Construction, comparison and evolution of networks in life sciences and other disciplines

Noncoding RNAs improve the predictive power of network medicine

Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.

Network analysis of cognitive deficit in patients with schizophrenia spectrum disorders

Background

Cognitive impairments are found in 80% of patients with schizophrenia. The severity of these impairments significantly affects the recovery of patients in terms of social functioning.

Network analysis is the most suitable approach for studying complex relationships among cognitive functions.

Aim

To build a network model of neurocognitive functions for identifying both the severity of impairments in individual functions and the vertices central to the whole model.

Methods

The study included 115 patients with schizophrenia and schizophrenia spectrum disorders and a comparison group, comprising 99 healthy subjects. The severity of clinical symptoms was assessed using the PANSS, CDSS and YMRS, and the SAS and BARS for extrapyramidal symptoms and akathisia. Subjects from the comparison group completed screening questionnaires QIDS-SR and PQ-16. Neurocognitive functions were assessed using the BACS.

Results

The patients performed worse than the healthy subjects on all tests. In the cognitive network models of healthy subjects, fewer connections were revealed and the central place was occupied by working memory, the functioning of which depends upon everyday functioning in the community. In the cognitive models of patients there was a greater connectedness of neurocognitive functions. Furthermore, the central place of the networks in patients is occupied by the processing speed, evaluated primarily using the Symbol Coding test, which reflects the dependence of patient activity on lower-order functions.

Conclusion

The processing speed deficit is key to schizophrenia and it may be considered a potential endophenotype of the disease.

Testing cophylogeny between coral reef invertebrates and their bacterial and archaeal symbionts

Marine invertebrates harbour a complex suite of bacterial and archaeal symbionts, a subset of which are probably linked to host health and homeostasis. Within a complex microbiome it can be difficult to tease apart beneficial or parasitic symbionts from nonessential commensal or transient microorganisms; however, one approach is to detect strong cophylogenetic patterns between microbial lineages and their respective hosts. We employed the Procrustean approach to cophylogeny (PACo) on 16S rRNA gene derived microbial community profiles paired with COI, 18S rRNA and ITS1 host phylogenies. Second, we undertook a network analysis to identify groups of microbes that were co-occurring within our host species. Across 12 coral, 10 octocoral and five sponge species, each host group and their core microbiota (50% prevalence within host species replicates) had a significant fit to the cophylogenetic model. Independent assessment of each microbial genus and family found that bacteria and archaea affiliated to Endozoicomonadaceae, Spirochaetaceae and Nitrosopumilaceae have the strongest cophylogenetic signals. Further, local Moran's I measure of spatial autocorrelation identified 14 ASVs, including Endozoicomonadaceae and Spirochaetaceae, whose distributions were significantly clustered by host phylogeny. Four co-occurring subnetworks were identified, each of which was dominant in a different host group. Endozoicomonadaceae and Spirochaetaceae ASVs were abundant among the subnetworks, particularly one subnetwork that was exclusively comprised of these two bacterial families and dominated the octocoral microbiota. Our results disentangle key microbial interactions that occur within complex microbiomes and reveal long-standing, essential microbial symbioses in coral reef invertebrates.

Eye Movements and Cognitive Functioning in Patients With Schizophrenia Spectrum Disorders: Network Analysis

Background: Eye movement parameters are often used during cognitive functioning assessments of patients with psychotic spectrum disorders. It is interesting to compare these oculomotor parameters with cognitive functions, as assessed using psychometric cognitive tests. A network analysis is preferable for understanding complex systems; therefore, the aim of this study was to determine the multidimensional relationships that exist between oculomotor reactions and neurocognition in patients with schizophrenia spectrum disorders. Materials and Methods: A total of 134 subjects (93 inpatients with schizophrenia spectrum disorders (ICD-10) and 41 healthy volunteers) participated in this study. Psychiatric symptom severity was assessed using the Positive and Negative Syndrome Scale, the Calgary Depression Scale for Schizophrenia, and the Young Mania Rating Scale. Extrapyramidal symptoms were assessed using the Simpson-Angus Scale, and akathisia was assessed using the Barnes Akathisia Rating Scale. Eye movements were recorded using an eye-tracker SMI RED 500, and cognitive function was assessed using the Brief Assessment of Cognition in Schizophrenia. The statistical analyses were conducted using Minitab 17 Statistical Software, version 17.2.1. Data visualization and additional analyses were performed in the R 4.0.3 environment, using RStudio V 1.3.1093 software. Results: A network model of neurocognitive and oculomotor functions was constructed for the patients. In the full network (which includes all correlations) the median antisaccade latency value is the central element of the oculomotor domain, and the Symbol Coding test, the Digit Sequencing test, and the Verbal Fluency test are central elements in the neurocognitive domain. Additionally, there were connections between other cognitive and oculomotor functions, except for the antisaccade error latency in the oculomotor domain and the Token Motor Task in the neurocognitive domain. Conclusion: Network analysis provides measurable criteria for the assessment of neurophysiological and neurocognitive abnormalities in patients with schizophrenic spectrum disorders and allows to select key targets for their management and cognitive remediation.

A model of the transition to behavioural and cognitive modernity using reflexively autocatalytic networks

This paper proposes a model of the cognitive mechanisms underlying the transition to behavioural and cognitive modernity in the Upper Palaeolithic using autocatalytic networks. These networks have been used to model life's origins. More recently, they have been applied to the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations of knowledge and experiences play the role of catalytic molecules, the interactions among them (e.g. the forging of new associations or affordances) play the role of reactions, and thought processes are modelled as chains of these interactions. We posit that one or more genetic mutations may have allowed thought to be spontaneously tailored to the situation by modulating the degree of (i) divergence (versus convergence), (ii) abstractness (versus concreteness), and (iii) context specificity. This culminated in persistent, unified autocatalytic semantic networks that bridged previously compartmentalized knowledge and experience. We explain the model using one of the oldest-known uncontested examples of figurative art: the carving of the Hohlenstein-Stadel Löwenmensch, or lion man. The approach keeps track of where in a cultural lineage each innovation appears, and models cumulative change step by step. It paves the way for a broad scientific framework for the origins of both biological and cultural evolutionary processes.

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).