Biology subjects

Lin, Z.

Publications and source records attributed to Lin, Z..

At least 109 records · Page 6Linked to original sources

bioRxiv2024

Mechanism-guided engineering of a minimal biological particle for genome editing

The widespread application of genome editing to treat or even cure disease requires the delivery of genome editors into the nucleus of target cells. Enveloped Delivery Vehicles (EDVs) are engineered virally-derived particles capable of packaging and delivering CRISPR-Cas9 ribonucleoproteins (RNPs). However, the presence of lentiviral genome encapsulation and replication components in EDVs has obscured the underlying delivery mechanism and precluded particle optimization. Here we show that Cas9 RNP nuclear delivery is independent of the native lentiviral capsid structure. Instead, EDV-mediated genome editing activity corresponds directly to the number of nuclear localization sequences on the Cas9 enzyme. EDV structural analysis using cryo-electron tomography and small molecule inhibitors guided the removal of [~]80% of viral residues, creating a minimal EDV (miniEDV) that retains full RNP delivery capability. MiniEDVs are 25% smaller yet package equivalent amounts of Cas9 RNPs relative to the original EDVs, and demonstrated increased editing in cell lines and therapeutically-relevant primary human T cells. These results show that virally-derived particles can be streamlined to create efficacious genome editing delivery vehicles that could simplify production and manufacturing. SIGNIFICANCE STATEMENTOur results highlight the importance of understanding how virally-derived particles function to eliminate unnecessary viral proteins and create more efficacious and easier-to-produce delivery vehicles for therapeutic genome editing.

bioengineering↗

bioRxiv2024

Conditioned immunological memory: implication for cognition of immune cells

Associative learning refers to the process by which animals or human beings build the new relationship between two stimuli or events, which has been found throughout the animal kingdom. However, no studies have reported the associative learning in cells in the body to date. In the study, we focused on immune cells, as immunological memory is considered as one of the cardinal features of the immune system. As for a classical conditioning procedure, we designed the analogues of ginsenoside Rh1 (e.g., protopanaxadiol and panaxadiol) as the conditioned stimulus (CS) and the mcKLH-Rh1 conjugate as the unconditioned stimulus (US) and determined the CS-US association by detecting the dynamics of anti-Rh1 antibody production in serum. To test the hypothesis that immune cells also have the same associative learning ability as animals or human beings, mice were divided into control and CS group. Based on above CS and US, two groups of mice were subjected to conditioned learning, conditional memory recall, and deconditioning. When we used protopanaxadiol as the CS, the antibody response to Rh1 in the mice of CS group was slower and weaker than the control mice during conditional memory recall. Interestingly, the difference between two groups in antibody response to the immunogen disappeared during deconditioning. When we used panaxadiol as the CS, the antibody response to Rh1 in the mice of two groups was similar with that in studies regarding protopanaxadiol as the CS throughout three experimental phases. These results suggested that immune cells could acquire new association between a hapten analogue and a hapten-protein conjugate after undergoing a classical conditioning. Moreover, the conditioned immunological memory enables immune cells to make a conditioned response of humoral immunosuppression to prevent US induced immune damage. Our findings open a new frontier to explore cognitive functions of cells in the body and contribute to a novel understanding of cellular bioactivities.

immunology↗

bioRxiv2024

Supervised Deep Learning with Gene Annotation for Cell Classification

Gene-by-gene differential expression analysis is a widely used supervised approach for interpreting single-cell RNA-sequencing (scRNA-seq) data. However, modern scRNA-seq datasets often contain large numbers of cells, which can produce numerous differentially expressed genes with exceedingly small p-values but minimal effect sizes, and thus making biological interpretation difficult. To overcome this challenge, we developed Supervised Deep learning with gene ANnotation (SDAN), a method that integrates gene-annotation information with gene-expression profiles using a graph neural network. SDAN identifies functionally coherent gene sets that best classify cells, and the resulting cell-level classification scores can be aggregated to make individual-level predictions. We evaluated SDAN and two representative existing methods in three real-data applications to identify gene sets associated with severe COVID-19, dementia, and immunotherapy response in cancer. SDAN consistently outperformed alternative approaches by achieving two key objectives simultaneously: accurate classification of outcomes and unambiguous assignment of genes to gene sets of functionally related genes.

bioinformatics↗

bioRxiv2024

Vestigial like 4 regulates the adipogenesis of classical brown adipose tissue

Brown adipose tissue (BAT) is mammals primary non-shivering thermogenesis organ, and the molecular mechanisms regulating BAT growth and adipogenesis are largely unknown. The Hippo-YAP pathway has been well-known for controlling organ size, and Vestigial like 4 (VGLL4) is a transcriptional regulator that modulates the Hippo-YAP pathway by competing against YAP for binding to TEAD proteins. In this study, we dissected the function of VGLL4 in regulating BAT development. We generated a conventional Vgll4 mutant mouse line, in which the two Tondu (TDU) domains of VGLL4 were disrupted. We found that deletion of the TDU domains of VGLL4 resulted in perinatal lethality and paucity of the interscapular BAT. Histological and magnetic resonance imaging studies confirmed that the adipogenesis of BAT was impaired in Vgll4 mutants. Adeno-associated virus (AAV) mediated, brown adipocyte-specific overexpression of VGLL4 increased BAT volume and protected the adult male mice from acute cold stress. Genomic studies suggest that VGLL4/TEAD1 complex directly regulates the myogenic and adipogenic gene expression programs of BAT. In conclusion, our data identify VGLL4 as a previously unrecognized adipogenesis factor that regulates classical BAT development.

developmental biology↗

bioRxiv2024

Early-life Microbiome-Derived HCA Enhances Type 3 Immunity via FXR-dependent m6A RNA methylation

The gut microbiome plays a crucial role in the development of intestinal immunity during early life, but the underlying mechanisms remain largely unknown. Here, we found oxygen consumption in neonatal rats by S. boulardii accelerated the colonization of the microbiome and the development of type 3 immunity, which protected against S. typhimurium. Microbiome maturation increased the abundance of microbiome-encoded bile salt hydrolase (BSH) genes and elevated the levels of Hyocholic acid (HCA). HCA promotes the development of {gamma}{delta}T and type 3 innate lymphoid cell (ILC3) by sustaining the stability of Rorc mRNA via the FXR-WTAP- N6-methyl-adenosine (m6A) axis. L. reuteri, a bacterium encoding BSH, was enriched by oxygen consumption in the intestine and promoted intestinal type 3 immunity. However, inhibition of BSH blocks the L. reuteri-induced development of intestinal type 3 immunity. These results reveal the role of microbiome-derived HCA in the regulation of intestinal type 3 immunity during early life.

microbiology↗

bioRxiv2024

A safe and effective method for thin cornea cross-linking

The mechanical properties of the collagen matrix determine the corneal regularity. Thus, corneal thinning, cone-like protrusion, and loss of corneal astigmatism characterize corneal ecstatic diseases, including keratoconus and post-LASIK ectasia. Currently, riboflavin (RF)/ultraviolet A (UVA) based corneal collagen cross-linking (CXL) is commonly used in clinics to stiffen cornea and halt disease progression in patients. However, due to tissue damage, especially irreversible endothelium loss, standard RF/UVA protocol is only allowed in eyes with corneal thickness over 400 m after epithelium debridement. Thus, lots of patients with thin cornea have limited therapeutic options. Here, we introduced a new strategy to cross-link cornea collagens using synthesized photo-initiators lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) and visible blue light (BL). The LAP/BL protocol could effectively increase corneal stiffness, similar to the RF/UVA protocol in both porcine and rabbit cornea. When the LAP/BL protocol was used, both epithelial wound healing, stromal cell repopulation, and corneal edema recovery rates were accelerated compared with the RF/UVA protocol. More importantly, as BL is used instead of UVA, corneal endothelium was mostly preserved in both rabbit and mouse models even when cornea thickness was as thin as 100 m. In summary, we propose that the LAP/BL protocol is a safe and effective choice for thin cornea CXL.

bioengineering↗

bioRxiv2024

Cellular Dynamics Upon Immune Checkpoint Inhibition

Immune checkpoint inhibitors (ICI) have transformed cancer therapy, yet the basis of variable patient responses remains unclear. We assembled a longitudinal single-cell RNA sequencing atlas of 441 samples from 241 patients across ten cancers to map treatment-associated remodeling of the tumor immune microenvironment (TIME). Using a hierarchical reference-guided deep-phenotyping framework, we defined 77 immune and stromal subtypes and resolved four conserved TIME subtypes. Approximately 40% of tumors shifted between states during therapy, and the transition was more predictive of outcome than the baseline state. Across 1,988 bulk transcriptomic tumors, favorable transitions toward inflamed or B-cell-enriched subtype tracked with improved response and survival, while persistence in or shifts towards myeloid dominance indicated resistance. We derived a transition score that predicted outcomes for baseline tumors across independent cohorts. These findings establish immunotype transitions as a central determinant of ICI, offering new avenues for response prediction and rational immunotherapy design. HighlightsO_LIA pan-cancer meta-analysis maps treatment-associated remodeling of the tumor immune microenvironment. C_LIO_LIFour conserved TIME states emerge across cancer subtypes. C_LIO_LITIME transition patterns during treatment are associated with clinical outcome. C_LIO_LIBaseline immune programs derived transition scores predict treatment response and survival. C_LI

cancer biology↗

bioRxiv2024

Simulating 500 million years of evolution with a language model

More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained on tokens generated by evolution can act as evolutionary simulators to generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. ESM3 can follow complex prompts combining its modalities and is highly responsive to biological alignment. We have prompted ESM3 to generate fluorescent proteins with a chain of thought. Among the generations that we synthesized, we found a bright fluorescent protein at far distance (58% identity) from known fluorescent proteins. Similarly distant natural fluorescent proteins are separated by over five hundred million years of evolution.

synthetic biology↗

bioRxiv2024

Modulation of ribosomal subunit associations by eIF6 is critical for mitotic exit and cancer progression.

Moderating the pool of active ribosomal subunits is critical for maintaining global translation rates. A factor crucial for modulating the 60S ribosomal subunits is eukaryotic translation initiation factor 6. Release of eIF6 from 60S is essential to permit 60S interactions with 40S. Here, using the N106S mutant of eIF6, we show that disrupting eIF6 interaction with 60S leads to an increase in vacant 80S. It further highlights a dichotomy in the anti-association activity of eIF6 that is distinct from its role in 60S biogenesis and shows that the nucleolar localization of eIF6 is not dependent on uL14-BCCIP interactions. Limiting active ribosomal pools markedly deregulates translation especially in mitosis and leads to chromosome segregation defects, mitotic exit delays and mitotic catastrophe. Ribo-Seq analysis of the eIF6-N106S mutant shows a significant downregulation in the translation efficiencies of mitotic factors and specifically transcripts with long 3'UTRs. eIF6-N106S mutation also limits cancer invasion, and this role is correlated with the overexpression of eIF6 only in high-grade invasive cancers suggesting that deregulation of eIF6 is probably not an early event in cancers. Thus, this study highlights the segregation of eIF6 functions and its role in moderating 80S availability for mitotic translation and cancer progression.

molecular biology↗

bioRxiv2024

Transcriptional pathobiology and multi-omics predictors for Parkinson's disease

Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinsons disease (PD) are urgently needed. In this study, we leverage the large-scale whole-blood total RNA-seq dataset from the Accelerating Medicine Partnership in Parkinsons Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). There were 1,111 significant marker RNAs, including 491 genes, 599 eRNAs, and 21 circRNAs, that were first discovered in the PPMI cohort (FDR < 0.05) and confirmed in the PDBP/BioFIND cohorts (nominal p < 0.05). Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-alpha signaling pathway. We further compare the PD-associated genes in blood with those in post-mortem brain dopamine neurons in our BRAINcode cohort. 44 genes show significant changes with the same direction in both PD brain neurons and PD blood, including neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Finally, we built a novel multi-omics machine learning model to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous studies and might aid the decision-making for PD diagnosis in clinical practice. In summary, this study delineates a wide spectrum of the known and novel RNAs linked to PD and are detectable in circulating blood cells in a harmonized, large-scale dataset. It provides a generally useful computational framework for further biomarker development and early disease prediction. Significance statementEarly and accurate diagnosis of Parkinsons disease (PD) is urgently needed. However, biomarkers for early detection of PD are still lacking. Also, the limit of sample size remains one of the main pitfalls of current PD biomarker studies. We employed an analysis of large-scale whole-blood RNA-seq data. By identifying 1,111 significant marker RNAs, we establish a robust foundation for early PD detection, which implicated in neutrophil activation, degranulation, and TNF-alpha signaling, offer unprecedented insights into PD pathogenesis. Our multi-omics machine learning model, boasting an AUC of 0.89, outperforms previous studies, promising a transformative tool for precise PD diagnosis in clinical settings. This study marks a pivotal step toward enhanced biomarker development and early disease prediction.

neuroscience↗

bioRxiv2024

Brain implantation of tissue-level-soft bioelectronics 1 via embryonic development

The design of bioelectronics capable of stably tracking brain-wide, single-cell, and millisecond-resolved neural activities in the developing brain is critical to the study of neuroscience and neurodevelopmental disorders. During development, the three-dimensional (3D) structure of the vertebrate brain arises from a 2D neural plate1,2. These large morphological changes previously posed a challenge for implantable bioelectronics to track neural activity throughout brain development3-9. Here, we present a tissue-level-soft, sub-micrometer-thick, stretchable mesh microelectrode array capable of integrating into the embryonic neural plate of vertebrates by leveraging the 2D-to-3D reconfiguration process of the tissue itself. Driven by the expansion and folding processes of organogenesis, the stretchable mesh electrode array deforms, stretches, and distributes throughout the entire brain, fully integrating into the 3D tissue structure. Immunostaining, gene expression analysis, and behavioral testing show no discernible impact on brain development or function. The embedded electrode array enables long-term, stable, brain-wide, single-unit-single-spike-resolved electrical mapping throughout brain development, illustrating how neural electrical activities and population dynamics emerge and evolve during brain development.

bioengineering↗

bioRxiv2024

An Unbiased Proteomic Platform for Activity-based Arginylation Profiling

Protein arginylation is an essential posttranslational modification (PTM) catalyzed by arginyl-tRNA-protein transferase 1 (ATE1) in mammalian systems. Arginylation features a post-translational conjugation of an arginyl to a protein, making it extremely challenging to differentiate from translational arginine residues with the same mass in a protein sequence. Here we present a general ATE1-based arginylation profiling platform for the unbiased discovery of arginylation substrates and their precise modification sites. This method integrates isotopic arginine labeling into an ATE1 assay utilizing biological lysates (ex vivo) rather than live cells, thus eliminating translational bias derived from the ribosomal activity and enabling bona fide arginylation identification using isotopic features. The method has been successfully applied to an array of peptide, protein, cell, patient, and animal tissue samples using 20 {micro}g sample input, with 235 unique arginylation sites revealed from human proteomes. Representative sites were validated and followed up for their biological functions. The developed platform is globally applicable to the aforementioned sample types and therefore paves the way for functional studies of this difficult-to-characterize protein modification.

biochemistry↗

bioRxiv2024

Interpretable Protein-DNA Interactions Captured by Structure-based Optimization

Sequence-specific DNA recognition underlies essential processes in gene regulation, yet methods for simultaneous predictions of genomic DNA recognition sites and their binding affinity remain lacking. Here, we present the Interpretable protein-DNA Energy Associative (IDEA) model, a residue-level, interpretable biophysical model capable of predicting binding sites and affinities of DNA-binding proteins. By fusing structures and sequences of known protein-DNA complexes into an optimized energy model, IDEA enables direct interpretation of physicochemical interactions among individual amino acids and nucleotides. We demonstrate that this energy model can accurately predict DNA recognition sites and their binding strengths across various protein families. Additionally, the IDEA model is integrated into a coarse-grained simulation framework that quantitatively captures the absolute protein-DNA binding free energies. Overall, IDEA provides an integrated computational platform that alleviates experimental costs and biases in assessing DNA recognition and can be utilized for mechanistic studies of various DNA recognition processes.

biophysics↗

bioRxiv2024

A chromosome-level genome assembly of the cabbage aphid Brevicoryne brassicae

The cabbage aphid, Brevicoryne brassicae, is a major pest on Brassicaceae plants, and causes significant yield losses annually. However, lacking genomic resources hinders the progress in understanding this pest at the level of molecular biology. Here, a high-quality, chromosomal-level genome was assembled for B. brassicae based on PacBio HIFI long-read sequencing and Hi-C data. The final assembled size was 429.99 Mb with a scaffold N50 of 93.31 Mb. Importantly, 96.2% of the assembled sequences were anchored to eight chromosomes. The genome recovered 98.50% of BUSCO genes and 92.30% of CEGMA genes, supporting the high level of completeness. By combining high-coverage transcriptome data, a total of 22,671 protein-coding genes and 3,594 lncRNA genes were annotated. Preliminary comparative genomic analyses were focused on genes related to host colonisation, such as chemosensory- and detoxification-related genes, as well as those encoding putative protein effectors and cross-kingdom lncRNA Ya. In summary, our study presents a contiguous and complete genome for B. brassicae that will benefit our understanding of the biology of it and other aphids.

genomics↗

bioRxiv2024

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

The ever increasing breadth of biological knowledge has led to recent efforts to combine information from various fields into cell- or tissue atlases. Anatomical features are the structural basis for such efforts, but unfortunately large scale analysis of subcellular anatomical traits is currently a missing feature. Similarly, small phenotypic alterations of organelle- or cell-specific anatomical traits, such as an increase of the total volume or the number of mitochondria in response to certain stimuli, are currently hard to quantify. To provide tools to extract quantitative information from available 3D microscopic datasets generated with methods such as serial block face scanning electron microscopy we a) developed much improved fixation and embedding protocols for plants to drastically reduce processing artifacts and b) generated an easy-to-use AI tool for quantitative analysis and visualization of large-scale data sets. We make this tool available as open source.

cell biology↗

bioRxiv2024

Topoisomerase I is an Evolutionarily Conserved Key Regulator for Satellite DNA Transcription

Repetitive satellite DNAs, divergent in nucleic-acid sequence and size across eukaryotes, provide a physical site for centromere assembly to orchestrate chromosome segregation during the cell cycle. These non-coding DNAs are transcribed by RNA polymerase (RNAP) II and the transcription has been shown to play a role in chromosome segregation, but a little is known about the regulation of centromeric transcription, especially in higher organisms with tandemly-repeated-DNA-sequence centromeres. Using RNA interference knockdown, chemical inhibition and AID/IAA degradation, we show that Topoisomerase I (TopI), not TopII, promotes the transcription of -satellite DNAs, the main type of satellite on centromeres in human cells. Mechanistically, TopI localizes to centromeres, binds RNAP II and facilitates RNAP II elongation on centromeres. Interestingly, in response to DNA double-stranded breaks (DSBs) induced by chemotherapy drugs or CRSPR/Cas9, -satellite transcription is dramatically stimulated in a DNA damage checkpoint-independent but TopI-dependent manner. These DSB-induced -satellite RNAs were predominantly derived from the -satellite high-order repeats of human centromeres and forms into strong speckles in the nucleus. Remarkably, TopI-dependent satellite transcription also exists in mouse 3T3 and Drosophila S2 cells and in Drosophila larval imaginal wing discs and tumor tissues. Altogether, our findings herein reveal an evolutionally conserved mechanism with TopI as a key player for the regulation of satellite transcription at both cellular and animal levels.

cell biology↗

bioRxiv2024

Yeast poly(A)-binding protein (Pab1) controls translation initiation in vivo primarily by blocking mRNA decapping and decay

Poly(A)-binding protein (Pab1 in yeast) is involved in mRNA decay and translation initiation, but its molecular functions are incompletely understood. We found that auxin-induced degradation of Pab1 reduced bulk mRNA and polysome abundance in a manner suppressed by deleting the catalytic subunit of decapping enzyme (dcp2{Delta}), demonstrating that enhanced decapping/degradation is the major driver of reduced mRNA abundance and protein synthesis at limiting Pab1 levels. An increased median poly(A) tail length conferred by Pab1 depletion was also nullified by dcp2{Delta}, suggesting that mRNA isoforms with shorter tails are preferentially decapped/degraded at limiting Pab1. In contrast to findings on mammalian cells, the translational efficiencies (TEs) of many mRNAs were altered by Pab1 depletion; however, these changes were broadly diminished by dcp2{Delta}, suggesting that reduced mRNA abundance is a major driver of translational reprogramming at limiting Pab1. Thus, assembly of the closed-loop mRNP via PABP-eIF4G interaction appears to be dispensable for normal translation of most yeast mRNAs in vivo. Interestingly, histone mRNAs and proteins are preferentially diminished on Pab1 depletion dependent on Dcp2, accompanied by activation of internal cryptic promoters in the manner expected for reduced nucleosome occupancies, revealing a new layer of post-transcriptional control of histone gene expression.

genomics↗

bioRxiv2024

RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the non-unique structure-sequence mapping, and the flexibility of RNA conformation. In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in-silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

bioinformatics↗