Biology subjects

Li, Y. I.

Publications and source records attributed to Li, Y. I..

16 recordsLinked to original sources

bioRxiv2025

Non-canonical cytokinesis driven by mechanical uncoupling via nematic flows and adhesion-based invagination

Cleavage - the series of rapid cell divisions that follow fertilization - marks the onset of metazoan development and represents a deeply conserved evolutionary process. Across animals, two principal modes exist: complete (holoblastic) and incomplete (meroblastic) cleavage. While holoblastic cleavage resembles conventional cytokinesis both in vitro and in vivo, the mechanisms underlying meroblastic cleavage have remained poorly understood. Using zebrafish embryos as a model, we show that meroblastic cleavage proceeds through a distinct two-step mechanism. The process begins with the assembly and contraction of a large, arc-shaped actomyosin cable. However, this contractile event alone is insufficient to complete division. A second phase, driven by cadherin-mediated membrane adhesion, is required to invaginate the furrow ridge. Strikingly, this transition depends on mechanical uncoupling of the contractile cable from the surrounding cortex. We demonstrate that such uncoupling arises from an active nematic instability, which both enhances contractility along the cable and generates actin depletion zones that relieve lateral connections. Together, these findings reveal that meroblastic cleavage is governed not by a single actomyosin-based event but by a sequential interplay between cytoskeletal contraction and cadherin-dependent adhesion, highlighting a mechanism fundamentally distinct from canonical cytokinesis.

cell biology↗

bioRxiv2025

Geometry-driven asymmetric cell divisions pattern cell cycles and zygotic genome activation in the zebrafish embryo

Early embryo geometry is one of the most invariant species-specific traits, yet its role in ensuring developmental reproducibility and robustness remains underexplored. Here, we show that in zebrafish, the geometry of the fertilized egg - specifically its curvature and volume - serves as a critical initial condition triggering a cascade of events that exert a lasting influence on development. Specifically, it guides asymmetric cell divisions in the blastoderm, generating radial gradients of cell volume and nucleocytoplasmic ratio. These gradients generate mitotic phase waves, with individual cell cycle periods determined largely cell-autonomously by the nucleocytoplasmic ratio. Modeling and perturbation experiments demonstrate that reducing cell autonomy reshapes these waves, emphasizing the instructive role of geometry-derived volume patterns in setting the intrinsic period of the cell cycle oscillator. Remarkably, in addition to organizing cell cycles, early embryo geometry also spatially patterns zygotic genome activation (ZGA) at the midblastula transition, a key step in establishing embryonic autonomy. Disrupting geometry alters the ZGA pattern and causes ectopic germ layer specification, underscoring its developmental significance. Together, our findings reveal a novel symmetry-breaking function of early embryo geometry in coordinating cell cycle and transcriptional patterning, establishing a blueprint for robust embryogenesis.

developmental biology↗

bioRxiv2025

Scaling of mouse somitogenesis by coupling of cell cycle to segmentation clock oscillations

While scaling developmental processes is fundamental to maintaining robust tissue patterning, the mechanisms underlying this process are enigmatic. Somitogenesis, the periodic segmentation of growing mesodermal tissue in vertebrate embryos1, involves precise scaling with the unsegmented presomitic mesoderm (PSM) over developmental time and under perturbation2-4. Somitogenesis is spatiotemporally regulated by FGF and Wnt morphogen gradients and the segmentation clock -- oscillations in Notch, Wnt, and FGF signalling5-7. Here, we find that cell proliferation is distributed throughout the oscillating PSM. Long-term single-cell tracking in mouse embryo tails uncovered a correlation between cell cycle progression and the segmentation clock, with microfluidics-based entrainment indicating coupling between the cell cycle and signalling oscillations, likely through S-phase inducing Cyclins. A theoretical model suggests this coupling ensures uniform PSM growth, uniform morphogen dilution and precise somite formation, which we validated experimentally by blocking cell proliferation. Our findings reveal that coupling cell proliferation to signalling oscillations is crucial for robust somitogenesis and precise somite scaling.

developmental biology↗

bioRxiv2024

Self-organization of spermatogenic wave coordinates sustained sperm production in the mouse testis

Spermatogenesis takes place in the testis, relying on the ordered turnover of differentiating cells supplied from stem cells. Classic histological analyses have revealed that this process shows hierarchical spatiotemporal patterning known as the spermatogenic cycle, wave, and descent of segmental order, indicative of currently underexplored mechanisms of tissue- and organ-scale homeostasis. Here, using mice, we conducted high-resolution, wide-field, and ultra long-term live imaging studies in vivo and ex vivo, combined with whole-organ mapping of differentiation stages. Such trans-scale measures demonstrate how stereotypic local cell turnover is coordinated into characteristic phase waves propagating along the seminiferous tubules, further organized into organ-scale patterning over the tubule loops. Minimal mathematical modeling shows that such higher-order dynamics can emerge from the local coupling of autonomous oscillators, which are rooted in delayed feedback interplay between stem and differentiating cells via retinoic acid signaling. These findings highlight a self-organization mechanism underpinning organ-scale homeostasis and constant sperm production.

developmental biology↗

bioRxiv2024

Protein-protein interactions shape trans-regulatory impact of genetic variation on protein expression and complex traits

Most genetic variants influence complex traits by affecting gene regulation. Yet, despite comprehensive catalogs of molecular QTLs, linking trait-associated variants to biological functions remains difficult. In this study, we re-analyzed large maps of protein QTLs (pQTLs) to show that genes with trans-pQTLs but without cis-pQTLs are under strong selective constraints and are highly enriched in GWAS loci. We found that trans-pQTLs and their trans targets are highly enriched in interacting protein pairs, and trans-pQTLs in coding regions are significantly enriched at protein-protein interactions (PPI) interfaces. By leveraging existing PPI annotations for trans-pQTL mapping, we identified 26,028 trans-pQTLs influencing 1,061 PPI clusters. The trans-pQTLs of PPIs colocalized with 66% GWAS loci per trait on average for 50 complex traits, helping in many cases to link GWAS loci to cellular function. Finally, we identified trans-pQTL effects at multiple autoimmune GWAS loci that converge on the same PPIs, pinpointing protein complexes and signaling pathways that show promising therapeutic target potential.

genetics↗

bioRxiv2024

Chromosome-scale genome assembly and gene annotation of the hydrothermal vent annelid Alvinella pompejana yield insight into animal evolution in extreme environments

The types of genomic change needed for environmental adaptation are of great interest. Annelid worms are a large phylum found in a rich diversity of habitats, giving opportunities to explore this issue. We report the chromosome level genome sequence of the Pompeii worm, the annelid Alvinella pompejana, an inhabitant of an extreme deep-sea hydrothermal vent environment. We find strong but heterogeneously distributed genetic divergence between populations taken from either side of the equator. Using transcript data, we produced a set of gene models and analysed the predicted protein set in the light of past hypotheses about the thermotolerance of Alvinella, and compared it to other recently sequenced annelid vent worms. We do not find evidence of a more extreme genome wide amino acid composition than other species, neither do we find evidence for rapid genome evolution in the form of disrupted synteny. We discount the hypothesis of loss of amino acid biosynthesis genes associated with obligate symbioses reported in siboglinid annelids. We do find evidence of a parallel increase in the number of globin encoding genes and loss of light sensitive opsins and cryptochromes. Alvinella encodes several respiratory enzymes unusual for bilaterian animals, suggesting an ability to better tolerate hypoxic environments.

genomics↗

bioRxiv2023

Cryptic splicing mediates genetic and therapeutic perturbation of human gene expression levels

Alternative splicing (AS) is pervasive in human genes, yet the specific function of most AS events remains unknown. It is widely assumed that the primary function of AS is to diversify the proteome, however AS can also influence gene expression levels by producing transcripts rapidly degraded by nonsense-mediated decay (NMD). Currently, there are no precise estimates for how often the coupling of AS and NMD (AS-NMD) impacts gene expression levels because rapidly degraded NMD transcripts are challenging to capture. To better understand the impact of AS on gene expression levels, we analyzed population-scale genomic data in lymphoblastoid cell lines across eight molecular assays that capture gene regulation before, during, and after transcription and cytoplasmic decay. Sequencing nascent mRNA transcripts revealed frequent aberrant splicing of human introns, which results in remarkably high levels of mRNA transcripts subject to NMD. We estimate that [~]15% of all protein-coding transcripts are degraded by NMD, and this estimate increases to nearly half of all transcripts for lowly-expressed genes with many introns. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are similarly likely to associate with NMD-induced expression level differences as with differences in protein isoform usage. Additionally, we used the splice-switching drug risdiplam to perturb AS at hundreds of genes, finding that [~]3/4 of the splicing perturbations induce NMD. Thus, we conclude that AS-NMD substantially impacts the expression levels of most human genes. Our work further suggests that much of the molecular impact of AS is mediated by changes in protein expression levels rather than diversification of the proteome.

genomics↗

bioRxiv2021

Profiling of Nascent Lariat Intermediates Reveals Key Genetic Determinants of the Timing of Human Co-transcriptional Splicing

As splicing is intimately coupled with transcription, understanding splicing mechanisms requires an understanding of splicing timing, which is currently limited. Here, we developed CoLa-seq (co-transcriptional lariat sequencing), a genomic assay that reports splicing timing relative to transcription through analysis of nascent lariat intermediates. In human cells, we mapped 165,282 branch points and characterized splicing timing for over 70,000 introns. Splicing timing varies dramatically across introns, with regulated introns splicing later than constitutive introns. Machine learning-based modeling revealed genetic elements predictive of splicing timing, notably the polypyrimidine tract, intron length, and regional GC content, which illustrate the significance of the broader genomic context of an intron and the impact of co-transcriptional splicing. The importance of the splicing factor U2AF in early splicing rationalizes surprising observations that most introns can splice independent of exon definition. Together, these findings establish a critical framework for investigating the mechanisms and regulation of co-transcriptional splicing. HighlightsO_LICoLa-seq enables cell-type specific, genome-wide branch point annotation with unprecedented efficiency. C_LIO_LICoLa-seq captures co-transcriptional splicing for tens of thousands of introns and reveals splicing timing varies dramatically across introns. C_LIO_LIModeling uncovers key genetic determinants of splicing timing, most notably regional GC content, intron length, and the polypyrimidine tract, the binding site for U2AF2. C_LIO_LIEarly splicing precedes transcription of a downstream 5 SS and in some cases accessibility of the upstream 3 SS, precluding exon definition. C_LI

molecular biology↗

bioRxiv2021

Predicting RNA splicing from DNA sequence using Pangolin

Recent progress in deep learning approaches have greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues that has been trained on RNA splicing and sequence data from four species. Pangolin outperforms state of the art methods for predicting RNA splicing on a variety of prediction tasks. We use Pangolin to study the impact of genetic variants on RNA splicing, including lineage-specific variants and rare variants of uncertain significance. Pangolin predicts loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense (AUPRC = 0.93), demonstrating remarkable potential for identifying pathogenic variants.

genomics↗

bioRxiv2020

Single-cell RNA-sequencing reveals pervasive but highly cell type-specific genetic ancestry effects on the response to viral infection

Humans vary in their susceptibility to infectious disease, partly due to variation in the immune response following infection. Here, we used single-cell RNA-sequencing to quantify genetic contributions to this variation in peripheral blood mononuclear cells, focusing specifically on the transcriptional response to influenza infection. We find that monocytes are the most responsive to influenza infection, but that all cell types mount a conserved interferon response, which is stronger in individuals with increased European ancestry. By comparing European American and African American individuals, we show that genetic ancestry effects on expression are common, influencing 29% of genes, but highly cell type-specific. Further, we demonstrate that much of this population-associated expression variation is explained by cis expression quantitative trait loci, which are enriched for signatures of recent positive selection. Our findings establish common cis-regulatory variants--including those that are differentiated by genetic ancestry--as important determinants of the antiviral immune response.

genomics↗

bioRxiv2020

Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

bioinformatics↗

bioRxiv2018

Imputation of single-cell gene expression with an autoencoder neural network

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.\n\nMethodsTo overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.\n\nResultsOn both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.\n\nConclusionsWe demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.

bioinformatics↗

bioRxiv2018

Trans effects on gene expression can drive omnigenic inheritance

Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns con[fl]ict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated - as seems likely - then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.

genetics↗

bioRxiv2018

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 241 mean expression QTLs (eQTLs) at 10% FDR, of which 82% replicate in bulk RNA-seq data from the same individuals. We further identified 14 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs) which could alter the variance of expression independently of the mean can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 424 individuals as a lower bound to achieve 80% power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.\n\nAuthor summaryCommon genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

genomics↗

bioRxiv2017

Prioritizing Parkinson’s Disease genes using population-scale transcriptomic data

Genome-wide association studies (GWAS) have identified over 41 susceptibility loci associated with late-onset Parkinsons Disease (PD) but identifying putative causal genes and the underlying mechanisms remains challenging. To address this, we leveraged large-scale transcriptomic datasets to prioritize genes that are likely to affect PD. We found 29 gene associations in peripheral monocytes, and 44 gene associations whose expression or differential splicing in prefrontal cortex is associated with PD. This includes many novel genes but also known associations such as MAPT, for which we found that variation in exon 3 splicing explains the common genetic association. Genes identified in our analyses are more likely to interact physically with known PD genes and belong to the same or related pathways including lysosomal and innate immune function. Overall, our study provides a strong foundation for further mechanistic studies that will elucidate the molecular drivers of PD.

genomics↗

bioRxiv2016

Impact of regulatory variation across human iPSCs and differentiated cells

Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation across different cell types and as models for studies of complex disease. We established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression, chromatin accessibility and DNA methylation. Regulatory variation between individuals is lower in iPSCs than in the differentiated cell types, consistent with the intuition that developmental processes are generally canalized. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific QTLs are in shared open chromatin. Finally, we developed a deep neural network to predict open chromatin regions from DNA sequence alone and were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.

genomics↗