Biology subjects

Fei, T.

Publications and source records attributed to Fei, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Dietary fiber intake associates with improved survival and microbiomecomposition in allogeneic hematopoietic cell transplantation

Diet is linked to changes in gut microbiota and metabolite production with clinical relevance in several disease settings, although these effects remain poorly defined. We performed prospective, real-time diet monitoring (37,929 food items, 3,837 patient days) and longitudinal microbiome and metabolite profiling (1,230 fecal samples) in a clinical cohort of 173 patients undergoing allogeneic hematopoietic cell transplantation. Patients with pre-transplant fiber intake above the cohort average had significantly improved overall survival (p=0.014) and reduced incidence of grades 2-4 acute graft-versus-host disease (GVHD) (p=0.032) post-transplant. Those consuming insoluble fiber had increased microbial diversity, enriched butyrate-producing taxa, and depleted Enterococcus. Those who developed lower gastrointestinal GVHD had reduced fecal butyrate levels. In a GVHD preclinical model, we confirmed that a fiber-enriched diet increased survival, cecal butyrate, and regulatory-to-conventional T cell ratio. Thus, we demonstrated that dietary fiber has clinical significance as a modifiable factor with microbiome-mediated effects.

cancer biology↗

bioRxiv2026

Differential effects of two common GVHD prophylaxis regimens on the gut microbiome: Results from the BMT CTN 1801 study

Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment for many hematological malignancies, but graft-versus-host disease (GVHD) is a common complication. Low gut microbiome diversity is associated with higher GVHD risk and shorter survival in multiple studies. Recently, the BMT CTN 1703 clinical trial demonstrated superiority of a GVHD-prophylaxis regimen including post-transplant cyclophosphamide (PTCy) compared to the standard prophylaxis (tacrolimus and methotrexate, Tac/MTX) in terms of GVHD-free, relapse-free survival at one year among reduced intensity conditioning allo-HCT recipients. However, the effect of PTCy on the gut microbiome and its association with clinical outcome have not been described. Here, we report on a companion randomized clinical controlled trial (BMT CTN 1801), which collected 2575 longitudinal stool samples from 304 study participants. Samples were obtained up to weekly up to day 84 post allo-HCT and at less frequent intervals thereafter, up to 2 years. Microbiome diversity and absolute microbial load were lower in the PTCy group compared to the Tac/MTX group on days 14-28 post-HCT. However, diversity at the timepoint closest to neutrophil engraftment was not significantly associated with non-relapse mortality after one year or other clinical outcomes, contrary to expectations from previous studies. Microbial domination events, when a single species exceeds 30% relative abundance, were comparable across treatment arms and reflected both pathogen blooms as well as less severe disruptions of the microbial community. Clostridium scindens and secondary bile acid metabolism pathways were less prevalent in the PTCy arm than in the Tac/MTX arm post-HCT, yet presence of secondary bile acid metabolism pathways was associated with a lower risk of chronic GVHD. Given that PTCy was associated with a greater disruption of the microbiome as measured by diversity, absolute microbial abundance, and bile acid metabolism capability, but better clinical outcomes overall, these data suggest that the importance of the microbiome in modulating the host immune systems after allo-HCT is specific to different types of GVHD prophylaxis.

microbiology↗

bioRxiv2026

eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolution

The human genome is densely populated with cis-regulatory elements (CREs), yet deciphering their functional regulatory syntax and combinational logic remains a fundamental challenge. Here, we integrate 379 genome-scale CRISPR screen experiments, encompassing 21 million perturbations across 23 cell types, to construct a compendium of 41,239 high-confidence functional CREs from 530,527 candidates. Leveraging this resource, we develop eScreen, a deep learning model built on the StripedHyena2 architecture to functionally decode the regulatory genome at single-nucleotide resolution. eScreen achieves three primary functions: (1) predicts genome-wide cell-type-specific CRE functional activity with high accuracy, outperforming existing models; (2) provides mechanistic interpretation of regulatory syntax at single-nucleotide resolution; (3) dissects the functional organization of enhancer clusters through in silico perturbation analysis. We perform multiple independent CRISPR knockout, CRISPR interference (CRISPRi), and base editing screens to validate these functions of eScreen both at scale and on individual cases. Furthermore, we provide an interactive web server (https://escreen.huanglabxmu.com/) for the community to access the integrated CRISPR screen resources and eScreen functions. Collectively, our work establishes a highly precise and convenient tool to decode the causal effects of the regulatory genome.

genomics↗

bioRxiv2025

Benchmarking Perturbation Tools for the Noncoding Genome

Deciphering the functionality of the noncoding genome which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancer, dual-SpCas9 outperforms other methods with superior efficiency of destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model DeepDC with associated web server is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

genetics↗

bioRxiv2025

Single-cell spatial map of cis-regulatory elements for disease-related genes in the macaque cortex

Single-cell spatial transcriptomes have demonstrated molecular and cellular diversity in the brain1, 2, 3, 4, 5, 6, 7, 8, but gene regulatory mechanisms underlying transcriptomic profiles and disease pathogenesis remain largely unknown in primates. Here we performed single-nucleus Assay for Transposase-Accessible Chromatin followed by sequencing (snATAC-seq) for [~]1.6 million cells from 142 cortical regions of two male cynomolgus monkeys (Macaca fascicularis), and identified distinct chromatin accessibility profiles of cis-regulatory elements (CREs) for various cell types. By integrative analysis with large-scale spatial transcriptome data, we found that these CREs showed laminar and regional preferences, with their regional accessibility exhibiting striking dependence on the regions hierarchical level. Cross-species comparison of snATAC-seq data revealed human/macaque-enriched layer-4 glutamatergic neurons and LAMP5/LHX6-expressing GABAergic neurons as well as human/macaque-biased CREs for genes related to neurodevelopment and psychiatric diseases. Importantly, risk single-nucleotide polymorphisms for many brain disorders strongly associated with human/macaque-biased CREs in glutamatergic neuronal types and those for Alzhemers disease strongly associated with CREs exclusively in microglia. Our results provided the basis for understanding the spatial gene regulatory mechanisms underlying cellular diversity and disease pathogenesis in the primate cortex.

neuroscience↗

bioRxiv2025

A study of different pre-treatment methods for cigarettes and their aroma differences

The study innovatively utilized a combination of smoking machine and Cambridge filter to effectively extract the aroma compounds generated during smoking of cigarettes, and employed three different pretreatment methods to obtain optimal cigarette pretreatment procedures. In addition, the aroma components of different cigarettes were identified by different instruments such as gas chromatography-mass spectrometry. Combined with the calculation of Odor Activity Values (OAV), PCA analysis and sensory evaluation were performed on the five types of cigarettes to reveal differences and similarities in their aroma profiles.

biochemistry↗

bioRxiv2025

Correlating High-dimensional longitudinal microbial features with time-varying outcomes with FLORAL

Correlating time-dependent patient characteristics and matched microbiome samples can identify biomarkers in longitudinal microbiome studies. Existing approaches typically repeat a pre-specified modeling approach for all taxonomic features, followed by a multiple testing adjustment step for false discovery rate (FDR) control. In this work, we develop an alternative strategy of using log-ratio penalized generalized estimating equations, which directly models the longitudinal patient characteristic of interest as the outcome variable and treats microbial features as high-dimensional compositional covariates. A cross validation procedure is developed for variable selection and model selection among different working correlation structures. In extensive simulations, the proposed method achieved superior sensitivity over the state-of-the-art methods with robustly controlled FDR. In the analyses of correlating longitudinal dietary intake, bloodstream infection status, and microbial features from matched samples of cancer patients, the proposed method effectively identified gut health indicators and clinically relevant microbial markers, showing robust utilities in real-world applications. The method is implemented under the open-source R package FLORAL, which is available at (https://vdblab.github.io/FLORAL/).

bioinformatics↗

bioRxiv2024

A comprehensive benchmark for multiple highly efficient base editors with broad targeting scope

As the toolbox of base editors (BEs) expands, selecting appropriate BE and guide RNA (gRNA) to achieve optimal editing efficiency and outcome for a given target becomes challenging. Here, we construct a set of 10 adenine and cytosine BEs with high activity and broad targeting scope, and comprehensively evaluate their editing profiles and properties head-to-head with 34,040 BE-gRNA-target combinations using genomically integrated long targets and tiling gRNA strategies. Interestingly, we observe widespread non-canonical protospacer adjacent motifs (PAMs) for these BEs. Using this large-scale benchmark data, we build a deep learning model, named BEEP (Base Editing Efficiency Predictor), for predicting the editing efficiency and outcome of these BEs. Guided by BEEP, we experimentally test and validate the installment of 3,558 disease-associated single nucleotide variants (SNVs) via BEs, including 20.1% of target sites that would be generally considered as "uneditable", due to the lack of canonical PAMs. We further predict candidate BE-gRNA-target combinations for modeling 1,752,651 ClinVar SNVs. We also identify several cancer-associated SNVs that drive the resistance to BRAF inhibitors in melanoma. These efforts benchmark the performance and illuminate the capabilities of multiple highly useful BEs for interrogating functional SNVs. A practical webserver (http://beep.weililab.org/) is freely accessible to guide the selection of optimal BEs and gRNAs for a given target.

molecular biology↗

bioRxiv2024

Sugar-rich foods exacerbate antibiotic-induced microbiome injury

Intestinal microbiota composition is implicated in several diseases; understanding the factors that influence it are key to elucidating host-commensal interactions and to designing microbiome-targeted therapies. We quantified how diet influences microbiome dynamics in hospitalized patients. We recorded 9,419 meals consumed by 173 patients undergoing hematopoietic cell transplantation and profiled the microbiome in 1,009 longitudinally collected stool samples from 158 of them. Caloric intake was correlated with fecal microbiota diversity. Bayesian inference revealed associations between intake of sweets or sugars during antibiotic exposure with microbiome disruption, as assessed by low diversity or expansion of the pathobiont Enterococcus. We validated this observation experimentally, finding that sucrose exacerbated antibiotic-induced Enterococcus expansion in mice. Taken together, our results suggest that avoiding sugar-rich foods during antibiotic treatment may reduce microbiome injury. One Sentence SummaryAnalyses of hematopoietic cell transplant patients and of mice uncover links between foods and microbiome disruption.

microbiology↗

bioRxiv2024

Low Kit expression identifies Hematopoietic Stem Cell subsets with enhanced lymphoid potential in mice and humans

Hematopoietic stem cells (HSCs) with multilineage potential are critical for effective T cell reconstitution and restoration of the adaptive immune system after allogeneic Hematopoietic Cell Transplantation (allo-HCT). The Kitlo subset of HSCs is enriched for multipotential precursors,1, 2 but their T-cell lineage potential has not been well-characterized. We therefore studied the thymic reconstituting and T-cell potential of Kitlo HSCs. Using a preclinical allo-HCT model, we demonstrate that Kitlo HSCs support better thymic recovery, and T-cell reconstitution resulting in improved T cell responses to infection post-HCT. Furthermore, Kitlo HSCs with augmented BM lymphopoiesis mitigate age-associated thymic alterations, thus enhancing T-cell recovery in middle-aged hosts. We find the frequency of the Kitlo subset declines with age, providing one explanation for the reduced frequency of T-competent HSCs and reduced T-lymphopoietic potential in BM precursors of aged mice.3, 4, 5 Chromatin profiling revealed that Kitlo HSCs exhibit higher activity of lymphoid-specifying transcription factors (TFs), including Zbtb1. Deletion of Zbtb1 in Kitlo HSCs diminished their T-cell potential, while reinstating Zbtb1 in megakaryocytic-biased Kithi HSCs rescued T-cell potential, in vitro and in vivo. Finally, we discover an analogous Kitlo HSC subset with enhanced lymphoid potential in human bone marrow. Our results demonstrate that Kitlo HSCs with enhanced lymphoid potential have a distinct underlying epigenetic program.

immunology↗

bioRxiv2024

CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

Resistance to chemotherapy has been a major hurdle that limits therapeutic benefits for many types of cancer. Here we systematically identify genetic drivers underlying chemoresistance by performing 30 genome-scale CRISPR knockout screens for seven chemotherapeutic agents in multiple cancer cells. Chemoresistance genes vary between conditions primarily due to distinct genetic background and mechanism of action of drugs, manifesting heterogeneous and multiplexed routes towards chemoresistance. By focusing on oxaliplatin and irinotecan resistance in colorectal cancer, we unravel that evolutionarily distinct chemoresistance can share consensus vulnerabilities identified by 26 second-round CRISPR screens with druggable gene library. We further pinpoint PLK4 as a therapeutic target to overcome oxaliplatin resistance in various models via genetic ablation or pharmacological inhibition, highlighting a single-agent strategy to antagonize evolutionarily distinct chemoresistance. Our study not only provides resources and insights into the molecular basis of chemoresistance, but also proposes potential biomarkers and therapeutic strategies against such resistance.

cancer biology↗

bioRxiv2023

A single-cell transposable element atlas of human cell identity

Single cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex biological systems. However, most sequencing studies overlook the contribution of transposable element (TE) expression to the transcriptome. In both scRNA-seq and bulk tissue RNA sequencing (RNA-seq), quantification of TE expression is challenging due to repetitive sequence content and poorly characterized TE gene models. Here, we developed a tool and analysis pipeline for Single cell Transposable Element Locus Level Analysis of scRNA Sequencing (Stellarscope) that reassigns multi-mapped reads to specific genomic loci using an expectation-maximization algorithm. Using Stellarscope, we built an atlas of TE expression in human PBMCs. We found that locus-specific TEs delineate cell types and define new cell subsets not identified by standard mRNA expression profiles. Altogether, this study provides comprehensive insights into the influence of transposable elements in human biology.

genomics↗

bioRxiv2023

Leucine zipper-based sorting system enables generation of multi-functional CAR T cells

Resistance to chimeric antigen receptor (CAR) T cell therapy develops through multiple mechanisms including antigen-loss escape and tumor-induced immune suppression. Expression of multiple CARs may overcome multi-antigen-loss escape. Similarly, expression of switch receptors that convert inhibitory immune checkpoint signals into positive costimulatory signals may enhance CAR T cell activity in the tumor microenvironment. Engineering multiple features into one cell product, however, is limited by transgene packaging constraints of current vector systems. Here, we describe a leucine zipper-based cell sorting methodology that enables selective single-step immunomagnetic purification of cells co-transduced with two vectors, designed to potentially double the number of incorporated transgenes. This "Zip-sorting" system facilitated generation of T cells simultaneously expressing up to four CARs and co-expressing up to three switch receptors. These multi-CAR multi-Switch receptor arrays enabled T cells to eliminate antigenically heterogeneous syngeneic leukemia populations co-expressing multiple inhibitory ligands. Zip-sorted multi-CAR multi-Switch receptor T cells represent a potent therapeutic strategy to overcome multiple mechanisms of CAR T cell resistance.

synthetic biology↗

bioRxiv2023

Locus specific human endogenous retroviruses reveal new lymphoma subtypes

The heterogeneity of cancers are driven by diverse mechanisms underlying oncogenesis such as differential cell-of-origin (COO) progenitors, mutagenesis, and viral infections. Classification of B-cell lymphomas have been defined by considering these characteristics. However, the expression and contribution of transposable elements (TEs) to B cell lymphoma oncogenesis or classification have been overlooked. We hypothesized that incorporating TE signatures would increase the resolution of B-cell identity during healthy and malignant conditions. Here, we present the first comprehensive, locus-specific characterization of TE expression in benign germinal center (GC) B-cells, diffuse large B-cell lymphoma (DLBCL), Epstein-Barr virus (EBV)-positive and EBV-negative Burkitt lymphoma (BL), and follicular lymphoma (FL). Our findings demonstrate unique human endogenous retrovirus (HERV) signatures in the GC and lymphoma subtypes whose activity can be used in combination with gene expression to define B-cell lineage in lymphoid malignancies, highlighting the potential of retrotranscriptomic analyses as a tool in lymphoma classification, diagnosis, and the identification of novel treatment groups.

cancer biology↗

bioRxiv2023

A deep learning-based drug repurposing screening and validation for anti-SARS-CoV-2 compounds by targeting the cell entry mechanism

The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still lacking. Here, we employ a deep learning-based drug repositioning strategy to systematically screen potential anti-SARS-CoV-2 drug candidates that target the cell entry mechanism of SARS-CoV-2 virus from 2,635 FDA-approved drugs and 1,062 active ingredients from Traditional Chinese Medicine herbs. In silico molecular docking analysis validates the interactions between the top compounds and host receptors or viral spike proteins. Using a SARS-CoV-2 pseudovirus system, we further identify several drug candidates including Fostamatinib, Linagliptin, Lysergol and Sophoridine that can effectively block the cell entry of SARS-CoV-2 variants into human lung cells even at a nanomolar scale. These efforts not only illuminate the feasibility of applying deep learning-based drug repositioning for antiviral agents by targeting a specified mechanism, but also provide a valuable resource of promising drug candidates or lead compounds to treat COVID-19.

microbiology↗

bioRxiv2023

Modeling kidney development, disease, and plasticity with clonal expandable nephron progenitor cells and nephron organoids

Nephron progenitor cells (NPCs) self-renew and differentiate into nephrons, the functional units of the kidney. Here we report manipulation of p38 and YAP activity creates a synthetic niche that allows the long-term clonal expansion of primary mouse and human NPCs, and induced NPCs (iNPCs) from human pluripotent stem cells. Cultured iNPCs resemble closely primary human NPCs, generating nephron organoids with abundant distal convoluted tubule cells, which are not observed in published kidney organoids. The synthetic niche reprograms differentiated nephron cells into NPC state, recapitulating the plasticity of developing nephron in vivo. Scalability and ease of genome-editing in the cultured NPCs allow for genome-wide CRISPR screening, identi-fying novel genes associated with kidney development and disease. A rapid, efficient, and scala-ble organoid model for polycystic kidney disease was derived directly from genome-edited NPCs, and validated in drug screen. These technological platforms have broad applications to kidney development, disease, plasticity, and regeneration.

developmental biology↗

bioRxiv2023

Scalable Log-ratio Lasso Regression Enhances Microbiome Feature Selection for Predictive Models

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL (https://vdblab.github.io/FLORAL/), an open-source computational tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility of longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for extended false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches, and better sensitivity over popular differential abundance testing methods for datasets with smaller sample size. In a survival analysis in allogeneic hematopoietic-cell transplant, we further demonstrated considerable improvement by FLORAL in microbial feature selection by utilizing longitudinal microbiome data over only using baseline microbiome data.

bioinformatics↗

bioRxiv2023

High-Dimensional Spectral Cytometry Reveals Therapeutically Relevant Immune Subtypes in Gastric Cancer

Identification of locally advanced gastric cancer (GC) patients who might potentially benefit from immune-based strategies is limited by both the poor predictive quality of existing biomarkers, including molecular subtypes, tumor mutational burden, and PD-L1 expression, as well as inadequate understanding of the gastric cancer immune microenvironment. Here, we leveraged high-dimensional spectral cytometry to re-classify locally advanced gastric tumors based on immune composition. The gastric cancer microenvironment was comprised of a diverse immune infiltrate including high proportions of plasmablasts, macrophages, and myeloid-derived suppressor cells. Computational cell typing and sample clustering based on tiered broad immune and T-cell focused phenotyping identified three distinct immune subtypes. The most immunogenic subtype exhibited high proportions of activated CD4+ T-cells and plasmablasts and included tumors that would have been classified as non-immunogenic based on prior classifications. Analysis of gastric cancer patients treated with immune checkpoint blockade indicates that patients who responded to immunotherapy had a pre-treatment tumor composition that corresponded to higher immune scores from our analysis. This work establishes a novel immunological classification of gastric cancer including identification of patients and immune networks likely to benefit from immune-based therapies.

cancer biology↗