Biology subjects

Kuang, R.

Publications and source records attributed to Kuang, R..

14 recordsLinked to original sources

bioRxiv2025

Developmental Trajectories of Addition and Subtraction in Early School-aged Children

Mastering basic arithmetic lays the foundation for lifelong mathematical achievement, yet how the neural mechanisms underlying addition and subtraction calculations develop during childhood remain unclear. This study investigated the developmental trajectories of addition and subtraction calculations in school-aged children, and explored how arithmetic-specific neural responses in the right parieto-occipital region are related to age-related improvements in behavioral performance. Here, we recorded electroencephalography (EEG) signals from 115 typically developing children divided into three age groups (7-8-, 9-10-, and 11-12-year-olds) during an arithmetic task involving addition and subtraction calculations. We analyzed the P300 component from the original waveforms and the {Delta}P300 component from the difference waveforms, which were generated by subtracting the event-related potentials (ERPs) elicited by the first operand from those elicited by the second operand to isolate arithmetic-specific neural processes. The results revealed that, in both calculations, older children showed better behavioral performance (faster reaction time and larger PI) and greater right parieto-occipital {Delta}P300 amplitudes, with a key developmental stage at 11-12 years. Furthermore, all groups of children exhibited greater right parieto-occipital P300 and {Delta}P300 amplitudes in subtraction, whereas the smaller right parieto-occipital {Delta}P300 amplitudes in addition significantly mediated the relationship between age and PI. These findings reveal distinct patterns for addition and subtraction calculations over development, highlighting the right parieto-occipital {Delta}P300 amplitude as a neural marker of arithmetic performance, particularly for addition calculation.

neuroscience↗

bioRxiv2025

Phylogenetic Inference of Copy Number Alterations and Single Nucleotide Variants from Longitudinal Single-Cell Sequencing

Longitudinal phylogenetic reconstruction reveals how cancers evolve over time and respond to treatments. Advances in targeted single-cell sequencing, combined with longitudinal sampling, now enable detailed longitudinal tracking of single nucleotide variants (SNVs) and copy number alterations (CNAs) at single-cell resolution. Here, we introduce LoPhy, the first method designed to reconstruct the evolution of SNVs and CNAs from these new longitudinal single-cell data. LoPhy is a sequential tree-building algorithm that reconstructs longitudinally-consistent phylogenies of SNVs and CNAs by maximizing a new factorized tree reconstruction objective. The algorithm incrementally grows a clone tree, adding SNVs and CNAs in the order they are observed across time points. Applied to a cohort of 15 acute myeloid leukemias (AMLs) and 4 TP53-mutated AMLs, LoPhy produced phylogenies that are biologically and temporally consistent with clinical observations, with many inferred CNAs validated by orthogonal bulk sequencing from the same cancer. These reconstructions highlight the role of CNAs in disease progression and resistance, revealing that AML clones selected after therapy are often defined by both large-scale CNAs and SNVs. More broadly, LoPhy can help uncover how SNVs and CNAs jointly shape the evolutionary trajectories of individual cancers at single-cell resolution. The LoPhy source code is available under a CC-BY-ND license at https://github.com/ethanumn/LoPhy. Author summaryLongitudinal single-cell DNA sequencing is increasingly used to track somatic mutations in cancer, including single nucleotide variants (SNVs) and copy number alterations (CNAs). Phylogenetic analysis of such data can reveal the mutations that characterize key subpopulations of cancerous cells and how they evolve over time. However, no existing methods are designed to reconstruct the joint evolution of SNVs and CNAs from longitudinal single-cell data. To address this gap, we developed LoPhy, an algorithm that infers a phylogenetic tree from longitudinal single-cell data to capture the joint evolutionary history of SNVs and CNAs in an individual cancer. We applied LoPhy to simulated datasets and 19 acute myeloid leukemias (AMLs). LoPhys reconstructions reveal that AML subpopulations selected after treatment are frequently defined by both SNVs and large-scale CNAs--highlighting that joint longitudinal modeling of SNVs and CNAs is crucial for understanding disease progression and therapeutic resistance.

bioinformatics↗

bioRxiv2024

Reconstructing 3D Spatial Structures of RNA-Tomography Transcriptomes by Tensor Factorization

Cells are organized to form three-dimensional structures of complex tissues. To map the complete 3D organization of a tissue, technologies based on tissue microdissections provide deep bulk RNA sequencing of orthographically arranged cryosections of a tissue, such that the full 3D spatial structure could be inferred from deeply sequenced transcriptomes in three views projected similarly as 3D tomography. Here, we introduce CTFacTomo to learn a Collapsed Tensor Factorization for RNA tomography data from cryosections to reconstruct 3D spatially resolved gene expressions. CTFacTomo combines tensor factorization with collapsing tensor entries to match the bulk gene expressions in each cryosection, enriched by a regularization of a product graph of protein-protein interaction network and spatial graphs. In the experiments, CTFacTomo is first validated on three datasets projected from fully profiled 3D spatial gene expressions to demonstrate that CTFacTomo significantly outperforms the benchmark methods for predicting the ground-truth gene expressions based on the projected 1D spatial gene expressions of three orthographic views. CTFacTomo is then applied to two RNA tomography datasets from zebrafish embryo and mouse olfactory mucosa, respectively. In both datasets, CTFacTomo detects 3D spatial expressions of several marker genes that are consistent with the developmental or functional regions in comparison to accompanying ISH staining images. In addition, a qualitative comparison between the reconstructed zebrafish embryo gene expressions with a matched external 3D Stereo-seq dataset also suggests that CTFacTomo reconstructs more spatially coherent patterns in the whole transcriptome with state-of-the-art performance.

bioinformatics↗

bioRxiv2024

Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs

MotivationSpatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images, provides a scalable alternative approach to decoding tissue complexity. ResultsHere, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. Availabilityhttps://github.com/song0309/asGNN/

bioinformatics↗

bioRxiv2024

BiGCN: Leveraging Cell and Gene Similarities for Single-cell Transcriptome Imputation with Bi-Graph Convolutional Networks

MotivationRNA profiling at the single-cell level is essential for characterizing the molecular activities and functions of individual cells. The current technical limitations of single-cell RNA sequencing (scRNA-seq) technologies can lead to a phenomenon known as "dropout", where a significant portion of gene expression is not captured. Dropout is particularly prominent in genes with low or sparse expression, greatly impacting the reliability and interpretability of scRNA-seq data. Consequently, various techniques have been developed to estimate missing gene expression using imputation, often by either modeling similarities in gene expression among cells or using gene co-expression, but rarely both. ResultsIn this study, we introduce a Bi-Graph Convolutional Network (BiGCN), a deep learning method that leverages both cell similarities and gene co-expression to capture cell-type-specific gene co-expression patterns for imputing scRNA-seq data. BiGCN constructs both a cell similarity graph and a gene co-expression graph, and employs them for convolutional smoothing in a dual two-layer Graph Convolutional Networks (GCNs). The embeddings from the two GCNs can subsequently be combined to facilitate the final imputation. BiGCN demonstrates superior performance compared to state-of-the-art imputation methods on both real and simulated scRNA-seq data. Additionally, BiGCN outperforms existing methods when tasked with clustering cells into cell types. We also perform a novel validation using a PBMC scRNA-seq dataset, and this experiment supports that BiGCNs imputations are more realistic than competing imputation methods. In both the imputation and the cluster tasks, BiGCN consistently outperformed two variants of BiGCN that solely relied on either the gene co-expression graph or cell similarity graph. This indicates that the two graphs offer complimentary information for imputation and cell clustering, underscoring the importance of incorporating both types of information. Code Availabilityhttps://github.com/inoue0426/scBiGCN. Contactkuang@umn.edu

bioinformatics↗

bioRxiv2024

Positive Selection on Rare Variants Underlying the Cold Adaptation of Wild Boar

The wide geographical distribution of Eurasian wild boar (Sus scrofa) offers a natural experiment to study the thermoregulation. Here, we conducted whole-genome resequencing and chromatin profiling experiments on the local populations from cold regions (northern and northeastern Asia) and warm regions (southeastern Asia and southern China). Using genome-wide scans of four methods, we detected candidate genes underlying cold-adaptation with significant enrichment of pathways related to thermogenesis, fat cell development, and adipose tissue regulation. We also found two enhancer variants under positive selection, an intronic variant of IGF1R (rs341219502) and an exonic variant of BRD4 (rs327139795), which showed the highest differentiation between cold and warm region populations of wild boar and domestic pigs. Moreover, these rare variants were absent in outgroup species and warm-region wild boar but nearly fixed in cold-region populations, suggesting their de novo origins in cold-region populations. The experiments of CUT&Tag chromatin profiling showed that rs341219502 of IGF1R is associated with the gain of three novel transcription factors involving regulatory changes in enhancer function, while rs327139795 of BRD4 could result in the loss of a phosphorylation site due to amino acid alteration. We also found three genes (SLCO1C1, PDE3A, and TTC28) with selection signals in both wild boar and native human populations from Siberia, which suggests convergent molecular adaptation in mammals. Our study shows the adaptive evolution of genomic molecules underlying the remarkable environmental flexibility of wild boar.

evolutionary biology↗

bioRxiv2024

Screening and assessment of functional variants by regulatory features from epigenomic data in livestock species

BackgroundUnderstanding the functional impact of genetic variants is essential for advancing animal genomics and improving livestock breeding. Variants that disrupt transcription factor (TF) motifs provide a means to assess functional potential, but the lack of TF ChIP-seq data for farm animals presents a challenge. ResultsTo address this, we curated nearly 900 epigenomic datasets from 10 farm animal species and annotated eight regulatory regions to assess how variants affect TF motifs. Over 127 million candidate functional variants were classified into five functional confidence categories across the species. Variants with high confidence were enriched in eQTLs and trait-associated SNPs, showing greater potential to affect gene expression and phenotypes. Incorporating these functional variants into genomic prediction models improved the accuracy of Estimated Breeding Values (EBVs). Active variants also revealed trait-related tissues, and single-cell RNA sequencing (scRNA-seq) identified the cell types most associated with production traits. To facilitate research, we developed the Integrated Functional Mutation (IFmut) platform, enabling users to explore variant functions easily. Our study provides a flexible platform and resource for studying genomic variation in farm animals, setting a new standard for research and breeding strategies. ConclusionThe results indicated that evaluating functional potential by annotating and categorizing variants that interfere with transcription factor motifs can help elucidate changes in gene expression and phenotype. By focusing on high-confidence variants enriched in eQTL and trait-associated SNPs, it improves the accuracy of genomic predictions in research and breeding strategies.

genomics↗

bioRxiv2022

FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completion

Functional interpretation of spatial transcriptomics data usually requires non-trivial pre-processing steps and other supporting data in the analysis due to the high sparsity and incompleteness of spatial RNA profiling, especially in 3D constructions. As a solution, we present a new software tool FIST-nD, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion in n-Dimensions for imputing 3D as well as 2D spatial transcriptomics data. FIST-nD is implemented based on a novel graph-regularized tensor decomposition method, which imputes spatial gene expression data using 4-way high-order tensor structure and relations in spatial and gene functional graphs. The implementation, accelerated by GPU or multicore parallel computing, can efficiently impute high-resolution 3D spatial transcriptomics data within a few minutes. The experiments on three 3D Spatial Transcriptomics datasets and one 3D high-resolution Stereo-seq dataset confirm the high accuracy of the imputation by FIST-nD and demonstrate that the imputed spatial transcriptomes provide a more complete gene expression landscape for downstream analyses such as spatial gene expression clustering and visualizations.

bioinformatics↗

bioRxiv2020

A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-based Multiple Sequence Alignment

Human chromosome 19q13.4 contains genes encoding killer-cell immunoglobulin-like receptors (KIR). Reported haplotype lengths range from 67 to 269 kilobases and contain 4 to 18 genes. The region has certain properties such as single nucleotide variation, structural variation, homology, and repetitive elements that make it hard to align accurately beyond single gene alleles. To the best of our knowledge, a multiple sequence alignment of KIR haplotypes has never been published or presented. Such an alignment would be useful to precisely define KIR haplotypes and loci, provide context for assigning alleles (especially fusion alleles) to genes, infer evolutionary history, impute alleles, interpret and predict co-expression, and generate markers. In order to extend the framework of KIR haplotype sequences in the human genome reference, 27 new sequences were generated including 24 haplotypes from 12 individuals of African American ancestry that were selected for genotypic diversity and novelty to the reference, to bring the total to 68 full length genomic KIR haplotype sequences. We leveraged these data and tools from our long-read KIR haplotype assembly algorithm to define and align KIR haplotypes at <5 kb resolution on average. We then used a standard alignment algorithm to refine that alignment down to single base resolution. This processing demonstrated that the high-level alignment recapitulates human-curated annotation of the human haplotypes as well as a chimpanzee haplotype. Further, assignments and alignments of gene alleles were consistent with their human curation in haplotype and allele databases. These results define KIR haplotypes as 14 loci containing 9 genes. The multiple sequence alignments have been applied in two software packages as probes to capture and annotate KIR haplotypes and as markers to genotype KIR from WGS.

bioinformatics↗

bioRxiv2020

Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. One major limitation of in-situ capturing is the high dropout rate of mRNAs that fail the capture or the amplification, which leads to incomplete profiling of the gene expressions. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed several best performing single-cell RNAseq data imputation methods. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney. The source code and data are available at https://github.com/kuanglab/FIST. Author summaryBiological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Similar to single-cell RNA sequencing data, spatial transcriptomics data also suffers from a high dropout rate of mRNAs in in-situ capture. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

bioinformatics↗

bioRxiv2020

Efficient Sequencing, Assembly, and Annotation of Human KIR Haplotypes

The homology, recombination, variation, and repetitive elements in the natural killer-cell immunoglobulin-like receptor (KIR) region has made full haplotype DNA interpretation impossible without physical separation of chromosomes. Here, we present a new approach using long-read sequencing to efficiently capture, sequence, and assemble diploid human KIR haplotypes. Sequences for capture probe design were derived from public full-length gene and haplotype sequences. IDT xGen(R) Lockdown probes were used to capture 2-8 kb of sheared DNA fragments followed by sequencing on a PacBio Sequel. The sequences were error corrected, binned, and then assembled using the Canu assembler. The assembly was evaluated on 16 individuals (8 African American and 8 Europeans) from whom ground truth was known via long-range sequencing on fosmid-isolated chromosomes. Using only 18 capture probes, the results show that the assemblies cover 97% of the GenBank reference, are 99.97% concordant, and it takes only 1.8 contigs to cover 75% of the reference. We also report the first assembly of diploid KIR haplotypes from long-read WGS, including the first sequencing of cB05[~]tB01, which pairs a KIR2DS2/KIR2DS3 fusion with the tB01 region. Our targeted hybridization probe capture and sequencing approach is the first of its kind to fully sequence and phase all diploid human KIR haplotypes, and it is efficient enough for population-scale studies and clinical use.

bioinformatics↗

bioRxiv2019

Predicting KIR Structural Haplotypes with Novel Sequence Signatures from Short-read Whole Genome Sequencing

The killer cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the bodys reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for in silico KIR probe interpretation (KPI) to accurately interpret individuals KIR genes and haplotype-pairs from KIR sequencing reads. We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72-100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPIs accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, although the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https://github.com/droeatumn/kpi.

bioinformatics↗

bioRxiv2017

DARPP-32 and t-DARPP promote lung cancer growth through IKKα-dependent cell migration and Akt/Erk-mediated cell survival

Lung cancer is the leading cause of cancer-related death worldwide. In this study, we demonstrate that elevated expression of dopamine and cyclic adenosine monophosphate-regulated phosphoprotein, Mr 32000 (DARPP-32) and its truncated splice variant t-DARPP promotes lung tumor growth, while abrogation of DARPP-32 expression in human non-small cell lung cancer (NSCLC) cells reduces tumor growth in orthotopic mouse models. We observe a novel physical interaction between DARPP-32 and inhibitory kappa B kinase- (IKK) that promotes NSCLC cell migration through non-canonical nuclear factor kappa-light-chain-enhancer of activated B cells 2 (NF-{kappa}B2) signaling. Bioinformatics analysis of 513 lung adenocarcinoma patients reveals elevated t-DARPP isoform expression is associated with poor overall survival. Histopathological investigation of 62 human lung adenocarcinoma tissues also showed that t-DARPP expression is elevated with increasing tumor (T) stage. Our data suggest that DARPP-32 is a negative prognostic marker associated with increasing stages of NSCLC and may represent a novel therapeutic target.

cancer biology↗

bioRxiv2017

Revealing Complete Complex KIR Haplotypes Phased By Long-Read Sequencing Technology

The killer cell immunoglobulin-like receptor (KIR) region of human chromosome 19 contains up to sixteen genes for natural killer (NK) cell receptors that recognize human leukocyte antigen (HLA)/peptide complexes and other ligands. The KIR proteins fulfill functional roles in infections, pregnancy, autoimmune diseases, and transplantation. However, their characterization remains a constant challenge. Not only are the genes highly homologous due to their recent evolution by tandem duplications, but the region is structurally dynamic due to frequent transposon-mediated recombination. A sequencing approach that precisely captures the complexity of KIR haplotypes for functional annotation is desirable.\n\nWe present a unique approach to haplotype the KIR loci using Single Molecule, Real-Time (SMRT(R)) Sequencing. Using this method, we have - for the first time - comprehensively sequenced and phased sixteen KIR haplotypes from eight individuals without imputation. The information revealed four novel haplotype structures, a novel gene-fusion allele, novel and confirmed insertion/deletion events, a homozygous individual, and overall diversity for the structural haplotypes and their alleles.\n\nThese KIR haplotypes augment our existing knowledge by providing high-quality references, evolutionary informers, and source material for imputation. The haplotype sequences and gene annotations provide alternative loci for the KIR region in the human genome reference GrCh38.p8.\n\nAuthor contributionsDR, CVG, SS, SR, DEG, MM designed the project. CWP, KE, RH performed the preparation and sequencing experiments. DR, CVG, RK, SR, DWG wrote the majority of the manuscript.

genomics↗