Biology subjects

Dou, J.

Publications and source records attributed to Dou, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-Cell Indel Detection Enhances Genetic Ancestry and Cellular Lineage Analysis

Small insertions and deletions (Indels) provide critical information for cancer genomics and clonal evolution, yet their detection from single-cell sequencing (SCS) such as scRNA-seq and scATAC-seq remains challenging due to sparse coverage, alignment artifacts, and RNA editing. Here, we present Monopogen-Indel, a bioinformatics framework for accurate germline and somatic Indel detection through haplotype-aware variant calling, dynamic template matching in repetitive regions, and cell-population- based allele segregation analysis. We validated germline Indel detection in human retina snRNA-seq with matched bulk whole genome sequencing (WGS). Monopogen-Indel detected 41,000-45,000 germline Indels per sample, with >70% precision and >90% genotyping accuracy. Using 65 heart left ventricle snATAC-seq samples, indel-based global ancestry inference segregated genetic ancestry comparably to SNVs, establishing indels as an independent marker of genetic diversity in SCS. In scRNA-seq from 43,717 cells across four anatomic sites of a patient with high grade serous ovarian cancer (HGSOC), Monopogen- Indel identified ~50,000 germline Indels per sample at 86% WGS-validated precision and an average of 1,040 de novo Indels per sample. Approximately 50% of de novo Indels reflected biological sources, including variants WGS variants, RNA editing, and cell-type-specific patterns. Somatic SNVs were correctly restricted to CD45- cells, indicating high specificity in delineating somatic from germline variants. In summary, Monopogen-indel is the first framework dedicated to indel detection from SCS and expands the utility of existing single-cell data for population genetics and cancer evolution. The module is integrated into Monopogen repository https://github.com/KChen-lab/Monopogen.

genomics↗

bioRxiv2026

Attention or prediction? Characterizing the top-down influence of predictive context on speech encoding

Theories of predictive coding propose that perception is the process of inferring the causes of our sensory input by comparing that input with predictions derived from our internal models of the world. Such predictive processes are thought to play a central role in language comprehension, however, robust neurophysiological evidence for such processes, particularly during natural speech perception, remains limited. Previous work has suggested that the early auditory encoding of words in natural speech is influenced by their preceding linguistic context. However, it remains unclear whether this effect is driven by prediction per se or dynamic modulations of attention based on contextual uncertainty. To distinguish between these alternatives, we recorded electroencephalography from 17 healthy adults while they listened to slightly changed audiobook. Specifically, we identified and replaced several unsurprising content words with more surprising words. We quantified the early auditory encoding of words using speech-envelope reconstruction accuracy within 100-ms time window after word onset and examined its relationship to word surprisal and contextual uncertainty. We found that more surprising words showed enhanced early auditory encoding despite matched contextual constraint. Moreover, the temporal profile of this enhancement depended on when the incoming speech signal diverged from the predicted phonological sequence, consistent with the emergence of prediction-error responses. Linear mixed-effects modeling further revealed that word surprisal had a substantially stronger influence on early auditory encoding than contextual uncertainty. Together, these findings indicate that the early auditory encoding of words during naturalistic speech perception is more strongly associated with predictive computations than with uncertainty-driven attentional gain. Significance StatementDuring natural speech comprehension, contextual information influences how the brain processes incoming sensory input. However, whether this context-dependent modulation of the early auditory encoding of words reflects predictive computations or dynamic changes in attentional gain has remained unresolved. By combining a naturalistic speech paradigm with a stimulus manipulation that varies word surprisal while controlling contextual uncertainty, we show that the early auditory encoding of words is driven by word surprisal under matched contextual constraint. Moreover, the temporal dynamics of this modulation closely follow the point at which the incoming speech signal departs from the predicted phonological sequence. These findings provide neurophysiological evidence that predictive computations contribute to the context- dependent modulation of early auditory processing during natural speech comprehension.

neuroscience↗

bioRxiv2026

Estimating the Explainable Variance of EEG Responses to Natural Speech

Substantial progress has been made in recent years on understanding how the human brain parses and processes natural speech. Much of this progress has been based on modeling how brain activity relates to the different acoustic and linguistic features of speech. By fitting and testing models based on those features, one can test hypotheses about the kinds of computations and representations the brain uses to convert speech sounds into understanding. While much of this work has focused on modeling BOLD activity using functional neuroimaging or intracranially recorded electrophysiological signals, the approach has also proven useful with MEG and EEG. Indeed, noninvasive EEG has certain advantages for studying speech processing in terms of translational research and application. Research over the last decade or so has shown that EEG can be successfully modeled based on numerous acoustic, linguistic, and paralinguistic speech features. However, an important unanswered question hangs over all of this work: namely, what constitutes a good model of EEG responses to natural speech? Or, to put it another way, how much variance in EEG recorded during natural speech listening is explainable as having derived from that speech input? The present study aims to tackle this issue. We do so under the assumption that the best model for a persons EEG response to natural speech is a set of EEG responses from other people listening to the same speech. Using this assumption, we construct inter-subject models using EEG from 19 healthy adult native speakers of English who all listened to the same audiobook. The model for each subject involves predicting their EEG data using (dimensionality-reduced) EEG from different numbers of other subjects and then extrapolating to estimate the total explainable variance in the target individuals response to speech. Following this, we show that linear models (temporal response functions) based on several commonly used acoustic and linguistic speech features can predict most - but importantly not all - of the estimated total explainable variance in EEG responses across subjects.

neuroscience↗

bioRxiv2026

Somatic variant detection in normal tissues from single-cell sequencing data

A crucial advantage of single-cell sequencing (SCS) is its ability to identify somatic variants in individual cells, enabling phylogenetic analysis of cellular populations within bulk tissues. While identifying somatic variants in tumor tissues via SCS has become a common practice, doing so in normal tissues remains challenging due to the rarity of somatic variants in normal cells. To evaluate the feasibility of somatic variant calling from widely available single-nucleus RNA-seq (snRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) data, we profiled a Cell-line mix of six HapMap samples prepared by the SMaHT consortium using 10x Genomics 5 snRNA-seq (12k cells with 36k mean reads per cell) and snATAC-seq (11k cells with 14k median high-quality fragments per cell) for variant calling. PacBio long-read whole genome sequencing (WGS) data (109x) generated from individual cell lines were used as ground truth. Two computational tools, Monopogen and SComatic, were used for somatic variant calling from the SCS data. Monopogen achieved single nucleotide variant (SNV) detection accuracies of 93.30% in the snRNA-seq and 99.64% in the snATAC-seq data, both of which outperformed SComatic (74.35% and 94.29%, respectively). Monopogen also consistently detected somatic SNVs at cellular fractions as low as 0.5% (2.54% in snRNA and 0.81% in snATAC) in individual samples. Notably, snATAC-seq exhibited higher genomic coverage breadth and larger number of variants detected than snRNA-seq. While the SCS data have lower overall genome coverage than that of the bulk WGS, the single-cell level variant resolution allows Monopogen to assign variants to their cells of origin with over 80% accuracy in both RNA and ATAC modalities, thereby facilitating studies of clonal evolution and cell-type-specific mutagenesis. Other benchmarking methods were also evaluated (DeepVariant, Cellsnp-lite and Mutect2) for comparison. In conclusion, our study demonstrated the feasibility of performing reliable single-cell somatic mutation calling in a cell-line mixture and discussed the strengths and limitations of current computational methods when applied to normal tissues.

bioinformatics↗

bioRxiv2025

PhysDock: A Physics-Guided All-Atom Diffusion Model for Protein-Ligand Complex Prediction

Accurate prediction of protein-ligand complexes remains a central challenge in structural biology. Traditional methods are computationally inefficient and prone to local minima, whereas deep learning approaches struggle to capture structural flexibility and physical plausibility. We introduce PhysDock, a physics-guided diffusion model that uniquely integrates (i) all-atom diffusion to model ligand flexibility and protein precision-flexibility (i.e., subtle conformational adjustments); (ii) physical priors as diffusion conditioning, alongside two-phase physics guidance during the denoising diffusion to ensure physical plausibility. PhysDock demonstrates state-of-the-art performance in redocking benchmarks and excels in the more challenging cross-docking assessments. For practical utility, PhysDock (i) resolves cannabinoid receptor selectivity across diverse molecules, achieving accuracy comparable to experiments; (ii) distinguishes most drug candidates from weak binders in virtual screening of NTRK3 kinase, while uncovering novel candidates with structural insights. PhysDock serves as a versatile tool for protein-ligand complex prediction, with substantial potential to accelerate structure-based drug discovery.

bioinformatics↗

bioRxiv2024

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples. In addition to objective assessment of automatic cell segmentation and phenotyping, we performed pixel-resolution manual evaluation of phenotyping to carry out pathologically meaningful comparison between ST platforms. Our study detailed the intricate differences between the ST platforms, revealed the importance of parameters such as tissue age and probe design in determining the data quality, and suggested reliable workflows for accurate spatial profiling and molecular discovery.

bioinformatics↗

bioRxiv2024

Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable words

In recent years, it has become clear that EEG indexes the comprehension of natural, narrative speech. One particularly compelling demonstration of this fact can be seen by regressing EEG responses to speech against measures of how individual words in that speech linguistically relate to their preceding context. This approach produces a so-called temporal response function that displays a centro-parietal negativity reminiscent of the classic N400 component of the event-related potential. One shortcoming of previous implementations of this approach is that they have typically assumed a linear, time-invariant relationship between the linguistic speech features and the EEG responses. In other words, the analysis typically assumes that the response has the same shape and timing for every word - and only varies (linearly) in terms of its amplitude. In the present work, we relax this assumption under the hypothesis that responses to individual words may be processed more rapidly when they are predictable. Specifically, we introduce a framework wherein the standard linear temporal response function can be modulated in terms of its amplitude, latency, and temporal scale based on the expectedness of the current and prior words. We use the proposed approach to model EEG recorded from a set of participants who listened to an audiobook narrated by a single talker, and a separate set of participants who attended to one of two concurrently presented audiobooks. We show that expected words are processed faster - evoking lower amplitude N400-like responses with earlier peaks - and that this effect is driven both by the words own predictability and the predictability of the immediately preceding word. Additional analysis suggests that this finding is not simply explained based on how quickly words can be disambiguated from their phonetic neighbors. As such, our study demonstrates that the timing and amplitude of brain responses to words in natural speech depend on their predictability. By accounting for these effects, our framework also improves the accuracy with which neural responses to natural speech can be modeled.

neuroscience↗

bioRxiv2024

Placental and Immune Cell DNA Methylation Reference Panel for Bulk Tissue Cell Composition

To distinguish DNA methylation (DNAm) from cell proportion changes in whole placental tissue research, we developed a robust cell type-specific DNAm reference to estimate cell composition. We collated newly collected and existing cell type DNAm profiles quantified via Illumina EPIC or 450k microarrays. To estimate cell composition, we deconvoluted whole placental samples (n=36) with robust partial correlation based on the top 50 hyper- and hypomethylated sites per cell type. To test deconvolution performance, we evaluated RMSE in predicting principal component one of DNAm variation in 204 external placental samples. We analyzed DNAm profiles (n=368,435 sites) from 12 cell types: cytotrophoblasts (n=18), endothelial cells (n=19), Hofbauer cells (n=26), stromal cells (n=21), syncytiotrophoblasts (n=4), six lymphocyte types (n=36), and nucleated red blood cells (n=11). Median cell composition was consistent with placental biology: 60.4% syncytiotrophoblast, 17.1% stromal, 8.8% endothelial, 4.5% cytotrophoblast, 3.9% Hofbauer, 1.7% nucleated red blood cells, and 1.2% neutrophils. Our expanded reference outperformed an existing reference in predicting DNAm variation (15.4% variance explained, IQR=21.61) with cell composition estimates (RMSE:10.51 vs. 11.43, p-value<0.001). This cell type reference can robustly estimate cell composition from whole placental DNAm data to detect important cell types, reveal biological mechanisms, and improve casual inference.

genomics↗

bioRxiv2024

Synthetic bacterial consortium for degradation of plastic pyrolysis oil waste

The plasic crisis is ominipresent, from littering macroplastic to reports that document plastic in every niche of this planet, including the human body. In order to achieve higher recycling quotas, especially of mixed plastic waste, pyrolysis seems to be a viable option. However, depending on the process parameters, plastic pyrolysis oil waste is encountered, which is difficult to valorize, due to the enormous spread of the molecules included. To reduce the molecular heterogeneity, we here artificially compounded, monitored, and optimized the performance of a bacterial consortium, which has the ability to tolerate organic pollutants and use them as energy and carbon sources for their own metabolic activity. The primary constituents of the here used plastic pyrolysis oil waste (PPOW) were alkanes and {varepsilon}-caprolactam. The bacterial community exhibited noteworthy efficacy in eliminating alkanes of diverse chain lengths ranging from 71% to 100%. Additionally, within 7-days, the microbial community demonstrated a removal efficiency surpassing 50% for various aromatic hydrocarbons, along with complete eradication of {varepsilon}-caprolactam and naphthalene. Besides, a back-propagation (BP) neural network method is applied to evaluate O2 consumption as a measure of microbial activity. The insights gained were used to build a model, which is able to predict O2 depletion in long-time experiments and other experimental conditions. The results are discussed in the context of a developing (open) circular plastic economy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/590079v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@628f68org.highwire.dtl.DTLVardef@b5274eorg.highwire.dtl.DTLVardef@1278ceaorg.highwire.dtl.DTLVardef@194887a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightO_LISynthetic bacterial communities are used to remove plastic hydrolysis oil waste C_LIO_LIThe optimized biphase reaction system can remove the majority of pollutants C_LIO_LIThe biodegradation process can be monitored in a real-time bioprocess software C_LIO_LINeural network techniques are used to model and predict the removal process C_LI

bioengineering↗

bioRxiv2023

Temporal recording of mammalian development and precancer

Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo, while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.

developmental biology↗

bioRxiv2023

Intrinsic epigenetic state of primary osteosarcoma drives metastasis

Osteosarcoma (OS) is the most common primary malignant bone tumor affecting the pediatric population with high potential to metastasize to distal sites, most commonly the lung. Insights into defining molecular features contributing to metastatic potential are lacking. We have mapped the active chromatin landscapes of OS tumors by integrating histone H3 lysine acetylated chromatin (H3K27ac) profiles (n=13), chromatin accessibility profiles (n=11) and gene expression (n=13) to understand the differences in their active chromatin profiles and its impact on molecular mechanisms driving the malignant phenotypes. Primary OS tumors from patients with metastasis (primary met) have a distinct active chromatin landscape compared to primary tumors from patients without metastatic disease (localized). The difference in chromatin activity shapes the transcriptional profile of OS. We identified novel candidate genes involved in OS pathogenesis and metastasis, including PPP1R1B, PREX1 and IGF2BP1, which exhibit increased chromatin activity in primary met along with higher transcript levels. Overall, differential chromatin activity in primary met occurs in proximity of genes regulating actin cytoskeleton organization, cellular adhesion, and extracellular matrix suggestive of their role in facilitating OS metastasis. Furthermore, chromatin profiling of tumors from metastatic lung lesions noted increases in chromatin activity in genes involved in cell migration and key intracellular signaling cascades, including the Wnt pathway. Thus, this data demonstrates that metastatic potential is intrinsically present in primary metastatic tumors and the cellular chromatin profiles further adapt to allow for successful dissemination, migration, and colonization at the distal metastatic site.

cancer biology↗

bioRxiv2023

Integration of microbial and chemical synthesis for the efficient production of plitidepsin, a promising anticancer and antiviral agent

Plitidepsin, a marine-derived anticancer medicine, is being tested in phase III clinical trials for treating COVID-19. However, the current supply of plitidepsin relies on laborious chemical synthesis processes. Here, we present a new approach that combines microbial and chemical synthesis to produce plitidepsin. We screened a Tistrella strain library to identify a high-yield didemnin B producer, and then introduced a second copy of the didemnin biosynthetic gene cluster into its genome, resulting in the highest yield of didemnin B reported in the literature. Next, we developed two straightforward chemical strategies to convert didemnin B to plitidepsin, one of which involved a one-step synthetic route giving over 90% overall yield. We also synthesized two new didemnin analogues and assessed their anticancer and antiviral activities. Our findings offer a practical and sustainable solution for producing plitidepsin and its derivatives, potentially expediting didemnin drug development.

synthetic biology↗

bioRxiv2023

Placental Cell Conditioned Media Modifies Hematopoietic Stem Cell Transcriptome In Vitro

BackgroundHematopoietic stem cells are cells that differentiate into all blood cell types. Although the placenta secretes hormones, proteins and other factors important for maternal and fetal health, cross-talk between placental cells and hematopoietic stem cells is poorly understood. Moreover, toxicant impacts on placental-hematopoietic stem cell communication is understudied. The goals of this study were to determine if factors secreted from placental cells alter transcriptomic responses in hematopoietic stem cells and if monoethylhexyl phthalate (MEHP), the bioactive metabolite of the pollutant diethylhexyl phthalate, modifies these effects. MethodsWe used K-562 and BeWo cells as in vitro models of hematopoietic stem cells and placental syncytiotrophoblasts, respectively. We treated K-562 cells with medium conditioned by incubation with BeWo cells, medium conditioned with BeWo cells treated with 10 M MEHP for 24 hours, or controls treated with unconditioned medium. We extracted K-562 cell RNA, performed RNA sequencing, then conducted differential gene expression and pathway analysis by treatment group. ResultsRelative to controls, K-562 cells treated with BeWo cell conditioned medium differentially expressed 173 genes (FDR<0.05 and fold-change>2.0), including 2.4 fold upregulatation of TPM4 and 3.3 fold upregulatation of S1PR3. Upregulated genes were enriched for pathways including stem cell maintenance, cell proliferation and immune processes. Downregulated genes were enriched for terms involved in protein translation and transcriptional regulation. MEHP treatment differentially expressed eight genes (FDR<0.05), including genes involved in lipid metabolism (PLIN2, fold-change: 1.4; CPT1A, fold-change: 1.4). ConclusionK-562 cells, a model of hematopoietic stem cells, are responsive to media conditioned by placental cells, potentially impacting pathways like stem cell maintenance and proliferation.

developmental biology↗

bioRxiv2022

In situ genetically targeted chemical assembly of polymers on living neuronal membranes

Multicellular biological systems, most notably living neural networks, exhibit highly complex physical organization properties that pose challenges for building cell-specific and biocompatible interfaces. We developed a novel approach to genetically program cells to chemically assemble artificial structures that modify the electrical properties of neurons in situ, opening up the possibility of minimally-invasive cell-specific interfaces with neural circuits in living animals. However, the efficiency and biocompatibility of this approach were challenged by limited membrane targeting of the constructed material. Here, we report a method with significantly improved molecular construct properties, which expresses highly localized enzymes targeted to the plasma membrane of primary neurons with minimal intracellular retention. Polymers synthesized in situ by this approach form dense clusters on the targeted cell membrane, and neurons remain viable after polymerization. This platform can be readily extended to incorporate a broad range of materials onto the surface membranes of specific cells within complex tissues, using chemistry that may further enable the next generation of interfaces with living biological systems.

bioengineering↗

bioRxiv2022

Monopogen: single nucleotide variant calling from single cell sequencing

Distinguishing how genetics impact cellular processes can improve our understanding of variable risk for diseases. Although single-cell omics have provided molecular characterization of cell types and states on diverse tissue samples, their genetic ancestry and effects on cellular molecular traits are largely understudied. Here, we developed Monopogen, a computational tool enabling researchers to detect single nucleotide variants (SNVs) from a variety of single cell transcriptomic and epigenomic sequencing data. It leverages linkage disequilibrium from external reference panels to identify germline SNVs from sparse sequencing data and uses Monovar to identify novel SNVs at cluster (or cell type) levels. Monopogen can identify 100K~3M germline SNVs from various single cell sequencing platforms (scRNA-seq, snRNA-seq, snATAC-seq etc), with genotyping accuracy higher than 95%, when compared against matched whole genome sequencing data. We applied Monopogen on human retina, normal breast and Asian immune diversity atlases, showing that that derived genotypes enable accurate global and local ancestry inference and identification of admixed samples from ancestrally diverse donors. In addition, we applied Monopogen on ~4M cells from 65 human heart left ventricle single cell samples and identified novel variants associated with cardiomyocyte metabolic levels and epigenomic programs. In summary, Monopogen provides a novel computational framework that brings together population genetics and single cell omics to uncover genetic determinants of cellular quantitative traits.

bioinformatics↗

bioRxiv2022

Characterizing transition cells in developmental processes from scRNA-seq data

Multi-cellular organism development involves orchestrated gene regulations of different cell types and cell states. Single-cell RNA-Seq, enable simultaneous observation of cells in various states, making it possible to study the underlying molecular mechanisms. However, most of the analytical methods do not make full use of the dynamics captured. Here, we model single-cell RNA-seq data obtained from a developmental process as a function of gene regulatory network using stochastic differential equations (SDEs). Based on dynamical systems theory, we showed that pair-wise gene expression correlation coefficients can accurately infer cell state transitions and validated it using mouse muscle cell regeneration scRNA-seq data. We then applied our analytical framework to the PDAC (Pancreatic ductal adenocarcinoma) mouse model scRNA-seq data. Through transition cells found in the pancreatic preinvasive lesions scRNA-seq data, we can better explain the heterogeneity and predict distinct cell fate even at early tumorigenesis stage. This suggests that the biomarkers identified by transition cells can be potentially used for diagnosis, prognosis and therapeutics of diseases.

bioinformatics↗

bioRxiv2022

The Trichloroethylene Metabolite S-(1,2-dichlorovinyl)-l-cysteine Inhibits Lipopolysaccharide-induced Transcriptomic Inflammation Pathways and Cytokine Secretion in a Macrophage Cell Model

Previous studies have shown that the trichloroethylene metabolite S-(1,2-dichlorovinyl)-l-cysteine (DCVC) inhibits cytokine secretion in pathogen stimulated fetal membrane tissue but little is known about the mechanism for these effects, including which cell types or transcriptomic pathways are impacted. Macrophages play a critical role in the fetal membrane innate immune response during infection. We tested the hypothesis that DCVC inhibits lipopolysaccharide (LPS) stimulated inflammation pathways in differentiated (macrophage-like) THP-1 cells. THP-1 cells were differentiated with phorbol 12-myristate 13-acetone for 24 hours and subsequently treated with 1, 5, or 10 {micro}M DCVC for 24 hours. After an additional 4 hour incubation with lipopolysaccharide (LPS), we collected RNA and cell media. We performed transcriptomic analysis using RNA sequencing analysis for 5{micro}M DCVC treatments and quantified cytokine release (IL-1{beta}, IL-6, and TNF-) into cell media for 1, 5 and 10 {micro}M DCVC treatments. RNAseq analysis revealed 1,399 differentially expressed genes (FDR<0.05 and log2fold change magnitude>2.5) in the cells co-treated with DCVC and LPS compared to LPS alone. For example, TNF was 9-fold downregulated with the addition of DCVC. Major pathways downregulated (adjusted p-value<0.05) in DCVC+LPS treatments versus LPS-only treatments, included: "acute inflammatory response", "production of molecular mediator of immune response" and "phagocytosis". LPS increased IL-1{beta}, IL-6, and TNF- levels in culture media (p<0.001), but this effect which was inhibited by co-treatment with DCVC (p<0.001 for LPS vs. LPS+DCVC treatments). Our results demonstrate that DCVC suppresses inflammatory responses in macrophages.

pharmacology and toxicology↗

bioRxiv2021

ANTIBODY-INDEPENDENT ANTITUMOR EFFECTS OF CD32A-CHIMERIC RECEPTOR T CELLS: IMPLICATIONS FOR BREAST CANCER PROGNOSIS AND TREATMENT.

Fc{gamma} RIIA (CD32A) and their ligands, including the immunoglobulin Fc fragment and pentraxins, are key players in a variety of innate immune responses. Still unclear is whether additional ligands of CD32A do exist. The objective of this study is to demonstrate that CD32A-chimeric receptor (CR) can be utilized for the identification of CD32A cell surface ligand(s). Among fifteen cancer cell lines tested, CD32A-CR T cells recognized three of breast cancer (BC) including the MDA-MB-468 and one colorectal carcinoma (HT29) in the absence of targeting antibodies. Conjugation of sensitive BC cells with CD32A-CR T cells induced CD32A polarization and down-regulation, CD107 release, and mutual cell elimination in vitro. Conversely, normal fibroblasts and myoblasts were not affected while normal HUVEC cells promoted CD32A down-regulation. CD32A-CR T cell activity was not inhibited by human IgGs or human serum, but; it was rather enhanced by cetuximab antibody. RNAseq analysis of sensitive vs resistant BC cells identified a fingerprint of 42 genes predicting the sensitivity of BC cells to CD32A-CR T cells and their association with favorable prognostic significance in advanced BC patients. Our data also identify ICAM 1 as a major regulator of CD32A-CR T cell-mediated cytotoxicity. Finally, CD32A-CR T cell administration protected immunodeficient mice from subcutaneous growth of MDA-MB-468 cells in the absence of tumor-specific antibodies. These data indicate that CD32A-CR can be utilized for the identification of (1) cell surface CD32A ligand(s); (2) rational therapeutic strategies to target BC; and (3) novel transcriptomic signatures prognostically relevant for advanced BC patients.

immunology↗