Specific Gene Expression Research Articles

Design of a tunable bacterial gene expression system using engineered σ factors.

The synthesis of organic compounds involves the action of multiple enzymes in a biosynthetic pathway. Incorporating such biosynthetic pathways into microbes often leads to substantial cellular and metabolic stress resulting in low titers of the target compound. This limitation can be offset, in part, by optimizing enzyme efficiency and cellular enzyme concentration. The former involves significant efforts to achieve improvements in catalytic efficiency with the caveat that the metabolic load on a microbial cell imposed by the overexpression of the exogenous enzyme could result in reduced cell fitness. Here, we demonstrate the feasibility of engineered σ factors to modulate gene expression levels without significant genetic engineering. We note that changing the sequence of two flexible polypeptide loops without any changes to the structural scaffold of the transcription initiation factor σE could modulate the expression levels of the target genes. This ability provides a route to improve the efficiency of a biosynthetic pathway without altering the overall genomic makeup. The σE chimera library thus provides an avenue for pre-determined conditional gene expression of specific genes in Escherichia coli.

Comparative Screening of the Liver Gene Expression Profiles from Type 1 and Type 2 Diabetes Rat Models.

Experimental animal models of diabetes can be useful for identifying novel targets related to disease, for understanding its physiopathology, and for evaluating emerging antidiabetic treatments. This study aimed to characterize two rat diabetes models: HFD + STZ, a high-fat diet (60% fat) combined with streptozotocin administration (STZ, 35 mg/kg BW), and a model with a single STZ dose (65 mg/kg BW) in comparison with healthy rats. HFD + STZ- induced animals demonstrated a stable hyperglycemia range (350-450 mg/dL), whereas in the STZ-induced rats, we found glucose concentration values with a greater dispersion, ranging from 270 to 510 mg/dL. Moreover, in the HFD + STZ group, the AUC value of the insulin tolerance test (ITT) was found to be remarkably augmented by 6.2-fold higher than in healthy animals (33,687.0 ± 1705.7 mg/dL/min vs. 5469.0 ± 267.6, respectively), indicating insulin resistance (IR). In contrast, a more moderate AUC value was observed in the STZ group (19,059.0 ± 3037.4 mg/dL/min) resulting in a value 2.5-fold higher than the average exhibited by the control group. After microarray experiments on liver tissue from all animals, we analyzed genes exhibiting a fold change value in gene expression <-2 or >2 (p-value <0.05). We found 27,686 differentially expressed genes (DEG), identified the top 10 DEGs and detected 849 coding genes that exhibited opposite expression patterns between both diabetes models (491 upregulated genes in the STZ model and 358 upregulated genes in HFD + STZ animals). Finally, we performed an enrichment analysis of the 849 selected genes. Whereas in the STZ model we found cellular pathways related to lipid biosynthesis and metabolism, in the HFD + STZ model we identified pathways related to immunometabolism. Some phenotypic differences observed in the models could be explained by transcriptomic results; however, further studies are needed to corroborate these findings. Our data confirm that the STZ and the HFD + STZ models are reliable experimental models for human T1D and T2D, respectively. These results also provide insight into alterations in the expression of specific liver genes and could be utilized in future studies focusing on diabetes complications associated with impaired liver function.

Abstract LB199: Condensate modulator sequesters beta-catenin into depot condensates and demonstrates competitive anti-tumor activity in animal models ofcolorectal cancer

Abstract Constitutive activation of beta-catenin is a well-known driver of malignancy. However, traditional drug discovery approaches have proven challenging and largely unsuccessful in identifying therapeutic agents to modulate the function of beta-catenin. Biomolecular condensates have recently been demonstrated to play key roles in regulating most cellular processes and biological pathways by compartmentalizing biomolecules in membranelles organelles, thus lending novel opportunities to drug previously “undruggable” targets. Here, we leverage condensate biology to discover small molecules that reverse the hyperactive function of beta-catenin in colorectal cancer by entrapping it into depot condensates. We developed a high-throughput phenotypic assay that identifies beta-catenin condensate-modifying compounds (c-mods). Through condensate screening and functional secondary assays, we have identified c-mods that sequester beta-catenin into depots, induce selective cancer cell killing, and reverse oncogenic beta-catenin specific gene expression programs. C-mods identified in our screen are effective in genetically diverse colorectal cancers and a range of Wnt-associated cancers, providing the opportunity to treat a broad patient population. Furthermore, oral dosing of lead c-mod demonstrates competitive tumor growth inhibition as a single agent in xenograft and PDX models of colorectal cancer. Taken together, these results highlight the promise of condensate biology in drugging previously intractable high-value targets in oncology and developing novel treatments for patients suffering from diseases of high unmet need. Citation Format: Costa Salojin, Douglas Baumann, Adam Talbot, Kip West, Thomas Durand-Reville, Isaac Klein, Ann Boija. Condensate modulator sequesters beta-catenin into depot condensates and demonstrates competitive anti-tumor activity in animal models ofcolorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB199.

Metabolic Regulation of Longevity and Immune Response in Caenorhabditis elegans by Ingestion of Lacticaseibacillus rhamnosus IDCC 3201 Using Multi-Omics Analysis.

Probiotics, specifically Lacticaseibacillus rhamnosus, have garnered attention for their potential health benefits. This study focuses on evaluating the probiotic properties of candidate probiotics L. rhamnosus IDCC 3201 (3201) using the Caenorhabditis elegans surrogate animal model, a well-established in vivo system for studying host-bacteria interactions. The adhesive ability to the host's gastrointestinal tract is a crucial criterion for selecting potential probiotic bacteria. Our findings demonstrated that 3201 exhibits significantly higher adhesive capabilities compared with Escherichia coli OP50 (OP50), a standard laboratory food source for C. elegans and is comparable with the widely recognized probiotic L. rhamnosus GG (LGG). In lifespan assay, 3201 significantly increased the longevity of C. elegans compared with OP50. In addition, preconditioning with 3201 enhanced C. elegans immune response against four different foodborne pathogenic bacteria. To uncover the molecular basis of these effects, transcriptome analysis elucidated that 3201 modulates specific gene expression related to the innate immune response in C. elegans. C-type lectin-related genes and lysozyme-related genes, crucial components of the immune system, showed significant upregulation after feeding 3201 compared with OP50. These results suggested that preconditioning with 3201 may enhance the immune response against pathogens. Metabolome analysis revealed increased levels of fumaric acid and succinic acid, metabolites of the citric acid cycle, in C. elegans fed with 3201 compared with OP50. Furthermore, there was an increase in the levels of lactic acid, a well-known antimicrobial compound. This rise in lactic acid levels may have contributed to the robust defense mechanisms against pathogens. In conclusion, this study demonstrated the probiotic properties of the candidate probiotic L. rhamnosus IDCC 3201 by using multi-omics analysis.

272 Integrative Genomics Implicates Primary Impact on Cortical Development in Spina Bifida

INTRODUCTION: Spina bifida is one of the most common malformation of the central nervous system that is associated with poor neurodevelopmental outcomes. Altered neurodevelopment in spina bifida has been attributed to venting of CSF through the neural tube defect. Despite correction of the spinal defect, some patients still develop hydrocephalus with poor neurodevelopmental outcomes, suggesting incomplete understanding of disease pathogenesis. Recent human genetic efforts have identified candidate genes that confer spina bifida risk, however, the mechanisms linking gene mutations to altered neurodevelopment remain poorly understood. METHODS: We curated a list of 439 genes that confer risk to spina bifida from a whole-genome sequencing study of 310 individuals. We then mapped the spatiotemporal and cell-type specific expression of these risk genes onto bulk and single-cell RNA sequencing data of normally developing human brains covering the entire lifespan of human development. RESULTS: We found that spina bifida risk genes are enriched at post-conception weeks 5-6 and 25-38 in the developing human brain. After birth, spina bifida risk genes are enriched at postnatal months 6-10 and then after 60 years of age. At the cell type level, spina bifida risk genes are enriched in proliferative neuroprogenitor cells and microglia in the prenatal human cortex. CONCLUSIONS: Our analyses show that spina bifida risk genes are expressed in the developing human brain, suggesting a primary impact on brain development that is independent from the open caudal end of the neural tube defect. The finding that spina bifida risk genes continue to be expressed postnatally suggests a window for therapeutic intervention to optimize neurodevelopmental outcomes after birth in at least a subset of patients.

Gene expression of protein synthesis, immunity and brain pathways specifically altered in Anorexia Nervosa

IntroductionAnorexia nervosa (AN) is a severe and chronic psychiatric disorder, resulting from a voluntary food restriction, vomiting, use of laxatives and excessive exercises, leading in dramatic weight loss and high mortality. AN is a multifactorial disease involving genetic and epigenetic factors supporting that AN is a metabo-psychiatric disorder. The molecular mechanisms involved in the etiology of AN remain unclear. One work reported gene expression by RNA sequencing in peripheral blood before and after weight restoration in 6 AN patients (Kim 2013), and one RNA sequencing in human iiPSC-derived neurons from 4 patients and 4 controls (Negraes 2017). To date, the profile of expression of genes and proteins in AN is undetermined.ObjectivesIn this study, our goal is to identify specific gene expression signatures from circulating blood nuclear cells to decipher the pathophysiology of AN and characterize biomarkers that can be used for diagnostic or prognostic of AN.MethodsAll consented participants are recruited at Sainte-Anne Hospital, Paris, France, using DSM5 criteria. They had a blood draw in Paxgene tube for the collection of RNAs. Total RNA was extracted from peripheral blood mononuclear cells of 15 patients suffering of AN and 15 healthy controls. All messenger RNAs are sequenced on a Novaseq plateform. Reads are aligned to the human genome 19 and statistical analyses on the read counts for differentially expressed genes are computed with DESeq2.ResultsThe total RNA sequencing allows us to identify 673 dysregulates genes (p adjusted value &lt;0.01, fold change &gt;1,5). Among them, 248 are down-regulated and 425 are up-regulated genes in AN patients compared to controls. From them, 151 transcripts are annotated as pseudogene and 45 are referenced as antisense RNA. Of the 522 remaining transcripts, 424 correspond to a transcript or protein annotated by HGNC and ENSEMBL and 93 are known pseudogenes. A large number of proteins resulting from the expression of deregulated genes interact with each other and form a statistically enriched network impacting biological processes. They are mainly increased and acting in the cellular machinery allowing protein synthesis (biological process: transcription, ribosome, spliceosome and mitochondria). In contrast, down-regulated genes present an enrichment in genes involved in immunity pathways. Finally, several genes are also expressed in the brain. We observed a significant enrichment of genes expressed in the blood and brain tissues.ConclusionsWe identify specific profiles of gene expression in AN. Several genes are both blood and brain tissue expression. Some genes are good candidates for biomarker of the diagnostic in AN that need to be investigated in a longitudinal study to evaluate their useful as prognostic biomarker of AN.This work is supported by Fondation de France &amp; Fédération Recherche sur le Cerveau.Disclosure of InterestNone Declared

Silence of five F. graminearum genes in wheat host confers resistance to Fusarium head blight

Fusarium head blight (FHB), mainly caused by fungus Fusarium graminearum (F. graminearum), is a devastating wheat disease worldwide, leading to reduced yield production and compromised grain quality due to contamination by mycotoxins, such as deoxynivalenol (DON). Manipulating the specific gene expression in microorganisms through RNA interference (RNAi) presents an opportunity for new-generation double-stranded RNA (dsRNA)-based formulations to combat a large number of plant diseases. Here, we applied both spray-induced gene silencing (SIGS) and host-induced gene silencing (HIGS) to target five virulence-related and DON-synthesized genes in F. graminearum, including protein kinase gene Gpmk1, zinc finger protein gene FgChy1, transcription factor FgSR, DON synthesis gene TRI5 and the cell-end marker protein gene FgTeaA, aiming to effectively control FHB in wheat. Direct spraying of individual or combined siRNAs (small interfering RNA) from the fungus showed reduced expression of target genes and suppressed pathogenic symptoms during F. graminearum infection in wheat leaves, with the combination of all five siRNAs demonstrating superior resistance. Furthermore, we generated transgenic wheat lines expressing chimeric RNAi cassettes targeting these five genes, and two independent lines exhibited strong resistance to FHB and Fusarium crown rot, and the reduced DON accumulation. Notably, the HIGS transgenic lines did not adversely impact plant growth and yield traits. Collectively, our findings support that SIGS and HIGS represent effective strategies targeting key pathogenic genes for bolstering disease resistance in crops.

Cyclosporine a inhibits bone regeneration and induces bone loss in a rat model

Cyclosporine A (CSA) is an immunosuppressant that has been extensively studied for its side effects on inhibiting osseointegration of titanium implants. However, the impact of CSA on bone healing in postmenopausal osteoporosis remains unknown. Therefore, this study aimed to investigate the effect of CSA on bone repair in an ovariectomized (OVX) rat model through both in vitro and in vivo experiments. We examined the interventions of CSA on osteoblast progenitor cells MC3T3-E1 and assessed their effects on biological function using RT-qPCR, CCK-8 assay, alizarin red staining, and alkaline phosphatase staining. Furthermore, we evaluated the effects of CSA on bone regeneration and bone mass in both OVX rat models and femoral diaphysis bone defect models. The results from the CCK-8 experiment indicated a positive influence of experimental doses of CSA on osteogenic differentiation of MC3T3-E1 cells. ALP expression levels and calcified nodules were also evaluated, suggesting that CSA intervention promoted osteogenic differentiation in MC3T3-E1 cells. Additionally, specific gene expressions including OPN, Runx-2, OC, and Col1a1 were up-regulated after CSA intervention. Biomechanical parameters aligned with histological analysis as well as micro-CT scans confirmed worse bone microstructure and strength following CSA intervention. Our findings preliminarily suggest that whether it is normal or osteoporotic bones, CSA has adverse effects on bone health which are associated with elevated-bone turnover.

Coencapsulation of Immunosuppressive Drug with Anti-Inflammatory Molecule in Pickering Emulsions as an Innovative Therapeutic Approach for Inflammatory Dermatoses

Psoriasis is an inflammatory skin disease characterized by epidermal and immune dysfunctions. Although efficient, current topical treatments display adverse effects, including skin atrophy and burning sensation, leading to poor patient adherence. To overcome these downsides, pickering emulsions were formulated in which the calcitriol-containing dispersed phase was stabilized with either cyclosporin A- or tacrolimus-loaded poly(lactic-co-glycolic) acid nanoparticles. This study aimed to investigate their biological effects on lymphocytes and epidermal cells and their effectiveness in an imiquimod-induced psoriasis-like mouse model. Results showed that both emulsions significantly inhibited nuclear factor of activated T cell translocation in T lymphocytes as well as their IL-2 production, cell activation, and proliferation. In keratinocytes, inhibition of nuclear factor of activated T cell translocation decreased the production of IL-8 and TNF-α. Topical application of emulsions over skin biopsies exvivo showed accumulation of rhodamin B-coupled poly(lactic-co-glycolic) acid nanoparticles throughout the epidermis by immunofluorescence and significantly decreased the antigen-presenting capacity of Langerhans cells in relation to a reduced expression of activation markers CD40, CD86, and HLA-DR. Using an imiquimod-induced psoriasis model invivo, pickering emulsions significantly alleviated psoriasiform lesions potentially attributed to the decreased cutaneous expression of T-cell markers, proinflammatory cytokines, chemokines, and specific epidermal cell genes. Altogether, pickering emulsion might be a very efficient formulation for treating inflammatory dermatoses.

Abstract 552: Non-canonical disulfide isomerases are strongly selective, understudied targets for cancer therapy

Abstract A major challenge in cancer therapy is the identification of drug targets that are essential for cancer cells but not non-transformed cells. The Broad Institute’s Dependency Map (DepMap) evaluates whether knocking down or knocking out expression of specific genes is “strongly selective” against subsets of cancer cells, in contrast with genes that are “common essential.” Of the 22 human Protein Disulfide Isomerases (PDIs), only four, ERp44, TMX1, AGR2, and AGR3, were identified as strongly selective in the DepMap. Importantly, our team previously identified ERp44, AGR2, and AGR3 as targets of bicyclic thiosulfonate compounds termed Disulfide bond Disrupting Agents (DDAs). DDAs exhibit striking anti-cancer activity in xenograft tumor studies. DDA treatment of cancer cells blocks ERp44, AGR2, and AGR3 active site Cys residues, ablating their catalytic activity and binding to client proteins. Evaluation of cancer cell lines predicted to be sensitive to DDAs, based on their AGR2, AGR3, or ERp44 dependence, accurately predicts DDA sensitivity. Cell lines from a variety of tumor types exhibit AGR2, AGR3, or ERp44 dependencies. Our previous work identified HER-family members (i.e., EGFR, HER2, and HER3) and TRAIL receptors (Death Receptors 4 and 5) as important clients of DDA-sensitive PDIs in breast cancer cells, but it was unknown if these observations extend to other tumor types. Initial studies indicate that AGR2 DepMap dependencies are a strong predictor of sensitivity of cancer cells to DDAs, irrespective of their site of origin. It will be important in future studies to determine what the key client proteins for these DDA-sensitive PDIs are and whether they vary across tumor types. Citation Format: Mary E. Law, Zaafir M. Dulloo, Elham Yaaghubi, Grace M. Alexandrow, Bianca Forsyth, Hanyu Su, Ronald K. Castellano, Brian K. Law. Non-canonical disulfide isomerases are strongly selective, understudied targets for cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 552.

Exploring salivary gene expression clusters: A bioinformatics approach for advanced diagnosis and prognosis in head and neck squamous cell carcinoma

ObjectiveThis study postulates that distinct patterns in salivary gene expression clusters among HNSCC patients provide valuable insights into diagnostic and prognostic markers for the disease. The aim is to identify critical genes and their protein-protein interactions through bio-computational analyses, contributing to a deeper understanding of HNSCC progression. Ultimately, the hypothesis anticipates the discovery of novel molecular signatures in saliva, potentially enhancing early detection and prognostic precision in head and neck squamous cell carcinoma. Materials and methodsA comprehensive keyword search was conducted to extract 74 salivary gland secretion genes from the Human Protein Atlas. GeneMANIA and STRING databases were employed to analyse protein-protein interactions and predict functional partners. UALCAN facilitated the visualization of salivary gland secretion gene expression patterns in HNSCC, providing insights into survival analysis. ResultsIn HNSCC compared to normal tissue, CHRM3, GYLTL1B, MMP3, and WDR91 were identified as statistically upregulated genes, while the downregulated genes included AGFG2, CCDC64B, CD24, CRISP3, CXCL17, ELF5, KRT7, MANSC1, MYO5C, PRR4, SCGB3A1, SH3YL1, TCN1, TRPV6, and ZG16B. Kaplan–Meier survival curve analysis revealed that high expression of salivary genes such as AGFG2, AMY1A, CCDC64B, CCL28, C4ORF7, GYLTL1B, TMEM211, LMX1B, ODAM, SCGB3A1, and SLC26A9 was significantly associated with poorer overall survival outcomes in HNSCC patients. ConclusionThis study identifies distinct salivary gene expression patterns in HNSCC, highlighting potential diagnostic and prognostic markers. Specific upregulated genes, such as CHRM3 and GYLTL1B, and downregulated genes, including AGFG2 and CCDC64B, offer insights into HNSCC progression. Elevated expression of particular genes correlates with poorer overall survival, emphasising their prognostic significance.

Abstract 3457: Distinct signatures of tumor-associated macrophage subpopulations predict survival in renal cell carcinoma

Abstract Background: Comprising 90% of renal cancer cases in adults, renal cell carcinoma (RCC) is marked by significant heterogeneity in its tumor microenvironment. This study aims to test the hypothesis that tumor-associated macrophages (TAMs) play a role in modulating RCC progression and patient response to treatment. We explored the prognostic implications of TAM signatures on RCC survival, leveraging immunomics to decipher TAM-related alterations in the tumor microenvironment. Methods: Utilizing independent single-cell RNA-seq data from human RCC samples, we crafted 8 distinct RCC TAM signatures reflective of TAM presence. A LASSO Cox regression model was developed to prognosticate survival, tested against the TCGA dataset and independently validated across multiple RCC cohorts. Model performance was corroborated through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis, and t-SNE analyses. Results: We identified two TAM signatures that correlate positively with patient survival, indicative of the abundance of specific subsets of TAMs in RCC. These signatures showed a strong association of specific TAM markers and macrophage infiltration. Survival analysis demonstrated that specific TAM signature gene expressions are significant prognostic markers. Forest plots underscored their prognostic relevance. A 32-gene risk score model was established, successfully stratifying patients into distinct risk categories, with low-risk patients exhibiting markedly improved overall survival. Conclusions: The study underscores the positive association between abundance of specific TAM subsets of and RCC patient survival. The precision of our LASSO Cox regression model, informed by novel abundance markers of specific TAM subsets, advances our understanding of TAM roles in TIME and RCC progression. These TAM signatures could potentially provide additional insights and targets to enhance the efficacy of RCC treatments. Keywords: Tumor associated macrophage, renal cell carcinoma, prognosis, tumor microenvironment, immunomics. Citation Format: Youngsoo Han, Aidan Shen, Zhaohui Wang, Aliesha Garrett, Chongming Jiang. Distinct signatures of tumor-associated macrophage subpopulations predict survival in renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3457.

Abstract 2278: Unraveling immune dynamics of CD4+ in non-small cell lung cancer through single-cell analysis and gene expression profiling

Abstract Non-small cell lung cancer (NSCLC) is a type of epithelial lung cancer other than small cell lung cancer and represents the majority of cases in lung cancer. The immune response against NSCLC relies significantly on the vital role by CD4+ T cells, contributing to the regulation of immune system and the suppression of tumor growth. Various CD4+ T cell subtypes, such as T conventional cells (Tconv cells) and T regulatory cells (Treg cells), perform distinct functions within the immune system. Tconv cells boost immune responses against specific pathogens, while Treg cells play a role in suppressing immune reactions. Investigating the correlation between Tconv cells and Treg cells in NSCLC could serve a valuable biomarker for evaluating the immune status of lung cancer patients. We examine the connection between particular CD4+ T cells in the lung and specific gene expressions associated with NSCLC. Through single-cell analysis, we classified the proportions of Treg cells and Tconv cells within CD4+ T cells in both the lungs and spleen of mice. Our discovery revealed that the proportion of Tconv cells in the lungs exceeded that in the spleen. From the differential gene expression (DEG) analysis using RNA-Seq data for Treg cells and Tconv cells in NSCLC, compared to the control group of Peripheral Blood Mononuclear Cells (PMBC), we identified 1823 genes uniquely expressed in NSCLC Treg cells and 711 genes in Tconv cells. Within these genes, we verified the expression of IL2RA and CTLA4, known to be expressed in NSCLC. Additionally, we identified high expression of genes TM4SF1 and KRT6A. By comparing the genes specific to Tconv and Treg cells in the lungs with those significantly expressed in NSCLC, we were able to identify 114 co-expressed genes in Treg cells and 71 in Tconv cells. We conducted GO and KEGG pathway analyses on these genes and examined the interaction networks among the associated genes. Furthermore, we found receptor interactions between Tconv cell subtypes (Th1, Th2, Th17, and Tfh) and Treg cells through cell-cell interaction analysis. We explored the mouse phenotypes associated with these genes. These discoveries unveiled connections within lung-specific gene networks of Treg and Tconv cells associated with NSCLC, highlighting candidate genes for targeted therapy in cancer. Citation Format: Hyunmin Woo, JiHye Yang, Siyoung Yang, Seong-il Eyun. Unraveling immune dynamics of CD4+ in non-small cell lung cancer through single-cell analysis and gene expression profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2278.

Abstract 6126: Understanding disparities in lung cancer using single cell RNA sequencing data transformed by the Gerchberg Saxton algorithm

Abstract Purpose An overarching goal of this study is to reduce disparities in lung cancer research commonly manifested by racial and/or gender under-representations. By employing the Gerchberg-Saxton (GS) algorithm, we aim to diminish data noise/bias in single-cell RNA sequencing (scRNA-seq) gene expression data, thereby enabling more equitable research outcomes. An immediate goal is to remove extensive data bias associated with scRNA-seq data thus enhancing downstream Machine Learning analyses. Background: We have successfully applied the GS algorithm for fairer mortality rate predictions across different racial groups [1]. In this study, we seek to apply the GS algorithm to the scRNA-seq data that interrogate the cellular landscapes of lung cancer and the tumor microenvironment. Methodology: The application of the GS algorithm to single cell RNA data involved a series of steps. Initially, all data frames were transposed, switching columns and rows to represent gene expressions and single cells, respectively. The algorithm was then meticulously applied to each column, allowing for the uniform distribution of certain specific gene expression information across all single cells within the data frame. This column-wise application was crucial to maintain the integrity of individual cell characteristics while ensuring a fair structural distribution of gene expression data. The process aimed to balance the representation of genes across all cells, addressing inherent biases and noises in the original data structure. Data: The study utilizes a previously unpublished scRNA-seq dataset comprising 14 lung cancer patients from Wake Forest Baptist Comprehensive Cancer Center, including 6 African American and 8 Caucasian patients. Results: One out of 14 preliminary clustering results are presented in Figure 2 using ScanPy [2]. The initial findings are based on a restricted set of cell marker genes, which will be further developed with additional markers in subsequent analyses. With the application of the Gerchberg-Saxton algorithm, the Shannon Entropy [3] analysis revealed a more uniform randomness across gene expressions (Figure 1), and the unsupervised clustering indicated a clearer separation of cell types, significantly enhancing the ability for downstream analyses. The data transformation will be applied to the whole dataset for comparative analyses of cellular landscapes between lung cancer from different races and genders, to be reported at the AACR 2024 Annual Meeting. Conclusions: Our preliminary studies showed that the Gerchberg-Saxton algorithm is effectiveness in normalizing data distribution for scRNA-seq data, which has led to enhanced resolution of cell type differentiations in clustering analysis. With this refined methodology, we are better poised to better address lung cancer health disparities revealed by single cell sequencing analysis. Citation Format: Seha Ay, Liang Liu, Elizabeth Forbes, Umit Topaloglu, Wei Zhang. Understanding disparities in lung cancer using single cell RNA sequencing data transformed by the Gerchberg Saxton algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6126.

Abstract 6560: Cell-free RNA analysis for non-invasive cancer detection and characterization

Abstract Blood-based liquid biopsies enable non-invasive characterization of cancers. Cell-free RNA (cfRNA) analysis could potentially complement circulating tumor DNA (ctDNA) analysis and allow broader molecular characterization of cancers but has not been extensively explored. Here, we describe RARE-Seq, a novel cfRNA sequencing method designed to maximize sensitivity by targeting rare, tissue-specific transcripts, and simultaneously detecting both tumor-derived expression signatures and somatic mutations in circulating tumor RNA (ctRNA). To interrogate samples for specific gene expression signatures, we developed and optimized a framework to measure tumor- and tissue-specific enrichment scores (ES) within cfRNA. We then empirically determined the limit of detection (LOD95) of RARE-Seq using admixtures of tumor cell line RNA and control cfRNA across a wide range of fractions. We found RARE-Seq to have an estimated LOD95 of 0.05% for cancer-derived RNA, which was 52-fold better than whole transcriptome cfRNA profiling. To explore its potential clinical utility, we profiled 192 samples from 170 subjects with various cancers and non-cancer controls using RARE-Seq. Non-small cell lung cancer (NSCLC) expression signatures were significantly detected in 40 out of 51 NSCLC samples (78% sensitivity at 90% specificity). Detection increased with stage, ranging from 40% in stage I to 87% in stage IV. In a subset of stage IV NSCLC patients with matching ctDNA data (n=16), ctRNA levels were significantly correlated with ctDNA allele frequencies (Pearson R=0.84, P=0.005). Intriguingly, 86% of samples with undetectable ctDNA contained detectable ctRNA, suggesting potential complementarity of ctRNA and ctDNA analysis for cancer detection. We also developed and benchmarked a direct ctRNA genotyping approach, allowing noninvasive detection of driver mutations in 42% of patients with advanced NSCLC. When applied to other tumors, RARE-Seq demonstrated 80-100% sensitivity at 90% specificity for detection of pancreatic adenocarcinomas (PAAD), prostate adenocarcinomas (PRAD), and hepatocellular carcinomas (LIHC). Furthermore, when evaluating tissue of origin (TOO) classification performance, RARE-Seq achieved 85% median accuracy for the top predicted tissue and 96% median accuracy for the top two tissues. These results highlight the potential value of ultrasensitive cfRNA analysis, providing proof-of-principle for detection of multiple tumor types using RARE-Seq. Citation Format: Monica C. Nesselbush, Bogdan A. Luca, Young-Jun Jeon, Isabel Jabara, Catherine B. Meador, Michael S. Binkley, Nova Xu, Angela B. Hui, Andrea Garofalo, William Y. Shi, Kevin J. Liu, Takeshi Sugio, Noah Kastelowitz, Emily G. Hamilton, Rene F. Bonilla, Yi Peng Wang, Alice Jiang, Brianna Lau, Jordan Eichholz, Mandeep Banwait, Joseph Schroers-Martin, Jan Boegeholz, Daniel A. King, Mohammad S. Esfahani, Tyler Raclin, Robert Tibshirani, Sandy Srinivas, Viswam S. Nair, James M. Isbell, Bob T. Li, Zofia Piotrowska, Lecia V. Sequist, Aaron N. Hata, Joel W. Neal, Heather A. Wakelee, Andrew J. Gentles, Ash A. Alizadeh, Maximilian Diehn. Cell-free RNA analysis for non-invasive cancer detection and characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6560.

Abstract 6191: Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression

Abstract Understanding the Tumor Microenvironment (TME) is crucial for tailoring personalized cancer treatments and improving patient outcomes. Using deep learning, we can predict properties of the TME from standard Hematoxylin and Eosin (H&amp;E) stained slides, including specific gene expression signatures. While past models were limited to single signature predictions, we explore the potential of multi-task deep learning to identify multiple biomarkers at once, allowing more complex relationships and interactions to be modeled simultaneously. Our study focuses on the prediction of expression patterns of tumor, stromal, and immune cells in breast cancer (BC). We developed a multi-task transformer-based deep regression model, trained in a weakly-supervised setting, to predict biomarkers in the TME from routine H&amp;E-stained pathology slides of a BC cohort (n=876). The performance was evaluated using Pearson’s correlation coefficient (r) and spatial heatmaps generated via gradient-weighted class activation mapping. Our model achieved a significant correlation coefficient above 0.40 (p&amp;lt;0.0001) for all predicted TME biomarkers in the holdout test set (n=176). This includes the biomarkers lymphocyte infiltrating signature score (r=0.46, p&amp;lt;0.0001), tumor infiltrating lymphocyte regional fraction (r=0.48, p&amp;lt;0.0001), leukocyte fraction (r=0.43, p&amp;lt;0.0001), stromal fraction (r=0.42, p&amp;lt;0.0001), and tumor cell proliferation (r=0.47, p&amp;lt;0.0001). Notably, the model's heatmaps highlighted the pertinent cells and histological characteristics for its predictions. These data show that our deep learning model accurately predicts multiple TME biomarkers concurrently from routine pathology slides. This approach offers a promising avenue for cost-effective and efficient biomarker quantification in the TME, with potential applicability across various cancer types. Citation Format: Omar S. El Nahhas, Marta Ligero, Jakob N. Kather. Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6191.

Abstract 7045: RNA sequencing approaches enable tissue specific B and T cell gene expression and immune repertoire profiling

Abstract The adaptive immune system defends the human body from pathogens through the recognition ability of antigen receptors from B and T cells. Different organs play various immune function roles, and therefore present varied B and T cell gene expression and immune repertoire profiles. Abnormality of these profiles may reflect immunological disorder, presence of neoplastic tissue, or response to infectious agent. High-throughput sequencing technology has facilitated the understanding of gene expression and related cell functions, but in-depth understanding of B and T cell phenotype and clonotype profiling is an emerging application that has yet to take full advantage of sequencing technology for diagnosis and prognosis of disease. Here, we share results of RNA sequencing of immune system tissues; including B cell and T cell specific gene expression and full-length immune repertoire profiling. Total RNA was extracted from various tissue samples including healthy donor peripheral blood mononuclear cells (PBMC), lymph nodes, bone marrow, spleen, thyroid, thymus and colon, as well as diseased PBMC and colon samples. RNA-seq and Immune-seq libraries were made from these samples and sequenced on Illumina NextSeq2000. Gene expression analysis and immune repertoire profiling from RNA-seq data were performed using edgeR and TRUST4. Immune repertoire sequencing data was processed using pRESTO and IgBlast. We correlated RNA-seq with Immune-seq results for B-cell receptor and T-cell receptor clonotypes. Differential expression and clonotype profiling comparisons were also performed between normal and diseased RNA samples. The effect of reference on immune repertoire detection sensitivity and accuracy was further investigated by comparing IMGT reference and AIRR-C Human IG Reference Sets. This study has identified tissue specific B/T cell phenotype and clonality patterns that relate to tissue functions and disease progression. RNA-seq enables highly multiplex immunophenotyping which is traditionally performed by flow cytometry. RNA-seq can detect high-abundant clones that correlate with Immune-seq, with the latter targeted approach providing more in depth clonality signatures. References comparison results show the importance of continued community effort to develop more representative and accurate immunorepertoire germ line references for clonotype annotation in diverse populations. Integrating both RNA sequencing and immune repertoire sequencing approaches allows accurate phenotype and clonotype determination for immune landscape in various tissues. Citation Format: Chen Song, Evan Janzen, Tessa Devoe, Ariel Erijman, Gautam Naishadham, Li Song, Bradley W. Langhorst. RNA sequencing approaches enable tissue specific B and T cell gene expression and immune repertoire profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7045.

Modified in-vitro AATCC-100 procedure to measure viable bacteria from wound dressings.

Chronic wounds are reoccurring healthcare problems in the United States and cost up to $50 billion annually. Improper wound care results in complications such as wound debridement, surgical amputation, and increased morbidity/ mortality due to opportunistic infections. To eliminate wound infections, many antimicrobial dressings are developed and submitted to FDA for evaluation. AATCC-100 is a standard method widely used to evaluate cloth wound dressings. This method, requires enrichment, followed by culturing to measure the concentration of culturable organisms; a caveat to this method could result in neglected viable but nonculturable (VBNC) bacteria and overestimate the antimicrobial properties of wound dressings. Therefore, the objectives of this study were to assess this accepted protocol with quantitative real-time polymerase chain reaction (qRT-PCR), to measure time dependent antimicrobial efficacy of wound dressing, and to examine for potential viable bacteria but non-culturable as compared with traditional plating methods. The test organisms included opportunistic pathogens: Pseudomonas aeruginosa (ATCC 15692) and Staphylococcus aureus (ATCC 43300). To mimic a wound dressing environment, samples of commercially available wound dressings (McKesson Inc.) with silver ion (positive control) and dressings without silver ion (positive control) were assessed under sterile conditions. All samples were examined by the original protocol (the extended AATCC-100 method) and qRT-PCR. The expression of specific housekeeping genes was measured (proC for P. aeruginosa and 16s rRNA for S. aureus). Based on these tests, log reduction of experimental conditions was compared to identify time dependent and precise antimicrobial properties from wound dressing samples. These results showed antimicrobial properties of wound dressings diminished as incubation days are increased for both methods from day 1 PCR result of 4.31 ± 0.54 and day 1 plating result of 6.31 ± 3.04 to day 3 PCR result of 1.22 ± 0.97 and day 3 plating result of 5.89 ± 2.41. These results show that data from qRT-PCR generally produced lower standard deviation than that of culture methods, hence shown to be more precise. Complementary parallel analysis of samples using both methods better characterized antimicrobial properties of the tested samples.

High-throughput mRNA sequencing of human placenta shows sex differences across gestation

IntroductionFetal sex affects fetal and maternal health outcomes in pregnancy, but this connection remains poorly understood. As the placenta is the route of fetomaternal communication and derives from the fetal genome, placental gene expression sex differences may explain these outcomes. ObjectivesWe utilized next generation sequencing to study the normal human placenta in both sexes in first and third trimester to generate a normative transcriptome based on sex and gestation. Study designWe analyzed 124 first trimester (T1, 59 female and 65 male) and 43 third trimester (T3, 18 female and 25 male) samples for sex differences within each trimester and sex-specific gestational differences. ResultsPlacenta shows more significant sexual dimorphism in T1, with 94 T1 and 26 T3 differentially expressed genes (DEGs). The sex chromosomes contributed 60.6% of DEGs in T1 and 80.8% of DEGs in T3, excluding X/Y pseudoautosomal regions. There were 6 DEGs from the pseudoautosomal regions, only significant in T1 and all upregulated in males. The distribution of DEGs on the X chromosome suggests genes on Xp (the short arm) may be particularly important in placental sex differences. Dosage compensation analysis of X/Y homolog genes shows expression is primarily contributed by the X chromosome. In sex-specific analyses of first versus third trimester, there were 2815 DEGs common to both sexes upregulated in T1, and 3263 common DEGs upregulated in T3. There were 7 female-exclusive DEGs upregulated in T1, 15 female-exclusive DEGs upregulated in T3, 10 male-exclusive DEGs upregulated in T1, and 20 male-exclusive DEGs upregulated in T3. DiscussionThis is the largest cohort of placentas across gestation from healthy pregnancies defining the normative sex dimorphic gene expression and sex common, sex specific and sex exclusive gene expression across gestation. The first trimester has the most sexually dimorphic transcripts, and the majority were upregulated in females compared to males in both trimesters. The short arm of the X chromosome and the pseudoautosomal region is particularly critical in defining sex differences in the first trimester placenta. As pregnancy is a dynamic state, sex specific DEGs across gestation may contribute to sex dimorphic changes in overall outcomes.

ICA/SDF-1α/PBMSCs loaded onto alginate and gelatin cross-linked scaffolds promote damaged cartilage repair.

A three-dimensional alginate-coated scaffold (GAIS) was constructed in the present study to showcase the multidifferentiation potential of peripheral blood mesenchymal stem cells (PBMSCs) and to investigate the role and mechanism by which Icariin (ICA)/stromal cell-derived factor (SDF-1α)/PBMSCs promote damaged articular repair. In addition, the ability of ICA, in combination with SDF-1α, to promote the migration and proliferation of stem cells was validated through the utilization of CCK-8 and migration experiments. The combination of ICA and SDF-1α inhibited the differentiation of PBMSCs into cartilage, as demonstrated by invivo experiments and histological staining. Both PCR and western blot experiments showed that GAIS could upregulate the expression of particular genes in chondrocytes. In comparison to scaffolds devoid of alginate (G0), PBMSCs seeded into GAIS scaffolds exhibited a greater rate of proliferation, and the conditioned medium derived from scaffolds containing SDF-1α enhanced the capacity for cell migration. Moreover, after a 12-week treatment period, GAIS, when successfully transplanted into osteochondral defects of mice, was found to promote cartilage regeneration and repair. The findings, therefore, demonstrate that GAIS enhanced the invitro capabilities of PBMSCs, including proliferation, migration, homing and chondrogenic differentiation. In addition, ICA and SDF-1α effectively collaborated to support cartilage formation invivo. Thus, the ICA/SDF-1α/PBMSC-loaded biodegradable alginate-gelatin scaffolds showcase considerable potential for use in cartilage repair.