Biology subjects

He, S.

Publications and source records attributed to He, S..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

bioinformatics↗

bioRxiv2024

Microglial-Mediated Prevention of Axonal Degeneration in the Injured Spinal Cord: Insights from an In Vivo Imaging Study

Microglia, the primary immune cells in the central nervous system, play a critical role in regulating neuronal function and fate through their interaction with neurons. Despite extensive research, the specific functions and mechanisms of microglia-neuron interactions remain incompletely understood. In this study, we demonstrate that microglia establish direct contact with myelinated axons at Nodes of Ranvier in the spinal cord of mice. Under normal physiological conditions, microglia-node contact occurs in a random scanning pattern and is associated with neuronal activity. However, in response to axonal injury, microglia rapidly transform their contact into a robust wrapping form, preventing acute axonal degeneration from extending beyond the nodes. This neuroprotective wrapping behavior of microglia is dependent on the function of their P2Y12 receptors, which may be activated by ATP released through axonal volume-activated anion channels at the nodes. Additionally, voltage-gated sodium channels (NaV) contribute to the interaction between nodes and glial cells following injury, and inhibition of NaV delays axonal degeneration. Through in vivo imaging, our findings reveal a neuroprotective role of microglia during the acute phase of spinal cord injury, achieved through a novel form of neuron-glia interaction.

neuroscience↗

bioRxiv2024

Discovery and heterologous expression of functional 4-O-dimethylallyl-L-tyrosine synthases from lichen-forming fungi.

Fungal DMATS-type aromatic prenyltransferases are a family of biosynthetic enzymes that catalyze the prenylation of a range of aromatic substrates during the biosynthesis of bioactive indole alkaloids, diketopiperazines, and meroterpenoids. Together with their broad substrate scope and soluble nature, this makes DMATS-type prenyltransferases particularly adept for applications in biocatalysis, for example to derivatize aromatic drug leads and improve their bioactivity. Here, we investigated four putative DMATS-type prenyltransferases from lichen-forming fungi, an underexplored group of organisms that produce more than 1,000 unique metabolites. We were able to successfully express two functional lichen prenyltransferases in the heterologous host A. oryzae, which allowed us to identify them as 4-O-dimethylallyltyrosine synthases. Our extensive bioinformatic analysis shows that related lichen prenyltransferases are likely not active on indoles but rather on aromatic polyketides and phenylpropanoids, common metabolites in these organisms. Overall, our work not only provides new insights into fungal DMATS-type prenyltransferases at the family level, but it also enables future efforts aimed at identifying new candidates for biocatalytic transformations of aromatic compounds.

microbiology↗

bioRxiv2024

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently. This framework maps proteins onto a concise CG-scale complex graph, where nodes represent CG beads and edges encode chemically plausible interactions. The GNN-based encoder is tailored to extract high-quality representations from this graph, efficiently capturing the overall properties of the protein complex structure. Extensive experiments on three different downstream PPI property prediction tasks demonstrate that MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of the computational resource consumption. Furthermore, the CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

bioinformatics↗

bioRxiv2024

Intrinsically linked lineage-specificity of transposable elements and lncRNAs reshapes transcriptional regulation species- and tissue-specifically

Unlike protein-coding genes, many mammalian lncRNAs are lineage-specific (LS), but which human and mouse reference lncRNAs are lineage-specific, how they are generated, and whether their regulatory elements are lineage-specific remain unclear. To resolve their origins, properties, and impacts, we systematically classified primate-, simian-, rodent-, human-, and mouse-specific lncRNAs in GENCODE-annotated human and mouse genomes using structure-aware covariance modeling. We predicted their DNA-binding domains (DBDs) and genomic binding sites (DBSs) and traced the origins of embedded transposable elements (TEs). LS TEs contribute to over 84% (human) and 98% (mouse) of LS lncRNAs, 61% and 95% of their DBDs, and 46% and 73% of their DBSs. Analyses of multi-omics data and a developmental process suggest that LS TE-linked lineage-specificity substantially rewires transcriptional regulation and that many evolutionary novelties may be structurally predisposed. These findings bridge the gap between human diseases and mouse models, providing valuable resources for identifying human-specific therapeutic lncRNA targets.

genomics↗

bioRxiv2024

Discovering Genetic Modulators of the Protein Homeostasis System through Multilevel Analysis

Every protein progresses through a natural lifecycle from birth to maturation to death; this process is coordinated by the protein homeostasis system. Environmental or physiological conditions trigger pathways that maintain the homeostasis of the proteome. An open question is how these pathways are modulated to respond to the many stresses that an organism encounters during its lifetime. To address this question, we tested how the fitness landscape changes in response to environmental and genetic perturbations using directed and massively parallel transposon mutagenesis in Caulobacter crescentus. We developed a general computational pipeline for the analysis of gene-by-environment interactions in transposon mutagenesis experiments. This pipeline uses a combination of general linear models (GLMs), statistical knockoffs, and a nonparametric Bayesian statistical model to identify essential genetic network components that are shared across environmental perturbations. This analysis allows us to quantify the similarity of proteotoxic environmental perturbations from the perspective of the fitness landscape. We find that essential genes vary more by genetic background than by environmental conditions, with limited overlap among mutant strains targeting different facets of the protein homeostasis system. We also identified 146 unique fitness determinants across different strains, with 19 genes common to at least two strains, showing varying resilience to proteotoxic stresses. Experiments exposing cells to a combination of genetic perturbations and dual environmental stressors show that perturbations that are quantitatively dissimilar from the perspective of the fitness landscape are likely to have a synergistic effect on the growth defect. Significance StatementThis study provides critical insights into how cells adapt to environmental and genetic challenges affecting protein homeostasis. Using multilevel statistical analysis and transposon mutagenesis, we find that a model organism, Caulobacter crescentus, lacks a universal redundancy mechanism for coping with stress, as evidenced by the limited overlap in essential genes across different environmental and genetic perturbations. Our methods also pinpoint key fitness determinants and enable the prediction of perturbation combinations that synergistically affect cell growth.

systems biology↗

bioRxiv2024

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

biophysics↗

bioRxiv2024

Transcriptional and phenotypic heterogeneity underpinning venetoclax resistance in AML

The venetoclax BCL2 inhibitor in combination with hypomethylating agents represents a cornerstone of induction therapy for older AML patients, unfit for intensive chemotherapy. Like other targeted therapies, venetoclax-based therapies suffer from innate and acquired resistance. While several mechanisms of resistance have been identified, the heterogeneity of resistance mechanism across patient populations is poorly understood. Here we utilized integrative analysis of transcriptomic and ex-vivo drug response data in AML patients to identify four transcriptionally distinct VEN resistant clusters (VR_C1-4), with distinct phenotypic, genetic and drug response patterns. VR_C1 was characterized by enrichment for differentiated monocytic- and cDC-like blasts, transcriptional activation of PI3K-AKT-mTOR signaling axis, and energy metabolism pathways. They showed sensitivity to mTOR and CDK inhibition. VR_C2 was enriched for NRAS mutations and associated with distinctive transcriptional suppression of HOX expression. VR_C3 was characterized by enrichment for TP53 mutations and higher infiltration by cytotoxic T cells. This cluster showed transcriptional expression of erythroid markers, suggesting tumor cells mimicking erythroid differentiation, activation of JAK-STAT signaling, and sensitivity to JAK inhibition, which in a subset of cases synergized with venetoclax. VR_C4 shared transcriptional similarities with venetoclax-sensitive patients, with modest over-expression of interferon signaling. They were also characterized by high rates of DNMT3A mutations. Finally, we projected venetoclax-resistance states onto single cells profiled from a patient who relapsed under venetoclax therapy capturing multiple resistance states in the tumor and shifts in their abundance under venetoclax selection, suggesting that single tumors may consist of cells mimicking multiple VR_Cs contributing to intra-tumor heterogeneity. Taken together, our results provide a strategy to evaluate inter- and intra-tumor heterogeneity of venetoclax resistance mechanisms and provide insights into approaches to navigate further management of patients who failed therapy with BCL2 inhibitors.

cancer biology↗

bioRxiv2024

Human vascular organoids with a mosaic AKT1 mutation recapitulate Proteus syndrome

Vascular malformation, a key clinical phenotype of Proteus syndrome, lacks effective models for pathophysiological study and drug development due to limited patient sample access. To bridge this gap, we built a human vascular organoid model replicating Proteus syndromes vasculature. Using CRISPR/Cas9 genome editing and gene overexpression, we created induced pluripotent stem cells (iPSCs) embodying the Proteus syndrome-specific AKTE17K point mutation for organoid generation. Our findings revealed that AKT overactivation in these organoids resulted in smaller sizes yet increased vascular connectivity, although with less stable connections. This could be due to the significant vasculogenesis induced by AKT overactivation. This phenomenon likely stems from boosted vasculogenesis triggered by AKT overactivation, leading to increased vascular sprouting. Additionally, a notable increase in dysfunctional PDGFR{beta} + mural cells, impaired in matrix secretion, was observed in these AKT-overactivated organoids. The application of AKT inhibitors (ARQ092, AZD5363, or GDC0068) reversed the vascular malformations; the inhibitors effectiveness was directly linked to reduced connectivity in the organoids. In summary, our study introduces an innovative in vitro model combining organoid technology and gene editing to explore vascular pathophysiology in Proteus syndrome. This model not only simulates Proteus syndrome vasculature but also holds potential for mimicking vasculatures of other genetically driven diseases. It represents an advance in drug development for rare diseases, historically plagued by slow progress.

bioengineering↗

bioRxiv2024

Integrative Omics Uncovers Low Tumorous Magnesium Content as A Driver Factor of Colorectal Cancer

Magnesium (Mg) deficiency is associated with increased risk and malignancy of colorectal cancer (CRC), yet the underlying mechanisms remain elusive. Here we used genomic, proteomic, and phosphoproteomic data to elucidate the impact of Mg deficiency on CRC. Genomic analysis identified 160 genes with higher mutation frequencies in Low-Mg tumors, including key driver genes such as KMT2C and ERBB3. Unexpectedly, initiation driver genes of CRC, such as TP53 and APC, displayed higher mutation frequencies in High-Mg tumors. Additionally, proteomics and phosphoproteomics indicated that low tumorous Mg content may activate epithelial-mesenchymal transition (EMT) by modulating inflammation or remodeling the phosphoproteome of cancer cells. Notably, we observed a negative correlation between the phosphorylation of DBN1 at S142 (DBN1S142p) and Mg content. A mutation in S142 to D (DBN1S142D) mimicking DBN1S142p upregulated MMP2 and enhanced cell migration, while treatment with MgCl2 reduced DBN1S142p, thereby reversing this phenotype. Mechanistically, Mg2+ attenuated the DBN1-ACTN4 interaction by decreasing DBN1S142p, which, in turn, enhanced the binding of ACTN4 to F-actin and promoted F-actin polymerization, ultimately reducing MMP2 expression. These findings shed new light on the crucial role of Mg deficiency in CRC progression and suggest that Mg supplementation may offer a promising preventive and therapeutic strategy for CRC.

cancer biology↗

bioRxiv2024

Molecular basis for the transcriptional regulation of an epoxide-based virulence circuit in Pseudomonas aeruginosa

The opportunistic pathogen Pseudomonas aeruginosa infects cystic fibrosis (CF) patient airways and produces a virulence factor Cif that is associated with worse outcomes. Cif is an epoxide hydrolase that reduces cell-surface abundance of the cystic fibrosis transmembrane conductance regulator (CFTR) and sabotages pro-resolving signals. Its expression is regulated by a divergently transcribed TetR family transcriptional repressor. CifR represents the first reported epoxide-sensing bacterial transcriptional regulator, but neither its interaction with cognate operator sequences nor the mechanism of activation has been investigated. Using biochemical and structural approaches, we uncovered the molecular mechanisms controlling this complex virulence operon. We present here the first molecular structures of CifR alone and in complex with operator DNA, resolved in a single crystal lattice. Significant conformational changes between these two structures suggest how CifR regulates the expression of the virulence gene cif. Interactions between the N-terminal extension of CifR with the DNA minor groove of the operator play a significant role in the operator recognition of CifR. We also determined that cysteine residue Cys107 is critical for epoxide sensing and DNA release. These results offer new insights into the stereochemical regulation of an epoxide-based virulence circuit in a critically important clinical pathogen.

biochemistry↗

bioRxiv2023

A high-throughput screening approach to discover potential colorectal cancer chemotherapeutics: Repurposing drugs to disrupt 14-3-3 protein-BAD interactions

Inducing apoptosis in different types of cancer cells is an effective therapeutic strategy. However, the success of existing chemotherapeutics can be compromised by tumor cell resistance and systemic off-target effects. Therefore, the discovery of pro-apoptotic compounds with minimal systemic side-effects is crucial. 14-3-3 proteins are molecular scaffolds that serve as important regulators of cell survival. Our previous study demonstrated that 14-3-3{zeta} can sequester BAD, a pro-apoptotic member of the BCL-2 protein family, in the cytoplasm and prevent its translocation to mitochondria to inhibit the induction of apoptosis. Despite being a critical mechanism of cell survival, it is unclear whether disrupting 14-3-3 protein:BAD interactions could be harnessed as a chemotherapeutic approach. Herein, we established a BRET-based high-throughput drug screening approach (Z-score= 0.52) capable of identifying molecules that can disrupt 14-3-3{zeta}BAD interactions. An FDA-approved drug library containing 1971 compounds was used for screening, and the capacity of identified hits to induce cell death was examined in NIH3T3-fibroblasts and colorectal cancer cell lines, HT-29 and Caco-2. Our in vitro results suggest that terfenadine, penfluridol, and lomitapide could be potentially repurposed for treating colorectal cancer. Moreover, our screening method demonstrates the feasibility of identifying pro-apoptotic agents that can be applied towards conditions where aberrant cell growth or function are key determinants of disease pathogenesis.

cancer biology↗

bioRxiv2023

Molecular definition of the BAK:VDAC2 interaction as a target to manipulate apoptosis

BAK and BAX execute intrinsic apoptosis by permeabilising the mitochondrial outer membrane. Their activity is regulated through interactions with pro-survival BCL-2 family proteins and with non-BCL-2 proteins including the mitochondrial porin VDAC2. VDAC2 is important for bringing both BAK and BAX to mitochondria where they execute their apoptotic function. Despite this important function in apoptosis, whilst interactions with pro-survival family members are well characterised and have culminated in the development of drugs that target these interfaces to induce cancer cell apoptosis, the interaction between BAK and VDAC2 remains largely undefined. Deep scanning mutagenesis coupled with cysteine linkage identified key residues in the interaction between BAK and VDAC2. Obstructive labelling of specific residues in the BH3 domain or hydrophobic groove of BAK disrupted this interaction. Conversely, mutating specific residues in a cytosol-exposed region of VDAC2 stabilised the interaction with BAK, and inhibited BAK apoptotic activity. Thus, this VDAC2-BAK interaction site can potentially be targeted to either inhibit BAK-mediated apoptosis in scenarios where excessive apoptosis contributes to disease, or to promote BAK-mediated apoptosis for cancer therapy.

cell biology↗

bioRxiv2023

Liver Protects Cytoarchitecture, Neuron Viability and Electrocortical Activity in Post-Cardiac Arrest Brain Injury

BACKGROUNDBrain injury is the major reason for patient deaths in victims who survive after cardiac arrest. Clinical studies have shown that the presence of hypoxic hepatitis and pre-cardiac arrest liver disease is associated with increased mortality and inferior neurological recovery. However, how the liver might impact the pathogenesis of post-cardiac arrest is still unknown. METHODSAn in vivo global cerebral ischemia model was established to assess how simultaneous liver ischemia affected the recovery of brain ischemic injury. In addition, an ex vivo brain normothermic machine perfusion (NMP) model was established to evaluate how addition of a functioning liver might impact the circulation, cytoarchitecture, neuron viability and electrocortical activity of the reperfused brain post-cardiac arrest. RESULTSIn the in vivo model, we observed a larger infarct area in the frontal lobe, elevated tissue injury scores in the CA1 region, as well as increased intravascular immune cell adhesion in the reperfused brains with hepatic ischemia, compared to those without simultaneous hepatic ischemia. The results of the ex vivo model demonstrated that the addition of a functioning liver to the brain NMP circuit significantly reduced post-cardiac arrest brain injury, increased neuronal viability and improved electrocortical activity. Furthermore, we observed significant alterations in gene expressions and metabolites in the presence or absence of hepatic ischemia. CONCLUSIONSOur research highlights the crucial role of the liver in the pathogenesis of post-cardiac arrest brain injury. These findings shed lights on a cardio-pulmonary-hepatic brain resuscitation strategy for patients with cardiac arrest.

neuroscience↗

bioRxiv2023

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures

Turnover numbers (kcat), which indicate an enzymes catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) architectures. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D structures. Consequently, our model exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes kcat values with superior accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

bioinformatics↗

bioRxiv2023

CRISPR-HOLMES-based NAD+ detection

Studies have indicated that intracellular nicotinamide adenine dinucleotide (NAD+) level is associated with the occurrence and development of many diseases. While traditional NAD+ detection techniques are time-consuming and may require large and expensive instruments. We recently found that the CRISPR-Cas12a protein can be inactivated by AcrVA5-mediated acetylation and reactivated by CobB, using NAD+ as the co-factor. Therefore, here in this study, we created a CRISPR-Cas12a-based one-step HOLMES(NAD) system for rapid and convenient NAD+ detection with the employment of both acetylated Cas12a and CobB. In HOLMES(NAD), acetylated Cas12a losses its trans-cleavage activates and can be reactivated by CobB at the presence of NAD+, cutting ssDNA reporters to generate fluorescence signals. HOLMES(NAD) shows both sensitivity and specificity in NAD+ detection and can be used for quantitative determination of intracellular NAD+ concentrations. Therefore, HOLMES(NAD) not only provides a convenient and rapid approach for target NAD+ quantitation but also expands the application scenarios of HOLMES to non-nucleic acid detection.

molecular biology↗

bioRxiv2023

Tuning the brakes: Modulatory role of transcranial random noise stimulation on inhibition

BackgroundEveryday decision-making requires the ability to flexibly modify and sometimes terminate our actions, such as avoiding a tempting slice of cake to hitting the brakes in an emergency. Neural oscillations, such as beta-band rhythms observed over the medial prefrontal cortex(mPFC), help regulate these context-dependent behaviours. However, how random noise stimulation would modulate neural rhythms and corresponding inhibitory behaviors remain understudied. ObjectivesTo target the mPFC using random noise stimulation and modulate neural activity underlying inhibitory behaviours. MethodsUsing a single-blinded within-subject design, fifteen participants received random noise or sham stimulation in a pseudo-randomized order while performing a Go/Conflict/No-Go task. We measured neural activity and behavior before and after stimulation. ResultsWe show that random noise stimulation significantly improved inhibitory behaviors (4.6{+/-}4.42percent) by reducing the number of errors in No-Go trials. This improvement was a function of participants impulsivity-levels and baseline performance, i.e., impulsive individuals who made more baseline errors improved more after receiving stimulation. At the neural level, we show that random noise stimulation increases low-beta power at stimulation site, mediated by an increase in the duration of intermittent beta-bursts. ConclusionWe show for the first time that random noise stimulation improves the ability to withhold response to unexpected inhibitory cues as a function of an individuals impulsivity level. This improvement could be attributed to increased low-beta band power and intermittent-burst duration. These results suggest that random noise stimulation of the mPFC could potentially be used as a neuromodulatory intervention to target maladaptive behaviors in impulse control disorders.

neuroscience↗

bioRxiv2023

RAG1 and RAG2 non-core regions are implicated in the leukemogenesis and off-target V(D)J recombination in BCR-ABL1-driven B cell lineage lymphoblastic leukemia.

The evolutionary conservation of non-core RAG regions suggests significant roles that might involve quantitative or qualitative alterations in RAG activity. Off-target V(D)J recombination contributes to lymphomagenesis and is exacerbated by RAG2 C-terminus absence in Tp53-/- mice thymic lymphomas. However, the genomic stability effects of non-core regions from both cRAG1 and cRAG2 in BCR-ABL1+ Blymphoblastic leukemia (BCR-ABL1+ B-ALL), the characteristics, and mechanisms of non-core regions in suppressing off-target V(D)J recombination remains unclear. Here, we established three mouse models of BCR-ABL1+ B-ALL in mice expressing full-length RAG (fRAG), core RAG1 (cRAG1), and core RAG2 (cRAG2). The cRAG (cRAG1 and cRAG2) leukemia cells exhibited greater malignant tumor characteristics compared to fRAG cells. Additionally, cRAG cells showed higher frequency of off-target V(D)J recombination and oncogenic mutations than fRAG. We also revealed decreased RAG cleavage accuracy in cRAG cells and a smaller recombinant size in cRAG1 cells, which could potentially exacerbate off-target V(D)J recombination in cRAG cells. In conclusion, these findings indicate that the non-core RAG regions, particularly the non-core region of RAG1, play a significant role in preserving V(D)J recombination precision and genomic stability in BCR-ABL1+ B-ALL.

immunology↗