Biology subjects

Vo, D. T.

Publications and source records attributed to Vo, D. T..

7 recordsLinked to original sources

bioRxiv2025

Distinct Colitis-Associated Macrophages Drive NOD2-DependentBacterial Sensing and Gut Homeostasis

Single-cell studies have revealed that intestinal macrophages maintain gut homeostasis through the balanced actions of reactive (inflammatory) and tolerant (non-inflammatory) subpopulations. How such balance is impaired in inflammatory bowel diseases (IBD), including Crohns disease (CD) and ulcerative colitis (UC), remains unresolved. Here, we define colon-specific macrophage states and reveal the critical role of non-inflammatory colon-associated macrophages (niColAMs) in IBD recovery. Through trans-scale analyses--integrating computational transcriptomics, proteomics, and in vivo interventional studies--we identified GIV (CCDC88A) as a key regulator of niColAMs. GIV emerged as the top-ranked gene in niColAMs that physically and functionally interacts with NOD2, an innate immune sensor implicated in CD and UC. Myeloid-specific GIV depletion exacerbates infectious colitis, prolongs disease, and abolishes the protective effects of the NOD2 ligand, muramyl dipeptide, in colitis and sepsis models. Mechanistically, GIVs C-terminus binds the terminal leucine-rich repeat (LRR#10) of NOD2 and is required for NOD2 to dampen inflammation and clear microbes. The CD-associated 1007fs NOD2-variant, which lacks LRR#10, cannot bind GIV--providing critical insights into how this clinically relevant variant impairs microbial sensing and clearance. These findings illuminate a critical GIV-NOD2 axis essential for gut homeostasis and highlight its disruption as a driver of dysbiosis and inflammation in IBD.

immunology↗

bioRxiv2024

Molecular Signatures for Microbe-Associated Colorectal Cancers

BackgroundGenetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics. MethodsA Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum (Fn), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice (CPC-APCMin+/-) and patients (FAP, Lynch Syndrome, PJS, and JPS). ResultsThe MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APCMin+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs. ConclusionsComputational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

microbiology↗

bioRxiv2022

Artificial Intelligence Guided Discovery of Gastric Cancer Continuum

BackgroundDetailed understanding of pre, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to Gastric Cancers (GCs) and medical treatment to intercept such progression. MethodsWe built a Boolean Implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low-to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean Implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean Implication relationships can decipher fundamental time series underlying the biological data. Pursuing this method, we developed a healthy mucosa [->]GC continuum model based on this approach. ResultsOur model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia [->]dysplasia [->]neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression. ConclusionsA Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression. MINI-ABSTRACTWe developed predictive models of early and late neoplastic states in gastric cancer and identified gene clusters that are up/down-regulated at various points along the gastric cancer disease continuum.

bioinformatics↗

bioRxiv2022

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance that Predict Disease Outcomes

Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate. Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species and immune cells (> 12,500 diverse datasets). This 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates. These findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics. One Sentence SummarySignatures of macrophage reactivity and tolerance (SMaRT) predict disease outcomes

systems biology↗

bioRxiv2022

Coupling of NOD2 to GIV is Required for Bacterial Sensing

ABSTRACT/SUMMARYSensing of pathogens by Nucleotide oligomerization domain (NOD)-like 2 receptor (NOD2) induces a protective inflammatory response that coordinates bacterial clearance. Polymorphisms in NOD2 impair bacterial clearance, leading to chronic gut inflammation in Crohns disease (CD) via mechanisms that remain incompletely understood. We identify GIV/Girdin (CCDC88A) as a NOD2-interactor that shapes bacterial sensing-and-signaling in macrophages. Myeloid-specific GIV depletion exacerbated and protracted infectious colitis and abolished the protective effect of muramyl dipeptide (MDP) in both chemical colitis and severe sepsis. In the presence of GIV, macrophages enhance anti-bacterial pathways downstream of NOD2, clear microbes rapidly and concomitantly suppress inflammation. GIVs actions are mediated via its C-terminus, which directly binds the terminal leucine-rich repeat (LRR#10) of NOD2; binding is augmented by MDP and ATP, precedes receptor oligomerization, and is abolished by the 1007fs CD-risk variant which lacks LRR#10. Findings illuminate mechanisms that underlie protective NOD2 signaling and loss of function in the major 1007fs variant. In briefThis work reveals a mechanism by which macrophages use their innate immune sensor, NOD2, to protect the host against overzealous inflammation during bacterial infections, and the consequences of its loss, as occurs in the most important Crohns disease-risk variant. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/489574v2_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@139a1feorg.highwire.dtl.DTLVardef@ba5354org.highwire.dtl.DTLVardef@b4d933org.highwire.dtl.DTLVardef@11fe12e_HPS_FORMAT_FIGEXP M_FIG GRAPHIC ABSTRACT C_FIG HIGHLIGHTSO_LIGIV is a functional and direct interactor of the terminal LRR repeat of NOD2 C_LIO_LIMice lacking M[FE] GIV develop dysbiosis, protracted ileocolitis and sepsis C_LIO_LIMDP/NOD2-dependent protective host responses require GIV C_LIO_LICD-risk NOD2 1007fs variant lacking the terminal LRR#10 cannot bind GIV C_LI

immunology↗

bioRxiv2022

AI-assisted Discovery of an Ethnicity-influenced Driver of Cell Transformation in Esophageal and Gastroesophageal Junction Adenocarcinomas

Although Barretts metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of the metaplasia[->]dysplasia[->]neoplasia cascade in the esophagus remains incompletely understood. Using an AI-guided network transcriptomics approach, in which EAC initiation and progression is modeled as networks to simplify complex multi-cellular processes, we first predict cellular continuum states and disease driving processes with an unprecedented degree of precision. Key AI-guided predictions are subsequently validated in a human organoid model and patient-derived biopsies of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. We find that all EACs must originate from BE, pinpoint a CXCL8/IL8{leftrightarrow}neutrophil immune microenvironment as a driver of cellular transformation in both EACs and gastroesophageal junction-ACs. This driver is prominent in Caucasians (Cau), but notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with ethnic changes in ANC modify that risk. Thus, findings define a racially influenced immunological basis for cell transformation and suggest that benign ethnic neutropenia in AAs may serve as a deterrent to BE[->]EAC progression. BRIEF SUMMARYEsophageal adenocarcinoma (EAC) is a highly lethal cancer among Caucasians, while African Americans are somewhat protected; what factors drive transformation with racial disparity remain unknown. AI-enabled creation of the first computational map of neoplastic progression in the esophagus built and validated using transcriptomic datasets from diverse cohorts of human samples pinpointed CXCL8{leftrightarrow}neutrophil tumor immune-microenvironment as a racially influenced driver of EACs and GEJ-ACs. Computational tools pinpoint a racially influenced driver of cell transformation during BE[->]EAC progression; in doing so, it reveals new novel biology, informs disease modeling, therapeutic strategies, and biomarkers. LAY SUMMARYBy modeling diseases as networks, this work unravels a fundamental race-influenced immunologic driver of cell transformation in adenocarcinomas of the esophagus and the gastroesophageal junction.

cancer biology↗

bioRxiv2019

Integrated analyses of early responses to radiation in glioblastoma identify new alterations in RNA processing and candidate target genes to improve treatment outcomes

BackgroundHigh-dose radiation is the main component of glioblastoma therapy. Unfortunately, radio-resistance is a common problem and a major contributor to tumor relapse. Understanding the molecular mechanisms driving response to radiation is critical for identifying regulatory routes that could be targeted to improve treatment response. MethodsWe conducted an integrated analysis in the U251 and U343 glioblastoma cell lines to map early alterations in the expression of genes at three levels: transcription, splicing, and translation in response to ionizing radiation. ResultsChanges at the transcriptional level were the most prevalent response. Downregulated genes are strongly associated with cell cycle and DNA replication and linked to a coordinated module of expression. Alterations in this group are likely driven by decreased expression of the transcription factor FOXM1 and members of the E2F family. Genes involved in RNA regulatory mechanisms were affected at the mRNA, splicing, and translation levels, highlighting their importance in radiation-response. We identified a number of oncogenic factors, with an increased expression upon radiation exposure, including BCL6, RRM2B, IDO1, FTH1, APIP, and LRIG2 and lncRNAs NEAT1 and FTX. Several of these targets have been previously implicated in radio-resistance. Therefore, antagonizing their effects post-radiation could increase therapeutic efficacy. ConclusionsOur integrated analysis provides a comprehensive view of early response to radiation in glioblastoma. We identify new biological processes involved in altered expression of various oncogenic factors and suggest new target options to increase radiation sensitivity and prevent relapse.

cancer biology↗