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Bo Wang

Publications and source records attributed to Bo Wang.

18 recordsLinked to original sources
PubMed2026

Ancient DNA unveils distinctive ancestries in the Bronze and Iron Ages of East Tianshan.

The East Tianshan Mountains occupy a key corridor between Central and East Asia, but their population history remains poorly understood. Here we report genome-wide data from 135 ancient individuals from 11 archaeological sites. We identify a previously unrecognized Bronze Age admixture between populations related to Yellow River millet farmers and steppe pastoralists associated with the Chemurchek culture. In contrast, we find little genetic contribution from contemporaneous middle-to-late Bronze Age steppe pastoralists, despite their eastward expansion across the Eurasian Steppe. By the Iron Age, regional populations had become more heterogeneous, incorporating additional eastern and steppe-related sources while retaining variable contributions from Early Bronze Age groups. These results reveal sustained demographic interactions in eastern Central Asia nearly 1800 years preceding the establishment of the historic Silk Road.

DNA, Ancient↗
PubMed2026

Flexible use of conserved motifs constrains genome access in cell type evolution.

Cell types can be organized into related families, but the regulatory mechanisms that define and maintain these families across deep evolutionary time remain unknown. Here, combining single-nucleus multi-omic sequencing with deep learning to analyse the accessible genomes of two groups of vastly divergent animals including flatworms and vertebrates, we find that hundreds of accessibility-dictating sequence motifs partition into distinct yet conserved sets, or 'vocabularies', each associated with a specific cell type family. However, combinatorial relationships among these motifs preferred by individual cell types are largely species specific. Deep-learning models trained on one species accurately predict family-level chromatin accessibility in distantly related species, albeit frequently rely on different motifs from shared vocabularies to reach convergent predictions. By contrast, models trained on individual cell types within a family lose cross-species predictive power, indicating that the regulatory syntax governing cell type-level identity evolves rapidly. We propose a 'collective maintenance' model in which motif vocabularies defining cell type families are evolutionarily stable, while recombination of these motifs generates cell type-specific regulatory programmes. This suggests that family identity is maintained collectively by large, conserved pools of regulatory factors, analogous to the logic of developmental homology, where character identity persists through network-level conservation despite extensive rewiring.

Journal Article↗
PubMed2026

Heterologous expression of DobHLH25 from Dendrobium officinale enhances drought tolerance in Arabidopsis.

Drought stress severely constrains the growth, yield, and accumulation of bioactive compounds in Dendrobium officinale (D. officinale), a valuable medicinal orchid, and this challenge is exacerbated under simulated wild cultivation where plants are inevitably exposed to recurring water deficits. Basic helix-loop-helix (bHLH) transcription factors are well-established regulators of plant abiotic stress responses. However, the molecular mechanisms by which bHLH transcription factors respond to drought stress in this species remain largely unknown. In this study, a bHLH transcription factor gene, DobHLH25, was cloned from D. officinale. Phylogenetic analysis revealed that DobHLH25 shares the highest sequence identity with its ortholog in Dendrobium nobile. Additionally, subcellular localization analysis indicated that DobHLH25 is targeted to the nucleus and possesses a functional transcriptional activation domain. Expression pattern analysis showed that DobHLH25 is most abundantly expressed in old leaves, and its expression in roots, stems, and leaves is induced by polyethylene glycol treatments. Heterologous expression of DobHLH25 in Arabidopsis thaliana resulted in higher seed germination rates and longer root lengths under mannitol-induced osmotic stress compared to wild-type plants. Under drought stress, DobHLH25 heterologous expression lines exhibited higher survival rates, reduced leaf water loss, lower malondialdehyde accumulation, and increased proline content. Moreover, the activities of antioxidant enzymes such as superoxide dismutase and peroxidase were significantly enhanced, and the expression levels of multiple drought-responsive genes were markedly upregulated. Collectively, these findings suggest a correlation between DobHLH25 expression and plant drought tolerance, as evidenced by reduced oxidative damage, increased osmolyte accumulation, enhanced antioxidant enzyme activities, and upregulation of drought-responsive genes. Together, these results suggest that DobHLH25 plays a positive role in drought tolerance, and provides a basis for future dissection of its regulatory network in D. officinale.

Drought Resistance↗
PubMed2026

Long-read sequencing reveals widespread novel splicing and neojunction-derived neoantigens in nasopharyngeal carcinoma.

The widespread transcriptomic diversity driven by alternative splicing (AS) contributes to all hallmarks of cancer and represents a critical source of neoantigens for personalized immunotherapy. However, unlike other major malignancies, the full repertoire of AS in nasopharyngeal carcinoma (NPC) remains underexplored. Here, we employ long-read sequencing (LR-seq) to generate a high-resolution, isoform-level transcriptomic atlas from a cohort of 14 NPC tumor samples and four immortalized nasopharyngeal epithelial cell lines. We identify a substantial number of full-length novel transcripts (22,687; ∼44.38%), which reveal diverse splicing patterns and previously unannotated splicing events. By integrating short-read RNA-seq data to quantify isoform expression, we discover a subset of novel transcripts that are differentially expressed between tumor samples and immortalized nasopharyngeal epithelial cell lines. Furthermore, LR-seq enables precise identification of chimeric readthrough fusion transcripts, such as CLDN15-FIS1 and FOXRED2-TXN2 Finally, we develop a computational framework, tumor-specific splicing neoantigen detection (TS-SNAD), to predict neoantigens originating from novel exon-exon junctions (neojunctions) in tumor-specific novel transcripts. Using this framework, we identify neojunction-derived neoantigens and experimentally validate the immunogenicity of selected HLA-B*40:01-restricted neoantigens. These neojunction-derived peptides constitute a new class of noncanonical neoantigens with significant potential for developing personalized cancer vaccines for NPC.

Humans↗
PubMed2026

Distress Intolerance, Social Anxiety, and Depressive Symptoms in Adolescents: Evidence from Random-Intercept Cross-Lagged Panel and Cross-Lagged Panel Network Analyses.

Social anxiety and depressive symptoms frequently co-occur during adolescence, yet the mechanisms underlying their longitudinal associations remain insufficiently understood. Distress intolerance has been proposed as a transdiagnostic risk factor implicated across internalizing symptoms. However, it remains unclear whether distress intolerance is predicted by social anxiety and depressive symptoms, as well as the underlying mechanisms among these three constructs. The specific symptoms most centrally involved in these cross-domain associations remain poorly understood. The present study investigated the longitudinal associations among distress intolerance, social anxiety, and depressive symptoms using a dual-method framework combining random-intercept cross-lagged panel models (RI-CLPM) and cross-lagged panel network (CLPN) analyses. A total of 1,378 Chinese adolescents (Mage = 12.57, SDage = 0.63; 50.4% female) were assessed at three time points with six-month intervals between waves. The RI-CLPM analyses revealed that higher distress intolerance prospectively predicted subsequent increases in both social anxiety and depressive symptoms, whereas elevated social anxiety and depressive symptoms in turn predicted subsequent increases in distress intolerance. Moreover, distress intolerance mediated the longitudinal associations between social anxiety and depressive symptoms. Additionally, distress intolerance was also indirectly associated with its own subsequent levels through social anxiety and depressive symptoms. The CLPN analyses revealed that fear of negative evaluation and fatigue were the strongest predictors of other network nodes from T1 to T2 and from T2 to T3, respectively. In contrast, distress intolerance symptoms were predominantly predicted by other nodes in both cross-lagged networks. These findings extend prior views of distress intolerance as a unidirectional vulnerability by showing that distress intolerance is also predicted by social anxiety and depressive symptoms and accounts for part of their longitudinal associations across adolescence.

Humans↗
PubMed2026

Genome-wide characterization of the TGF-β superfamily identifies bmp15, gdf9, and gsdf as sex-biased candidate regulators of gonadal differentiation in the synchronous hermaphrodite Plectropomus leopardus.

The transforming growth factor-β (TGF-β) superfamily plays conserved roles in vertebrate reproduction and gonadal sex differentiation. However, its genomic repertoire and sex-biased expression patterns remain unclear in the leopard coral grouper (Plectropomus leopardus), a species with synchronous hermaphroditism. Here, we performed a genome-wide identification of the TGF-β superfamily, identifying 42 genes from the chromosome-level genome. Phylogenetic and synteny analyses indicated that segmental duplication under purifying selection contributed to family expansion. Expression profiling across multiple tissues and four gonadal developmental stages (undifferentiated, 120 dph; early differentiated, 15 months; mature testis, 3 years; mature ovary, 3 years) identified eight gonad-enriched genes, among which bmp15 and gdf9 exhibited pronounced female-biased expression, with transcripts localized exclusively to the oocyte cytoplasm, particularly in stage II-III oocytes. In contrast, gsdf showed male-biased expression and was localized in spermatogenic cells of the testis. These reciprocal expression patterns indicate that bmp15/gdf9 and gsdf are candidate factors associated with gonadal sex differentiation. Our study provides the first comprehensive characterization of the TGF-β superfamily in P. leopardus and highlights bmp15, gdf9, and gsdf as candidate sex-differentiation factors in this hermaphroditic species.

Animals↗
PubMed2026

ChromCall: assigning chromatin status to defined genomic regions using epigenomic profiling data.

MOTIVATION: Chromatin regulation is crucial for modulating gene expression and cellular function by altering DNA accessibility. Defining and understanding chromatin regulation across diverse biological conditions, including health and disease, requires quantification of both the presence and enrichment level of diverse DNA-binding factors and chromatin modifications across defined genomic regions. Existing approaches mainly rely on peak-based or genome-wide models, which identify high-signal regions but do not annotate chromatin status at predefined functional genomic regions, such as promoters or enhancers. This lack of region-based annotation limits downstream comparative and integrative analyses across multiple factors and datasets, prompting us to create ChromCall. RESULTS: ChromCall is an R package for region-based chromatin enrichment analysis that provides a robust and extensible foundation for transparent and reproducible epigenomic profiling at predefined genomic regions. We applied ChromCall to ChIP-seq data from glioblastoma (GBM) brain tumours and found that the promoters of genes implicated in treatment resistance are significantly more likely to exhibit a combination of histone marks associated with phenotypic plasticity. This highlights a potential novel mechanism of therapeutic escape in these deadly tumours. AVAILABILITY AND IMPLEMENTATION: The R package is available on https://github.com/GliomaGenomics/ChromCall and the version used in this paper is archived at https://doi.org/10.5281/zenodo.19580967.

Chromatin↗
PubMed2026

Phytochrome-interacting factor 1b (SlPIF1b) affects the fruit quality of tomato by regulating chloroplast development.

The increased abundance and functionality of fruit chloroplasts could promote the accumulation of nutrients and flavor in the fruit. Tomato fruit has fully developed fruit chloroplasts, whose abundance and functionality have much untapped potential in improving fruit quality by controlling fruit chloroplast development. Previous studies have identified many regulatory factors that specifically regulate fruit chloroplast development in tomatoes, but there are fewer reports on tomato phytochrome-interacting factors (SlPIFs). Arabidopsis AtPIFs have been implicated in chloroplast development and chlorophyll biosynthesis. In this study, we identified and characterized an SlPIF1b mutant in tomato, named GS, which exhibited a dark green fruit shoulder with enhanced chloroplast development. RNA-seq and genotyping analysis identified a - 21 bp (A → T) mutation in the promoter of SlPIF1b, resulting in the absence of the TATA-box core transcriptional element and inhibiting SlPIF1b transcription. The overexpression of SlPIF1b in GS inhibited chloroplast development of fruits, leading to a lighter green shoulder color, decreased chlorophyll content, reduced photosynthetic activity, diminished starch accumulation, and compromised fruit quality upon ripening. Conversely, the down expression of SlPIF1b significantly enhanced fruit chloroplast development and functionality in fruits, resulting in increased chlorophyll and carotenoid accumulation. Further analysis of expression profile and transcriptional activity indicated that SlPIF1b could bind to G/PBE-box elements present in SlGLK2, SlTKN4, SlCAO1a, SlPOR1, SlPOR3, SlCAB1 and SlCAB1b promoters, thereby inhibiting their expression. This study revealed the specific regulatory mechanism by which SlPIF1b modulates chloroplast development and chlorophyll synthesis in tomato fruit and provided valuable genetic resources and a theoretical basis for tomato quality improvement.

Solanum lycopersicum↗
PubMed2025

NR2F6 regulates Temozolomide resistance in glioma via the E2F2-PARP1 pathway.

BACKGROUND: Glioma is the most common primary malignant brain tumor in adults. Temozolomide (TMZ) represents a standard-of-care chemotherapeutic agent in glioblastoma (GBM). However, the development of drug resistance constitutes a significant hurdle in the treatment of malignant glioma. Elucidating the mechanisms of temozolomide (TMZ) resistance in glioma is of critical clinical importance for improving patient prognosis and developing novel therapeutic strategies. METHODS: We obtained RNA sequencing (RNA-seq) data of 648 glioma samples from The Cancer Genome Atlas (TCGA) and 325 samples from the Chinese Glioma Genome Atlas (CGGA) as study cohorts. Additionally, we validated the expression characteristics of the NR2F6 gene in our in-house cohort of glioma patients. Furthermore, we investigated the potential mechanism of NR2F6 in TMZ resistance in glioma by constructing TMZ-resistant cell lines in vitro. Statistical analyses and graphical work were primarily performed using R language and GraphPad Prism software. RESULTS: We observed a significant upregulation of NR2F6 expression in high-grade gliomas, which is associated with an unfavorable prognosis in patients. Concurrently, our findings revealed a significant upregulation of NR2F6 in drug-resistant cells, which induced TMZ resistance in glioma cells via the E2F2-PARP1 axis. CONCLUSION: In brief, NR2F6, as a nuclear transcription factor, enhances the transcription of E2F2.The increased expression of E2F2 enhances PARP1 expression, which in turn facilitates TMZ-mediated DNA damage repair, thereby diminishing glioma sensitivity to TMZ.

Journal Article↗
PubMed2025

Multi-omic underpinnings of heterogeneous aging across multiple organ systems.

Aging is the main determinant of chronic diseases and mortality, yet organ-specific aging trajectories vary, and the molecular basis underlying this heterogeneity remains unclear. To elucidate this, we integrated genomic, epigenomic, transcriptomic, proteomic, and metabolomic data, employing post-genome-wide association study methodologies to systematically investigate the molecular mechanisms of nine organ-specific aging clocks and four blood-based epigenetic clocks. We uncovered genetic correlations and specific phenotypic clusters among these aging-related traits, identified prioritized genetic drug targets for heterogeneous aging, and elucidated downstream proteomic and metabolomic effects mediated by heterogeneous aging. We constructed a cross-layer molecular interaction network of heterogeneous aging across multiple organ systems and characterized detectable biomarkers of this heterogeneity. Integrating these findings, we developed an R/Shiny-based framework that provides a comprehensive multi-omic molecular landscape of heterogeneous aging, thereby advancing the understanding of aging heterogeneity and informing precision medicine strategies to delay organ-specific aging and prevent or treat its associated chronic diseases.

Aging↗
PubMed2025

Proteomic-based identification of novel EV-derived protein antibodies biomarkers for melioidosis diagnosis.

Melioidosis, caused by Burkholderia pseudomallei (Bp), is a life-threatening disease characterized by diverse clinical manifestations and limited diagnostic capabilities. Extracellular vesicles (EVs) have emerged as critical carriers of novel antibody targets for serodiagnosis. In this study, we established a Bp-infected BEAS-2B cell model (Bp/BEAS-2B) and isolated EV from both Bp and Bp/BEAS-2B cells to generate EV proteome, identifying potential antigenic biomarkers for melioidosis diagnosis. Bioinformatics analysis identified PPEP and POMCR proteins as candidate antigens, with BLF1 and omp A serving as positive controls. Using a self-developed IgM-ELISA, serum samples from 43 melioidosis patients and 47 healthy volunteers were analyzed to detect antibodies against these antigens. Anti-POMCR IgM demonstrated exceptional diagnostic performance, with an AUC of 0.9872 (95% CI: 0.9713-1.003), sensitivity of 93.02% and specificity of 97.92% at a cutoff value of OD450 = 0.118. Similarly, IgM against PPEP, BLF1, and omp A also showed high diagnostic accuracy, with AUC values of 0.969, 0.9621, and 0.976, respectively. The accuracy of anti-POMCR and anti-PPEP were 96.43% and 95.54%, respectively, equivalent to anti-omp A (93.75%) and anti-BLF1 (91.96%). Antibodies to EV-derived proteins effectively differentiated melioidosis patients from other bacterial infections and healthy volunteers, highlighting their clinical potential as diagnostic tools for melioidosis.

Humans↗
PubMed2025

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals↗
PubMed2025

In silico generation of synthetic cancer genomes using generative AI.

Understanding how genomic alterations drive cancer is key to advancing precision oncology. To detect these alterations, accurate algorithms are used; however, due to privacy concerns, few deeply sequenced cancer genomes can be shared, limiting benchmarking and representing a major obstacle to the improvement of analytic tools. To address this, we developed OncoGAN, a generative AI model combining adversarial networks and variational autoencoders to create realistic synthetic cancer genomes. Trained on large-scale genomic datasets, OncoGAN accurately reproduces somatic mutations, copy number alterations, and structural variants across cancer types while preserving donors' privacy. The synthetic genomes reflect tumor-specific mutational signatures and positional mutation patterns. Using DeepTumour, we validated the synthetic data's fidelity, showing high concordance between generated and predicted tumors. Moreover, augmenting the training data with synthetic genomes improved DeepTumour's accuracy, underscoring OncoGAN's potential to generate shareable datasets with known ground truths for benchmarking and enhancement of cancer genome analysis tools.

Humans↗
PubMed2025

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge. Here, we introduce Orthrus, a Mamba-based mature RNA foundation model pre-trained using a novel self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between curated pairs of RNA transcripts, where pairs are formed from splice isoforms of 10 model organisms and transcripts from orthologous genes in 400+ mammalian species from the Zoonomia Project. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. We find that the generalized mature RNA isoform representations learned by Orthrus significantly outperform genomic foundation models on mRNA property prediction tasks, and requires only a fraction of fine-tuning data to do so. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.

Journal Article↗
PubMed2025

Comparative Genomic Screening Identifies Developmental Constraint Loci Underscoring the Phenotypic Evolution of Syngnathids.

Seahorses and their relatives (syngnathids) exhibit remarkable diversity in morphology and function, characterized by their distinctive body shapes and specialized feeding mechanisms. Despite recent advances in uncovering the genetic basis of some traits, the genotype-phenotype map in syngnathids remains incomplete. In this study, we employed forward-genomic approaches and developed a method to enrich for human disease amino acid loci at a genomic scale. Our aim was to identify genetic loci associated with fin size reduction, tooth loss, and spinal curvature in syngnathids. Intriguingly, we identified a convergent amino acid change in the lat4a gene shared by syngnathids and some flying fishes, with in vitro analysis confirming its role in fin size evolution in both lineages. While genes critical for tooth development are conserved in syngnathids, the absence of key regulatory elements, such as pitx2, likely contributes to tooth loss. Additionally, we implicated col6a3 in spinal curvature development in seadragons. These findings reveal novel genetic signatures and developmental constraints underlying syngnathid diversity, demonstrating the utility of comparative genomics and targeted gene enrichment in exploring vertebrate evolution.

Animals↗
PubMed2024

The centromere landscapes of four karyotypically diverse Papaver species provide insights into chromosome evolution and speciation.

Understanding the roles played by centromeres in chromosome evolution and speciation is complicated by the fact that centromeres comprise large arrays of tandemly repeated satellite DNA, which hinders high-quality assembly. Here, we used long-read sequencing to generate nearly complete genome assemblies for four karyotypically diverse Papaver species, P. setigerum (2n = 44), P. somniferum (2n = 22), P. rhoeas (2n = 14), and P. bracteatum (2n = 14), collectively representing 45 gapless centromeres. We identified four centromere satellite (cenSat) families and experimentally validated two representatives. For the two allopolyploid genomes (P. somniferum and P. setigerum), we characterized the subgenomic distribution of each satellite and identified a "homogenizing" phase of centromere evolution in the aftermath of hybridization. An interspecies comparison of the peri-centromeric regions further revealed extensive centromere-mediated chromosome rearrangements. Taking these results together, we propose a model for studying cenSat competition after hybridization and shed further light on the complex role of the centromere in speciation.

Centromere↗
PubMed2023

Towards mechanistic models of mutational effects: Deep learning on Alzheimer's Aβ peptide.

Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and depth of mutational coverage. Here, we apply deep learning to comprehensively model the mutational effect of the Alzheimer's Disease associated peptide Aβ42 on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks and Recurrent Neural Networks are found to be the most cost-effective models with high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory prediction from neural networks, geometric-structural features further enhance the prediction performance. Notably, we demonstrate how mechanistic insights into phenotype may be extracted from the neural networks themselves suitably designed. This methodological benefit is particularly relevant for biochemical systems displaying a strong coupling between structure and phenotype such as the conformation of Aβ42 aggregate and nucleation, as shown here using a Graph Convolutional Neural Network (GCN) developed from the protein atomic structure input. In addition to accurate imputation of missing values (which here ranged up to 55% of all phenotype values at key residues), the mutationally-defined nucleation phenotype generated from a GCN shows improved resolution for identifying known disease-causing mutations relative to the original DMS phenotype. Our study suggests that neural network derived sequence-phenotype mapping can be exploited not only to provide direct support for protein engineering or genome editing but also to facilitate therapeutic design with the gained perspectives from biological modeling.

Alzheimer's disease↗