Biology subjects

Liu, X. S.

Publications and source records attributed to Liu, X. S..

31 records · Page 2Linked to original sources

bioRxiv2021

CHIPS: A Snakemake pipeline for quality control and reproducible processing of chromatin profiling data

MotivationThe chromatin profile measured by ATAC-seq, ChIP-seq, or DNase-seq experiments can identify genomic regions critical in regulating gene expression and provide insights on biological processes such as diseases and development. However, quality control and processing chromatin profiling data involve many steps, and different bioinformatics tools are used at each step. It can be challenging to manage the analysis. ResultsWe developed a Snakemake pipeline called CHIPS (CHromatin enrichment Processor) to streamline the processing of ChIP-seq, ATAC-seq, and DNase-seq data. The pipeline supports single- and paired-end data and is flexible to start with FASTQ or BAM files. It includes basic steps such as read trimming, mapping, and peak calling. In addition, it calculates quality control metrics such as contamination profiles, PCR bottleneck coefficient, the fraction of reads in peaks, percentage of peaks overlapping with the union of public DNaseI hypersensitivity sites, and conservation profile of the peaks. For downstream analysis, it carries out peak annotations, motif finding, and regulatory potential calculation for all genes. The pipeline ensures that the processing is robust and reproducible. AvailabilityCHIPS is available at https://github.com/liulab-dfci/CHIPS

bioinformatics↗

bioRxiv2021

FGFR inhibitor mediated dismissal of SWI/SNF complexes from YAP-dependent enhancers induces adaptive therapeutic resistance

How cancer cells adapt to evade the therapeutic effects of drugs targeting oncogenic drivers is poorly understood. Here we report an epigenetic mechanism leading to the adaptive resistance of triple-negative breast cancer (TNBC) to fibroblast growth factor receptor (FGFR) inhibitors. Prolonged FGFR inhibition suppresses the function of BRG1-dependent chromatin remodeling leading to an epigenetic state that derepresses YAP-associated enhancers. These chromatin changes induce the expression of several amino acid transporters resulting in increased intracellular levels of specific amino acids that reactivate mTORC1. Collectively, these findings reveal a novel feedback loop involving an epigenetic state transition and metabolic reprogramming that leads to adaptive therapeutic resistance.

cancer biology↗

bioRxiv2020

Hippocampal cells multiplex positive and negative engrams

The hippocampus is involved in processing a variety of mnemonic computations specifically the spatiotemporal components and emotional dimensions of contextual memory.1-3 Recent studies have demonstrated vast structural and functional heterogeneity along the dorsal-ventral axis1, 5 of the hippocampus. The ventral hippocampus has been shown to be important in the processing of emotion and valence.6-9 Here, we combine transgenic and all-virus based activity-dependent tagging strategies to visualize multiple valence-specific engrams in the vHPC and demonstrate two partially segregated cell populations and projections that respond to appetitive and aversive experiences. Next, using RNA sequencing and DNA methylation sequencing approaches, we find that vHPC appetitive and aversive engram cells display distinct transcriptional programs and DNA methylation landscapes compared to a neutral engram population. Additionally, while optogenetic manipulation of tagged cell bodies in vHPC is not sufficient to drive appetitive or aversive behavior in real-time place preference, stimulation of tagged vHPC terminals projecting to the amygdala and nucleus accumbens (NAc), but not the prefrontal cortex (PFC), had the capacity drive preference and avoidance. These terminals can also undergo a "switch" or "reset" in their capacity to drive either, thereby demonstrating their adaptable contributions to behavior. We conclude that the vHPC contains genetically, cellularly, and behaviorally distinct populations of cells processing appetitive and aversive memory engrams. Together, our findings provide a novel means by which to visualize multiple engrams within the same brain and point to their unique genetic signatures as reference maps for the future development of new therapeutic strategies. One sentence summaryThe hippocampus contains neurons that correspond to positive and negative engrams, which are segregated by their molecular, cellular, and projection-specific features.

neuroscience↗

bioRxiv2020

In Vivo CRISPR Screens Identify E3 Ligase Cop1 as a Modulator of Macrophage Infiltration and Cancer Immunotherapy Target

Despite remarkable clinical efficacies of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that inhibition of the E3 ubiquitin ligase Cop1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, and shows synergy in anti-tumor immunity with ICB. Transcriptomics, epigenomics, and proteomics analyses revealed Cop1 functions through proteasomal degradation of the C/ebp{delta} protein. Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebp{delta}, which leads to polyubiquitination of C/ebp{delta}. Cop1 inhibition stabilizes C/ebp{delta} to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy by regulating chemokine secretion and macrophage levels in the TNBC tumor microenvironment. HighlightsO_LILarge-scale in vivo CRISPR screens identify new immune targets regulating the tumor microenvironment C_LIO_LICop1 knockout in cancer cells enhances anti-tumor immunity C_LIO_LICop1 modulates chemokine secretion and macrophage infiltration into tumors C_LIO_LICop1 targets C/ebp{delta} degradation via Trib2 and influences ICB response C_LI

cancer biology↗

bioRxiv2020

Identification of Gli1 as a progenitor cell marker for meniscus development and injury repair

Meniscal tears are associated with a high risk of osteoarthritis but currently have no disease-modifying therapies. Using Gli1-CreER tdTomato mice, we found that Gli1+ cells contribute to the development of meniscus horns from 2 weeks of age. In adult mice, Gli1+ cells resided at the superficial layer of meniscus and expressed known mesenchymal progenitor markers. In culture, meniscal Gli1+ cells possessed high progenitor activities under the control of Hh signal. Meniscus injury at the anterior horn induced a quick expansion of Gli1+ cells. Normally, the tissue healed slowly, leading to cartilage degeneration. Ablation of Gli1+ cells further hindered this repair process. Strikingly, intra-articular injection of Gli1+ meniscal cells or an Hh activator right after injury accelerated the bridging of the interrupted ends and attenuated signs of osteoarthritis. Taken together, our work identified a novel progenitor population in meniscus and proposes a new treatment for repairing injured meniscus and preventing osteoarthritis.

developmental biology↗

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

Subtype Heterogeneity and Epigenetic Convergence in Neuroendocrine Prostate Cancer

Neuroendocrine carcinomas (NEC) are tumors expressing markers of neuronal differentiation that can arise at different anatomic sites but have strong histological and clinical similarities. Here we report the chromatin landscapes of a range of human NECs and show convergence to the activation of a common epigenetic program. With a particular focus on treatment emergent neuroendocrine prostate cancer (NEPC), we analyzed cell lines, patient-derived xenograft (PDX) models and human clinical samples to show the existence of two distinct NEPC subtypes based on the expression of the neuronal transcription factors ASCL1 and NEUROD1. While in cell lines and PDX models these subtypes are mutually exclusive, single cell analysis of human clinical samples exhibit a more complex tumor structure with subtypes coexisting as separate sub-populations within the same tumor. These tumor sub-populations differ genetically and epigenetically contributing to intra- and inter-tumoral heterogeneity in human metastases. Overall our results provide a deeper understanding of the shared clinicopathological characteristics shown by NECs. Furthermore, the intratumoral heterogeneity of human NEPCs suggests the requirement of simultaneous targeting of coexisting tumor populations as a therapeutic strategy.

cancer biology↗

bioRxiv2020

Bone marrow adipogenic lineage precursors (MALPs) promote osteoclastogenesis in bone remodeling and pathologic bone loss

Bone is maintained by coupled activities of bone-forming osteoblasts/osteocytes and bone-resorbing osteoclasts and an alternation of this relationship can lead to pathologic bone loss such as in osteoporosis. It is well known that osteogenic cells support osteoclastogenesis via synthesizing RANKL. Interestingly, our recently identified bone marrow mesenchymal cell population--marrow adipogenic lineage precursors (MALPs) that form a multi-dimensional cell network in bone--was computationally demonstrated to be the most interactive with monocyte-macrophage lineage cells through highly and specifically expressing several osteoclast regulatory factors, including RANKL. Using an adipocyte-specific Adipoq-Cre to label MALPs, we demonstrated that mice with RANKL deficiency in MALPs have a drastic increase of trabecular bone mass in long bones and vertebrae starting from 1 month of age but that their cortical bone is normal. This phenotype was accompanied by diminished osteoclast number and attenuated bone formation at the trabecular bone surface. Reduced RANKL signaling in calvarial MALPs also abolished osteolytic lesions after lipopolysaccharide (LPS) injections. Furthermore, in ovariectomized mice, elevated bone resorption was partially attenuated by RANKL deficiency in MALPs. In summary, our studies identified MALPs as a critical player in controlling bone remodeling during normal bone metabolism and pathological bone loss in a RANKL-dependent fashion.

cell biology↗

bioRxiv2020

Single-cell analysis reveals diverse stromal subsets associated with immune evasion in triple-negative breast cancer

The tumour stroma regulates nearly all stages of carcinogenesis. Stromal heterogeneity in human triple-negative breast cancers (TNBCs) remains poorly understood, limiting the development of stromal-targeted therapies. Single cell RNA-sequencing of five TNBCs revealed two cancer-associated fibroblast (CAF) and two perivascular-like (PVL) subpopulations. CAFs clustered into two states, the first with features of myofibroblasts and the second characterised by high expression of growth factors and immunomodulatory molecules. PVL cells clustered into two states consistent with a differentiated and immature phenotype. We showed that these stromal states have distinct morphologies, spatial relationships and functional properties in regulating the extracellular matrix. Using cell-signalling predictions, we provide evidence that stromal-immune crosstalk acts via a diverse array of immunoregulatory molecules. Importantly, the investigation of gene signatures from inflammatory-CAFs and differentiated-PVL cells in independent TNBC patient cohorts revealed strong associations with cytotoxic T-cell dysfunction and exclusion, respectively. Such insights present promising candidates to further investigate for new therapeutic strategies in the treatment of TNBCs.

cancer biology↗

bioRxiv2019

Inferring transcriptional regulators through integrative modeling ofpublic chromatin accessibility and ChIP-seq data

We developed Lisa (http://lisa.cistrome.org) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses compendia of public histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Then using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes, and outperformed alternative methods in identifying the perturbed TRs.

bioinformatics↗

bioRxiv2019

DeepBCR: Deep learning framework for cancer-type classification and binding affinity estimation using B cell receptor repertoires.

We developed a deep learning framework to model the binding specificity of B-cell receptors (BCRs). The DeepBCR framework can predict the cancer type from a repertoire of BCRs and estimate the binding affinity of a single BCR. We designed a peptide encoding network that includes an amino acid encoding layer, k-mer motif layer, and immunoglobulin isotype layer, and used transfer learning to reduce parameters and over-fitting. When we applied the framework to evaluate the three commercial anti-PD1 drugs (Opdivo, Keytruda, and Libtayo), the predicted binding affinities correlate with the real affinities measured in kD values. This validates the prediction and indicates that we can use the framework to select strong antigen-specific binders.

bioinformatics↗

bioRxiv2019

Determinants of transcription factor regulatory range

To characterize the genomic distances over which transcription factors (TFs) influence gene expression, we examined thousands of TF and histone modification ChIP-seq datasets and thousands of gene expression profiles. A model integrating these data revealed two classes of TF: one with short-range regulatory influence, the other with long-range regulatory influence. The two TF classes also had distinct chromatin-binding preferences and auto-regulatory properties. The regulatory range of a single TF bound within different topologically associating domains (TADs) depended on intrinsic TAD properties such as local gene density and G/C content, but also on the TAD chromatin state in specific cell types. Our results provide evidence that most TFs belong to one of these two functional classes, and that the regulatory range of long-range TFs is chromatin-state dependent. Thus, consideration of TF type, distance-to-target, and chromatin context is likely important in identifying TF regulatory targets and interpreting GWAS and eQTL SNPs.

genomics↗

bioRxiv2017

Integrative analysis and refined design of CRISPR knockout screens

Genome-wide CRISPR-Cas9 screen has been widely used to interrogate gene functions. However, the analysis remains challenging and rules to design better libraries beg further refinement. Here we present MAGeCK-NEST, which integrates protein-protein interaction (PPI), improves the inference accuracy when fewer guide-RNAs (sgRNAs) are available, and assesses screen qualities using information on PPI. MAGeCK-NEST also adopts a maximum-likelihood approach to remove sgRNA outliers, which are characterized with higher G-nucleotide counts, especially in regions distal from the PAM motif. Using MAGeCK-NEST, we found that choosing non-targeting sgRNAs as negative controls lead to strong bias, which can be mitigated by sgRNAs targeting the \"safe harbor\" regions. Custom-designed screens confirmed our findings, and further revealed that 19nt sgRNAs consistently gave the best signal-to-noise separation. Collectively, our method enabled robust calling of CRISPR screen hits and motivated the design of an improved genome-wide CRISPR screen library.

bioinformatics↗