Deciphering the regulatory networks involved in mild and severe salt stress responses in the roots of wild grapevine Vitis vinifera spp. sylvestris

Transcriptional regulatory networks are pivotal components of plant's response to salt stress. However, plant adaptation strategies varied as a function of stress intensity, which is mainly modulated by climate change. Here, we determined the gene regulatory networks based on transcription factor (TF) TF_gene co-expression, using two transcriptomic data sets generated from the salt-tolerant "Tebaba" roots either treated with 50 mM NaCl (mild stress) or 150 mM NaCl (severe stress). The analysis of these regulatory networks identified specific TFs as key regulatory hubs as evidenced by their multiple interactions with different target genes related to stress response. Indeed, under mild stress, NAC and bHLH TFs were identified as central hubs regulating nitrogen storage process. Moreover, HSF TFs were revealed as a regulatory hub regulating various aspects of cellular metabolism including flavonoid biosynthesis, protein processing, phenylpropanoid metabolism, galactose metabolism, and heat shock proteins. These processes are essentially linked to short-term acclimatization under mild salt stress. This was further consolidated by the protein-protein interaction (PPI) network analysis showing structural and plant growth adjustment. Conversely, under severe salt stress, dramatic metabolic changes were observed leading to novel TF members including MYB family as regulatory hubs controlling isoflavonoid biosynthesis, oxidative stress response, abscisic acid signaling pathway, and proteolysis. The PPI network analysis also revealed deeper stress defense changes aiming to restore plant metabolic homeostasis when facing severe salt stress. Overall, both the gene co-expression and PPI network provided valuable insights on key transcription factor hubs that can be employed as candidates for future genetic crop engineering programs.

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.

Distance-dependent reconfiguration of hubs in Alzheimer's disease: a cross-tissue functional network study

The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimer's disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive network's GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience network's GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.

The organization of individually mapped structural and functional semantic networks in aging adults

Language function in the brain, once thought to be highly localized, is now appreciated as relying on a connected but distributed network. The semantic system is of particular interest in the language domain because of its hypothesized integration of information across multiple cortical regions. Previous work in healthy individuals has focused on group-level functional connectivity (FC) analyses of the semantic system, which may obscure interindividual differences driving variance in performance. These studies also overlook the contributions of white matter networks to semantic function. Here, we identified semantic network nodes at the individual level with a semantic decision fMRI task in 53 typically aging adults, characterized network organization using structural connectivity (SC), and quantified the segregation and integration of the network using FC. Hub regions were identified in left inferior frontal gyrus. The individualized semantic network was composed of three interacting modules: (1) default-mode module characterized by bilateral medial prefrontal and posterior cingulate regions and also including right-hemisphere homotopes of language regions; (2) left frontal module extending dorsally from inferior frontal gyrus to pre-motor area; and (3) left temporoparietal module extending from temporal pole to inferior parietal lobule. FC within Module3 and integration of the entire network related to a semantic verbal fluency task, but not a matched phonological task. These results support and extend the tri-network semantic model (Xu in Front Psychol 8: 1538 1538, 2017) and the controlled semantic cognition model (Chiou in Cortex 103: 100 116, 2018) of semantic function.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Functional Connectivity Differences Between Two Culturally Distinct Prairie Vole Populations: Insights Into the Prosocial Network

BACKGROUND

The goal of this study was to elucidate the fundamental connectivity-resting-state connectivity-within and between nodes in the olfactory and prosocial (PS) cores, which permits the expression of social monogamy in males; and how differential connectivity accounts for differential expression of prosociality and aggression.

METHODS

Using resting-state functional magnetic resonance imaging, we integrated graph theory analysis to compare functional connectivity between two culturally/behaviorally distinct male prairie voles (Microtusochrogaster).

RESULTS

Illinois males display significantly higher levels of prosocial behavior and lower levels of aggression than KI (Kansas dam and Illinois sire) males, which are associated with differences in underlying neural mechanisms and brain microarchitecture. Shared connectivity 1) between the anterior hypothalamic area and the paraventricular nucleus and 2) between the medial preoptic area and bed nucleus of the stria terminalis and the nucleus accumbens core suggests essential relationships required for male prosocial behavior. In contrast, Illinois males displayed higher levels of global connectivity and PS intracore connectivity, a greater role for the bed nucleus of the stria terminalis and anterior hypothalamic area, which were degree connectivity hubs, and greater PS and olfactory intercore connectivity.

CONCLUSIONS

These findings suggest that behavioral differences are associated with PS core degree of connectivity and postsignal induction. This transgenerational system may serve as powerful mental health and drug abuse translational model in future studies.

Variability of regional glucose metabolism and the topology of functional networks in the human brain

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using ¹⁸F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

The role of the default mode network in longitudinal functional brain reorganization of brain gliomas

The study of patients after glioma resection offers a unique opportunity to investigate brain reorganization. It is currently unknown how the whole-brain connectomic profile evolves longitudinally after surgical resection of a glioma and how this may be associated with tumor characteristics and cognitive outcome. In this longitudinal study, we investigate the impact of tumor lateralization and grade on functional connectivity (FC) in highly connected networks, or hubs, and cognitive performance. Twenty-eight patients (17 high-grade, 11 low-grade gliomas) underwent longitudinal pre/post-surgery resting-state fMRI scans and neuropsychological assessments (73 total measures). FC matrices were constructed considering as functional hubs the default mode (DMN) and fronto-parietal networks. No-hubs included primary sensory functional networks and any other no-hubs nodes. Both tumor hemisphere and grade affected brain reorganization post-resection. In right-hemisphere tumor patients, regardless of grade and relative to left-hemisphere gliomas, FC increased longitudinally after the intervention, both in terms of FC within hubs (p_hubs = 0.0004) and FC between hubs and no-hubs (p_hubs-no-hubs = 0.005). Regardless of tumor side, only lower-grade gliomas showed longitudinal FC increases relative to high-grade tumors within a precise hub network, the DMN. The neurocognitive profile was longitudinally associated with spatial features of the connectome, mainly within the DMN. We provide evidence that clinical glioma features, such as lateralization and grade, affect post-surgical longitudinal functional reorganization and cognitive recovery. The data suggest a possible role of the DMN in supporting cognition, providing useful information for prognostic prediction and surgical planning.

DNA damage response proteins synergistically affect the cancer prognosis and resistance

Amplification of oxidative stress can be utilized as a strategy to attenuate cancer progression by instigating apoptosis. However, the duration of positive response to such therapies is limited, as cancer cells eventually develop resistance. The underlying molecular mechanisms of cancer cells to escape apoptosis under oxidative stress is unknown. Employing big data, and its integration with transcriptome, proteome and network analysis in six cancer types revealed system-level interactions between DNA damage response (DDR) proteins, including; DNA damage repair, cell cycle checkpoints and anti-apoptotic proteins. Cancer system biology is used to elucidate mechanisms for cancer progression, but networks defining mechanisms causing resistance is less explored. Using system biology, we identified DDR hubs between G1-S and M phases that were associated with bad prognosis. The increased expression of DDR network was involved in resistance under high oxidative stress. We validated our findings by combining H₂O₂ induced oxidative stress and DDR inhibitors in human lung cancer cells to conclude the necessity of targeting a 'disease-causing network'. Collectively, our work provides insights toward designing strategies for network pharmacology to combat resistance in cancer research.

Gene co-expression analysis identifies modules related to insufficient sleep in humans

BACKGROUND

Insufficient sleep and circadian rhythm disruption may cause cancer, obesity, cardiovascular disease, and cognitive impairment. The underlying mechanisms need to be elucidated.

METHOD

Weighted gene co-expression network analysis (WGCNA) was used to identify co-expressed modules. Connectivity Map tool was used to identify candidate drugs based on top connected genes. R ptestg package was utilized to detected module rhythmicity alteration. A hypergeometric test was used to test the enrichment of insomnia SNP signals in modules. Google Scholar was used to validate the modules and hub genes by literature.

RESULTS

We identified a total of 45 co-expressed modules. These modules were stable and preserved. Eight modules were correlated with sleep restriction duration. Module rhythmicity was disrupted in sleep restriction subjects. Hub genes that involve in insufficient sleep also play important roles in sleep disorders. Insomnia GWAS signals were enriched in six modules. Finally, eight drugs associated with sleep disorders were identified.

CONCLUSION

Systems biology method was used to identify sleep-related modules, hub genes, and candidate drugs. Module rhythmicity was altered in sleep insufficient subjects. Thiamphenicol, lisuride, timolol, and piretanide are novel candidates for sleep disorders.

Understanding neural flexibility from a multifaceted definition

Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.

ComHub: Community predictions of hubs in gene regulatory networks

BACKGROUND

Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.

RESULTS

We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.

CONCLUSIONS

In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla

Measures of resting-state functional connectivity allow the description of neuronal networks in humans and provide a window on brain function in normal and pathological conditions. Characterizing neuronal networks in animals is complementary to studies in humans to understand how evolution has modelled network architecture. The mouse lemur (Microcebus murinus) is one of the smallest and more phylogenetically distant primates as compared to humans. Characterizing the functional organization of its brain is critical for scientists studying this primate as well as to add a link for comparative animal studies. Here, we created the first functional atlas of mouse lemur brain and describe for the first time its cerebral networks. They were classified as two primary cortical networks (somato-motor and visual), two high-level cortical networks (fronto-parietal and fronto-temporal) and two limbic networks (sensory-limbic and evaluative-limbic). Comparison of mouse lemur and human networks revealed similarities between mouse lemur high-level cortical networks and human networks as the dorsal attentional (DAN), executive control (ECN), and default-mode networks (DMN). These networks were however not homologous, possibly reflecting differential organization of high-level networks. Finally, cerebral hubs were evaluated. They were grouped along an antero-posterior axis in lemurs while they were split into parietal and frontal clusters in humans.

Unexpected hubness: a proof-of-concept study of the human connectome using pagerank centrality and implications for intracerebral neurosurgery

INTRODUCTION

Understanding the human connectome by parcellations allows neurosurgeons to foretell the potential effects of lesioning parts of the brain during intracerebral surgery. However, it is unclear whether there exist variations among individuals such that brain regions that are thought to be dispensable may serve as important networking hubs.

METHODS

We obtained diffusion neuroimaging data from two healthy cohorts (OpenNeuro and SchizConnect) and applied a parcellation scheme to them. We ranked the parcellations on average using PageRank centrality in each cohort. Using the OpenNeuro cohort, we focused on parcellations in the lower 50% ranking that displayed top quartile ranking at the individual level. We then queried whether these select parcellations with over 3% prevalence would be reproducible in the same manner in the SchizConnect cohort.

RESULTS

In the OpenNeuro (n = 68) and SchizConnect cohort (n = 195), there were 27.9% and 43.1% of parcellations, respectively, in the lower half of all ranks that displayed top quartile ranks. We noted three outstanding parcellations (L_V6, L_a10p, and L_7PL) in the OpenNeuro cohort that also appeared in the SchizConnect cohort. In the larger Schizconnect cohort, L_V6, L_a10p, and L_7PL had unexpected hubness in 3.08%, 5.13%, and 8.21% of subjects, respectively.

CONCLUSIONS

We demonstrated that lowly-ranked parcellations may serve as important hubs in a subset of individuals, highlighting the importance of studying parcellation ranks at the personalized level in planning supratentorial neurosurgery.

Nonhuman primate meso-circuitry data: a translational tool to understand brain networks across species

The foundation for understanding brain connections and related psychiatric diseases lies in human and animal circuitry studies. In rodents and nonhuman primates (NHPs), axonal tracing methods provide the ground-truth connectivity information of brain circuits, coupled with the ability to experimentally manipulate them when combined with other methods. In humans, neuroimaging approaches have taken the lead for studying connectivity patterns in vivo and the changes in network profiles associated with disease. To integrate knowledge from animal models and humans, a critical question is how similar the animal brains and circuits are to the humans'. In this review, we demonstrate the use of meso-circuitry information from tracing studies in NHPs to understand common network connections across species. We show that the meso-circuitry information help establish homologies of cortical and striatal regions and fiber pathways between rodents and NHPs, facilitate the translation of connections that are detailed in animal models to humans, and can locate critical hubs in large-scale brain networks. This review combines anatomic studies across animal models and imaging studies across NHPs and humans to provide a more comprehensive understanding of the hard-wired connectivity that underlies neuroimaging-derived brain networks.

Kinless hubs are potential target genes in prostate cancer network

Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN → SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.

Temporal flow of hubs and connectivity in the human brain

Hubs in brain network connectivity have previously been observed using neuroimaging techniques and are generally believed to be of pivotal importance to establish and maintain a functional platform on which cognitively meaningful and energy-efficient neuronal communication can occur. However, little is known if hubs are static (i.e. a brain region is always a hub) or if these properties change over time (i.e. brain regions fluctuate in their 'hubness'). To address this question, we introduce two new methodological concepts, the flow of brain connectivity and node penalized shortest paths which are then applied to time-varying functional connectivity fMRI BOLD data. We show that the constellations of active hubs change over time in a non-trivial way and that activity of hubs is dependent on the temporal scale of investigation. Slower fluctuations in the number of active hubs that exceeded the degree expected by chance alone were detected primarily in subcortical structures. Moreover, we observed faster fluctuations in hub activity residing predominately in the default mode network that suggests dynamic events in brain connectivity. Our results suggest that the temporal behavior of connectivity hubs is a multilayered and complex issue where method-specific properties of temporal sensitivity to time-varying connectivity must be taken into account. We discuss our results in relation to the on-going discussion of the existence of discrete and stable states in the resting-brain and the role of network hubs in providing a scaffold for neuronal communication across time.

Computational analysis of microarray data of Arabidopsis thaliana challenged with Alternaria brassicicola for identification of key genes in Brassica

Background

Alternaria blight, a recalcitrant disease caused by Alternaria brassicae and Alternaria brassicicola, has been recognized for significant losses of oilseed crops especially rapeseed-mustard throughout the world. Till date, no resistance source is available against the disease; hence, plant breeding methods cannot be used to develop disease-resistant varieties. Therefore, in the present study, efforts have been made to identify resistance and defense-related genes as well as key components of JA-SA-ET-mediated pathway involved in resistance against Alternaria brasscicola through computational analysis of microarray data and network biology approach. Microarray profiling data from wild type and mutant Arabidopsis plants challenged with Alternaria brassicicola along with control plant were obtained from the Gene Expression Omnibus (GEO) database. The data analysis, including DEGs extraction, functional enrichment, annotation, and network analysis, was used to identify genes associated with disease resistance and defense response.

Results

A total of 2854 genes were differentially expressed in WT9C9; among them, 1327 genes were upregulated and 1527 genes were downregulated. A total of 1159 genes were differentially expressed in JAM9C9; among them, 809 were upregulated and 350 were downregulated. A total of 2516 genes were differentially expressed in SAM9C9; among them, 1355 were upregulated and 1161 were downregulated. A total of 1567 genes were differentially expressed in ETM9C9; among them, 917 were upregulated and 650 were downregulated. Besides, a total of 2965 genes were differentially expressed in contrast WT24C24; among them, 1510 genes were upregulated and 1455 genes were downregulated. A total of 4598 genes were differentially expressed in JAM24C24; among them, 2201 were upregulated and 2397 were downregulated. A total of 3803 genes were differentially expressed in SAM24C24; among them, 1819 were upregulated and 1984 were downregulated. A total of 4164 genes were differentially expressed in ETM24C24; among them, 1895 were upregulated and 2269 were downregulated. The upregulated genes of Arabidopsis thaliana were mapped and annotated with CDS sequences of Brassica rapa obtained from PlantGDB database. Additionally, PPI network of these genes were constructed to investigate the key components of hormone-mediated pathway involved in resistance during pathogenesis.

Conclusion

The obtained information from present study can be used to engineer resistance to Alternaria blight caused by Alternaria brasscicola through molecular breeding or genetic manipulation-based approaches for improving Brassica oilseed productivity.

Connectivity and morphology of hubs of the cerebral structural connectome are associated with brain resilience in AD- and age-related pathology

The physiological basis of resilience to age-associated and AD-typical neurodegenerative pathology is still not well understood. So far, the established resilience factor intelligence has been shown to be associated with white matter network global efficiency, a key constituent of which are highly connected hubs. However, hub properties have also been shown to be impaired in AD. Individual predisposition or vulnerability of hub properties may thus modulate the impact of pathology on cognitive outcome and form part of the physiological basis of resilience. 85 cognitively normal elderly subjects and patients with MCI with DWI, MRI and AV45-PET scans were included from ADNI. We reconstructed the global WM networks in each subject and characterized hub-properties of GM regions using graph theory by calculating regional betweenness centrality. Subsequently, we investigated whether regional GM volume (GMV) and structural WM connectivity (WMC) of more hub-like regions was more associated with resilience, quantified as cognitive performance independent of amyloid burden, tau and WM lesions. Subjects with higher resilience showed higher increased regional GMV and WMC in more hub-like compared to less hub-like GM-regions. Additionally, this association was in some instances further increased at elevated amounts of brain pathology. Higher GMV and WMC of more hub-like regions may contribute more to resilience compared to less hub-like regions, reflecting their increased importance to brain network efficiency, and may thus form part of the neurophysiological basis of resilience. Future studies should investigate the factors leading to higher GMV and WMC of hubs in non-demented elderly with higher resilience.

Circuits, Networks, and Neuropsychiatric Disease: Transitioning From Anatomy to Imaging

Since the development of cellular and myelin stains, anatomy has formed the foundation for understanding circuitry in the human brain. However, recent functional and structural studies using magnetic resonance imaging have taken the lead in this endeavor. These innovative and noninvasive approaches have the advantage of studying connectivity patterns under different conditions directly in the human brain. They demonstrate dynamic and structural changes within and across networks linked to normal function and to a wide range of psychiatric illnesses. However, these indirect methods are unable to link networks to the hardwiring that underlies them. In contrast, anatomic invasive experimental studies can. Following a brief review of prefrontal cortical, anterior cingulate, and striatal connections and the different methodologies used, this article discusses how data from anatomic studies can help inform how hardwired connections are linked to the functional and structural networks identified in imaging studies.

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption.

Intact pancreatic islets and dispersed beta-cells both generate intracellular calcium oscillations but differ in their responsiveness to glucose

Pancreatic islets produce pulses of insulin and other hormones that maintain normal glucose homeostasis. These micro-organs possess exquisite glucose-sensing capabilities, allowing for precise changes in pulsatile insulin secretion in response to small changes in glucose. When communication among these cells is disrupted, precision glucose sensing falters. We measured intracellular calcium patterns in 6-mM-steps between 0 and 16 mM glucose, and also more finely in 2-mM-steps from 8 to 12 mM glucose, to compare glucose sensing systematically among intact islets and dispersed islet cells derived from the same mouse pancreas in vitro. The calcium activity of intact islets was uniformly low (quiescent) below 4 mM glucose and active above 8 mM glucose, whereas dispersed beta-cells displayed a broader activation range (2-to-10 mM). Intact islets exhibited calcium oscillations with 2-to-5-min periods, yet beta-cells exhibited longer 7-10 min periods. In every case, intact islets showed changes in activity with each 6-mM-glucose step, whereas dispersed islet cells displayed a continuum of calcium responses ranging from islet-like patterns to stable oscillations unaffected by changes in glucose concentration. These differences were also observed for 2-mM-glucose steps. Despite the diversity of dispersed beta-cell responses to glucose, the sum of all activity produced a glucose dose-response curve that was surprisingly similar to the curve for intact islets, arguing against the importance of "hub cells" for function. Beta-cells thus retain many of the features of islets, but some are more islet-like than others. Determining the molecular underpinnings of these variations could be valuable for future studies of stem-cell-derived beta-cell therapies.

The development of brain network hubs

Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.

Changed hub and functional connectivity patterns of the posterior fusiform gyrus in chess experts

The hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, the effects of long-term practice on functional network hubs in chess experts are largely undefined. Here, we investigated whether alterations of hubs can be detected in chess experts using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods. We first mapped the whole-brain voxel-wise functional connectivity and calculated the functional connectivity strength (FCS) map in each of the 28 chess players and 27 gender- and age-matched healthy novice players. Whole-brain resting-state functional connectivity analyses for the changed hub areas were conducted to further elucidate the corresponding changes of functional connectivity patterns in chess players. The hub analysis revealed increased FCS in the right posterior fusiform gyrus of the chess players, which was supported by analyses of this area's regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and fractional amplitude of low frequency fluctuations (fALFF). The following functional connectivity analyses revealed increased functional connectivities between the right posterior fusiform gyrus and the visuospatial attention and motor networks in chess players. These findings demonstrate that cognitive expertise has a positive influence on the functions of the brain regions associated with the chess expertise and that increased functional connections might in turn facilitate within and between networks communication for expert behavior to get superior performance.

Predication of hub target genes of differentially expressed microRNAs contributing to Helicobacter pylori infection in gastric non-cancerous tissue

AIM

The main goal of this investigation was to provide an overview on H.pylori effect on gastric tissue via bioinformatics analysis of microarray-identified miRNAs and its target genes.

BACKGROUND

MicroRNAs which control about 30 to 60% of gene expression in human body play a critical role in different cell growth stages. Expression modification of non-coding (NC) RNAs in H.pylori infections requires further investigations to provide better understanding of their roles in the body.

METHODS

GSE54397, the microRNA microarray dataset, was analyzed by GEO2R, the online GEO database for detection of differentially expressed microRNAs and lastly the potential target genes as well as their associated pathways.

RESULTS

A total of 244 miRNAs were detected as differentially expressed (p<0.05 and FC>2) in non-cancerous tissue of gastric with H.pylori infection in comparison with tissues without H.pylori infection. The findings indicated that hub microRNAs and target genes of up-regulated network are KIF9, DCTN3, and CA5BP1 along with hsa-miR-519d, hsa-miR-573, hsa-miR-646, hsa-miR-92a-1, hsa-miR-186, and hsa-miR-892a, respectively. For the down-regulated network, genes of RABGAP1, HSPB11 and microRNAs of hsa-miR-620, hsa-miR-19b-2, hsa-miR-555, and hsa-let-7f-2 were hubs. Most of the up-regulated microRNAs are involved in gastric cancer development while there is no evidence for the down-regulated ones. Yet, all of the hub down-regulated miRNAs are reported to have associations with different kinds of cancer.

CONCLUSION

The introduced hub miRNAs and genes may serve as feasible markers in the mechanisms of H.pylori infection for different kinds of gastric diseases, in particular gastric cancer. However, their role requires further investigations.

Evaluating scenarios of locations and capacities for vaccine storage in Nigeria

Many developing countries still face the prevalence of preventable childhood diseases because their vaccine supply chain systems are inadequate by design or structure to meet the needs of their populations. Currently, Nigeria is evaluating options in the redesign of the country's vaccine supply chain. Using Nigeria as a case study, the objective is to evaluate different regional supply chain scenarios to identify the cost minimizing optimal hub locations and storage capacities for doses of different vaccines to achieve a 100% fill rate. First, we employ a shortest-path optimization routine to determine hub locations. Second, we develop a total cost minimizing routine based on stochastic optimization to determine the optimal capacities at the hubs. This model uses vaccine supply data between 2011 and 2014 provided by Nigeria's National Primary Health Care Development Agency (NPHCDA) on Tuberculosis, Polio, Yellow Fever, Tetanus Toxoid, and Hepatitis B. We find that a two-regional system with no central hub (NC2) cut costs by 23% to achieve a 100% fill rate when compared to optimizing the existing chain of six regions with a central hub (EC6). While the government's leading redesign alternative - no central three-hub system (Gov NC3) - reduces costs by 21% compared with the current EC6, it is more expensive than our NC2 system by 3%. In terms of capacity increases, optimizing the current system requires 42% more capacity than our NC2 system. Although the proposed Gov NC3 system requires the least increase in storage capacity, it requires the most distance to achieve a 100% coverage and about 15% more than our NC2. Overall, we find that improving the current system with a central hub and all its variants, even with optimal regional hub locations, require more storage capacities and are costlier than systems without a central hub. While this analysis prescribes the no central hub with two regions (NC2) as the least cost scenario, it is imperative to note that other configurations have benefits and comparative tradeoffs. Our approach and results offer some guidance for future vaccine supply chain redesigns in countries with similar layouts to Nigeria's.

Estimating the impact of structural directionality: How reliable are undirected connectomes?

Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.

Altered network hub connectivity after acute LSD administration

LSD is an ambiguous substance, said to mimic psychosis and to improve mental health in people suffering from anxiety and depression. Little is known about the neuronal correlates of altered states of consciousness induced by this substance. Limited previous studies indicated profound changes in functional connectivity of resting state networks after the administration of LSD. The current investigation attempts to replicate and extend those findings in an independent sample. In a double-blind, randomized, cross-over study, 100 μg LSD and placebo were orally administered to 20 healthy participants. Resting state brain activity was assessed by functional magnetic resonance imaging. Within-network and between-network connectivity measures of ten established resting state networks were compared between drug conditions. Complementary analysis were conducted using resting state networks as sources in seed-to-voxel analyses. Acute LSD administration significantly decreased functional connectivity within visual, sensorimotor and auditory networks and the default mode network. While between-network connectivity was widely increased and all investigated networks were affected to some extent, seed-to-voxel analyses consistently indicated increased connectivity between networks and subcortical (thalamus, striatum) and cortical (precuneus, anterior cingulate cortex) hub structures. These latter observations are consistent with findings on the importance of hubs in psychopathological states, especially in psychosis, and could underlay therapeutic effects of hallucinogens as proposed by a recent model.

Mapping the changed hubs and corresponding functional connectivity in idiopathic restless legs syndrome

OBJECTIVE

The hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, whether the changed pattern in functional network hubs contributes to the onset of leg discomfort symptoms in restless legs syndrome (RLS) patients remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods, we investigated whether alterations of hubs can be detected in RLS.

METHODS

First, we constructed the whole-brain voxelwise functional connectivity and calculated a functional connectivity strength (FCS) map in each of 16 drug-naive idiopathic RLS patients and 26 gender- and age-matched healthy control (HC) subjects. Next, a two-sample t test was applied to compare the FCS maps between HC and RLS patients, and to identify significant changes in FCS in RLS patients. To further elucidate the corresponding changes in the functional connectivity patterns of the aberrant hubs in RLS patients, whole-brain resting-state functional connectivity analyses for the hub areas were performed.

RESULTS

The hub analysis revealed decreased FCS in the cuneus, fusiform gyrus, paracentral lobe, and precuneus, and increased FCS in the superior frontal gyrus and thalamus in idiopathic drug-naive RLS patients. Subsequent functional connectivity analyses revealed decreased functional connectivity in sensorimotor and visual processing networks and increased functional connectivity in the affective cognitive network and cerebellar-thalamic circuit. Furthermore, the mean FCS value in the superior frontal gyrus was significantly correlated with Hamilton Anxiety Rating Scale scores in RLS patients, and the mean FCS value in the fusiform gyrus was significantly correlated with Hamilton Depression Rating Scale scores.

CONCLUSIONS

These findings may provide novel insight into the pathophysiology of RLS.

Hubs in the human fetal brain network

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Network analysis has identified highly connected regions, or hubs, in the human brain.

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Whether network hubs emerge in utero has yet to be examined.

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We found that fetal hubs were located in both primary and association cortices.

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Interestingly, hubs were identified close to fusiform facial and Wernicke’s areas.

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These putative hubs may be points of vulnerability in fetal brain development.

Advances in neuroimaging and network analyses have lead to discovery of highly connected regions, or hubs, in the connectional architecture of the human brain. Whether these hubs emerge in utero, has yet to be examined. The current study addresses this question and aims to determine the location of neural hubs in human fetuses. Fetal resting-state fMRI data (N = 105) was used to construct connectivity matrices for 197 discrete brain regions. We discovered that within the connectional functional organization of the human fetal brain key hubs are emerging. Consistent with prior reports in infants, visual and motor regions were identified as emerging hub areas, specifically in cerebellar areas. We also found evidence for network hubs in association cortex, including areas remarkably close to the adult fusiform facial and Wernicke areas. Functional significance of hub structure was confirmed by computationally deleting hub versus random nodes and observing that global efficiency decreased significantly more when hubs were removed (p < .001). Taken together, we conclude that both primary and association brain regions demonstrate centrality in network organization before birth. While fetal hubs may be important for facilitating network communication, they may also form potential points of vulnerability in fetal brain development.

In silico identification of genes involved in chronic metabolic acidosis

AIMS

Chronic metabolic acidosis (CMA) refers to increased plasma acidity due to disturbed acid-base equilibrium in human body. CMA leads to many dysfunctions including disorders of intestinal metabolism and barrier functions. The human body responds to these intestinal dysfunctions by creating a compensatory mechanism at genomic level in intestinal epithelial cells. This study was to identify the molecular pathways involved in metabolic dysfunction and compensatory adaptations in intestinal epithelium during CMA.

MAIN METHODS

In silico approaches were utilized to characterize a set of 88 differentially expressed genes (DEGs) from intestinal cells of rat CMA model. Interaction networks were constructed for DEGs by GeneMANIA and hub genes as well as enriched clusters in the network were screened using GLay. Gene Ontology (GO) was used for enriching functions in each cluster.

KEY FINDINGS

Four gene hubs, i.e., trefoil factor 1, 5-hydroxytryptamine (serotonin) receptor 5a, solute carrier family 6 (neurotransmitter transporter), member 11, and glutamate receptor, ionotropic, n-methyl d-aspartate 2b, exhibiting the highest node degree were predicted. Six biologically related gene clusters were also predicted. Functional enrichment of GO terms predicted neurological processes such as neurological system process regulation and nerve impulse transmission which are related to negative and positive regulation of digestive system processes., intestinal motility and absorption and maintenance of gastrointestinal epithelium.

SIGNIFICANCE

The study predicted several important genomic pathways that potentially play significant roles in metabolic disruptions or compensatory adaptations of intestinal epithelium induced by CMA. The results provide a further insight into underlying molecular mechanisms associated with CMA.

Altered structural network organization in cognitively normal individuals with amyloid pathology

Recent findings show that structural network topology is disrupted in Alzheimer's disease (AD), with changes occurring already at the prodromal disease stages. Amyloid accumulation, a hallmark of AD, begins several decades before symptom onset, and its effects on brain connectivity at the earliest disease stages are not fully known. We studied global and local network changes in a large cohort of cognitively healthy individuals (N = 299, Swedish BioFINDER study) with and without amyloid-β (Aβ) pathology (based on cerebrospinal fluid Aβ42/Aβ40 levels). Structural correlation matrices were constructed based on magnetic resonance imaging cortical thickness data. Despite the fact that no significant regional cortical atrophy was found in the Aβ-positive group, this group exhibited an altered global network organization, including decreased global efficiency and modularity. At the local level, Aβ-positive individuals displayed fewer and more disorganized modules as well as a loss of hubs. Our findings suggest that changes in network topology occur already at the presymptomatic (preclinical) stage of AD and may precede detectable cortical thinning.

Executive Dysfunction in Autism Spectrum Disorder Is Associated With a Failure to Modulate Frontoparietal-insular Hub Architecture

BACKGROUND

Comorbid executive dysfunction in autism spectrum disorder (ASD) is a barrier to adaptive functioning, despite remittance of core social-communication symptoms. Network models of ASD address core symptoms but not comorbid executive dysfunction. Following recent demonstrations in healthy adults that, with increasing executive demands, hubs embedded within frontoparietal-insular control networks interact with a more diverse set of networks, we hypothesized that the capability of hubs to do so is perturbed in ASD and predicts executive behavior.

METHODS

Seventy-five 7- to 13-year-old children with ASD (n = 35) and age- and IQ-matched typically developing control subjects (n = 40) completed both a resting-state and a selective attention task functional magnetic resonance imaging session. We assessed changes in the participation coefficient, a graph theory metric indexing hubness, of 264 brain regions comprising 12 functional networks between the two sessions. Parent reported executive impairment in everyday life was measured using the Behavior Rating Inventory of Executive Function.

RESULTS

The participation coefficient of the frontoparietal-insular cortex, including core nodes of the frontoparietal control and salience networks, significantly increased in typically developing children but not in children with ASD during the task relative to rest. Change in frontoparietal-insular participation coefficient predicted Behavior Rating Inventory of Executive Function scores indexing the ability to attend to task-oriented output, plan and organize, and sustain working memory.

CONCLUSIONS

Our results suggest that executive impairments in ASD emerge from a failure of frontoparietal-insular control regions to function as adaptive and integrative hubs in the brain's functional network architecture. Our results also demonstrate the utility of examining dynamic network function for elucidating potential biomarkers for disorders with comorbid executive dysfunction.

Mapping how local perturbations influence systems-level brain dynamics

The human brain exhibits a distinct spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our findings highlight the importance of a periphery-to-core hierarchy to determine the effect of local stimulation on the brain network. This study also provides novel resources to orient empirical work aiming at manipulating functional connectivity using non-invasive brain stimulation.

Connectomic profile and clinical phenotype in newly diagnosed glioma patients

Gliomas are primary brain tumors, originating from the glial cells in the brain. In contrast to the more traditional view of glioma as a localized disease, it is becoming clear that global brain functioning is impacted, even with respect to functional communication between brain regions remote from the tumor itself. However, a thorough investigation of glioma-related functional connectomic profiles is lacking. Therefore, we constructed functional brain networks using functional MR scans of 71 glioma patients and 19 matched healthy controls using the automated anatomical labelling (AAL) atlas and interregional Pearson correlation coefficients. The frequency distributions across connectivity values were calculated to depict overall connectomic profiles and quantitative features of these distributions (full-width half maximum (FWHM), peak position, peak height) were calculated. Next, we investigated the spatial distribution of the connectomic profile. We defined hub locations based on the literature and determined connectivity (1) between hubs, (2) between hubs and non-hubs, and (3) between non-hubs. Results show that patients had broader and flatter connectivity distributions compared to controls. Spatially, glioma patients particularly showed increased connectivity between non-hubs and hubs. Furthermore, connectivity distributions and hub-non-hub connectivity differed within the patient group according to tumor grade, while relating to Karnofsky performance status and progression-free survival. In conclusion, newly diagnosed glioma patients have globally altered functional connectomic profiles, which mainly affect hub connectivity and relate to clinical phenotypes. These findings underscore the promise of using connectomics as a future biomarker in this patient population.

Differential variation patterns between hubs and bottlenecks in human protein-protein interaction networks

BACKGROUND

The identification, description and understanding of protein-protein networks are important in cell biology and medicine, especially for the study of system biology where the focus concerns the interaction of biomolecules. Hubs and bottlenecks refer to the important proteins of a protein interaction network. Until now, very little attention has been paid to differentiate these two protein groups.

RESULTS

By integrating human protein-protein interaction networks and human genome-wide variations across populations, we described the differences between hubs and bottlenecks in this study. Our findings showed that similar to interspecies, hubs and bottlenecks changed significantly more slowly than non-hubs and non-bottlenecks. To distinguish hubs from bottlenecks, we extracted their special members: hub-non-bottlenecks and non-hub-bottlenecks. The differences between these two groups represent what is between hubs and bottlenecks. We found that the variation rate of hubs was significantly lower than that of bottlenecks. In addition, we verified that stronger constraint is exerted on hubs than on bottlenecks. We further observed fewer non-synonymous sites on the domains of hubs than on those of bottlenecks and different molecular functions between them.

CONCLUSIONS

Based on these results, we conclude that in recent human history, different variation patterns exist in hubs and bottlenecks in protein interaction networks. By revealing the difference between hubs and bottlenecks, our results might provide further insights in the relationship between evolution and biological structure.

Identification of hub glycogenes and their nsSNP analysis from mouse RNA-Seq data

Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.

Unravelling the relationship between protein sequence and low-complexity regions entropies: Interactome implications

Low-complexity regions are sub-sequences of biased composition in a protein sequence. The influence of these regions over protein evolution, specific functions and highly interactive capacities is well known. Although protein sequence entropy has been largely studied, its relationship with low-complexity regions and the subsequent effects on protein function remains unclear. In this work we propose a theoretical and empirical model integrating the sequence entropy with local complexity parameters. Our results indicate that the protein sequence entropy is related with the protein length, the entropies inside and outside the low-complexity regions as well as their number and average size. We found a small but significant increment in the sequence entropy of hubs proteins. In agreement with our theoretical model, this increment is highly dependent of the balance between the increment of protein length and average size of the low-complexity regions. Finally, our models and proteins analysis provide evidence supporting that modifications in the average size is more relevant in hubs proteins than changes in the number of low-complexity regions.

Dynamics of the connectome in Huntington's disease: A longitudinal diffusion MRI study

Objectives

To longitudinally investigate the connectome in different stages of Huntington's disease (HD) by applying graph theoretical analysis to diffusion MRI data.

Experimental design

We constructed weighted structural networks and calculated their topological properties. Twenty-two premanifest (preHD), 10 early manifest HD and 24 healthy controls completed baseline and 2 year follow-up scans. We stratified the preHD group based on their predicted years to disease onset into a far (preHD-A) and near (preHD-B) to disease onset group. We collected clinical and behavioural measures per assessment time point.

Principle observations

We found a significant reduction over time in nodal betweenness centrality both in the early manifest HD and preHD-B groups as compared to the preHD-A and control groups, suggesting a decrease of importance of specific nodes to overall network organization in these groups (FDR adjusted ps < 0.05). Additionally, we found a significant longitudinal decrease of the clustering coefficient in preHD when compared to healthy controls (FDR adjusted p < 0.05), which can be interpreted as a reduced capacity for internodal information processing at the local level. Furthermore, we demonstrated dynamic changes to hub-status loss and gain both in preHD and early manifest HD. Finally, we found significant cross-sectional as well as longitudinal relationships between graph metrics and clinical and neurocognitive measures.

Conclusions

This study demonstrates divergent longitudinal changes to the connectome in (pre) HD compared to healthy controls. This provides novel insights into structural correlates associated with clinical and cognitive functions in HD and possible compensatory mechanisms at play in preHD.

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Investigates characteristics of the connectome in Huntington's disease (HD).

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HD patients showed longitudinal changes in their structural connectome.

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Connectome dynamics correlated with changes in clinical and cognitive measures.

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Connectomics provides novel insights into compensatory strategies of the diseased brain.

Specimen Referral Network to Rapidly Scale-Up CD4 Testing: The Hub and Spoke Model for Haiti

OBJECTIVES

Regular and quality CD4 testing is essential to monitor disease progression in people living with HIV. In Haiti, most laboratories have limited infrastructure and financial resources and have relied on manual laboratory techniques. We report the successful implementation of a national specimen referral network to rapidly increase patient coverage with quality CD4 testing while at the same time building infrastructure for referral of additional sample types over time.

METHOD

Following a thorough baseline analysis of facilities, expected workload, patient volumes, cost of technology and infrastructure constraints at health institutions providing care to HIV patients, the Haitian National Public Health Laboratory designed and implemented a national specimen referral network. The specimen referral network was scaled up in a step-wise manner from July 2011 to July 2014.

RESULTS

Fourteen hubs serving a total of 67 healthcare facilities have been launched; in addition, 10 healthcare facilities operate FACSCount machines, 21 laboratories operate PIMA machines, and 11 healthcare facilities are still using manual CD4 tests. The number of health institutions able to access automated CD4 testing has increased from 27 to 113 (315%). Testing volume increased 76% on average. The number of patients enrolled on ART at the first healthcare facilities to join the network increased 182% within 6 months following linkage to the network. Performance on external quality assessment was acceptable at all 14 hubs.

CONCLUSION

A specimen referral network has enabled rapid uptake of quality CD4 testing, and served as a backbone to allow for other future tests to be scaled-up in a similar way.

Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification

Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit.

Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in complex networks

Modeling and topological analysis of networks in biological and other complex systems, must venture beyond the limited consideration of very few network metrics like degree, betweenness or assortativity. A proper identification of informative and redundant entities from many different metrics, using recently demonstrated techniques, is essential. A holistic comparison of networks and growth models is best achieved only with the use of such methods.