Biology subjects

Ren, C.

Publications and source records attributed to Ren, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Differential Survival of Retinal Ganglion Cell Types Following Branch Retinal Vein Occlusion

Background. Branch retinal vein occlusion (BRVO) is a common cause of retinal ischemia, rapidly inducing neuronal death and often producing irreversible vison loss. Interventions such as thrombolysis restore perfusion but cannot recover neurons already lost, and most patients retain permanent visual deficits. Identifying which neuronal populations die, and how rapidly, may therefore reveal timing windows and targets for neuroprotective treatment. Aims. To determine the timing of pathology, we evaluated vascular changes and neuronal degeneration in a photocoagulation-induced mouse model of BRVO. To determine differences in retinal ganglion cells (RGC) type susceptibility, we tracked the survival of RGC subpopulations using immunofluorescence markers. Methods. BRVO was induced by retro-orbital injection of Rose Bengal and FITC dextran followed by targeted photocoagulation of a branch vein. Obstruction was confirmed by fluorescein angiography and vascular damage was viewed by optical coherence tomography angiography. Cell survival was quantified by immunostaining at 3, 7, and 14 days using RNA Binding Protein with Multiple Splicing (RBPMS, pan RGC). RGC subtype markers for intrinsically photosensitive RGCs (TBR2), RGCs (SPP1), ON OFF direction-selective RGCs (CART), and F RGCs (FOXP2) were used to examine differences in survival across RGC types. Results. Occlusion selectively disrupted the superficial and intermediate capillary plexus, sparing the deep plexuses. All examined neuronal populations were significantly depleted, but with markedly different magnitude and timing. RGCs and F RGCs were the most vulnerable subtypes, both approaching complete loss by one week, whereas intrinsically photosensitive RGCs were the most resistant. Conclusion. RGCs, typically resilient in glaucoma, trauma, and excitotoxicity, were markedly vulnerable in BRVO, indicating that RGC subtype susceptibility is insult-specific.

neuroscience↗

bioRxiv2026

Intranasal Fritillaria taipaiensis nanovesicles alleviate acute lung injury via the lung-brain axis

Acute lung injury (ALI) is a severe inflammatory syndrome frequently complicated by brain injury, which worsens outcomes. Here, we explored whether regulation of the lung-brain axis represents a therapeutic strategy for the management of ALI. We show that intranasal administration of Fritillaria taipaiensis-derived exosome-like nanovesicles (Ft-ELNs) ameliorates LPS-induced ALI and ALI-associated hypothalamic inflammation in mice. Ft-ELNs preferentially accumulate in the lungs, inhibiting TRPV1 in both the lungs and the hypothalamus to establish the lung-brain axis. In parallel, Ft-ELNs primarily regulate systemic humoral immunity by inhibiting the VEGFR1/PI3K/AKT/NF?B signaling pathway. In vitro experiments further confirmed that Ft-ELNs are efficiently internalized by alveolar epithelial MLE-12 cells, exerting protective effects by suppressing VEGFR1 expression. Mechanistically, we identified Ft-miR1 of Ft-ELNs as the key functional effector that directly targets the Flt1 3'UTR to inhibit VEGFR1 expression. These data suggest that Ft-ELNs attenuate lung-brain axis inflammation by targeting VEGFR1 and may hold potential as novel therapeutic agents for the treatment of severe ALI.

immunology↗

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗

bioRxiv2026

PASTRI: Resolving Stage-Specific Cell-State Dynamics from Annotated Cell Lineage Trees

Cellular transitions between phenotypic states are fundamental to development and disease, yet quantitative analysis of their dynamics remains challenging. Here we present PASTRI (Phylogenetic Adjacency-based State Transition Rate Inference), a computational framework that infers transition rates from cell lineages/phylogenies annotated with terminal phenotypic states, such as single-cell transcriptomes. We validate PASTRI using simulated lineages and the Caenorhabditis elegans embryonic lineage. Importantly, by leveraging cell pairs at varying phylogenetic distances, PASTRI accurately resolves stage-specific transition rates, circumventing the issue of developmental changes in dynamics. Applied to three cell phylogeny datasets from our lineage-tracing experiments spanning diverse developmental/disease models and tracing systems, PASTRI uncovers rate-limiting steps in the activation of hepatic stellate cells and the differentiation of primordial lung progenitors, as well as attractor states that support cancer cell proliferation. PASTRI thus opens up a venue for dissecting cell state transition dynamics from annotated cell lineage/phylogeny.

bioinformatics↗

bioRxiv2025

A Machine Learning- 3D Microvessel Platform Identifies Kinase Targets Restoring Blood-Brain-Barrier Endothelial Integrity.

Disruption of the brain endothelial barrier is a hallmark of traumatic brain injury (TBI), and contributes to cerebral edema, coagulopathy, and delayed neurological deficits, yet there are no mechanism-guided therapies that directly stabilize human brain vessels. Here we used a three-dimensional (3D) human brain endothelial vessel platform with quantifiable macromolecular barrier function to assess responses to thrombin and plasma from patients with TBI. This endothelial-focused 3D system isolates the central cellular regulator of blood-brain barrier function and enables mechanistic analysis of endothelial signaling and drug responses as a foundation for personalized vascular therapeutics. By integrating this platform with machine learning-guided kinase analysis, we identified kinase pathways associated with barrier stabilization versus disruption and nominated inhibitors that reversed thrombin- and TBI plasma-induced leakage as well as compounds that exacerbated endothelial loss. This work establishes an important first step toward precision vascular therapeutics in TBI, by linking patient-derived plasma phenotypes to specific endothelial barrier responses and pharmacologically tractable kinase signaling, and it provides a generalizable framework for mechanistic drug discovery across neurovascular disorders. SIGNIFICANCE STATEMENTBlood-brain barrier (BBB) disruption drives cerebral edema, coagulopathy, ischemia, and neurological deficits after TBI. Targeting kinases as therapeutics is promising but challenged by complex inhibitor profiles and patient variability. We present a machine learning-guided platform built on 3D perfusable human brain endothelial-centric microvessels to systematically screen and select kinase inhibitors that reverse TBI-induced endothelial barrier breakdown. By pinpointing key kinase and gene regulators and demonstrating restoration of endothelial barrier integrity in patient-derived plasma models, our approach provides a transformative strategy for stabilizing the endothelial barrier after TBI and offers a versatile framework for therapeutic discovery across neurovascular disorders.

bioengineering↗

bioRxiv2025

BiomarkerML: A cloud-based proteomics ML workflow for biomarker discovery

BackgroundHigh-throughput affinity and mass-spectrometry-based proteomic studies of large clinical cohorts generate high-dimensional proteomic data useful for accelerated disease biomarker discovery. A powerful approach to realizing the potential of these big, complex, and non-linear data, whilst ensuring reproducible results, is to use automated machine learning (ML) and deep learning (DL) pipelines for their analysis. However, there remains a gap in comprehensive ML workflows tailored to proteomic biomarker discovery and designed for biomedical researchers who need pipelines to optimally self-configure and automatically avoid over-fitting. FindingsWe present BiomarkerML, a cloud-based workflow for automated, reproducible, and efficient ML/DL analysis of proteomic data for biomarker discovery, designed for novice-ML users and implemented in Python, R and Workflow Description Language (WDL). BiomarkerML: ingests proteomic and clinical data alongside sample labels; pre-processes data for model fitting and optionally performs dimensionality reduction and visualization; fits a catalogue of ML and DL classification and regression models; and calculates model performance metrics for model comparison. Next, the workflow applies mean SHapley Additive exPlanations (SHAP) to quantify the contribution of each protein to model predictions across all samples. Finally, proteins with high mean SHAP values, and their co-expressed protein network interactors, are identified as candidate biomarkers. Importantly, hyperparameters - configuration variables set prior to training models - are automatically fine-tuned via grid-search, and BiomarkerML employs weighted, nested cross-validation to avoid model over-fitting and data leakage. ConclusionsBiomarkerML is scalable, provides a standardized, user-friendly interface, and streamlines analyses to ensure reproducibility of results. Overall, BiomarkerML is a significant advancement, enabling novice-ML researchers to use cutting-edge ML/DL tools to identify disease biomarkers in complex proteomic data.

bioinformatics↗

bioRxiv2025

Analysis of the phosphorylation networks characterizing distinct phenotypic states in glioblastoma cell Populations

Cancer cells, including the most aggressive brain cancer, glioblastoma, can exhibit a high degree of phenotypic plasticity. However, it is not well understood whether and how distinct phenotypic states might be associated with or driven by specific signaling processes. Here, we use a novel approach to the identification of phenotype-specific signaling networks in cells undergoing the Go-or-Grow switch in populations of GBM cell lines. We find that the transition to invasive spread may be associated with the onset of DNA damage response, cell cycle arrest, and activation of the AKT-mTOR signaling. We reconstructed the large-scale signaling networks, revealing integration of diverse pathways and possible feedback interactions mediating phenotypic stability. We further show that phosphorylation outcomes may be preferentially mediated by 14-3-3 proteins, previously implicated in stress response and control of the cell cycle. Finally, we demonstrate that the phenotype-specific phospho-site signatures have predictive power for disease-free patient survival. This study paves the way for a more comprehensive understanding of phenotypic plasticity in GBM and other aggressive cancers.

systems biology↗

bioRxiv2025

Differential encoding of social identity, valence and unfamiliarity in the amygdala and piriform cortex

To appropriately respond to social situations, animals must rapidly identify conspecifics as individuals, gauge their familiarity and recall any good or bad associations. In previous work (Mazuski & OKeefe, 2022), we characterized populations of neurons in the basolateral amygdala complex (BLA) that were strongly tuned to social conspecifics. In this current study, we asked whether these or other populations in the BLA encode properties necessary for social interaction: individual recognition, familiarity detection and reward association. In addition to cells in the rat lateral amygdala (LA) and basolateral amygdala (BLA), we recorded from cells in the anatomically-related piriform cortex (PIR) to see how much of the amygdala response was inherited from this input. Here, we show that these three interrelated areas encode distinct features of social conspecifics at the population and single neuron level. We recorded from large populations of neurons using 4-shank Neuropixels probes while rats learned a social discrimination task, where one male conspecific was paired with a food reward (S+) and the other its absence (S-). For comparison, two unfamiliar animals were presented on occasional probe trials. After successful discrimination learning, we looked for transfer effects to our previously studied task where implanted rats freely interacted with all four conspecifics in an open-field environment. The LA dynamically encoded social reward learning. Initially, LA neuronal population activity could not distinguish between the S+ and S-conspecifics, but after learning distinct representations emerged. In contrast, the PIR showed no effect of social reward learning. Single PIR units encoded social identity with strong tuning to individual conspecifics both before and after learning. The BLA encoded both reward learning and social identity suggesting this is where the PIR and LA information streams converge. Unexpectedly, the BLA and LA strongly coded for the unfamiliarity of the rat - single neurons responded to the probe trial animals with firing rates 2-5x higher than to the task animals. This cross-region encoding of identity, reward and unfamiliarity was reactivated during social interaction in the new open-field environment, demonstrating that the learning was not context specific. The results throw light on how distinct neural circuits contribute to social recognition and memory and how they interact with each other.

neuroscience↗

bioRxiv2025

Proteomic analysis reveals distinct germination strategies in polymorphic fruits of Haloxylon ammodendron

Fruit polymorphism, the production of multiple fruit morphotypes within a species, is an adaptive bet-hedging strategy in variable environments. However, researches about perennial plant and the adaptive mechanisms are not well understood. Haloxylon ammodendron, a constructive desert shrub, exhibits three fruit morphotypes: YY (yellow wings with yellow pericarp), YP (yellow wings with pink pericarp), and PP (pink wings with pink pericarp). We investigated their ecophysiological and molecular mechanisms through germination assays under salt and drought stress, combined with proteomic analysis. YP consistently showed the highest germination percentage (GP) and germination rate index (GRI) under stress, while PP displayed well germination success under low salinity and well-watered conditions (GP=32.7%, 36.7%; GRI=0.018, 0.020), but significantly impaired viability under stress (GP=12.7%, 12.0%; GRI=0.006, 0.006). Proteomics identified 721 differentially expressed proteins (DEPs), with the most (662) between YP and PP, linked to stress response and germination. YPs high abundance of stress-resistant proteins enabled rapid germination, whereas PPs delayed germination aligns with a persistent seed bank strategy. This polymorphism promotes niche differentiation: YP ensures quick colonization, PP enhances long-term resilience, and YY offers an intermediate strategy. Our findings reveal molecular-ecological adaptations in H. ammodendron, aiding targeted germplasm use for desert restoration.

plant biology↗

bioRxiv2025

Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording

Associating unfamiliar stimuli with appropriate behavior through experience is crucial for survival. While task-relevant information was found to be distributed across multiple brain regions, how regional nodes in this distributed network reorganize their functional interactions throughout learning remains to be elucidated. Here, we performed chronic, large-scale single-unit recording across 10 cortical and subcortical regions using ultra-flexible microelectrode arrays in mice performing a visual decision-making task and tracked mesoscale functional network dynamics throughout learning. Task learning reshaped interregional functional connectivity, leading to the emergence of a subnetwork involving visual and frontal regions during the acquisition of correct No-Go responses. This reorganization was accompanied by a more widespread representation of visual stimulus across regions, and a regions network rank strongly predicted its peak timing of visual information encoding. Together, our findings revealed that mesoscale networks undergo dynamic restructuring during learning, with functional connectivity ranks influencing the propagation of sensory information across the network.

neuroscience↗

bioRxiv2025

Large-scale single-neuron recording using the uFINE array in human cortex

Monitoring neural population activity at single-cell resolution is essential for driving fundamental research and clinical innovations. However, translating advanced recording techniques from animal models to humans remains a significant challenge. Flexible neural electrodes have recently emerged as powerful tools for large-scale single-unit recordings due to their superior biocompatibility and high recording density. Here, we demonstrate reliable, high-density single-unit recordings during intraoperative procedures in human patients using ultra-Flexible Implantable Neural Electrode (uFINE) arrays. The uFINE array exhibited sufficient mechanical robustness to maintain structural integrity throughout surgical operations. We successfully recorded 616 single units from 10 patients, with up to 135 single units simultaneously recorded. The flexibility of uFINE array minimized signal disturbances from brain pulsations, enabling stable and continuous single-unit detection. Stimulus and response tuning were observed at the level of individual neurons in awake patients. This uFINE-based recording approach offers unique opportunities to investigate human-specific cognitive functions and develop next-generation brain-machine interfaces.

bioengineering↗

bioRxiv2025

Acquired resistance to sotorasib in mutant NSCLC is vulnerable to PI3K-mTOR pathway inhibition mediated by 4E-BP1 regulator of cap-dependent translation

Sotorasib and adagrasib have shown significant efficacy in KRASG12C mutant NSCLC; however, acquired resistance (AR) occurs within 6-12 months. While some resistance arises from new mutations, over half of the resistant cases lack identifiable genomic alterations. We hypothesize that resistance is driven by signaling network rewiring, creating new therapeutic vulnerabilities. To investigate AR mechanisms, multiple AR models, including cell lines, PDXs, CDXs, and PDXOs, were developed. H23AR and H358AR cells displayed >600-fold and 200-fold; and PDXO303AR and PDXO314AR organoids exhibited >300-fold and >100-fold resistance to sotorasib, respectively; however, no additional mutations in KRAS or other potential genetic alterations were identified. The AR cells and PDXOs also showed comparable resistance to adagrasib. Distinct protein signatures associated with KRAS reactivation, mTORC1 signaling upregulation, and PI3K/AKT/mTOR pathway activation were identified in TC303AR & TC314AR PDXs. PI3K protein levels were significantly elevated in AR PDXs, H23AR, and H358AR cells. Pharmacological inhibition of PI3K with copanlisib or genetic knockout via CRISPR-Cas9 restored sotorasib sensitivity, suppressed colony formation, and inhibited downstream effectors, including p-AKT, p-mTOR, p-S6, p70S6K, p-GSK3{beta}, and p-PRAS40 in AR cells. Copanlisib also sensitized both acquired and primary resistant PDXOs and synergized with sotorasib in restoring drug sensitivity. p4E-BP1 was significantly upregulated in H23AR and H358AR cells, which is suppressed by copanlisib. The level of p4E-BP1 expression was correlated with Sotorasib sensitivity in PI3K knockout clones, where the most sensitive clone displayed reduced or no p4E-BP1 expression. CRISPR-Cas9-mediated knockout of 4E-BP1, either alone or in combination with PI3K knockout, dramatically restored sotorasib sensitivity to levels comparable to parental cells. Suppression of 4E-BP1 hyperphosphorylation required dual inhibition of mTORC1 and mTORC2, and treatment with AZD8055 or sapanisertib (mTORC1/2 dual inhibitors) significantly dephosphorylated 4E-BP1 and restored sotorasib sensitivity in resistant cells and PDXOs. In PDX, CDX, and xenograft models in vivo, the combination of sotorasib with either copanlisib or sapanisertib resulted in robust, synergistic, and durable tumor regression at well-tolerated doses. These findings showed the critical role of PI3K/mTOR signaling as a bypass mechanism of resistance to KRASG12C inhibitors. We conclude that mTORC1/2 mediated inhibition of p4E-BP1 and combination strategies targeting this pathway effectively overcome acquired resistance to KRASG12C inhibitors in NSCLC.

cancer biology↗

bioRxiv2025

Predicting PROTAC-targeted Degradation and Designing Androgen Receptor Degraders with AiPROTAC

Proteolysis-targeting chimeras (PROTACs), a pioneering class of heterobifunctional ligands, have emerged as transformative tools in combating cancer and immune-related diseases due to their ability to target previously undruggable proteins and overcome drug resistance. Accurate assessment of the degradation potential of PROTACs is essential for advancing their therapeutic applications. However, the development of robust predictive models is hindered by the scarcity of large-scale datasets and domain-specific tools, as well as the underutilization of existing unlabeled data. To address these challenges, we developed an innovative method called AiPROTAC to predict the degradation capacity of PROTACs. In particular, AiPROTAC integrates graph augmentation, message-passing enhanced encoders and cross-attention mechanisms within a contrastive learning framework. Moreover, AiPROTAC leverages a curated specialized dataset combined with the PROTAC-DB dataset to enhance prediction accuracy. Experimental results across two datasets and six evaluation metrics demonstrate that AiPROTAC consistently outperforms state-of-the-art models. Further case studies underline its superior sensitivity and reliability. Notably, AiPROTAC facilitated the design of a novel androgen receptor (AR) degrader, PROTAC GT19, which achieved enhanced in vitro degradation compared to Bavdegalutamide (ARV-110). This advancement highlights our AiPROTACs potential to accelerate PROTAC-based therapeutic development and optimization.

bioinformatics↗

bioRxiv2025

Interplay between cytokine and FGF2 signaling in induction of entosis and vasculogenic mimicry response in glioblastoma

Tumor vascularization is critical to survival of cancer cells, but is frequently perturbed leading to disorganized angiogenesis and emergence of alternative means of delivery of oxygen and nutrients, such as vasculogenic mimicry (VM). Understanding of VM and its relationship to endothelial vascularization has been hampered by the lack of comprehensive combination of in vivo clinical data and relevant in vitro models. We address this challenge by analyzing glioblastoma (GBM) tumors and clinically isolated cancer cells. This analysis strongly suggests a key role of macrophage-induced controlled cell death in emergence of VM. The results further point to entosis of cancer cells as a critical intermediate state in this process, enabled by mechano-chemical cell heterogeneity. We find evidence that macrophages can regulate endothelial angiogenesis and VM as two alternative vascularization mechanisms. These results reveal mechanistic underpinnings of VM and pave the way to predictive analysis of tumor progression.

cancer biology↗

bioRxiv2024

Cholinergic feedback for context-specific modulation of sensory representations

The brains ability to prioritize behaviorally relevant sensory information is crucial for adaptive behavior, yet the underlying mechanisms remain unclear. Here, we investigated the role of basal forebrain cholinergic neurons in modulating olfactory bulb (OB) circuits in mice. Calcium imaging of cholinergic feedback axons in OB revealed that their activity is strongly correlated with orofacial movements, with little responses to passively experienced odor stimuli. However, when mice engaged in an odor discrimination task, OB cholinergic axons rapidly shifted their response patterns from movement-correlated activity to odor-aligned responses. Notably, these odor responses during olfactory task engagement were absent in cholinergic axons projecting to the dorsal cortex. The level of odor responses correlated with task performance. Inactivation of OB-projecting cholinergic neurons during task engagement impaired performance and reduced odor responses in OB granule cells. Thus, the cholinergic system dynamically modulates sensory processing in a modality-specific and context-dependent manner, providing a mechanism for a flexible and adaptive sensory prioritization.

neuroscience↗

bioRxiv2024

Two distinct oscillatory auxin signals define the plasticity of lateral rooting in Arabidopsis thaliana

The phytohormone auxin defines lateral root pre-branch sites (PBS) in the growing primary root tip. How PBS contribute to the plasticity of the root system architecture remains incompletely understood1,2,3,4. Here, we reveal in the model plant Arabidopsis thaliana that two distinct oscillatory systems for the phytohormone auxin coordinate the spatial and temporal identity of PBS, jointly defining the lateral root density. We followed auxin signalling dynamics for days and thereby detected a systemic auxin signal oscillating along the mature primary root, where PBS potentially form functional root branches. While a previously proposed local oscillatory auxin zone spatially primes the PBS in the growing root tip, we reveal that the systemic oscillatory auxin signal temporally controls the auxin-dependent identity of these PBS. Light perception in the shoot defines the strength of the systemic auxin signal and thereby controls the auxin-reliant ability of PBS to develop into lateral roots. Moreover, PHYB and CRY1 mediate the light-dependent integration of other environmental signals, such as ambient temperature, into the control systemic auxin signalling and lateral root density. Our work reveals how two spatially distinct oscillatory auxin signals define the plasticity of plant root development in response to fluctuating conditions.

plant biology↗

bioRxiv2024

Unraveling the Web of Life: Incomplete lineage sorting and hybridization as primary mechanisms over polyploidization in the evolutionary dynamics of pear species

In contrast to the traditional Tree of Life (ToL) paradigm, the Web of Life (WoL) model provides a more nuanced and precise depiction of organismal phylogeny, particularly considering the prevalent incongruence observed among gene/species trees. The lack of a generalized pipeline for teasing apart potential evolutionary mechanisms--such as Incomplete Lineage Sorting (ILS), hybridization, introgression, polyploidization, and Whole-Genome Duplication--poses significant challenges to the delineation of the WoL. The pear genus Pyrus, characterized by extensive hybridization events, serves as an excellent model for investigating the WoL. This study introduces a novel Step-by-Step Exclusion (SSE) approach to deciphering the complexities inherent in the WoL. Our findings indicate: 1) ILS, rather than polyploidization, is identified as the primary driver behind the origin of Pyrus from the arid regions of the Himalayas-Central Asia; 2) the two subgenera of Pyrus have independent evolutionary trajectories, facilitated by the geographical barriers that arose via the uplift of the Tibetan Plateau and increased aridity in Central Asia; 3) ILS and hybridization have facilitated the diversification of Oriental pears, while hybridization alone has driven the reticulate evolution of Occidental pears; 4) the establishment of the Silk Road during the Han Dynasty acted as a conduit for genetic exchange between Occidental and Oriental pears. The novel SSE approach provides a universally applicable framework for investigating evolutionary mechanisms defining the WoL paradigm.

systems biology↗

bioRxiv2024

Proteome analysis provides insights into sex differences in Holothuria Scabra

Sex-determining mechanism is still ambiguous for sea cucumber Holothuria scabra which only manifests gonochorism in gonad. In this study, proteomic analysis was employed to delineate sex-related proteins and genes in gonads of H. scabra, subsequently validated through Quantitative real-time polymerase chain reaction (qRT-PCR). A total of 5,313 proteins were identified via proteome sequencing. Among these, 817 proteins exhibited expression in both the ovary and testis, with 445 proteins displaying up-regulation and 372 proteins showing down-regulation. Furthermore, 136 and 69 proteins were identified as ovary-specific and testis-specific Differentially Abundant Proteins (DAPs), respectively. For the validation of 75 DAP coding genes, 9 genes were considered to be reliable. Notably, 25 ovary-bias proteins enriched in ribosome pathway strongly indicated the crucial role of ribosome in ovary. And 5S/18S rRNA ratio in H. Scabra may have potencial to establish a nondestructive method to distinguish sexes unambiguously. This study serves to furnish novel evidence pertaining to sex differences in H. scabra.

molecular biology↗