mechanistic graphical modeling, to consolidate existing knowledge around mRNA vaccines mode of action and enhance the translatability of preclinical hypotheses into clinical evidence. A Natural Language Processing (NLP) pipeline for automated knowledge extraction retrieved key biological evidences that were joined into an interactive mechanistic graphical model representing the chain of immune events induced by mRNA vaccines administration. The achieved mechanistic graphical model will help the design of future experiments, foster the generation of new hypotheses and set the basis for the development of mathematical models capable of simulating and predicting the immune response to mRNA vaccines.

Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis.

Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.

MOUSSE: Multi-Omics Using Subject-Specific SignaturEs.

High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.

A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy.

Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come.

Folate metabolism biomarkers from two randomised placebo-controlled clinical studies with paroxetine and venlafaxine.

OBJECTIVES: Based on the hypothesis of a role for folate and vitamin B12 in major depressive disorders (MDD), we aimed at validating the association between folate pathway biomarkers and depression or antidepressant response in clinical trial populations. METHODS: We investigated serum levels erythrocyte folate and serum levels of homocysteine, vitamin B12, and folate as disease and response biomarkers for MDD in two independent randomised, placebo-controlled clinical trials, where paroxetine or venlafaxine were used as active controls, for a total of 881 patients. RESULTS: Significant but weak correlations between depression severity and biomarker levels could be detected in the paroxetine study for serum folate and vitamin B12, with no correlations for any biomarker in the venlafaxine study. Besides a weak association for erythrocyte folate in the venlafaxine study, no significant associations were observed between treatment response and pre-treatment levels of any of the biomarkers tested. CONCLUSIONS: Notwithstanding the relatively large number of patients tested, we did not find consistent associations between folate biomarkers and MDD severity, or response to paroxetine and venlafaxine. Our results may be related to the particular study design or clinical population; however, our findings do not support the hypothesis of a dysfunction of one-carbon metabolism in MDD.

MLL4-associated condensates counterbalance Polycomb-mediated nuclear mechanical stress in Kabuki syndrome.

The genetic elements required to tune gene expression are partitioned in active and repressive nuclear condensates. Chromatin compartments include transcriptional clusters whose dynamic establishment and functioning depend on multivalent interactions occurring among transcription factors, cofactors and basal transcriptional machinery. However, how chromatin players contribute to the assembly of transcriptional condensates is poorly understood. By interrogating the effect of KMT2D (also known as MLL4) haploinsufficiency in Kabuki syndrome, we found that mixed lineage leukemia 4 (MLL4) contributes to the assembly of transcriptional condensates through liquid-liquid phase separation. MLL4 loss of function impaired Polycomb-dependent chromatin compartmentalization, altering the nuclear architecture. By releasing the nuclear mechanical stress through inhibition of the mechanosensor ATR, we re-established the mechanosignaling of mesenchymal stem cells and their commitment towards chondrocytes both in vitro and in vivo. This study supports the notion that, in Kabuki syndrome, the haploinsufficiency of MLL4 causes an altered functional partitioning of chromatin, which determines the architecture and mechanical properties of the nucleus.

Depression-Associated Gene Negr1-Fgfr2 Pathway Is Altered by Antidepressant Treatment.

The Negr1 gene has been significantly associated with major depression in genetic studies. Negr1 encodes for a cell adhesion molecule cleaved by the protease Adam10, thus activating Fgfr2 and promoting neuronal spine plasticity. We investigated whether antidepressants modulate the expression of genes belonging to Negr1-Fgfr2 pathway in Flinders sensitive line (FSL) rats, in a corticosterone-treated mouse model of depression, and in mouse primary neurons. Negr1 and Adam10 were the genes mostly affected by antidepressant treatment, and in opposite directions. Negr1 was down-regulated by escitalopram in the hypothalamus of FSL rats, by fluoxetine in the hippocampal dentate gyrus of corticosterone-treated mice, and by nortriptyline in hippocampal primary neurons. Adam10 mRNA was increased by nortriptyline administration in the hypothalamus, by escitalopram in the hippocampus of FSL rats, and by fluoxetine in mouse dorsal dentate gyrus. Similarly, nortriptyline increased Adam10 expression in hippocampal cultures. Fgfr2 expression was increased by nortriptyline in the hypothalamus of FSL rats and in hippocampal neurons. Lsamp, another IgLON family protein, increased in mouse dentate gyrus after fluoxetine treatment. These findings suggest that Negr1-Fgfr2 pathway plays a role in the modulation of synaptic plasticity induced by antidepressant treatment to promote therapeutic efficacy by rearranging connectivity in corticolimbic circuits impaired in depression.

Transcriptome analysis of the zebrafish atoh7-/- Mutant, lakritz, highlights Atoh7-dependent genetic networks with potential implications for human eye diseases.

Expression of the bHLH transcription protein Atoh7 is a crucial factor conferring competence to retinal progenitor cells for the development of retinal ganglion cells. Several studies have emerged establishing ATOH7 as a retinal disease gene. Remarkably, such studies uncovered ATOH7 variants associated with global eye defects including optic nerve hypoplasia, microphthalmia, retinal vascular disorders, and glaucoma. The complex genetic networks and cellular decisions arising downstream of atoh7 expression, and how their dysregulation cause development of such disease traits remains unknown. To begin to understand such Atoh7-dependent events in vivo, we performed transcriptome analysis of wild-type and atoh7 mutant (lakritz) zebrafish embryos at the onset of retinal ganglion cell differentiation. We investigated in silico interplays of atoh7 and other disease-related genes and pathways. By network reconstruction analysis of differentially expressed genes, we identified gene clusters enriched in retinal development, cell cycle, chromatin remodeling, stress response, and Wnt pathways. By weighted gene coexpression network, we identified coexpression modules affected by the mutation and enriched in retina development genes tightly connected to atoh7. We established the groundwork whereby Atoh7-linked cellular and molecular processes can be investigated in the dynamic multi-tissue environment of the developing normal and diseased vertebrate eye.