Search bioRxiv⌕ Search

Biology subjects

Shu, H.

Publications and source records attributed to Shu, H..

At least 19 recordsLinked to original sources

Xenograft supervision and joint methylation-fragment-end encoding define a reusable molecular scoring layer for cell-free tumor DNA

Circulating tumor DNA is often scarce in plasma, and regional methylation summaries can obscure informative differences between individual molecules. Here we present ctDNA-BERT, a Transformer that scores individual bisulfite reads without predefined differentially methylated regions by jointly encoding CpG methylation state, nucleotide context and the fragment 3' end. Xenografts provide molecule-level tumor supervision. The resulting frozen scorer supported tumor-fraction estimation in synthetic and patient-derived xenograft mixtures and enriched tumor-concordant methylation and chromatin-accessibility signals in independent hepatocellular carcinoma and stage III pan-cancer plasma without cohort-specific retraining. Features derived from selected reads discriminated stage I cancers from healthy controls in a retrospective cohort of 1,477 specimens, achieving an out-of-fold area under the receiver operating characteristic curve of 0.884. By separating molecular scoring from sample-level inference, ctDNA-BERT provides a reusable read-level scoring layer for cell-free DNA methylation analysis.

bioinformatics↗

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

bioinformatics↗

SpatialMOC: Accurate reconstruction of spatial multi-omics landscapes through cross-modality prediction

Spatial multi-omics technologies provide unprecedented opportunities to characterize tissue organization by measuring complementary molecular layers within their native spatial context. However, simultaneous profiling of multiple molecular modalities remains technically challenging, limiting the widespread application of spatial multi-omics and leaving most studies reliant on single-modality measurements. Here, we present Spatial Multi-Omics Cross-prediction (Spatial-MOC), a computational framework that reconstructs missing spatial molecular modalities by integrating spatial context with cross-modality representation learning. Across multiple tissues, molecular modalities, developmental stages, sequencing platforms, and degraded datasets, SpatialMOC consistently outper-formed existing computational approaches in bidirectional molecular prediction. Beyond accurate prediction, SpatialMOC faithfully reconstructed tissue architecture, preserved dynamic molecular and regulatory heterogeneity, and recovered biologically meaningful spatial landscapes from technically compromised measurements. Together, these results establish SpatialMOC as a general framework for spatial multi-omics reconstruction, extending the analytical value of existing spatial omics datasets and facilitating comprehensive investigations of tissue organization, development, and disease.

bioinformatics↗

Epistasis facilitates the long-term antigenic evolution of the influenza B virus hemagglutinin

The antigenic drift of viral glycoproteins must be balanced by purifying selection pressure to maintain functionality. Understanding these evolutionary processes is key to predicting and combating viral evolution but is primarily based on influenza A(H3N2), which may limit generalisability. By characterising the influenza B virus haemagglutinin (HA) over 8 decades of circulation in humans, we found continuous genetic diversification, punctuated with antigenic changes that did not follow a linear path in antigenic space. Antigenic change is primarily underpinned by re-occurring mutations and deletions at positions 136, 150, 162-165, 197 and 203. These residues form complex epistatic networks that modulate the antigenic impact of mutation recycling. They also generate permissive backbones on which immune escape can emerge with limited replicative fitness cost. Our study identifies critical similarities and differences with A(H3N2) evolution and demonstrates the role of epistasis in balancing antigenic novelty with viral fitness. Our findings and genetic, antigenic and phenotypic datasets support the development of genotype-to-phenotype prediction tools, but such predictions need to capture the complex outcomes of epistasis.

microbiology↗

Uridine cytidine kinases dictate the therapeutic response of molnupiravir via its bioactivation

Molnupiravir is a nucleoside analogue antiviral drug with activities against a broad spectrum of RNA viruses, including its clinical indication SARS-CoV-2. Whilst its antiviral mechanism-of-action is well defined, host pharmacogenetic factors that regulate its therapeutic responses - efficacy as well as selectivity - have not been mechanistically deciphered and characterized. Here we identified that uridine cytidine kinase (UCK), the first and rate-limiting kinase of the pyrimidine salvage pathway, could effectively phosphorylate N4-hydroxycytidine (NHC), the active compound of molnupiravir, and thereby dictate its anti-SARS-CoV-2 efficacy, as well as its selectivity index. Using target engagement and enzyme kinetic assays, we demonstrate that recombinant UCK isoform 1 (UCK1) and 2 (UCK2) effectively bind and phosphorylate NHC, with UCK2 displaying 9-fold higher catalytic efficiency. Accordingly, in SARS-CoV-2-infected cells, downregulation of UCK2 via siRNA hampered the intracellular accumulation of the triphosphate antiviral metabolite of NHC, resulting in 10-fold reduction of antiviral efficacy. Furthermore, we could recapitulate this using a pan-UCK small molecule inhibitor. Critically, both UCK downregulation and inhibition significantly reduced the selectivity index of molnupiravir/NHC. Altogether, this work underscores the pivotal roles of UCK enzymes in upholding molnupiravir efficacy and therapeutic window, and furthermore, highlights their potential as pharmacologically tractable targets for tailoring the therapeutic response to this antiviral agent.

microbiology↗

DisConST: Deciphering Spatial Domains Using Distribution-aware Contrastive Learning for Spatial Transcriptomics

Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling the gene expression information together with the spatial locations, resulting in suboptimal accuracy. We introduce DisConST (Distribution-aware Contrastive Learning for Spatial Transcriptomics), a novel deep-learning method designed to improve spatial domain detection within spatial transcriptomics datasets. DisConST addresses key challenges, such as the high dropout rates and the complex integration of spatial and gene expression data, by incorporating contrastive learning strategies that are aware of the underlying data distributions. It employs the zero-inflated negative binomial (ZINB) distribution, along with graph contrastive learning, to generate more informative latent representations. These representations efficiently integrate spatial positions, transcriptomic profiles, and cell-type proportions within spots. We validated DisConST across diverse ST datasets of tissues, organs, and embryos from various sequencing platforms in both normal and disease states. Our results consistently demonstrated that DisConST achieves superior spatial domain recognition accuracy compared to existing state-of-the-art methods. Furthermore, our experiments highlighted the utility of DisConST in advancing research on tissue organization, embryonic development, and tumor immune microenvironment dissection. The source code for DisConST is freely available at https://github.com/Zhenpm/DisConST/.

bioinformatics↗

Harnessing Neutrophils to Deliver Antibiotics Non-Invasively across the Tympanic Membrane for Otitis Media Treatment

Acute otitis media (AOM) is a leading cause of pediatric antibiotic prescriptions. Systemic antibiotics cause side effects and antibiotic resistance, whereas local delivery of antibiotics (directly to the middle ear) is hindered by an impermeable biological barrier, the tympanic membrane (TM). Here, we report on a liposome that hitchhikes on neutrophils to deliver antibiotics to the site of infection. Distinct from previous immune-cell-based therapies, we enable neutrophil hitchhiking via a topical application of hydroxylated liposomes, thus bypassing the invasive neutrophil harvesting procedures. The hydroxylated liposomes are opsonized by the complement protein fragments and subsequently internalized by native neutrophils. A simple topical application completely cures AOM in an established chinchilla model. It points to a low-cost and non-invasive treatment for this prevalent disease, poised to reduce pediatric antibiotic usage.

bioengineering↗

Distinct duration and diet dependent lipid profiles and renal complicationsin GCK-MODY

Heterozygous inactivating mutations in the glucokinase (GCK) gene cause one of the most common types of maturity-onset diabetes of the young type 2 (MODY2), also named GCK-MODY. Studies suggest that, unlike other types of diabetes, patients with GCK-MODY do not have increased risk of diabetic complications and therefore do not typically require antihyperglycemic therapy. However, long-term outcomes of GCK-MODY on lipid profiles and chronic complications under different dietary scenarios remain unclear. Herein, using a knock-in mouse model expressing a novel MODY causing mutation GCK-Q26L (GCKMut), we examined age- and diet-related lipid profiles and diabetic complications. We found that, although GCKMut mice exhibited mild elevated blood glucose, the lipid profiles, body fat composition, and diabetic kidney disease (DKD) were initially improved in high-fat-diet (HFD) fed mice at the age of 28 weeks, supporting potential beneficial effects of GCKMut on lipid metabolism and kidney health. Unexpectedly however, those protective effects diminished by 40 weeks, and became more severe dyslipidemia and kidney injury associated with renal fibrosis and inflammation at 60-week-old mice fed with normal diet (ND) or HFD. Those findings revealed distinct duration- and diet-dependent effects of inactivating GCK mutation on lipid profiles and DKD, highlighting previously unrealized long-term chronic complications in GCK-MODY.

physiology↗

Robust identification of orthologous synteny with the Orthology Index and its applications in reconstructing the evolutionary history of plant genomes

With the explosive growth of whole-genome datasets, the accurate detection of orthologous synteny has become crucial for the reconstruction of evolutionary history. However, the currently available methods for the identification of orthologous synteny have great limitations: the methods are difficult to scale with varying polyploidy histories, and the accurate removal of out-paralogy is challenging. In this study, we developed a scalable and robust approach, the Orthology Index (OI), to identify orthologous synteny. Our evaluation of a large-scale dataset with diverse polyploidization events demonstrated that the technique is highly reliable. This discovery highlights OI as a potentially unified criterion for the identification of orthologous synteny, and this is further validated using simulation-based benchmarks. In addition, we explore its broad applications in reconstructing the evolutionary histories of plant genomes, including inference of polyploidy, identification of reticulation, and phylogenomics. In conclusion, OI offers a robust, interpretable, and scalable approach for identifying orthologous synteny, significantly enhancing our analytical prowess in plant evolutionary genomics.

genomics↗

Nearly complete genome assembly of a critically endangered pine illuminates evolution and conservation of conifers

Population genetic theory predicts that severe bottlenecks and extremely small effective population sizes (Ne) should reduce the ability of natural selection to eliminate harmful mutations. Under this framework, deleterious alleles are expected to accumulate and even fix, eroding fitness, constraining evolutionary rescue, and potentially precipitating mutational meltdown. Yet, empirical tests of these predictions in species at the extreme lower bound of Ne remain rare. We address this gap using Pinus squamata, one of the rarest tree species on Earth, with only 35 wild individuals remaining. We generated a near-complete reference genome (29.2 Gb) for this species and performed population genomic analyses across nearly all of its extant individuals, together with two closely related species. P. squamata exhibits extraordinarily low nucleotide diversity ({pi} = 3.35 x 10-), the lowest reported for any plant. Demographic inference reveals a recent and severe bottleneck ([~]20 generations ago) that reduced Ne to [~]2.7 and resulted in intense inbreeding. Contrary to theoretical expectations, we uncover evidence for highly efficient purifying selection: strongly deleterious mutations are markedly depleted, indicating substantial purging despite the extremely small Ne. Genome-wide patterns further implicate selection at linked sites--including background selection and pseudo-overdominance--as dominant forces shaping genomic variation in the species. These results challenge the prevailing view that drift overwhelms selection in extremely small populations. Instead, they suggest that, under certain genomic and demographic conditions, purifying selection can remain unexpectedly effective, potentially mitigating the risk of mutational meltdown in the rarest species.

genomics↗

Efficient integration of multiple spatial transcriptomics data for 3D domain detection, matching, and alignment with stMSA

Spatial omics (SO) is a powerful methodology that enables the study of genes, proteins, and other molecular features within the spatial context of tissue architecture. With the growing availability of SO datasets, researchers are eager to extract biological insights from larger datasets for a more comprehensive understanding. However, existing approaches focus on batch effect correction, often neglecting complex biological patterns in tissue slices, complicating feature integration and posing challenges when combining transcriptomics with other omics layers. Here, we introduce stMSA (SpaTial Multi-Slice/omics Analysis), a deep graph contrastive learning model that incorporates graph auto-encoder techniques. stMSA is specifically designed to produce batch-corrected representations while retaining the distinct spatial patterns within each slice, considering both intra- and inter-batch relationships during integration. Extensive evaluations show that stMSA outperforms state-of-the-art methods in distinguishing tissue structures across diverse slices, even when faced with varying experimental protocols and sequencing technologies. Furthermore, stMSA effectively deciphers complex developmental trajectories by integrating spatial proteomics and transcriptomics data, and excels in cross-slice matching and alignment for 3D tissue reconstruction.

bioinformatics↗

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individuals genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimers disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

genetics↗

Biogenic phosphonate utilization by globally distributed diatom Thalassiosira

Phosphonate is a class of enigmatic organic phosphorus compounds that contribute [~]25% of total dissolved organic phosphorus (DOP). Recent studies reveal the important role of phosphonate mediated by prokaryotes in the marine P redox cycle, however, its bioavailability and metabolic process by eukaryotic phytoplankton are under debate. 2-Aminoethylphosphonic acid and (2-AEP) and 2-amino-3-phosphonopropionic acid (2-AP3) are two biogenic phosphonates that ubiquitously exist in marine environment. Here, we report that Thalassiosira pseudonana, a dominant diatom species in the ocean, are able to recover growth from phosphorus starvation with individual supplement of 2-AEP and 2-AP3. Moreover, the cellular stoichiometric C:P and N:P ratios of cells grown under 2-AEP are in-between the P-depleted and DIP-replete groups. This study provided evidence that biogenic phosphonates can be adopted as alternative phosphorus sources to support diatom growth and might have a profound effect on the elemental stoichiometry in the oligotrophic ocean. Scientific significance statementPhosphorus (P) is a major limiting macronutrient for primary productivity in the ocean, whilst the biologically mediated cycling of different P forms is less understood compared to carbon and nitrogen. Accounting for 25% of the marine dissolved organic phosphorus pool, phosphonate bioavailability by eukaryotic phytoplankton still remains discrepancies. In line with our hypothesis, here we report that a cosmopolitan diatom Thalassiosira pseudonana can utilize biogenic phosphonates 2-aminoethylphosphonic acid and 2-amino-3-phosphonopropionic acid to support cell growth, with deviated cellular C:N:P in comparison to that of P-replete cells. Furthermore, cells grown under lower temperature exhibit physiological adaptation (K-selection strategy) with the benefit of 2-AEP supplied in the medium. We provide the evidence that utilization of biogenic phosphonate is ubiquitous in diatom and it might have profound effect on elemental stoichiometry ratios in the environment. Filling in this missing puzzle piece provides us with a fresh perspective to elucidate the role of phosphonates in marine phosphorus cycle and biological pump.

ecology↗

Identification and evaluation of small-molecule inhibitors against the dNTPase SAMHD1 via a comprehensive screening funnel

Sterile alpha motif and histidine-aspartic acid domain containing protein-1 (SAMHD1) is a deoxynucleoside triphosphate (dNTP) triphosphohydrolase central to cellular nucleotide pool homeostasis. Recent literature has also demonstrated how SAMHD1 can detoxify chemotherapy metabolites thereby controlling their clinical responses. To further understand SAMHD1 biology and to investigate the potential of targeting this enzyme as a neoadjuvant to existing chemotherapies we set out to discover selective small molecule-based inhibitors of SAMHD1. Here we report a discovery pipeline encompassing a biochemical screening campaign and a set of complementary biochemical, biophysical, and cell-based readouts for further characterisation of the screen output. The identified hit compound TH6342 and its analogues, accompanied by their inactive negative control analogue TH7126, demonstrated specific, low M potency in inhibiting the hydrolysis of both natural substrates and nucleotide analogue therapeutics, shown using complementary enzyme-coupled and direct enzymatic activity assays. Their mode of inhibition was subsequently detailed by coupling kinetic studies with thermal shift assays, where TH6342 and analogues were shown to engage with pre-tetrameric SAMHD1 and deter the oligomerisation and allosteric activation of SAMHD1 without occupying nucleotide binding pockets. We further outline the development and application of multiple cellular assays for assessing cellular target engagement and associated functional effects, including CETSA and an in-cell dNTP hydrolase activity assay, which highlighted future optimisation strategies of this chemotype. In summary, with a novel mode of inhibition, TH6342 and analogues broaden the set of tool compounds available in deciphering SAMHD1 enzymology and functions, and furthermore, the discovery pipeline reported herein represents a thorough framework for future SAMHD1 inhibitor development. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/524275v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@dc706forg.highwire.dtl.DTLVardef@59a9b4org.highwire.dtl.DTLVardef@9473e2org.highwire.dtl.DTLVardef@4427dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

A genome wide association study identifies a new variant associated with word reading fluency in Chinese children

Reading disability exhibited defects in different cognitive domains, including word reading fluency, word reading accuracy, phonological awareness, rapid automatized naming, and morphological awareness. To identify the genetic basis of Chinese reading disability, we conducted a genome wide association study (GWAS) of the cognitive traits related to Chinese reading disability in 2284 unrelated Chinese children. Among the traits analyzed in the present GWAS, we detected one genome wide significant association (p<5x10-8) on reading fluency for one SNP on 4p16.2, within EVC genes (rs6446395, p=7.55x10-10). Rs6446395 also showed significant association with word reading accuracy (p=3.39x10-4), phonological awareness (p=7.12x10-3), and rapid automatized naming (p=4.71x10-3), implying multiple effects of this variant. Gene-based analyses identified a gene to be associated with reading fluency at the genome-wide level. The eQTL data showed that rs6446395 affected EVC expression in the cerebellum. Our study discovered a new candidate susceptibility variant for reading ability and provide new insights into the genetics of development dyslexia in Chinese Children.

genetics↗

Genome-wide analyses of histone modifications and chromatin accessibility reveal the distinct genomic compartments in the Irish potato famine pathogen Phytophthora infestans

Phytophthora infestans, the causal agent of potato late blight, is a devastating plant disease that leads to Irish potato famine and threatens world-wide food security. Despite the genome of P. infestans has provided fundamental resource for studying the aggressiveness of this pandemic pathogen, the epigenomes remain poorly understood. Here, utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS), we demonstrate post-translational modifications (PTM) at P. infestans core histone H3. The PTMs not only include these prevalent modifications in eukaryotes, and also some novel marks, such as H3K53me2 and H3K122me3. We focused on the trimethylations of H3K4, H3K9 and H3K27 and H3K36, and profiled P. infestans epigenomes employing Native Chromatin Immunoprecipitation followed by sequencing (N-ChIP-seq). In parallel, we mapped P. infestans chromomatin accessibility by Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq). We found that adaptive genomic compartments display significantly higher levels of H3K9me3 and H3K27me3, and are generally in condense chromatin. Interestingly, we observed that genes encoding virulence factors, such as effectors, are enriched in open chromatin regions that barely have the four histone modifications. With a combination of genomic, epigenomic, transcriptomic strategies, our study illustrates the epigenetic states in P. infestans, which will help to study genomic functions and regulations in this pathogen. Author summaryEpigenetics play an important role in various biological processes of eukaryotes, including pathogenicity of plant pathogens. However, the epigenetic landscapes are marginally known in oomycetes that are fungal-like organisms and comprise lots of destructive plant pathogens. In this study, using the Irish potato famine pathogen Phytophthora infestans as a model, we conducted genome-wide studies of histone post modifications and chromatin accessibility, and demonstrate the relationship of gene expression and evolution with the epigenetic marks. We found that one of the most important classes of virulence proteins, effectors, are enriched in open chromatin regions that barely have eu- and hetero-chromatic marks. This study provides an overview of the oomycete epigenetic atlas, and advances our understanding of the regulation of virulence factors in plant pathogens.

microbiology↗

Comparative transcriptomics unveil distinctive metabolic pathway of phosphonate utilization by diatom Phaeodactylum tricornutum

Phosphonates are important constituents of marine organic phosphorus, however, the bioavailability and catabolism of phosphonates by eukaryotic phytoplankton remain enigmatic. Here, we use diatom Phaeodactylum tricornutum to investigate the bioavailability of phosphonates and elaborate the underlying molecular mechanism. Our results showed that 2-aminoethylphosphonic (2-AEP) can be utilized as alternative phosphorus source. Comparative transcriptomics unveil the 2-AEP utilization comprising two steps, molecular uptake through clathrin-mediated endocytosis and incorporation into the membrane phospholipids in the form of diacylglyceryl-2-AEP (DAG-2-AEP). In the global ocean, we found the prevalence of key genes responsible for vesicle formation (CLTC, AP-2) and DAG-AEP synthesis (PCYT2 and EPT1) in diatom assemblage. In accordance with the observation of elevated transcript abundance in cold waters, our culture experiments showed that cells grown in 2-AEP are more active at lower temperature. This study elucidated a distinctive mechanism of phosphonate utilization by diatom and inspected the ecological implications in adaptive mechanism.

microbiology↗

Atypical relationships between neurofunctional features of print-sound integration and reading abilities in Chinese children with dyslexia

Conquering print-sound mappings (e.g., grapheme-phoneme correspondence rules) is vital for developing fluent reading skills. In neuroimaging research, this ability can be indexed by activation differences between audiovisual congruent against incongruent conditions in brain areas such as the left superior temporal cortex. In line with it, individuals with dyslexia have difficulty in tasks requiring print-sound processing, accompanied by a reduced neural integration. However, existing evidence is almost restricted to alphabetic languages. Whether and how multisensory processing of print and sound is impaired in Chinese dyslexia remains underexplored. In this study, we applied a passive audiovisual integration paradigm with functional magnetic resonance imaging to investigate the possible dysfunctions in processing character-sound (opaque; semantics can be automatically accessed) and pinyin-sound associations (transparent; no particular meaning can be confirmed) in Chinese dyslexic children. Unexpectedly, the dyslexic group did not show reduced neural integration compared with typically developing readers in either character or pinyin experiment. However, the results revealed atypical correlations between neural integration and different reading abilities in dyslexia. Specifically, while the neural integration in the left inferior frontal cortex in processing character-sound pairs correlated with silent reading comprehension in both children with and without dyslexia, it was associated with morphological awareness (semantic-related) in controls but with rapid naming (phonological-related) in dyslexics. This result indicates Chinese dyslexic children may not use the same grapho-semantic processing strategy as their typical peers do. As for pinyin-sound processing, while a stronger neural integration in the direction of "congruent > incongruent" in the left occipito-temporal cortex and bilateral superior temporal cortices was associated with better oral reading fluency in the control group, an opposite pattern was found in dyslexia. This finding may reflect dyslexias dysfunctional recruitment of the regions in grapho-phonological processing, which further impedes character learning. HighlightsO_LINeurofunctional correlates of print-sound integration in Chinese children with and without dyslexia are investigated. C_LIO_LIDyslexic children show atypical relationships between neural audiovisual integration and reading abilities. C_LIO_LIChinese children with dyslexia are likely to use inefficient strategies to process characters and pinyin. C_LI

neuroscience↗