Biology subjects

Gu, S.

Publications and source records attributed to Gu, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

A molecular switch between mammalian MLL complexes dictates response to Menin-MLL inhibition

The chromatin adaptor Menin interacts with oncogenic fusion proteins encoded by MLL1-rearrangements (MLL1-r), and small molecules that disrupt these associations are currently in clinical trials for the treatment of leukemia. By integrating chromatin-focused and genome-wide CRISPR screens with genetic, pharmacological, and biochemical approaches in mouse and human systems, we discovered a molecular switch between the MLL1-Menin and MLL3/4-UTX chromatin modifying complexes that dictates response to Menin-MLL inhibitors. We show that MLL1-Menin safeguards leukemia survival by impeding binding of the MLL3/4-UTX complex at a subset of target gene promoters. Disrupting the interaction between Menin and MLL1 leads to UTX-dependent transcriptional activation of a tumor suppressor gene-program that is crucial for a therapeutic response in murine and human leukemia. We establish the therapeutic relevance of this mechanism by showing that CDK4/6 inhibitors allow re-activation of this tumor-suppressor program in Menin-inhibitor insensitive leukemia cells, mitigating treatment resistance. The discovery of a molecular switch between MLL1-Menin and MLL3/4-UTX complexes on chromatin sheds light on novel functions of these evolutionary conserved epigenetic mediators and is particularly relevant to understand and target molecular pathways determining response and resistance in ongoing phase 1/2 clinical trials.

cancer biology↗

bioRxiv2021

MetaTrass: High-quality Metagenomic Taxonomic Read Assembly of Single-Species based on co-barcoding sequencing data and references

With the development of sequencing technologies and computational analysis in metagenomics, the genetic diversity of non-conserved regions has been receiving intensive attention to unravel the human gut microbial community. However, it remains a challenge to obtain enough microbial draft genomes at a high resolution from a single sample. In this work, we presented MetaTrass with a strategy of binning first and assembling later to assemble high-quality draft genomes based on metagenomics co-barcoding reads and the public reference genomes. We applied the tool to the single tube long fragment reads datasets for four human faecal samples, and generated more high-quality draft genomes with longer contiguity and higher resolution than the common combination strategies of genome assembling and binning. A total of 178 high-quality genomes was successfully assembled by MetaTrass, but the maximum of 58 was generated by the optimal common combination strategy in our tests. These high-quality genomes paved the way for genetic diversity and lineage analysis among different samples. With the high capability of assembling high-quality genomes of metagenomics datasets, MetaTrass will facilitate the study of spatial characters and dynamics of complex microbial communities at high resolution. The open-source code of MetaTrass is available at https://github.com/BGI-Qingdao/MetaTrass.

bioinformatics↗

bioRxiv2021

Effective Ensemble of Deep Neural Networks Predicts Neural Responses to Naturalistic Videos

This report provides a review of our submissions to the Algonauts Challenge 2021. In this challenge, neural responses in the visual cortex were recorded using functional neuroimaging when participants were watching naturalistic videos. The goal of the challenge is to develop voxel-wise encoding models which predict such neural signals based on the input videos. Here we built an ensemble of models that extract representations based on the input videos from 4 perspectives: image streams, motion, edges, and audio. We showed that adding new modules into the ensemble consistently improved our prediction performance. Our methods achieved state-of-the-art performance on both the mini track and the full track tasks.

neuroscience↗

bioRxiv2021

Flexible pri-miRNA structures enable tunable production of 5' isomiRs

Drosha cleavage of a pri-miRNA defines mature microRNA sequence. Drosha cleavage at alternative positions generates 5 isoforms (isomiRs) which have distinctive functions. To understand how pri-miRNA structures influence Drosha cleavage, we performed a systematic analysis of the maturation of endogenous pri-miRNAs and their variants both in vitro and in vivo. We show that, in addition to previously known features, the overall structural flexibility of pri-miRNA impacts Drosha cleavage fidelity. Internal loops and nearby G{middle dot}U wobble pairs on the pri-miRNA stem induce the use of non-canonical cleavage sites by Drosha, resulting in 5 isomiR production. By analyzing patient data deposited in The Cancer Genome Atlas, we provide evidence that alternative Drosha cleavage of pri-miRNAs is a tunable process that responds to the level of pri-miRNA-associated RNA-binding proteins. Together, our findings reveal that Drosha cleavage fidelity can be modulated by altering pri-miRNA structure, a potential mechanism underlying 5 isomiR biogenesis in tumors. HIGHLIGHTSO_LIFlexible pri-miRNA structures lead to 5 isomiR production C_LIO_LIInternal loops and G{middle dot}U pairs of pri-miRNA contribute to alternative Drosha cleavages C_LIO_LIAlternative Drosha cleavage results in 5 isomiRs from both strands of pre-miRNAs C_LIO_LI5 isomiR production is upregulated by pri-miRNA-associated RBPs in cancers C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/456839v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@cab9aorg.highwire.dtl.DTLVardef@1d613b1org.highwire.dtl.DTLVardef@1a6e200org.highwire.dtl.DTLVardef@13f7607_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2021

Scaffolding protein GspB/OutB facilitates assembly of the Dickeya dadantii type 2 secretion system by anchoring the outer membrane secretin pore to the inner membrane and to the peptidoglycan cell wall.

The phytopathogenic proteobacterium Dickeya dadantii secretes an array of plant cell wall degrading enzymes and other virulence factors via the type 2 secretion system (T2SS). T2SSs are widespread among important plant, animal and human bacterial pathogens. This multi-protein complex spans the double membrane cell envelope and secretes fully folded proteins through a large outer membrane pore formed by 15 subunits of the secretin GspD. Secretins are also found in the type 3 secretion system and the type 4 pili. Usually, specialized lipoproteins termed as pilotins assist the targeting and assembly of secretins into the outer membrane. Here, we show that in Dickeya, the pilotin acts in concert with the scaffolding protein GspB. Deletion of gspB profoundly impacts secretin assembly, pectinase secretion, and virulence. Structural studies reveal that GspB possesses a conserved periplasmic Homology Region domain that interacts directly with the N-terminal secretin domain. Site-specific photo cross-linking unravels molecular details of the GspB-GspD complex in vivo. We show that GspB facilitates outer membrane targeting and assembly of the secretin pores and anchors them to the inner membrane while the C-terminal extension of GspB scaffolds the secretin channel in the peptidoglycan cell wall. Phylogenetic analysis shows that in other bacteria, GspB homologs vary in length and domain composition and act in concert with either a cognate ATPase GspA or a pilotin GspS. ImportanceGram-negative bacteria have two cell membranes sandwiching a peptidoglycan net that form together a robust protective cell envelope. To translocate effector proteins across this multilayer envelope, bacteria have evolved several specialized secretion systems. In the type 2 secretion system and some other bacterial machineries, secretins form large multimeric pores that allow transport of effector proteins or filaments across the outer membrane. The secretins are essential for nutrient acquisition and pathogenicity and constitute a target for development of new antibacterials. Targeting of secretin subunits into the outer membrane is often facilitated by a special class of lipoproteins called pilotins. Here, we show that in Dickeya and some other bacteria, the scaffolding protein GspB acts in concert with pilotin, facilitating the assembly of the secretin pore and its anchoring to both the inner membrane and the bacterial cell wall. GspB homologs of varied domain composition are present in many other T2SSs.

microbiology↗

bioRxiv2021

The transcription factor Zeb2 drives differentiation of age-associated B cells

Age-associated B-cells (ABCs) accumulate during infection, aging and autoimmunity, contributing to lupus pathogenesis. Here, we screen for transcription factors driving ABC formation and find Zeb2 is required for human and mouse ABC differentiation in-vitro. ABCs are reduced in ZEB2 haploinsufficient individuals and in mice lacking Zeb2 in B-cells. In mice with TLR7-driven lupus, Zeb2 is essential for ABC formation and autoimmune pathology. Zeb2 binds to the +20kb intronic enhancer of Mef2b, repressing Mef2b-mediated germinal center B-cell differentiation and promoting ABC formation. Zeb2 also targets genes important for ABC specification and function including Itgax. Zeb2-driven ABC differentiation requires Jak-Stat signaling, and treatment with the Jak1/3 inhibitor tofacitinib reduces ABC accumulation in autoimmune mice and patients. Zeb2 thus emerges as a driver of B-cell autoimmunity. One-Sentence SummaryZeb2 is essential for Age-associated B cells differentiation and function.

immunology↗

bioRxiv2021

Network controllability mediates the relationship between rigid structure and flexible dynamics

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brains structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain regions functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes boundary controllability, suggesting that a regions strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity. AUTHOR SUMMARYPrecisely how the relatively rigid white matter wiring of the human brain gives rise to a diverse repertoire of functional neural dynamics is not well understood. In this work, we combined tools from network science and control theory to address this question. Capitalizing on a large developmental cohort, we demonstrated that the ability of a brain region to flexibly change its functional module allegiance over time (i.e., its modular flexibility), was positively correlated with its proportion of anatomical edges projecting to multiple cognitive networks (i.e., its structural participation coefficient). Moreover, this relationship was strongly mediated by the regions boundary controllability, a metric capturing its capacity to integrate information across multiple cognitive domains.

neuroscience↗

bioRxiv2021

MYC drives aggressive prostate cancer by disrupting transcriptional pause release at androgen receptor targets

c-MYC (MYC) is a major driver of prostate cancer tumorigenesis and progression. Although MYC is overexpressed in both early and metastatic disease and associated with poor survival, its impact on prostate transcriptional reprogramming remains elusive. We demonstrate that MYC overexpression significantly diminishes the androgen receptor (AR) transcriptional program (the set of genes directly targeted by the AR protein) in luminal prostate cells without altering AR expression. Importantly, analyses of clinical specimens revealed that concurrent low AR and high MYC transcriptional programs accelerate prostate cancer progression toward a metastatic, castration-resistant disease. Data integration of single-cell transcriptomics together with ChIP-seq revealed an increased RNA polymerase II (Pol II) promoter-proximal pausing at AR-dependent genes following MYC overexpression without an accompanying deactivation of AR-bound enhancers. Altogether, our findings suggest that MYC overexpression antagonizes the canonical AR transcriptional program and contributes to prostate tumor initiation and progression by disrupting transcriptional pause release at AR-regulated genes. STATEMENT OF SIGNIFICANCEAR and MYC are key to prostate cancer etiology but our current understanding of their interplay is scarce. Here we show that the oncogenic transcription factor MYC can pause the transcriptional program of the master transcription factor in prostate cancer, AR, while turning on its own, even more lethal program.

genomics↗

bioRxiv2021

Ligand Strain Energy in Large Library Docking

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast pre-calculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC {beta}-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing high-scoring decoy molecules that were strained. In a 40 target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules, and one based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on pre-calculated population distributions, this approach is rapid, taking less than 0.04 second to evaluate a conformation on a standard core, making it pragmatic for pre-calculating strain in even ultra-large libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org

bioinformatics↗

bioRxiv2020

Quantitative Control of Noise in Mammalian Gene Expression by Dynamic Histone Regulations

Fluctuation ( noise) in gene expression is critical for mammalian cellular processes. Numerous mechanisms contribute to its origins, yet large noises induced by single transcriptional activator species remain to be experimentally understood. Here, we combined the dynamic regulation of transcriptional activator binding, histone regulator inhibitors, and single-cell quantification of chromatin accessibility, mRNA, and protein to probe putative mechanisms. Using a light-induced expression system, we show that the transcriptional activator forms a positive feedback loop with histone acetyltransferases CBP/p300. It generates epigenetic bistability in H3K27ac, which contributes to large noise. Disable of the positive feedback loop by CBP/p300 and HDAC4/5 inhibitors also reduces heterogeneity in endogenous genes, suggesting a universal mechanism. We showed that the noise was reduced by pulse-wide modulation of transcriptional activator binding due to alternating the system between high and low monostable states. Our findings could provide a mechanism-based approach to modulate noise in synthetic and endogenous gene expressions.

molecular biology↗

bioRxiv2020

Polygenic Prediction of Complex Traits with Iterative Screen Regression Models

Although genome-wide association studies have successfully identified thousands of markers associated with various complex traits and diseases, our ability to predict such phenotypes remains limited. A perhaps ignored explanation lies in the limitations of the genetic models and statistical techniques commonly used in association studies. However, using genotype data for individuals to perform accurate genetic prediction of complex traits can promote genomic selection in animal and plant breeding and can lead to the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling genetic variants together via polygenic methods. Here, we also utilize our proposed polygenic methods, which refer to as the iterative screen regression model (ISR) for genome prediction. We compared ISR with several commonly used prediction methods with simulations. We further applied ISR to predicting 15 traits, including the five species of cattle, rice, wheat, maize, and mice. The results of the study indicate that the ISR method performs well than several commonly used polygenic methods and stability.

genetics↗

bioRxiv2020

CoBRA: Containerized Bioinformatics workflow for Reproducible ChIP/ATAC-seq Analysis - from differential peak calling to pathway analysis

ChIP-seq and ATAC-seq have become essential technologies used as effective methods of measuring protein-DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates correct normalization between samples, adjustment of copy number variations, and integration of new downstream analysis tools. Here we present CoBRA, a modularized computational workflow which quantifies ChIP and ATAC-seq peak regions and performs unsupervised and supervised analysis. CoBRA provides a comprehensive state-of-the-art ChIP and ATAC-seq analysis pipeline that is usable by scientists with limited computational experience. This enables researchers to gain rapid insight into protein-DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference DB and pathway analysis. Code availability: https://bitbucket.org/cfce/cobra

bioinformatics↗

bioRxiv2020

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or ground high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2=0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

plant biology↗

bioRxiv2020

A new approach of dissecting genetic effects for complex traits

During the past decades, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits included in humans, animals, and plants. All common genome-wide association (GWA) methods rely on population structure correction to avoid false genotype and phenotype associations. However, population structure correction is a stringent penalization, which also impedes the identification of real associations. Here, we used recent statistical advances and proposed iterative screen regression (ISR), which enables simultaneous multiple marker associations and shown to appropriately correction population stratification and cryptic relatedness in GWAS. Results from analyses of simulated suggest that the proposed ISR method performed well in terms of power (sensitivity) versus FDR (False Discovery Rate) and specificity, also less bias (higher accuracy) in effect (PVE) estimation than the existing multi-loci (mixed) model and the single-locus (mixed) model. We also show the practicality of our approach by applying it to rice, outbred mice, and A.thaliana datasets. It identified several new causal loci that other methods did not detect. Our ISR provides an alternative for multi-loci GWAS, and the implementation was computationally efficient, analyzing large datasets practicable (n>100,000).

genetics↗

bioRxiv2020

Tumor IsomiR Encyclopedia (TIE): a pan-cancer database of miRNA isoforms

MicroRNAs (miRNAs) function as master regulators of gene expression in many physiological and pathological conditions including cancer. Sequence variants or isoforms (isomiRs) can account for between 40 to 60% of total miRNA counts, yet despite this overwhelming abundance, their function continues to be debated. Recent studies demonstrate that certain isomiRs can regulate unique sets of target mRNAs by altering their seed sequence or stabilizing 3 pairing, while others are decay intermediates indicating an active miRNA turnover. Given their short sequence length and high heterogeneity, mapping isomiRs can be challenging; without adequate depth and data aggregation, low frequency events are often disregarded. To address these challenges, we present the Tumor IsomiR Encyclopedia (TIE): a dynamic database of isomiRs from over 10,000 adult and pediatric tumor samples in The Cancer Genome Atlas (TCGA) and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects. A key novelty of TIE is its ability to annotate heterogeneous isomiR sequences and aggregate the variants obtained across all samples and datasets. The database provides annotation of templated and non-templated nucleotides as well as other advanced analysis. All data can be browsed online or downloaded as simple spreadsheets. Here we show analysis of isomiRs of miR-21 and miR-30a to demonstrate the utility of TIE. TIE search engine and data are hosted at https://isomir.ccr.cancer.gov/.

genomics↗

bioRxiv2020

Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Growing evidence supports the importance of understanding tumor-immune spatial relationship in the tumor microenvironment in order to achieve precision cancer therapy. However, existing methods, based on oversimplistic cell-to-cell proximity, are largely confounded by immune cell density and are ineffective in capturing tumor-immune spatial patterns. Here we developed a novel computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), to offer an effective solution for spatially informed tumor subtyping. Our method could measure the extent of immune cell partitioning between tumor epithelial and stromal areas as well as the degree of immune cell clustering. Using a U.S. nation-wide colorectal cancer database, we showed that TIPC could determine tumor subtypes with unique tumor-immune spatial patterns that were significantly associated with patient survival and key tumor molecular features. We also demonstrated that TIPC was robust to parameter settings and readily applicable to different immune cell types. The capability of TIPC in delineating clinically relevant patient subtypes that encapsulate tumor-immune spatial relationship, immune density, and tumor morphology is expected to shed light on underlying immune mechanisms. Hence, TIPC can be a useful bioinformatics tool for effective characterization of the spatial composition of the tumor-immune microenvironment to inform precision immunotherapy.

cancer biology↗

bioRxiv2020

Reliability of dynamic network reconfiguration: Impact of code implementation, parameter selection, scan duration, and task condition

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization. HighlightsO_LIDynamic network reliability is highly dependent on many methodological decisions C_LIO_LIThe default multilayer community detection algorithm generates erroneous results C_LIO_LIReliability-optimized intra-/inter-layer coupling parameters are dataset-dependent C_LIO_LIScan duration is a much stronger determinant of reliability than scan condition C_LIO_LIMovies are the most reliable condition, requiring at least 20 min of data C_LI

neuroscience↗

bioRxiv2020

Counteracting Genome Instability by p53-dependent Mintosis

Entosis was proposed to promote aneuploidy and genome instability by cell-in-cell mediated engulfment in tumor cells. We reported here, in epithelial cells, that entosis coupled with mitotic arrest functions to counteract genome instability by targeting aneuploid mitotic progenies for engulfment and elimination. We found that the formation of cell-in-cell structures associated with prolonged mitosis, which was sufficient to induce entosis. This process was controlled by the tumor suppressor p53 (wild type) that upregulates Rnd3 expression in response to DNA damages associated with prolonged metaphase. Rnd3 compartmentalized RhoA activities accumulated during prolonged metaphase to drive cell-in-cell formation. Remarkably, this prolonged mitosis-induced entosis (mintosis) selectively targets non-diploid progenies for internalization, blockade of which increased aneuploidy. Thus, our work uncovered a heretofore unrecognized mechanism of mitotic surveillance for entosis, which eliminates newly-born abnormal daughter cells in a p53-depedent way to maintain genome integrity.

cell biology↗