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Biology subjects

Shen, Y.

Publications and source records attributed to Shen, Y..

At least 199 records · Page 11Linked to original sources

The PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2

Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenetic approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued proliferation inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in antagonizing G1 progression in a diversity of cell linages, including CML, breast cancer and immortalized cell lines.

molecular biology↗

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 {micro}m, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

bioinformatics↗

Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline AbGen that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. Our AbGen centers around a novel antibody language model (AbLM) that is pretrained on 12 million generic protein domain sequences and fine-tuned on 4,000+ paired VH-VL sequences, with IgG-specific CDR-masking and VH-VL cross-attention. AbLM provides a latent space of IgG sequence embeddings for AbGen, including (a) landscapes of IgGs activities in neutralizing the wild-type virus are analyzed through structure prediction for IgG and IgG-antigen (viral protein spikes receptor binding domain, RBD) interactions; and (b) landscapes of IgGs susceptibility in neutralizing variant viruses are predicted through Gaussian process regression, despite that as few as 14 clinical antibodies responses to variants of concern are available. The AbGen pipeline was applied to over 1300 IgG sequences we collected from RBD-binding B cells of convalescent patients. With experimental validations, AbGen efficiently prioritized IgG candidates against a broad spectrum of viral variants (wildtype, Delta, and Omicron), preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. Compared to other existing protein language models that require 10-100 times more model parameters, AbLM improved the precision from around 50% to 75% to predict IgGs with low variant susceptibility. Furthermore, AbGen enables structure-based computational protein redesign for selected IgG clones with single amino acid substitutions at the RBD-binding interface that doubled the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screen and re- design combining data-driven protein language models and Kriging for antibody sequence analysis and activity prediction, in synergy with physics-driven protein docking and design for antibody-antigen interface analyses and functional optimization.

immunology↗

PreMode predicts mode of action of missense variants by deep graph representation learning of protein sequence and structural context

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreModes prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.

bioinformatics↗

Dnmt3a mutations limit normal and autoreactive Tfh differentiation

Rheumatoid arthritis (RA) is an autoimmune disease characterized by joint inflammation, strongly associated with the activity of autoreactive CD4+ T cells. DNMT3A mutations are the most common somatic mutations found in the hematopoietic system of patients with rheumatoid arthritis. However, the role of DNMT3A in CD4+ T cells and CD4+ T follicular helper (Tfh) cells is poorly understood. Since somatic mutations are not identified in standard genome-wide association studies, somatic mutations impact on the etiology of diseases could be underestimated. Here, we thoroughly characterized and used the KRN+ splenocyte transfer model of autoimmune joint inflammation and inactivated Dnmt3a using CRISPR-Cas9 and standard Cre/loxP approaches. Experiments with competitive bone marrow (BM) chimeras identified a positive role for Dnmt3a in Tfh differentiation, which was validated by comparing mice with Dnmt3a mutations in CD4+ cells to animals with WT Dnmt3a. In conclusion, We identify that Dnmt3a mutations limit normal and autoreactive Tfh differentiation. Key findings- Dnmt3a mutations limit Tfh differentiation, which could contribute to reduced immune responses in individuals with somatic DNMT3A mutations. - Deep characterization of the KRN+ splenocyte transfer model defines a dynamic process leading to reproducible autoimmune joint inflammation. - The immuno-CRISPR (iCR) methodology can be used to test the role of candidate genes in disease models.

immunology↗

Non-Invasive dsRNA Delivery via Feeding for Effective Gene Silencing in Teleost Fish: A Novel Approach in the Study of Gene Function Analysis

RNA interference is a powerful technique for gene silencing, involving post-transcriptional regulation of gene expression. Successful application of RNAi has been demonstrated in various organisms like nematodes, insects, and oysters by ingesting dsRNA-expressing bacteria. In this study, we attempted a non-invasive feeding method in the marine economy teleost fish, Sebastes schlegelii. To validate the feasibility of this approach in S. schlegelii, we utilized rotifers (Brachionus plicatilis), Artemia nauplii, and commercial pellet feed as vectors to deliver Escherichia coli strain HT115 engineered to express double-stranded RNA (dsRNA), targeting dead end (dnd) gene, known to be important for primordial germ cells (PGCs) migration and proliferation in fish. Our experimental results consistently showed that knockdown of the dnd gene effectively inhibited the development of PGCs in S. schlegelii, resulting in gonadal dysplasia and even sex reversal. This study marks a significant advancement in understanding gene function in teleost fish, laying the groundwork for future large-scale research in this field.

genetics↗

Biallelic pathogenic variants in DNAH3 cause male infertility in humans and mice

Axonemal protein complexes, including the outer and inner dynein arms (ODA/IDA), are highly ordered structures of the sperm flagella that drive sperm motility. Deficiencies in several axonemal proteins have been associated with male infertility, which is characterized by asthenozoospermia or asthenoteratozoospermia. Dynein axonemal heavy chain 3 (DNAH3) resides in the IDA and is highly expressed in the testis. However, the relationship between DNAH3 and male infertility is still unclear. Herein, we identified biallelic variants of DNAH3 in four unrelated Han Chinese infertile men with asthenoteratozoospermia through whole-exome sequencing (WES). These variants contributed to deficient DNAH3 expression in the patients sperm flagella. Importantly, the patients represented the anomalous sperm flagellar morphology, and the flagellar ultrastructure was severely disrupted. Intriguingly, Dnah3 knockout (KO) male mice were also infertile, especially showing the severe reduction in sperm movement with the abnormal IDA and mitochondrion structure. Mechanically, nonfunctional DNAH3 expression resulted in decreased expression of IDA-associated proteins in the spermatozoa flagella of patients and KO mice, including DNAH1, DNAH6, and DNALI1, the deletion of which has been involved in disruption of sperm motility. Moreover, the infertility of patients with DNAH3 variants and Dnah3 KO mice could be rescued by intracytoplasmic sperm injection (ICSI) treatment. Our findings indicated that DNAH3 is a novel pathogenic gene for asthenoteratozoospermia and may further contribute to the diagnosis, genetic counseling, and prognosis of male infertility.

genetics↗

Arginine Di-methylation of RIPK3 Safeguards Necroptosis for Intestinal Homeostasis

The necroptosis mediated by RIPK3 is stringently regulated for intestinal homeostasis. Here we found that mice lacking Prmt5 (Protein arginase methyltransferase 5) in intestinal epithelial cells (IECs) caused premature death with IECs necroptosis, villus atrophy and loss of Paneth cells. This pathology can be partially rescued by antibiotic treatment, germ-free breeding condition and pharmaceutical inhibition of RIPK1 and RIPK3, but aggravated for embryonic lethality by Caspase-8 deficiency, which demonstrating the importance of commensal bacteria and necroptosis for the Prmt5-IEC deficiency. Intriguingly, tumor-necrosis factor (TNF) receptor 1(Tnfr1) deficiency could not completely rescue the pathology, and mice deficit in Z- DNA binding protein 1(ZBP1) exhibited shorter lifespan compared with Prmt5 null mice, suggesting Prmt5 loss might trigger TNFR-RIPK1-depenfent and ZBP1- dependent necroptosis. Mechanically, we identified the 479-arginine residue of RIPK3 di-methylated by PRMT5 was an endogenous checkpoint for necroptosis. Furthermore, RIPK3-R479K mutation had higher affinity with both RIPK1 and ZBP1 by immunoprecipitation and STORM (Stochastic Optical Reconstruction Microscopy) analysis, which might explain the endogenous necroptosis triggered by mutated RIPK3 even without upstream stimuli. Moreover, the peptide of RIPK3-SDMA (Symmetric dimethylarginine of 479) could rescue lethality of Prmt5 lacking mice through necrosome formation inhibition, which demonstrating the great potential for necroptosis-related disease treatment through RIPK3 dimethylation targeting.

cell biology↗

Multi-species numerical validation of an efficient algorithm for auditory brainstem response hearing threshold estimation

The Auditory Brainstem Response (ABR) can be used to evaluate hearing sensitivity of animals who are unable to respond to behavioral tasks. However, typical data collection methods are time consuming; decreasing the measurement time may save resources or allow researchers to spend more time on other tasks. Here, an adaptive algorithm is proposed for efficient estimation of ABR thresholds. The algorithm relies on the online update of the predicted hearing threshold from a Gaussian process model as ABR data are collected using iteratively optimized stimuli. To validate the algorithm, ABR threshold estimation is simulated by adaptively sub-sampling pre-collected ABR datasets for which the stimuli were systematically varied in frequency and level. The simulated experiment is performed on 5 datasets of Mouse (2 different datasets), Budgerigar, Gerbil, and Guinea Pig ABRs collected by different laboratories, with a total of 27 ears. The original datasets contain between 68 and 106 stimuli conditions, while the adaptive algorithm is run up to a total of 20 stimuli conditions. The adaptive algorithm ABR threshold estimate is compared against human rater estimates who view the full ABR dataset. The adaptive algorithm threshold matches the human estimates within 10 dB, averaged over frequency, for 19 out of 27 ears. The adaptive procedure is able to provide threshold estimates that are comparable to the human rater estimated thresholds while reducing the measurement time by a factor of 3 to 5. The standard deviation of threshold estimates from successive runs is smaller than the inter-human rater differences, indicating adequate test/retest reliability.

bioengineering↗

Anti-capsule human monoclonal antibodies protect against hypervirulent and pandrug-resistant Klebsiella pneumoniae

The silent pandemic caused by antimicrobial resistance (AMR) requires innovative therapeutic approaches. Human monoclonal antibodies (mAbs), which are among the most transformative, safe and effective drugs in oncology and autoimmunity, are rarely used for infectious diseases and not yet used for AMR. Here we applied an antigen-agnostic strategy to isolate extremely potent human mAbs against Klebsiella pneumoniae (Kp) sequence type 147 (ST147), a hypervirulent and pandrug-resistant clonotype which is spreading globally. Isolated mAbs target the bacterial capsule and the O-antigen. Surprisingly, although both capsule- and O-antigen-specific mAbs displayed bactericidal activity in the picomolar range in vitro, only the capsule-specific mAbs were protective against fulminant ST147 bloodstream infection. Protection correlated with in vitro bacterial uptake by macrophages and enchained bacterial growth. Our study describes the only drug able to protect against pandrug-resistant Kp and provides a strategy to isolate mAbs and identify correlates of protection against AMR bacteria.

microbiology↗

Label free, capillary-scale blood flow mapping in vivo reveals that low intensity focused ultrasound evokes persistent dilation in cortical microvasculature

Non-invasive, low intensity focused ultrasound (FUS) is an emerging neuromodulation technique that offers the potential for precision, personalized therapy. An increasing body of research has identified mechanosensitive ion channels that can be modulated by FUS and support acute electrical activity in neurons. However, neuromodulatory effects that persist from hours to days have also been reported. The brains ability to provide targeted blood flow to electrically active regions involve a multitude of non-neuronal cell types and signaling pathways in the cerebral vasculature; an open question is whether persistent effects can be attributed, at least partly, to vascular mechanisms. Using a novel in vivo optical approach, we found that microvascular responses, unlike larger vessels which prior investigations have explored, exhibit persistent dilation following sonication without the use of microbubbles. This finding and approach offers a heretofore unseen aspect of the effects of FUS in vivo and indicate that concurrent changes in neurovascular function may partially underly persistent neuromodulatory effects.

neuroscience↗

DNA representations and generalization performance of sequence-to-expression models

The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a mechanism-agnostic representation of nucleotide sequences. Moreover, studies have consistently shown that training on mechanistic sequence features leads to much poorer predictions, even with features that are known to correlate with expression, such as DNA sequence motifs, codon usage or mRNA secondary structures. However, despite their excellent local accuracy, current sequence-to-expression models can fail to generalize predictions far away from the training data. Through a comparative study across datasets in Escherichia coli and Saccharomyces cerevisiae, here we show that mechanistic sequence features can provide gains on model generalization, and thus improving their utility for predictive sequence design. We explore several strategies to integrate one-hot encodings and mechanistic features into a single predictive model, including feature stacking, ensemble model stacking, and geometric stacking, a novel architecture based on graph convolutional neural networks. Our work casts new light on mechanistic sequence features, underscoring the importance of domain-knowledge and feature engineering for accurate prediction of protein expression levels.

synthetic biology↗

Pre-existing Immunity to Influenza Aids Ferrets in Developing Stronger and Broader Vaccine-induced Antibody Responses

Influenza seasons occur annually, building immune history for individuals, but the influence of this history on subsequent influenza vaccine protection remains unclear. We extracted data from an animal trial to study its potential impact. The trial involved 80 ferrets, each receiving either one type of infection or a placebo before vaccination. We quantified the vaccine protection by evaluating hemagglutination inhibition (HAI) antibody titer responses. We tested whether hosts with different infection histories exhibited similar level of responses when receiving the same vaccine for all homologous and heterologous outcomes. We observed that different pre-existing immunities were generally beneficial to vaccine induced responses, but varied in magnitude. Without pre-immunity, post-vaccination HAI titers after the 1st dose of the vaccine were less likely to be above 1:40, and a booster shot was needed. Our study suggests that pre-existing immunity may strengthen and extend the homologous and heterologous vaccine protection.

immunology↗

Adaptive, sample-specific parameter selection for more accurate transcript assembly

We introduce AutoTuneX, a data-driven, AI system designed to automatically predict optimal parameters for transcript assemblers -- tools for reconstructing expressed transcripts from the reads in a given RNA-seq sample. AutoTuneX is built by learning parameter knowledge from existing RNA-seq samples and transferring this knowledge to unseen samples. On 1588 human RNA-seq samples tested with two transcript assemblers, AutoTuneX predicts parameters that resulted in 98% of samples achieving more accurate transcript assembly compared to using default parameter settings, with some samples experiencing up to a 600% improvement in AUC. AutoTuneX offers a new strategy for automatically optimizing use of sequence analysis tools.

bioinformatics↗

Microplastics from disposable paper cups are enriched in the placenta and fetus, leading to metabolic and reproductive toxicity during pregnancy

The health implications of microplastics (MPs), especially those originating from hot drinks in disposable paper cups (DPCs), are increasingly alarming. We investigated the accumulation and metabolic and reproductive toxicological effects of MPs from DPCs filled with hot water in various tissues in a pregnant mouse model. Simulating human intake of 0.3, 3.3, and 33.3 cups daily, we found MPs exposure-induced dose-responsive harmful effects on murine fetal development and maternal physiology. MPs were detected in all 13 examined tissues, with the highest accumulation in the cecal contents, followed by significant depositions in the fetus, placenta, kidney, spleen, lung, and heart. A higher proportion of smaller MPs (90.35% < 10 m) was identified in brain tissues. Dose-responsive changes in functional microbiome and gene pathways were observed. Moderate MPs intake of 3.3 cups daily significantly altered cecal microbiome composition and metabolic functions. The transcriptomic functional variations in maternal blood, placenta, and mammary gland underscore the significant impacts of realistic MPs exposure on metabolic and immune health and posing neurodegenerative and miscarriage risks. The benchmark dose framework analysis using tissue-specific gene biomarkers revealed safe exposure limits at 2 to 4 cups/day during pregnancy. Our results indicate selective tissue accumulation and potential metabolic and reproductive toxicities of MPs at exposure levels presumed non-hazardous. Such risks remain unaddressed within current food safety regulations, impacting vulnerable groups such as pregnant women and fetuses. Research HighlightsO_LIMicroplastics released from disposable paper cups filled with hot water showed preferential accumulations in the murine fetus, placenta, kidney, spleen, lung, and heart, with significant adverse impacts on fetal development. C_LIO_LIMicroplastic exposure led to dose-responsive maternal microbiome changes associated with increased fatty acid biosynthesis and elevated expressions of genes related to viral infections, neurodegenerative diseases, oxidative stress, and miscarriage risk. C_LIO_LIA consumption level of 3.3 cups/day was sufficient to elicit systemic metabolic and reproductive toxicity, with a predicted safe exposure limit of 2 to 4 cups/day during pregnancy by benchmark dose framework analysis with molecular biomarkers. C_LI

molecular biology↗

AXOLOTL: an accurate method for detecting aberrant gene expression in rare diseases using coexpression constraints

BackgroundThe assessment of aberrant transcription events in patients with rare diseases holds promise for significantly enhancing the prioritization of causative genes, a practice already widely employed in clinical settings to increase diagnostic accuracy. Nevertheless, the entangled correlation between genes presents a substantial challenge for accurate identification of causal genes in clinical diagnostic scenarios. Currently, none of the existing methods are capable of effectively modeling gene correlation. MethodsWe propose a novel unsupervised method, AXOLOTL, to identify aberrant gene expression events in an RNA expression matrix. AXOLOTL effectively addresses biological confounders by incorporating coexpression constraints. ResultsWe demonstrated the superior performance of AXOLOTL on representative RNA-seq datasets, including those from the GTEx healthy cohort, mitochondrial disease cohort and Collagen VI-related dystrophy cohort. Furthermore, we applied AXOLOTL to real case studies and demonstrated its ability to accurately identify aberrant gene expression and facilitate the prioritization of pathogenic variants.

bioinformatics↗

In vitro antimicrobial mechanism of diacerein and potential to reverse MRSE resistance to β-lactams

Staphylococcus epidermidis is the most common pathogens causing ocular infection. With the increase of drug resistance rate, it poses a new challenge to anti-infection treatment. In this study, we analyzed the drug resistance of S. epidermidis isolated from the eye in the past 5 years to guide empirical antibiotics treatment. Then, the MIC and MBC of diacerein against MRSE were detected, and continuous induced resistance experiments confirmed that MRSE not easily induce resistance to diacerein. In addition, it was observed that diacerein induced MRSE cell lysis, increased membrane permeability and resulted in intracellular ROS accumulation. Diacerein does not have toxic effects to HCEC at effective bacteriostatic concentrations. The results of checkerboard assay indicated that the combination of diacerein and {beta}-lactams had additive effect on MRSE. We also observed that diacerein may reverse MRSE resistance to {beta}-lactam drugs by affecting the active efflux system. In conclusion, our results provide strong evidence for the high therapeutic potential of diacerein against MRSE.

microbiology↗

SCC3 acts as the cohesin and inhibits inter-sister chromatids repair during rice meiosis

Cohesin is a multi-subunit protein that plays a pivotal role in holding sister chromatids together during cell division. Sister chromatid cohesion 3 (SCC3), constituents of cohesin complex, is highly conserved from yeast to mammals. Since the deletion of individual cohesin subunit always causes lethality, it is difficult to dissect its biological function in both mitosis and meiosis. Here, we obtained scc3 weak mutants using CRISPR-Cas9 system to explore its function during rice mitosis and meiosis. The scc3 weak mutants displayed obvious vegetative defects and complete sterility, underscoring the essential roles of SCC3 in both mitosis and meiosis. SCC3 is localized on chromatin from interphase to prometaphase in mitosis. However, in meiosis, SCC3 acts as an axial element during early prophase I and subsequently situates onto centromeric regions following the disassembly of the synaptonemal complex. The loading of SCC3 onto meiotic chromosomes depends on REC8. scc3 shows severe defects in homologous pairing and synapsis. Consequently, SCC3 functions as an axial element that is essential for maintaining homologous chromosome pairing and synapsis during meiosis.

genetics↗