Biology subjects

Wu, P.-Y.

Publications and source records attributed to Wu, P.-Y..

13 recordsLinked to original sources

bioRxiv2026

A Distinct Layer 1 Astrocyte Program Shapes Perisynaptic Structure and Calcium Signaling in Mouse Motor Cortex

Although layer-specific molecular and morphological diversity among cortical astrocytes is increasingly well established, how these distinct states are linked to specialized calcium signaling and maintained in the adult cortex remains unclear. Here, combining super-resolution structural imaging, two-photon calcium imaging, and analysis of public single-cell and spatial transcriptomic resources, we identify Layer 1 (L1) astrocytes in mouse primary motor cortex as a distinct superficial astrocyte program. These cells occupy compact territories yet contain dense synapse-associated loop-like structures and display frequent, fast, broadly spreading calcium events that engage a large fraction of the territory. Transcriptomic analyses identify Id1 and Id3 as enriched components of this superficial program. CRISPR-Cas9 deletion of Id1 and Id3 in adult astrocytes selectively disrupts L1 and superficial Layer 2/3 (L2/3) astrocytes, expanding territory size, reducing fine-process complexity, and suppressing calcium activity. Thus, adult layer-specific transcriptional programs maintain specialized astrocyte structure and signaling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=185 SRC="FIGDIR/small/712002v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@89c916org.highwire.dtl.DTLVardef@1f0d71eorg.highwire.dtl.DTLVardef@bb02f3org.highwire.dtl.DTLVardef@1bedf7e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LILayer 1 astrocytes in mouse motor cortex have compact, loop-rich nanoarchitecture C_LIO_LILayer 1 astrocytes show fast, frequent, widespread calcium events C_LIO_LISingle-cell data define a superficial astrocyte state enriched for Id1 and Id3 C_LIO_LIAdult Id1/Id3 loss selectively disrupts Layer 1 astrocyte structure and calcium C_LI In BriefBhattacharjee et al., identify a specialized Layer 1 astrocyte state in mouse motor cortex, defined by compact, loop-rich nanoarchitecture and unusually high calcium signaling. Id1/Id3 deletion selectively disrupts this superficial program, linking layer-specific astrocyte form and function to adult transcriptional control.

neuroscience↗

bioRxiv2025

Assessment of segregation variance estimates from derivation, simulations, and empirical data in autotetraploid species exemplified in potato

The optimal choice of parents and crosses and, therefore, the prediction of the segregation variance are of high relevance to maximize genetic gain in breeding programs. Several methods have been developed for the prediction of segregation variance, including correlation with genotypic diversity, progeny simulations, or algebraic derivations in case of a diploid inheritance. To the best of our knowledge, no algebraic derivation using parental genotypic information is available to predict segregation variance for autotetraploid species. The objectives of our study were to (1) derive algebraic derivation based on linkage disequilibrium (LD) between linked loci to predict the segregation variance in autotetraploid species; (2) compare the performance of segregation variance estimated based on simulated progenies and the algebraic derivation; (3) investigate by simulations how experimental parameters affect the accuracy of segregation variance prediction; and (4) compare the segregation variance estimated in empirical data of potato and the one based on the algebraic derivation. The segregation variance estimated by the developed derivations showed very high correlations with the one observed in large simulated progenies, but those were lower when phased parental haplotypes were not available or family size decreased. The correlation between segregation variance estimated by the developed derivation and the empirical data was low. This could be attributed to the small family size used in the study, which we could show to increase LD between unlinked loci. The proposed algebraic derivations promise to be a precise alternative to simulations to help breeders in optimizing their family choices and sizes considering the segregation variance.

genetics↗

bioRxiv2025

Genetic architecture and cellular basis of flag leaf size variation in barley

Flag leaf is the major contributor of photosynthetic assimilates to developing grains. We investigated the genetic architecture and cellular basis of flag leaf length (FLL) and width (FLW) in a multi-parent population comprised of 45 recombinant inbred line (RIL) populations (HvDRR) in barley. Fine mapping of a major quantitative trait locus (QTL) was performed as a first step to isolate the causal gene. Natural variation of FLL and FLW across multi-environment was highly heritable and genotypes adapted to warm climate produced longer and wider flag leaf than those adapted to cooler regions. The variation in flag leaf size was quantitatively inherited and influenced by 24 consensus QTLs of which 17 have not been reported earlier. Further, validation of QTL qHvDRR-FLS-8 and qHvDRR-FLS-17 in nearly isogenic RILs demonstrated that these QTLs also controlled length and width of leaves older than flag leaf. The number of epidermal cells primarily determined the differences in FLL and FLW. In addition, we identified the previously unknown effect of genic- and epiallele at Vrn-H1 on flag leaf size variation in spring barley. Furthermore, we fine-mapped qHvDRR-FLS-8, narrowing the interval from 8.7 to 3.5 Mb. In conclusion, our study identified the genomic regions associated with morphological and anatomical variation for leaf size and set the stage to uncover causal genes.

genetics↗

bioRxiv2024

Investigating the barley methylome, its variation and association with genomic, transcriptomic, and phenotypic variation

Epigenetic variation contributes to explaining the missing heritability of complex traits. In order to understand the genome-wide methylation variation in spring barley, our objectives were to gain fundamental insight into the barley methylome through whole genome bisulfite sequencing, characterizing methylation variation among 23 parental inbreds of a community resource for genetic mapping of phenotypic traits, and assessing the association of differentially methylated regions (DMRs) with single nucleotide polymorphisms (SNPs) and gene expression variation. Compared to other angiosperms, barley was found to have a highly methylated genome with an average genome wide methylation level of 88.6%, 58.1%, and 1.4% in the CpG, CHG, and CHH sequence context, respectively. We identified just below 500 000 differentially methylated regions (DMRs) among the inbreds. About 64%, 64%, and 83% of the DMRs were not associated with genomic variation in the CpG, CHG, and CHH context, respectively. The methylation level of around 6% of all DMRs was significantly associated with gene expression, where the directionality of the correlation was depended on the relative location of the DMR to the respective gene with a recognizable pattern. Notably, this pattern was much more specific and spatially confined than the association of methylation with gene expression across genes in a singular inbred line. We exemplified this association between DNA methylation and gene expression on the known flowering promoting gene VRN-H1 and identified a highly methylated epiallele associated with earlier flowering time. Finally, methylation was shown to improve the prediction abilities of genomic prediction models for a variety of traits over models using solely SNPs and gene expression as predictors. These observations highlight the independence of DNA methylation to sequence variation and their difference in information content. Our discoveries suggest that epigenetic variation provides a layer of information likely not predictable by other means and is therefore a valuable addition to genomic prediction models.

plant biology↗

bioRxiv2024

Optimal implementation of genomic selection in clone breeding programs - exemplified in potato: II. Effect of selection strategy and cross-selection method on long-term genetic gain

Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato is lacking so far. The objectives of our study were to (i) assess how different CS methods incorporating GS with or without consideration of genetic variability affect both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selection intensities had a higher short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) reached the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) kept the same level of genetic gain but resulted in a higher diversity and a lower number of fixed QTL. Moreover, EUCD had a relatively high efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeders objectives. Our results provide breeders with concrete methods to improve their potato breeding programs. Key messageCross-selection method considering progeny mean performance and genetic variability can improve long-term genetic gain and keep genetic diversity in clone breeding.

genetics↗

bioRxiv2023

Genetic mapping reveals new loci and alleles for flowering time and plant height using the double round-robin population of barley

Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Although their role as key traits, a comprehensive understanding of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Through a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as identify new genetic variants by single and multi-population quantitative trait loci (QTL) analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected one new QTL for flowering time and two new QTL for plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect or low frequency, genetic variants underlie the control of these two traits.

genetics↗

bioRxiv2022

Optimal implementation of genomic selection in clone breeding programs - exemplified in potato: I. Effect of selection strategy, implementation stage, and selection intensity on short-term genetic gain

Genomic selection (GS) is used in many animal and plant breeding programs to enhance genetic gain for complex traits. However, its optimal integration in clone breeding programs that up to now relied on phenotypic selection (PS) requires further research. The objectives of this study were to (i) investigate under a fixed budget how the weight of GS relative to PS, the stage of implementing GS, the correlation between an auxiliary trait assessed in early generations and the target trait, the variance components, and the prediction accuracy affect the genetic gain of the target trait of GS compared to PS, (ii) determine the optimal allocation of resources maximizing the genetic gain of the target trait in each selection strategy and for varying cost scenarios, and (iii) make recommendations to breeders how to implement GS in clone and especially potato breeding programs. In our simulation results, any selection strategy involving GS had a higher short-term genetic gain for the target trait than Standard-PS. In addition, we show that implementing GS in consecutive selection stages can largely enhance short-term genetic gain and recommend the breeders to implement GS at single hills and A clone stages. Furthermore, we observed for selection strategies involving GS that the optimal allocation of resources maximizing the genetic gain of the target trait differed considerably from those typically used in potato breeding programs. Therefore, our study provides new insight for breeders regarding how to optimally implement GS in a commercial potato breeding program to improve the short-term genetic gain for their target trait.

genetics↗

bioRxiv2022

lncRNA HOTAIRM1 Coordinates with RNA Processing Factors in DNA Damage Repair

The eukaryotic RNA processing factor Y14 participates in double-strand break (DSB) repair via its RNA-dependent interaction with the non-homologous end-joining (NHEJ) complex. We identified the long non-coding RNA HOTAIRM1 as a candidate that mediates this interaction. HOTAIRM1 localized to DNA damage sites induced by ionizing radiation. Depletion of HOTAIRM1 delayed the recruitment of DNA damage response and repair factors to DNA lesions and reduced DNA repair efficiency. Identification of the HOTAIRM1 interactome revealed a large set of RNA processing factors including mRNA surveillance factors. The surveillance factors Upf1 and SMG6 localized to DNA damage sites in a HOTAIRM1-dependent manner. Depletion of Upf1 or SMG6 increased the level of DSB-induced non-coding transcripts at damaged sites, indicating a pivotal role for Upf1/SMG6-mediated RNA degradation in DNA repair. We conclude that HOTAIRM1 serves as an assembly scaffold for both DNA repair and RNA processing factors that act in concert to repair DSBs.

molecular biology↗

bioRxiv2022

The double round-robin population unravels the genetic architecture of grain size in barley

Grain number, size and weight primarily determine the yield of barley. Although the genes regulating grain number are well studied in barley, the genetic loci and the causal gene for sink capacity are poorly understood. Therefore, the primary objective of our work was to dissect the genetic architecture of grain size and weight in barley. We used a multi-parent population developed from a genetic cross between 23 diverse barley inbreds in a double round-robin design. Seed size-related parameters such as grain length, grain width, grain area and thousand-grain weight were evaluated in the HvDRR population comprising 45 recombinant inbred line sub-populations. We found significant genotypic variation for all seed size characters and observed 84 % or higher heritability across four environments. The results of the quantitative trait locus (QTL) detection indicate that the genetic architecture of grain size is more complex than reported previously. In addition, both cultivars and landraces contributed positive alleles at grain size QTLs. Candidate genes identified using genome-wide variant calling data for all parental inbred lines indicated overlapping and potential novel regulators of grain size in cereals. Furthermore, our results indicated that sink capacity was the primary determinant of grain weight in barley. HighlightMulti parent population uncovered the natural allelic series across quantitative loci associated with grain size and weight that will contribute to identifying causal genes and yield improvement in barley.

plant biology↗

bioRxiv2022

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants (SNV). However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity >70% and precision >95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

genomics↗

bioRxiv2022

Omicron-specific mRNA vaccine induced potent neutralizing antibody against Omicron but not other SARS-CoV-2 variants

The emerging SARS-CoV-2 variants of concern (VOC) harbor mutations associated with increasing transmission and immune escape, hence undermine the effectiveness of current COVID-19 vaccines. In late November of 2021, the Omicron (B.1.1.529) variant was identified in South Africa and rapidly spread across the globe. It was shown to exhibit significant resistance to neutralization by serum not only from convalescent patients, but also from individuals receiving currently used COVID-19 vaccines with multiple booster shots. Therefore, there is an urgent need to develop next generation vaccines against VOCs like Omicron. In this study, we develop a panel of mRNA-LNP-based vaccines using the receptor binding domain (RBD) of Omicron and Delta variants, which are dominant in the current wave of COVID-19. In addition to the Omicron- and Delta-specific vaccines, the panel also includes a "Hybrid" vaccine that uses the RBD containing all 16 point-mutations shown in Omicron and Delta RBD, as well as a bivalent vaccine composed of both Omicron and Delta RBD-LNP in half dose. Interestingly, both Omicron-specific and Hybrid RBD-LNP elicited extremely high titer of neutralizing antibody against Omicron itself, but few to none neutralizing antibody against other SARS-CoV-2 variants. The bivalent RBD-LNP, on the other hand, generated antibody with broadly neutralizing activity against the wild-type virus and all variants. Surprisingly, similar cross-protection was also shown by the Delta-specific RBD-LNP. Taken together, our data demonstrated that Omicron-specific mRNA vaccine can induce potent neutralizing antibody response against Omicron, but the inclusion of epitopes from other variants may be required for eliciting cross-protection. This study would lay a foundation for rational development of the next generation vaccines against SARS-CoV-2 VOCs.

microbiology↗

bioRxiv2021

CpG-adjuvanted stable prefusion SARS-CoV-2 spike protein protected hamsters from SARS-CoV-2 challenge

The COVID-19 pandemic presents an unprecedented challenge to global public health. Rapid development and deployment of safe and effective vaccines are imperative to control the pandemic. In the current study, we applied our adjuvanted stable prefusion SARS-CoV-2 spike (S-2P)-based vaccine, MVC-COV1901, to hamster models to demonstrate immunogenicity and protection from virus challenge. Golden Syrian hamsters immunized intramuscularly with two injections of 1 {micro}g or 5 {micro}g of S-2P adjuvanted with CpG 1018 and aluminum hydroxide (alum) were challenged intranasally with SARS-CoV-2. Prior to virus challenge, the vaccine induced high levels of neutralizing antibodies with 10,000-fold higher IgG level and an average of 50-fold higher pseudovirus neutralizing titers in either dose groups than vehicle or adjuvant control groups. Six days after infection, vaccinated hamsters did not display any weight loss associated with infection and had significantly reduced lung pathology and most importantly, lung viral load levels were reduced to lower than detection limit compared to unvaccinated animals. Vaccination with either 1 g or 5 g of adjuvanted S-2P produced comparable immunogenicity and protection from infection. This study builds upon our previous results to support the clinical development of MVC-COV1901 as a safe, highly immunogenic, and protective COVID-19 vaccine.

microbiology↗

bioRxiv2020

A Statistical Framework for QTL Hotspot Detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These detection methods attempt to address some of the concerns, including the correlation structure among traits, the magnitude of LOD scores within a hotspot and computational cost, that arise during the process of QTL hotspot detection. In this article, we describe a statistical framework that can handle both types of data as well as address all the concerns at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computation cost, and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top {gamma}n, profile, are introduced into the framework. The trait grouping attempts to group the closely linked or pleiotropic traits together to take care of the true linkages and cope with the underestimation of hotspot thresholds due to non-genetic correlations (arising from ignoring the correlation structure among traits), so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top {gamma}n, profile is designed to outline the LOD-score pattern of a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD score distributions. Real examples, numerical analysis and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

genetics↗