Phylum Platyhelminthes belongs to the primitive bilateria group including diverse flatworms with features of triplobalstic unsegmented, acoelomate body, fresh water, terrestrial, free living or parasitic forms. Body shows the absence of circulatory system, use of cilia instead of muscles for locomotion and the presence of protonephridia for excretion. They include four classes of flatworms including Classes Trematoda, Monogenea and Cestoidea which show entirely parasitic existence. The fourth class Turbellaria are mostly aquatic, marine, free living with few pelagic forms.
Planaria is a member of order Tricladida under Class Turbellaria (Table 1) with a small sized body ranging from microscopic to more than 60 cm in length. Turbellarians are characterised by the presence of gland cells located either in the epidermis or completely submerged below the muscles, which provide for adhesion, mucous and other secretions. They are largely carnivorous and prey on invertebrates like protozoans, rotifers, insect larvae, small crustaceans, snails and annelids. Nutrients enter into tissues by diffusion from the central gut. They excrete nitrogen in the form of ammonia that diffuses out of the body and use protonephridia to release water and other waste metabolites as they lack a circulatory system. The Turbellarian nerve cord, subepidermal ring like brain and a nerve net constitute the nervous system. Amongst the Planarian members Schmidtea mediterranea (S. med) and Dugesia japonica (Dj) are the most studied. In Dugesia, the peripheral nerve net is joined by longitudinal cords at regular intervals by transverse cords, thereby showing a highly organised ladder like orientation with hierarchic order and control. The large fresh water Planarians show asexual reproduction by transverse fission and show regeneration. Of the two types of regeneration observed in the Turbellians, Architomy and Paratomy, the former includes transverse fission and thereafter generation of new organs, while in the later, the split occurs perpendicular to the antero-posterior (AP) axis and regeneration of new organs start even before complete separation. In the freshwater Planarians, Architomy (Figure 1) is observed and it finds great importance in biology due to the immense regenerative potential of its totipotent adult stem cells called neoblasts. The uniqueness of the adult stem cell in Planaria arises from its hallmark property to differentiate into different cell lineages . Molecules and pathways in the Planarian regeneration are discussed in Table 2. But the intricate mechanisms controlling the plasticity of the stem cells and homeostasis are not yet known. Although decades of study through morphological and molecular phylogenetic evidences were conducted, yet the evolutionary relationships amongst the members of the phylum were poorly understood. Only recently by the application of high Bayesian posterior probabilities analysis of transcriptomic data, the evolutionary relationship amongst the members of flatworms has been deciphered .
Hidden Markov Models (HMMs) is a type of dynamic Bayesian network, a statistical model that finds tremendous application in pattern recognition. They find wide scale application in computational biology in modelling of data from proteins, Deoxyribonucleic acid (DNA) and Ribonucleic acid (RNA) sequences . Information on the protein family or domain can be inferred at. While profile-HMMs is helpful in analysis and modelling DNA and protein sequences, pair-HMM enables pairwise analysis of DNA and protein sequences but context sensitive HMMs finds application in analysis of RNA sequences . Multiple Sequence Alignment (MSA) is another bioinformatics tool that enables alignment of different protein or nucleic acid sequences similar in length from other members of the group, closely or distantly related species, and infers at the homology and the evolutionary relationships amongst them. Both HMM and MSA have been applied in designing of computational tools, identification of different receptors and other important signalling molecules in Planaria. They also find application in designing of computational tools that is helping our understanding of regeneration experiments, morphology and manipulation experiments in Planaria. In this article we discuss the different computational approaches, databases, data mining tools, analysis tools for transcriptomic data, gene curration strategies employed in understanding of the biology of the model organism Planaria (Table 3).
Despite conventional methods very limited information exists globally on a highly complex organism Planaria. This study particularly finds importance as many questions still remain unknown in Planaria biology. Questions like how and why the system of regeneration, is so unique to Planaria amongst its near relatives, the mechanism of plasticity of stem cells, homeostasis of regeneration, unique characteristics of these adult stem cells in Planaria are thought provoking.
2 Problems in Planaria Research that are Not Well Understood
The current day research in Planaria revolves around the several questions related to the plasticity of stem cells, the molecular mechanisms, signalling mechanisms involved in stem cells and their regulation and homeostasis events, designing networks and generating models to understand regeneration experiments. Despite studies with morphological and molecular phyllogenetic approaches, the diversity within the Planarians and the members of Platyhelminthes phylum are poorly understood. Besides the immune system vital to Planaria and its responses to repair after injury is less understood. Although modern day transcriptomic studies have generated huge data, problems related to their accessibility and appropriate analysis calls in for designing of suitable tools that will help in our understanding of the biology of Planaria.
3 Data Mining Tools and Computational Approaches and their Current Applications in Planaria Research
Although only a few computational strategies and data mining approaches are currently applied to research in Planaria, they have been instrumental in addressing some of the problems in Planaria biology. Text mining tools InterMine and PlanMine designed with automated pipelines, are enabling effective easy access, mining and analysis of Planarian transcriptome sequence data and functions to understand their diversity and interspecies relationships . PlanMine is an integrated query-able database which is used by entering identifiers or keywords for contigs, to search databases like InterPro, SMART, PROSITE, Treefam and gene ontology . InterPro enables protein sequence analysis, predicting domain and categorising into families and predicting domains. PFam enables grouping of protein families, by applying multiple sequence alignment (MSA) and Hidden Markov Models (HMMs). Simple Modular Architecture Research Tool (SMART) database enables protein domain analysis. PROSITE is the biological database for proteins, domain, families. TreeFam is a gene family database and gene ontology (GO) is a database to predict ontology.
PlanMine uses automated pipelines integrating basic local alignment search tool (BLAST) for homology search utilising heuristic algorithm for comparing nucleotide sequences, BLASTX to search protein database NCBI RefSeq using translated nucleotide query, InterProScan suite to search for protein domains and EMBOSS getorf tool for open reading frames (ORF). It uses Reciprocal BLASTP to predict orthologues i.e. genes in different species with common ancestor . Information on the anatomy, habitat, location, distribution and reproductive strategies for the Planarians including Planaria torva (Müller, 1774), Polycelis tenuis (Ijima, 1884), Dendrocoelum lacteum (Müller, 1774), Polycelis nigra (Müller, 1774), Schmidtea mediterranea S2F2 sexual strain, Schmidtea polychroa and transcript information of Schmidtea mediterranea can be mined from this database .
Genome and transcriptome data mining approaches have enabled understanding of the molecules involved in maintaining left-right asymmetry, namely signalling molecules, tumor growth factor-β (TGF-β), Nodal and its downstream effector Paired-like homeodomain transcription factor 2 (also known as pituitary homeobox 2, Pitx) are conserved across all organisms that show spiral cleavage at the third division, grouped under Spiralia including the Planarians .
Despite studies on the immune responses and injury induced repair, the immune system in Planarians remains poorly understood. Studies encompassing strategies of comparative genomic analysis and data mining has made it possible to decipher that Planarian immune system houses homologs of the innate immune system common to all Bilateria which are activated as a result of injury thereafter leading to its repair . Receptor mining and gene curration approaches has enabled the identification of the G protein-coupled receptors (GPCRs), comprising families of Rhodopsin, Glutamate, Adhesion, Secretin and Frizzled which play role in eukaryotic signal transduction, in Planarian Schmidtea mediterranea. With a transmembrane focussed strategy using Hidden Markov Models (HMMs) it has been possible to predict the occurrence of GPCRs in the proteomes of S. mansoni and S. mediterranea, extracting information from the GPCR Database (GPCRDB). The mining strategy and formal schemes of analytical processes is discussed in detail . Such strategies has also made possible the identification of novel receptors, including a large and highly-diverged Platyhelminthes specific Rhodopsin subfamily (PROF1), a Planarian-specific Adhesion-like family (PARF1), and a typical Glutamate-like receptor .
With the help of ImagePlane high-throughput image analysis and information extraction tool based on novel self-parameterizing adaptive thresholding algorithm and automated pipeline, written in Python, identification of morphology of Planaria through observation of wild phenotype and those with morphological alterations mediated by RNAi induced methods has been possible. We refer the reader to the multiresolution algorithm capable of segmenting the image into anterior-posterior and or left-right quadrants ab initio for detailed discussion in the cited article .
Perhaps the most important challenge in computational biology is the development of a tool in a quantitative approach to infer the regulatory networks from experimental data on morphology of Planaria. An in vitro simulation approach aided by a machine learning module is used to build regulatory networks from experimental data . Virtual worms and simulated experiments are tested by network model to understand pattern formation and algorithm is designed to compare the resultant phenotypes from the simulation studies with real data from planarian database. We refer the readers to the cited article  for a detailed discussion on the analytical processes and algorithms. In another study the pattern formation during development is studied  from experimental phenotypic morphological data to determine the regulatory networks based on published results in Planarian regeneration , . Approaches for understanding Planarian morphology from regeneration and manupulation experiments is being tested through a manually currated database and software tool termed as “Planform” . PlanformDB provides information on experiments on Planarian morphologies, regeneration and manipulations. It enables a graphical interface with interactive graphs that enable easy to query and user friendly study of Planaria biology , . The Schmidtea mediterranea Genome Database (SmedGD) is designed by integrating data associated with the Planarian genome, including predicted and annotated genes, expressed sequence tags (ESTs), protein and gene expression patterns and RNA interference (RNAi) phenotypes in a single web portal and is a powerful tool to enable the study of Planaria biology . SmedGD provides data structures compatible with other model organism databases and thus is instrumental in the study of developmental and evolutionary biology, comparative genomics, stem cell biology and regeneration and comparative genomics with other databases like Flybase and Wormbase .
The study of regulatory networks in the development and regeneration of Planaria, is a complex dynamic process that is self-regulated and has been modelled for identifying the underlying control mechanisms responsible for the dynamic regulation in regeneration and growth .
Computational tools have undoubtedly helped immensely in documenting, analysing and recording of high throughput data in biological sciences. Application of computational tools , , , , , , , , , , , ,  in the understanding of the Planaria biology is but a very recent approach developed in the last decade (Figure 1). Even at its infancy, the strategies immensely promise at better understanding of Planaria biology analysing the experimental data. The era of transcriptomics has generated huge data in all sphere of biology and also in Planaria biology, however, their analysis through efficient computational tools and resources are being helpful in deciphering the complexity in Planaria. Still there are many questions in Planaria related to its entire network of functioning of the stem cells, immune system and neurobiology which are not yet understood in a holistic approach in Planaria. The challenge for either experimental or computational biology still remains at targeting these vital questions to understand completely the Planaria biology. Scope remains in designing of the biochemical pathways in Planarians to understand the role in regeneration. The future of this article remains at the designing of an integrated approach of experimental and computational resources towards the understanding of complex biology of Planaria. We propose a unique model, to understand the genes and proteins involved in the regeneration pathway comprising the signalling cascades (Table 2) in the Planarian stem cell or neoblast and their structural-functional correlation with biochemical pathways involved and their comparison with the normal somatic cells, through both experimental and computational approaches, so that the information is not only restricted to the genes and proteins involved in the regeneration but also bears direct correlation of the signalling pathways downstream controlling the Planarian regeneration. Such biomolecules recorded from neoblast cells when compared with the somatic cells would help in the understanding of the uniqueness of pathways in Planarian stem cell. We also propose to incorporate the information on homology of such proteins reported from other known organisms so as to decode the uniqueness of Planarian regeneration biology in structure and function in a systems biology approach.
The author acknowledges the School of Biological Sciences, National Institute of Science Education and Research (NISER), Bhubaneswar, Odisha 752050, India; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India for the support.
Egger B, Lapraz F, Tomiczek B, Müller S, Dessimoz C, Girstmair J, et al. A transcriptomic-phylogenomic analysis of the evolutionary relationships of flatworms. Curr Biol. 2015;25:1347–53. Web of ScienceCrossrefPubMedGoogle Scholar
Peiris TH, Oviedo NJ. Gap junction proteins: master regulators of the planarian stem cell response to tissue maintenance and injury. Biochim Biophys Acta. 2013;1828:109–17. Web of SciencePubMedCrossrefGoogle Scholar
Grande C, Martín-Durán JM, Kenny NJ, Truchado-García M, Hejnol A. Evolution, divergence and loss of the Nodal signalling pathway: new data and a synthesis across the Bilateria. Int J Dev Biol. 2014;58:521–32. CrossrefWeb of ScienceGoogle Scholar
Zamanian M, Kimber MJ, McVeigh P, Carlson SA, Maule AG, Day TA. The repertoire of G protein-coupled receptors in the human parasite Schistosoma mansoni and the model organism Schmidtea mediterranea. BMC Genom. 2011;12:596. CrossrefGoogle Scholar
Flygare S, Campbell M, Ross RM, Moore B, Yandell M. ImagePlane: an automated image analysis pipeline for high-throughput screens using the planarian Schmidtea mediterranea. J Comput Biol. 2013;20:583–92. PubMedCrossrefWeb of ScienceGoogle Scholar
Lobo D, Levin M. Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration. PLoS Comput Biol. 2015;e100429511(6). Web of SciencePubMedGoogle Scholar
Lobo D, Malone TJ, Levin M. Towards a bioinformatics of patterning: a computational approach to understanding regulative morphogenesis. Biol Open. 2013;2:156–69. PubMedCrossrefWeb of ScienceGoogle Scholar
About the article
Published Online: 2017-07-06
Conflict of interest statement: Authors state no conflict of interest. All authors have read the journal’s Publication ethics and publication malpractice statement available at the journal’s website and hereby confirm that they comply with all its parts applicable to the present scientific work.
Citation Information: Journal of Integrative Bioinformatics, Volume 14, Issue 4, 20170007, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2017-0007.
©2017, Shyamasree Ghosh, published by De Gruyter, Berlin/Boston. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0