Biology subjects

Shen, X.

Publications and source records attributed to Shen, X..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Role of histone variants H2BC1 and H2AZ.2 in H2AK119ub nucleosome organization and Polycomb gene silencing

Ubiquitination of histone H2A at lysine 119 residue (H2AK119ub) plays critical roles in a wide range of physiological processes, including Polycomb gene silencing 1,2, replication 3-5, DNA damage repair 6-10, X inactivation 11,12, and heterochromatin organization 13,14. However, the underlying mechanism and structural basis of H2AK119ub remains largely elusive. In this study, we report that H2AK119ub nucleosomes have a unique composition, containing histone variants H2BC1 and H2AZ.2, and importantly, this composition is required for H2AK119ub and Polycomb gene silencing. Using the UAB domain of RSF1, we purified H2AK119ub nucleosomes to a sufficient amount and purity. Mass spectrometry analyses revealed that H2AK119ub nucleosomes contain the histone variants H2BC1 and H2AZ.2. A cryo-EM study resolved the structure of native H2AK119ub nucleosomes to a 2.6A resolution, confirming H2BC1 in one subgroup of H2AK119ub nucleosomes. Tandem GST-UAB pulldown, Flag-H2AZ.2, and HA-H2BC1 immunoprecipitation revealed that H2AK119ub nucleosomes could be separated into distinct subgroups, suggesting their composition heterogeneity and potential dynamic organization. Knockout or knockdown of H2BC1 or H2AZ.2 reduced cellular H2AK119ub levels, establishing H2BC1 and H2AZ.2 as critical determinants of H2AK119ub. Furthermore, genomic binding profiles of H2BC1 and H2AZ.2 overlapped significantly with H2AK119ub binding, with the most significant overlapping in the gene body and intergenic regions. Finally, assays in developing embryos reveal an interaction of H2AZ.2, H2BC1, and RING1A in vivo. Thus, this study revealed, for the first time, that the H2AK119ub nucleosome has a unique composition, and this composition is required for H2AK119ub and Polycomb gene silencing.

molecular biology↗

bioRxiv2024

Reformer: Deep learning model for characterizing protein-RNA interactions from sequence at single-base resolution

Protein-RNA interactions play an essential role in the regulation of transcription, translation, and metabolism of cellular RNA. Here, we develop Reformer, a deep learning model that predicts protein-RNA binding affinity purely from sequence. We developed Reformer with 155 RNA binding protein (RBP) targets from 3 cell lines. Reformer achieved high prediction accuracy at single-base resolution when tasking with inferring protein- and cell-type-specific binding affinity. We conducted electrophoretic mobility shift assays to validate high-impact RNA regulation mutations predicted by Reformer. In addition, Reformer learned to capture protein binding motifs that cannot be discovered by eCLIP-seq experiments. Furthermore, we demonstrated that motif signatures related to RNA processing functions are encoded within Reformer. In conclusion, Reformer will facilitate interpretation of the regulation mechanisms underlying RNA processing.

bioinformatics↗

bioRxiv2024

OmniNA: A foundation model for nucleotide sequences

Foundation models have demonstrated exceptional efficacy across diverse downstream tasks. However, within the realms of genomics and transcriptomics, a notable gap persists in the availability of models that afford a comprehensive understanding of nucleotide sequence principles across various species. Here, we present OmniNA, a foundation generative model designed for comprehensive nucleotide sequence learning. The model was pre-trained on 91.7 million nucleotide sequences and the corresponding annotations encompassing 1076.2 billion bases and 197 million words spanning a multitude of species. We demonstrated OmniNA gains the capacity to understand the semantics of the nucleotide sequence and textual annotations by analyzing the learned representation of the pre-trained model. OmniNA can be fine-tuned to align multiple nucleotide learning tasks with natural language paradigms. We demonstrate OmniNA-1.7B surpasses or rivals state-of-the art methods in 17 nucleotide tasks, encompassing nucleotide sequences detection and species classification. The models understanding of nucleotide grammars enhances its capability to reveal the mutation effect of nucleotide sequence on DNA and RNA processing. We hereby release the OmniNA-1.7B model as an open-source contribution to the research community. This foundation model signifies a step toward advancing our comprehension of nucleotide sequences across diverse species and holds substantial promise to facilitating genomics and transcriptomics research.

bioinformatics↗

bioRxiv2023

Aberrant Connectivity Across the Lifespan in a Mouse Model of Alzheimer's disease and Rescue by mGlur5 Modulator Treatment

Amyloid accumulation in Alzheimers disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

neuroscience↗

bioRxiv2023

Single-cell transcriptomic analysis of immune cell dynamics in the healthy human endometrium

The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analyses on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages. Our investigation revealed that proliferative natural killer (NK) cells, a potential source of endometrial NK cells, exhibit the most robust proliferative and differentiation potential during non-pregnant stages. During early pregnancy, NK cells display high oxidative phosphorylation metabolism activity, and together with macrophages and T cells, exhibit a strong type II interferon response. Based on our cell-cell interaction analyses, we identify a large majority of interaction pairs to occur in late pregnancy. Finally, we explored the correlation between stage-specific alterations in transcriptomics and the risk genes of common reproductive diseases, unveiling that MHC class I/II molecules, along with TGFBR1, exhibited the potential to serve as biomarkers. Our study provides insights into the dynamics of the endometrial immune microenvironment during different reproductive cycle stages, thus serving as a reference for detecting pathological changes during pregnancy.

bioinformatics↗

bioRxiv2023

Germline-targeting SOSIP trimer immunization elicits precursor CD4 binding-site targeting broadly neutralizing antibodies in infant macaques.

A vaccine that can achieve protective immunity prior to sexual debut is critical to prevent the estimated 410,000 new HIV infections that occur yearly in adolescents. As children living with HIV can make broadly neutralizing antibody (bnAb) responses in plasma at a faster rate than adults, early childhood is an opportune window for implementation of a multi-dose HIV immunization strategy to elicit protective immunity prior to adolescence. Therefore, the goal of our study was to assess the ability of a B cell lineage-designed HIV envelope SOSIP to induce bnAbs in early life. Infant rhesus macaques (RMs) received either BG505 SOSIP or the germline-targeting BG505 GT1.1 SOSIP (n=5/group) with the 3M-052-SE adjuvant at 0, 6, and 12 weeks of age. All infant RMs were then boosted with the BG505 SOSIP at weeks 26, 52 and 78, mimicking a pediatric immunization schedule of multiple vaccine boosts within the first two years of life. Both immunization strategies induced durable, high magnitude binding antibodies and plasma autologous virus neutralization that primarily targeted the CD4-binding site (CD4bs) or C3/465 epitope. Notably, three BG505 GT1.1-immunized infants exhibited a plasma HIV neutralization signature reflective of VRC01-like CD4bs bnAb precursor development and heterologous virus neutralization. Finally, infant RMs developed precursor bnAb responses at a similar frequency to that of adult RMs receiving a similar immunization strategy. Thus, a multi-dose immunization regimen with bnAb lineage designed SOSIPs is a promising strategy for inducing protective HIV bnAb responses in childhood prior to adolescence when sexual HIV exposure risk begins.

immunology↗

bioRxiv2023

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in pig.

Understanding the molecular and cellular mechanisms that underlie complex traits in pigs is crucial for enhancing their genetic improvement program and unleashing their substantial potentials in human biomedicine research. Here, we conducted a meta-GWAS analysis for 232 complex traits with 28.3 million imputed whole-genome sequence variants in 70,328 individuals from 14 pig breeds. We identified a total of 6,878 genomic regions associated with 139 complex traits. By integrating with the Pig Genotype-Tissue Expression (PigGTEx) resource, we systemically explored the biological context and regulatory circuits through which these trait-associated variants act and finally prioritized 16,664 variant-gene-tissue-trait circuits. For instance, rs344053754 regulates the expression of UGT2B31 in the liver by affecting the activity of regulatory elements and ultimately influences litter weight at weaning. Furthermore, we investigated the conservation of genetic and regulatory mechanisms underlying 136 human traits and 232 pig traits. Overall, our multi-breed meta-GWAS in pigs provides invaluable resources and novel insights for understanding the regulatory and evolutionary mechanisms of complex traits in both pigs and humans.

genetics↗

bioRxiv2023

Hybrid hydrogel-extracellular matrix scaffolds identify distinct ligand and mechanical signatures in cardiac aging

Extracellular matrix (ECM) remodeling of cardiac tissue is a key contributor to age-related cardiovascular disease and dysfunction. Aberrant secretion, structural perturbations, and degradation of specific ECM components lead to significant alterations in ECM properties that disrupt healthy cell and tissue homeostasis. These changes in ECM are multifaceted, as alterations in ligand presentation, including both biochemical and architectural aspects, are often accompanied by stiffness changes, clouding our understanding of how and which ECM properties contribute to a dysfunctional state. To identify the specific roles of these interconnected ECM cues and elucidate their mechanistic regulation in cellular function, we developed a material system that can independently present these two distinct matrix properties, i.e., ligand presentation and stiffness, to cultured cells in vitro. We describe a decellularized ECM-synthetic hydrogel hybrid scaffold that maintains native matrix composition and organization of young or aged murine cardiac tissue with independently tunable scaffold mechanics that mimic young or aged tissue stiffness. Seeding these scaffolds with primary cardiac fibroblasts (CFs) from young or aged mice, we identify distinct age- and ECM-dependent mechanisms of CF activation. Importantly, we show that ligand presentation of young ECM can outweigh profibrotic stiffness cues typically present in aged ECM in maintaining or driving CF quiescence, thereby highlighting the unique roles of ECM in aging. Ultimately, these tunable scaffolds can enable the discovery of specific ECM targets to prevent aging dysfunction and promote rejuvenation. DECIPHER: DECellularized In Situ Polyacrylamide Hydrogel-ECM hybRid

bioengineering↗

bioRxiv2023

Comparative Analysis of SARS-CoV-2 Antigenicity across Assays and in Human and Animal Model Sera

The antigenic evolution of SARS-CoV-2 requires ongoing monitoring to judge the immune escape of newly arising variants. A surveillance system necessitates an understanding of differences in neutralization titers measured in different assays and using human and animal sera. We compared 18 datasets generated using human, hamster, and mouse sera, and six different neutralization assays. Titer magnitude was lowest in human, intermediate in hamster, and highest in mouse sera. Fold change, immunodominance patterns and antigenic maps were similar among sera. Most assays yielded similar results, except for differences in fold change in cytopathic effect assays. Not enough data was available for conclusively judging mouse sera, but hamster sera were a consistent surrogate for human first-infection sera.

bioinformatics↗

bioRxiv2023

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

Microbial communities exert a substantial influence on human health and have been unequivocally associated with a spectrum of human maladies, encompassing conditions such as anxiety1, depression2, hypertension3, cardiovascular diseases4, obesity4,5, diabetes6, inflammatory bowel disease7, and cancer8,9. This intricate interplay between microbiota community structures and host pathophysiology has kindled substantial interest and spurred active research endeavors across various scientific domains. Despite significant strides in sequencing technologies, which have unveiled the vast diversity of microbial populations across diverse ecosystems, the analysis of microbiome data remains a formidable challenge. The complexity inherent in such data, compounded by the absence of standardized data processing and analysis workflows, continues to pose substantial hurdles. The tidyverse paradigm, comprised of a suite of R packages meticulously crafted to facilitate efficient data manipulation and visualization, has garnered considerable acclaim within the data science community10. Its appeal stems from its innate simplicity and efficacy in organizing and processing data10. In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project11, tidyomics project12, tidymicro13, and MicrobiotaProcess13,14. However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis. To address this burgeoning demand for standardized and reproducible microbiome data analysis, we introduce microbiomedataset, an R package that embraces the tidyverse ethos to furnish a structured framework for the organization and processing of microbiome data. Microbiomedataset offers a comprehensive, customizable solution for the management, structuring, and processing of microbiome data. Importantly, this package seamlessly integrates with established bioinformatics tools, facilitating its incorporation into existing analytical pipelines11,13,14,15. Within this manuscript, we proffer an in-depth overview of the microbiomedataset package, elucidating its multifarious functionalities. Moreover, we substantiate its utility through illustrative case studies employing a publicly available microbiome dataset. It is imperative to underscore that microbiomedataset constitutes an integral component of the larger tidymicrobiome project, accessible via www.tidymicrobiome.org. Tidymicrobiome epitomizes an ecosystem of R packages that share a coherent design philosophy, grammar, and data structure, collectively engendering a robust, reproducible, and object-oriented computational framework. This project's development has been guided by several key tenets: (1) Cross-platform compatibility, (2) Uniformity, shareability, traceability, and reproducibility, and (3) Flexibility and extensibility. We further expound upon the advantages inherent in adopting a tidyverse-style framework for microbiome data analysis, underscoring the pronounced benefits in terms of standardization and reproducibility that microbiomedataset offers. In sum, microbiomedataset furnishes an accessible and efficient avenue for microbiome data analysis, catering to both neophyte and seasoned R users alike.

bioinformatics↗

bioRxiv2023

The effects of CYP2C19 metaboliser status on DNA methylation

CYP2C19 metabolises many medications e.g., antidepressants, proton pump inhibitors and its enzymatic activity can be inferred from genetic variants within its encoding gene, linking to the efficacy of drug treatments and their side effects. It is however unclear if enzymatic activity is associated with local or widespread differences in DNA methylation. DNA methylation differences associated with CYP2C19 metabolising status may also have the potential to reveal interacting genes and pathways that underly CYP2C19 effects on drug response and health consequences. A methylome-wide association study was conducted in the Generation Scotland cohort (n=18,396) to investigate the linear and non-linear effects of CYP2C19 metaboliser status on genome-wide DNA methylation. Pathway enrichment analysis was conducted for the cytosine-guanine dinucleotide (CpG) probes significantly associated with CYP2C19 metaboliser status. We examined whether the associations between CYP2C19 metaboliser status and DNA methylation were independent of theuse of drugs that are inducers, inhibitors, or substrates of the CYP2C19 enzyme by interaction analysis. Forty-eight CpG probes were significantly associated with both linear and quadratic terms of CYP2C19 metaboliser status (PBonferroni < 0.05). These CpG are annotated to genes involving drug metabolism, inflammation, lipid level, and Type 2 diabetes. Pathway enrichment analysis showed enrichment in biological processes involving metabolic activities and the Cytochrome P450 pathway. However, DNA methylation signals associated with CYP2C19 metaboliser statusdid not vary by CYP2C19-related medication use. This research suggests that genetically-determined CYP2C19 metaboliser status is associated with both local and distal DNA methylation. These associations are independent of whether individuals were receiving drugs that are related to this enzyme.

pharmacology and toxicology↗

bioRxiv2023

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis

The integration of cell transcriptomics and spatial coordinates to organize differentiation trajectories remains a challenge. Here we introduce spaTrack, a trajectory inference method using optimal transport to incorporate both transcriptomics and distance of spatial transcriptomics sequencing data into transition costs. spaTrack could construct fine spatial trajectories reflecting the true differentiation topology, as well as trace cell dynamics across multiple samples with temporal intervals. To capture the dynamic drivers, spaTrack models the cell fate as a function of expression profile along temporal intervals driven by transcription factors. Applying spaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Furthermore, we uncover diverse malignant lineages expanding in a primary tumor. One of the lineages with upregulated extracellular matrix organization implants to the metastatic site and subsequently colonizes to a secondary tumor. Overall, spaTrack greatly facilitates trajectory inference from spatial transcriptomics, providing insights in cell differentiation of broad areas.

bioinformatics↗

bioRxiv2023

Voxel-wise regression without intensity normalizationprovides better sensitivity when identifying injuryregions after stroke in FDG-PET images

BackgroundIn the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET), intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of an appropriate normalization method in stroke studies remains unclear, leading to inconsistent findings in the literature. Materials and methodsHere, we propose a regression-based and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other comprising 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination was calculated to measure the linearity. The results between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those having reduced linearity. Our method was compared with existing intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. ResultsThe proposed method detected more injury areas compared to existing approaches, as confirmed by the ground truth provided by TTC. The area under the curve (AUC) of the average receiver operating characteristic (ROC) curves using our method reached 0.84, whereas the AUCs using existing methods ranged from 0.77 [~] 0.79. The average false positive rate (FPR) and true positive rate (TPR) of the individual analysis results using our method (FPR = 0.06, TPR = 0.56) were better than the group-wise analysis results using t-tests (FPR = 0.10, TPR = 0.51). The identified injury regions were consistent in the two independent data sets. Some of them were confirmed by other publications. ConclusionsThe proposed method offers a new quantitative approach to analyzing FDG-PET images. The calculation does not involve intensity normalization and can be applied to a single subject. The method yields more sensitive results than existing intensity normalization methods.

neuroscience↗

bioRxiv2023

Mutational basis of serum cross-neutralization profiles elicited by infection or vaccination with SARS-CoV-2 variants

A series of SARS-CoV-2 variants emerged during the pandemic under selection for neutralization resistance. Convalescent and vaccinated sera show consistently different cross-neutralization profiles depending on infecting or vaccine variants. To understand the basis of this heterogeneity, we modeled serum cross-neutralization titers for 165 sera after infection or vaccination with historically prominent lineages tested against 18 variant pseudoviruses. Cross-neutralization profiles were well captured by models incorporating autologous neutralizing titers and combinations of specific shared and differing mutations between the infecting/vaccine variants and pseudoviruses. Infecting/vaccine variant-specific models identified mutations that significantly impacted cross-neutralization and quantified their relative contributions. Unified models that explained cross-neutralization profiles across all infecting and vaccine variants provided accurate predictions of holdout neutralization data comprising untested variants as infecting or vaccine variants, and as test pseudoviruses. Finally, comparative modeling of 2-dose versus 3-dose mRNA-1273 vaccine data revealed that the third dose overcame key resistance mutations to improve neutralization breadth. HIGHLIGHTSO_LIModeled SARS-CoV-2 cross-neutralization using mutations at key sites C_LIO_LIIdentified resistance mutations and quantified relative impact C_LIO_LIAccurately predicted holdout variant and convalescent/vaccine sera neutralization C_LIO_LIShowed that the third dose of mRNA-1273 vaccination overcomes resistance mutations C_LI

immunology↗

bioRxiv2023

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome

BackgroundSmall studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intra-pancreatic microbial dysbiosis may drive malignant transformation. We sought to comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. MethodsPancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n=20; IPMN, n=20; PDAC, n=19) and pancreatic cyst fluid (IPMN, n=30; SCA, n=10; MCN, n=10). Assessment of bacterial DNA by quantitative PCR and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium (CPTAC) were analyzed to validate findings. ResultsSequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade dysplasia (LGD) and high-grade dysplasia (HGD) IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in HGD cyst fluid. Decontamination analysis using the CPTAC database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. ConclusionsOur analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation. Significance of this studyO_ST_ABSWhat is already known on this subject?C_ST_ABSO_LIMicrobial colonization, infection, and dysbiosis have been implicated in the oncogenesis of various gastrointestinal tumors (e.g.: Helicobacter pylori in gastric cancer, hepatitis B and C in hepatocellular carcinoma, colon dysbiosis in colon cancer progression). C_LIO_LIFew studies have analyzed the intrinsic pancreatic microbiome, and these have produced conflicting results regarding microbial presence and alterations associated with malignant disease. C_LIO_LIIPMN is a precursor lesion to pancreatic cancer that represents a whole gland defect without an established driver event, and microbiome changes have been implicated as a possible etiology of cyst formation and dysplastic progression. C_LI What are the new findings?O_LIA low-biomass, low-diversity intrinsic pancreatic microbiome is present in both pancreatic tissue and cyst fluid. C_LIO_LIThis intrinsic pancreatic microbiome does not differ in terms of abundance, composition, or diversity between patients with PDAC, IPMN, or other benign conditions of the pancreas. C_LI How might it impact clinical practice in the foreseeable future?O_LIMicrobiome dysbiosis does not appear to be a driver of malignant degeneration of IPMN, and further research is needed to identify drivers of oncogenesis in order for possible chemoprevention strategies to be developed. C_LI

microbiology↗

bioRxiv2023

DePARylation is critical for S phase progression and cell survival

Poly(ADP-ribose)ylation or PARylation by PAR polymerase 1 (PARP1) and dePARylation by poly(ADP-ribose) glycohydrolase (PARG) are equally important for the dynamic regulation of DNA damage response. PARG, the most active dePARylation enzyme, is recruited to sites of DNA damage via pADPr-dependent and PCNA-dependent mechanisms. Targeting dePARylation is considered an alternative strategy to overcome PARP inhibitor resistance. However, precisely how dePARylation functions in normal unperturbed cells remains elusive. To address this challenge, we conducted multiple CRISPR screens and revealed that dePARylation of S phase pADPr by PARG is essential for cell viability. Loss of dePARylation activity initially induced S phase-specific pADPr signaling, which resulted from unligated Okazaki fragments and eventually led to uncontrolled pADPr accumulation and PARP1/2-dependent cytotoxicity. Moreover, we demonstrated that proteins involved in Okazaki fragment ligation and/or base excision repair regulate pADPr signaling and cell death induced by PARG inhibition. In addition, we determined that PARG expression is critical for cellular sensitivity to PARG inhibition. Additionally, we revealed that PARG is essential for cell survival by suppressing pADPr. Collectively, our data not only identify an essential role for PARG in normal proliferating cells but also provide a potential biomarker for the further development of PARG inhibitors in cancer therapy.

cancer biology↗

bioRxiv2023

Generative Neural Spike Prediction from Upstream Neural Activity via Behavioral Reinforcement

It is quite challenging to predict dynamic stimulation patterns on downstream cortical regions from upstream neural activities. Spike prediction models used in traditional methods are trained by downstream neural activity as the reference signal in a supervised manner. However, downstream activity is unavailable when neurological disorders exist. This study proposes a reinforcement learning-based point process framework to generatively predict spike trains through behavior-level rewards, solving the difficulty. The framework is evaluated to reconstruct the transregional spike communication during motor control through behavioral reinforcement. We show that our methods can generate spike trains beyond the collected neural recordings and achieve better behavioral performance.

bioengineering↗

bioRxiv2023

Tailoring Tfh Profiles Enhances Antibody Persistence to a Clade C HIV-1 Vaccine in Rhesus Macaques

CD4 T follicular helper cells (Tfh) are essential for establishing serological memory and have distinct helper attributes that impact both the quantity and quality of the antibody response. Insights into Tfh subsets that promote antibody persistence and functional capacity can critically inform vaccine design. Based on the Tfh profiles evoked by the live attenuated measles virus vaccine, renowned for its ability to establish durable humoral immunity, we investigated the potential of a Tfh1/17 recall response during the boost phase to enhance persistence of HIV-1 Envelope (Env) antibodies in rhesus macaques. Using a DNA-prime encoding gp160 antigen and Tfh polarizing cytokines (interferon protein-10 (IP-10) and interleukin-6 (IL-6)), followed by a gp140 protein boost formulated in a cationic liposome-based adjuvant (CAF01), we successfully generated germinal center (GC) Tfh1/17 cells. In contrast, a similar DNA-prime (including IP-10) followed by gp140 formulated with monophosphoryl lipid A (MPLA)+QS-21 adjuvant predominantly induced GC Tfh1 cells. While the generation of GC Tfh1/17 cells with CAF01 and GC Tfh1 cells with MPLA+QS-21 induced comparable peak Env antibodies, the latter group demonstrated significantly greater antibody concentrations at week 8 after final immunization which persisted up to 30 weeks (gp140 IgG ng/ml-MPLA; 5500; CAF01, 2155; p <0.05). Notably, interferon {gamma}+ Env-specific Tfh responses were consistently higher with gp140 in MPLA+QS-21 and positively correlated with Env antibody persistence. These findings suggest that vaccine platforms maximizing GC Tfh1 induction promote persistent Env antibodies, important for protective immunity against HIV.

immunology↗