Biology subjects

Huo, D.

Publications and source records attributed to Huo, D..

14 recordsLinked to original sources

bioRxiv2025

Spatially Organized IGF1 mTOR Signaling Controls Human Forebrain Progenitor Fate Through Coordinated Transcriptional and Translational Programs

The specification and maintenance of human forebrain neural progenitor cells (NPCs) depend on both intrinsic gene networks and spatially localized niche signals, but the interplay between these cues remains incompletely understood. Here, we identify a spatially organized, paracrine IGF1 signaling architecture that regulates human FOXG1 NPCs through multilayered transcriptional and translational control. Using a pluripotent stem cell-derived forebrain model, we show that FOXG1 NPCs express IGF1 receptors but lack endogenous IGF1, instead depending on neighboring epithelial-like domains that secrete IGF1. IGF1 promotes progenitor proliferation, clonal expansion, and vertical tissue growth by activating PI3K-AKT-mTOR and MEK-ERK pathways. Ribosome profiling and 5'UTR reporter assays reveal that mTOR signaling selectively enhances translation of neurodevelopmental and biosynthetic transcripts-- including GSX1, a ventral fate determinant implicated in interneuron specification and autism. These findings uncover a human-specific regulatory mechanism in which spatially restricted IGF1-mTOR signaling integrates niche signals with translational output to support progenitor identity, biosynthetic capacity, and developmental resilience.

neuroscience↗

bioRxiv2025

APOE genotype confers context dependent neurovascular vulnerability in immune-vascularized human forebrain organoids

The APOE gene is a major genetic determinant of neurovascular and immune function, yet the mechanisms by which its isoforms modulate brain vulnerability to pathogenic stress remain incompletely understood. Here, we employ isogenic human iPSC-derived immune-vascularized--Forebrain Organoid-based Multicellular Assembled Cerebral Organoids (FORMA-COs)--to dissect isoform-specific responses to a clinically relevant viral challenge. We find that APOE2/2 and APOE4/4 FORMA-COs exhibit heightened viral RNA burden and distinct neuroinflammatory profiles compared to APOE3/3. Specifically, APOE4/4 promotes IL-1 and VEGFA induction, whereas APOE2/2 leads to elevated TNF-{beta} and VEGFA protein accumulation, indicating divergent pathways of injury. Integrated transcriptomic analyses, combined with known and predicted APOE protein-protein interaction networks, reveal genotype-dependent enrichment of cytokine signaling, angiogenic remodeling, and immune dysregulation. In vivo validation using humanized mouse models corroborates APOE genotype- specific vascular remodeling, microglial activation, and oligodendrocyte perturbation. These findings demonstrate that APOE genotype confers context-specific susceptibility to neuroimmune and vascular injury, providing insight into genetic risk mechanisms underlying infection-related and neurodegenerative brain disorders.

neuroscience↗

bioRxiv2024

Comparative genomic analysis reveals reduced pathogenicity of Ralstonia spp. in water

Ralstonia spp., known for their adaptability across various habitats, are known to cause infections. The adaptive metabolic diversity of those inhabiting aquatic ecosystems remains poorly understood. We report four new Ralstonia pickettii genomes enriched in the cyanobacterial culture derived from bloom-forming cyanobacteria, Dolichospermum. A total of 228 complete genomes from the Ralstonia genus were utilized for phylogenetic inference, categorizing them based on isolation environment and host: water, soil, plant, and human groups. Meanwhile, the abundance of carbohydrate-active enzymes and secondary metabolites in water and human groups differed from the plant-host associate habitat. CeoB and two {beta}-lactamases types of OXA were identified in the water habitat, showing similarities to certain strains in the human-host but differences from other habitats. The infectivity within water habitats seems to diminish, as evidenced by the decreased abundance of T3SS virulence proteins. Moreover, a distinctive pyrimidine degradation pathway in water degrades exogenous pyrimidines to supply nitrogen and other compounds for energy metabolism to provide a potential for broader habitat adaptability. Fluorescence in situ hybridization results confirmed that R. pickettii rarely attached to cyanobacterial cells, indicating that they are not parasitic relationships. We postulate that the absence of T3SS and the unique metabolic profile represent adaptations of Ralstonia to an aquatic free-living lifestyle.

microbiology↗

bioRxiv2024

Temporal Dynamics and Adaptive Mechanisms of Microbial Communities: Divergent Responses and Network Interactions

Microbial communities are integral to aquatic ecosystems, playing key roles in biogeochemical cycles, nutrient recycling, and ecosystem functioning. Despite their importance, the temporal dynamics of these communities, especially in response to short-term environmental changes, remain inadequately understood. This study utilizes high-throughput amplicon sequencing and Co-occurrence network analysis to elucidate how prokaryotic and eukaryotic microbial communities dynamically respond to short-term environmental fluctuations, and dynamics of networks. Our results reveal distinct temporal patterns, with eukaryotic communities showing consistent increases in diversity, while prokaryotic communities exhibited more pronounced fluctuations and directional changes. Co-occurrence network analysis further highlighted the differences in community structure between phases of homogenization and heterogenization. Specifically, we observed a shift from a more modular network during the homogenization phase to a more centralized network in the heterogenization phase, resulting in increased network fragility. Environmental factors, such as nutrient levels and climatic conditions, were found to play pivotal roles in shaping these dynamics. This study provides critical insights into the resilience and adaptability of microbial communities, emphasizing the intricate interplay between biodiversity, network structure, and ecosystem stability under varying environmental conditions.

microbiology↗

bioRxiv2023

Sequence-based drug design using transformers

Protein-ligand interactions play central roles in biological processes and are of key importance in drug design. Deep learning-based approaches are emerging as cost-effective alternatives to high-throughput experimental methods for the screening of large libraries of ligands. Here, to predict the binding affinity between proteins and small molecules, we introduce Ligand-Transformer, a deep learning framework based on the AlphaFold2 transformer architecture. We applied Ligand-Transformer to screen inhibitors targeting the mutant EGFRLTC kinase, identifying compounds with low nanomolar potency. We then used this approach to predict the conformational population shifts induced by ABL kinase inhibitors. To show the applicability of Ligand-Transformer to disordered proteins, we explored the binding of small molecules to the Alzheimers A{beta} peptide, identifying compounds that delayed its aggregation. Overall, Ligand-Transformer illustrates the potential of transformers in accurately predicting the interactions of small molecules with both ordered and disordered proteins, thus uncovering molecular mechanisms and facilitating the initial steps in drug discovery.

bioinformatics↗

bioRxiv2022

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.833 versus 0.765 in an external validation cohort, p = 0.003), and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

cancer biology↗

bioRxiv2022

Aridity modulates biogeographic distribution and community assembly of cyanobacterial morphotypes in drylands

The patterns of biogeographical distribution and assembly processes of microbiota are of vital importance for understanding ecological adaptation and functioning maintenance that microorganisms provide. Although the assembly of microbial communities was increasingly inspected, the role of their morphological characteristics is still poorly ascertained. Here, by integrating high-throughput sequencing and traditional trait-based classification, we investigated taxonomic and phylogenetic turnovers of different morphotypes of terrestrial cyanobacteria in biocrusts, as a model system, to evaluate the contributions of deterministic and stochastic processes across a large scale in drylands. The results showed that the bundle-forming category dominated in arid ecosystems with high tolerance against environmental fluctuations. Despite strong distance-decay relationships of {beta}-diversity in all categories, both spatial composition and phylogenetic turnover rates of bundle-forming cyanobacteria were significantly lower than unicellular/colonial, heterocystous, and other non-heterocyst filamentous cyanobacteria. Null model analysis of phylogenetic signals and abundance-based neutral model found that stochastic processes prevailed in the assembly, while aridity mediated the balance between determinism and stochasticity and prompted a shifting threshold among morphotypes. Our findings provide a unique perspective to understand the role of microbial morphology, highlighting the differentiation of biogeographic distribution, environmental response, and species preference between morphotypes with consideration of potential functional consequences, and therefore facilitated the prediction of biodiversity loss under climate change.

ecology↗

bioRxiv2020

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. This site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the digital image characteristics constituting this histologic batch effect. As an example, we show that patient ethnicity within the TCGA breast cancer cohort can be inferred from histology due to site-level batch effect, which must be accounted for to ensure equitable application of DL. Batch effect also leads to overoptimistic estimates of model performance, and we propose a quadratic programming method to guide validation that abrogates this bias.

bioinformatics↗

bioRxiv2020

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

Black women of African ancestry experience more aggressive breast cancer with higher mortality rates than White women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast tumors, with RNA-seq in a subset, from indigenous African patients in Nigeria in comparison to The Cancer Genome Atlas (n=76) revealed a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations and a 10.5-year younger age at diagnosis. We also found evidence for non-coding mutations in two novel drivers (ZNF217 and SYPL1) and a novel INDEL signature strongly associated with African ancestry proportion. This comprehensive analysis of an understudied population underscores the need to incorporate diversity of genomes as a key parameter in fundamental research with potential to tailor clinical intervention and promote equity in precision oncology care.

genomics↗

bioRxiv2020

Generation and Validation of Versatile Inducible CRISPRi Embryonic Stem Cell and Mouse Models

CRISPR-Cas9 has been widely used far beyond genome editing. Fusions of deactivated Cas9 (dCas9) to transcription effectors enable interrogation of the epigenome and controlling of gene expression. However the large transgene size of dCas9-fusion hinders its applications especially in somatic tissues. Here, we develop a robust CRISPR interference (CRISPRi) system by transgenic expression of doxycycline (Dox) inducible dCas9-KRAB in mouse embryonic stem cells (iKRAB ESC). After introduction of specific gRNAs, the induced dCas9-KRAB efficiently maintains gene inactivation, though it exerts modest effects on active gene expression. Proper timing of Dox addition during cell differentiation or reprogramming allows us to study or screen spatiotemporally activated promoters or enhancers and thereby the gene functions. Furthermore, taking the ESC for blastocyst injection, we generate an iKRAB knockin (KI) mouse model that enables shut-down of gene expression and loss-of-function studies ex vivo and in vivo by a simple transduction of gRNAs. Thus, our inducible CRISPRi ESC line and KI mouse provide versatile and convenient platforms for functional interrogation and high-throughput screens of specific genes and potential regulatory elements in the setting of development or diseases.

genetics↗

bioRxiv2019

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

Anesthesia is an important part in modern surgery, and the way how to effectively monitor the depth of anesthesia (DOA) is core issue in the anesthesia work. Since anesthetics mainly affected the brain of patients, it is very effective to monitor DOA by electroencephalogram (EEG). This paper proposes a method for monitoring DOA using EEG. First, the sample entropy (SampEn) of EEG were calculated as a feature vector. Simultaneously, the Burg recursive algorithm was used to solve the autoregressive model (AR model) and AR coefficients were extracted as feature vectors. Later, according to the characteristics of uneven distribution of sample points, the weighted k-nearest neighbor (WKNN) classifier was selected. The Anesthesia was divided into awake, mild, moderate and deep by WKNN classifier. According to the results, the correlation coefficient between the SampEn of the EEG and Bispectral Index (BIS) is above 0.8. There is a correlation between the first five orders of AR coefficient and the BIS index, and the correlation of the second order reaches 0.8. Through the validation of 30 patients, this method can assessment of DOA effectively and accurately.

bioengineering↗

bioRxiv2018

The association between weight at birth and breast cancer risk revisited using Mendelian randomisation

Observational studies suggest that higher birth weight (BW) is associated with increased risk of breast cancer in adult life. We conducted a two-sample Mendelian randomisation (MR) study to assess whether this association is causal. Sixty independent single nucleotide polymorphisms (SNPs) known to be associated at P < 5 x 10-8 with BW were used to construct (1) a 41-SNP instrumental variable (IV) for univariable MR after removing SNPs with pleiotropic associations with other breast cancer risk factors and (2) a 49-SNP IV for multivariable MR after filtering SNPs for data availability. BW predicted by the 41-SNP IV was not associated with overall breast cancer risk in inverse-variance weighted (IVW) univariable MR analysis of genetic association data from 122,977 breast cancer cases and 105,974 controls (odds ratio = 0.86 per 500 g higher BW; 95% confidence interval: 0.73--1.01). Sensitivity analyses using four alternative methods and three alternative IVs, including an IV with 59 of the 60 BW-associated SNPs, yielded similar results. Multivariable MR adjusting for the effects of the 49-SNP IV on birth length, adult height, adult body mass index, age at menarche, and age at menopause using IVW and MR-Egger methods provided estimates consistent with univariable analyses. Results were also similar when all analyses were repeated after restricting to estrogen receptor-positive or -negative breast cancer cases. Point estimates of the odds ratios from most analyses performed indicated an inverse relationship between genetically-predicted BW and breast cancer. Thus, there is little evidence from MR to suggest that the previously observed association between higher BW and increased risk of breast cancer in adult life is causal.

epidemiology↗

bioRxiv2018

The association between low-density lipoprotein cholesterol predicted by HMGCR genetic variants and breast cancer risk may be mediated by body mass index

Orho-Melander et al. recently reported that lower low-density lipoprotein cholesterol (LDLC) as predicted by the T-allele of the variant rs12916 in HMGCR is associated with a decreased risk of developing breast cancer [odds ratio (OR) = 0.89; 95% confidence interval (CI): 0.82-0.96].1 This analysis was embedded in a wider Mendelian randomization (MR) study performed using genotype data from a prospective cohort of 26,589 individuals that included 16,022 women and 1176 incident breast cancer cases. HMGCR encodes 3-hydroxy-3-methylglutaryl-coenzyme A reductase, the enzyme inhibited by statins. The T-allele of rs12916 is associated with reduced HMGCR expression and therefore, in principle, its effects should be analogous to the effects of lifelong statin administration starting at birth.2 The MR study of Orho-Melander et al. also found that a genome-wide LDLC score based on 32 independent LDLC-associated single nucleotide polymorphisms (SNPs) was not associated with breast cancer. In light of this finding, they suggest that the protective effect of the rs12916 T-allele on breast cancer may either be specific to LDLC lowering via genetic inhibition of HMGCR or be the result of a distinct mechanism that is regulated by rs12916 and HMGCR.

epidemiology↗

bioRxiv2018

Identification of Novel Common Breast Cancer Risk Variants in Latinas at the 6q25 Locus

Background: Breast cancer is a partially heritable trait and over 180 common genetic variants have been associated with breast cancer in genome wide association studies (GWAS). We have previously performed breast cancer GWAS in Latinas and identified a strongly protective single nucleotide polymorphism (SNP) at 6q25 with the protective minor allele originating from Indigenous American ancestry. Here we report on additional GWAS and replication in Latinas.\n\nMethods: We performed GWAS in 2385 cases and 7342 controls who were either U.S. Latinas or Mexican women. We replicated 2412 cases and 1620 controls of U.S Latina, Mexican, and Colombian women. In addition, we replicated the top novel variants in study of African American and African women and in one study of Chinese women. In each dataset we used logistic regression models to test the association between SNPs and breast cancer risk and corrected for genetic ancestry using either principal components or genetic ancestry inferred from ancestry informative markers using a model based approach.\n\nResults: We identified 3 SNPs (p=1.9x10-8 - 2.8x10-8) at 6q25 locus not in linkage disequilibrium (LD) with variants previously reported at this locus. These SNPs were in high LD with each other, with the top SNP, rs3778609, associated with breast cancer with an odds ratio (OR) and 95% confidence interval (95% CI) of 0.75 (0.68-0.83). In a replication in women of Latin American origin, we also observed a consistent effect (OR: 0.88; 95% CI: 0.78-0.99; p=0.037). Since the minor allele was common in East Asians and African American but not European ancestry populations, we replicated in a meta-analysis of those populations and also observed a consistent effect (OR 0.94; 95% CI: 0.91 - 0.97; p=0.013).\n\nConclusion: The effect size of this variant is relatively large compared to other common variants associated with breast cancer and adds to evidence about the importance of the 6q25 locus for breast cancer susceptibility. Our finding also highlights the utility of performing additional searches for genetic variants for breast cancer in non-European populations.

genetics↗