Network Signatures Research Articles

Determining synchronously updated fixed points and attractors in a prototype boolean gene regulation model of diphtheria pathogenesis

This study employs dynamic modeling and simulation to provide theoretical insights into the systemic behaviors underlying diphtheria pathogenesis. A Boolean network model was developed to formalize the hypothesized interactions among eight genes identified in the literature as central to toxin production, immune response, and disease transmission. Computational exploration of the state-space dynamics within this model revealed three distinct attractors, each hypothesized to represent key disease states. Structural analysis of these attractors and their basins of attraction offered insights into network architectures potentially responsible for bistable switches between chronic infection and recovery, endogenous inflammatory oscillations reflective of periodic fever cycles, and modular topologies enabling alternative developmental pathways. These findings demonstrate the utility of Boolean modeling in uncovering organizing principles—such as periodicity, bistability, and evolvability—that govern disease emergence in complex systems. The study highlights testable network signatures that could refine our understanding of diphtheria and similar pathologies, and while preliminary, it underscores the potential of iterative computational and experimental approaches to inform more effective control strategies.

The Phylogenetic Architecture of Recruitment Networks

ABSTRACTAimPlant recruitment involves both stochastic and deterministic processes. Recruits may establish independently or interact nonrandomly with canopy plants. We explore this deterministic aspect by testing whether recruitment patterns are influenced by the phylogenetic history of canopy and recruiting plants. Since the effect of canopy plants in recruitment can be positive (facilitation), negative (competition) or neutral, we also estimated the phylogenetic signal separately for each interaction type. Furthermore, we assessed whether environmental stress influenced the phylogenetic signal, under the expectation that more severe environmental conditions will lead to stronger phylogenetic signatures in network structure.LocationGlobal.Time Period1998–2021.Major Taxa StudiedAngiospermae.MethodsWe analysed recruitment interactions occurring in 133 plant communities included in the RecruitNet database, which encompasses a wide range of biomes and vegetation types. The phylogenetic signal in canopy–recruit interactions was quantified in different dimensions of the recruitment niche, represented by the level of interaction generalisation, and by the taxonomic and evolutionary composition of the group of canopy plants.ResultsWe found significant phylogenetic signals in more networks than expected by chance. Canopies’ evolutionary history influenced facilitative and competitive but not neutral interactions. The phylogenetic signal in the recruitment niche strengthened in arid regions, suggesting that stressful habitats promote the occurrence of conserved recruitment interactions where closely related species recruit in association with closely related canopy species.Main ConclusionsDespite the strong influence of stochastic processes on plant recruitment, evolutionary history plays a significant role in driving the recruitment process, especially in harsh environments. In particular, the historical effect becomes more important when canopy species have a significant impact on the performance of recruits, either through facilitation or competition. More generally, we show that the analysis of different dimensions of the ecological niche can reveal important insights on the functional roles of interacting species.

Synaptic acetylcholine induces sharp wave ripples in the basolateral amygdala through nicotinic receptors.

While the basolateral amygdala (BLA) is critical in the consolidation of emotional memories, mechanisms underlying memory consolidation in this region are not well understood. In the hippocampus, memory consolidation depends upon network signatures termed sharp wave ripples (SWR). These SWRs largely occur during states of awake rest or slow wave sleep and are inversely correlated with cholinergic tone. While high frequency cholinergic stimulation can inhibit SWRs through muscarinic acetylcholine receptors, it is unclear how nicotinic acetylcholine receptors or different cholinergic firing patterns may influence SWR generation. SWRs are also present in BLA in vivo. Interestingly, the BLA receives extremely dense cholinergic inputs, yet the relationship between acetylcholine (ACh) and BLA SWRs is unexplored. Here, using brain slice electrophysiology in male and female mice, we show that brief stimulation of ACh inputs to BLA reliably induces SWRs that resemble those that occur in the BLA in vivo. Repeated ACh-SWRs are induced with single pulse stimulation at low, but not higher frequencies. ACh-SWRs are driven by nicotinic receptors which recruit different classes of local interneurons and trigger glutamate release from external inputs. In total, our findings establish a previously undefined mechanism for SWR induction in the brain. They also challenge the previous notion of neuromodulators as purely modulatory agents gating these events but instead reveal these systems can directly instruct SWR induction with temporal precision. Further, these results intriguingly suggest a new role for the nicotinic system in emotional memory consolidation.

Association of sleep network functional connectivity with hyperexcitability and cognition in Alzheimer’s disease

AbstractBackgroundSleep disturbances are common in Alzheimer's disease (AD) and occur at early stages. Hyperexcitability also arises during sleep and can lead to epileptiform activity and seizures that impact memory consolidation. The underlying mechanisms of sleep disturbances and hyperexcitability in AD pathology remain unclear but are likely associated with changes in brain networks and altered functional connectivity (FC).MethodUsing ambulatory scalp EEG recordings, we examined 33 cognitively normal healthy older adult controls (HC), 36 individuals with early clinical stages of AD without history or risk factors for epilepsy (AD‐NoEp), and 14 individuals with early clinical stages of AD who developed epilepsy related to Alzheimer's (AD‐Ep). The analysis involved analyzing FC measures such as amplitude envelope correlation with leakage correction, coherence‐based measures, and graph theory to examine FC across sleep‐wake stages, frequency bands, and its associations with cognitive performance.ResultWe found specific FC changes in Alzheimer's during sleep stages and frequency bands, including increased gamma connectivity during N2 sleep (N2‐gamma), increased delta connectivity during REM‐delta, and decreased alpha connectivity in N2‐alpha and REM‐alpha. AD‐Ep showed a distinct network signature compared to AD‐NoEp, with changes in sleep‐stage and frequency band combinations not observed in AD‐NoEp and reversal of FC changes associated with AD‐NoEp. Specific measures (N2‐gamma, REM‐alpha, Awake‐theta, and Awake‐delta connectivity) were linked to longitudinal global cognitive decline in AD. Machine learning algorithms using combinations of awake and asleep FC features accurately distinguished AD‐NoEp from HC (AUCroc = 0.905) and AD‐Ep from AD‐NoEp (AUCroc = 0.881). Moroever, we identified "fast cognitive decliners" among AD participants with high classification performance (AUCroc = 0.865).ConclusionThe results highlight specific changes in sleep network functional connectivity in AD, offering insights for disease monitoring and finding therapeutic targets.

TMIC-42. CONQUERING SPP1+ MYELOID-DRIVEN RESISTANCE OF GLIOBLASTOMA TO IL13RA2-TARGETED CAR T CELL THERAPY

Abstract In the evolving landscape of glioblastoma (GBM) therapy, the recent proliferation of clinical trials exploring chimeric antigen receptor (CAR) T cell interventions has garnered attention, although their therapeutic efficacy remains limited. Despite recent progress in elucidating the role of the GBM microenvironment in immunotherapy resistance, particularly regarding intercellular networks and macrophages, significant ambiguities endure, impeding a comprehensive understanding of resistance mechanisms amidst the patient-specific heterogeneity of the tumor microenvironment (TME). In this study, we utilized single-cell RNA sequencing to analyze tumors from 41 glioma patients undergoing IL13Rα2-targeted CAR T cell therapy. Our findings unveiled heightened suppressive extracellular network signatures, particularly SPP-1, predominantly observed within myeloid cells from non-responsive patients. The clinical significance of both SPP1+ myeloid cell abundance and overall expression levels revealed associations with patient outcomes. Subsequently, transcriptomic insights were translated to myeloid cell functions in patients exhibiting high versus low SPP1 expression. SPP1-expressing macrophages displayed diminished antigen presentation, phagocytosis, and cell-mediated cytotoxicity, along with augmented extracellular matrix biogenesis and degradation, iron efflux, and suppressive interleukin pathways. Moreover, SPP1-high macrophages exhibited enhanced ligand-receptor interactions, implying a predisposition towards suppressive activities. Immunometabolism pathways, including lipid and ketone body biogenesis and regeneration, were upregulated in SPP1-expressing macrophages, indicating an increased reliance on non-glycolysis-mediated ATP production. To assess translational implications, the therapeutic efficacy of SPP1 blockade was evaluated in a syngeneic glioma model. Blockade of SPP1 with an anti-SPP1 antibody prior to CAR T cell infusion effectively mitigated the immunosuppressive milieu, reversing resistance in preclinical GBM models to murine IL13Rα2-targeted CAR T cell therapy. Subsequent profiling of tumors post-treatment provided insights into TME alterations following SPP1 blockade therapy. This investigation illuminates the intricate interplay between the TME and CAR T cell therapy resistance in GBM, underscoring SPP1 as a promising therapeutic target to surmount CAR T cell resistance and enhance treatment outcomes.

DISTINCT FUNCTIONAL CEREBRAL HYPERSENSITIVITY NETWORKS DURING INCISIONAL AND INFLAMMATORY PAIN IN RATS

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus modalities processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. GT-analysis has uncovered specific features in pain modality processing that align well with those previously identified in humans. These include areas such as S1, M1, CPu, HC, piriform, and cingulate cortex. Additionally, we have identified unique Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA. This leads to a diminished ability to differentiate between stimulus modalities in both pain models compared to control conditions, while also enhancing aversion processing and descending pain modulation. Our findings further show that different pain entities modulate sensory input through distinct NSPHs. These neuroimaging signatures are an important step toward identifying novel cerebral pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic and diagnostic options, which ultimately can be translated to the clinic.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness.

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patients capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Cortical sites critical to language function act as connectors between language subnetworks

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site’s pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.

Estimating the all-terminal signatures for networks by using deep neural network

Computing the signature of a network is both significant and challenging. Addressing the limitations of existing methods in batch processing of large-scale network signatures, in this paper we propose a novel DNN (Deep Neural Network)-based framework for estimating the all-terminal signature and reliability for networks with varying topologies. Our framework involves constructing a DNN model with four efficient and compact network topological features (the numbers of nodes, and links, the node degrees and the link connectivity) as input features and the signature as the response. Additionally, we propose to estimate the all-terminal network reliability based on the signature estimated by the DNN, termed the two-stage DNN approach, which does not require the link reliability as one of the inputs, resulting in better estimation accuracy and generation performance compared to traditional DNN approaches. A case study is conducted and the results show that the estimation accuracy of our DNN model for the signature is satisfactory, and the two-stage DNN approach for network reliability outperforms existing DNN approaches in the literature.

Characterizing Solar Center-to-limb Radial-velocity Variability with SDO

Stellar photospheric inhomogeneities are a significant source of noise, which currently precludes the discovery of Earth-mass planets orbiting Sun-like stars with the radial-velocity (RV) method. To complement several previous studies that have used ground- and spaced-based facilities to characterize the RV of the Sun, here we characterize the center-to-limb variability (CLV) of solar RVs arising from various solar-surface inhomogeneities observed by the Solar Dynamics Observatory (SDO)/Helioseismic Magnetic Imager and SDO/Atmospheric Imaging Assembly. By using various SDO observables to classify pixels and calculate line-of-sight velocities as a function of pixel classification and limb angle, we show that each identified feature type, including the umbrae and penumbrae of sunspots, quiet-Sun magnetoconvective cells, magnetic network, and plage, exhibit distinct and complex CLV signatures, including a notable limb-angle dependence in the observed suppression of convective blueshift for magnetically active regions. We discuss the observed distributions of velocities by identified region type and limb angle, offer interpretations of the physical phenomena that shape these distributions, and emphasize the need to understand the RV signatures of these regions as astrophysical signals, rather than simple (un)correlated noise processes.

Epigenetic heterogeneity shapes the transcriptional landscape of regional microglia.

Microglia, the innate immune cells in the central nervous system, exhibit distinct transcriptional profiles across brain regions that are important for facilitating their specialized function. There has been recent interest in identifying the epigenetic modifications associated with these distinct transcriptional profiles, as these may improve our understanding of the underlying mechanisms governing the functional specialization of microglia. One obstacle to achieving this goal is the large number of microglia required to obtain a genome-wide profile for a single histone modification. Given the cellular and regional heterogeneity of the brain, this would require pooling many samples which would impede biological applications that are limited by numbers of available animals. To overcome this obstacle, we have adapted a method of chromatin profiling known as Cleavage Under Targets and Tagmentation (CUT&Tag-Direct) to profile histone modifications associated with regional differences in gene expression throughout the brain reward system. Consistent with previous studies, we find that transcriptional profiles of microglia vary by brain region. However, here we report that these regional differences also exhibit transcriptional network signatures specific to each region. Additionally, we find that these region-dependent network signatures are associated with differential deposition of H3K27ac and H3K7me3, and while the H3K27me3 landscape is remarkably stable across brain regions, the H3K27ac landscape is most consistent with the anatomical location of microglia which explain their distinct transcriptional profiles. Altogether, these findings underscore the established role of H3K27me3 in cell fate determination and support the active role of H3K27ac in the dynamic regulation of microglial gene expression. In this study, we report a molecular and computational framework that can be applied to improve our understanding of the role of epigenetic regulation in microglia in both health and disease, using as few as 2,500 cells per histone mark.

Network signatures define consciousness state during focal seizures.

Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally. We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types. We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures. This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.

Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson’s disease

PurposeWhile fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level.MethodsIn the current study, this technique was employed to explore Parkinson’s disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI.ResultsOur findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni−Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni−Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036).ConclusionMetabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration.

High-dimensional longitudinal immune profiling uncovers a dual role of the CXCL9/CXCR3, CXCL13/CXCR5, and CCL11/CCL3 axis in the coupling of immune-related adverse events to immune checkpoint inhibitor response.

2512 Background: Additional characterization is required to understand the immune network signatures and immunophenotypes that link immune-related adverse events (irAEs) to immune checkpoint inhibitor (ICI) therapy responses in cancer patients. Methods: A comprehensive immune profiling analysis was conducted on whole blood and peripheral blood mononuclear cells (PBMC) from 165 oncology patients with various irAEs, including colitis (n=64), myocarditis (n=19), pneumonitis (n=25), arthritis (n=24), and cytokine release syndrome CRS (n=33), and compared to a cohort of 219 cancer patients prior to ICI treatment. Results: Following ICI therapy on cycle 2 day 1 (C2D1), we observed significant increases in IL-6, CCL2, CXCL9, CXCL10, and CXCL13 levels preceding irAEs. CXCL9 was specifically upregulated during the development of colitis, arthritis, CRS, and myocarditis, while CXCL13 increased only in CRS, and CCL3 only in myocarditis and CRS, with no CCL11 upregulation in any irAE. High levels of CXCL9, high CCL11 and low CXCL13 strongly correlated with improved oncologic outcomes in different irAEs. Compared to other cluster, in the high CXCL9 cluster, 5-year overall survival (OS) was significantly improved (HR = 10.26 [95% CI, 1.27-82.82], p = 0.029) in colitis (n=20), in CRS (n=21) (HR = 6.45 [95% CI, 1.60-25.99], p = 0.009), and in arthritis. In the high CCL11 cluster, 5-year OS was significantly improved in pneumonitis (n=15), (HR = 4.04 [95% CI, 1.05-15.46]). Conversely, in myocarditis, the high CXCL13 cluster (n=9) was associated with decreased 2-year OS (HR = 6.05 [95% CI, 1.03-35.48], p = 0.046). It is important to note that the CXCL9-driven immunophenotypes were characterized by low IL-6. Notable observations include CD38 upregulation and large-scale activation of CD8 memory subsets during ICI therapy, concomitant with increased circulation of immature neutrophils at the onset of irAEs diagnosis. This event may increase the migration of TCRγδ, NK, DC, and CD8 cells to tumor sites through CXCR3/CXCL9 interactions. Conclusions: This study unravels intricate connections between immune activation and tumor response. CXCL9, CCL11, and CXCL13 emerge as pivotal biomarkers bridging the gap between ICI therapy effectiveness and distinct irAEs-associated immunophenotypes.

Gut microbial network signatures of early colonizers in preterm neonates with extrauterine growth restriction

BackgroundExtrauterine growth restriction (EUGR) represents a prevalent condition observed in preterm neonates, which poses potential adverse implications for both neonatal development and long-term health outcomes. The manifestation of EUGR has been intricately associated with perturbations in microbial and metabolic profiles. This study aimed to investigate the characteristics of the gut microbial network in early colonizers among preterm neonates with EUGR.MethodsTwenty-nine preterm infants participated in this study, comprising 14 subjects in the EUGR group and 15 in the normal growth (AGA) group. Meconium (D1) and fecal samples were collected at postnatal day 28 (D28) and 1 month after discharge (M1). Subsequently, total bacterial DNA was extracted and sequenced using the Illumina MiSeq system, targeting the V3-V4 hyper-variable regions of the 16S rRNA gene.ResultsThe outcomes of principal coordinates analysis (PCoA) and examination of the microbial network structure revealed distinctive developmental trajectories in the gut microbiome during the initial three months of life among preterm neonates with and without EUGR. Significant differences in microbial community were observed at the D1 (P = 0.039) and M1 phases (P = 0.036) between the EUGR and AGA groups, while a comparable microbial community was noted at the D28 phase (P = 0.414). Moreover, relative to the AGA group, the EUGR group exhibited significantly lower relative abundances of bacteria associated with secretion of short-chain fatty acids, including Lactobacillus (P = 0.041) and Parabacteroides (P = 0.033) at the D1 phase, Bifidobacterium at the D28 phase, and genera Dysgonomonas (P = 0.042), Dialister (P = 0.02), Dorea (P = 0.042), and Fusobacterium (P = 0.017) at the M1 phase.ConclusionOverall, the present findings offer crucial important insights into the distinctive gut microbial signatures exhibited by earlier colonizers in preterm neonates with EUGR. Further mechanistic studies are needed to establish whether these differences are the cause or a consequence of EUGR.

Network-based drug repurposing for schizophrenia

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Electrophysiological signatures of anxiety in Parkinson’s disease

Anxiety is a common non-motor symptom in Parkinson’s disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting-state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N = 18) to both PD non-anxious (PD-NA, N = 50) and HC groups (N = 25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety and followed their progression trend over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and can ultimately support personalized psychiatric follow-up and the development of new therapeutic strategies.

SUMER: A New Family of Lightweight and Traditional Block Ciphers with Multi-Modes

With the recent increase in the risks and attacks facing our daily lives and digital environment around us,the trend towards securing data has become inevitable. Block ciphers play a crucial role in modern crypto-applicationssuch as secure network storage and signatures and are used to safeguard sensitive information. The present paperdevelops a new variant of the symmetric model called SUMER family ciphers with three equivalent modes: lightweight,conventional (traditional), and extended ciphers. SUMER name belongs to one of the oldest civilizations inMesopotamia and stands for Secure Universal Model of Encryption Robust Cipher. The SUMER cipher is based on asimple and robust symmetric structure and involves solid algebraic theories that completely depend on the Galois FieldGF(28). SUMER cipher is designed to work with two involutional structures of the Substitution-Permutation Network(SPN) and Feistel structure. These two involutional structures mean that the same algorithm is used for the encryptionand decryption process, and only the algorithm of the ciphering key is used in reverse order in both structures. TheSUMER lightweight structure is an elegant mode that does not need building an S-Box that requires a large amount ofmemory and a number of electronic logical gates as S-Box construction has been canceled and replaced by the on-flycomputation clue, which does not need a reserved memory for building S-Box. SUMER family ciphers also can work ina traditional mode or as an extended mode with high margin security. This family of ciphers is applicable with multimodes of various utilizations. The proposed ciphers are designed to be byte-oriented, showing good evaluation andresults under several measurement tests for speed, time implementation, and efficiency.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

BackgroundBipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies. MethodsThis study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database. ResultsUsing a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments. LimitationsNon-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes. ConclusionsFurther investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Mathematical-based morphological classification of skin eruptions corresponding to the pathophysiological state of chronic spontaneous urticaria

BackgroundChronic spontaneous urticaria (CSU) is one of the most intractable human-specific skin diseases. However, as no experimental animal model exists, the mechanism underlying disease pathogenesis in vivo remains unclear, making the establishment of a curative treatment challenging.MethodsA novel approach combining mathematical modelling, in vitro experiments and clinical data analysis was used to infer the pathological state of CSU patients from geometric features of the skin eruptions.ResultsBased on our hierarchical mathematical modelling, the eruptions of CSU were classified into five categories, each with distinct histamine, basophils, mast cells and coagulation factors network signatures. The analysis of 105 real CSU patients with this classification by six individual dermatologists achieved 87.6% agreement. Furthermore, our network analysis revealed that the coagulation status likely determines boundary/area pattern of wheals, while the state of spontaneous histamine release from mast cells may contribute to the divergence of size and outline of the eruptions.ConclusionsOur multi-faceted approach was accurate in defining pathophysiological states of disease based on geometric features offering the potential to improve the accuracy of CSU diagnosis and better management of the disease in the clinic.