Ultrafast distant wound response is essential for whole-body regeneration.

Injury induces systemic responses, but their functions remain elusive. Mechanisms that can rapidly synchronize wound responses through long distances are also mostly unknown. Using planarian flatworms capable of whole-body regeneration, we report that injury induces extracellular signal-regulated kinase (Erk) activity waves to travel at a speed 10-100 times faster than those in other multicellular tissues. This ultrafast propagation requires longitudinal body-wall muscles, elongated cells forming dense parallel tracks running the length of the organism. The morphological properties of muscles allow them to act as superhighways for propagating and disseminating wound signals. Inhibiting Erk propagation prevents tissues distant to the wound from responding and blocks regeneration, which can be rescued by a second injury to distal tissues shortly after the first injury. Our findings provide a mechanism for long-range signal propagation in large, complex tissues to coordinate responses across cell types and highlight the function of feedback between spatially separated tissues during whole-body regeneration.

Propagation of Protein Aggregation in Neurodegenerative Diseases.

Most common neurodegenerative diseases feature deposition of protein amyloids and degeneration of brain networks. Amyloids are ordered protein assemblies that can act as templates for their own replication through monomer addition. Evidence suggests that this characteristic may underlie the progression of pathology in neurodegenerative diseases. Many different amyloid proteins, including Aß, tau, and α-synuclein, exhibit properties similar to those of infectious prion protein in experimental systems: discrete and self-replicating amyloid structures, transcellular propagation of aggregation, and transmissible neuropathology. This review discusses the contribution of prion phenomena and transcellular propagation to the progression of pathology in common neurodegenerative diseases such as Alzheimer's and Parkinson's. It reviews fundamental events such as cell entry, amplification, and transcellular movement. It also discusses amyloid strains, which produce distinct patterns of neuropathology and spread through the nervous system. These concepts may impact the development of new diagnostic and therapeutic strategies.

Single-cell RNA sequencing data imputation using bi-level feature propagation.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell-cell or gene-gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene-gene and cell-cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.

MOTIVATION: Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes. RESULTS: We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network.

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of ~5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

mHTT Seeding Activity: A Marker of Disease Progression and Neurotoxicity in Models of Huntington's Disease.

Self-propagating, amyloidogenic mutant huntingtin (mHTT) aggregates may drive progression of Huntington's disease (HD). Here, we report the development of a FRET-based mHTT aggregate seeding (FRASE) assay that enables the quantification of mHTT seeding activity (HSA) in complex biosamples from HD patients and disease models. Application of the FRASE assay revealed HSA in brain homogenates of presymptomatic HD transgenic and knockin mice and its progressive increase with phenotypic changes, suggesting that HSA quantitatively tracks disease progression. Biochemical investigations of mouse brain homogenates demonstrated that small, rather than large, mHTT structures are responsible for the HSA measured in FRASE assays. Finally, we assessed the neurotoxicity of mHTT seeds in an inducible Drosophila model transgenic for HTTex1. We found a strong correlation between the HSA measured in adult neurons and the increased mortality of transgenic HD flies, indicating that FRASE assays detect disease-relevant, neurotoxic, mHTT structures with severe phenotypic consequences in vivo.

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets.

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Low-distortion information propagation with noise suppression in swarm networks.

A method for low-distortion (low-dissipation, low-dispersion) information propagation in swarm-type networks with suppression of high-frequency noise is presented. Information propagation in current neighbor-based networks, where each agent seeks to achieve a consensus with its neighbors, is diffusion-like, dissipative, and dispersive and does not reflect the wave-like (superfluidic) behavior seen in nature. However, pure wave-like neighbor-based networks have two challenges: i) It requires additional communication for sharing information about time derivatives and ii) it can lead to information decoherence through noise at high frequencies. The main contribution of this work is to show that delayed self-reinforcement (DSR) by the agents using prior information (e.g., using short-term memory) can lead to the wave-like information propagation at low-frequencies as seen in nature without the need for additional information sharing between the agents. Moreover, it is shown that the DSR can be designed to enable suppression of high-frequency noise transmission while limiting the dissipation and dispersion of (lower-frequency) information content leading to similar (cohesive) behavior of agents. In addition to explaining noise-suppressed wave-like information transfer in natural systems, the result impacts the design of noise-suppressing cohesive algorithms for engineered networks.

Otoacoustic emissions reveal the micromechanical role of organ-of-Corti cytoarchitecture in cochlear amplification.

The intricate, crystalline cytoarchitecture of the mammalian organ of Corti presumably plays an important role in cochlear amplification. As currently understood, the oblique, Y-shaped arrangement of the outer hair cells (OHCs) and phalangeal processes of the Deiters cells serves to create differential "push-pull" forces that drive the motion of the basilar membrane via the spatial feedforward and/or feedbackward of OHC forces. In concert with the cochlear traveling wave, the longitudinal separation between OHC sensing and forcing creates phase shifts that yield a form of negative damping, amplifying waves as they propagate. Unlike active forces that arise and act locally, push-pull forces are inherently directional-whereas forward-traveling waves are boosted, reverse-traveling waves are squelched. Despite their attractions, models based on push-pull amplification must contend with otoacoustic emissions (OAEs), whose existence implies that amplified energy escapes from the inner ear via mechanisms involving reverse traveling waves. We analyze hybrid local/push-pull models to determine the constraints that reflection-source OAEs place on the directionality of cochlear wave propagation. By implementing a special force-mixing control knob, we vary the mix of local and push-pull forces while leaving the forward-traveling wave unchanged. Consistency with stimulus-frequency OAEs requires that the active forces underlying cochlear wave amplification be primarily local in character, contradicting the prevailing view. By requiring that the oblique cytoarchitecture produce predominantly local forces, we reinterpret the functional role of the Y-shaped geometry, proposing that it serves not as a push-pull amplifier, but as a mechanical funnel that spatially integrates local OHC forces.

Speckle-correlation imaging through a kaleidoscopic multimode fiber.

Speckle-correlation imaging techniques are widely used for noninvasive imaging through complex scattering media. While light propagation through multimode fibers and scattering media share many analogies, reconstructing images through multimode fibers from speckle correlations remains an unsolved challenge. Here, we exploit a kaleidoscopic memory effect emerging in square-core multimode fibers and demonstrate fluorescence imaging with no prior knowledge on the fiber. Experimentally, our approach simply requires to translate random speckle patterns at the input of a square-core fiber and to measure the resulting fluorescence intensity with a bucket detector. The image of the fluorescent object is then reconstructed from the autocorrelation of the measured signal by solving an inverse problem. This strategy does not require the knowledge of the fragile deterministic relation between input and output fields, which makes it promising for the development of flexible minimally invasive endoscopes.

Nanotopography modulates intracellular excitable systems through cytoskeleton actuation.

Cellular sensing of most environmental cues involves receptors that affect a signal-transduction excitable network (STEN), which is coupled to a cytoskeletal excitable network (CEN). We show that the mechanism of sensing of nanoridges is fundamentally different. CEN activity occurs preferentially on nanoridges, whereas STEN activity is constrained between nanoridges. In the absence of STEN, waves disappear, but long-lasting F-actin puncta persist along the ridges. When CEN is suppressed, wave propagation is no longer constrained by nanoridges. A computational model reproduces these experimental observations. Our findings indicate that nanotopography is sensed directly by CEN, whereas STEN is only indirectly affected due to a CEN-STEN feedback loop. These results explain why texture sensing is robust and acts cooperatively with multiple other guidance cues in complex, in vivo microenvironments.

Tryptophan residues in TDP-43 and SOD1 modulate the cross-seeding and toxicity of SOD1.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease of motor neurons. Neuronal superoxide dismutase-1 (SOD1) inclusion bodies are characteristic of familial ALS with SOD1 mutations, while a hallmark of sporadic ALS is inclusions containing aggregated WT TAR DNA-binding protein 43 (TDP-43). We show here that co-expression of mutant or WT TDP-43 with SOD1 leads to misfolding of endogenous SOD1 and aggregation of SOD1 reporter protein SOD1G85R-GFP in human cell cultures and promotes synergistic axonopathy in zebrafish. Intriguingly, this pathological interaction is modulated by natively solvent-exposed tryptophans in SOD1 (tryptophan-32) and TDP-43 RNA-recognition motif RRM1 (tryptophan-172), in concert with natively sequestered TDP-43 N-terminal domain tryptophan-68. TDP-43 RRM1 intrabodies reduce WT SOD1 misfolding in human cell cultures, via blocking tryptophan-172. Tryptophan-68 becomes antibody-accessible in aggregated TDP-43 in sporadic ALS motor neurons and cell culture. 5-fluorouridine inhibits TDP-43-induced G85R-GFP SOD1 aggregation in human cell cultures and ameliorates axonopathy in zebrafish, via its interaction with SOD1 tryptophan-32. Collectively, our results establish a novel and potentially druggable tryptophan-mediated mechanism whereby two principal ALS disease effector proteins might directly interact in disease.

Construing the function of N-terminal domain of D29 mycobacteriophage LysA endolysin in phage lytic efficiency and proliferation.

Endolysins produced by bacteriophages hydrolyze host cell wall peptidoglycan to release newly assembled virions. D29 mycobacteriophage specifically infects mycobacteria including the pathogenic Mycobacterium tuberculosis. D29 encodes LysA endolysin, which hydrolyzes mycobacterial cell wall peptidoglycan. We previously showed that LysA harbors two catalytic domains (N-terminal domain [NTD] and lysozyme-like domain [LD]) and a C-terminal cell wall binding domain (CTD). While the importance of LD and CTD in mycobacteriophage biology has been examined in great detail, NTD has largely remained unexplored. Here, to address NTD's significance in D29 physiology, we generated NTD-deficient D29 (D29∆NTD) by deleting the NTD-coding region from D29 genome using CRISPY-BRED. We show that D29∆NTD is viable, but has a longer latent period, and a remarkably reduced burst size and plaque size. A large number of phages were found to be trapped in the host during the D29∆NTD-mediated cell lysis event. Such poor release of progeny phages during host cell lysis strongly suggests that NTD-deficient LysA produced by D29∆NTD, despite having catalytically-active LD, is unable to efficiently lyse host bacteria. We thus conclude that LysA NTD is essential for optimal release of progeny virions, thereby playing an extremely vital role in phage physiology and phage propagation in the environment.

Propagation of Tau Aggregates and Neurodegeneration.

A pathway from the natively unfolded microtubule-associated protein Tau to a highly structured amyloid fibril underlies human Tauopathies. This ordered assembly causes disease and represents the gain of toxic function. In recent years, evidence has accumulated to suggest that Tau inclusions form first in a small number of brain cells, from where they propagate to other regions, resulting in neurodegeneration and disease. Propagation of pathology is often called prion-like, which refers to the capacity of an assembled protein to induce the same abnormal conformation in a protein of the same kind, initiating a self-amplifying cascade. In addition, prion-like encompasses the release of protein aggregates from brain cells and their uptake by neighboring cells. In mice, the intracerebral injection of Tau inclusions induces the ordered assembly of monomeric Tau, followed by its spreading to distant brain regions. Conformational differences between Tau aggregates from transgenic mouse brain and in vitro assembled recombinant protein account for the greater seeding potency of brain aggregates. Short fibrils constitute the major species of seed-competent Tau in the brains of transgenic mice. The existence of multiple human Tauopathies with distinct fibril morphologies has led to the suggestion that different molecular conformers (or strains) of aggregated Tau exist.

Improved drug response prediction by drug target data integration via network-based profiling.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson's correlation coefficient.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data.

With the development of chromosome conformation capture technique, the study of spatial conformation of a genome based on Hi-C technique has made a quantum leap. Previous studies reveal that genomes are folded into hierarchy of three-dimensional (3D) structures associated with topologically associating domains (TADs), and detecting TAD boundaries is of great significance in the chromosome-level analysis of 3D genome architecture. In this paper, we propose a novel TAD identification method, LPAD, which first extracts node correlations from global interactions of chromosomes based on the random walk with restart and then builds an undirected graph from Hi-C contact matrix. Next, LPAD designs a label propagation-based approach to discover communities and generates TADs. Experimental results verify the effectiveness and quality of TAD detections compared with existing methods. Furthermore, experimental evaluation of chromatin immunoprecipitation sequencing data shows that LPAD performs high enrichment of histone modifications remarkably nearby the TAD boundaries, and these results demonstrate LPAD's advantages on TAD identification accuracy.

Explaining slow seizure propagation with white matter tractography.

Epileptic seizures recorded with stereoelectroencephalography (SEEG) can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and SEEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread, and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared to regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-IV). The seizure onset zone was more tract connected to regions of seizure spread than to non-involved regions (p<0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared to non-involved regions. In seizure free patients only, regions of seizure spread were more tract connected to the seizure onset zone than to other regions of spread (p<0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract connected to regions of early spread compared to regions of late spread in seizure free patients only (p<0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone may be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds may be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship may eventually provide insight into selecting the correct targets for epilepsy surgery.