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Wang, G.

Publications and source records attributed to Wang, G..

At least 163 records · Page 9Linked to original sources
bioRxiv2024

Sexual Failure Decreases Sweet Taste Perception in Male Drosophila via Dopaminergic Signaling

Sweet taste perception, a critical aspect of the initiation of feeding behavior, is primarily regulated by an animals internal metabolic state. However, non-metabolic factors, such as motivational and emotional states, can also influence peripheral sensory processing and hence feeding behavior. While mating experience is known to induce motivational and emotional changes, its broader impact on other innate behaviors, such as feeding, remains largely uncharacterized. In this study, we demonstrated that mating failure of male fruit flies suppressed sweet taste perception via dopamine signaling in specific neural circuitry. Upon repetitive failure in courtship, male flies exhibited a sustained yet reversible decline of sweet taste perception, as measured by the proboscis extension reflex (PER) towards sweet tastants as well as the neuronal activity of sweet-sensing Gr5a+ neurons in the proboscis. Mechanistically, we identified a small group of dopaminergic neurons projecting to the subesophageal zone (SEZ) and innervating with Gr5a+ neurons as the key modulator. Repetitive sexual failure decreased the activity of these dopaminergic neurons and in turn suppressed Gr5a+ neurons via Dop1R1 and Dop2R receptors. Our findings revealed a critical role for dopaminergic signaling in integrating reproductive experience with appetitive sensory processing, providing new insights into the complex interactions between different innate behaviors and the role of brains reward systems in regulating internal motivational and emotional states.

neuroscience↗
bioRxiv2024

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

Understanding cellular responses to genetic perturbations is essential for understanding gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional responses to perturbations at the single-cell level, there remains a pressing need for computational models that can decode the mechanisms driving these responses and accurately predict outcomes to prioritize target genes for experimental design. Here, we present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations. By leveraging gene embeddings derived from large language models, scLAMBDA effectively integrates prior biological knowledge and disentangles basal cell states from perturbation-specific salient representations. Through comprehensive evaluations on multiple single-cell CRISPR Perturb-seq datasets, scLAMBDA consistently outperformed state-of-the-art methods in predicting perturbation outcomes, achieving higher prediction accuracy. Notably, scLAMBDA demonstrated robust generalization to unseen target genes and perturbations, and its predictions captured both average expression changes and the heterogeneity of single-cell responses. Furthermore, its predictions enable diverse downstream analyses, including the identification of differentially expressed genes and the exploration of genetic interactions, demonstrating its utility and versatility.

bioinformatics↗
bioRxiv2024

Multi-omics analysis of the mechanism of alfalfa and wheat-induced rumen flatulence in Xizang sheep

This study induced a rumen flatulence model in Xizang sheep using alfalfa (HRF) and wheat grass (MRF) to explore related mechanisms. The pH of HRF and MRF groups was lower than the natural grass group. SCFA concentrations varied in different groups. Microbial analysis showed HRF and MRF groups had lower alpha and beta diversity and different compositions. Transcriptome analysis found different numbers of differentially expressed genes in each group. Spearmans correlation analysis indicated Butyrivibrio was related to GLRX and DUOX2, and Quinella to several genes.

microbiology↗
bioRxiv2024

Trikafta rescues F508del-CFTR by tightening specific phosphorylation-dependent interdomain interactions

Trikafta effectively corrects the thermal and gating defects associated with the F508del mutation, the most common cause of cystic fibrosis, even at physiological temperatures. However, the exact correction pathway is still unclear. Here, noncovalent interactions among two transmembrane domains (TMD1 and TMD2), the regulatory (R) domain and two nucleotide binding domains (NBD1 and NBD2) were analyzed. The thermal stability of NBD1 was also evaluated through its tertiary constrained noncovalent interaction networks or thermoring structures. The results demonstrated that Trikafta binding to flexible TMD1 and TMD2 rearranged their interactions with the R domain upon phosphorylation, coupling tightened cytoplasmic TMD1-TMD2 interactions to tightened Mg/ATP-dependent NBD1-NBD2 dimerization, which stabilized NBD1 above human body temperature. In essence, while the F508 deletion primarily causes a thermal defect in NBD1, leading to a gating defect at the TMD1-TMD2 interface, Trikafta allosterically reverses these effects. These mechanistic insights into the precise correction pathway of this misfolded channel facilitate optimizing cystic fibrosis treatment. (155 words) Key pointsO_LITrikafta binding to flexible TMD1 and TMD2 tightened their cytoplasmic interactions. C_LIO_LITight cytoplasmic TMD1-TMD2 interactions primed the specific binding of the dynamic phosphorylated S813 site to the TMD1/TMD2/NBD1 interfaces. C_LIO_LIThe tight binding of the S813 site to the TMD1/TMD2/NBD1 interfaces strengthened NBD1-NBD2 dimerization which stabilizes NBD1. C_LI

biophysics↗
bioRxiv2024

Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiT

Perturb-seq enabled the profiling of transcriptional effects of genetic perturbations in single cells but lacks the ability to examine the impact on tissue environments. We present Perturb-DBiT for simultaneous co- sequencing of spatial transcriptome and guide RNAs (gRNAs) on the same tissue section for in vivo CRISPR screen with genome-scale gRNA libraries, offering a comprehensive understanding of how genetic modifications affect cellular behavior and tissue architecture. This platform supports a variety of delivery vectors, gRNA library sizes, and tissue preparations, along with two distinct gRNA capture methods, making it adaptable to a wide range of experimental setups. In applying Perturb-DBiT, we conducted un-biased knockouts of tens of genes or at genome-wide scale across three cancer models. We mapped all gRNAs in individual colonies and corresponding transcriptomes in a human cancer metastatic colonization model, revealing clonal dynamics and cooperation. We also examined the effect of genetic perturbation on the tumor immune microenvironment in an immune-competent syngeneic model, uncovering differential and synergistic perturbations in promoting immune infiltration or suppression in tumors. Perturb-DBiT allows for simultaneously evaluating the impact of each knockout on tumor initiation, development, metastasis, histopathology, and immune landscape. Ultimately, it not only broadens the scope of genetic inquiry, but also lays the groundwork for developing targeted therapeutic strategies.

genomics↗
bioRxiv2024

Analysis of Categorical Data with Logistic Regression and the Cochran-Mantel-Haenszel Tests in Biological Experiments

The choice of statistical test is a fundamentally important one when analyzing experimental data. Here, we consider the question of categorical data, defined by their properties (for example color) rather than by continuous numbering. Using simple and complex example datasets generated from Caenorhabditis elegans research, we conduct a statistical analysis of (1) a rare cellular event involving the formation of a neuronal extrusion called an exopher, and of (2) a variable behavioral response across a timescale. Two tests we use here are the Cochran- Mantel-Haenszel (CMH) test and logistic regression. These two tests pose practical challenges to researchers that include lack of easy access to statistical software and the need for prior programming knowledge. To this end we provide step-by-step tutorials and example code. We emphasize the flexibility of logistic regression in handling both simple and complex datasets, emphasizing the capacity of logistic regression to provide more comprehensive insights into experimental outcomes than simpler tests like CMH. By analyzing real biological examples and demonstrating their analysis with R code, we provide a practical guide for biologists to enhance the rigor and reproducibility of categorical data analysis in experimental studies.

genetics↗
bioRxiv2024

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygote with few-shot learning

The CRISPR-Cas system has revolutionized gene editing, while its outcome prediction remains unsatisfactory, especially in new cell states due to their distinct DNA repair preferences. In this study, we introduce inDecay, a flexible system for predicting CRISPR editing outcomes from target sequence, returning probabilities of nearly the full spectrum of indel events. Uniquely, inDecay utilizes informative and parameter-efficient features for each indel event and incorporates cell-type-specific repair preferences through a multi-stage design. While both inDecay and existing methods achieve accurate results for prediction within cell lines, only inDecay with transfer learning can retain the high performance for cross-cell line prediction. We then applied inDecay to mouse embryo editing using our newly generated data and observed remarkable accuracy by including as few as 30 fine-tuning embryonic samples. Notably, inDecay is the first software to predict embryonic editing. Therefore, our few-shot learning-supported system may accelerate guide RNA prioritization in mouse model generation, mammal embryonic gene editing, and cellular therapeutics.

bioinformatics↗
bioRxiv2024

Spatial quantitative metabolomics enables identification of remote and sustained ipsilateral cortical metabolic reprogramming after stroke

Mass spectrometry imaging (MSI) has become a cornerstone of spatial biology research. However, various factors that are intrinsic to the technology limit quantitative capacity of MSI-based spatial metabolomics and thus reliable interpretation. Here, we developed a quantitative MSI workflow (Q-MSI), based on isotopically 13C-labeled yeast extract as internal standards, to overcome these pitfalls. Using brain and kidney tissue, we demonstrate that this approach allows for (absolute) quantification of hundreds of metabolites and lipids. Applying our workflow to a stroke model allowed us to not only map metabolic remodeling of the infarct and peri-infarct area over time, but also discover hitherto unnoted remote metabolic remodeling in the histologically unaffected ipsilateral sensorimotor cortex. At day 7 post-stroke, increased levels of neuroprotective lysine and reduced excitatory glutamate levels were found compared to the contralateral cortex. By day 28 post-stroke, lysine and glutamate levels had been normalized, while decreased precursor pools of UDP-GlcNAc and linoleate persisted that have previously been associated with vulnerability. Importantly, traditional normalization strategies not employing internal standards were unable to visualize these differences. Using 13C-labeled yeast extracts as a normalization strategy establishes a new paradigm in quantitative MSI-based spatial metabolomics that greatly enhances reliability and interpretive strength.

biochemistry↗
bioRxiv2024

Clinical characteristics, drug-resistance genes and virulence analysis of carbapenem-resistant Klebsiella pneumoniae

OBJECTIVEThe aim was to investigate the clinical infection characteristics, risk factors and convergence of carbapenem-resistant and hypervirulent Klebsiella pneumoniae(hv-CRKP) in our hospital, in order to provide scientific basis for the rational use of antibiotics in the clinic. METHODSCRKP strains were isolated from clinical specimens of 44 patients in central intensive care unit (CICU), neurosurgery intensive care unit (NICU) and emergency intensive care unit (EICU) of our hospital in 41 months. The clinical manifestations of the patients were analyzed, and the hypermucoviscous phenotype was defined by string test. The drug resistance gene, virulence gene and capsular specific serotype of the isolated strains were detected by polymerase chain reaction (PCR) technology, and the gene of CRKP producing KPC enzyme was sequence. RESULTSThe incidence of sepsis in 44 patients with CRKP infection was 25.0%, and the overall treatment failure rate was 38.6%. All the 44 strains of CRKP produced KPC enzyme, and all of them were blaKPC-2 (100%);14 strains (31.8%) were positive in string test; 23 strains (52.3%) were found to have virulence related genes, including 10 strains (22.7%) of rmpA, 16 strains (36.4%) of iucA and 19 strains (43.2%) of peg-344. 5 strains (11.4%) were capsule-specific serotypes and 13 strains (29.5%) were identified as hv-CRKP. CONCLUSIONThe mechanism of CRKP resistance in our hospital is blaKPC-2, the detection rates of virulence gene, string test positive and hypervirulent capsular specific serotype are all relatively high in CRKP, there are not a few Klebsiella pneumoniae with carbapenem-resistant and hypervirulent.

microbiology↗
bioRxiv2024

A genome-wide-level insight into HSF gene family of Rhodomyrtus tomentosa and the functional characterization of RtHSFA2s genes in thermal adaption

Heat shock transcription factor (HSF) is one of the most important regulatory elements in plant development and stress response. Rhohomyrtus tomentosa has many advantages in adapting to high temperature and humidity climates, whereas the inherence has barely been elucidated. In this study, we aimed to characterize the HSF family and investigate the thermal adaption mechanisms of R. tomentosa. We identified 25 HSF genes in the R. tomentosa genome. They could be classified into three classes: HSFA, HSFB, and HSFC. Gene duplication event is a major motivation for the expansion of the RtHSF gene family. Most of the genes in the same subclass share similar conserved motifs and gene structures. The cis-acting elements of the promoter regions of RtHSF genes are related to development, phytohormone signaling, and stress responses, and they vary among the genes even in the same subclass, resulting in different expression patterns. Especially, there exists subfunctionalization in the RtHSFA2 subfamily in responding to various abiotic stresses, viz. RtHSFA2a is sensitive to drought, salt, and cold stresses, whilst RtHSFA2b is mainly induced by heat stress. We further prove that RtHSFA2b might be of more importance in R. tomentosa thermotolerance, for Arabidopsis with overexpressed RtHSFA2b outperformed those with RtHSFA2a under heat stress, and RtHSFA2b has much higher transcription activity than RtHSFA2a in regulating certain heat shock response (HSR) genes. RtHSFA2a plays a role in transactivating RtHSFA2b. All these results provide a general prospect of the RtHSF gene family and enclose a basal thermal adaption mechanism of R. tomentosa.

plant biology↗
bioRxiv2024

Manipulation of the brown glume and internode 1 gene leads to alterations in lignified tissue coloration, lignification, and pathogen resistance in wheat

Lignin is a crucial component of the cell wall, providing mechanical support and protection against biotic and abiotic stresses. However, little is known about wheat lignin-related mutants and their roles in pathogen defense. In this study, we identified an ethyl methanesulfonate (EMS)-derived Aegilops tauschii mutant named brown glume and internode 1 (bgi1), which exhibits reddish-brown pigmentation in various tissues, including internodes, spikes, and glumes. Using map-based cloning and single nucleotide polymorphism (SNP) analysis, we identified AET6Gv20438400 (BGI1) as the leading candidate gene, encoding the TaCAD1 protein. The mutation occurred in the splice acceptor site of the first intron, resulting in a premature stop codon in BGI1. We validated the function of BGI1 using loss-of-function EMS and gene editing knockout mutants, both of which displayed reddish-brown pigmentation in lignified tissues. BGI1 knockout mutants exhibited reduced lignin content and shearing force relative to wild type, while BGI1 overexpression transgenic plants showed increased lignin content and enhanced disease resistance against common root rot and Fusarium crown rot. We confirmed that BGI1 exhibits CAD activity both in vitro and in vivo, playing an important role in lignin biosynthesis. BGI1 was highly expressed in the stem and spike and localized in the cytoplasm. Transcriptome analysis revealed the regulatory networks associated with BGI1. Finally, we demonstrated that BGI1 interacts with TaPYL-1D, potentially involved in the abscisic acid signaling pathway. The identification and functional characterization of BGI1 significantly advance our understanding of CAD proteins in lignin biosynthesis and plant defense against pathogen infection in wheat.

plant biology↗
bioRxiv2024

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODALs effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.

bioinformatics↗
bioRxiv2024

Characterization of hairpin loops and cruciforms across 118,065 genomes spanning the tree of life

Inverted repeats (IRs) can form alternative DNA secondary structures called hairpins and cruciforms, which have a multitude of functional roles and have been associated with genomic instability. However, their prevalence across diverse organismal genomes remains only partially understood. Here, we examine the prevalence of IRs across 118,065 complete organismal genomes. Our comprehensive analysis across taxonomic subdivisions reveals significant differences in the distribution, frequency, and biophysical properties of perfect IRs among these genomes. We identify a total of 29,589,132 perfect IRs and show a highly variable density across different organisms, with strikingly distinct patterns observed in Viruses, Bacteria, Archaea, and Eukaryota. We report IRs with perfect arms of extreme lengths, which can extend to hundreds of thousands of base pairs. Our findings demonstrate a strong correlation between IR density and genome size, revealing that Viruses and Bacteria possess the highest density, whereas Eukaryota and Archaea exhibit the lowest relative to their genome size. Additionally, the study reveals the enrichment of IRs at transcription start and termination end sites in prokaryotes and Viruses and underscores their potential roles in gene regulation and genome organization. Through a comprehensive overview of the distribution and characteristics of IRs in a wide array of organisms, this largest-scale analysis to date sheds light on the functional significance of inverted repeats, their contribution to genomic instability, and their evolutionary impact across the tree of life.

genomics↗
bioRxiv2024

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.

bioinformatics↗
bioRxiv2024

The fetal specific gene LIN28B is essential for human fetal B-lymphopoiesis and initiation of KMT2A::AFF1 infant leukemia

Infant ALL (iALL) is initiated in utero, most often by rearrangement of the KMT2A gene (KMT2Ar). It carries a very poor prognosis despite a lack of additional oncogenic driver mutations common in childhood ALL. Here, we aimed to identify specific properties of human fetal hematopoietic stem/progenitor cells (HSPC) that promote leukemic transformation in KMT2Ar iALL using molecular, functional and in vivo assays. First, by comparing transcriptomes of human fetal HSPC to adult HSPC we derived a fetal-specific gene signature and identified the fetal oncogene LIN28B and its downstream effectors among the top hits. These genes were also expressed in iALL. Functional assays revealed that LIN28B was essential in human fetal liver (FL) CD34+ cells to maintain proliferation and stemness, and support B- and NK-lymphopoiesis. To interrogate the role of LIN28B in iALL, we utilised a human FL-derived CRISPR-Cas9 KMT2A::AFF1 model. In this model, LIN28B-expressing leukemias were more proliferative in vitro and in vivo, with this advantage being lost upon LIN28B knockdown. Mechanistic studies showed that LIN28B acts by stabilizing key early B-lymphoid genes, epigenetic regulators, and cell cycle and anti-apoptotic genes. Finally, In the absence of LIN28B, human FL CD34+ cells fail to transform upon induction of KMT2A::AFF1 translocation. Thus, LIN28B has an essential role in normal human fetal B-lymphopoiesis, and is necessary for the initiation of KMT2A::AFF1 iALL in fetal cells in the absence of co-operating mutations. It has a role in making leukemias more aggressive, suggesting it is a potential target in LIN28B-expressing leukemias.

cancer biology↗
bioRxiv2024

Contrasting Cognitive, Behavioral, and Physiological Responses to Breathwork vs. Naturalistic Stimuli in Reflective Chamber and VR Headset Environments

The MindGym, a novel immersive technology utilizing a reflective chamber environment, was developed to create standardized experiential content, including anxiolytic experiences. This study examined whether therapeutic experiences originally created in the MindGym could maintain their efficacy when delivered via 360-degree recordings through virtual reality (VR) headsets. A randomized controlled trial (N=126) compared anxiety reduction, cognitive performance, and physiological responses across four conditions: MindGym and VR platforms, each delivering either breathwork or rain stimuli. Results demonstrated significant improvements across all conditions in cognitive performance (Trail Making Test RTACC, p.fdr<.001; Architex Total Speed, p.fdr<.001) and anxiety reduction (STAI, p.fdr<.001). Breathwork conditions produced greater decreases in breath rate compared to rain stimuli (p.fdr=.002). Treatment responses were moderated by individual differences, with absorption (MODTAS) predicting both awe (p.fdr=.004) and ego dissolution (p.fdr=.015), while openness to experience interacted with stimuli type to influence anxiety reduction (p.fdr=.038). The anxiolytic effects originally generated in the MindGym maintained full efficacy when translated to VR delivery, with no significant differences in effectiveness or immersion between the original environment and its virtual reproduction. These findings establish the MindGym as a viable content creation platform for immersive, anxiety-reducing experiences that can be successfully adapted to more accessible delivery systems, while highlighting the potential for personalization based on individual differences. Future research should investigate the translation of more complex MindGym-generated experiences to expand accessible anxiety management tools.

neuroscience↗
bioRxiv2024

Differential Immunoregulation by Human Surfactant Protein A Variants Determines Severity of SARS-CoV-2-induced Lung Disease

AbstractCOVID-19 remains a significant threat to public health globally. Infection in some susceptible individuals causes life-threatening acute lung injury (ALI/ARDS) and/or death. Human surfactant protein A (SP-A) is a C-type lectin expressed in the lung and other mucosal tissues, and it plays a critical role in host defense against various pathogens. The human SP-A genes (SFTPA1 and SFTPA2) are highly polymorphic and comprise several common genetic variants, i.e., SP-A1 (variants 6A2, 6A4) and SP-A2 (variants 1A0, 1A3). Here, we elucidated the differential antiviral and immunoregulatory roles of SP-A variants in response to SARS-CoV-2 infection in vivo. Six genetically-modified mouse lines, expressing both hACE2 (SARS-CoV-2 receptor) and individual SP-A variants: (hACE2/6A2 (6A2), hACE2/6A4 (6A4), hACE2/1A0 (1A0), and hACE2/1A3(1A3), one SP-A knockout (hACE2/SP-A KO (KO) and one hACE2/mouse SP-A (K18) mice, were challenged intranasally with 103 PFU SARS-CoV-2 or saline (Sham). Infected KO and 1A0 mice had more weight loss and mortality compared to other mouse lines. Relative to other infected mouse lines, a more severe ALI was observed in KO, 1A0, and 6A2 mice. Reduced viral titers were generally observed in the lungs of infected SP-A mice relative to KO mice. Transcriptomic analysis revealed an upregulation in genes that play central roles in immune responses such as MyD88, Stat3, IL-18, and Jak2 in the lungs of KO and 1A0 mice. However, Mapk1 was significantly downregulated in 6A2 versus 1A0 mice. Analysis of biological pathways identified those involved in lung host defense and innate immunity, including pathogen-induced cytokine, NOD1/2, and Trem1 signaling pathways. Consistent with the transcriptomic data, levels of cytokines and chemokines such as G-CSF, IL-6 and IL-1{beta} were comparatively higher in the lungs and sera of KO and 1A0 mice with the highest mortality rate. These findings demonstrate that human SP-A variants differentially modulate SARS-CoV-2-induced lung injury and disease severity by differentially inhibiting viral infectivity and regulating immune-related gene expressions.

immunology↗