Dynamic Gene Expression Analysis Research Articles

Three-dimensional computer vision for exploring heterogeneity in collective Cancer Invasion

Collective cancer invasion exhibits a hierarchical structure characterized by leader-follower organization. Dynamic gene expression analysis of invading cells using nanobiosensors within 3D microenvironments provides a valuable means to explore the regulation of leader cells during collective cancer invasion. Nonetheless, the analysis of time-lapse, multimodal images that capture the intricacies of complex invading structures and gene expression profiles in 3D tumor spheroids poses a significant technological challenge. Here, we present a computer vision-based workflow that streamlines the identification of protrusions and detached clusters from 3D tumor spheroids. This methodology not only discerns invading multicellular structures and quantifies their physical properties, but also captures gene expression patterns associated with these invasive mechanisms using an intracellular nanobiosensor. Consequently, it empowers a systematic exploration of the genotypic and phenotypic heterogeneities inherent in cancer invasion. To illustrate the effectiveness of this approach, we applied it to the analysis of a long noncoding RNA, MALAT1, in tumor spheroids derived from patients with muscle-invasive bladder cancer. Our investigation delved into the heterogeneity of cancer invasion and its relationship to MALAT1 expression. Overall, this workflow represents a valuable tool for gaining insights into the complexities of cancer invasion.

Effect of Maternal Body Mass Index on the Transcriptomic Network of Human First-Trimester Chorionic Villi.

The relationship between fertility and maternal body weight is shaped like an inverted "U," meaning that fertility is negatively affected in overweight or underweight women. Timely and appropriate maternal-fetal interaction is a crucial part of successful pregnancy. However, it is not clear how body weight affects maternal-fetal interaction. Placental villi are the bridge for maternal-fetal interaction. Therefore, we collected villi from pregnant women with different body mass indexes (BMI), who voluntarily underwent induced abortion, to construct a molecular network via RNA-seq. Surprisingly, based on global and significant gene network analysis, we found that dysregulation of inflammatory reaction, cell adhesion, and immune response were the most significantly enriched pathways. We also conducted dynamic gene expression analysis with BMI as a variable, and identified several distinct clusters. Among them, cluster 9 showed an inverted "U" shape and genes in it were mainly enriched in chemical synaptic transmission and cell-cell adhesion via plasma-membrane adhesion molecules. Additionally, genes in the "U" shaped cluster (cluster 5) were enriched in regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains and negative regulation of immune response. We thus conclude that maternal body weight can affect maternal-fetal interaction through alterations or aberrant activation of inflammatory reaction and immune response. Regulating inflammatory reaction may be a potential therapeutic strategy to improve fertility of overweight and underweight people.

A novel group of genes that cause endocrine resistance in breast cancer identified by dynamic gene expression analysis.

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of endocrine resistance and identified a novel group of genes whose time course expression significantly change during cell modelling of endocrine resistant BC development. Expression of a subset of these genes was also differentially expressed in microarray analysis of endocrine-resistant and endocrine-sensitive tumor samples. Surprisingly, a subset of those genes was also differentially genes expressed in triple-negative breast cancer (TNBC) as compared with ER-positive BC. The findings suggest shared genetic mechanisms may underlie the development of endocrine resistant BC and TNBC. Our findings identify 34 novel genes for further study as potential therapeutic targets for treatment of endocrine-resistant BC and TNBC.

Abstract PD14-06: Dynamic changes of PD-L1 and T-cell activation in ECLIPSE: A phase II study investigating preoperative immune combination strategies in untreated, operable ER+ primary breast cancer

Abstract Background: Whilst Atezolizumab + nab-paclitaxel is the new standard of care in patients with PD-L1+ metastatic triple negative breast cancer (BC), the role of immunotherapy in ER+ BC has yet to be defined. ECLIPSE, an open label, phase II trial, aims to evaluate the effect of short-term pre-operative immune-modulatory agent combinations on ER+ tumour immune-phenotypes (NCT03395899). Here, we examined the first results of the dynamic changes of PD-L1 expression (≥1% positive immune cell by VENTANA PD-L1 SP142 IHC) and T-cell expansion and activation after 1 cycle of atezolizumab alone or in combination with AKT, MEK or VEGF inhibitors. Methods: Patients with histologically confirmed, untreated, operable ER+/HER2- primary BC received 1 cycle of atezolizumab alone or atezolizumab + ipatasertib, atezolizumab + cobimetinib, or atezolizumab + cobimetinib + bevacizumab 3 weeks prior to surgery or neoadjuvant therapy. Biopsies were obtained pre and on completion of study treatment. Dynamic changes in biomarkers were compared in 48 evaluable patients, including 41 paired samples. Results: PD-L1 IC expression significantly increased after 1 cycle of treatment (p < 0.0001). At a 1% IC cutoff, 21/45 patients (47%) were PD-L1 IC-positive (+) at baseline while 34/44 (77%) were PD-L1 IC + post-treatment. Increased tumour cell expression was also found in post-treatment samples (p < 0.01). All post-treatment PD-L1 negative patients (n = 9) were also negative at baseline. Overall, there was a small but significant increase in CD8 + T-cells post-treatment compared to baseline (p = 0.03). In parallel, the number of T-cells that were positive for both CD8 and granzyme B (GZMB) - a T-cell activation marker - increased substantially (p < 0.01). Single GZMB+ cells and single FOXP3+ cells did not change with treatment. In 2 cases, no tumour was detected post-treatment, suggesting a major pathological response. Baseline biopsies of both patients stood out as having the largest amount of CD8+GZMB+ T-cells. Post-treatment tumour bed profiling of these patients showed major infiltration of CD8+ and CD8+GZMB+ T-cells. Dynamic gene expression analysis showed upregulation of genes related to innate immunity and a cytotoxic T-cell transcriptional signature (tGE8), (p < 0.001). Patients were grouped as immune-responsive or non-responsive based on a dynamic increase of CD8+GZMB+ cells. The immune-response group was characterized by increased IFN response signatures and decreased luminal gene signatures at baseline. Conclusions: Our data indicate that short-term induction treatment with atezolizumab ± targeted treatments induce dynamic changes toward inflamed immune phenotypes both at the morphological and transcriptional level, in patients with operable ER+/HER2- primary BC. These findings might help identify novel immunotherapy combination strategies in this setting, alongside the most appropriate biomarkers that can aid in identifying patients most likely to benefit from these treatments. Citation Format: Peter Schmid, Mark Kockx, Sung-Bae Kim, Duncan Wheatley, Melissa Mary Phillips, Oliver Hoffmann, Jens U. Blohmer, Seock-Ah Im, Andreas Schneeweiss, Esther Zamora, Yannick Waumans, Pieter-Jan Van Dam, Dieter Peeters, Akhila Wimalasingham, Patricia Marosics, Aaron Prendergast, Kelly Mousa, Javier Cortes, Sherko Kuemmel. Dynamic changes of PD-L1 and T-cell activation in ECLIPSE: A phase II study investigating preoperative immune combination strategies in untreated, operable ER+ primary breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD14-06.

Abstract P2-11-20: Endocrine resistance in breast cancer: A dynamic gene expression analysis approach reveals potential new responsible genes

Abstract While ER-targeted endocrine therapies are the most successful therapies for ER-positive breast cancer, tumor recurrence and endocrine-resistance happens in many of the cases. Despite efforts, the underlying genes and mechanisms of this acquired resistance remain largely unknown. Here, we utilized a recently developed pipeline for dynamic gene regulatory network analysis1to re-analyze the publically available datasets related to endocrine resistance in breast cancer in order to find new target genes related to endocrine resistance. The pipeline exploits the time-resolved expression pattern of genes to identify dynamic responsive genes (DRG) and group them into multiple modules based on the similarity in their dynamic activities, which leads us to identify more significant genes compared with previous gene expression analysis models that solely focus on expression value of the genes. Here, we analyzed a dataset of time-resolved gene expression during MCF-7 cell transition to endocrine-resistant cells by long-term deprivation of estrogen, which is an accepted model of endocrine resistance2. The dataset is publically available as GES20361 in the Gene Expression Omnibus (GEO) database. Based on their dynamic expression pattern over time, our method of analysis grouped genes into three main modules. Interestingly, the majority of well-studied genes in the literature related to endocrine resistance are part of one of these modules, while there are many genes such as GABPA, TFDP1, MCM7, MDC1, and FOXC1 that are potential novel targets for treatments against endocrine resistance. Module One included genes related to endocrine resistance, mainly those related to cell cycle, such as CCNA1, CCNA2, CCND1, and CDK family, and DNA replication and repair, such as MDC1 and PARP1, as well as EGFR. Consistent with the previous literature on endocrine resistant breast cancer cells, these genes all showed similar up-regulated expression patterns over time. Furthermore, our model confirmed the identified genes shown in the previous paper2 (POLD1, TERF1, RAF1, and E2F1) as part of Module One and as important genes in endocrine resistance regulation. Module Two was enriched for genes related to PI3K/AKT/mTOR signaling pathway, while Module Three mostly contained tyrosine kinase related genes, which not only confirms the previous findings related to the importance of these genes in acquiring endocrine resistance, but also indicates the difference between the expression patterns of these genes compare to cell cycle genes and those in Module One. Our analysis revealed that the genes in Module One continued their up-regulating trend beyond day 90, when the marked transition toward resistance happens and resistance phenotype starts to arise, while genes in Module Two got up-regulated before the 90 days and down-regulated afterward, suggesting different roles from the genes in Module One. Lastly, we studied the gene regulatory networks, transcription factors, and miRNAs upstream of the identified genes, and discovered several interesting targets, such as MiR-193b and MiR-16, which have integral parts in the regulation of our identified genes and thus can be of great importance toward endocrine resistance.

Dynamic gene expression analysis in a H1N1 influenza virus mouse pneumonia model.

H1N1, a major pathogenic subtype of influenza A virus, causes a respiratory infection in humans and livestock that can range from a mild infection to more severe pneumonia associated with acute respiratory distress syndrome. Understanding the dynamic changes in the genome and the related functional changes induced by H1N1 influenza virus infection is essential to elucidating the pathogenesis of this virus and thereby determining strategies to prevent future outbreaks. In this study, we filtered the significantly expressed genes in mouse pneumonia using mRNA microarray analysis. Using STC analysis, seven significant gene clusters were revealed, and using STC-GO analysis, we explored the significant functions of these seven gene clusters. The results revealed GOs related to H1N1 virus-induced inflammatory and immune functions, including innate immune response, inflammatory response, specific immune response, and cellular response to interferon-beta. Furthermore, the dynamic regulation relationships of the key genes in mouse pneumonia were revealed by dynamic gene network analysis, and the most important genes were filtered, including Dhx58, Cxcl10, Cxcl11, Zbp1, Ifit1, Ifih1, Trim25, Mx2, Oas2, Cd274, Irgm1, and Irf7. These results suggested that during mouse pneumonia, changes in the expression of gene clusters and the complex interactions among genes lead to significant changes in function. Dynamic gene expression analysis revealed key genes that performed important functions. These results are a prelude to advancements in mouse H1N1 influenza virus infection biology, as well as the use of mice as a model organism for human H1N1 influenza virus infection studies.

Dynamic gene expression analysis of maternal, cytoplasmic and embryo genetic systems for linolenic and erucic acid contents in rapeseed (Brassica napus L.)

The genetic effects including genetic main effects and genotype × environment (GE) interaction effects were analyzed for linolenic acid content (LLAC) and erucic acid content (EAC) in rapeseed using unconditional and conditional genetic models for diploid seed quantitative traits. The results from unconditional analysis revealed that GE effects were important for both traits during seed development. Also the higher magnitude of embryo effects at different developmental times indicated that seed selection would be useful for improving these quality traits in rapeseed. The conditional analysis showed that new expression of genes from maternal and embryo genetic systems occurred during different developmental stages and their magnitude could be influenced by the GE effects. The total narrow-sense heritability was high for both LLAC and EAC, with general heritability being more prominent for EAC at 15 days (32.87 %), 29 days (31.44 %), 36 days (60.55 %) and 43 days (76.45 %), and GE interaction heritability dominating for LLAC at 22 days (40.57 %), 29 days (60.91 %), 36 days (63.83 %) and 43 days (47.45 %) after flowering, respectively. Phenotypic and genotypic correlations for both traits were positive among some pairwise developmental times indicating that genes expressed at these times complemented each other to improve LLAC or EAC in rapeseed. Significant negative correlation between LLAC and EAC at most developmental times indicated that it may not be possible to simultaneously reduce the contents of both traits by indirect selection.

Resolving the TCA cycle and pentose‐phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis

Solventogenic clostridia are an important class of microorganisms that can produce various biofuels. One of the bottlenecks in engineering clostridia stems from the fact that central metabolic pathways remain poorly understood. Here, we utilized the power of (13) C-based isotopomer analysis to re-examine central metabolic pathways of Clostridium acetobutylicum ATCC 824. We demonstrate using [1,2-(13) C]glucose, MS analysis of intracellular metabolites, and enzymatic assays that C. acetobutylicum has a split TCA cycle where only Re-citrate synthase (CS) contributes to the production of α-ketoglutarate via citrate. Furthermore, we show that there is no carbon exchange between α-ketoglutarate and fumarate and that the oxidative pentose-phosphate pathway (oxPPP) is inactive. Dynamic gene expression analysis of the putative Re-CS gene (CAC0970), its operon, and all glycolysis, pentose-phosphate pathway, and TCA cycle genes identify genes and their degree of involvement in these core pathways that support the powerful primary metabolism of this industrial organism.

Dynamic Gene Expression Analysis Links Melanocyte Growth Arrest with Nevogenesis

Like all primary cells in vitro, normal human melanocytes exhibit a physiologic decay in proliferative potential as it transitions to a growth-arrested state. The underlying transcriptional program(s) that regulate this phenotypic change is largely unknown. To identify molecular determinants of this process, we performed a Bayesian-based dynamic gene expression analysis on primary melanocytes undergoing proliferative arrest. This analysis revealed several related clusters whose expression behavior correlated with the melanocyte growth kinetics; we designated these clusters the melanocyte growth arrest program (MGAP). These MGAP genes were preferentially represented in benign melanocytic nevi over melanomas and selectively mapped to the hepatocyte fibrosis pathway. This transcriptional relationship between melanocyte growth stasis, nevus biology, and fibrogenic signaling was further validated in vivo by the demonstration of strong pericellular collagen deposition within benign nevi but not melanomas. Taken together, our study provides a novel view of fibroplasia in both melanocyte biology and nevogenesis.

Molecular stages of rapid and uniform neuralization of human embryonic stem cells

Insights into early human development are fundamental for our understanding of human biology. Efficient differentiation of human embryonic stem cells (hESCs) into neural precursor cells is critical for future cell-based therapies. Here, using defined conditions, we characterized a new method for rapid and uniform differentiation of hESCs into committed neural precursor cells (designated C-NPCs). Dynamic gene expression analysis identified several distinct stages of ESC neuralization and revealed functional modules of coregulated genes and pathways. The first wave of gene expression changes, likely corresponding to the transition through primitive ectoderm, started at day 3, preceding the formation of columnar neuroepithelial rosettes. The second wave started at day 5, coinciding with the formation of rosettes. The majority of C-NPCs were positive for both anterior and posterior markers of developing neuroepithelium. In culture, C-NPCs became electrophysiologically functional neurons; on transplantation into neonatal mouse brains, C-NPCs integrated into the cortex and olfactory bulb, acquiring appropriate neuronal morphologies and markers. Compared to rosette-NPCs,(1) C-NPCs exhibited limited in vitro expansion capacity and did not express potent oncogenes such as PLAG1 or RSPO3. Concordantly, we never detected tumors or excessive neural proliferation after transplantation of C-NPCs into mouse brains. In conclusion, our study provides a framework for future analysis of molecular signaling during ESC neuralization.