Computational analysis of tissue-specific gene networks: application to murine retinal functional studies

Boolean implication analysis of single-cell data predicts retinal cell type markers

Background

The retina is a complex tissue containing multiple cell types that are essential for vision. Understanding the gene expression patterns of various retinal cell types has potential applications in regenerative medicine. Retinal organoids (optic vesicles) derived from pluripotent stem cells have begun to yield insights into the transcriptomics of developing retinal cell types in humans through single cell RNA-sequencing studies. Previous methods of gene reporting have relied upon techniques in vivo using microarray data, or correlational and dimension reduction methods for analyzing single cell RNA-sequencing data computationally. We aimed to develop a state-of-the-art Boolean method that filtered out noise, could be applied to a wide variety of datasets and lent insight into gene expression over differentiation.

Results

Here, we present a bioinformatic approach using Boolean implication to discover genes which are retinal cell type-specific or involved in retinal cell fate. We apply this approach to previously published retina and retinal organoid datasets and improve upon previously published correlational methods. Our method improves the prediction accuracy of marker genes of retinal cell types and discovers several new high confidence cone and rod-specific genes.

Conclusions

The results of this study demonstrate the benefits of a Boolean approach that considers asymmetric relationships. We have shown a statistically significant improvement from correlational, symmetric methods in the prediction accuracy of retinal cell-type specific genes.

Furthermore, our method contains no cell or tissue-specific tuning and hence could impact other areas of gene expression analyses in cancer and other human diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04915-4.

Dynamics of transcription regulatory network during mice-derived retina organoid development

The retina is a complex system containing several neuron types arranged in distinct layers. Many aspects of the retina's development and the molecular events in the human light-sensing system have been previously unveiled. However, there is limited information about regulatory networks governing the transitional stages during retina development. To address this issue, we have studied the transcriptome dynamics of mice-derived retinal organoid development in 10 successive time-points, from stem cell to functional retina. For the first time, we have identified the main modules of genes related to different stages of development and predicted all possible transcription factors. A major shift in the transcriptome occurs during the transition of cells from D0 to D10 and again at the late stages of retina development. Transcription, nervous system development, cell cycle, neurotransmitter transport, glycosylation, and lipid metabolisms are the most important biological processes during retina development. Altogether, we have identified and reported 15 TFs, including Irx2, Irx3, Lmo2, Tead2, Tbx20, and Zeb1, which are potentially involved in the regulation of retinal organoid development. In conclusion, using several rigorous analyses, we have found main stage-specific biological processes in the retina development and predicted TFs with strong potency in controlling this structure.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

BaiHui: cross-species brain-specific network built with hundreds of hand-curated datasets

MOTIVATION

Functional gene networks, representing how likely two genes work in the same biological process, are important models for studying gene interactions in complex tissues. However, a limitation of the current network-building scheme is the lack of leveraging evidence from multiple model organisms as well as the lack of expert curation and quality control of the input genomic data.

RESULTS

Here, we present BaiHui, a brain-specific functional gene network built by probabilistically integrating expertly-hand-curated (by reading original publications) heterogeneous and multi-species genomic data in human, mouse and rat brains. To facilitate the use of this network, we deployed a web server through which users can query their genes of interest, visualize the network, gain functional insight from enrichment analysis and download network data. We also illustrated how this network could be used to generate testable hypotheses on disease gene prioritization of brain disorders.

AVAILABILITY AND IMPLEMENTATION

BaiHui is freely available at: http://guanlab.ccmb.med.umich.edu/BaiHui/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Global Identification of Small Ubiquitin-related Modifier (SUMO) Substrates Reveals Crosstalk between SUMOylation and Phosphorylation Promotes Cell Migration

Proteomics studies have revealed that SUMOylation is a widely used post-translational modification (PTM) in eukaryotes. However, how SUMO E1/2/3 complexes use different SUMO isoforms and recognize substrates remains largely unknown. Using a human proteome microarray-based activity screen, we identified over 2500 proteins that undergo SUMO E3-dependent SUMOylation. We next constructed a SUMO isoform- and E3 ligase-dependent enzyme-substrate relationship network. Protein kinases were significantly enriched among SUMOylation substrates, suggesting crosstalk between phosphorylation and SUMOylation. Cell-based analyses of tyrosine kinase, PYK2, revealed that SUMOylation at four lysine residues promoted PYK2 autophosphorylation at tyrosine 402, which in turn enhanced its interaction with SRC and full activation of the SRC-PYK2 complex. SUMOylation on WT but not the 4KR mutant of PYK2 further elevated phosphorylation of the downstream components in the focal adhesion pathway, such as paxillin and Erk1/2, leading to significantly enhanced cell migration during wound healing. These studies illustrate how our SUMO E3 ligase-substrate network can be used to explore crosstalk between SUMOylation and other PTMs in many biological processes.

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells

Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA–TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA–TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process.

Despite their importance in the signaling transduction, there is no systematic effort in identifying and characterizing the scaffold proteins in humans. In this work, we predicted scaffold proteins by integrating the available protein-protein interactions and kinase-substrate relationships. The predicted scaffold proteins showed characteristics for known scaffold proteins, suggesting the fidelity of our prediction. More importantly, the systematic prediction of scaffold proteins provides biological insights in the scaffold-mediated signal transduction. We found that scaffold proteins are likely to form complexes, suggesting that scaffold proteins could participate in diverse signaling pathways through the combinatorial interactions among scaffold proteins. Furthermore, the regulation of scaffold proteins’ activities has not been extensively studied. Our bioinformatics analysis proposed that scaffold proteins themselves might be regulated through phosphorylation process.

Retinal Disorders and Sleep Disorders: Are They Genetically Related?

Introduction

Sleep is important for optimal physical health and vitality. Recent studies have shown that individuals with visual impairments may be at risk for sleep problems. This research examines the prevalence of sleep problems among those with retinal disorders and the possibility of a genetic link.

Methods

Subjects with retinitis pigmentosa ( n = 33), Stargardt's disease ( n = 31) and age-related macular degeneration ( n = 43) were recruited from the ophthalmology department of Montreal Children's Hospital. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS). Genetic testing was conducted by the Radboud University Medical Center in Nijmegen, Netherlands. Retinal genes were identified as having retina only or pineal and retinal expression.

Results

The expression patterns of genes causing retinal disorders did not predict sleep quality. The PSQI indicated poor sleep quality in 56% of participants with retinitis pigmentosa, 48% of those with Stargardt's disease, and 53% of those with age-related macular degeneration. The ESS showed that daytime sleepiness was experienced by 20% of individuals with retinitis pigmentosa or Stargardt's disease, and by only one individual with age-related macular degeneration.

Discussion

Approximately 50% of people with retinal disease have sleep problems. This number compares with up to one-third of the general population. Gene expression did not correlate with sleep quality, and the explanation for such a large percentage of sleep disorders needs further investigation.

Implications for practitioners

Eye care and rehabilitation specialists need to be aware of the high prevalence of poor sleep quality in individuals with retinal disorders, since this situation may have an important impact on memory and learning, both of which are vital in successful rehabilitation.

Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases

An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases.

An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the entire human body. In order to answer this question, we created an extensive resource of protein expression and interactions across 16 main human tissues. Using this resource, we first show that the genes underlying hundreds of hereditary diseases are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. We then identify two distinct, statistically-significant factors that could lead to tissue-specific vulnerability in the face of this broad expression: (i) many disease-causing genes have elevated expression levels in their disease tissues, and (ii) disease-causing genes have a significantly higher tendency for tissue-specific interactions in their disease tissues. As we show for several disease-causing genes, these tissue-specific interactions highlight disease mechanisms and provide an efficient filter for interrogating the molecular basis of diseases. Together the two factors we identified are relevant for as many as two thirds of the tissue-specific hereditary diseases. Our comparative tissue analysis therefore provides a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases.

Computational methods and opportunities for phosphorylation network medicine

Protein phosphorylation, one of the most ubiquitous post-translational modifications (PTM) of proteins, is known to play an essential role in cell signaling and regulation. With the increasing understanding of the complexity and redundancy of cell signaling, there is a growing recognition that targeting the entire network or system could be a necessary and advantageous strategy for treating cancer. Protein kinases, the proteins that add a phosphate group to the substrate proteins during phosphorylation events, have become one of the largest groups of 'druggable' targets in cancer therapeutics in recent years. Kinase inhibitors are being regularly used in clinics for cancer treatment. This therapeutic paradigm shift in cancer research is partly due to the generation and availability of high-dimensional proteomics data. Generation of this data, in turn, is enabled by increased use of mass-spectrometry (MS)-based or other high-throughput proteomics platforms as well as companion public databases and computational tools. This review briefly summarizes the current state and progress on phosphoproteomics identification, quantification, and platform related characteristics. We review existing database resources, computational tools, methods for phosphorylation network inference, and ultimately demonstrate the connection to therapeutics. Finally, many research opportunities exist for bioinformaticians or biostatisticians based on developments and limitations of the current and emerging technologies.

PhosphoNetworks: a database for human phosphorylation networks

SUMMARY

Phosphorylation plays an important role in cellular signal transduction. Current phosphorylation-related databases often focus on the phosphorylation sites, which are mainly determined by mass spectrometry. Here, we present PhosphoNetworks, a phosphorylation database built on a high-resolution map of phosphorylation networks. This high-resolution map of phosphorylation networks provides not only the kinase-substrate relationships (KSRs), but also the specific phosphorylation sites on which the kinases act on the substrates. The database contains the most comprehensive dataset for KSRs, including the relationships from a recent high-throughput project for identification of KSRs using protein microarrays, as well as known KSRs curated from the literature. In addition, the database also includes several analytical tools for dissecting phosphorylation networks. PhosphoNetworks is expected to play a prominent role in proteomics and phosphorylation-related disease research.

AVAILABILITY AND IMPLEMENTATION

http://www.phosphonetworks.org

Evolution of the eye transcriptome under constant darkness in Sinocyclocheilus cavefish

In adaptating to perpetual darkness, cave species gradually lose eyes and body pigmentation and evolve alternatives for exploring their environments. Although troglodyte features evolved independently many times in cavefish, we do not yet know whether independent evolution of these characters involves common genetic mechanisms. Surface-dwelling and many cave-dwelling species make the freshwater teleost genus Sinocyclocheilus an excellent model for studying the evolution of adaptations to life in constant darkness. We compared the mature retinal histology of surface and cave species in Sinocyclocheilus and found that adult cavefish showed a reduction in the number and length of photoreceptor cells. To identify genes and genetic pathways that evolved in constant darkness, we used RNA-seq to compare eyes of surface and cave species. De novo transcriptome assemblies were developed for both species, and contigs were annotated with gene ontology. Results from cave-dwelling Sinocyclocheilus revealed reduced transcription of phototransduction and other genes important for retinal function. In contrast to the blind Mexican tetra cavefish Astyanax mexicanus, our results on morphologies and gene expression suggest that evolved retinal reduction in cave-dwelling Sinocyclocheilus occurs in a lens-independent fashion by the reduced proliferation and downregulation of transcriptional factors shown to have direct roles in retinal development and maintenance, including cone-rod homeobox (crx) and Wnt pathway members. These results show that the independent evolution of retinal degeneration in cavefish can occur by different developmental genetic mechanisms.

Global analysis of phosphorylation networks in humans

Phosphorylation-mediated signaling plays a crucial role in nearly every aspect of cellular physiology. A recent study based on protein microarray experiments identified a large number of kinase-substrate relationships (KSRs), and built a comprehensive and reliable phosphorylation network in humans. Analysis of this network, in conjunction with additional resources, revealed several key features. First, comparison of the human and yeast phosphorylation networks uncovered an evolutionarily conserved signaling backbone dominated by kinase-to-kinase relationships. Second, although most of the KSRs themselves are not conserved, the functions enriched in the substrates for a given kinase are often conserved. Third, the prevalence of kinase-transcription factor regulatory modules suggests that phosphorylation and transcriptional regulatory networks are inherently wired together to form integrated regulatory circuits. Overall, the phosphorylation networks described in this work promise to offer new insights into the properties of kinase signaling pathways, at both the global and the protein levels. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Conditional knockdown of DNA methyltransferase 1 reveals a key role of retinal pigment epithelium integrity in photoreceptor outer segment morphogenesis

Dysfunction or death of photoreceptors is the primary cause of vision loss in retinal and macular degenerative diseases. As photoreceptors have an intimate relationship with the retinal pigment epithelium (RPE) for exchange of macromolecules, removal of shed membrane discs and retinoid recycling, an improved understanding of the development of the photoreceptor-RPE complex will allow better design of gene- and cell-based therapies. To explore the epigenetic contribution to retinal development we generated conditional knockout alleles of DNA methyltransferase 1 (Dnmt1) in mice. Conditional Dnmt1 knockdown in early eye development mediated by Rx-Cre did not produce lamination or cell fate defects, except in cones; however, the photoreceptors completely lacked outer segments despite near normal expression of phototransduction and cilia genes. We also identified disruption of RPE morphology and polarization as early as E15.5. Defects in outer segment biogenesis were evident with Dnmt1 exon excision only in RPE, but not when excision was directed exclusively to photoreceptors. We detected a reduction in DNA methylation of LINE1 elements (a measure of global DNA methylation) in developing mutant RPE as compared with neural retina, and of Tuba3a, which exhibited dramatically increased expression in mutant retina. These results demonstrate a unique function of DNMT1-mediated DNA methylation in controlling RPE apicobasal polarity and neural retina differentiation. We also establish a model to study the epigenetic mechanisms and signaling pathways that guide the modulation of photoreceptor outer segment morphogenesis by RPE during retinal development and disease.

LASSO modeling of the Arabidopsis thaliana seed/seedling transcriptome: a model case for detection of novel mucilage and pectin metabolism genes

Whole genome transcript correlation-based approaches have been shown to be enormously useful for candidate gene detection. Consequently, simple Pearson correlation has been widely applied in several web based tools. That said, several more sophisticated methods based on e.g. mutual information or Bayesian network inference have been developed and have been shown to be theoretically superior but are not yet commonly applied. Here, we propose the application of a recently developed statistical regression technique, the LASSO, to detect novel candidates from high throughput transcriptomic datasets. We apply the LASSO to a tissue specific dataset in the model plant Arabidopsis thaliana to identify novel players in Arabidopsis thaliana seed coat mucilage synthesis. We built LASSO models based on a list of genes known to be involved in a sub-pathway of Arabidopsis mucilage synthesis. After identifying a putative transcription factor, we verified its involvement in mucilage synthesis by obtaining knock-out mutants for this gene. We show that a loss of function of this putative transcription factor leads to a significant decrease in mucilage pectin.

Construction of human activity-based phosphorylation networks

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)-based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase-substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high-quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high-resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B-cell receptor signaling. Overall, these studies provide global insights into kinase-mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.

Bioinformatics in Retina

Bioinformatics, a word coined for the applications of computer science in biology, is now promising as a major constituent in modern biology and biomedical research. Bioinformatics plays an important role for the integration of broad disciplines of biology to understand the complex mechanisms of the cell. Bioinformatics also aids the way in which biomedical investigators use the information in their testing. Development and implementation of this novel field enable efficient access and management of different types of biological information including those at the genomic, proteomic, and metabolomic level to understand about disease mechanisms and identify new molecular targets for drug discovery. Bioinformatics has expanded its wings in exploring out different important contributions in relation with medical sciences such as neurology, parasitology, hematology, and pathology including ophthalmology. Many bioinformatics-oriented studies have contributed a lot in ophthalmology and given birth to new avenues of occuloinformatics, hence, a new coined term, occuloinformatics: a new approach of research and diagnostics related to ocular disorders with significant inputs of bioinformatics. In this current review, we tried to focus on current avenues and significant contributions of bioinformatics with special reference to retinal disorders.

Dynamics of regulatory networks in the developing mouse retina

Understanding gene regulation is crucial to dissect the molecular basis of human development and disease. Previous studies on transcription regulatory networks often focused on their static properties. Here we used retinal development as a model system to investigate the dynamics of regulatory networks that are comprised of transcription factors, microRNAs and other protein-coding genes. We found that the active sub-networks are topologically different at early and late stages of retinal development. At early stages, the active sub-networks tend to be highly connected, while at late stages, the active sub-networks are more organized in modular structures. Interestingly, network motif usage at early and late stages is also distinct. For example, network motifs containing reciprocal feedback regulatory relationships between two regulators are overrepresented in early developmental stages. Additionally, our analysis of regulatory network dynamics revealed a natural turning point at which the regulatory network undergoes drastic topological changes. Taken together, this work demonstrates that adding a dynamic dimension to network analysis can provide new insights into retinal development, and we suggest the same approach would likely be useful for the analysis of other developing tissues.

Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks

The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.