A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration

Formalizing Phenotypes of Regeneration

Regeneration experiments can produce complex phenotypes including morphological outcomes and gene expression patterns that are crucial for the understanding of the mechanisms of regeneration. However, due to their inherent complexity, variability between individuals, and heterogeneous data spreading across the literature, extracting mechanistic knowledge from them is a current challenge. Toward this goal, here we present protocols to unambiguously formalize the phenotypes of regeneration and their experimental procedures using precise mathematical morphological descriptions and standardized gene expression patterns. We illustrate the application of the methodology with step-by-step protocols for planaria and limb regeneration phenotypes. The curated datasets with these methods are not only helpful for human scientists, but they represent a key formalized resource that can be easily integrated into downstream reverse engineering methodologies for the automatic extraction of mechanistic knowledge. This approach can pave the way for discovering comprehensive systems-level models of regeneration.

Curation and annotation of planarian gene expression patterns with segmented reference morphologies

Motivation

Morphological and genetic spatial data from functional experiments based on genetic, surgical and pharmacological perturbations are being produced at an extraordinary pace in developmental and regenerative biology. However, our ability to extract knowledge from these large datasets are hindered due to the lack of formalization methods and tools able to unambiguously describe, centralize and interpret them. Formalizing spatial phenotypes and gene expression patterns is especially challenging in organisms with highly variable morphologies such as planarian worms, which due to their extraordinary regenerative capability can experimentally result in phenotypes with almost any combination of body regions or parts.

Results

Here, we present a computational methodology and mathematical formalism to encode and curate the morphological outcomes and gene expression patterns in planaria. Worm morphologies are encoded with mathematical graphs based on anatomical ontology terms to automatically generate reference morphologies. Gene expression patterns are registered to these standard reference morphologies, which can then be annotated automatically with anatomical ontology terms by analyzing the spatial expression patterns and their textual descriptions. This methodology enables the curation and annotation of complex experimental morphologies together with their gene expression patterns in a centralized standardized dataset, paving the way for the extraction of knowledge and reverse-engineering of the much sought-after mechanistic models in planaria and other regenerative organisms.

Availability and implementation

We implemented this methodology in a user-friendly graphical software tool, PlanGexQ, freely available together with the data in the manuscript at https://lobolab.umbc.edu/plangexq.

Supplementary information

Supplementary data are available at Bioinformatics online.

Continuous Dynamic Modeling of Regulated Cell Adhesion: Sorting, Intercalation, and Involution

Cell-cell adhesion is essential for tissue growth and multicellular pattern formation and crucial for the cellular dynamics during embryogenesis and cancer progression. Understanding the dynamical gene regulation of cell adhesion molecules (CAMs) responsible for the emerging spatial tissue behaviors is a current challenge because of the complexity of these nonlinear interactions and feedback loops at different levels of abstraction-from genetic regulation to whole-organism shape formation. To extend our understanding of cell and tissue behaviors due to the regulation of adhesion molecules, here we present a novel, to our knowledge, model for the spatial dynamics of cellular patterning, growth, and shape formation due to the differential expression of CAMs and their regulation. Capturing the dynamic interplay between genetic regulation, CAM expression, and differential cell adhesion, the proposed continuous model can explain the complex and emergent spatial behaviors of cell populations that change their adhesion properties dynamically because of inter- and intracellular genetic regulation. This approach can demonstrate the mechanisms responsible for classical cell-sorting behaviors, cell intercalation in proliferating populations, and the involution of germ layer cells induced by a diffusing morphogen during gastrulation. The model makes predictions on the physical parameters controlling the amplitude and wavelength of a cellular intercalation interface, as well as the crucial role of N-cadherin regulation for the involution and migration of cells beyond the gradient of the morphogen Nodal during zebrafish gastrulation. Integrating the emergent spatial tissue behaviors with the regulation of genes responsible for essential cellular properties such as adhesion will pave the way toward understanding the genetic regulation of large-scale complex patterns and shapes formation in developmental, regenerative, and cancer biology.

EDEn-Electroceutical Design Environment: Ion Channel Tissue Expression Database with Small Molecule Modulators

The emerging field of bioelectricity has revealed numerous new roles for ion channels beyond the nervous system, which can be exploited for applications in regenerative medicine. Developing such biomedical interventions for birth defects, cancer, traumatic injury, and bioengineering first requires knowledge of ion channel targets expressed in tissues of interest. This information can then be used to select combinations of small molecule inhibitors and/or activators that manipulate the bioelectric state. Here, we provide an overview of electroceutical design environment (EDEn), the first bioinformatic platform that facilitates the design of such therapeutic strategies. This database includes information on ion channels and ion pumps, linked to known chemical modulators and their properties. The database also provides information about the expression levels of the ion channels in over 100 tissue types. The graphical interface allows the user to readily identify chemical entities that can alter the electrical properties of target cells and tissues.

RegenDbase: a comparative database of noncoding RNA regulation of tissue regeneration circuits across multiple taxa

Regeneration is an endogenous process of tissue repair that culminates in complete restoration of tissue and organ function. While regenerative capacity in mammals is limited to select tissues, lower vertebrates like zebrafish and salamanders are endowed with the capacity to regenerate entire limbs and most adult tissues, including heart muscle. Numerous profiling studies have been conducted using these research models in an effort to identify the genetic circuits that accompany tissue regeneration. Most of these studies, however, are confined to an individual injury model and/or research organism and focused primarily on protein encoding transcripts. Here we describe RegenDbase, a new database with the functionality to compare and contrast gene regulatory pathways within and across tissues and research models. RegenDbase combines pipelines that integrate analysis of noncoding RNAs in combination with protein encoding transcripts. We created RegenDbase with a newly generated comprehensive dataset for adult zebrafish heart regeneration combined with existing microarray and RNA-sequencing studies on multiple injured tissues. In this current release, we detail microRNA-mRNA regulatory circuits and the biological processes these interactions control during the early stages of heart regeneration. Moreover, we identify known and putative novel lncRNAs and identify their potential target genes based on proximity searches. We postulate that these candidate factors underscore robust regenerative capacity in lower vertebrates. RegenDbase provides a systems-level analysis of tissue regeneration genetic circuits across injury and animal models and addresses the growing need to understand how noncoding RNAs influence these changes in gene expression.

MoCha: Molecular Characterization of Unknown Pathways

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

A dynamic architecture of life

In recent decades, a profound conceptual transformation has occurred comprising different areas of biological research, leading to a novel understanding of life processes as much more dynamic and changeable. Discoveries in plants and animals, as well as novel experimental approaches, have prompted the research community to reconsider established concepts and paradigms. This development was taken as an incentive to organise a workshop in May 2014 at the Academia Nazionale dei Lincei in Rome. There, experts on epigenetics, regeneration, neuroplasticity, and computational biology, using different animal and plant models, presented their insights on important aspects of a dynamic architecture of life, which comprises all organisational levels of the organism. Their work demonstrates that a dynamic nature of life persists during the entire existence of the organism and permits animals and plants not only to fine-tune their response to particular environmental demands during development, but underlies their continuous capacity to do so. Here, a synthesis of the different findings and their relevance for biological thinking is presented.

Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.

Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.

Limbform: a functional ontology-based database of limb regeneration experiments

SUMMARY

The ability of certain organisms to completely regenerate lost limbs is a fascinating process, far from solved. Despite the extraordinary published efforts during the past centuries of scientists performing amputations, transplantations and molecular experiments, no mechanistic model exists yet that can completely explain patterning during the limb regeneration process. The lack of a centralized repository to enable the efficient mining of this huge dataset is hindering the discovery of comprehensive models of limb regeneration. Here, we introduce Limbform (Limb formalization), a centralized database of published limb regeneration experiments. In contrast to natural language or text-based ontologies, Limbform is based on a functional ontology using mathematical graphs to represent unambiguously limb phenotypes and manipulation procedures. The centralized database currently contains >800 published limb regeneration experiments comprising many model organisms, including salamanders, frogs, insects, crustaceans and arachnids. The database represents an extraordinary resource for mining the existing knowledge of functional data in this field; furthermore, its mathematical nature based on a functional ontology will pave the way for artificial intelligence tools applied to the discovery of the sought-after comprehensive limb regeneration models.