bioRxiv Science⌕ Search

Biology subjects

Jiang, P.

Publications and source records attributed to Jiang, P..

At least 37 records · Page 2Linked to original sources

The discovery and patterns of the underlying long-standing mild-effect mutator alleles in S. cerevisiae populations

Most mutations are neutral or deleterious, and mutator alleles that substantially increase the mutation rate of an organism are believed to be short-lived in natural populations. Theory suggests that mutator alleles of modest effect have the potential to segregate nearly neutrally, but this prediction has been difficult to test experimentally. Here, we report strong genomic signatures consistent with the transmission and long-term maintenance of at least one natural mutator allele in Saccharomyces cerevisiae isolates. Specifically, such signatures are consistent with segregation of mutator alleles that disproportionately increase A>C mutations in the African Beer strains of S. cerevisiae. Remarkably, the mutation spectra of African Beer strains deviate from that of other S. cerevisiae natural isolates more than some Saccharomyces species differ from each other. Furthermore, computational analysis suggests mutator allele introgression from the African Beer population into a subset of French Dairy yeast, motivating us to experimentally measure the de novo mutation rates and spectra of these strains. We observed a consistent but weak enrichment of A>C and A>G de novo mutations in the African Beer population, with the A>C enrichment also evident in the French Dairy subset. Unexpectedly, other de novo mutation types varied more substantially among strains, including one outlier African Beer strain (AFL) with an excess of C>A mutations. Overall, our measurements reveal that de novo mutation spectra show greater variability among populations compared to the mutation spectra of rare polymorphisms, which in turn show greater variability than the spectra of all polymorphisms. Together, while additional mutator alleles may segregate in natural populations, mild enrichment of A>C and A>G types in African Beer strains reflects mutator alleles with weak effects that can persist and accumulate mutations through evolution.

evolutionary biology↗

De Novo Design of a Protein Binder to Probe Gas Channel and Enhance the Oxygen Tolerance of Hydrogenase

[NiFe]-hydrogenases exhibit outstanding catalytic performance for H2 production and oxidation but suffer from extreme O2 sensitivity, limiting their biotechnological potential. The identity and functional roles of gas channels that connect to the buried [NiFe] active site have remained unclear, impeding the rational engineering of O2-tolerant enzyme variants. In this study, we present an artificial intelligence-guided de novo protein binder design strategy, utilizing compact protein binders as selective molecular probes to map hydrophobic O2 diffusion pathways in Escherichia coli hydrogenase-2 (Hyd-2). By integrating RFdiffusion, ProteinMPNN, and AlphaFold, approximately 100,000 candidate binders were computationally screened, leading to the identification of two high-affinity binders, L1 and L2. Structural, biophysical, and electrochemical analyses reveal that L1 specifically occludes the primary O2 ingress channel, thereby enhancing the enzymes oxygen tolerance by more than threefold, whereas L2, which targets a putative secondary channel, exerts a negligible effect, demonstrating the nonfunctionality of this pathway. These results provide the first direct experimental evidence for a hierarchical organization of O2 diffusion channels in [NiFe]-hydrogenases and establish binder-mediated channel occlusion as a generalizable, mutation-free approach for probing gas transport mechanisms and selectivity in metalloenzymes.

biochemistry↗

SpaTranslator: A deep generative framework for universal spatial multi-omics cross-modality translation

Recent advances in spatial omics technologies have enabled the simultaneous analysis of multiple molecular patterns in tissue sections, offering unprecedented insights into cellular microenvironments. However, the high cost of measurements and the sparsity of data restrict the availability of paired spatial multi-omics datasets. Here, we present SpaTranslator, a deep generative framework that integrates graph neural networks with an adversarial variational generative model to fully capture spatial characteristics and enable effective cross-modality translation of spatial omics data, enabling simulation of paired spatial multi-omics data from single-omics measurements. Extensive experiments demonstrate that SpaTranslator consistently outperforms baseline methods in both clustering accuracy and biological coherence across various real-world scenarios, including spatial transcriptomics-epigenomics and spatial transcriptomics-proteomics translation tasks. Furthermore, SpaTranslator provides biologically meaningful insights through marker genes and proteins recovery, motif enrichment analysis, and gene regulation inference. Our work offers an effective and adaptable solution for spatial multi-omics cross-modality translation, supporting a broad range of biological and biomedical research.

bioinformatics↗

Web engine for tumor pathology image retrievals on massive scales

Hematoxylin and Eosin staining (H&E) is widely used in clinical practice, but efficient and versatile image retrieval tools are lacking. We developed the H&E Retrieval Engine (HERE, https://hereapp.ccr.cancer.gov) to analyze patient cases based on image similarities to database records. Using H&E image regions as input, HERE searches 21.2 terabytes of whole-slide images from multiple tumor histopathology cohorts through a 12.1-gigabyte memory index, and returns top images containing regions similar to the query. HERE scans high-resolution images in the database using accurate artificial intelligence encoding and ultra-efficient hierarchical skip indexing. HERE demonstrated performance superior to existing image retrieval tools based on blinded pathologist scoring using benchmark queries that represent key image features of human tumors. By pairing spatial transcriptomics with H&E images, HERE also enables retrieving image features from gene transcriptomics input and identifies molecular pathways associated with tumor histologies.

bioinformatics↗

Human olfactory organoids as an in vitro model of the olfactory epithelium

Abstract/Key PointsO_LIA novel human olfactory epithelial organoid model has been generated from primary cell culture. C_LIO_LIHuman olfactory organoids exhibit immunostaining and gene expression for olfactory cells. C_LIO_LIOlfactory organoids mobilize Ca2+ in response to odorants, a surrogate for neuronal function. C_LI

cell biology↗

Widespread genomic heterogeneity at the type II NAD(P)H dehydrogenase locus predisposes Cryptosporidium to clofazimine resistance

The parasite Cryptosporidium is a leading cause of life-threatening diarrheal disease, and effective treatment is not available. The discovery of potent anti-Cryptosporidium activity of clofazimine, offered the opportunity to repurpose a drug already used to treat leprosy and tuberculosis. However, clofazimine failed in a human trial, which was attributed to poor bioavailability. Here, we observed differential susceptibility among parasite isolates which we exploit to map the mode of action to type II NADH dehydrogenase (NDH2) in an unbiased genetic cross. Targeted genetic ablation of NDH2 resulted in profound clofazimine resistance, and biochemical studies demonstrated NDH2 mediated electron transfer to clofazimine. Through genomic analyses we uncovered heterogeneity at the NDH2 locus for C. parvum and C. hominis and widespread carriage of a conserved attenuated allele across multiple continents. This heterogeneity allows parasites genomically linked through frequent sexual recombination to adjust to changing NDH2 requirements and predisposes Cryptosporidium to evade clofazimine treatment.

microbiology↗

Fluorescein Angiography Image-AI Based Early Diabetic Retinopathy Detection

Fluorescein angiography (FA) is widely regarded as the clinical gold standard for diagnosing diabetic retinopathy (DR), based on a range of observable symptoms, including microaneurysms, retinal hemorrhages, and vascular leakage. However, early detection of DR remains challenging, as these symptoms may not manifest simultaneously and are often too subtle to detect. Acellular capillary density is currently the only quantitative metric used to assess the risk and severity of DR, particularly in its early stages when these clinical symptoms are minimal or absent. Unfortunately, measuring acellular capillary density to detect pre-symptomatic diabetic retinopathy (DR) requires destructive techniques such as trypsin digest to remove non-vascular tissues, making it unsuitable for human patients, while current imaging techniques such as FA does not have resolution to quantify acellular capillary density, limiting the utility of non- invasive approaches for early detection of DR. In this study, we used Nile rat as a DR model to train an AI deep learning model based on FA images to detect retinas with acellular capillary density [&ge;]18 counts per mm{superscript 2}, which is considered as pre-symptomatic (before clinically observed DR symptoms) DR. Our model achieves an accuracy of 80.85%, with a sensitivity of 84.21%, specificity of 75.68%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.86. We also showed that integrating the duration of diabetes as a pre-knowledge into a Bayesian framework with real time AI-image prediction can enhance the prediction power with AUC of ROC of 0.9. Using the first sequenced Nile rat genome and our RNA-seq data, we identified 43 gene markers that signified a shift from a normal state to a transitional phase preceding early diabetic retinopathy (DR). While PDGF-B/PDGFR{beta} signaling is typically associated with pericyte loss and increased acellular capillary density, which are hallmarks of early DR, none of the identified markers were related to this pathway. Instead, three genes (Bcl2a1, Birc5, and Il20rb) out of these 43 biomarkers are associated with inflammation or response to inflammation suggesting that inflammation may contribute to the shift in acellular capillary density from <16 (normal) to 17-18 counts (transitional phase preceding early DR) per mm{superscript 2} before pericyte loss occurs, providing new insights into the early pathogenesis of DR. Author SummaryDiabetic retinopathy (DR) is a leading cause of vision loss worldwide. Early detection is critical to prevent progression, yet current diagnostic methods rely on symptoms that may appear too late. The density of acellular capillaries--tiny, non-functioning blood vessels in the retina--is a key early marker of DR, but measuring it requires destructive techniques that are not suitable for human patients. In this study, we used Nile rats, a rodent model that naturally develops diabetes and DR, to train an artificial intelligence (AI) model using fluorescein angiography (FA) images. Our AI system detects retinas with acellular capillary density at levels associated with pre- symptomatic DR with high accuracy. We further improved predictive power by integrating diabetes duration into a Bayesian framework. In addition, our transcriptomic study identified 43 markers associated with the transition from a normal state to a transitional phase preceding early diabetic retinopathy. Interestingly, none were linked to a well-known pathway involving pericyte loss, which has been thought to drive early DR. Instead, three markers (Bcl2a1, Birc5, and Il20rb) suggest inflammation may contribute to early changes before pericyte loss occurs. These findings offer a new perspective on the earliest stages of DR.

bioengineering↗

Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery

Omics-based molecular subtyping in large-scale and multicentric cohort studies is a prerequisite for proteomics-driven precision medicine (PDPM). However, keeping the subtypes with robust molecular features and significant associations with prognosis among different cohorts is challenging due to the biological heterogeneity and technical inconsistency. Herein, we propose a subtyping algorithm, named Survival Reinforced Patient Stratification (SRPS), to adapt the known subtypes from the discovery cohort to another by simultaneously preserving the distinct prognosis and molecular characteristics of each subtype. SRPS has been benchmarked on simulated and real-world datasets, where it shows a 12% increase in classification accuracy and possesses the best prognostic discriminations. Moreover, based on the calculated subtype significance score, an unpopular protein, Peptidylprolyl Isomerase C (PPIC), was identified as the top-1 remarkable protein for subtyping the hepatocellular carcinoma (HCC) patients with the worst prognosis. Eventually, PPIC was experimentally proved to be a pro-cancer protein in HCC, confirming our work as a practice of interpretable machine learning guided biological discovery in PDPM research.

bioinformatics↗

Distinct Nuclear and Cytoplasmic Transcriptomic Signatures Reveal Transcription Alone Is Insufficient to Determine Glucose-Induced Transcriptomic Dynamics

Glucose profoundly influences cellular transcriptomes, but whether these changes are primarily driven by transcription remains unclear. Traditional bulk RNA sequencing, which interrogates total mRNA from whole cells, obscures distinct dynamics of nuclear and cytoplasmic transcriptomes. Nuclear RNA levels primarily reflect transcriptional activity, whereas cytoplasmic RNA levels are shaped by both transcription and post-transcriptional processes, such as RNA export, stability, and degradation. In this study, we systematically investigate glucose-induced transcriptomic responses in a subcellular location- and cell type-specific manner using three cell lines: FHC (normal colonic epithelial cells), MCF10A (normal breast epithelial cells), and MCF7 (metastatic breast cancer cells). Our findings reveal that, although nuclear and cytoplasmic mRNA levels show strong global correlations, glucose-induced changes in mRNA abundance exhibit minimal concordance between the nucleus and cytoplasm. Additionally, glucose-induced changes in exon inclusion levels often diverge between the nucleus and cytoplasm, underscoring the importance of post-transcriptional processes in shaping the cytoplasmic transcriptome response to glucose level changes. Glucose-induced differentially expressed genes (DEGs) and differentially spliced exons ({Delta}PSI) are enriched in distinct pathways exhibiting unique enrichment patterns depending on the subcellular location and cell line. These findings underscore the complexity of glucose-induced transcriptomic regulation, demonstrating that transcription alone is insufficient to explain the observed dynamics.

bioinformatics↗

Transcriptomic Landscape and Immune Microenvironment AroundWound Bed Define Regenerative versus Non-regenerative Outcomesin Mouse Digit Amputation

In the mouse distal terminal phalanx (P3), it remains unclear why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (BMP2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, with potential applications in personalized treatment strategies and assessing the impact of interventions on regenerative outcomes.

developmental biology↗

Structure of the human taurine transporter TauT reveals substrate recognition and mechanisms of inhibition

Taurine, a sulfur-containing amino acid, plays a crucial role in human health due to its antioxidant, anti-inflammatory, osmoregulatory properties 1,2. Taurine levels are primarily regulated by the sodium-/chloride-dependent taurine transporter (TauT) 3. Inhibitors of TauT have potential in the treatment of certain diseases, including neurological disorders and cancer 4,5. Here, we present five structures of TauT: in the substrate-free apo state, in complex with taurine and in complex with taurine-mimetic inhibitors {beta}-alanine, GABA and guanidinoethyl sulfonate (GES), each with varying length of linear structure, in both inward-facing and occluded conformations. The taurine-, {beta}-alanine- and GABA-bound hTauT structures, in the presence of NaCl, adopt an occluded conformation, with ligands binding in the central pocket. In the presence of KCl, GES-bound hTauT adopts an inward-facing conformation, with two molecules positioned along the substrate translocation pathway with one into the deep central cavity and the other precluded conformational change from inward-facing to occluded state. Combined with function analysis, our structures provided insights into the overall architecture, substrate coordination and inhibitor recognition mechanisms of TauT.

biophysics↗

Inactivation of necroptosis-promoting protein MLKL creates a therapeutic vulnerability in colorectal cancer cells.

Mortality from colorectal cancer (CRC) is significant, and novel CRC therapies are needed. A pseudokinase MLKL typically executes necroptotic cell death, and MLKL inactivation protects cells from such death. However, we found unexpectedly that MLKL gene knockout enhanced CRC cell death caused by a protein synthesis inhibitor homoharringtonine used for chronic myeloid leukemia treatment. In an effort to explain this finding, we observed that MLKL gene knockout reduced CRC cell autophagy and rendered such autophagy critically dependent on the presence of VPS37A, a component of the ESCRT-I complex. Moreover, homoharringtonine-induced activation of p38 MAP kinase (p38MAPK) prevented VPS37A from supporting autophagy in MLKL-deficient cells and triggered their parthanatos, a cell death type driven by poly(ADP-ribose) polymerase hyperactivation. Finally, a pharmacological MLKL inhibitor necrosulfonamide strongly cooperated with homoharringtonine in suppressing CRC cell tumorigenicity in mice. Thus, while MLKL mediates necroptosis, MLKL protects CRC cells from death caused by drugs blocking basal autophagy, e.g., homoharringtonine, and MLKL inhibition creates a therapeutic vulnerability that could be utilized for CRC treatment.

cancer biology↗

Integrative Analysis of Gene Expression, Protein Abundance, and Metabolomic Profiling Elucidates Complex Relationships in Chronic Hyperglycemia-Induced Changes in Human Aortic Smooth Muscle Cells

Type 2 diabetes mellitus (T2DM) is a major public health concern with significant cardiovascular complications (CVD). Despite extensive epidemiological data, the molecular mechanisms relating hyperglycemia to CVD remain incompletely understood. We here investigated the impact of chronic hyperglycemia on human aortic smooth muscle cells (HASMCs) cultured under varying glucose conditions in vitro, mimicking normal (5 mmol/L), pre-diabetic (10 mmol/L), and diabetic (20 mmol/L) conditions, respectively. Patient-derived T2DM-SMCs served as disease phenotype controls. Results showed significant increases in cellular proliferation, area, perimeter, and F-actin expression with increasing glucose concentration (p < 0.01), albeit not exceeding the levels in T2DM cells. Atomic force microscopy analysis revealed significant decreases in Youngs moduli, membrane tether forces, membrane tension, and surface adhesion in SMCs at higher glucose levels (p < 0.001), with T2DM-SMCs being the lowest among all the cases (p < 0.001). T2DM-SMCs exhibited elevated levels of pro-inflammatory markers such as IL-6, IL-8, and MCP-1 compared to glucose-treated SMCs (p < 0.01). Conversely, growth factors such as FGF-2 and TGF-{beta} were higher in SMCs exposed to 10 mmol/L glucose but lower in T2DM-SMCs (p < 0.01). Pathway enrichment analysis showed significant increases in the expression of inflammatory cytokine-associated pathways, especially involving IL-10, IL-4 and IL-13 signaling in genes that are up-regulated by elevated glucose levels. Differentially regulated gene (DGE) analysis showed that compared to normal glucose receiving SMCs, 513 genes were upregulated and 590 genes were downregulated in T2DM-SMCs; fewer genes were differentially expressed in SMCs receiving higher glucose levels. Finally, the altered levels in genes involved in ECM organization, elastic fiber synthesis and formation, laminin interactions, and ECM proteoglycans were identified, which highlight the role of hyperglycemia in vascular remodeling and CVD progression. Our results collectively suggest that chronic hyperglycemia in vascular SMCs leads to morphological, biomechanical, and functional alterations, potentially contributing to the pathogenesis of T2DM-associated CVD. The observed differences in gene expression patterns between in vitro hyperglycemic models and patient-derived T2DM-SMCs highlight the complexity of T2DM pathophysiology and underline the need for further studies.

bioengineering↗

CGPA: multi-context insights from the cancer gene prognosis atlas

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPAs comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies. SignificanceCGPA is a streamlined, interactive platform for multi-context gene-centric prognostic analysis, simplifying biomarker discovery and validation in oncology for clinicians and basic scientists, and bridging a critical gap in translational cancer research.

bioinformatics↗

Co-Transplantation-Based Human-Mouse Chimeric Brain Models to Study Human Glial-Glial and Glial-Neuronal Interactions

Human-mouse chimeric brain models, generated by transplanting human induced pluripotent stem cell (hiPSC)-derived neural cells, are valuable for studying the development and function of human neural cells in vivo. Understanding glial-glial and glial-neuronal interactions is essential for unraveling the complexities of brain function and developing treatments for neurological disorders. To explore these interactions between human neural cells in vivo, we co-engrafted hiPSC-derived neural progenitor cells together with primitive macrophage progenitors into the neonatal mouse brain. This approach creates human-mouse chimeric brains containing human microglia, macroglia (astroglia and oligodendroglia), and neurons. Using super-resolution imaging and 3D reconstruction techniques, we examine the dynamics between human neurons and glia, and observe human microglia pruning synapses of human neurons, and often engulfing neurons themselves. Single-cell RNA sequencing analysis of the chimeric brain uncovers a close recapitulation of the human glial progenitor cell population, along with a dynamic stage in astroglial development that mirrors the processes found in the human brain. Furthermore, cell-cell communication analysis highlights significant neuronal-glial and macroglial-microglial interactions, especially the interaction between adhesion molecules neurexins and neuroligins between neurons and astroglia, emphasizing their key role in synaptogenesis. We also observed interactions between microglia and astroglia mediated by SPP1, crucial for promoting microglia growth and astrogliosis, and the PTN-MK pathways, instrumental in homeostatic maintenance and development in macroglial progenitors. This innovative co-transplantation model opens up new avenues for exploring the complex pathophysiological mechanisms underlying human neurological diseases. It holds particular promise for studying disorders where glial-neuronal interactions and non-cell-autonomous effects play crucial roles.

neuroscience↗

URA6 mutations provide an alternative mechanism for 5-FOA resistance in Saccharomyces cerevisiae

The URA3 gene is frequently used in the budding yeast community as the mutation target for 5-fluoroorotic acid (5-FOA) resistance. We identified a class of ura6 mutants that can grow in the presence of 5-FOA. Unlike ura3 mutants, ura6 mutants remain prototrophic and are able to grow in the absence of uracil. In addition to 5-FOA resistance, we found that ura6 mutants are also resistant to 5-fluorocytosine (5-FC) and 5-fluorouracil (5-FU). In total, we identified 50 unique missense mutations across 32 unique amino acid positions of Ura6 which confer resistance to 5-FOA. We found that 28 out of the 32 affected positions are located in regions conserved between Saccharomyces cerevisiae and three clinically relevant pathogenic fungi. Metabolic analysis revealed a build-up of uridine monophosphate (UMP) and 5-fluorouridine monophosphate (5-FUMP) in ura6 mutants, indicating a reduction in Ura6 activity. Despite the accumulation of UMP in ura6 mutants, uridine diphosphate and triphosphate (UDP, UTP) levels were similar across mutants and wild type. These findings suggest that missense mutations to URA6, can result in reduction of uridylate kinase activity and lead to cross resistance to fluorinated prodrugs that incorporate into both the de novo pyrimidine synthesis and the pyrimidine salvage pathway.

genetics↗

The novel role of Kallistatin in linking metabolic syndromes and cognitive memory deterioration by inducing amyloid-β plaques accumulation and tau protein hyperphosphorylation

Accumulation of amyloid {beta} (A{beta}) peptides and hyperphosphorylated tau proteins in the hippocampus triggers cognitive memory decline in Alzheimers disease (AD). The incidence and mortality of sporadic AD were tightly associated with diabetes and hyperlipidemia, while the exact linked molecular is uncertain. Here, we reported that serum Kallistatin concentrations were meaningfully higher in AD patients, with a higher concentration of fasting blood glucose and triglyceride. In addition, the constructed Kallistatin-transgenic (KAL-TG) mice defined its cognitive memory impairment phenotype and lower LTP in hippocampal CA1 neurons accompanied by increased A{beta} deposition and tau phosphorylation. Mechanistically, Kallistatin could directly bind to the Notch1 receptor and thereby upregulate BACE1 expression by inhibiting PPAR{gamma} signaling, resulting in A{beta} cleavage and production. Besides, Kallistatin could promote the phosphorylation of tau by activating GSK-3{beta}. Fenofibrate, a hypolipidemic drug, could alleviate cognitive memory impairment by down-regulating A{beta} and tau phosphorylation of KAL-TG mice. Collectively, our data clarified a novel mechanism for A{beta} accumulation and tau protein hyperphosphorylation regulation by Kallistatin, which might play a crucial role in linking metabolic syndromes and cognitive memory deterioration, and suggested that fenofibrate might have the potential for treating metabolism-related AD. HighlightsO_LIKallistatin-transgenic (KAL-TG) mice defined its cognitive memory impairment phenotype accompanied by increased A{beta} deposition and tau phosphorylation. C_LIO_LIKallistatin could directly bind to the Notch1 receptor and thereby upregulate BACE1 expression by inhibiting PPAR{gamma} signaling. C_LIO_LIFenofibrate could alleviate cognitive memory impairment and down-regulate the serum Kallistatin level. C_LI

neuroscience↗

Contraction-induced endocardial id2b plays a dual role in regulating myocardial contractility and valve formation

Biomechanical cues play an essential role in sculpting organ formation. Comprehending how cardiac cells perceive and respond to biomechanical forces is a biological process with significant medical implications that remains poorly understood. Here we show that biomechanical forces activate endocardial id2b (inhibitor of DNA binding 2b) expression, thereby promoting cardiac contractility and valve formation. Taking advantage of the unique strengths of zebrafish, particularly the viability of embryos lacking heartbeats, we systematically compared the transcriptomes of hearts with impaired contractility to those of control hearts. This comparison identified id2b as a gene sensitive to blood flow. By generating a knockin reporter line, our results unveiled the presence of id2b in the endocardium, and its expression is sensitive to both pharmacological and genetic perturbations of contraction. Furthermore, id2b loss-of-function resulted in progressive heart malformation and early lethality. Combining RNA-seq analysis, electrophysiology, calcium imaging, and echocardiography, we discovered profound impairment in atrioventricular (AV) valve formation and defective excitation-contraction coupling in id2b mutants. Mechanistically, deletion of id2b reduced AV endocardial cell proliferation and led to a progressive increase in retrograde blood flow. In the myocardium, id2b directly interacted with the bHLH component tcf3b (transcription factor 3b) to restrict its activity. Inactivating id2b unleashed its inhibition on tcf3b, resulted in enhanced repressor activity of tcf3b, which subsequently suppressed the expression of nrg1 (neuregulin 1), an essential mitogen for heart development. Overall, our findings identify id2b as an endocardial cell-specific, biomechanical signaling-sensitive gene, which mediates intercellular communications between endocardium and myocardium to sculpt heart morphogenesis and function.

developmental biology↗