Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

Lateral mammillary body neurons in mouse brain are disproportionately vulnerable in Alzheimer's disease

The neural circuits governing the induction and progression of neurodegeneration and memory impairment in Alzheimer's disease (AD) are incompletely understood. The mammillary body (MB), a subcortical node of the medial limbic circuit, is one of the first brain regions to exhibit amyloid deposition in the 5xFAD mouse model of AD. Amyloid burden in the MB correlates with pathological diagnosis of AD in human postmortem brain tissue. Whether and how MB neuronal circuitry contributes to neurodegeneration and memory deficits in AD are unknown. Using 5xFAD mice and postmortem MB samples from individuals with varying degrees of AD pathology, we identified two neuronal cell types in the MB harboring distinct electrophysiological properties and long-range projections: lateral neurons and medial neurons. lateral MB neurons harbored aberrant hyperactivity and exhibited early neurodegeneration in 5xFAD mice compared with lateral MB neurons in wild-type littermates. Inducing hyperactivity in lateral MB neurons in wild-type mice impaired performance on memory tasks, whereas attenuating aberrant hyperactivity in lateral MB neurons ameliorated memory deficits in 5xFAD mice. Our findings suggest that neurodegeneration may be a result of genetically distinct, projection-specific cellular dysfunction and that dysregulated lateral MB neurons may be causally linked to memory deficits in AD.

APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes

APOE4 is the strongest genetic risk factor for Alzheimer's disease1-3. However, the effects of APOE4 on the human brain are not fully understood, limiting opportunities to develop targeted therapeutics for individuals carrying APOE4 and other risk factors for Alzheimer's disease4-8. Here, to gain more comprehensive insights into the impact of APOE4 on the human brain, we performed single-cell transcriptomics profiling of post-mortem human brains from APOE4 carriers compared with non-carriers. This revealed that APOE4 is associated with widespread gene expression changes across all cell types of the human brain. Consistent with the biological function of APOE2-6, APOE4 significantly altered signalling pathways associated with cholesterol homeostasis and transport. Confirming these findings with histological and lipidomic analysis of the post-mortem human brain, induced pluripotent stem-cell-derived cells and targeted-replacement mice, we show that cholesterol is aberrantly deposited in oligodendrocytes-myelinating cells that are responsible for insulating and promoting the electrical activity of neurons. We show that altered cholesterol localization in the APOE4 brain coincides with reduced myelination. Pharmacologically facilitating cholesterol transport increases axonal myelination and improves learning and memory in APOE4 mice. We provide a single-cell atlas describing the transcriptional effects of APOE4 on the aging human brain and establish a functional link between APOE4, cholesterol, myelination and memory, offering therapeutic opportunities for Alzheimer's disease.

Neurons burdened by DNA double-strand breaks incite microglia activation through antiviral-like signaling in neurodegeneration

DNA double-strand breaks (DSBs) are linked to neurodegeneration and senescence. However, it is not clear how DSB-bearing neurons influence neuroinflammation associated with neurodegeneration. Here, we characterize DSB-bearing neurons from the CK-p25 mouse model of neurodegeneration using single-nucleus, bulk, and spatial transcriptomic techniques. DSB-bearing neurons enter a late-stage DNA damage response marked by nuclear factor κB (NFκB)-activated senescent and antiviral immune pathways. In humans, Alzheimer's disease pathology is closely associated with immune activation in excitatory neurons. Spatial transcriptomics reveal that regions of CK-p25 brain tissue dense with DSB-bearing neurons harbor signatures of inflammatory microglia, which is ameliorated by NFκB knockdown in neurons. Inhibition of NFκB in DSB-bearing neurons also reduces microglia activation in organotypic mouse brain slice culture. In conclusion, DSBs activate immune pathways in neurons, which in turn adopt a senescence-associated secretory phenotype to elicit microglia activation. These findings highlight a previously unidentified role for neurons in the mechanism of disease-associated neuroinflammation.

BACE-1 inhibition facilitates the transition from homeostatic microglia to DAM-1

BACE-1 is required for generating β-amyloid (Aβ) peptides in Alzheimer's disease (AD). Here, we report that microglial BACE-1 regulates the transition of homeostatic to stage 1 disease-associated microglia (DAM-1) signature. BACE-1 deficiency elevated levels of transcription factors including Jun, Jund, Btg2, Erg1, Junb, Fos, and Fosb in the transition signature, which transition from more homeostatic to highly phagocytic DAM-1. Consistently, similar transition-state microglia in human AD brains correlated with lowered levels of BACE-1 expression. Targeted deletion of Bace-1 in adult 5xFAD mice microglia elevated these phagocytic microglia, correlated with significant reduction in amyloid plaques without synaptic toxicity. Silencing or pharmacologically inhibiting BACE-1 in cultured microglia-derived cells shows higher phagocytic function in microglia. Mechanistic exploration suggests that abolished cleavage of IL-1R2 and Toll-like receptors via BACE-1 inhibition contributes to the enhanced signaling via the PI3K and p38 MAPK kinase pathway. Together, targeted inhibition of BACE-1 in microglia may offer AD treatment.

MEF2 is a key regulator of cognitive potential and confers resilience to neurodegeneration

[Figure: see text].

In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

The increasing availability of single-cell data revolutionizes the understanding of biological mechanisms at cellular resolution. For differential expression analysis in multi-subject single-cell data, negative binomial mixed models account for both subject-level and cell-level overdispersions, but are computationally demanding. Here, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA). The speed gain is achieved by analytically solving high-dimensional integrals instead of using the Laplace approximation. We demonstrate that NEBULA is orders of magnitude faster than existing tools and controls false-positive errors in marker gene identification and co-expression analysis. Using NEBULA in Alzheimer's disease cohort data sets, we found that the cell-level expression of APOE correlated with that of other genetic risk factors (including CLU, CST3, TREM2, C1q, and ITM2B) in a cell-type-specific pattern and an isoform-dependent manner in microglia. NEBULA opens up a new avenue for the broad application of mixed models to large-scale multi-subject single-cell data.

Mapping the epigenomic and transcriptomic interplay during memory formation and recall in the hippocampal engram ensemble

The epigenome and three-dimensional (3D) genomic architecture are emerging as key factors in the dynamic regulation of different transcriptional programs required for neuronal functions. In this study, we used an activity-dependent tagging system in mice to determine the epigenetic state, 3D genome architecture and transcriptional landscape of engram cells over the lifespan of memory formation and recall. Our findings reveal that memory encoding leads to an epigenetic priming event, marked by increased accessibility of enhancers without the corresponding transcriptional changes. Memory consolidation subsequently results in spatial reorganization of large chromatin segments and promoter-enhancer interactions. Finally, with reactivation, engram neurons use a subset of de novo long-range interactions, where primed enhancers are brought in contact with their respective promoters to upregulate genes involved in local protein translation in synaptic compartments. Collectively, our work elucidates the comprehensive transcriptional and epigenomic landscape across the lifespan of memory formation and recall in the hippocampal engram ensemble.

Reconstruction of the human blood-brain barrier in vitro reveals a pathogenic mechanism of APOE4 in pericytes

In Alzheimer's disease, amyloid deposits along the brain vasculature lead to a condition known as cerebral amyloid angiopathy (CAA), which impairs blood-brain barrier (BBB) function and accelerates cognitive degeneration. Apolipoprotein (APOE4) is the strongest risk factor for CAA, yet the mechanisms underlying this genetic susceptibility are unknown. Here we developed an induced pluripotent stem cell-based three-dimensional model that recapitulates anatomical and physiological properties of the human BBB in vitro. Similarly to CAA, our in vitro BBB displayed significantly more amyloid accumulation in APOE4 compared to APOE3. Combinatorial experiments revealed that dysregulation of calcineurin-nuclear factor of activated T cells (NFAT) signaling and APOE in pericyte-like mural cells induces APOE4-associated CAA pathology. In the human brain, APOE and NFAT are selectively dysregulated in pericytes of APOE4 carriers, and inhibition of calcineurin-NFAT signaling reduces APOE4-associated CAA pathology in vitro and in vivo. Our study reveals the role of pericytes in APOE4-mediated CAA and highlights calcineurin-NFAT signaling as a therapeutic target in CAA and Alzheimer's disease.

Inferring multimodal latent topics from electronic health records

Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization

Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features.

Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration

Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.

Author Correction: Single-cell transcriptomic analysis of Alzheimer's disease

Change history: In this Article, the Acknowledgements section should have included that the work was supported in part by the Cure Alzheimer's Fund (CAF), and the final NIH grant acknowledged should have been 'U01MH119509' instead of 'RF1AG054012'. In Supplementary Table 2, the column labels 'early.pathology.mean' and 'late.pathology.mean' were reversed in each worksheet (that is, columns Y and Z). These errors have been corrected online.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

Structural robustness of mammalian transcription factor networks reveals plasticity across development

Network biology aims to understand cell behavior through the analysis of underlying complex biomolecular networks. Inference of condition-specific interaction networks from epigenomic data enables the characterization of the structural plasticity that regulatory networks can acquire in different tissues of the same organism. From this perspective, uncovering specific patterns of variation by comparing network structure among tissues could provide insights into systems-level mechanisms underlying cell behavior. Following this idea, here we propose an empirical framework to analyze mammalian tissue-specific networks, focusing on characterizing and contrasting their structure and behavior in response to perturbations. We structurally represent the state of the cell/tissue by condition specific transcription factor networks generated using DNase-seq chromatin accessibility data, and we profile their systems behavior in terms of the structural robustness against random and directed perturbations. Using this framework, we unveil the structural heterogeneity existing among tissues at different levels of differentiation. We uncover a novel and conserved systems property of regulatory networks underlying embryonic stem cells (ESCs): in contrast to terminally differentiated tissues, the promiscuous regulatory connectivity of ESCs produces a globally homogeneous network resulting in increased structural robustness. We show that this property is associated with a more permissive, less restrictive chromatin accesibility state in ESCs. Possible biological consequences of this property are discussed.

Modeling the Epigenetic Landscape in Plant Development

Computational mechanistic models enable a systems-level understanding of plant development by integrating available molecular experimental data and simulating their collective dynamical behavior. Boolean gene regulatory network dynamical models have been extensively used as a qualitative modeling framework for such purpose. More recently, network modeling protocols have been extended to model the epigenetic landscape associated with gene regulatory networks. In addition to understanding the concerted action of interconnected genes, epigenetic landscape models aim to uncover the patterns of cell state transition events that emerge under diverse genetic and environmental background conditions. In this chapter we present simple protocols that naturally extend gene regulatory network modeling and demonstrate their use in modeling plant developmental processes under the epigenetic landscape framework. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature. The protocols presented here can be applied to any well-characterized gene regulatory network in plants, animals, or human disease.

Gene regulatory network underlying the immortalization of epithelial cells

BACKGROUND

Tumorigenic transformation of human epithelial cells in vitro has been described experimentally as the potential result of spontaneous immortalization. This process is characterized by a series of cell-state transitions, in which normal epithelial cells acquire first a senescent state which is later surpassed to attain a mesenchymal stem-like phenotype with a potentially tumorigenic behavior. In this paper we aim to provide a system-level mechanistic explanation to the emergence of these cell types, and to the time-ordered transition patterns that are common to neoplasias of epithelial origin. To this end, we first integrate published functional and well-curated molecular data of the components and interactions that have been found to be involved in such cell states and transitions into a network of 41 molecular components. We then reduce this initial network by removing simple mediators (i.e., linear pathways), and formalize the resulting regulatory core into logical rules that govern the dynamics of each of the network components as a function of the states of its regulators.

RESULTS

Computational dynamic analysis shows that our proposed Gene Regulatory Network model recovers exactly three attractors, each of them defined by a specific gene expression profile that corresponds to the epithelial, senescent, and mesenchymal stem-like cellular phenotypes, respectively. We show that although a mesenchymal stem-like state can be attained even under unperturbed physiological conditions, the likelihood of converging to this state is increased when pro-inflammatory conditions are simulated, providing a systems-level mechanistic explanation for the carcinogenic role of chronic inflammatory conditions observed in the clinic. We also found that the regulatory core yields an epigenetic landscape that restricts temporal patterns of progression between the steady states, such that recovered patterns resemble the time-ordered transitions observed during the spontaneous immortalization of epithelial cells, both in vivo and in vitro.

CONCLUSION

Our study strongly suggests that the in vitro tumorigenic transformation of epithelial cells, which strongly correlates with the patterns observed during the pathological progression of epithelial carcinogenesis in vivo, emerges from underlying regulatory networks involved in epithelial trans-differentiation during development.

Dynamic network modelling to understand flowering transition and floral patterning

Differentiation and morphogenetic processes during plant development are particularly robust. At the cellular level, however, plants also show great plasticity in response to environmental conditions, and can even reverse apparently terminal differentiated states with remarkable ease. Can we understand and predict both robust and plastic systemic responses as a general consequence of the non-trivial interplay between intracellular regulatory networks, extrinsic environmental signalling, and tissue-level mechanical constraints? Flower development has become an ideal model system to study these general questions of developmental biology, which are especially relevant to understanding stem cell patterning in plants, animals, and human disease. Decades of detailed study of molecular developmental genetics, as well as novel experimental techniques for in vivo assays in both wild-type and mutant plants, enable the postulation and testing of experimentally grounded mathematical and computational network dynamical models. Research in our group aims to explain the emergence of robust transitions that occur at the shoot apical meristem, as well as flower development, as the result of the collective action of key molecular components in regulatory networks subjected to intra-organismal signalling and extracellular constraints. Here we present a brief overview of recent work from our group, and that of others, focusing on the use of simple dynamical models to address cell-fate specification and cell-state stochastic dynamics during flowering transition and cell-state transitions at the shoot apical meristem of Arabidopsis thaliana. We also focus on how our work fits within the general field of plant developmental modelling, which is being developed by many others.

Reshaping the epigenetic landscape during early flower development: induction of attractor transitions by relative differences in gene decay rates

BACKGROUND

Gene regulatory network (GRN) dynamical models are standard systems biology tools for the mechanistic understanding of developmental processes and are enabling the formalization of the epigenetic landscape (EL) model.

METHODS

In this work we propose a modeling framework which integrates standard mathematical analyses to extend the simple GRN Boolean model in order to address questions regarding the impact of gene specific perturbations in cell-fate decisions during development.

RESULTS

We systematically tested the propensity of individual genes to produce qualitative changes to the EL induced by modification of gene characteristic decay rates reflecting the temporal dynamics of differentiation stimuli. By applying this approach to the flower specification GRN (FOS-GRN) we uncovered differences in the functional (dynamical) role of their genes. The observed dynamical behavior correlates with biological observables. We found a relationship between the propensity of undergoing attractor transitions between attraction basins in the EL and the direction of differentiation during early flower development - being less likely to induce up-stream attractor transitions as the course of development progresses. Our model also uncovered a potential mechanism at play during the transition from EL basins defining inflorescence meristem to those associated to flower organs meristem. Additionally, our analysis provided a mechanistic interpretation of the homeotic property of the ABC genes, being more likely to produce both an induced inter-attractor transition and to specify a novel attractor. Finally, we found that there is a close relationship between a gene's topological features and its propensity to produce attractor transitions.

CONCLUSIONS

The study of how the state-space associated with a dynamical model of a GRN can be restructured by modulation of genes' characteristic expression times is an important aid for understanding underlying mechanisms occurring during development. Our contribution offers a simple framework to approach such problem, as exemplified here by the case of flower development. Different GRN models and the effect of diverse inductive signals can be explored within the same framework. We speculate that the dynamical role of specific genes within a GRN, as uncovered here, might give information about which genes are more likely to link a module to other regulatory circuits and signaling transduction pathways.

Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development

Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.

Descriptive vs. mechanistic network models in plant development in the post-genomic era

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

Molecular evolution constraints in the floral organ specification gene regulatory network module across 18 angiosperm genomes

The gene regulatory network of floral organ cell fate specification of Arabidopsis thaliana is a robust developmental regulatory module. Although such finding was proposed to explain the overall conservation of floral organ types and organization among angiosperms, it has not been confirmed that the network components are conserved at the molecular level among flowering plants. Using the genomic data that have accumulated, we address the conservation of the genes involved in this network and the forces that have shaped its evolution during the divergence of angiosperms. We recovered the network gene homologs for 18 species of flowering plants spanning nine families. We found that all the genes are highly conserved with no evidence of positive selection. We studied the sequence conservation features of the genes in the context of their known biological function and the strength of the purifying selection acting upon them in relation to their placement within the network. Our results suggest an association between protein length and sequence conservation, evolutionary rates, and functional category. On the other hand, we found no significant correlation between the strength of purifying selection and gene placement. Our results confirm that the studied robust developmental regulatory module has been subjected to strong functional constraints. However, unlike previous studies, our results do not support the notion that network topology plays a major role in constraining evolutionary rates. We speculate that the dynamical functional role of genes within the network and not just its connectivity could play an important role in constraining evolution.