Biology subjects

Jiang, H.

Publications and source records attributed to Jiang, H..

At least 145 records · Page 8Linked to original sources

bioRxiv2024

Nuclear FAK aggravates CD8+ T cell exhaustion via the SP1-IL-6 axis in colorectal cancer

Nuclear abnormalities such as nuclear deformation are hallmarks of many diseases, including cancer. Accumulating evidence suggests that the dense and mechanically stiff tumor microenvironment promotes nuclear deformation in cancer cells. However, little is known about how nuclear deformation in neoplastic cells regulates immune exhaustion in the tumor microenvironment. Here, we found that lamin A/C-mediated nuclear stiffening in neoplastic cells promotes the nuclear translocation of phosphorylated focal adhesion kinase (pFAK), which is strongly correlated with the heterogeneity and exhaustion of CD8+ T cells within the spatial context of the tumor microenvironment in human colorectal cancer. Mechanistically, we revealed that increased nuclear tension within tumor cells promotes pFAK nuclear translocation, where nuclear pFAK was found to regulate SP1/IL-6-mediated T-cell exhaustion and the transcription of proinflammatory cytokines/chemokines. Pharmacological inhibition or disruption of pFAK nuclear translocation enhanced antitumor immune responses and synergistically potentiated PD-1 and TIM-3 immunotherapy by increasing CD8+ T-cell cytotoxicity and restoring exhaustion in preclinical models of colorectal cancer. These findings highlight the pivotal role of nuclear tension-mediated pFAK translocation into the tumor cell nucleus in regulating CD8+ T-cell exhaustion, suggesting that pFAK is a promising target for advancing cancer immunotherapy.

immunology↗

bioRxiv2024

Membranous translation platforms in the chloroplast of Chlamydomonas reinhardtii

A small genome in chloroplasts encodes polypeptide subunits of the photosynthetic electron transport complexes in the membranes of thylakoid vesicles in the chloroplast stroma. Trans-membrane subunits of these complexes undergo co-translational membrane insertion during their synthesis by ribosomes of the bacterial-like genetic system of this semiautonomous organelle. While thylakoid membranes are sites of translation, evidence in the unicellular alga Chlamydomonas reinhardtii supports translation also on non-canonical membranes in a discrete translation-zone in the chloroplast. To characterize the membranous platforms for translation and the biogenesis of thylakoid membrane, we profiled membranes during chloroplast development, using the yellow-in-the-dark 1 mutant, and carried out proteomic analyses on membranes of interest. The results support roles of two membrane types in preliminary and ongoing stages of translation: a "low-density membrane" and a denser "chloroplast translation membrane", respectively. These roles are based on correlations of the relative levels of each membrane type and the translational status of the chloroplast before, during and after chloroplast differentiation and results of proteomic analyses. Our results support a model of photosynthesis complex biogenesis in a spatiotemporal "assembly line" involving LDM and CTM as sequential stages leading to photosynthetic thylakoid membranes.

cell biology↗

bioRxiv2024

BOOST: a robust ten-fold expansion method on hour-scale

Expansion microscopy (ExM) enhances the microscopy resolution by physically expanding biological specimens and improves the visualization of structural and molecular details. Numerous ExM techniques and labeling methods have been developed and refined over the past decade to cater to specific research needs. Nonetheless, a shared limitation among current protocols is the extensive time required for sample processing, particularly for challenging-to-expand biological specimens (e.g., formalin-fixed paraffin-embedded (FFPE) sections and large three-dimensional specimens). Here, we have developed a rapid and robust ExM workflow named BOOST, which leverages a series of novel microwave (MW)-accelerated ExM chemistry, resulting in a single-step linear expansion of [~]10x. Specifically, BOOST facilitates a [~]10-fold expansion of cultured cells, tissue sections, and even the challenging-to-expand FFPE sections under merely 90 minutes with heat and surfactant-based protein denaturation. Furthermore, BOOST employs microwave-assisted proteomic staining and immunostaining to facilitate high-resolution visualization of structural and molecular details with significantly enhanced throughput. Noteworthily, BOOST has pioneered a [~]10-fold expansion of large millimeter-sized three-dimensional specimens in approximately three hours. BOOST offers an easily adaptable workflow based on stable and common reagents, thus boosting the potential adoption of ExM methods in biological investigations.

bioengineering↗

bioRxiv2024

Sequence-specific targeting of intrinsically disordered protein regions

Intrinsically disordered proteins and peptides play key roles in biology, but the lack of defined structures and the high variability in sequence and conformational preferences has made targeting such systems challenging. We describe a general approach for designing proteins that bind intrinsically disordered protein regions in diverse extended conformations with side chains fitting into complementary binding pockets. We used the approach to design binders for 39 highly diverse unstructured targets and obtain designs with pM to 100 nM affinities in 34 cases, testing [~]22 designs per target (including polar targets). The designs function in cells and as detection reagents, and are specific for their intended targets in all-by-all binding experiments. Our approach is a major step towards a general solution to the intrinsically disordered protein and peptide recognition problem.

biochemistry↗

bioRxiv2024

IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation

As an emerging class of RNA molecules, circular RNAs play pivotal roles in various biological processes, thereby determining their three-dimensional (3D) structure is crucial for a deep understanding of their biological significances. Similar to linear RNAs, the development of computational methods for circular RNA 3D structure prediction is challenging, especially considering the inherent flexibility and potentially long length of circular RNAs. Here, we introduce an extension of our previous IsRNA2 model, named IsRNAcirc, to enable circular RNA 3D structure predictions through coarse-grained molecular dynamics simulations. The workflow of IsRNAcirc consists of four main steps, including input preparation, end closure, structure prediction, and model refinement. Our results demonstrate that IsRNAcirc can provide reasonable 3D structure predictions for circular RNAs, which significantly reduce the locally irrational elements contained in the initial input. Moreover, for a validation test set comprising 34 circular RNAs, our IsRNAcirc can generate 3D models with better scores than the template-based 3dRNA method. These findings demonstrate that our IsRNAcirc method is a promising tool to explore the structural details along with intricate interactions of circular RNAs. Author SummaryKnowledge of the 3D structure of circular RNA molecules can help us better understand their roles in eukaryotic cells. However, experimental determination of circular RNA 3D structure remains challenging, with currently only one complete circular RNA 3D structure available in the Protein Data Bank. Thus, extending from their linear counterparts, computational methods that can predict circular RNA 3D structure are urgently needed. In particular, physics-based de novo 3D structure prediction methods are promising due to their powerful sampling capability in the conformational space, which avoid the problem of limited available templates in template-based approaches. We have developed IsRNAcirc, a new physics-based method that employs an accurate coarse-grained force field and replica-exchange molecular dynamics simulations to predict the 3D structure of circular RNAs. IsRNAcirc is shown to significantly reduce the number of locally irrational structures in the input trials, which are caused by the lack of suitable templates and the improper assembly of fragments. Furthermore, in the absence of experimental reference structures, the predicted 3D models were validated using the popular scoring functions, such as DFIRE-RNA and rsRNASP, demonstrating that IsRNAcirc outperforms the template-based 3dRNA method in generating low-energy conformations.

biophysics↗

bioRxiv2024

MetaGXplore: Integrating Multi-Omics Data with Graph Convolutional Networks for Pan-cancer Patient Metastasis Identification

The spread of cancer cells from the primary tumor to distant anatomical sites, known as tumor metastasis, poses a significant challenge in clinical prognosis, impairing treatment efficacy and reducing patient survival time. Current methods for predicting and diagnosing tumor metastasis rely heavily on extensive examinations and subjective clinical judgments. Accurate and rapid prediction of tumor metastasis likelihood remains an unresolved challenge, crucial for guiding effective clinical interventions and extending patient survival. Additionally, identifying key genes highly associated with metastasis probability is a pressing issue, essential for providing valuable insights into the potential identification of tumor metastasis-specific biomarkers. We developed MetaGXplore, a pioneering Graph Convolutional Neural Network (GCN)-based framework designed to predict metastasis probability by integrating pan-cancer multi-omic datasets with a protein-protein interaction network, while also identifying key genes involved in the metastatic process. Multi-omics datasets offer a comprehensive view of cancer biology, enhancing accuracy in metastasis forecasting through superior deep learning algorithms. Our classification model results were interpreted with GNNExplainer. The efficacy of MetaGXplore was validated via model evaluations, graph structure analysis, and multi-omics data assessment. Enrichment analysis of key genes further explored their biological roles.

bioinformatics↗

bioRxiv2024

Inhibition of serine racemase prevents diabetic retinopathy

Retinal diabetic neuropathy (RDN) precedes retinal microvascular pathology in diabetic retinopathy (DR), but the therapy by antagonizing RDN to stall DR is rare. Emerging evidence including ours suggest that serine racemase (SRR) drives DR at least by promoting RDN. Thus, we explore the therapy by antagonizing SRR to stall and treat DR. We herein indicate that inhibition of SRR by oral gavage of l-aspartic acid {beta}-hydroxymate (L-ABH), a competitive inhibitor of SRR, mitigated photoreceptor dysfunction, loss of retinal ganglion cells (RGC) and retinal endothelial cell and pericyte in db/db mice, a type II diabetes model. To dissect the mechanism, intravitreal injection of L-ABH mitigates glutamate-induced neurotoxicity in the retina. In the whole body, gastrointestinal-based delivery of L-ABH maintained euglycemia and improved glucose tolerance either in db/db mice or in diet-induced obesity mice. In conclusion, inhibition of SRR prevented retinal neurovascular abnormalities in diabetic animals through executing neuroprotection in the retina and maintaining glucose homeostasis in the system. Our study reveals a novel strategy to prevent DR.

biochemistry↗

bioRxiv2024

RNA interactome of hypervirulent Klebsiella pneumoniae reveals a small RNA inhibitor of capsular mucoviscosity and virulence

Hypervirulent Klebsiella pneumoniae (HvKP) is an emerging bacterial pathogen causing invasive infection in immune-competent humans. The hypervirulence is strongly linked to the overproduction of hypermucovisous capsule, but the underlining regulatory mechanism of hypermucoviscosity (HMV) has been elusive, especially at the post-transcriptional level mediated by small noncoding RNAs (sRNAs). Using a recently developed RNA interactome profiling approach, we interrogate the Hfq-associated sRNA regulatory network and establish the intracellular RNA-RNA interactome in HvKP. Our data reveal numerous interactions between sRNAs and HMV-related mRNAs, and identify a plethora of sRNAs that repress or promote HMV. One of the strongest repressors of HMV is ArcZ, which is activated by catabolite regulator CRP and targets many HMV-related genes including mlaA and fbp. We discover that MlaA and its function in phospholipid transport is crucial for capsule retention and HMV, inactivation of which abolished Klebsiella virulence in mice. ArcZ overexpression significantly reduced bacterial burden in mice and reduced HMV in multiple carbapenem-resistant and hypervirulent clinical isolates with diverse genetic background, indicating it is a potent RNA inhibitor of bacterial pneumonia with therapeutic potential. In summary, our work unravels a comprehensive map of the RNA-RNA interaction network of HvKP and identifies a novel CRP-ArcZ-MlaA regulatory circuit of HMV, providing mechanistic insights into the posttranscriptional virulence control in a superbug of global concern. HIGHLIGHTSO_LIGlobal RNA-RNA interactome map in hypervirulent Klebsiella pneumoniae C_LIO_LIHfq and multiple small RNAs regulate capsular hypermucoviscosity C_LIO_LIArcZ targets mlaA that is required for lipid transport, hypermucoviscosity and virulence C_LIO_LICrp is a transcriptional activator of ArcZ governing a new virulence circuit C_LI

microbiology↗

bioRxiv2024

Inducible biosynthesis of bacterial cellulose in recombinant Enterobacter sp. FY-07

Bacterial cellulose (BC) is an extracellular polysaccharide with myriad unique properties, such as high purity, water-holding capacity and biocompatibility, making it attractive in materials science. However, genetic engineering techniques for BC-producing microorganisms are rare. Herein, the electroporation-based gene transformation and the {lambda} Red-mediated gene knockout method with a nearly 100% recombination efficiency were established in the fast-growing and BC hyperproducer Enterobacter sp. FY-07. This genetic manipulation toolkit was validated by inactivating the protein subunit BcsA in the cellulose synthase complex. Subsequently, the inducible BC-producing strains from glycerol were constructed through inducible expression of the key gene fbp in the gluconeogenesis pathway, which recovered more than 80% of the BC production. Finally, the BC properties analysis results indicated that the induced-synthesized BC pellicles were looser, more porous and reduced crystallinity, which could further broaden the application prospects of BC. To our best knowledge, this is the first attempt to construct the completely inducible BC-producing strains. Our work paves the way for increasing BC productivity by metabolic engineering and broadens the available fabrication methods for BC-based advanced functional materials.

synthetic biology↗

bioRxiv2024

RBET guides case-specific choice of batch correction methods for single-cell transcriptomic data with overcorrection awareness and large batch effect robustness

Integrating single-cell RNA-sequencing datasets from different sources is a common practice to empower in-depth interrogation for biological insights, where batch effect correction (BEC) is of vital importance. However, an inappropriate BEC may lead to overcorrection and report misleading results on downstream analyses including cell annotation, trajectory inference and cell-cell communication. Hence, we develop the Reference-based Batch Effect Testing (RBET), a novel statistical framework for evaluating the performance of different BEC methods by leveraging housekeeping-gene inspired reference genes and MAC statistics for distribution comparison. Comparing with existing methods, RBET is more powerful on detecting batch effect, overcorrection sensitive, computationally efficient, and robust to large batch effect sizes. Furthermore, extensive multi-scenario real examples show that RBET selects optimal BEC tools for consistent downstream analysis results, which confirm prior biological knowledge. This comprehensive BEC decision-making tool is available as an R package.

bioinformatics↗

bioRxiv2024

ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models

Microbiome differential abundance analysis remains a challenging problem despite multiple methods proposed in the literature. The excessive zeros and compositionality of metagenomics data are two main challenges for differential abundance analysis. We propose a novel method called "analysis of differential abundance by pooling Tobit models" (ADAPT) to overcome these two challenges. ADAPT uniquely treats zero counts as left-censored observations to facilitate computation and enhance interpretation. ADAPT also encompasses a theoretically justified way of selecting non-differentially abundant microbiome taxa as a reference for hypothesis testing. We generate synthetic data using independent simulation frameworks to show that ADAPT has more consistent false discovery rate control and higher statistical power than competitors. We use ADAPT to analyze 16S rRNA sequencing of saliva samples and shotgun metagenomics sequencing of plaque samples collected from infants in the COHRA2 study. The results provide novel insights into the association between the oral microbiome and early childhood dental caries.

bioinformatics↗

bioRxiv2024

Asymmetric Engagement of Dimeric CRL3KBTBD4 by the Molecular Glue UM171 Licenses Degradation of HDAC1/2 Complexes

UM171 is a potent small molecule agonist of ex vivo human hematopoietic stem cell (HSC) self-renewal1, a process that is tightly controlled by epigenetic regulation. By co-opting KBTBD4, a substrate receptor of the CULLIN3-RING E3 ubiquitin ligase complex, UM171 promotes the degradation of members of the CoREST transcriptional corepressor complex, thereby limiting HSC attrition2,3. However, the direct target and mechanism of action of UM171 remain unclear. Here, we reveal that UM171 acts as a molecular glue to induce high-affinity interactions between KBTBD4 and HDAC1 to promote the degradation of select HDAC1/2 corepressor complexes. Through proteomics and chemical inhibitor studies, we discover that the principal target of UM171 is HDAC1/2. Cryo-electron microscopy (cryo-EM) analysis of dimeric KBTBD4 bound to UM171 and the LSD1-HDAC1-CoREST complex unveils an unexpected asymmetric assembly, in which a single UM171 molecule enables a pair of KBTBD4 KELCH-repeat propeller domains to recruit HDAC1 by clamping on its catalytic domain. One of the KBTBD4 propellers partially masks the rim of the HDAC1 active site pocket, which is exploited by UM171 to extend the E3-neo-substrate interface. The other propeller cooperatively strengthens HDAC1 binding via a separate and distinct interface. The overall neomorphic interaction is further buttressed by an endogenous cofactor of HDAC1-CoREST, inositol hexakisphosphate, which makes direct contacts with KBTBD4 and acts as a second molecular glue. The functional relevance of the quaternary complex interaction surfaces defined by cryo-EM is demonstrated by in situ base editor scanning of KBTBD4 and HDAC1. By delineating the direct target of UM171 and its mechanism of action, our results reveal how the cooperativity offered by a large dimeric CRL E3 family can be leveraged by a small molecule degrader and establish for the first time a dual molecular glue paradigm.

molecular biology↗

bioRxiv2024

KBTBD4 Cancer Hotspot Mutations Drive Neomorphic Degradation of HDAC1/2 Corepressor Complexes

Cancer mutations can create neomorphic protein-protein interactions to drive aberrant function1. As a substrate receptor of the CULLIN3-RBX1 E3 ubiquitin ligase complex, KBTBD4 is recurrently mutated in medulloblastoma (MB)2, the most common embryonal brain tumor in children, and pineoblastoma3. These mutations impart gain-of-function to KBTBD4 to induce aberrant degradation of the transcriptional corepressor CoREST4. However, their mechanism of action remains unresolved. Here, we elucidate the mechanistic basis by which KBTBD4 mutations promote CoREST degradation through engaging HDAC1/2, the direct neomorphic target of the substrate receptor. Using deep mutational scanning, we systematically map the mutational landscape of the KBTBD4 cancer hotspot, revealing distinct preferences by which insertions and substitutions can promote gain-of-function and the critical residues involved in the hotspot interaction. Cryo-electron microscopy (cryo-EM) analysis of two distinct KBTBD4 cancer mutants bound to LSD1-HDAC1-CoREST reveals that a KBTBD4 homodimer asymmetrically engages HDAC1 with two KELCH-repeat propeller domains. The interface between HDAC1 and one of the KBTBD4 propellers is stabilized by the MB mutations, which directly insert a bulky side chain into the active site pocket of HDAC1. Our structural and mutational analyses inform how this hotspot E3-neo-substrate interface can be chemically modulated. First, our results unveil a converging shape complementarity-based mechanism between gain-of-function E3 mutations and a molecular glue degrader, UM171. Second, we demonstrate that HDAC1/2 inhibitors can block the mutant KBTBD4-HDAC1 interface, the aberrant degradation of CoREST, and the growth of KBTBD4-mutant MB models. Altogether, our work reveals the structural and mechanistic basis of cancer mutation-driven neomorphic protein-protein interactions and pharmacological strategies to modulate their action for therapeutic applications.

molecular biology↗

bioRxiv2024

FKBP11 targeted plasma cells promote abdominal aortic aneurysm progression through an m6A-dependent mechanism

ObjectiveDespite surgical advance, effective targeted drugs for non-surgical treatment of abdominal aortic aneurysm (AAA) are lacking because of the unclear pathogenesis of AAA. N6-methyladenosine (m6A) methylation, acknowledged for its pivotal influence on RNA metabolism, including aspects such as stability, transport, translation, and splicing, is largely implied for its role in AAA mechanism. This study aims to elucidate the involvement of m6A methylation in the progression of AAA through an integrative multi-omics and machine learning approach. Methods and ResultsWe utilized methylated RNA immunoprecipitation sequencing (MeRIP-seq) to map the m6A methylation landscape in AAA tissues and combined this with RNA sequencing (RNA-seq) from the GEO database, to explore the interplay between m6A methylation and gene expression. A machine learning-based AAA m6A-related mRNA signature (AMRMS) was developed to predict the risk of AAA dilation. The AMRMS showed robust predictive power in distinguishing between patients with large and small AAAs. Notably, FKBP11 was identified as a key gene significantly influencing the predictive model, and up-regulated in large AAAs compared to its in small AAAs. Further single-cell RNA sequencing (scRNA-seq) and histological analysis highlighted the critical role of FKBP11 in mediating the endoplasmic reticulum stress of plasma cells within the AAA walls and its correlation with m6A methylation. ConclusionsThe m6A modification regulatory network plays a vital role in the progression of AAA, and the AMRMS offers promising potential in assessing the risk of AAA dilation. Our findings suggest that elevated FKBP11, by activating endoplasmic reticulum stress in plasma cells, may significantly contribute to AAA expansion.

cell biology↗

bioRxiv2024

Frequency-dependence in multidimensional diffusion-relaxation correlation MRI of the brain: overfitting or meaningful parameter?

Time- or frequency-dependent ("restricted") diffusion potentially provides useful information about cellular-scale structures in the brain but is challenging to interpret because of the intravoxel tissue heterogeneity. Frequency-dependence was recently incorporated in a multidimensional diffusion-relaxation correlation MRI framework relying on tensor-valued diffusion encoding at multiple frequencies, as well as variable echo and repetition times, to give model-free characterization of intravoxel heterogeneity by Monte Carlo data inversion into nonparametric distributions of frequency-dependent diffusion tensors and nuclear relaxation rates. While microimaging equipment with high-amplitude magnetic field gradients allows exploration of frequencies from tens to hundreds of Hz, clinical scanners with more moderate gradient capabilities are limited to a narrow frequency range, which may be insufficient to observe effects of restricted diffusion for brain tissues. We here investigate the effects of including or omitting frequency-dependence in the data inversion from isotropic and anisotropic liquids, excised tumor tissue, ex vivo mouse brain, and in vivo human brain. For microimaging measurements covering a wide frequency range, from 35 to 320 Hz at b-values over 4{middle dot}109 sm-2, the inclusion of frequency-dependence drastically reduces the fit residuals and avoids bias in the diffusion metrics for tumor and brain voxels with micrometer-scale structures. Conversely, for the case of in vivo human brain investigated in the narrow frequency range from 5 to 11 Hz at b = 3{middle dot}109 sm-2, analyses with and without inclusion of frequency-dependence yield similar fit residuals and diffusion metrics for all voxels. These results indicate that frequency-dependent inversion may be generally applied to diffusion-relaxation correlation MRI data with and without observable effects of restricted diffusion.

biophysics↗

bioRxiv2024

Delta-5 elongase knockout reduces DHA and TAG synthesis coupled with an increase of heat sensitivity in a marine diatom

Recent global marine lipidomic analysis reveals a strong relationship in the ocean between temperature and phytoplanktonic abundance of omega-3 long-chain polyunsaturated fatty acids (LC-PUFAs), especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which are essential for human nutrition and primarily from phytoplankton in marine food webs. In phytoplanktonic organisms, EPA may play a major role in regulating the phase transition temperature of membranes, while the function of DHA remains to be explored. In the oleaginous diatom Phaeodactylum tricornutum, DHA is distributed mainly on extraplastidial phospholipids, which is very different from the EPA enriched in thylakoid lipids. Here, CRISPR/Cas9-mediated knockout of ptELO5a, which encodes a delta-5 elongase catalyzing the elongation of EPA to synthesize DHA, led to a substantial interruption of DHA synthesis in P. tricornutum. The ptELO5a mutants show significant alterations in transcriptome and glycerolipidomes including membrane lipids and triacylglycerols under normal temperature (22{degrees}C), and are more sensitive to elevated temperature (28{degrees}C) than wild type. We conclude that the PtELO5a-mediated synthesis of small amounts of DHA has indispensable functions in regulating the membrane lipid, and indirectly contributing storage lipid accumulation and maintaining thermomorphogenesis in P. tricornutum. This study also highlights the significance of DHA synthesis and lipid composition for environmental adaptation of P. tricornutum.

plant biology↗

bioRxiv2024

Structural basis for the inhibition of coronaviral main proteases by PF-00835231

The main protease (Mpro) of coronaviruses plays a key role in viral replication, thus serving as a hot target for drug design. It has been proven that PF-00835231 is promising inhibitor of SARS-CoV-2 Mpro. Here, we report the inhibition potency of PF-00835231 against SARS-CoV-2 Mpro and seven Mpro mutants (G15S, M49I, Y54C, K90R, P132H, S46F, and V186F) from SARS-CoV-2 variants. The results confirm that PF-00835231 has broad-spectrum inhibition against various coronaviral Mpros. In addition, the crystal structures of SARS-CoV-2 Mpro, SARS-CoV Mpro, MERS-CoV Mpro, and seven SARS-CoV-2 Mpro mutants (G15S, M49I, Y54C, K90R, P132H, S46F, and V186F) in complex with PF-00835231 are solved. A detailed analysis of these structures reveal key determinants essential for inhibition and elucidates the binding modes of different coronaviral Mpros. Given the importance of the main protease for the treatment of coronaviral infection, structural insights into the Mpro inhibition by PF-00835231 can accelerate the design of novel antivirals with broad-spectrum efficacy against different human coronaviruses.

molecular biology↗

bioRxiv2024

UK BioCoin: Swift Trait-Specific Summary Statistics Regression for UK Biobank

Summary statistics derived from large-scale biobanks facilitate the sharing of genetic discoveries while minimizing the risk of compromising individual-level data privacy. However, these summary statistics, such as those from the UK Biobank (UKB) provided by Neales lab, are often adjusted by a fixed set of covariates to all traits (12 covariates including 10 PCs, sex and age), preventing the exploration of trait-specific summary statistics. In this study, we present a novel computational device UK BioCoin (UKC), which is designed to provide an efficient framework for trait-specific adjustment for covariates. Without requiring access to individual-level data from UKB, UKC leverages summary statistics regression technique and resources from UKB (289 GB of 199 phenotypes and 10 million SNPs), to enable the generation of GWAS summary statistics adjusted by user-specified covariates. Through a comprehensive analysis of height under trait-specific adjustments, we demonstrate that the GWAS summary statistics generated by UKC closely mirror those generated from individual-level UKB GWAS ({rho} [≥] 0.99 for effect sizes and{rho} [≥] 0.99 for p-values). Furthermore, we demonstrate the results for GWAS, SNP-heritability estimation, polygenic score, and Mendelian randomization, after various trait-specific covariate adjustments as allowed by UKC, indicating UKC a platform that harnesses in-depth exploration for researchers lacking access to UKB. The whole framework of UKC is portable for other biobank, as demonstrated in Westlake Biobank, which can equivalently be converted to a UKC-like" platform and promote data sharing. UKC has its computational engine fully optimized, and the computational efficiency of UKC is about 70 times faster than that of UKB. We package UKC as a Docker image of 20 GB (https://github.com/Ttttt47/UKBioCoin), which can be easily deployed on an average computer (e.g. laptop). One sentence summaryWe develop UK BioCoin (UKC), which allows fine-tuning of covariates for each UK Biobank trait but does not relay on UK Biobank individual-level data. It will change the current landscape of GWAS and reshape its downstream analyses.

genetics↗