Biology subjects

Shang, E.

Publications and source records attributed to Shang, E..

7 recordsLinked to original sources

bioRxiv2026

Chromosome-level genome assembly of macroalgae Gracilariopsis lemaneiformis

As an important cultivated red alga, Gracilariopsis lemaneiformis has great economic and ecological value. However, its existing genome assembly is highly fragmented and inadequately annotated. In this study, we constructed the first high-quality chromosome-level genome of Gp. lemaneiformis using PacBio long reads, Illumina short reads and Hi-C sequencing data. The assembled genome was approximately 86.66 Mb and the assembled sequences were anchored to 28 pseudo-chromosomes with lengths ranging from 1.70 to 7.81 Mb. 99.91% of the PacBio reads could be mapped to our assembly. In total, 8,664 genes were annotated, and the repeat elements identified in Gp. lemaneiformis constituted 65.04% of the whole genome, including 2.24% tandem repeat sequences and 62.81% interspersed repeats. We also established a high-evidence phylogenetic tree from 19 representative algae species, with the main aim to calculate their divergence times. This high-quality genome of Gp. lemaneiformis provides a crucial foundation for understanding genetic characteristics, investigating the genomic evolution, and facilitating molecular breeding.

genomics↗

bioRxiv2025

Genetic Convergence Analysis of CRISPR Perturbations Deciphers Gene Functional Similarity

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique to determine the functionality of a gene by directly perturbing the DNA of the gene. One of the most intriguing recent problems is quantifying the similarity between CRISPR perturbations, for example, whether they upregulate the same set of downstream genes. In this context, genetic convergence refers to the phenomenon where CRISPR disruptions of different genes lead to a similar downstream outcome. Existing methods are mostly heuristic. We present XConTest, a two-step, cross-validated procedure for assessing the genetic convergence problem. The test statistics calculated from that procedure are approximately standard normal when the two perturbations have an orthogonal influence on the cell expression profile. We apply XConTest to two studies: an investigation of the common impact of a suite of autism genes, and a large-scale study of genes associated with immune response to determine sets of genes with common functionality.

genomics↗

bioRxiv2025

Predicting the unseen: a diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution

Predicting cellular responses to genetic perturbations is critical for advancing our understanding of gene regulation. While single-cell CRISPR perturbation assays such as Perturb-seq provide direct measurements of gene function, the scale of these experiments is limited by cost and feasibility. This motivates the development of computational approaches that can accurately infer responses to unmeasured perturbations from related experimental data. We introduce dbDiffusion, a generative framework that integrates diffusion models with classifier-free guidance derived from perturbation information, operating in latent space through a variational autoencoder (VAE). Diffusion models are probabilistic generative models that approximate data distributions by reversing a Markovian diffusion process, progressively denoising Gaussian noise into structured outputs. By exploiting biological similarities in gene expression profiles and relationships among perturbations, dbDiffusion enables the conditional generation of gene expressions for previously unobserved perturbations. In contrast to competing approaches, dbDiffusion does not rely on LLM or foundation models, which have been found to yield unsatisfactory results. Rather, it leverages embeddings derived from measured perturbations to generalize to unseen pertur-bations, effectively transferring information across related experimental conditions. In benchmarking against state-of-the-art methods on Perturb-seq datasets, dbDiffusion demonstrates superior accuracy in predicting perturbation responses. A methodological innovation of dbDiffusion is the integration of prediction-powered inference, which corrects for biases inherent in generative models and enables statistically rigorous downstream tasks, including identification of differentially expressed genes. By combining deep generative modeling with principled inference, dbDiffusion establishes a scalable computational framework for predicting and analyzing transcriptomic perturbation responses, significantly extending the utility of Perturb-seq experiments.

genomics↗

bioRxiv2023

Plasma-free samples for transcriptomic analysis: a potential alternative to whole blood samples

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the viability of employing PFB or SFB samples as substitute RNA sources in transcriptomic analysis and performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples in terms of protein-coding gene expression patterns, differential expression gene profiling, and immunological characterizations, suggesting that PFB can be a viable alternative for transcriptomic analysis. This contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

bioinformatics↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2021

The Flowering Repressor SVP recruits the TOPLESS co-repressor to control flowering in chrysanthemum and Arabidopsis

Plant flowering time is a consequence of the perception of environmental and endogenous signals. The MCM1-AGAMOUSDEFICIENS-SRF-box (MADS-box) gene SHORT VEGETATIVE PHASE (SVP) is a pivotal repressor that negatively regulates the floral transition during the vegetative phase. The transcriptional corepressor TOPLESS (TPL) plays critical roles in many aspects of plant life. An interaction first identified between the second LXLXLX motif (LRLGLP) of CmSVP with CmTPL1-2, which can repress the expression of a key flowering factor CmFTL3 by binding its promotor CArG element in chrysanthemum. Genetic analysis suggested that the CmSVP-CmTPL1-2 transcriptional complex is a prerequisite for SVP to act as a floral repressor, which reduces CmFTL3 transcriptional activity. CmSVP rescued the phenotype of the svp-31 mutant in Arabidopsis, and overexpression of AtSVP or CmSVP in the Arabidopsis dominant negative mutation tpl-1 led to a loss-of-function in late flowering, which confirmed the highly conserved function of SVP in the two completely different species. Thus, we have validated a conserved machinery wherein SVP relies on TPL to inhibit flowering through the direct regulation of FT, which is more meaningful for the evolution of species and could be translated to high-quality cultivation and breeding of crops.

plant biology↗

bioRxiv2020

The Potential for SARS-CoV-2 to Evade Both Natural and Vaccine-induced Immunity

SARS-CoV-2 attaches to the surface of susceptible cells through extensive interactions between the receptor binding domain (RBD) of its spike protein and angiotensin converting enzyme type 2 (ACE2) anchored in cell membranes. To investigate whether naturally occurring mutations in the spike protein are able to prevent antibody binding, yet while maintaining the ability to bind ACE2 and viral infectivity, mutations in the spike protein identified in cases of human infection were mapped to the crystallographically-determined interfaces between the spike protein and ACE2 (PDB entry 6M0J), antibody CC12.1 (PDB entry 6XC2), and antibody P2B-2F6 (PDB entry 7BWJ). Both antibody binding interfaces partially overlap with the ACE2 binding interface. Among 16 mutations that map to the RBD:CC12.1 interface, 11 are likely to disrupt CC12.1 binding but not ACE2 binding. Among 12 mutations that map to the RBD:P2B-2F6 interface, 8 are likely to disrupt P2B-2F6 binding but not ACE2 binding. As expected, none of the mutations observed to date appear likely to disrupt the RBD:ACE2 interface. We conclude that SARS-CoV-2 with mutated forms of the spike protein may retain the ability to bind ACE2 while evading recognition by antibodies that arise in response to the original wild-type form of the spike protein. It seems likely that immune evasion will be possible regardless of whether the spike protein was encountered in the form of infectious virus, or as the immunogen in a vaccine. Therefore, it also seems likely that reinfection with a variant strain of SARS-CoV-2 may occur among people who recover from Covid-19, and that vaccines with the ability to generate antibodies against multiple variant forms of the spike protein will be necessary to protect against variant forms of SARS-CoV-2 that are already circulating in the human population.

immunology↗