Biology subjects

Zhang, Z.

Publications and source records attributed to Zhang, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Median Preoptic Brs3-Expressing Neurons Regulate Heat-Evoked Defensive Escalation

Animals confronted with increasing heat must switch from exploratory behavior to vigorous actions that limit further exposure, but the forebrain circuits controlling this transition are poorly understood. We exposed male mice to progressively stronger floor heating while providing continuous access to a climbing mesh separated from the heating plate. Higher plate settings shifted behavior from rearing toward wall-directed digging and climbing, with jumping at the highest setting, and increased c-Fos labeling in the median preoptic nucleus. Neurons expressing bombesin receptor subtype 3 showed greater c-Fos recruitment under strong heating, and their population calcium signals increased when mice entered the plate-controlled zone and decreased when they left. Optogenetic activation without heating produced place avoidance, vigorous actions, and increased locomotion. Inhibition reduced locomotion and, during strong heating, shifted behavior from climbing and jumping toward rearing and weakened avoidance of a localized heat source. These findings identify median preoptic Brs3-expressing neurons as regulators of behavioral escalation and heat avoidance.

neuroscience↗

bioRxiv2026

Causal event structure shapes convergent reactivation signatures in human cortex and language model

Humans adapt to changing environments by understanding the latent causal structure of events, and modern large language models (LLMs) increasingly exhibit analogous abilities. However, elucidating how causal knowledge is represented and integrated in the human brain and in LLMs poses a substantial challenge for cognitive science. To address this, we combine topological analysis of representational manifolds with neuroscientific analyses of reactivation. We find that event-level causal relations modulate temporally structured neural reactivation in the human brain, particularly in the angular gyrus, precuneus and prefrontal cortex. By contrast, LLMs express these relations through positionally structured representational reactivation. Despite these differences, the resulting reactivation patterns exhibit similar intrinsic manifold structure across the two systems. Together, these findings suggest that the human brain and LLM share a common principle for organizing causal knowledge.

neuroscience↗

bioRxiv2026

YGEP: An Integrated Web Platform for Genomic Evaluation and Intelligent Breeding in Yak

Background: The yak (Bos grunniens) is an essential livestock species in high-altitude alpine ecosystems, serving key roles in meat, milk, and wool production as well as ecological maintenance. However, traditional yak breeding systems face several limitations, including fragmented herd data management, inefficient phenotypic collection, missing pedigree records, and high technical barriers to applying genomic technologies. These factors constrain the widespread application of modern genetic evaluation methods in yak breeding programs. Description: We developed the Yak Genomic Evaluation Platform (YGEP, https://yakbreeding.swun.edu.cn/en/), an integrated digital system for yak genetic selection and herd management. YGEP integrates three core analytical modules: pedigree reconstruction, genotype imputation and genomic breeding value estimation. Benchmarking on 706 yaks showed that GBLUP achieved a prediction accuracy of r = 0.96 for cannon bone circumference (CCB), and KBeagle reached 84.0% imputation accuracy with the combined reference panel. Conclusion: YGEP bridges the gap between advanced genomic analytics and practical yak breeding. By integrating pedigree reconstruction, genotype imputation, and GEBV estimation into a user-friendly cloud platform, YGEP effectively eliminates technical and data bottlenecks. Overall, it provides an accessible, scalable, and standardized solution to advance digital herd management and precision genetic improvement in alpine livestock.

bioinformatics↗

bioRxiv2026

Adenosine refines the balance of spermatogonial proliferation and differentiation

The balance between spermatogonial proliferation and differentiation is essential for sustaining male fertility, yet the contribution of cellular metabolism remains poorly understood. Here, we showed that adenosine levels increased significantly during spermatogonial differentiation and promoted the formation of differentiating spermatogonia. Using a conditional knockout mouse model in which Ahcy, a gene encoding an enzyme in adenosine synthesis, is deleted from spermatogonia, we found that disruption of adenosine generation blocked spermatogonial differentiation, resulting in a transient delay in spermatogenesis. We further demonstrated that adenosine exerted its effects partially through ADORA2A- and ADORA2B-mediated signaling. Downregulating these receptor activities by specific inhibitors in spermatogonial culture or by genetic ablation in mice led to increased spermatogonial proliferation. Taken together, our study identified adenosine as a metabolic signal in refining the balance between spermatogonial proliferation and differentiation, linking cellular metabolism to critical developmental timing and stem cell fate decisions.

developmental biology↗

bioRxiv2026

WNT/β-catenin increases 3D chromatin mobility and reorganisation during posterior cell state transitions

Chromatin mobility, the exploration of 3D conformations by the chromatin fibre, is implicated in transcription and DNA repair, and is thought to decrease progressively as cells differentiate. Here, combining live-cell 3D single-nucleosome tracking with genomic approaches, we show that chromatin mobility instead changes in a cell-type-specific manner, increasing as pluripotent cells acquire posterior fates. This is accompanied by 3D genome reorganisation, including loss of Polycomb-associated interactions and emergence of CDX2-associated interactions. Withdrawal of posteriorising WNT/{beta}-catenin signalling rapidly reduces chromatin mobility and partially restores Polycomb-associated chromatin interactions, while Cdx1/2/4-null cells fail to acquire these WNT/{beta}-catenin-dependent changes. Together, our findings establish chromatin mobility as a regulated property of cell state, with WNT/{beta}-catenin signalling and CDX factors acting as mediators that fine-tune the sampling of 3D chromatin interactions during cell state transitions.

cell biology↗

bioRxiv2026

Reconstructing donor genotypes from scRNA-seq for downstream eQTL and HLA-TCR analysis

Single-cell eQTL studies can reveal heterogeneous genetic effects on gene expression that vary with cell type, but their wider application has been constrained by the frequent absence of matched genotype data. Here, we present single-cell Transcriptome Allelic Prediction and Association Studies (scTAPAS), a bioinformatic framework for reconstructing donor genotypes directly from single-cell RNA-sequencing reads and downstream genetic analysis. Applying scTAPAS to 5' single-cell RNA-sequencing data from the COVID-19 Multi-omic Blood Atlas (COMBAT), we show that reference-based imputation of sparse RNA-derived genetic information generates genotype dosages sufficiently accurate for association testing. Despite testing only 18.0% of the variants available from conventional array-based genotyping and imputation, scTAPAS recovers 68.6% of cell type-eGene pairs identified using conventional array-based genotypes, with strong concordance in effect sizes. We further show that scTAPAS-derived variants can support imputation of classical HLA alleles and enable HLA-aware QTL analyses, including associations between HLA variation and TCR repertoire at single-cell resolution. Using matched single-cell TCR-sequencing data, we identify CD4+ T cell-specific associations between HLA class II alleles and T-cell receptor (TCR) Va; gene segment usage, highlighting the potential of analyses that complement TCR sequencing at single-cell resolution with HLA types. These results show that genetic information recovered from single-cell RNA sequencing data can support both regulatory and immunogenetic association analyses, expanding the discovery potential of single-cell cohorts for which conventional genotyping or HLA typing is unavailable.

bioinformatics↗

bioRxiv2026

Long-form hibernation-promoting factor coordinates 100S assembly and translational repression

Ribosome hibernation mediated by long-form hibernation-promoting factor (HPF) requires coordination of ribosome dimerization and translational repression, but how these functions are integrated remains unclear. Here, we present cryo-EM structures of native 100S from a deep-sea Bacillus isolate, revealing a conformationally heterogeneous 100S population. Beyond the established roles of the HPF N-terminal domain (NTD) in decoding-center occlusion and the C-terminal domain (CTD) in dimerization, our analyses reveal additional contributions from the Linker and a charged CTD surface near the 16S rRNA 3' terminus containing the anti-Shine-Dalgarno (aSD) sequence. Linker substitutions differentially affect translational repression while retaining 100S formation, with distinct effects of the two Linker segments. Mutation of the charged CTD surface enhances translational repression while retaining robust 100S formation, suggesting an additional role beyond CTD-mediated ribosome dimerization. Functional analyses in an E. coli cell-free translation system further show that the inhibitory activity of individual HPF regions varies with translational context. Together, these findings support a modular model in which the NTD, Linker, and CTD make distinct but interconnected contributions to 100S assembly and translational repression.

biochemistry↗

bioRxiv2026

AREION: A Foundation Model for Atlas-Agnostic Representation Learning of Brain Connectome

Brain foundation models have shown promising advances in learning generalizable representations from functional magnetic resonance imaging (fMRI) data. However, existing models are typically tied to specific brain atlases and cannot be applied to fMRI data represented using different atlases without full retraining, thereby severely limiting their broader utility across neuroimaging studies. To address this bottleneck, we introduce AREION, atlas-agnostic representation learning of brain connectomes. AREION overcomes atlas dependency through two integrated innovations. First, a Brain Region Contextualizing technique maps brain regions and connectivity entries from diverse atlases into a unified representation space based on their spatial, functional, and morphological characteristics. Second, a Hierarchical Brain Transformer Encoder processes region-specific connectivity profiles as variable-length token sequences and jointly captures local functional structure and global brain functional organization, conditioned on brain regional context, without assuming a fixed number of regions. Together, these two techniques enable the learning of generalizable representations across brain atlases with different parcellation resolutions and connectivity dimensions. AREION is pretrained on a large-scale multi-atlas cohort of fMRI data via self-supervised modeling and evaluated across diverse downstream tasks. Extensive experiments show that it outperforms strong supervised baselines and fMRI foundation models while using substantially fewer parameters, demonstrating strong transferability to atlases unseen during pretraining, generalization to external datasets, and excellent interpretability at the brain regional level.

neuroscience↗

bioRxiv2026

Memory-based and surround-based pattern completion show distinct laminar profiles in human V1

Visual input is often incomplete, requiring the brain to infer missing information from prior experience or contextual cues. Establishing whether memory-derived and context-derived information shape pattern completion through shared or distinct top-down pathways is central to understanding how perception emerges from the integration of sensory input and prior knowledge. Across two experiments, we used 7T layer fMRI to measure activity in an occluded region of human V1 while participants viewed natural scenes. Memory-based completion was enabled by varying the level of exposure to full scene images before scanning, while surround-based completion was implemented via two scenes that were repeatedly presented in partially occluded form but never seen intact. In both fMRI experiments, category decoding in the occluded V1 region only emerged after multiple prior exposures, whereas novel scenes did not show reliable decoding. Repeated partially occluded scenes also supported decoding, despite the hidden region never being shown, consistent with surround-based pattern completion. Critically, mnemonic and surround-based pattern completion showed opposite laminar decoding profiles across V1 depths, with mnemonic feedback dominating deep layers, and surround-based feedback dominating superficial layers. Together, these findings suggest that completion in the early visual cortex depends on the sources of inferred information, revealing dissociable feedback pathways for memory- and context-driven inference.

neuroscience↗

bioRxiv2026

Chronic Social Isolation Reshapes the Metabolome in Drosophila

Sleep, feeding, and metabolism are tightly coordinated outputs of the circadian system. Previous studies showed that chronic social isolation reduces sleep and increases feeding in Drosophila. Here, untargeted metabolomic profiling revealed that chronic social isolation induces a distinct metabolomic signature spanning amino acids, carbohydrates, and lipids, establishing a foundation for understanding the physiological consequences of social isolation.

neuroscience↗

bioRxiv2026

Biological rationales from language models enable leakage-resistant forecasts of target-indication success

Identifying which target-indication (T-I) hypotheses can translate into clinical success remains a central challenge in drug discovery. We present PRIORITI (Prospective Rationale-Informed Outcome Reasoning for Integrated Target-indication Intelligence), a leakage-resistant framework that separates upstream biological evidence synthesis from outcome prediction. Given only a target gene and disease indication, a domain-instructed large language model (LLM) synthesizes drug-agnostic human genetics across prespecified channels. These evidence were embedded and combined with static entity context derived from gene summaries and disease definitions. The resulting representation is used to fit a supervised model trained on 6,784 temporally curated historical T-I outcomes, which is one of the largest cohorts assembled for this task. On a held-out historical test set of 1,696 T-I pairs, PRIORITI achieved a ROC-AUC of 0.883 and an expected calibration error (ECE) of 0.023, outperforming both a static target and indication context baseline and a human-curated genetic-evidence reference. On a strictly post-cutoff out-of-time benchmark of 765 T-I pairs, the largest such evaluation cohort reported to date, performance remained robust, with ROC-AUC 0.817, PR-AUC 0.357 and ECE 0.044, exceeding static target and indication context baseline and direct zero-shot LLM forecasting. A label-blinded LLM-based explainer further converted locked predictions into auditable prioritization rationales. Together, PRIORITI provides a calibrated, biology-first probability together with decision-oriented rationales for T-I prioritization before downstream translational investment.

bioinformatics↗

bioRxiv2026

Fitting dynamics is not identifying causal edges: a white-box masked ODE benchmark for trans-omics digital twins of drug action mechanisms

Multi-component drug regimens, with traditional Chinese medicine formulas as the hardest case, act across signalling, transcriptional, proteomic and metabolic layers, and elucidating their mechanisms requires dynamic models that predict molecular trajectories rather than static association networks. Trainable ordinary differential equation (ODE) systems fitted to time-series omics are increasingly used for this purpose; yet their validation remains fit-based: at genomic scale, no ground truth has existed to test whether a good fit implies correct mechanisms. Here we build that ground truth: a white-box masked ODE benchmark on the real trans-omics topology of insulin action in mouse liver (transcriptome GEO GSE166336, proteome ProteomeXchange PXD022728, phosphoproteome PXD022823, metabolome source-publication Tables S2-S3; 2,106 molecular species; 4,912 ground-truth edges), with controllable noise, missingness and sampling budgets. Four instruments quantify identifiability: an oracle-perturbation basin curve, a held-out-layer corruption assay, a saturation audit, and an ideal-budget ceiling test. We find that a static baseline (FD + LASSO) performs at chance (AUROC ~ 0.50, except GE at 0.567); that from-scratch training remains at chance even with noise-free, fully observed, densely sampled data (per-layer AUROC 0.48-0.53); that held out layers act as corruption sinks whose failure decomposes into an information floor, a scale-mismatch amplifier, and an edge-gradient drag, curable only jointly; that tanh saturation silently zeroes entire regulator columns; and that a 12-knockout validation battery decomposes intervention reliability by network distance. We distill these into operational prescriptions. The binding constraint is not fitting but structural identifiability. Benchmark, code and audit tools are planned for open release upon publication.

bioinformatics↗

bioRxiv2026

Prognostic value, signal interaction network, and immune infiltration characteristics of MET gene expression in gastric cancer analyzed by multi-database bioinformatics

Objective Based on the bioinformatics method of multi-database integration, this study systematically analyzes the expression characteristics, clinical pathological correlation, prognostic value, potential molecular mechanisms, and immune infiltration patterns of hepatocyte growth factor receptor (MET) in gastric cancer. Methods The UALCAN and GEPIA databases were employed to examine the differential expression of MET between gastric cancer and normal gastric mucosal tissues, as well as its associations with clinicopathological features. Kaplan-Meier Plotter was utilized to evaluate the impact of MET expression on overall survival (OS) and progression-free survival (PFS). Protein-protein interaction (PPI) network was constructed via LinkedOmics, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of co-expressed genes. Four algorithms, TIMER, CIBERSORT, EPIC, and MCPcounter, were used to cross evaluate the correlation between MET expression and immune cell infiltration; Further validate the cell type specific expression of MET using the gastric cancer single-cell sequencing queues (GSE134520, GSE167297) built into the TISCH database. Results MET expression was significantly elevated in gastric cancer tissues compared with normal gastric mucosa (P < .05), and its expression level was significantly correlated with tumor grade and TNM stage. Patients with high MET expression exhibited significantly poorer OS and PFS than those with low MET expression (P < .05). The PPI network revealed that MET could interact with 20 key proteins, including EGFR, ERBB2, HGF, STAT3, and GRB2 etc. GO enrichment analysis suggests that differentially expressed genes are significantly enriched in functions such as the ERBB signaling pathway, cadherin binding, and DNA repair complexes; KEGG enrichment analysis showed that MET related genes were significantly enriched in pathways such as homologous recombination, nuclear cytoplasmic transport, mismatch repair, and oxidative phosphorylation. Immune infiltration analysis showed that the negative association between MET and B cells infiltration has cross algorithm robustness, while the association with neutrophils, CD8+ T cells, CD4+ T cells, and macrophages exhibits algorithmic heterogeneity or insignificance; There is no significant correlation between MET and common immune checkpoint molecules such as PD-1, PD-L1, CTLA4, etc. Single cell validation further confirmed that MET is mainly enriched in malignant epithelial cells and endothelial cells, and is almost not expressed in immune cells. Conclusions Multidimensional bioinformatic analyses demonstrate that elevated MET expression serves as an independent risk factor for unfavorable prognosis in gastric cancer. MET may mediate dual drug resistance in gastric cancer via crosstalk with multiple signaling molecules (including EGFR, ERBB2, HGF, STAT3 and GRB2) and dysregulation of the homologous recombination repair pathway. Results from multiple-algorithm immune infiltration analysis, single-cell dataset analysis and immune checkpoint correlation analysis indicate that MET exerts only modest direct regulatory effects on the gastric cancer immune microenvironment. This exploratory study offers systematic bioinformatic evidence supporting MET as a candidate prognostic biomarker and potential therapeutic target for gastric cancer. Further functional experiments and prospective cohort studies are required to validate its molecular mechanisms and clinical utility.

cancer biology↗

bioRxiv2026

YAP/TAZ-controlled ERK dynamics coordinate progenitor expansion and differentiation commitment

Progenitor cells must proliferate to expand the cell population, yet terminal differentiation requires this proliferative state to end. How signaling controls the duration of this proliferative window remains poorly understood. Using adipogenesis and live single-cell imaging of differentiation, cell-cycle, and ERK-activity reporters, we show that YAP and TAZ coordinate progenitor expansion with differentiation commitment by regulating ERK dynamics. YAP/TAZ maintain cells in a fluctuating high-ERK state that promotes proliferation while actively keeping the differentiation driver PPARG below the threshold for irreversible commitment. Crucially, this differentiation block is not explained by proliferation alone: inhibiting CDK4/6 or AKT suppressed proliferation without restoring differentiation, whereas MEK-ERK inhibition restored differentiation even when YAP/TAZ activity remained high. As YAP/TAZ activity decreases, dampened ERK fluctuations trigger PPARG activation. These findings support a self-limiting model in which YAP/TAZ-driven progenitor expansion progressively increases cell density and contact-dependent Hippo signaling, reducing YAP/TAZ activity and terminating the proliferative phase. Consequently, transient YAP/TAZ activation expands the progenitor pool while preserving subsequent differentiation, whereas sustained activation suppresses commitment. Together, these findings identify YAP/TAZ-controlled ERK dynamics as the nexus coordinating progenitor expansion with terminal differentiation and suggest that slower density-dependent Hippo feedback may set the duration of this proliferative window to regulate differentiated cell-number output.

cell biology↗

bioRxiv2026

m6A-Driven Intratumoral Cholesterol Biosynthesis Fuels Castration-Resistant Prostate Cancer Progression

Both nuclear pore complexes (NPCs) and RNA N6-methyladenosine (m6A) machinery are indispensable for proper cellular function. Although their collaborative roles in the nuclear export of messenger RNAs (mRNAs) have been reported, it remains ambiguous whether and how this collaboration may contribute to cancer progression. Here we identify a functional cooperation between NPCs and m6A signaling that promotes the development of castration-resistant prostate cancer (CRPC). We showed that nuclear export of m6A-modified mRNAs, mediated by the interaction between RNA methyltransferase METTL3 and the nucleoporin NUP93, is functionally coupled to cholesterol biosynthesis. Given that cholesterol-fueled intratumoral androgen production is one of the mechanisms driving CRPC, we demonstrated that overexpression of the wild-type METTL3 or NUP93, but neither the enzymatically dead METTL3 nor the mutant NUP93 that loses METTL3-interacting capability, elevates intracellular levels of androgens, activates AR signaling under castrate condition, and promotes androgen-independent growth of prostate cancer cells both in vitro and in vivo. Importantly, pharmacological inhibition of METTL3 or targeted demethylation on mRNAs encoding key cholesterol biosynthesis enzymes effectively suppressed CRPC malignancy. Together, these findings uncover a therapeutically targetable m6A-METTL3-NUP93 axis that links nuclear mRNA export and metabolic reprogramming to fuel CRPC progression, providing a conceptually new strategy for the treatment of this lethal disease.

cancer biology↗

bioRxiv2026

Computational design of a versatile, zero-radius proximity labeling enzyme

The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo compatibility. Here we report FlexID, an engineered promiscuous ligase that catalyzes the rapid attachment of diverse small-molecule probes to proximal endogenous proteins. Critically, FlexID operates through a zero-radius, direct-contact mechanism, offering superior spatial precision compared to existing PL tools. We engineered FlexID by combining the strengths of sequence- and structure-trained computational models to enhance its catalytic activity and structural stability. Biophysical analysis revealed that specific conformational changes in FlexID improve its ability to recognize diverse target proteins while simultaneously preventing the premature release of the reactive intermediate. We demonstrate FlexID's versatility through in vivo proximity labeling, comprehensive organelle proteome mapping, and a high-throughput, fluorescence-based screen for molecular glues. Our work shows that computational methods can be harnessed to create mechanistically distinct PL enzymes and establishes FlexID as a flexible, high-resolution tool for mapping protein interactions and proteomes in living cells.

bioengineering↗

bioRxiv2026

Gene therapy with doxycycline-controlled expression of human Kv1.1 reduces neuronal excitability and increases sociability of Scn2a-deficient mice

Genetic loss-of-function (LoF) variants in SCN2A, a gene encoding the voltage-gated sodium channel Nav1.2, have been identified as one of the foremost monogenic causes of autism spectrum disorder (ASD). ASD encompasses a broad spectrum of behavioral phenotypes, with impaired sociability as a core characteristic. We have established Scn2a-deficient mice (Scn2agt/gt) to model Scn2a-related ASD and found that this model recapitulates social impairment, exhibiting severe social deficits. Scn2agt/gt mice exhibit neuronal hyperexcitability and a marked global reduction in potassium channel expression, which plays a crucial role in maintaining the resting membrane potential and repolarizing neurons after an action potential. Among these downregulated potassium channels, potassium voltage-gated channel subfamily A member 1 (Kv1.1) was one of the most affected. To explore whether Kv1.1 could be a potential therapeutic target in SCN2A-related ASD, we evaluated the in vivo efficacy of a genetic construct driven by the CaMKII promoter that allows for exogenous expression of human Kv1.1 (hKv1.1) in principal neurons. In Scn2agt/gt mice, we found that hKv1.1 expression normalizes neuronal hyperexcitability. Importantly, doxycycline-induced hKv1.1 expression enhances sociability in Scn2a-deficient mice without influencing social behavior in wild-type mice, and this effect was reversed upon doxycycline withdrawal. Overall, we demonstrate the successful use of an inducible AAV-mediated gene delivery system to supplement hKv1.1 expression to mitigate neuronal hyperexcitability and ameliorate social impairments in a mouse model of SCN2A-related ASD. These findings highlight the contribution of Kv1.1 to SCN2A-associated pathophysiology and its potential as a therapeutic target for severe social deficits.

neuroscience↗

bioRxiv2026

MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling

Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferred transcription factor (TF) activity and miRNA expression across pathway classification and similarity-based MoA retrieval. HubmiRNet infers 414 pan-cancer hub miRNAs (HubmiRs) from 977 L1000 landmark genes, achieving a Pearson correlation coefficient of 87.72\%; its 1,298-output variant also outperformed SiCmiR on the full-miRNA task (71.21\% versus 67.30\%). In the evaluated comparisons, miRNA augmentation provided more consistent gains than TF activity. Generic embedding controls showed model-dependent utility, while complementarity analyses identified a distinct, partially linearly recoverable representation that retained gene-derived structure. Illustrative rescue cases linked improved classification to biologically plausible miRNA patterns in samples with weak transcriptional signatures. These findings support inferred HubmiRs as a biologically informed recoding of transcriptomic data for perturbational drug modeling, while leaving recovery of measured perturbational miRNA responses to further validation.

bioinformatics↗