Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood

Relationships between depression, anxiety, and motivation in the real-world: Effects of physical activity and screentime

Background

Mood and anxiety disorders are highly prevalent and comorbid worldwide, with variability in symptom severity that fluctuates over time. Digital phenotyping, a growing field that aims to characterize clinical, cognitive and behavioral features via personal digital devices, enables continuous quantification of symptom severity in the real world, and in real-time.

Methods

In this study, N=114 individuals with a mood or anxiety disorder (MA) or healthy controls (HC) were enrolled and completed 30-days of ecological momentary assessments (EMA) of symptom severity. Novel real-world measures of anxiety, distress and depression were developed based on the established Mood and Anxiety Symptom Questionnaire (MASQ). The full MASQ was also completed in the laboratory (in-lab). Additional EMA measures related to extrinsic and intrinsic motivation, and passive activity data were also collected over the same 30-days. Mixed-effects models adjusting for time and individual tested the association between real-world symptom severity EMA and the corresponding full MASQ sub-scores. A graph theory neural network model (DEPNA) was applied to all data to estimate symptom interactions.

Results

There was overall good adherence over 30-days (MA=69.5%, HC=71.2% completion), with no group difference (t(58)=0.874, p=0.386). Real-world measures of anxiety/distress/depression were associated with their corresponding MASQ measure within the MA group (t's > 2.33, p's < 0.024). Physical activity (steps) was negatively associated with real-world distress and depression (IRRs > 0.93, p's ≤ 0.05). Both intrinsic and extrinsic motivation were negatively associated with real-world distress/depression (IRR's > 0.82, p's < 0.001). DEPNA revealed that both extrinsic and intrinsic motivation significantly influenced other symptom severity measures to a greater extent in the MA group compared to the HC group (extrinsic/intrinsic motivation: t(46) = 2.62, p < 0.02, q FDR < 0.05, Cohen's d = 0.76; t(46) = 2.69, p < 0.01, q FDR < 0.05, Cohen's d = 0.78 respectively), and that intrinsic motivation significantly influenced steps (t(46) = 3.24, p < 0.003, q FDR < 0.05, Cohen's d = 0.94).

Conclusions

Novel real-world measures of anxiety, distress and depression significantly related to their corresponding established in-lab measures of these symptom domains in individuals with mood and anxiety disorders. Novel, exploratory measures of extrinsic and intrinsic motivation also significantly related to real-world mood and anxiety symptoms and had the greatest influencing degree on patients' overall symptom profile. This suggests that measures of cognitive constructs related to drive and activity may be useful in characterizing phenotypes in the real-world.

Evolution, immunity and the emergence of brain superautoantigens

While some autoimmune disorders remain extremely rare, others largely predominate the epidemiology of human autoimmunity. Notably, these include psoriasis, diabetes, vitiligo, thyroiditis, rheumatoid arthritis and multiple sclerosis. Thus, despite the quasi-infinite number of "self" antigens that could theoretically trigger autoimmune responses, only a limited set of antigens, referred here as superautoantigens, induce pathogenic adaptive responses. Several lines of evidence reviewed in this paper indicate that, irrespective of the targeted organ (e.g. thyroid, pancreas, joints, brain or skin), a significant proportion of superautoantigens are highly expressed in the synaptic compartment of the central nervous system (CNS). Such an observation applies notably for GAD65, AchR, ribonucleoproteins, heat shock proteins, collagen IV, laminin, tyrosine hydroxylase and the acetylcholinesterase domain of thyroglobulin. It is also argued that cognitive alterations have been described in a number of autoimmune disorders, including psoriasis, rheumatoid arthritis, lupus, Crohn's disease and autoimmune thyroiditis. Finally, the present paper points out that a great majority of the "incidental" autoimmune conditions notably triggered by neoplasms, vaccinations or microbial infections are targeting the synaptic or myelin compartments. On this basis, the concept of an immunological homunculus, proposed by Irun Cohen more than 25 years ago, is extended here in a model where physiological autoimmunity against brain superautoantigens confers both: i) a crucial evolutionary-determined advantage via cognition-promoting autoimmunity; and ii) a major evolutionary-determined vulnerability, leading to the emergence of autoimmune disorders in Homo sapiens. Moreover, in this theoretical framework, the so called co-development/co-evolution model, both the development (at the scale of an individual) and evolution (at the scale of species) of the antibody and T-cell repertoires are coupled to those of the neural repertoires (i.e. the distinct neuronal populations and synaptic circuits supporting cognitive and sensorimotor functions). Clinical implications and future experimental insights are also presented and discussed.

Hypotheses of the origin of natural antibodies: a glycobiologist's opinion

It is generally accepted that the generation of antibodies proceeds due to immunization of an organism by alien antigens, and the level and affinity of antibodies are directly correlated to the presence of immunogen. At the same time, vast experimental material has been obtained providing evidence of antibodies whose level remains unchanged and affinity is constant during a lifetime. In contrast to the first, adaptive immunoglobulins, the latter are named natural antibodies (nAbs). The nAbs are produced by B1 cells, whereas adaptive Abs are produced by B2. This review summarizes general data on nAbs and presents in more detail data on antigens of carbohydrate origin. Hypotheses on the origin of nAbs and their activation mechanisms are discussed. We present our thoughts on this matter supported by our experimental data on nAbs to glycans.

Reconstructing directed gene regulatory network by only gene expression data

Background

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues.

Results

In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer’s disease; 2. ZNF329 and RB1 significantly regulate those ‘mesenchymal’ gene expression signature genes for brain tumors.

Conclusion

By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.

Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes

Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (D_EPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the D_EPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the D_EPNA correctly captures the network’s hierarchy of influence. Applying D_EPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that D_EPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions.

Seroprofiling at the Candida albicans protein species level unveils an accurate molecular discriminator for candidemia

Serum antibodies to specific Candida proteins have been reported as potential diagnostic biomarkers for candidemia. However, their diagnostic usefulness at the protein species level has hardly been examined. Using serological proteome analysis, we explored the IgG-antibody responses to Candida albicans protein species in candidemia and control patients. We found that 87 discrete protein species derived from 34 unique proteins were IgG-targets, although only 43 of them were differentially recognized by candidemia and control sera. An increase in the speciation of the immunome, connectivity and modularity of antigenic species co-recognition networks, and heterogeneity of antigenic species recognition patterns was associated with candidemia. IgG antibodies to certain discrete protein species were better predictors of candidemia than those to their corresponding proteins. A molecular discriminator delineated from the combined fingerprints of IgG antibodies to two distinct species of phosphoglycerate kinase and enolase accurately classified candidemia and control patients. These results provide new insight into the anti-Candida IgG-antibody response development in candidemia, and demonstrate that an immunoproteomic signature at the molecular level may be useful for its diagnosis. Our study further highlights the importance of defining pathogen-specific antigens at the chemical and molecular level for their potential application as immunodiagnostic reagents or even vaccine candidates.

Dependency Relations among International Stock Market Indices

We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them.

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links

BACKGROUND

Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes.

RESULTS

We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone.

CONCLUSIONS

The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.

A dynamic network model of the similia principle

The use of drugs in high dilutions and the principle of similarity (or "similia") are two basic tenets of homeopathy. However, the plausibility of both is a subject of debate. Although several models have been proposed to explain the similia principle, it can be best understood and appreciated in the framework of complexity science and dynamic systems theory. This work applies a five-node Boolean network to show how self-organization and adaptation are relevant to rationalizing this traditional medical principle. Simulating the trajectories and attractors of the network system in the energy state-space provides a rudimentary and qualitative illustration of how targeted external perturbations can have pathological effects, leading to permanent, self-sustaining alterations. Similarly, changes that conversely enable the system to find its way back to the original state can induce therapeutic effects, by causing specific shifts in attractors when suitable conditions are satisfied. Extrapolating these mechanisms to homeopathy, we can envisage how major changes in the evolution of homeodynamic systems (and, eventually, healing of the entire body) can be achieved through carefully selected remedies that reproduce the whole symptom pattern of the ill state.

Semantic organization in children with cochlear implants: computational analysis of verbal fluency

PURPOSE

Cochlear implants (CIs) enable children with severe and profound hearing impairments to perceive the sensation of sound sufficiently to permit oral language acquisition. So far, studies have focused mainly on technological improvements and general outcomes of implantation for speech perception and spoken language development. This study quantitatively explored the organization of the semantic networks of children with CIs in comparison to those of age-matched normal hearing (NH) peers.

METHOD

Twenty seven children with CIs and twenty seven age- and IQ-matched NH children ages 7-10 were tested on a timed animal verbal fluency task (Name as many animals as you can). The responses were analyzed using correlation and network methodologies. The structure of the animal category semantic network for both groups were extracted and compared.

RESULTS

Children with CIs appeared to have a less-developed semantic network structure compared to age-matched NH peers. The average shortest path length (ASPL) and the network diameter measures were larger for the NH group compared to the CIs group. This difference was consistent for the analysis of networks derived from animal names generated by each group [sample-matched correlation networks (SMCN)] and for the networks derived from the common animal names generated by both groups [word-matched correlation networks (WMCN)].

CONCLUSIONS

The main difference between the semantic networks of children with CIs and NH lies in the network structure. The semantic network of children with CIs is under-developed compared to the semantic network of the age-matched NH children. We discuss the practical and clinical implications of our findings.

Individual and meta-immune networks

Networks can be found everywhere-in technology, in nature and in our bodies. In this paper we present how antigen networks can be used as a model to study network interaction and architecture. Utilizing antigen microarray data of the reactivity of hundreds of antibodies of sera of ten mothers and their newborns, we reconstruct networks, either isotype specific (IgM or IgG) or person specific-mothers or newborns-and investigate the network properties. Such an approach makes it possible to decipher fundamental information regarding the personal immune network state and its unique characteristics. In the current paper we demonstrate how we are successful in studying the interaction between two dependent networks, the maternal IgG repertoire and the one of the offspring, using the concept of meta-network provides essential information regarding the biological phenomenon of cross placental transfer. Such an approach is useful in the study of coupled networks in variety of scientific fields.

The natural autoantibody repertoire in newborns and adults: a current overview

Antibody networks have been studied in the past based on the connectivity between idiotypes and anti-idiotypes-antibodies that bind one another. Here we call attention to a different network of antibodies, antibodies connected by their reactivities to sets of antigens-the antigen-reactivity network. The recent development of antigen microarray chip technology for detecting global patterns of antibody reactivities makes it possible to study the immune system quantitatively using network analysis tools. Here, we review the analyses of IgM and IgG autoantibody reactivities of sera of mothers and their offspring (umbilical cords) to 300 defined self-antigens; the autoantibody reactivities present in cord blood represent the natural autoimmune repertories with which healthy humans begin life and the mothers' reactivities reflect the development of the repertoires in healthy young adults. Comparing the cord and maternal reactivities using several analytic tools led to the following conclusions: (1) The IgG repertoires showed a high correlation between each mother and her newborn; the IgM repertoires of all the cords were very similar and each cord differed from its mother's IgM repertoire. Thus, different humans are born with very similar IgM autoantibodies produced in utero and with unique IgG autoantibodies found in their individual mothers. (2) Autoantibody repertoires appear to be structured into sets of reactivities that are organized into cliques-reactivities to particular antigens are correlated. (3) Autoantibody repertoires are organized as networks of reactivities in which certain key antigen reactivities dominate the network-the dominant antigen reactivities manifest a "causal" relationship to sets of other correlated reactivities. Thus, repertoires of autoantibodies in healthy subjects, the immunological homunculus, are structured in hierarchies of antigen reactivities.

CORRELATIONS AND DEPENDENCIES IN THE GLOBAL FINANCIAL VILLAGE

The high degree of coupling between global financial markets has made the financial village prone to systemic collapses. Here we present a new methodology to assess and quantify inter-market relations. The approach is based on meta-correlations (correlations between the intra-market correlations), and a Dependency Network analysis approach. We investigated the relations between six important world markets — U.S., U.K., Germany, Japan, China and India from January 2000 until December 2010. Our findings show that while the developed Western markets (U.S., U.K., Germany), are highly correlated, the inter-dependencies between these markets and the Eastern markets (India and China) are very volatile and with noticeable maxima at times of global world events. Finally, using the Dependency network approach, we quantify the flow of information between the different markets, and how markets affect each other. We observe that German and U.K. stocks show a large amount of coupling, while other markets are more segmented. These and additional reported findings illustrate that this methodological framework provides a way to quantify interdependencies in the global market and their evolvement, to evaluate the world financial network, and quantify changes in inter-market relations. Such changes can be used as precursors to the agitation of the global financial village.

Introduction to focus issue: mesoscales in complex networks

Although the functioning of real complex networks is greatly determined by modularity, the majority of articles have focused, until recently, on either their local scale structure or their macroscopical properties. However, neither of these descriptions can adequately describe the important features that complex networks exhibit due to their organization in modules. This Focus Issue precisely presents the state of the art on the study of complex networks at that intermediate level. The reader will find out why this mesoscale level has become an important topic of research through the latest advances carried out to improve our understanding of the dynamical behavior of modular networks. The contributions presented here have been chosen to cover, from different viewpoints, the many open questions in the field as different aspects of community definition and detection algorithms, moduli overlapping, dynamics on modular networks, interplay between scales, and applications to biological, social, and technological fields.