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Yang, J.

Publications and source records attributed to Yang, J..

At least 487 records · Page 27Linked to original sources
bioRxiv2024

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Genetic variation in the regulation of gene expression contributes substantially to phenotypic variation. Understanding how variation in DNA sequences and epigentic modifications leads to gene expression variation remains a challenging task. In hybrid animals where cellular environments are homogeneous, differences between the expression of paternal and maternal alleles must be due to cis sequence or epigenic differences. Therefore, hybrid mapping is a powerful approach to efficiently identify and characterize genes under regulation through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we performed a comprehensive multi-omic characterization of regulatory variation across brain, liver, muscle, and placenta in four developmental stages. We produced one of the largest multi-omic datasets to date in pigs, including 16 whole genome sequenced genomes, 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We developed a novel read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We showed that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identified 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. By integrating methylation, chromatin accessibility, and gene expression data, many of these allele specific expression can be explained by allele specific methylation and/or chromatin accessibility. This study provides one of the most comprehensive characterizations of regulatory variation across multiple tissues and developmental stages in pigs and offers new opportunity of genetic improvement for this important food animal species.

developmental biology↗
bioRxiv2023

Fine-tuning Large Language Models for Rare Disease Concept Normalization

ObjectiveWe aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). MethodsWe developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concepts synonyms as well as identifiers. Subsequently, we fine-tuned Llama2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms. ResultsWhen the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only [~]20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy. ConclusionOur fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymens terms. Our approach provides a solution for the use of LLM to identify named medical entities from the clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.

bioinformatics↗
bioRxiv2023

Exploration of the intricacies of low light-induced changes in cigar tobacco leaf anticlinal growth: A holistic approach from anatomical and hormonal levels to gene expression

Cigar tobacco stands as a pivotal economic crop, with its leaf growth and development profoundly influenced by light intensity. It specifically aims to investigate how leaf morphology and anticlinal growth respond to varying light intensities, including normal light intensity (NL-300 mol m-2 s-1) and lower light intensity (LL-100 mol m-2 s-1). The research elucidates significant morphological shifts in cigar tobacco leaves under LL, revealing notable alterations in leaf area, leaf length, and leaf width. Early reductions in leaf dimensions, ranging from 30% to 48%, were succeeded by a substantial enhancement in expansion rates from day 9 to day 26, contributing to expanded leaf surfaces at later stages. Upper epidermis thickness declined by 29-19%, with a notably slower expansion rate in the initial 20 days. Palisade cell length consistently decreased by 52-17%, corresponding with upper epidermis trends. Spongy tissue thickness was reduced by 31-12%, with a slower expansion rate in LL for the initial 14 days, and leaf thickness dropped by 34-11%. LL resulted in slower leaf anticlinal expansion, leading to reduced leaf thickness (LT). LL significantly influenced phytohormones in cigar tobacco leaves. Gibberellic acid (41% to 16%) and auxin (20% to 35%) levels were found in higher amounts, while cytokinin levels (19% to 5%) were lowered compared to NL, indicating the intricate regulatory role of light in hormonal dynamics. The observed increase in LT and different cell layers at specific time points (day 8, day 12, day 24, and day 28) under LL, although lower than NL, may be attributed to elevated expression of genes related to cell expansion, including GRF1, XTH, and SAUR19 at those time points. This comprehensive understanding elucidates the intricate mechanisms by which light intensity orchestrates the multifaceted processes governing leaf anatomy and anticlinal expansion in cigar tobacco plants.

physiology↗
bioRxiv2023

Camel milk affects serum metabolites by modulating the intestinal microflora

Gut microbes play a vital role in human health and are influenced by numerous factors including diet, genetics, and environment. (Fermented) Camel milk, which is abundant in nutrients and lacks allergenic proteins, has been consumed for its edible and medicinal properties for centuries. Research on camel milks impact on gut microbiota and host metabolism is still limited. The results found that sour camel milk contained various beneficial bacteria such as Lactobacillus helveticus, Acinetobacter lwoffii, Eubacterium coprostanoligenes group, Lachnospiraceae, which could be transported to the recipients intestines by diet. This study specified that the transportation of microbiome happened both intra- and inter-species and played a principal role in the formation of progeny gut microflora. An investigation on type 2 diabetic rats revealed that the composition of gut microflora and serum metabolites of those fed with high-dose camel whey was closer to that of the normal. Eubacterium limnetica, which can reduce the risk of diseases by producing MtcB protein, was found in the gut microflora of the ones taking camel milk. These results evidenced the high potential of camel milk as a functional food.

microbiology↗
bioRxiv2023

Bayesian Hidden Mark Interaction Model forDetecting Spatially Variable Genes inImaging-Based Spatially ResolvedTranscriptomics Data

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energybased framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

bioinformatics↗
bioRxiv2023

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

Numerous tools have been developed to infer active cell-cell communication (CCC) events, which are essential for understanding biological processes and diseases. However, existing downstream methods for assessing the relationships between CCC events and biological conditions lack clear interpretation, fail to adjust for confounders, and ignore dependencies among CCC events. To address these limitations, we introduce STACCato, a Supervised Tensor Analysis tool designed to identify Condition-related Cell-cell communication events. STACCato employs a tensor-based regression model to enable statistical inference related to the relationships between biological conditions (e.g., disease status, tissue types) and specific CCC events, while adjusting for confounders and CCC dependencies. Through extensive simulations and real-world applications on scRNA-seq datasets of lupus and autism, we demonstrate that STACCato consistently provides improved inference of condition-related CCC events compared to alternative methods. The computational tool implementing the STACCato framework is available on GitHub.

bioinformatics↗
bioRxiv2023

Battery-Free Optoelectronic Patch for Photodynamic and Light Therapies in Treating Bacteria-Infected Wounds

Light therapy is an effective approach for the treatment of a variety of challenging dermatological conditions. In contrast to existing methods involving high doses and large areas of illumination, alternative strategies based on wearable designs that utilize a low light dose over an extended period provide a precise and convenient treatment. In this study, we present a battery-free, skin-integrated optoelectronic patch that incorporates a coil-powered circuit, an array of microscale violet and red light emitting diodes (LEDs), and polymer microneedles (MNs) loaded with 5-aminolevulinic acid (5-ALA). This polymer MNs, based on the biodegradable composite materials of polyvinyl alcohol (PVA) and hyaluronic acid (HA), serves as light waveguides for optical access and a medium for drug release into deeper skin layers. Unlike conventional clinical photomedical appliances with a rigid and fixed light source, this flexible design allows for a conformable light source that can be applied directly to the skin. In animal models with bacterial-infected wounds, the experimental group with the combination treatment of metronomic photodynamic and light therapies reduced 2.48 log10 CFU mL-1 in bactericidal level compared to the control group, indicating an effective anti-infective response. Furthermore, post-treatment analysis revealed the activation of proregenerative genes in monocyte and macrophage cell populations, suggesting enhanced tissue regeneration, neovascularization, and dermal recovery. Overall, this optoelectronic patch design broadens the scope for targeting deep skin lesions, and provides an alternative with the functionality of standard clinical light therapy methods.

bioengineering↗
bioRxiv2023

High-resolution ultrastructure and morphology of marine ammonia-oxidizing archaea Nitrosopumilus maritimus SCM1 revealed by Cryo-ET and STEM-EDS

Nitrososphaerota, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their significance in Earths ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant question unanswered of how the machinery of these organisms underpins metabolic functions. In this study, we combined chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaerota. Our tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules in which one type is possibly composed of polyphosphate, while the other type is polyhydroxyalkanoate. Collectively, these findings provide visual evidence for the resilience of AOA in the vast oligotrophic marine environment.

microbiology↗
bioRxiv2023

An inducible genetic tool for tracking and manipulating specific microglial states in development and disease

Recent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative region-associated microglia (PAM) in developing white matter and disease-associated microglia (DAM) prevalent in various neurodegenerative conditions. PAM and DAM share a similar core gene signature and other functional properties. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we report the generation of an inducible Cre driver line, Clec7a-CreERT2, designed to target PAM and DAM in the brain parenchyma. Utilizing this tool, we profile labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM/DAM gene expression. Through long-term tracking, we demonstrate surprising levels of plasticity in these microglial states. Lastly, we specifically depleted DAM in cuprizone-induced demyelination, revealing their roles in disease progression and recovery.

neuroscience↗
bioRxiv2023

Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intra-cluster heterogeneity. We propose to move beyond coarse analyses of pre-defined subgroups by using contextualized learning, a multi-task learning paradigm that uses multi-view contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, Neighborhood Selection) and estimate context-specific GRNs for 7997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to novel subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized. Significance StatementNetwork estimation is essential for understanding the structure and function of biological systems, but current statistical approaches fail to capture inter-subject heterogeneity or cross-modality information flow, both of which are needed for understanding complex phenotypes and pathologies. We introduce contextualized network inference, leveraging multi-view contextual metadata to capture similarities and differences among heterogeneous observations during network estimation. Sharing information across contexts enables inference at sample-specific resolution, thus quantifying variation between subjects and revealing context-specific network rewiring. Applied to tumor-specific transcriptional network inference using clinical, molecular, and multi-omic data, contextualized networks improve accuracy, generalize to unseen cancer types, and discover novel prognostic tumor subtypes. By tailoring disease models to each sample, contextualized networks promise to enable precision medicine at unprecedented resolution.

bioinformatics↗
bioRxiv2023

Towards ultra-low-cost smartphone microscopy

The outbreak of COVID-19 exposed the inadequacy of our technical tools for home health surveillance, and recent studies have shown the potential of smartphones as a universal optical microscopic imaging platform for such applications. However, most of them use laboratory-grade optomechanical components and transmitted illuminations to ensure focus tuning capability and imaging quality, which keeps the cost of the equipment high. Here we propose an ultra-low-cost solution for smartphone microscopy. To realize focus tunability, we designed a seesaw-like structure capable of converting large displacements on one side into small displacements on the other (reduced to [~]9.1%), which leverages the intrinsic flexibility of 3D printing materials. We achieved a focus-tuning accuracy of [~] 5 {micro}m, which is 40 times higher than the machining accuracy of the 3D-printed lens holder itself. For microscopic imaging, we use an off-the-shelf smartphone camera lens as the objective and the built-in flashlight as the illumination. To compensate for the resulting image quality degradation, we developed a learning-based image enhancement method. We use the CycleGAN architecture to establish the mapping from smartphone microscope images to benchtop microscope images without pairing. We verified the imaging performance on different biomedical samples. Except for the smartphone, we kept the full costs of the device under 4 USD. We think these efforts to lower the costs of smartphone microscopes will benefit their applications in various scenarios, such as point-of-care testing, on-site diagnosis, and home health surveillance. Research highlightsWe propose a solution for ultra-low-cost smartphone microscopy. Utilizing the flexibility of 3D-printed material, we can achieve focusing accuracy of [~] 5 {micro}m. Such a low-cost device will benefit point-of-care diagnosis and home health surveillance.

bioengineering↗
bioRxiv2023

Imitating the 'breeder's eye': predicting grain yield from measurements of non-yield traits

Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency and rate of genetic improvement in future breeding cycles. However, field trials conducted in one environment provide limited insight into how well crop varieties will perform in other environments. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties indicates that plant breeders will need to make decisions about new crop varieties without knowing the future environments those crop varieties will encounter in farmers fields. Therefore, significant improvements in cross-environment prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the twenty-first century. To address this challenge, we conducted linked yield trials of 752 public maize genotypes in two distinct environments: Lincoln, Nebraska, and East Lansing, Michigan. Our findings confirmed that genomic predictions of yield can outperform direct yield measurements used to train the genomic prediction model in predicting yield in a second environment. Additionally, we developed and trained another trait-based yield prediction model, which we refer to as the Silicon Breeders Eye (SBE). Our results demonstrate that SBE prediction has comparable predictive power to genomic prediction models. SBE prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations of individuals, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

genetics↗
bioRxiv2023

Live Malassezia strains isolated from the mucosa of patients with ulcerative colitis

The human gut is inhabited by a complex ecosystem of diverse microorganisms. While most studies on the gut microbiota have focused on bacteria, accumulating evidence has underscored the role of the mycobiota in inflammatory bowel disease (IBD). This study is the first to isolate and characterize live Malassezia globosa strains from the intestinal mucosa of patients with ulcerative colitis. Malassezia species primarily inhabit the human skin. We therefore compared the M. globosa gut isolates with the M. globosa skin isolates and noted a striking disparity between them. The gut isolates led to a greater exacerbation of colitis in mice. Transcriptome analysis revealed that the gut isolates were more sensitive to normoxia than the skin isolates, suggesting adaptation to hypoxia prevalent in the intestinal environment. These findings provide novel insights into the potential impact of M. globosa on the pathogenesis of IBD and the influence of niche-specific adaptations on its virulence.

microbiology↗
bioRxiv2023

Successful spread of mcr-1-bearing IncX4 plasmids is associated with variant in replication protein of IncX4 plasmids

IncX4 plasmids are one of the most epidemiologically successful vehicles for mcr-1 spread. Here we found that the IncX4 plasmids carried two different replication proteins encoded by genes pir-1 and pir-2, respectively, but mcr-1 was only carried by IncX4 plasmid encoding pir-1. The copy number of pir-2 encoding plasmids (3.15{+/-}0.9 copies) are higher than that of pir-1 encoding plasmids (0.85{+/-}0.5 copies). When mcr-1 was cloned into IncX4 plasmid encoding pir-2, the higher copy number of these plasmids resulted in increased expression of mcr-1 and a greater fitness burden on their host cells. However, these plasmids exhibited a lower rate of invasion into the bacterial population compared to mcr-1 positive plasmids encoding the pir-1 gene. These findings collectively explain the absence of mcr-1 in all IncX4 plasmids encoding pir-2. Our results further confirmed that low-copy numbers are important for the spread of mcr-1 plasmid from the perspective of natural evolution.

microbiology↗
bioRxiv2023

LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model

Accurately predicting protein structure, from amino acid sequences to three-dimensional structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, has been proposed with promising success. Here, we report an ultra-lightweight deep graph network, named LightRoseTTA, to achieve accurate and high-efficient prediction for proteins. Notably, three highlights are possessed by our LightRoseTTA: (i) high-accurate structure prediction for proteins, being competitive with RoseTTAFold on multiple popular datasets including CASP14 and CAMEO; (ii) high-efficient training and inference with an ultra-lightweight model, costing only one week on one single general NVIDIA 3090 GPU for model-training (vs 30 days on 8 high-speed NVIDIA V100 GPUs for RoseTTAFold) and containing only 1.4M parameters (vs 130M in RoseTTAFold); (iii) low dependency on multi-sequence alignments (MSA, widely-used homologous information), achieving the best performance on three MSA-insufficient datasets: Orphan, De novo, and Orphan25. Besides, our LightRoseTTA is transferable from general proteins to antibody data, as verified in our experiments. We visualize some case studies to demonstrate the high-quality prediction, and provide some insights on how the structure predictions facilitate the understanding of biological functions. We further make a discussion on the time and resource costs of LightRoseTTA and RoseTTAFold, and demonstrate the feasibility of lightweight models for protein structure prediction, which may be crucial in the resource-limited research for universities and academy institutions. We release our code and model to speed biological research.

bioinformatics↗
bioRxiv2023

rNMPID: a database for riboNucleoside Mono-Phosphates In DNA

MotivationRibonucleoside monophosphates (rNMPs) are the most abundant non-standard nucleotides embedded in genomic DNA. If the presence of rNMP in DNA cannot be controlled, it can lead to genome instability. The actual positive functions of rNMPs in DNA remain mainly unknown. Considering the association between rNMPs embedment and various diseases and cancer, the phenomenon of rNMPs embedment in DNA has become a prominent area of research in recent years. ResultsWe introduce the rNMPID database, which is the first database revealing rNMP-embedment characteristics, strand bias, and preferred incorporation patterns in the genomic DNA of samples from bacterial to human cells of different genetic backgrounds. The rNMPID database uses datasets generated by different rNMP-mapping techniques. It provides the researchers with a solid foundation to explore the features of rNMPs embedded in the genomic DNA of multiple sources, and their association with cellular functions, and, in future, disease. It also significantly benefits researchers in the fields of genetics and genomics who aim to integrate their studies with the rNMP-embedment data. AvailabilityrNMPID is freely accessible on the web at https://www.rnmpid.org. Contactxph6113@gmail.com or storici@gatech.edu

bioinformatics↗
bioRxiv2023

Paternal genomic resources from the YanHuang cohort suggested a Weakly-Differentiated Multi-source Admixture model for the formation of Hans founding ancestral lineages

The large-scale human genome revolution and rapidly advanced statistical innovation have updated our understanding of the fine-scale and complex genetic structure, the entire landscape of genetic diversity and the evolutionary trajectories of spatiotemporally different ancients and ethnolinguistically diverse modern populations. Recent ancient DNA research provided a detailed and complex admixture picture of ancient Europeans but limited insights into East Asians as the few available genomes. Y-chromosome variations in the male-specific regions, served as molecular archaeological tool, have unique evolutionary features that can be utilized to reconstruct the origin and subsequent interaction of ancient East Asian paternal lineages. We launched the YanHuang cohort using our designed highest-resolution capture sequencing panel to explore the detailed evolutionary trajectory of the Han Chinese, one of the largest ethnic groups in the world. We reported one of the largest uniparental genomic resources and observed multiple founding paternal lineages dominant in ancient western Eurasian, Siberian and East Asian participating in the formation of the gene pool of the Han Chinese. We identified fine-scale paternal genetic structure correlated with different patterns of ancient population interaction and geographical mountain barriers (Qinling-Huaihe line and Nanling Mountains), suggesting isolation-enhanced and admixture-introduced genetic differentiation enhanced the complexity of the Han Chinese genomic diversity. We observed a strong direct correlation between the frequency of multiple founding lineages of the Han Chinese and the proportion of subsistence-related ancestry sources related to western pastoralists, Holocene Mongolian Plateau people and ancient East Asians, reflecting the ancient migration events contributed to our identified patterns of Chinese paternal genomic diversity. We finally provided one novel and the most plausible admixture-by-admixture model, the Weakly-Differentiated Multi-Source Admixture model, as the major genetic mechanism to illuminate our observed pattern of complex interactions of multiple ancestral sources and landscape of the Han Chinese paternal genetic diversity. Generally, we presented one large-scale uniparental genomic resource from the YanHuang cohort, portrayed one novel admixture formation model and presented the entire genomic landscape with multiple ancestral sources related to ancient herders, hunter-gatherers and farmers who participated in the ancestral formation of the Han Chinese.

evolutionary biology↗