Biology subjects

Lyu, B.

Publications and source records attributed to Lyu, B..

27 records · Page 2Linked to original sources

bioRxiv2023

The brain's topographical organization shapes dynamic interaction patterns to support flexible behavior

Understanding how human cognition flexibly supports distinct forms of behavior is a key goal of neuroscience. Adaptive behavior relies on context-specific rules that vary across situations, as well as on stable knowledge gained from experience. However, the mechanisms that allow these influences to be appropriately balanced remain elusive. Here, we show that this cognitive flexibility is partly supported by the topographical organization of the cortex. The frontoparietal control network (FPCN) is located between regions implicated in top-down attention and memory-guided cognition. We hypothesized that the FPCN is topographically divided into discrete systems that support these distinct forms of behavior. These FPCN subsystems exhibit multiple anatomical and functional similarities to their neighboring systems (the dorsal attention network and default mode network respectively). This topographic architecture is also mirrored in the functional patterns that emerge in different situations: the FPCN subnetworks act as a unified system when long-term memory can support behavior, but they segregate into discrete units when working memory, rather than long term memory, is necessary for behavioral control. In this way, the topographic organization of brain function provides crucial insights into how the human cortex supports flexible behavior.

neuroscience↗

bioRxiv2023

A conserved ion channel function of STING mediates non-canonical autophagy and cell death

The cGAS/STING pathway triggers inflammation in response to diverse cellular stresses such as infection, cellular damage, senescence, normal aging, and age-related disease. Besides inflammation, STING also triggers non-canonical autophagy and cell death, the former of which requires the proton pump V-ATPase- mediated LC3 lipidation to single membrane STING vesicles. V-ATPase is known to sense organelle de- acidification in other contexts and recruits the ATG16L1 complex for direct conjugation of LC3/ATG8 to single membranes (CASM). However, it is unclear how STING activates V-ATPase for non-canonical autophagy. Here we report that upon STING activation, the transmembrane domain (TMD) of STING significantly reorganizes and forms an electron-sparse pore in the center. Cellular imaging and in vitro ion flux assays revealed that STING is critical for proton efflux and pH neutralization of Golgi-derived STING vesicles. A chemical ligand of STING, C53, which binds to and blocks the channel of STING strongly inhibited STING-mediated proton flux in vitro and vesicular de-acidification in cells. C53 also abolished STING-dependent LC3 lipidation and cell death. Thus, the ion channel function of STING activates non-canonical autophagy and cell death through vesicle de-acidification.

biochemistry↗

bioRxiv2023

Exploring the Links between G-Quadruplex Structures and Pathogenicity Islands: Insights from Large-Scale Dataset Analysis

The dynamic interplay between guanine-quadruplex (G4) structures and pathogenicity islands (PAIs) represents a captivating area of research with implications for understanding the molecular mechanisms underlying pathogenicity. This study conducted a comprehensive analysis of a large-scale dataset from reported 89 pathogenic strains of bacteria to investigate the potential interactions between G4 structures and PAIs. G4 structures exhibited an uneven and non-random distribution within the PAIs and were consistently conserved within the same pathogenic strains. Additionally, this investigation identified positive correlations between the number and frequency of G4 structures and the GC content across different genomic features, including the genome, promoters, genes, tRNA, and rRNA regions, indicating a potential relationship between G4 structures and the GC-associated regions of the genome. The observed differences in GC content between PAIs and the core genome further highlight the unique nature of PAIs and underlying factors, such as DNA topology. High-confidence G4 structures within regulatory regions of Escherichia coli were identified, modulating the efficiency or specificity of DNA integration events within PAIs. Collectively, these findings pave the way for future research to unravel the intricate molecular mechanisms and functional implications of G4-PAI interactions, thereby advancing our understanding of bacterial pathogenicity and the role of G4 structures in pathogenic diseases.

pathology↗

bioRxiv2022

The Translation Initiation Factor Homolog, eif4e1c, Regulates Cardiomyocyte Metabolism and Proliferation During Heart Regeneration

The eIF4E family of translation initiation factors bind 5 methylated caps and act as the limiting-step for mRNA translation. The canonical eIF4E1A is required for cell viability, yet other related eIF4E families exist and are utilized in specific contexts or tissues. Here, we describe a family called Eif4e1c for which we find roles during heart development and regeneration in zebrafish. The Eif4e1c family is present in all aquatic vertebrates but is lost in all terrestrial species. A core group of amino acids shared over 500 million years of evolution forms an interface along the protein surface, suggesting Eif4e1c functions in a novel pathway. Deletion of eif4e1c in zebrafish caused growth deficits and impaired survival in juveniles. Mutants surviving to adulthood had fewer cardiomyocytes and reduced proliferative responses to cardiac injury. Ribosome profiling of mutant hearts demonstrated changes in translation efficiency of mRNA for genes known to regulate cardiomyocyte proliferation. Although eif4e1c is broadly expressed, its disruption had most notable impact on the heart and at juvenile stages. Our findings reveal context-dependent requirements for translation initiation regulators during heart regeneration.

developmental biology↗

bioRxiv2021

Humans, machines, and language: A deep alignment in underlying computational styles?

A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speakers intended meaning. This rapid process is subject to multi-dimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain.

neuroscience↗

bioRxiv2020

Genetic signatures of human brain structure: A comparison between GWAS and relatedness-based regression

Identifying the genetic variations impacting human brain structure and their further effects on cognitive functions, is important for our understanding of the fundamental bases of cognition. In this study, we take two different approaches to this issue: classical genome-wide association analysis (GWAS) and a relatedness-based regression approach (REL) to search for associations between genotype and brain structural measures of gray matter and white matter. Instead of searching genetic variants by testing the association between a phenotype trait and the genotype of each single-nucleotide polymorphism (SNP) as in GWAS, REL takes advantage of multiple SNPs within a genomic window as a single measure, which potentially find associations wherever the functional SNP is in linkage disequilibrium (LD) with SNPs that have been sampled. We also conducted a simulation analysis to systemically compare GWAS and REL with respect to different levels of LD. Both methods succeed in identifying genetic variations associated with regional and global brain structural measures and tend to give complementary results due to the different aspects of genetic properties used. Simulation results suggest that GWAS outperforms REL when the signal is relatively weak. However, the collective effects due to local LD boost the performance of REL with increasing signal strength, resulting in better performance than GWAS. Our study suggests that the optimal approach may vary across the genome and that pre-testing for LD could allow GWAS to be preferred where LD is high and REL to be used where LD is low, or the local pattern of LD is complex.

genetics↗

bioRxiv2020

Hydrogen evolution and absorption phenomena in plasma membrane of higher plants

Molecular hydrogen (H2) exhibits a wide breadth of botanical effects especially in improving the resistance to abiotic stress. However, the primary target is not clear. Bidirectional hydrogenases have been well studied in microbes and alga, but there is a paucity of research in higher plants. Here, we used real-time spectrophotometry, gas chromatography and H2 sensor to detect the H2-evolving and H2-uptake activity of the plasma membrane (PM) vesicles of higher plants. Net H+, Na+ and K+ fluxes were measured using Non-invasive Micro-test Technology (NMT). We found that the PM vesicles of Vigna radiata hypocotyls and Capsicum annuum stems have the ability of both oxidizing and producing H2. This two-way enzymatic activity coupled with the redox of ferricyanide/ferrocyanide and NADH/NAD+. H2 affected the H+ and Na+ fluxes on the surface of V. radiata hypocotyls. These results demonstrate that the PM vesicles of higher plants have the abilities of H2 evolution and absorption. The bidirectional activities may act as a valve to regulate the energy balance in plant cells.

biochemistry↗

bioRxiv2019

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations -- e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets -- have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

bioinformatics↗

bioRxiv2018

Deep Learning Based Tumor Type Classification Using GeneExpression Data

Differential analysis occupies the most significant portion of the standard practices of RNA-Seq analysis. However, the conventional method is matching the tumor samples to the normal samples, which are both from the same tumor type. The output using such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%, higher than another paper applying GA/KNN method on the same dataset. Based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumor-specific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply convolutional neural network on Pan-Cancer Atlas for classification, and we are also the first to match the significance of classification with the importance of genes. Our experiment results show that our method has a good performance and could also apply in other genomics data.

bioinformatics↗