The epigenome and top-down causation

Higher order interaction analysis quantifies coordination in the epigenome revealing novel biological relationships in Kabuki syndrome

Complex direct and indirect relationships between multiple variables, termed higher order interactions (HOIs), are characteristics of all natural systems. Traditional differential and network analyses fail to account for the omic datasets richness and miss HOIs. We investigated peripheral blood DNA methylation data from Kabuki syndrome type 1 (KS1) and control individuals, identified 2,002 differentially methylated points (DMPs), and inferred 17 differentially methylated regions, which represent only 189 DMPs. We applied hypergraph models to measure HOIs on all the CpGs and revealed differences in the coordination of DMPs with lower entropy and higher coordination of the peripheral epigenome in KS1 implying reduced network complexity. Hypergraphs also capture epigenomic trans-relationships, and identify biologically relevant pathways that escape the standard analyses. These findings construct the basis of a suitable model for the analysis of organization in the epigenome in rare diseases, which can be applied to investigate mechanism in big data.

A new species of Euclymene (Maldanidae, Annelida) from Brazil, with new combinations, and phylogenetic implications for Euclymeninae

Maldanids are tube-building polychaetes, known as bamboo-worms; inhabit diverse marine regions throughout the world. The subfamily Euclymeninae was proposed to include forms with anal and cephalic plates, a funnel-shaped pygidium, and a terminal anus. Euclymene, the type genus of Euclymeninae, has about 18 valid species. Euclymene vidali sp. nov. is defined and members of the species described from Northeastern Brazil. Members of this species have 23 chaetigers, and one pre-pygidial achaetous segment; nuchal grooves extend through three quarters of the cephalic plate, and there is one acicular spine with a denticulate tip. Euclymene africana, and E. watsoni, are here recognized, respectively, as Isocirrus africana comb. nov., and I. watsoni comb. nov. Three monotypic genera are invalid: Macroclymenella, Eupraxillella, and Pseudoclyemene; their species should be recognized as Clymenella stewartensis com. nov., Praxillella antarctica com. nov., and Praxillela quadrilobata com. nov., respectively. An identification key and a comparative table for all species of Euclymene are provided. A comparative table for all genera of Euclymeninae is also furnished. The paraphyletic status of Euclymene and Euclymeninae is discussed. The taxon Maldanoplaca is not code compliant and should only be regarded as an informal name.

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.

Who's who in Magelona: phylogenetic hypotheses under Magelonidae Cunningham & Ramage, 1888 (Annelida: Polychaeta)

Known as shovel head worms, members of Magelonidae comprise a group of polychaetes readily recognised by the uniquely shaped, dorso-ventrally flattened prostomium and paired ventro-laterally inserted papillated palps. The present study is the first published account of inferences of phylogenetic hypotheses within Magelonidae. Members of 72 species of Magelona and two species of Octomagelona were included, with outgroups including members of one species of Chaetopteridae and four of Spionidae. The phylogenetic inferences were performed to causally account for 176 characters distributed among 79 subjects, and produced 2,417,600 cladograms, each with 404 steps. A formal definition of Magelonidae is provided, represented by a composite phylogenetic hypothesis explaining seven synapomorphies: shovel-shaped prostomium, prostomial ridges, absence of nuchal organs, ventral insertion of palps and their papillation, presence of a burrowing organ, and unique body regionation. Octomagelona is synonymised with Magelona due to the latter being paraphyletic relative to the former. The consequence is that Magelonidae is monotypic, such that Magelona cannot be formally defined as associated with any phylogenetic hypotheses. As such, the latter name is an empirically empty placeholder, but because of the binomial name requirement mandated by the International Code of Zoological Nomenclature, the definition is identical to that of Magelonidae. Several key features for future descriptions are suggested: prostomial dimensions, presence/absence of prostomial horns, morphology of anterior lamellae, presence/absence of specialised chaetae, and lateral abdominal pouches. Additionally, great care must be taken to fully describe and illustrate all thoracic chaetigers in descriptions.

Towards a unified framework to study causality in Earth-life systems

There is considerable interest in better understanding how earth processes shape the generation and distribution of life on Earth. This question, at its heart, is one of causation. In this article I propose that at a regional level, earth processes can be thought of as behaving somewhat deterministically and may have an organized effect on the diversification and distribution of species. However, the study of how landscape features shape biology is challenged by pseudocongruent or collinear variables. I demonstrate that causal structures can be used to depict the cause–effect relationships between earth processes and biological patterns using recent examples from the literature about speciation and species richness in montane settings. This application shows that causal diagrams can be used to better decipher the details of causal relationships by motivating new hypotheses. Additionally, the abstraction of this knowledge into structural equation metamodels can be used to formulate theory about relationships within Earth–life systems more broadly. Causal structures are a natural point of collaboration between biologists and Earth scientists, and their use can mitigate against the risk of misassigning causality within studies. My goal is that by applying causal theory through application of causal structures, we can build a systems‐level understanding of what landscape features or earth processes most shape the distribution and diversification of species, what types of organisms are most affected, and why.

Informational architecture across non-living and living collectives

Collective behavior is widely regarded as a hallmark property of living and intelligent systems. Yet, many examples are known of simple physical systems that are not alive, which nonetheless display collective behavior too, prompting simple physical models to often be adopted to explain living collective behaviors. To understand collective behavior as it occurs in living examples, it is important to determine whether or not there exist fundamental differences in how non-living and living systems act collectively, as well as the limits of the intuition that can be built from simpler, physical examples in explaining biological phenomenon. Here, we propose a framework for comparing non-living and living collectives as a continuum based on their information architecture: that is, how information is stored and processed across different degrees of freedom. We review diverse examples of collective phenomena, characterized from an information-theoretic perspective, and offer views on future directions for quantifying living collective behaviors based on their informational structure.

Why is Complexity Science valuable for reaching the goals of the UN 2030 Agenda?

The goals and targets included in the 2030 Agenda compiled by the United Nations want to stimulate action in areas of critical importance for humanity and the Earth. These goals and targets regard everyone on Earth from both the health and economic and social perspectives. Reaching these goals means to deal with Complex Systems. Therefore, Complexity Science is undoubtedly valuable. However, it needs to extend its scope and focus on some specific objectives. This article proposes a development of Complexity Science that will bring benefits for achieving the United Nations' aims. It presents a list of the features shared by all the Complex Systems involved in the 2030 Agenda. It shows the reasons why there are certain limitations in the prediction of Complex Systems' behaviors. It highlights that such limitations raise ethical issues whenever new technologies interfere with the dynamics of Complex Systems, such as human beings and the environment. Finally, new methodological approaches and promising research lines to face Complexity Challenges included in the 2030 Agenda are put forward.

Rethinking the pragmatic systems biology and systems-theoretical biology divide: Toward a complexity-inspired epistemology of systems biomedicine

This paper examines some methodological and epistemological issues underlying the ongoing "artificial" divide between pragmatic-systems biology and systems-theoretical biology. The pragmatic systems view of biology has encountered problems and constraints on its explanatory power because pragmatic systems biologists still tend to view systems as mere collections of parts, not as "emergent realities" produced by adaptive interactions between the constituting components. As such, they are incapable of characterizing the higher-level biological phenomena adequately. The attempts of systems-theoretical biologists to explain these "emergent realities" using mathematics also fail to produce satisfactory results. Given the increasing strategic importance of systems biology, both from theoretical and research perspectives, we suggest that additional epistemological and methodological insights into the possibility of further integration between traditional experimental studies and complex modeling are required. This integration will help to improve the currently underdeveloped pragmatic-systems biology and system-theoretical biology. The "epistemology of complexity," I contend, acts as a glue that connects and integrates different and sometimes opposing viewpoints, perspectives, streams, and practices, thus maintaining intellectual and research coherence of systems research of life. It allows scientists to shift the focus from traditional experimental research to integrated, modeling-based holistic practices capable of providing a comprehensive knowledge of organizing principles of living systems. It also opens the possibility of the development of new practical and theoretical foundations of systems biology to build a better understanding of complex organismic functions.

The Awareness of the Fascial System

Fascia is a cacophony of functions and information, a completely adaptable entropy complex. The fascial system has a solid and a liquid component, acting in a perfect symbiotic synchrony. Each cell communicates with the other cells by sending and receiving signals; this concept is a part of quantum physics and it is known as quantum entanglement: a physical system cannot be described individually, but only as a juxtaposition of multiple systems, where the measurement of a quantity determines the value for other systems. Fascial continuum serves as a target for different manual approaches, such as physiotherapy, osteopathy and chiropractic. Cellular behaviour and the inclusion of quantum physics background are hardly being considered to find out what happens between the operator and the patient during a manual physical contact. The article examines these topics. According to the authors' knowledge, this is the first scientific text to offer manual operators’ new perspectives to understand what happens during palpatory contact. A fascial cell has not only memory but also the awareness of the mechanometabolic information it feels, and it has the anticipatory predisposition in preparing itself for alteration of its natural environment.

Biological information systems: Evolution as cognition-based information management

An alternative biological synthesis is presented that conceptualizes evolutionary biology as an epiphenomenon of integrated self-referential information management. Since all biological information has inherent ambiguity, the systematic assessment of information is required by living organisms to maintain self-identity and homeostatic equipoise in confrontation with environmental challenges. Through their self-referential attachment to information space, cells are the cornerstone of biological action. That individualized assessment of information space permits self-referential, self-organizing niche construction. That deployment of information and its subsequent selection enacted the dominant stable unicellular informational architectures whose biological expressions are the prokaryotic, archaeal, and eukaryotic unicellular forms. Multicellularity represents the collective appraisal of equivocal environmental information through a shared information space. This concerted action can be viewed as systematized information management to improve information quality for the maintenance of preferred homeostatic boundaries among the varied participants. When reiterated in successive scales, this same collaborative exchange of information yields macroscopic organisms as obligatory multicellular holobionts. Cognition-Based Evolution (CBE) upholds that assessment of information precedes biological action, and the deployment of information through integrative self-referential niche construction and natural cellular engineering antecedes selection. Therefore, evolutionary biology can be framed as a complex reciprocating interactome that consists of the assessment, communication, deployment and management of information by self-referential organisms at multiple scales in continuous confrontation with environmental stresses.

Why developmental niche construction is not selective niche construction: and why it matters

In the last decade, niche construction has been heralded as the neglected process in evolution. But niche construction is just one way in which the organism's interaction with and construction of the environment can have potential evolutionary significance. The constructed environment does not just select for, it also produces new variation. Nearly 3 decades ago, and in parallel with Odling-Smee's article ‘Niche-constructing phenotypes', West and King introduced the ‘ontogenetic niche’ to give the phenomena of exogenetic inheritance a formal name. Since then, a range of fields in the life sciences and medicine has amassed evidence that parents influence their offspring by means other than DNA (parental effects), and proposed mechanisms for how heritable variation can be environmentally induced and developmentally regulated. The concept of ‘developmental niche construction’ (DNC) elucidates how a diverse range of mechanisms contributes to the transgenerational transfer of developmental resources. My most central of claims is that whereas the selective niche of niche construction theory is primarily used to explain the active role of the organism in its selective environment, DNC is meant to indicate the active role of the organism in its developmental environment. The paper highlights the differences between the construction of the selective and the developmental niche, and explores the overall significance of DNC for evolutionary theory.

Sequence Data, Phylogenetic Inference, and Implications of Downward Causation

Framing systematics as a field consistent with scientific inquiry entails that inferences of phylogenetic hypotheses have the goal of producing accounts of past causal events that explain differentially shared characters among organisms. Linking observations of characters to inferences occurs by way of why-questions implied by data matrices. Because of their form, why-questions require the use of common-cause theories. Such theories in phylogenetic inferences include natural selection and genetic drift. Selection or drift can explain 'morphological' characters but selection cannot be causally applied to sequences since fitness differences cannot be directly associated with individual nucleotides or amino acids. The relation of selection to sequence data is by way of downward or top-down causation from those phenotypes upon which selection occurs. The application of phylogenetic inference to explain sequence data is thus restricted to instances where drift is the relevant theory; those nucleotides or amino acids that can be explained via downward causation are precluded from inclusion in the data matrix. The restrictions on the inclusion of sequence data in phylogenetic inferences equally apply to species hypotheses, precluding the more restrictive approach known as DNA barcoding. Not being able to discern drift and selection as relevant causal mechanisms can severely constrain the inclusion and explanations of sequence data. Implications of such exclusion are discussed in relation to the requirement of total evidence.

New scaling relation for information transfer in biological networks

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781-4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös-Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties.

Foundational perspectives on causality in large-scale brain networks

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

An automated training paradigm reveals long-term memory in planarians and its persistence through head regeneration

Planarian flatworms are a popular system for research into the molecular mechanisms that enable these complex organisms to regenerate their entire body, including the brain. Classical data suggest that they may also be capable of long-term memory. Thus, the planarian system may offer the unique opportunity to study brain regeneration and memory in the same animal. To establish a system for the investigation of the dynamics of memory in a regenerating brain, we developed a computerized training and testing paradigm that avoided the many issues that confounded previous, manual attempts to train planarians. We then used this new system to train flatworms in an environmental familiarization protocol. We show that worms exhibit environmental familiarization, and that this memory persists for at least 14 days - long enough for the brain to regenerate. We further show that trained, decapitated planarians exhibit evidence of memory retrieval in a savings paradigm after regenerating a new head. Our work establishes a foundation for objective, high-throughput assays in this molecularly tractable model system that will shed light on the fundamental interface between body patterning and stored memories. We propose planarians as key emerging model species for mechanistic investigations of the encoding of specific memories in biological tissues. Moreover, this system is lik ely to have important implications for the biomedicine of stem-cell-derived treatments of degenerative brain disorders in human adults.

Endogenous Voltage Potentials and the Microenvironment: Bioelectric Signals that Reveal, Induce and Normalize Cancer

Cancer may be a disease of geometry: a misregulation of the field of information that orchestrates individual cells' activities towards normal anatomy. Recent work identified molecular mechanisms underlying a novel system of developmental control: bioelectric gradients. Endogenous spatio-temporal differences in resting potential of non-neural cells provide instructive cues for cell regulation and complex patterning during embryogenesis and regeneration. It is now appreciated that these cues are an important layer of the dysregulation of cell: cell interactions that leads to cancer. Abnormal depolarization of resting potential (Vmem) is a convenient marker for neoplasia and activates a metastatic phenotype in genetically-normal cells in vivo. Moreover, oncogene expression depolarizes cells that form tumor-like structures, but is unable to form tumors if this depolarization is artificially prevented by misexpression of hyperpolarizing ion channels. Vmem triggers metastatic behaviors at considerable distance, mediated by transcriptional and epigenetic effects of electrically-modulated flows of serotonin and butyrate. While in vivo data on voltages in carcinogenesis comes mainly from the amphibian model, unbiased genetic screens and network profiling in rodents and human tissues reveal several ion channel proteins as bona fide oncogene and promising targets for cancer drug development. However, we propose that a focus on specific channel genes is just the tip of the iceberg. Bioelectric state is determined by post-translational gating of ion channels, not only from genetically-specified complements of ion translocators. A better model is a statistical dynamics view of spatial Vmem gradients. Cancer may not originate at the single cell level, since gap junctional coupling results in multi-cellular physiological networks with multiple stable attractors in bioelectrical state space. New medical applications await a detailed understanding of the mechanisms by which organ target morphology stored in real-time patterns of ion flows is perceived or mis-perceived by cells. Mastery of somatic voltage gradients will lead to cancer normalization or rebooting strategies, such as those that occur in regenerating and embryonic organs, resulting in transformative advances in basic biology and oncology.

The algorithmic origins of life

Although it has been notoriously difficult to pin down precisely what is it that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and, in particular, that top-down (or downward) causation-where higher levels influence and constrain the dynamics of lower levels in organizational hierarchies-may be a major contributor to the hierarchal structure of living systems. Here, we propose that the emergence of life may correspond to a physical transition associated with a shift in the causal structure, where information gains direct and context-dependent causal efficacy over the matter in which it is instantiated. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.

Analysis method of epigenetic DNA methylation to dynamically investigate the functional activity of transcription factors in gene expression

Background

DNA methylation is a fundamental component of epigenetic modification, which is intimately involved in the regulation of gene expression. One important DNA methylation pathway reduces the abilities of transcription factors to bind to gene promoter regions. Although many experiments have been designed to measure genome-wide DNA methylation levels at high resolution, the meaning of these different DNA methylation levels on transcription factor binding abilities remains poorly understood. We have, therefore, developed a method to quantitatively explore the extent to which DNA methylation levels can significantly reduce or even abolish the binding of certain transcription factors, resulting in reduced or non-expression of flanking genes. This method allows transcription factors that are functionally active in gene expression to be investigated.

Results

The method is based on a general model that depicts the relationship between DNA methylation and transcription factor binding ability based on intrinsic component properties, and the model parameters can be optimized through relative analysis of recognized transcription factor binding status and gene expression profiling. With fixed models, transcription factors functionally active in the regulation of gene expression and affected by epigenetic DNA methylation can be identified and subsequently confirmed. The method identified eleven apparently functionally active transcriptional factors in SH-SY5Y neuroblastoma cells.

Conclusions

Compared with gene regulatory elements, epigenetic modifications are able to change to dynamically respond to signals from physical, biological and social environments. Our proposed method is therefore designed to provide a dynamic assessment to investigate functionally active transcription factors. With the information deduced from our method, we can predict transcription factor binding status in promoter regions to further investigate how a particular gene is regulated by a specific group of transcription factors organized in a particular pattern. This will be helpful in the diagnosis and development of treatment for numerous diseases, including cancer. Although the method only investigates DNA methylation, it has the potential to be applied to more epigenetic factors, such as histone modification.

Top-down causation: an integrating theme within and across the sciences?

This issue of the journal is focused on ‘top-down (downward) causation'. The words in this title, however, already raise or beg many questions. Causation can be of many kinds. They form our ways of ordering our scientific understanding of the world, all the way from the reductive concept of cause as elementary objects exerting forces on each other, through to the more holistic concept of attractors towards which whole systems move, and to adaptive selection taking place in the context of an ecosystem. As for ‘top’ and ‘down’, in the present scientific context, these are clearly metaphorical, as some of the articles in this issue of the journal make clear. Do we therefore know what we are talking about? The meeting at the Royal Society on which this set of papers is based included philosophers as well as scientists, and some of those (Jeremy Butterfield, Barry Loewer, Alan Love, Samir Okasha and Eric Scerri) have contributed articles to this issue. We would like also to thank those (Claus Kiefer, Peter Menzies, Jerome Feldman and David Papineau) who contributed only to the discussion meeting. Their contributions were also valuable, both at the meeting and by influencing the articles that have been written by others. We include a glossary with this introduction, composed by one of us (O'Connor). The clarification of the use of words and their semantic frames is an important role of philosophy, and this was evident in the discussions at the meeting and is now evident in many of the articles published here. Moreover, philosophical analysis is not limited to the papers by the professional philosophers. The idea of top-down causation is intimately related to concepts of emergence; indeed, it is a key factor in strong theories of emergence.