Biology subjects

Cheng, H.

Publications and source records attributed to Cheng, H..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

GDC: An Integrated Resource to Explore the Pathogenesis of Hearing Loss through Genetics and Genomics

Effective research and clinical application in audiology and hearing loss (HL) often require the integration of diverse data. However, the absence of a dedicated database impeded understanding and insight extraction in HL. To address this, the Genetic Deafness Commons (GDC) was developed by consolidating extensive genetic and genomic data from 51 public databases and the Chinese Deafness Genetics Consortium, encompassing 5,983,613 variants across 201 HL genes. This comprehensive dataset detailed the genetic landscape of HL, identifying six novel mutational hotspots within DNA binding domains of transcription factor genes, which were eligible for evidence-based variant pathogenicity classification. Comparative phenotypic analyses highlighted considerable disparities between human and mouse models, with only 130 human HL genes exhibiting hearing abnormality in mice. Moreover, gene expression analyses in the cochleae of mice and rhesus macaques demonstrated a notable correlation (R2 = 0.76). Utilizing gene expression, function, pathway, and phenotype data, a SMOTE-Random Forest model identified 18 candidate HL genes, including TBX2 and ERCC2, newly confirmed as HL genes. The GDC, as a comprehensive and unified repository, significantly advances audiology research and clinical practice by enhancing data accessibility and usability, thereby facilitating deeper insights into hearing disorders.

genetics↗

bioRxiv2024

A single-nucleus census of immune and non-immune cell types for the major immune organ systems of chicken.

In the avian host, comprehensively cataloging immune cell types, their transcriptome profiles, and varying molecular responses to pathogen challenges are necessary steps toward a better understanding of the interplay between genetics and disease resilience. We present a first nuclei atlas of immune cell types derived from the three main immune organs of layer chickens, including spleen, bursa, and thymus. In bursa we also present, an accounting of cell type activation with the bacterial toxin lipopolysaccharide (LPS). Our analysis includes 36,370 total nuclei and 16, 12, and 12 transcriptionally distinct clusters for spleen, bursa, and thymus, respectively. We discover nuclei molecular profiles that uniquely distinguish states of the transcriptome within cell type that could serve as new means to characterize avian immune subtypes. We further subcluster refined immune cell type classifications, specifically highlighting the transcriptomic diversity of B and T cell subtypes. In the bursa, inferred intercellular communication and signaling pathway enrichment analyses across immune and non-immune cell types demonstrate the unappreciated complexity of the B cell repertoire in a model mimicking systemic bacterial infection. This census of all cell types in both primary and one major secondary avian immune organ system, although preliminary, provides a first review of how nuclei transcribe numerous genes, known and unknown, a critical prerequisite for the study avian immunogenetics by cell type.

immunology↗

bioRxiv2024

Cryo-EM phase-plate images reveal unexpected levels of apparent specimen damage

Apoferritin (apoF) is commonly used as a test specimen in single-particle electron cryo-microscopy (cryo-EM), since it consistently produces density maps that go to 3 [A] resolution or higher. When we imaged apoF with a laser phase plate (LPP), however, we observed more severe particle-to-particle variation in the images than we had previously thought to exist. Similarly, we found that images of ribulose bisphosphate carboxylase/oxygenase (rubisco) also exhibited a much greater amount of heterogeneity than expected. By comparison to simulations of images, we verified that the heterogeneity is not explained by the known features of the LPP, shot noise, or differences in particle orientation. We also demonstrate that our specimens are comparable to those previously used in the literature, based on using the final-reconstruction resolution as the metric for evaluation. All of this leads us to the hypothesis that the heterogeneity is due to damage that has occurred either during purification of the specimen or during preparation of the grids. It is not, however, our goal to explain the causes of heterogeneity; rather, we report that using the LPP has made the apparent damage too obvious to be ignored. In hindsight, similar heterogeneity can be seen in images of apoF and the 20S proteasome which others had recorded with a Volta phase plate. We therefore conclude that the increased contrast of phase-plate images (at low spatial frequencies) should also make it possible to visualize, on a single-particle basis, various forms of biologically functional heterogeneity in structure that had previously gone unnoticed. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/606536v1_ufig1.gif" ALT="Figure 1"> View larger version (101K): org.highwire.dtl.DTLVardef@1c85263org.highwire.dtl.DTLVardef@990352org.highwire.dtl.DTLVardef@1a920b3org.highwire.dtl.DTLVardef@a1fcf0_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIPhase plates recover low-frequency information with significantly improved SNR C_LIO_LILaser phase-plate images reveal unexpected amounts of structural heterogeneity C_LIO_LIIn retrospect, similar heterogeneity can also be seen in Volta phase-plate images C_LIO_LIParticle heterogeneity produces "structural noise", which may diminish map quality C_LI

biophysics↗

bioRxiv2024

A Ketogenic Diet Sensitizes Pancreatic Cancer to Inhibition of Glutamine Metabolism

Pancreatic cancer is the third leading cause of cancer death in the United States, and while conventional chemotherapy remains the standard treatment, responses are poor. Safe and alternative therapeutic strategies are urgently needed1. A ketogenic diet has been shown to have anti-tumor effects across diverse cancer types but will unlikely have a significant effect alone. However, the diet shifts metabolism in tumors to create new vulnerabilities that can be targeted (1). Modulators of glutamine metabolism have shown promise in pre-clinical models but have failed to have a marked impact against cancer in the clinic. We show that a ketogenic diet increases TCA and glutamine-associated metabolites in murine pancreatic cancer models and under metabolic conditions that simulate a ketogenic diet in vitro. The metabolic shift leads to increased reliance on glutamine-mediated anaplerosis to compensate for low glucose abundance associated with a ketogenic diet. As a result, glutamine metabolism inhibitors, such as DON and CB839 in combination with a ketogenic diet had robust anti-cancer effects. These findings provide rationale to study the use of a ketogenic diet with glutamine targeted therapies in a clinical context. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/604377v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1ebeefforg.highwire.dtl.DTLVardef@97e2ceorg.highwire.dtl.DTLVardef@1ab45c3org.highwire.dtl.DTLVardef@1713540_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Abstract Description: Mechanistic rationale for combining a ketogenic diet and glutamine metabolism inhibitors. The combination of low glucose from a ketogenic diet and pharmacologic glutamine inhibition impairs nutrient input to mitochondria, reducing cancer growth.

cancer biology↗

bioRxiv2024

Path-based reasoning in biomedical knowledge graphs

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimers, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNets interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

bioinformatics↗

bioRxiv2024

CAMSAP3 Forms Dimers via its lapha-helix Domain that Directly Stabilize Non-centrosomal Microtubule Minus Ends

Microtubules are vital components of the cytoskeleton. Their plus ends are dynamic and respond to changes in cell morphology, while the minus ends are stable and serve a crucial role in microtubule seeding and maintaining spatial organization. In mammalian cells, the calmodulin-regulated spectrin-associated proteins (CAMSAPs), play a key role in directly regulating the dynamics of non-centrosomal microtubules minus ends. However, the molecular mechanisms are not yet fully understood. Our study reveals that CAMSAP3 forms dimers through its C-terminal -helix; this dimerization not only enhances the microtubule-binding affinity of the CKK domain but also enables the CKK domain to regulate the dynamics of microtubules. Furthermore, CAMSAP3 also specializes in decorating at the minus end of microtubules through the combined action of the microtubule-binding domain (MBD) and the C-terminal -helix, thereby achieving dynamic regulation of the minus ends of microtubules. These findings are crucial for advancing our understanding and treatment of diseases associated with non-centrosomal microtubules. SignificanceOur study reveals the molecular mechanism of how CAMSAP3, a key regulator of non-centrosomal microtubule dynamics, directly regulates the dynamics of non-centrosomal microtubule minus ends through CKK domain. CAMSAP3 forms dimers through its C-terminal -helix, which enhances the CKK domain of CAMSAP3 binding to microtubule minus ends and confers stability of them. This finding is not limited to CAMSAP3, but can also be applied to the understanding of the regulation of non-centrosomal microtubule minus end stability by CAMSAP family proteins. Our findings deepen our comprehension of cellular structure and function, offering insights into the role of microtubules in cellular integrity and disease. This study fills a significant knowledge gap and lays the foundation for future research into the complex balance of microtubule dynamics required for cellular health and disease prevention.

cell biology↗

bioRxiv2024

Genomic and Genetic Insights into Mendel's Pea Genes

Pea, Pisum sativum, is an excellent model system through which Gregor Mendel established the foundational principles of inheritance. Surprisingly, till today, the molecular nature of the genetic differences underlying the seven pairs of contrasting traits that Mendel studied in detail remains partially understood. Here, we present a genomic and phenotypic variation map, coupled with haplotype-phenotype association analyses across a wide range of traits in a global Pisum diversity panel. We focus on a genomics-enabled genetic dissection of each of the seven traits Mendel studied, revealing many previously undescribed alleles for the four characterized genes, R, Le, I and A, and elucidating the gene identities and mutations for the remaining three uncharacterized traits. Notably, we identify: (1) a ca. 100kb deletion upstream of the Chlorophyll synthase (ChlG) gene, which generates aberrant transcripts and confers the yellow pod phenotype of gp mutants; (2) an in-frame premature stop codon mutation in a Dodeca-CLE41/44 signalling peptide which explains the parchmentless mutant phenotype corresponding to p; and (3) a 5bp in-frame deletion in a CIK-like receptor kinase gene corresponding to the fasciated stem phenotype fa, which Mendel described in terms of flower position, and we postulate the existence of a Modifier of fa (Mfa) locus that masks this meristem defect. Mendel noted the pleiotropy of the a mutation, including inhibition of axil ring anthocyanin pigmentation, a trait we found to be controlled by allelic variants of the gene D within an R2R3-MYB gene cluster. Furthermore, we characterize and validate natural variation of a quantitative genetic locus governing both pod width and seed weight, characters that Mendel deemed were not sufficiently demarcated for his analyses. This study establishes a cornerstone for fundamental research, education in biology and genetics, and pea breeding practices.

genetics↗

bioRxiv2024

Single-cell transcriptomics reveals immunosuppressive microenvironment and highlights stumor-promoting macrophage cells in Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive primary brain malignancy in adults. Nevertheless, the cellular heterogeneity and complexity within the GBM microenvironment (TME) are still not fully understood, posing a significant obstacle in the advancement of more efficient immunotherapies for GBM. In this study, we conducted an integrated analysis of 48 tumor fragments from 24 GBM patients at the single-cell level, uncovering substantial molecular diversity within immune infiltrates. We characterized molecular signatures for five distinct tumor-associated macrophage (TAM) subtypes. Notably, the TAM_MRC1 subtype displayed a pronounced M2 polarization signature. Additionally, we identified a subtype of natural killer (NK) cells, designated CD56dim_DNAJB1. This subtype is characterized by an exhausted phenotype, evidenced by an elevated stress signature and enrichment in the PD-L1/PD-1 checkpoint pathway. Our findings also highlight significant cell-cell interactions among malignant glioma cells, TAM, and NK cells within the TME. Overall, this research sheds light on the functional heterogeneity of glioma and immune cells in the TME, providing potential targets for therapeutic intervention in this immunologically cold cancer.

cancer biology↗

bioRxiv2024

5'-S-(3-aminophenyl)-5'-thioadenosine, a novel chemoprotective agent for reducing toxic side effects of fluorouracil in treatment of MTAP-deficient cancers

BackgroundNucleobase analogue (NBA) drugs are effective chemotherapeutics, but their clinical use is limited by severe side effects. Compelling evidence suggests the use of S-methyl-5-thioadenosine (MTA) can selectively reduce NBA toxicity on normal tissues while maintaining the efficacy of NBAs on methylthioadenosine phosphorylase (MTAP)-deficient cancers. However, we found that MTA induced hypothermia at its effective dose, limiting its translational potential. We intended to find an MTA analogue that can exert MTA function while minimize the undesired side effects of MTA. Thus, such an analogue can be used in combination with NBAs in selectively targeting MTAP-deficient cancers. MethodsWe screened a library of MTA analogues for the following criteria: 1) being substrates of MTAP; 2) selectively protection on MTAP-expressing cells from NBA toxicity using MTAP-isogenic cell lines; 3) ability to protect the host from NBA toxicity without hypothermic effect; and 4) lack of interference on the tumor-suppressive effect of NBA in mice bearing MTAP-deficient tumors. ResultsWe identified 5-S-(3-aminophenyl)-5-thioadenosine (m-APTA) that did not induce hypothermia at the effective doses. We demonstrated that m-APTA could be converted to adenine by MTAP. Consequently, m-APTA selectively protected mouse hosts from 5-FU-induced toxicity (i.e. anemia); yet it did not interfere with the drug efficacy on MTAP-deficient bladder cancers. In silico docking studies revealed that, unlike MTA, m-APTA interact inefficiently with adenosine A1 receptor, providing a plausible explanation of the superior safety profile of m-APTA. Conclusionm-APTA can significantly improve the translational potential of the NBA toxicity reduction strategy in selectively targeting MTAP-deficient cancers.

cancer biology↗

bioRxiv2024

Have AI-Generated Texts from LLM Infiltrated the Realm of Scientific Writing? A Large-Scale Analysis of Preprint Platforms

Since the release of ChatGPT in 2022, AI-generated texts have inevitably permeated various types of writing, sparking debates about the quality and quantity of content produced by such large language models (LLM). This study investigates a critical question: Have AI-generated texts from LLM infiltrated the realm of scientific writing, and if so, to what extent and in what setting? By analyzing a dataset comprised of preprint manuscripts uploaded to arXiv, bioRxiv, and medRxiv over the past two years, we confirmed and quantified the widespread influence of AI-generated texts in scientific publications using the latest LLM-text detection technique, the Binoculars LLM-detector. Further analyses with this tool reveal that: (1) the AI influence correlates with the trend of ChatGPT web searches; (2) it is widespread across many scientific domains but exhibits distinct impacts within them (highest: computer science, engineering sciences); (3) the influence varies with authors who have different language speaking backgrounds and geographic regions according to the location of their affiliations (Italy, China, etc.); (4) AI-generated texts are used in various content types in manuscripts (most significant: hypothesis formulation, conclusion summarization); (5) AI usage has a positive influence on papers impact, measured by its citation numbers. Based on these findings, suggestions about the advantages and regulation of AI-augmented scientific writing are discussed.

scientific communication and education↗

bioRxiv2024

deepGOLSA: Goal-directed planning with subgoal reduction models human brain activity

Goal-directed planning presents a challenge for classical RL algorithms due to the vastness of the combinatorial state and goal spaces, while humans and animals adapt to complex environments, especially with diverse, non-stationary objectives, often employing intermediate goals for long-horizon tasks. Here, we propose a goal reduction mechanism for effectively deriving subgoals from arbitrary and distant original goals, using a novel loop-removal technique.1 The product of the method, called goal-reducer, distills high-quality subgoals from a replay buffer, all without the need for prior global environmental knowledge. Simulations show that the goal-reducer can be integrated into RL frameworks like Deep Q-learning and Soft Actor-Critic. It accelerates performance in both discrete and continuous action space tasks, such as grid world navigation and robotic arm manipulation, relative to the corresponding standard RL models. Moreover, the goal-reducer, when combined with a local policy, without iterative training, outperforms its integrated deep RL counterparts in solving a navigation task. This goal reduction mechanism also models human problem-solving. Comparing the models performance and activation with human behavior and fMRI data in a treasure hunting task, we found matching representational patterns between a goal-reducer agents components and corresponding human brain areas, particularly the vmPFC and basal ganglia. The results suggest that humans may use a similar computational framework for goal-directed behaviors.

neuroscience↗

bioRxiv2024

Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error

In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction errors from the previous state predictions as inputs to the hidden units of the network. We show that the time course of sequence prediction errors generated by the model closely matched the activity time courses of populations of neurons in macaque prefrontal cortex. Hidden units in the model responded to combinations of task variables and exhibited sensitivity to changing stimulus probability in ways that closely resembled monkey prefrontal neurons. Moreover, the model generated prolonged response times to infrequent, unexpected events as did monkeys. The results suggest that prefrontal cortex may generate internal models of the temporal structure of the world even during tasks that do not explicitly depend on temporal expectation, using a sequence prediction error minimization learning rule to do so. As such, the SPEL model provides a unified, general-purpose theoretical framework for modeling the lateral prefrontal cortex.

neuroscience↗

bioRxiv2024

Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing to capture molecular snapshots within the first minutes, hours, and days of BRAF kinase inhibitor exposure in a human BRAF-mutant melanoma model of adaptive therapy resistance. By enriching specific phospho-motifs associated with mitogenic kinase activity, we monitored the dynamics of thousands of growth- and survival-related protein phosphorylation events under oncogenic BRAF inhibition and drug removal. We observed early and sustained inhibition of the BRAF-ERK axis, gradual downregulation of canonical cell cycle-dependent signals, and three distinct and reversible phase transitions toward quiescence. Statistical inference of kinetically-defined signaling and transcriptional modules revealed a concerted response to oncogenic BRAF inhibition and a dominant compensatory induction of SRC family kinase (SFK) signaling, which we found to be at least partially driven by accumulation of reactive oxygen species via impaired redox homeostasis. This induction sensitized cells to co-treatment with an SFK inhibitor across a panel of patient-derived melanoma cell lines and in an orthotopic mouse xenograft model, underscoring the translational potential for measuring the early temporal dynamics of signaling and transcriptional networks under therapeutic challenge.

systems biology↗

bioRxiv2024

A Novel Imaging Mass Spectrometry Computational Tool for Biomedical Discovery: Untargeted pixel-by-pixel imaging of Metabolite Ratio Pairs

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

bioinformatics↗

bioRxiv2023

Fast inhibition slows and desynchronizes auditory efferent neuron activity

The encoding of acoustic stimuli requires precise neuron timing. Auditory neurons in the cochlear nucleus (CN) and brainstem are well-suited for accurate analysis of fast acoustic signals, given their physiological specializations of fast membrane time constants, fast axonal conduction, and reliable synaptic transmission. The medial olivocochlear (MOC) neurons that provide efferent inhibition of the cochlea reside in the ventral brainstem and participate in these fast neural circuits. However, their modulation of cochlear function occurs over time scales of a slower nature. This suggests the presence of mechanisms that restrict MOC inhibition of cochlear function. To determine how monaural excitatory and inhibitory synaptic inputs integrate to affect the timing of MOC neuron activity, we developed a novel in vitro slice preparation ( wedge-slice). The wedge-slice maintains the ascending auditory nerve root, the entire CN and projecting axons, while preserving the ability to perform visually guided patch-clamp electrophysiology recordings from genetically identified MOC neurons. The in vivo-like timing of the wedge-slice demonstrates that the inhibitory pathway accelerates relative to the excitatory pathway when the ascending circuit is intact, and the CN portion of the inhibitory circuit is precise enough to compensate for reduced precision in later synapses. When combined with machine learning PSC analysis and computational modeling, we demonstrate a larger suppression of MOC neuron activity when the inhibition occurs with in vivo-like timing. This delay of MOC activity may ensure that the MOC system is only engaged by sustained background sounds, preventing a maladaptive hyper-suppression of cochlear activity. Significance StatementAuditory brainstem neurons are specialized for speed and fidelity to encode rapid features of sound. Extremely fast inhibition contributes to precise brainstem sound encoding. This circuit also projects to medial olivocochlear (MOC) efferent neurons that suppress cochlear function to enhance detection of signals in background sound. Using a novel brain slice preparation with intact ascending circuitry, we show that inhibition of MOC neurons can also be extremely fast, with the speed of the circuit localized to the cochlear nucleus. In contrast with the enhancement of precision afforded by fast inhibition in other brainstem auditory circuits, inhibition to MOC neurons instead has a variable onset that delays and desynchronizes activity, thus reducing precision for a slow, sustained response to background sounds.

neuroscience↗

bioRxiv2023

Macrophage Depletion Protects Against Cisplatin-Induced Ototoxicity and Nephrotoxicity

Cisplatin is a widely used and highly effective anti-cancer drug with significant side effects including ototoxicity and nephrotoxicity. Macrophages, the major resident immune cells in the cochlea and kidney, are important drivers of both inflammatory and tissue repair responses. To investigate the roles of macrophages in cisplatin-induced ototoxicity and nephrotoxicity, we used PLX3397, an FDA-approved inhibitor of the colony-stimulating factor 1 receptor (CSF1R), to eliminate tissue-resident macrophages during the course of cisplatin administration. Mice treated with cisplatin alone (cisplatin/vehicle) had significant hearing loss (ototoxicity) as well as kidney injury (nephrotoxicity). Macrophage ablation using PLX3397 resulted in significantly reduced hearing loss measured by auditory brainstem responses (ABR) and distortion-product otoacoustic emissions (DPOAE). Sensory hair cells in the cochlea were protected against cisplatin-induced death in mice treated with PLX3397. Macrophage ablation also protected against cisplatin-induced nephrotoxicity, as evidenced by markedly reduced tubular injury and fibrosis as well as reduced plasma blood urea nitrogen (BUN) and neutrophil gelatinase-associated lipocalin (NGAL) levels. Mechanistically, our data suggest that the protective effect of macrophage ablation against cisplatin-induced ototoxicity and nephrotoxicity is mediated by reduced platinum accumulation in both the inner ear and the kidney. Together our data indicate that ablation of tissue-resident macrophages represents a novel strategy for mitigating cisplatin-induced ototoxicity and nephrotoxicity. Brief summaryMacrophage ablation using PLX3397 was protective against cisplatin-induced ototoxicity and nephrotoxicity by limiting platinum accumulation in the inner ear and kidney.

immunology↗

bioRxiv2023

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

Emerging spatially resolved transcriptomics (SRT) technologies provide unprecedented opportunities to discover the spatial patterns of gene expression at the cellular or tissue levels. Currently, most existing computational tools on SRT are designed and tested on the ribbon-like brain cortex. Their present expressive power often makes it challenging to identify highly heterogeneous mosaic-like tissue architectures, such as tissues from kidney diseases. This demands heightened precision in discerning the cellular and morphological changes within renal tubules and their interstitial niches. We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, the proposed REGNN integrates equivariance to handle the rotational and translational symmetries of the spatial space, and Positional Encoding (PE) to identify and strengthen the relative spatial relations of the nodes uniformly distributed in the lattice. Our study finds that REGNN outperforms existing computational tools in identifying inherent mosaic-like heterogenous tissue architectures in kidney samples sourced from different kidney diseases using the 10X Visium platform. In case studies on acute kidney injury and chronic kidney diseases, the results identified by REGNN are also validated by experienced nephrology physicians. This proposed framework explores the expression patterns of highly heterogeneous tissues with an enhanced graph deep learning model, and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases. REGNN is publicly available at https://github.com/Mraina99/REGNN.

bioinformatics↗

bioRxiv2023

A seven-sex species recognizes self and non-self mating-type via a novel protein complex

Although most species have two sexes, multisexual (or multi-mating type) species are also widespread. However, it is unclear how mating-type recognition is achieved at the molecular level in multisexual species. The unicellular ciliate Tetrahymena thermophila has seven mating types, which are determined by the MTA and MTB proteins. In this study, we found that both proteins are essential for cells to send or receive complete mating-type information, and transmission of the mating-type signal requires both proteins to be expressed in the same cell. We found that MTA and MTB form a mating-type recognition complex that localizes to the plasma membrane, but not to the cilia. Stimulation experiments showed that the mating-type-specific regions of MTA and MTB mediate both self- and non-self-recognition, indicating that T. thermophila uses a dual approach to achieve mating-type recognition. Our results suggest that MTA and MTB form an elaborate multifunctional protein complex that can identify cells of both self and non-self mating types in order to inhibit or activate mating, respectively. Impact statementA giant multifunctional protein complex mediates mating-type recognition through a non-ligand-receptor mechanism in a multisexual species.

genetics↗