Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 973 records · Page 54Linked to original sources

bioRxiv2024

Engineered Whole Lungs for Tissue Biology

End-stage lung disease and lung cancer significantly contribute to global mortality, necessitating new research strategies for studying pulmonary biology. Here, we present an engineered whole-lung tissue model used to evaluate the effects of cellular communities on tissue organization and alveolar barrier function. Engineered lungs were grown ex vivo on decellularized whole-lung matrices as structurally biomimetic, bioactive scaffolds. Histologic architecture of engineered lungs improved with the addition of alveolar macrophages, coming to resemble neonatal lung. Incorporating alveolar macrophages maximized the differentiation of native-like cellular communities, including alveolar type I-like epithelium, bronchioalveolar stem cells, microvascular endothelium, and pericytes. Cell-cell signaling in engineered lungs showed activation of developmental and inflammatory pathways, including WNT, Notch, and FGF signaling pathways. Engineered lungs containing alveolar macrophages showed a 668% improvement in measured alveolar barrier function. This work demonstrates the potential utility of engineered lung models for studying principles of tissue biology and pulmonary regeneration.

bioengineering↗

bioRxiv2024

scGNN+: Adapting ChatGPT for Seamless Tutorial and Code Optimization

Foundation models have transformed AI by leveraging large-scale data to efficiently perform diverse tasks, and their applications in bioinformatics are primarily focused on data-centric tasks like cell type annotation and gene expression analysis. However, their potential extends beyond data analysis, offering significant opportunities in software development and optimization, such as code refinement, tutorial generation, and advanced visualization. For example, models like OpenAI Codex can suggest optimized code snippets and generate well-documented, reproducible workflows, enhancing accessibility and reducing computational complexity. Despite these advantages, the use of foundation models for improving computational tool engineering in single-cell research remains underutilized. To address this gap, we developed scGNN+, a web-based platform that combines the power of graph neural networks with the capabilities of ChatGPT to enhance reproducibility, code optimization, and visualization. scGNN+ further simplifies the process for users by generating standardized, well-annotated code, making complex procedures more accessible to non-programmers. Additionally, ChatGPT integration allows users to create high-quality, customizable visualizations through natural language prompts, improving data interpretation and presentation. Ultimately, scGNN+ offers a user-friendly, reproducible, and optimized solution for single-cell research, leveraging the full potential of foundation models in bioinformatics. scGNN+ is publicly available at https://bmblx.bmi.osumc.edu/scgnn+.

bioinformatics↗

bioRxiv2024

Evaluation of scalp-based targeting methods of DLPFC for TMS therapy: EEG cap F3, Beam F3, CPC F3, and adjusted Beam F3

BackgroundRepetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex (DLPFC) offers a promising approach for treating depression. Scalp-based targeting methods, including EEG Cap, Beam F3, CPC F3, and adjusted Beam F3, have evolved to improve clinical application. ObejectivesTo evaluate and compare the accuracy, reliability, speed, and training efficacy of these four methods. MethodsThree trained technicians conducted repeated DLPFC targeting measurements on 10 3D-printed head models. Accuracy was assessed by calculating targeting error and electric field strength at the subgenual anterior cingulate cortex anti-correlated peak. Reliability was evaluated through inter- and intra-rater variability, and training efficacy was examined by comparing targeting errors between trained technicians and novices. ResultsCPC F3, Beam F3, and adjusted Beam F3 demonstrated comparable accuracy in terms of electric field strength, but CPC F3 had the lowest targeting error (4.2 mm). CPC F3 also exhibited the highest reliability (inter-rater variability at 3.3 mm, intra-rater variability at 2.3 mm). The EEG cap was the fastest method (80 seconds), while CPC F3 was faster than Beam F3 but slower than EEG Cap. No significant differences in targeting error between trained technicians and novices were noted with CPC F3 and adjusted Beam F3. ConclusionThe comparative analysis of four scalp-based targeting methods provides insights that can enhance clinical decision-making and the practical application. While accuracy among CPC F3, Beam F3, and adjusted Beam F3 was comparable, CPC F3 emerges as highly practical due to its significantly better reliability, training efficacy, moderate speed, and straightforward implementation.

neuroscience↗

bioRxiv2024

Ligase-mediated programmable genomic integration (L-PGI): an efficient site-specific gene editing system that overcomes the limitations of reverse transcriptase-based editing systems

Since their discovery, CRISPR/Cas9 systems have been repurposed for programmable targeted genomic editing. This has led to unprecedented advancement of gene editing for therapeutic benefit. Initial uses of CRISPR/Cas9 were focused on gene disruption via DNA cleavage, but significant engineering led to systems for single base editing as well as insertion, deletion and manipulation of short stretches of genomic sequences using nicking Cas9 and RT-based methods. These technologies allowed safer and more precise editing but were limited to small corrections and showed significantly reduced efficiencies in nondividing cells, presenting difficulty for translation to in vivo therapies. To find an alternate editing strategy that could address these shortcomings, we revisited the mechanism of DNA nicking by nCas9. nCas9 nicking creates a free 5 phosphate group and a 3 hydroxyl group on the complementary strand of the target sequence. Under ordinary conditions in the cell these ends are re-joined by endogenously expressed ligases to repair DNA back to wild-type. If, however, a DNA fragment containing the desired edit were present, ligation of the nicked genomic DNA with the delivered fragment could result in gene editing. We demonstrate that optimization of each component and introduction of a chemically modified high affinity splinting DNA allows a variety of ligase-based edits, including longer edits not efficient with RT-based systems, at high efficiencies and fidelities that minimize genomic byproducts in both dividing and nondividing cells as well as in vivo in adult mice. Here we present the first therapeutically relevant ligation-based programmable gene editing technology, L-PGI.

bioengineering↗

bioRxiv2024

WIMOAD: Weighted Integration of Multi-omics data for Alzheimer's Disease (AD) Diagnosis

INTRODUCTIONAlzheimers disease (AD), the most prevalent subtype of dementia, is characterized by a gradual decline in brain cognitive function. Early detection is critical for initiating timely interventions that may delay the severe progression of the disease. Recent advances in next-generation sequencing (NGS) offer promising, non-invasive, and cost-effective strategies for AD screening. However, most current approaches rely on single-omics data, which may fail to capture the complex biological heterogeneity among individuals. METHODSWe introduce WIMOAD, a stacking ensemble and weighted multi-omics integration for AD diagnosis. It leverages paired gene expression and methylation data from ADNI and presents a meta learning framework for multi-cognitive stage classification during AD progression. RESULTS AND DISCUSSIONWIMOAD outperforms existing integration methods in AD diagnosis, effectively capturing complex multi-omics patterns linked to clinical outcomes. Its interpretability also facilitates the detection of novel biomarkers across different omics layers.

bioinformatics↗

bioRxiv2024

Continual integration of single-cell multimodal data with MIRACLE

Single-cell sequencing technologies have revolutionized our understanding of cellular heterogeneity and facilitated the construction of multi-omics cell atlases via data integration. However, updating these atlases with new data conventionally requires reintegration of all data and is computationally intensive, hindering timely updates and dynamic adjustments in biological and medical research. To address this challenge, we present Multimodal Integration with Continual Learning (MIRACLE), a novel online learning framework for the adaptive and efficient integration of single-cell multimodal data. MIRACLE employs dynamic architectures and data rehearsal strategies to support continual learning, allowing diverse data to be integrated while minimizing information loss over time. Our evaluations demonstrate that MIRACLE achieves accurate online integration with reduced computational requirements, effectively updating and expanding atlases with new cross-tissue and cross-modal data, and precisely identifying novel cell types and transferring labels across datasets. MIRACLE provides an efficient and flexible tool for single-cell community to integrate, share and explore biological knowledge from single-cell multimodal data.

bioinformatics↗

bioRxiv2024

Assessment of drive efficiency and resistance allele formation of a homing gene drive in the mosquito Aedes aegypti

Aedes aegypti, known for transmitting viruses such as dengue, zika, and yellow fever, poses a significant public health threat. Conventional insecticides give rise to a range of issues, including ecological contamination and insect resistance. Hence, there is a pressing demand for environmentally friendly, safer, and more efficacious strategies for mosquito control. With the rapid advancement of the CRISPR/Cas9 system in gene function exploration and pest population control, substantial progress has been achieved in utilizing CRISPR/Cas9-based gene drive systems across various mosquito species. Only a few studies on gene drive technology have been conducted in A. aegypti. In this study, we constructed two complete drives for A. aegypti with different Cas9 promoters, each targeting kmo. Our drive based on Pub-Cas9 had limited activity, but one with exu-Cas9 exhibited super-Mendelian inheritance rates of approximately 60%. We observed low but detectable somatic activity of the drive and no evidence of maternally deposited Cas9. Germline resistance allele formation rates were similar to drive conversion rates, but most wild-type alleles in the germline remained uncut. Injections into the exu-Cas9 drive line had 100% knockout efficiency among surviving offspring at three separate target genes. These results support the development and application of novel genetic pest control technologies aimed at combating A. aegypti.

genetics↗

bioRxiv2024

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-cell Acute Lymphoblastic Leukemia

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests and genomic profiling, which would be costly, complicated, and laborious. To overcome these challenges, we present RanBALL (an ensemble Random projection-based model for identifying B-ALL subtypes), an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on >1700 B-ALL patients demonstrated that RanBALL achieved remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than tSNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL.

bioinformatics↗

bioRxiv2024

Unveiling allopolyploidization-driven genome duplications through progressive analysis of deep genome skimming data

Whole-genome duplication (WGD) events are widespread across the Web of Life (WoL). Given the prevalence of WGDs in the polyploid Prunus (Rosaceae), this economically- and agriculturally-important angiosperm lineage provides an excellent model for exploring this mode of reticulation. We used the polyploid Prunus to demonstrate a progressive strategy for analyzing Deep Genome Skimming (DGS) data in the presence of WGDs. Phylogenomic discordance analyses indicated that allopolyploidization, rather than Incomplete Lineage Sorting (ILS), played a dominant role in the origin and dynamics of polyploid Prunus. This study underscores how a progressive strategy to identify WGD events at different depths in a phylogenetic tree reveals the nuances of evolutionary mechanisms driving allopolyploidization. We inferred that the continued uplift of the Himalayas from the Middle to Late Miocene drove the rapid diversification of the Eastern Asia endemic Maddenia clade, by facilitating frequent hybridization and allopolyploidization, specifically introgression between the Himalayas-Hengduan and the Central-Eastern China clades.

systems biology↗

bioRxiv2024

Impact of carbamate and pyrethroid pesticides on bile acid profiles in an in vitro gut microbiota model

The impact on the microbial community following exposure to commonly used pesticides has recently gained increasing interest. In this study, effects of selected carbamates (carbofuran, aldicarb) and pyrethroids (cypermethrin and cyhalothrin) on gut microbiota and related bile acid metabolism were quantified using a 24 h in vitro fermentation model system with mice feces. The results obtained reveal a pesticide induced significant increase the ratio of secondary over primary bile acids, particularly resulting from the enrichment of {beta}-muricholate ({beta}MCA) accompanied by the depletion of {omega}-muricholate ({omega}MCA). Besides, the bacterial profile showed significantly increased richness of Eggerthellaceae after 24 h exposure to carbofuran and cyhalothrin, and the genera Enterorhabdus belonging to Eggerthellaceae was found to be highly correlated to the fecal bile acid profile. In conclusion, in an in vitro gut microbial model carbamates and pyrethroids caused alterations of the gut microbial community resulting in the modulation of bile acid transformation. This illustrates that the gut microbiota and its metabolism may be a novel target to consider in future pesticide safety evaluations.

microbiology↗

bioRxiv2024

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics.

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

genomics↗

bioRxiv2024

High-Dimensional Mediation Analysis with Network Mediators: Applications to Pediatric Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, with survivors frequently experiencing long-term neurocognitive morbidities. Here, we utilize the TOTXVI clinical trial data to elucidate the mechanisms underlying treatment-related neurocognitive side effects in pediatric ALL patients by incorporating brain connectivity network data. To enable such analysis, we propose a high-dimensional mediation analysis method with a novel network mediation structural shrinkage (NMSS) prior, which is particularly suited for analyzing high-dimensional brain structural connectivity network data that serve as mediators. Our method is capable of addressing the structural dependencies of brain connectivity networks including sparsity, effective degrees of nodes, and modularity, yielding accurate estimates of the high-dimensional coefficients and mediation effects. We demonstrate the effectiveness and superiority of the proposed NMSS method through simulation studies and apply it to the TOTXVI data, revealing significant mediation effects of brain connectivity on visual processing speed directed by IT intensity. The findings shed light on the potential of targeted interventions to mitigate neurocognitive deficits in pediatric ALL survivors.

cancer biology↗

bioRxiv2024

Traumatic Brain Injury Exacerbates Alcohol Consumption and Neuroinflammation with Decline in Cognition and Cholinergic Activity

Traumatic brain injury (TBI) is a global health challenge, responsible for 30% of injury-related deaths and significantly contributing to disability. Annually, over 50 million TBIs occur worldwide, with most adult patients at emergency departments showing alcohol in their system. TBI is also a known risk factor for alcohol abuse, yet its interaction with alcohol consumption remains poorly understood. In this study, we demonstrate that the fluid percussion injury (FPI) model of TBI in mice significantly increases alcohol consumption and impairs cognitive function. At cellular levels, FPI markedly reduced the number and activity of striatal cholinergic interneurons (CINs) while increasing microglial cells. Notably, depleting microglial cells provided neuroprotection, mitigating cholinergic loss and enhancing cholinergic activity. These findings suggest that TBI may promote alcohol consumption and impair cognitive abilities through microglia activation and consequently reduced cholinergic function. Our research provides critical insights into the mechanisms linking TBI with increased alcohol use and cognitive deficits, potentially guiding future therapeutic strategies.

neuroscience↗

bioRxiv2024

Advancing Clinical Response Against Glioblastoma: Evaluating SHP1705 CRY2 Activator Efficacy in Preclinical Models and Safety in Phase I Trials

BackgroundIt has been reported that circadian clock components, Brain and Muscle ARNT-Like 1 (BMAL1) and Circadian Locomotor Output Cycles Kaput (CLOCK), are uniquely essential for glioblastoma (GBM) stem cell (GSC) biology and survival. Consequently, we developed a novel Cryptochrome (CRY) activator SHP1705, which inhibits BMAL1-CLOCK transcriptional activity. MethodsWe analyzed buffy coats isolated from Phase 1 clinical trial subjects blood to assess any changes to circadian, housekeeping, and blood transcriptome-based biomarkers following SHP1705 treatment. We utilized GlioVis to determine which circadian genes are differentially expressed in non-tumor versus GBM tissues. We employed in vitro and in vivo methods to test the efficacy of SHP1705 against patient-derived GSCs and xenografts in comparison to earlier CRY activator scaffolds. Additionally, we applied a novel-REV-ERB agonist SR29065, which inhibits BMAL1 transcription, to determine whether targeting both negative limbs of the circadian transcription-translation feedback loop (TTFL) would yield synergistic effects against various GBM cells. ResultsSHP1705 is safe and well-tolerated in Phase I clinical trials. SHP1705 has increased selectivity for the CRY2 isoform and potency against GSC viability compared to previously published CRY activators. SHP1705 prolonged survival in mice bearing GBM tumors established with GSCs. When combined with the novel REV-ERB agonist SR29065, SHP1705 displayed synergy against multiple GSC lines and differentiated GSCs (DGCs). ConclusionsThese demonstrate the efficacy of SHP1705 against GSCs, which pose for GBM patient outcomes. They highlight the potential of novel circadian clock compounds in targeting GBM as single agents or in combination with each other or current standard-of-care. KEY POINTSO_LISHP1705 is a novel CRY2 activator that has shown success in Phase 1 safety trials C_LIO_LISHP1705 has a significantly improved efficacy against GSCs and GBM PDX tumors C_LIO_LINovel REV-ERB agonist SR29065 and SHP1705 display synergistic effects against GSCs C_LI IMPORTANCE OF THE STUDYCRY2 is decreased in GBM tissues compared to CRY1 suggesting that promoting CRY2 activity will be an efficacious GBM treatment paradigm. SHP1705, a CRY2 activator that has shown success in Phase 1 safety trials, has significantly improved preclinical efficacy. Novel REV-ERB agonist SR29065 displays synergistic effects against diverse GBM cells.

cancer biology↗

bioRxiv2024

Room Temperature Aerosol Dehydration of Green Fluorescence Protein

Rapid Room-Temperature Aerosol Dehydration (RTAD) is a novel, scalable drying technology for powderization and thermal stabilization of pharmaceutical drug products. Compared to conventional spray drying, RTAD utilizes sub-20 {micro}m droplets that evaporate rapidly at room temperature, thereby reducing drying-induced stresses for thermally sensitive biologics. In this study, we employed Green Fluorescent Protein (GFP) as a model biological molecule to optimize the RTAD system design and process parameters. We investigated the effects of droplet size, multiphase flow patterns in the drying chamber, and usage of polysorbate 20 as a model surfactant on GFP fluorescence after drying and powder reconstitution. The experiments demonstrated that the presence of polysorbate 20 in the formulation significantly influenced GFP fluorescence intensity, especially for smaller droplets. The numerical studies using Computational Fluid Dynamics simulations revealed that the intensity of GFP fluorescence in the produced dry powders was dependent on the patterns of multiphase flow in the drying chamber. Non-axisymmetric flows and closed circulating streamlines near the drying gas inlet negatively impacted the GFP fluorescence intensity. Through iterative optimization of chamber design, process parameters, and feedstock formulation, we achieved the recovery (compared to the initial samples) of GFP fluorescence intensity exceeding 96% in the obtained dry powders. This work establishes GFP as a sensitive model biologic and its fluorescence intensity as a powerful tool for rapidly assessing bioactivity following dehydration. The insights gained from this study have broad implications for the design and scale-up of room-temperature drying technologies, which can potentially transform the production of dry powder biopharmaceuticals.

pharmacology and toxicology↗

bioRxiv2024

Microbial strategies of environmental adaptation revealed by trait-environmental relationships

Microbial trait variation along environment gradients is crucial to understanding their ecological adaptation mechanisms. With the increasing availability of microbial genomes, making full use of the genome-based traits to decipher their adaptation strategies becomes promising and urgent. Here, we examined microbial communities in water and sediments of 20 East African lakes with pH values ranging from 7.2 to 10.1 through taxonomic-profiling and genome-centric metagenomics. We identified functional traits important for microbial adaptation to the stresses of alkalinity and salinity based on the significant trait-environment relationships (TERs), including those involved in cytoplasmic pH homeostasis, compatible solute accumulation, cell envelope modification and energy requisition. By integrating these significant-TER traits, we further developed an environmental adaptation index to quantify the species-level adaptive capacity for environmental stresses, such as high pH environments. The adaptation index of pH showed consistently significant positive relationships with species pH optima across regional and global genomic datasets from freshwater, marine and soda lake ecosystems. The generality of the index for quantifying environmental adaptation was demonstrated by showing significant relationships with the species niche optima for the gradients of soil temperature and seawater salinity. These results highlight the importance of trait-environment relationships in facilitating the inference of microbial genomic-based adaptation mechanisms, and expand our understanding of ecological adaptative strategies along environmental gradients.

ecology↗

bioRxiv2024

Comprehensive mapping of the Interaction of levodopa and iron metabolism in Parkinson's disease

Levodopa remains the primary treatment for Parkinsons disease (PD), yet its long-term use has been associated with iron accumulation in the brain, a phenomenon linked to neurodegeneration. We utilize deep machine learning to determine plausible molecular mechanisms that may underlie the effects of levodopa on iron metabolism. Using the DRIFT platform, we performed a proteome-wide target identification of levodopa and uncovered significant interactions potentially involved in cellular iron transport. Pathway analysis revealed that levodopa may influence critical iron-related pathways, including the response of EIF2AK1 to heme deficiency, heme signaling, and ABC-family protein-mediated transport. These findings suggest that levodopa may contribute to iron dysregulation in PD by interacting with iron transporters and modulating iron-related pathways. Because levodopa is used at relatively high doses in PD, our findings provide new insight into secondary effects unrelated to being a precursor of dopamine. This highlights the need for careful consideration of its effects on iron metabolism as a consequence of use in the long-term management of PD. Further experimental validation is required to confirm these interactions, and also to explore potential strategies to mitigate iron-related side effects while preserving therapeutic efficacy.

bioinformatics↗

bioRxiv2024

RIPK1 is essential for Herpes Simplex Virus-triggered ZBP1-dependent necroptosis in human cells

Necroptosis initiated by the host sensor Z-NA Binding Protein-1 (ZBP1) is essential for host defense against a growing number of viruses, including Herpes Simplex Virus-1 (HSV-1). Studies with HSV-1 and other necroptogenic stimuli in murine settings have suggested that ZBP1 triggers necroptosis by directly complexing with the kinase RIPK3. Whether this is also the case in human cells, or whether additional co-factors are needed for ZBP1-mediated necroptosis, is unclear. Here, we show that ZBP1-induced necroptosis in human cells requires RIPK1. We have found that RIPK1 is essential for forming a stable and functional ZBP1-RIPK3 complex in human cells, but is dispensable for the formation of the equivalent murine complex. The RIP Homology Interaction Motif (RHIM) in RIPK3 is responsible for this difference between the two species, because replacing the RHIM in human RIPK3 with the RHIM from murine RIPK3 is sufficient to overcome the requirement for RIPK1 in human cells. These observations describe a critical mechanistic difference between mice and humans in how ZBP1 engages in necroptosis, with important implications for treating human diseases.

cell biology↗