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Mao, Y.

Publications and source records attributed to Mao, Y..

At least 109 records · Page 6Linked to original sources

A human-specific enhancer fine-tunes radial glia potency and corticogenesis

Humans evolved an extraordinarily expanded and complex cerebral cortex, associated with developmental and gene regulatory modifications1-3. Human accelerated regions (HARs) are highly conserved genomic sequences with human-specific nucleotide substitutions. Although there are thousands of annotated HARs, their functional contribution to human-specific cortical development is largely unknown4,5. HARE5 is a HAR transcriptional enhancer of the WNT signaling receptor Frizzled8 (FZD8) active during brain development6. Here, using genome-edited mouse and primate models, we demonstrate that human (Hs) HARE5 fine-tunes cortical development and connectivity by controlling the proliferative and neurogenic capacity of neural progenitor cells (NPCs). Hs-HARE5 knock-in mice have significantly enlarged neocortices containing more neurons. By measuring neural dynamics in vivo we show these anatomical features correlate with increased functional independence between cortical regions. To understand the underlying developmental mechanisms, we assess progenitor fate using live imaging, lineage analysis, and single-cell RNA sequencing. This reveals Hs-HARE5 modifies radial glial progenitor behavior, with increased self-renewal at early developmental stages followed by expanded neurogenic potential. We use genome-edited human and chimpanzee (Pt) NPCs and cortical organoids to assess the relative enhancer activity and function of Hs-HARE5 and Pt-HARE5. Using these orthogonal strategies we show four human-specific variants in HARE5 drive increased enhancer activity which promotes progenitor proliferation. These findings illustrate how small changes in regulatory DNA can directly impact critical signaling pathways and brain development. Our study uncovers new functions for HARs as key regulatory elements crucial for the expansion and complexity of the human cerebral cortex.

neuroscience↗

Comparative genomics of macaques and integrated insights into genetic variation and population history

The crab-eating macaques (Macaca fascicularis) and rhesus macaques (M. mulatta) are widely studied nonhuman primates in biomedical and evolutionary research. Despite their significance, the current understanding of the complex genomic structure in macaques and the differences between species requires substantial improvement. Here, we present a complete genome assembly of a crab-eating macaque and 20 haplotype-resolved macaque assemblies to investigate the complex regions and major genomic differences between species. Segmental duplication in macaques is [~]42% lower, while centromeres are [~]3.7 times longer than those in humans. The characterization of [~]2 Mbp fixed genetic variants and [~]240 Mbp complex loci highlights potential associations with metabolic differences between the two macaque species (e.g., CYP2C76 and EHBP1L1). Additionally, hundreds of alternative splicing differences show post-transcriptional regulation divergence between these two species (e.g., PNPO). We also characterize 91 large-scale genomic differences between macaques and humans at a single-base-pair resolution and highlight their impact on gene regulation in primate evolution (e.g., FOLH1 and PIEZO2). Finally, population genetics recapitulates macaque speciation and selective sweeps, highlighting potential genetic basis of reproduction and tail phenotype differences (e.g., STAB1, SEMA3F, and HOXD13). In summary, the integrated analysis of genetic variation and population genetics in macaques greatly enhances our comprehension of lineage-specific phenotypes, adaptation, and primate evolution, thereby improving their biomedical applications in human diseases.

genomics↗

A perfusion-independent high-throughput method to isolate liver sinusoidal endothelial cells

Liver sinusoidal endothelial cells (LSECs) critically regulate homeostatic liver function and liver pathogenesis. However, the isolation of LSECs remains a major technological bottleneck in studying molecular mechanisms governing LSEC functions. Current techniques to isolate LSECs, relying on perfusion-dependent liver digestion, are cumbersome with limited throughput. We here describe a perfusion-independent high-throughput procedure to isolate LSECs with high purity. Indifferently from previous perfusion-independent approaches, chopped liver tissue was incubated in the digestion mix for 30 minutes with intermittent mixing with a serological pipette. This led to the safeguarding of LSEC integrity and yielded 10 {+/-} 1.0 million LSECs per adult mouse liver, which is far higher than previous perfusion-independent protocols and comparable yield to established perfusion-dependent protocols for isolating LSECs. Combining magnetic and fluorescence-activated cell sorting (FACS), LSECs from different zones of the hepatic sinusoid can now be isolated in high numbers in less than two hours for downstream applications including proteomics. Our protocol enabled the isolation of LSECs from fibrotic liver tissues from mice and healthy liver tissues from higher vertebrate species (pigs), where traditional perfusion-based digestion protocols have very limited application. In conclusion, these technical advancements reduce post-mortem changes in the LSEC state and aid in reliable investigation of LSEC functions.

physiology↗

Deep learning modeling of ribosome profiling reveals regulatory underpinnings of translatome and interprets disease variants

Gene expression involves transcription and translation. Despite large datasets and increasingly powerful methods devoted to calculating genetic variants effects on transcription, discrepancy between mRNA and protein levels hinders the systematic interpretation of the regulatory effects of disease-associated variants. Accurate models of the sequence determinants of translation are needed to close this gap and to interpret disease-associated variants that act on translation. Here, we present Translatomer, a multimodal transformer framework that predicts cell-type-specific translation from mRNA expression and gene sequence. We train Translatomer on 33 tissues and cell lines, and show that the inclusion of sequence substantially improves the prediction of ribosome profiling signal, indicating that Translatomer captures sequence-dependent translational regulatory information. Translatomer achieves accuracies of 0.72 to 0.80 for de novo prediction of cell-type-specific ribosome profiling. We develop an in silico mutagenesis tool to estimate mutational effects on translation and demonstrate that variants associated with translation regulation are evolutionarily constrained, both within the human population and across species. Notably, we identify cell-type-specific translational regulatory mechanisms independent of eQTLs for 3,041 non-coding and synonymous variants associated with complex diseases, including Alzheimers disease, schizophrenia, and congenital heart disease. Translatomer accurately models the genetic underpinnings of translation, bridging the gap between mRNA and protein levels, and providing valuable mechanistic insights toward mapping "missing regulation" in disease genetics.

genomics↗

Multimodal single cell-resolved spatial proteomics reveals pancreatic tumor heterogeneity

Despite the advances in antibody-guided cell typing and mass spectrometry-based proteomics, their integration is hindered by challenges for processing rare cells in the heterogeneous tissue context. Here, we introduce Spatial and Cell-type Proteomics (SCPro), which combines multiplexed imaging and flow cytometry with ion exchange-based protein aggregation capture technology to characterize spatial proteome heterogeneity with single cell resolution. The SCPro was employed to explore the pancreatic tumor microenvironment and revealed the spatial alternations of over 5,000 proteins by automatically dissecting up to 100 single cells guided by multi-color imaging of centimeter-scale formalin-fixed, paraffin-embedded tissue slide. To enhance cell-type resolution, we characterized the proteome of 14 different cell types by sorting up to 1,000 cells from the same tumor, which allows us to deconvolute the spatial distribution of immune cell subtypes and leads to the discovery of a novel subtype of regulatory T cells. Together, the SCPro provides a multimodal spatial proteomics approach for profiling tissue proteome heterogeneity.

systems biology↗

Resting state global brain activity induces bias in fMRI motion estimates

Head motion is a significant source of artifacts in resting-state fMRI (rsfMRI) studies and has been shown to affect resting-state functional connectivity (rsFC) measurements. In many rsfMRI studies, motion parameters estimated from volume registration are used to characterize head motion and to mitigate motion artifacts in rsfMRI data. While prior task-based fMRI studies have shown that task-evoked brain activations may induce temporally correlated bias in the motion estimates, resulting in artificial activations after registration, relatively little is known about neural-related bias in rsfMRI motion parameters. In this study, we demonstrate that neural-related bias exists in rsfMRI motion estimates and characterize the potential effects of the bias on rsFC estimates. Using a public multi-echo rsfMRI dataset, we use the differences between motion estimates from the first echo and second echo data as a measure of neural-induced bias. We show that the resting-state global activity of the brain, as characterized with the global signal (GS), induces bias in the motion estimates in the y- and z-translational axes. Furthermore, we demonstrate that the GS-related bias reflects superior-inferior and anterior-posterior asymmetries in the GS beta coefficient map. Finally, we demonstrate that regression with biased motion estimates can negatively bias rsFC estimates and also reduce rsFC differences between young and old subjects.

neuroscience↗

Microglia facilitate and stabilize the response to general anesthesia

General anesthesia leads to a loss of consciousness and an unarousable state in patients. Although general anesthetics are widely used in clinical practice, their underlying mechanisms remain elusive. The potential involvement of nonneuronal cells is unknown. Microglia are important immune cells in the central nervous system (CNS) that play critical roles in CNS function and dysfunction. We unintentionally observed delayed anesthesia induction and early anesthesia emergence in microglia-depleted mice. We found that microglial depletion differentially regulates neuronal activities by suppressing the neuronal network of anesthesia-activated brain regions and activating emergence-activated brain regions. Thus, microglia facilitate and stabilize the anesthesia status. This influence is not mediated by dendritic spine plasticity. Instead, it relies on the activation of microglial P2Y12 and subsequent calcium influx, which facilitates the general anesthesia response. Together, we elucidate the regulatory role of microglia in general anesthesia, extending our knowledge of how nonneuronal cells modulate neuronal activities.

neuroscience↗

A conserved N-terminal motif of CUL3 mediates assembly and licenses E3 ligase activation of CRL3KLHL22

The CUL3-RING E3 ubiquitin ligases (CRL3s) play an essential role in response to extracellular nutrition and stress stimuli. The ubiquitin ligase function of CRL3s is activated through E3-E3 dimerization. However, how and why such a dimeric assembly is required for its ligase activity remains elusive. Here, we report the cryo-EM structure of the dimeric CRL3KLHL22 complex and reveal a conserved N-terminal motif in CUL3 that mediates the dimerization assembly and licenses the activation of CRL3KLHL22. Deletion of the CUL3 N-terminal motif leads to non-stochiometric assembly and impairs E3 activity of both CRL3KLHL22 and several other CRL3s. In addition, we found that the dynamics of dimeric E3-E3 superassembly generates a variable ubiquitination zone, potentially facilitating substrate recognition and ubiquitination. These findings demonstrate a CUL3 N-terminal motif-dependent E3-E3 superassembly mechanism and provide insights into the assembly and activation of CRL3s.

molecular biology↗

Synchronized long-read genome, methylome, epigenome, and transcriptome for resolving a Mendelian condition

Resolving the molecular basis of a Mendelian condition (MC) remains challenging owing to the diverse mechanisms by which genetic variants cause disease. To address this, we developed a synchronized long-read genome, methylome, epigenome, and transcriptome sequencing approach, which enables accurate single-nucleotide, insertion-deletion, and structural variant calling and diploid de novo genome assembly, and permits the simultaneous elucidation of haplotype-resolved CpG methylation, chromatin accessibility, and full-length transcript information in a single long-read sequencing run. Application of this approach to an Undiagnosed Diseases Network (UDN) participant with a chromosome X;13 balanced translocation of uncertain significance revealed that this translocation disrupted the functioning of four separate genes (NBEA, PDK3, MAB21L1, and RB1) previously associated with single-gene MCs. Notably, the function of each gene was disrupted via a distinct mechanism that required integration of the four omes to resolve. These included nonsense-mediated decay, fusion transcript formation, enhancer adoption, transcriptional readthrough silencing, and inappropriate X chromosome inactivation of autosomal genes. Overall, this highlights the utility of synchronized long-read multi-omic profiling for mechanistically resolving complex phenotypes.

genetics↗

Proteonano: a novel deep proteomics platform with 1000-plex profiling capacity and picogram sensitivity and its application in diabetic kidney disease

The development of blood-based multi-biomarker panels for screening diabetic patients, and as an easy-to-access tool for identifying individuals at greatest risk of developing diabetic kidney disease (DKD) and its progression, is essential. However, conventional blood biomarker-based methodologies (e.g. clinical tests and ELISA) are unable to predict DKD progression with high sensitivity and specificity. To overcome these challenges, we developed a deep, untargeted plasma proteome profiling technology (Proteonano platform) to identify potential multiple protein biomarkers involved in DKD progression. The Proteonano technology is an affinity selective mass spectrometric platform that comprises nanoparticle-based affinity binders (nanobinders) for low abundant protein enrichment, automated workflow for parallel sample preparation, and machine learning empowered bioinformatic software for data analysis. Using the Proteonano platform, we performed untargeted proteomics on 75 subjects (DKD progressors, n = 30; DKD non-progressors, n = 45) and identified an average of 953 {+/-} 80 (AVG {+/-} SD) protein groups, with a wide dynamic range of 8 orders of magnitude (with the lowest concentration down to 3.00 pg/mL). Among these, 38 proteins were differentially expressed between DKD progressors relative to non-progressors, and the predictive power for these proteins were assessed. Further, we performed random forest and LASSO analyses for additional variable selection. Variables selected by these approaches were assessed by Akaike information criterion method followed by ROC analysis, which identified a combination of multiple proteins (including VWF, PTGDS, B2M, BT3A2, and LCAT) that showed excellent predictive power over current methods, with an area under the curve value up to 0.97. Some of these plasma proteins are not previously recognized in the context of DKD progression, suggesting they are novel biomarkers. Our studies pave the way to develop multi-biomarker panels for DKD progression management. This study suggests that the Proteonano technology platform reported here can be employed as an established workflow enabling untargeted deep proteomic analysis to identify highly discriminative biomarkers for precise medicine.

bioinformatics↗

Comparative transcriptome in rhesus macaques and crab-eating macaques

Understanding the variations in gene expression between species is pivotal for deciphering the evolutionary diversity in phenotypes. Rhesus macaques and crab-eating macaques serve as crucial nonhuman primate biomedical models with different phenotypes, but the large-scale of comparative transcriptome research between these two species has yet to be fully elucidated. Here, we conduct systematic comparisons utilizing newly sequenced RNA-seq data from 84 samples encompassing 14 common tissues. Our findings reveal that a small fraction of genes ([~]3.7%) show differential expression between the two macaque species, while [~]36.5% of genes show tissue-specific expression in both macaques. We also compare gene expression between macaques and humans and [~]22.6 % of the orthologous genes show differential expression in at least 2 tissues. Moreover, [~]19.41% of genes overlapped with macaque-specific structural variants are more likely to show differential expression between humans and macaques. Of these, FAM220A shows elevated gene expression in humans compared to macaques because of lineage-specific duplication. In summary, our study presents a large-scale analysis of the transcriptomes within macaque species and between macaques and humans. These insights into gene expression variations will enhance the biomedical utility of macaque models and contributing to the broader realm of primate genomic studies.

bioinformatics↗

Complete genome-centric analysis reveals functional divergence and adaptive evolution of uncultured bacteria in anammox community

Anaerobic ammonium-oxidation (anammox) bacteria play a crucial role in global nitrogen cycling and wastewater nitrogen removal, but they share symbiotic relationships with various other microorganisms. No pure culture is available for anammox bacteria so far. Although shotgun metagenomics based on short reads has been widely used in anammox research, metagenome-assembled genomes (MAGs) are often discontinuous and highly contaminated, which limits in-depth analyses of anammox communities. Here, for the first time, we performed Pacific Biosciences high-fidelity (HiFi) long-read sequencing on the anammox granule sludge sample from a lab-scale bioreactor, and obtained 30 accurate and complete metagenome-assembled genomes (cMAGs). These cMAGs were obtained by selecting high-quality circular contigs from initial assemblies of long reads generated by HiFi sequencing, eliminating the need for Illumina short reads, binning, and reassembly. One new anammox species and species from three novel families were found in this anammox community. cMAG-centric analysis revealed divergences in general and nitrogen metabolism among members of the anammox community. Furthermore, we identified mobile genetic elements (MGEs) and putative horizontal gene transfer (HGT) events within these cMAGs to explore the adaptive evolution of the community. The results suggest that MGEs and HGT events, particularly transposons containing tnpA in anammox bacteria, might play important roles in the adaptive evolution of this anammox community. The cMAGs generated in the present study could be used to establish of a comprehensive database for anammox bacteria and associated microorganisms. Our findings highlight the advantages of HiFi sequencing for the studies of complex mixed cultures such as anammox communities and advance our understanding of anammox communities.

microbiology↗

Deep spatial proteomic exploration of severe COVID-19-related pulmonary injury in post-mortem specimens

The lung, as a primary target of SARS-CoV-2, exhibits heterogeneous microenvironment accompanied by various histopathological changes following virus infection. However, comprehensive insight into the protein basis of COVID-19-related pulmonary injury with spatial resolution is currently deficient. Here, we generated a region-resolved quantitative proteomic atlas of seven major pathological structures within the lungs of COVID-19 victims by integrating histological examination, laser microdissection, and ultrasensitive proteomic technologies. Over 10,000 proteins were quantified across 71 dissected FFPE post-mortem specimens. By comparison with control samples, we identified a spectrum of COVID-19-induced protein and pathway dysregulations in alveolar epithelium, bronchial epithelium, and pulmonary blood vessels, providing evidence for the proliferation of transitional-state pneumocytes. Additionally, we profiled the region-specific proteomes of hallmark COVID-19 pulmonary injuries, including bronchiole mucus plug, pulmonary fibrosis, airspace inflammation, and hyperplastic alveolar type 2 cells. Bioinformatic analysis revealed the enrichment of cell-type and functional markers in these regions (e.g. enriched TGFBI in fibrotic region). Furthermore, we identified the up-regulation of proteins associated with viral entry, host restriction, and inflammatory response in COVID-19 lungs, such as FURIN and HGF. Collectively, this study provides spatial proteomic insights for understanding COVID-19-caused pulmonary injury, and may serve as a valuable reference for improving therapeutic intervention for severe pneumonia.

systems biology↗

The variation and evolution of complete human centromeres

We completely sequenced and assembled all centromeres from a second human genome and used two reference sets to benchmark genetic, epigenetic, and evolutionary variation within centromeres from a diversity panel of humans and apes. We find that centromere single-nucleotide variation can increase by up to 4.1-fold relative to other genomic regions, with the caveat that up to 45.8% of centromeric sequence, on average, cannot be reliably aligned with current methods due to the emergence of new -satellite higher-order repeat (HOR) structures and two to threefold differences in the length of the centromeres. The extent to which this occurs differs depending on the chromosome and haplotype. Comparing the two sets of complete human centromeres, we find that eight harbor distinctly different -satellite HOR array structures and four contain novel -satellite HOR variants in high abundance. DNA methylation and CENP-A chromatin immunoprecipitation experiments show that 26% of the centromeres differ in their kinetochore position by at least 500 kbp--a property not readily associated with novel -satellite HORs. To understand evolutionary change, we selected six chromosomes and sequenced and assembled 31 orthologous centromeres from the common chimpanzee, orangutan, and macaque genomes. Comparative analyses reveal nearly complete turnover of -satellite HORs, but with idiosyncratic changes in structure characteristic to each species. Phylogenetic reconstruction of human haplotypes supports limited to no recombination between the p- and q-arms of human chromosomes and reveals that novel -satellite HORs share a monophyletic origin, providing a strategy to estimate the rate of saltatory amplification and mutation of human centromeric DNA.

genomics↗

Spatiotemporal transcriptome atlas of human embryos after gastrulation

The spatial and temporal atlas of gene expression in the human embryo at early gestation is critical in understanding embryo development, organogenesis, and disease origins. We obtained the spatiotemporal transcriptome from 90 sagittal sections of 16 whole human embryos from 3 to 8 post-conception weeks by Stereo-seq with high resolution and ultra-large field, establishing the development trajectory/regulatory profiling of 49 organs. We uncovered the organ-specific regulons as potential lineage-determining factors and identified the new regulatory networks during heart and brain development. The atlas refines the key organs/cell types vulnerable to virus infection and genetic disorders, and, reveals the dynamics of allelic gene expression in specific organs at different stages. These results present the first comprehensive delineation of the spatiotemporal transcriptomic dynamics of human organogenesis. One Sentence SummaryThe spatiotemporal transcriptome atlas presents a comprehensive delineation of human embryogenesis after gastrulation.

developmental biology↗

Fibertools: fast and accurate DNA-m6A calling using single-molecule long-read sequencing

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation as well as the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as co-processing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semi-supervised convolutional neural network for fast and accurate identification of m6A-marked bases using PacBio single-molecule long-read sequencing, as well as the co-processing of long-read genetic and epigenetic data produced using either PacBio or Oxford Nanopore sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kilobase long DNA molecules with a [~]1,000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

bioinformatics↗

Clinical Phenotype Prediction From Single-cell RNA-seq Data using Attention-Based Neural Networks

MotivationA patients disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown, or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. ResultsHere we propose the method ScRAT for clinical phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as COVID and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies.

bioinformatics↗

Structurally divergent and recurrently mutated regions of primate genomes

To better understand the pattern of primate genome structural variation, we sequenced and assembled using multiple long-read sequencing technologies the genomes of eight nonhuman primate species, including New World monkeys (owl monkey and marmoset), Old World monkey (macaque), Asian apes (orangutan and gibbon), and African ape lineages (gorilla, bonobo, and chimpanzee). Compared to the human genome, we identified 1,338,997 lineage-specific fixed structural variants (SVs) disrupting 1,561 protein-coding genes and 136,932 regulatory elements, including the most complete set of human-specific fixed differences. Across 50 million years of primate evolution, we estimate that 819.47 Mbp or ~27% of the genome has been affected by SVs based on analysis of these primate lineages. We identify 1,607 structurally divergent regions (SDRs) wherein recurrent structural variation contributes to creating SV hotspots where genes are recurrently lost (CARDs, ABCD7, OLAH) and new lineage-specific genes are generated (e.g., CKAP2, NEK5) and have become targets of rapid chromosomal diversification and positive selection (e.g., RGPDs). High-fidelity long-read sequencing has made these dynamic regions of the genome accessible for sequence-level analyses within and between primate species for the first time.

genomics↗