Actinobacteria are potential producers of various secondary metabolites with diverse bioactivities. Streptomyces are one of the largest and valuable resource of bioactive and complex secondary metabolites, many of which have important clinical applications. Recent advancements in high-throughput sequencing technologies have made possible the mapping of Streptomyces genome which helps to elucidate the dynamic changes in gene expression in response to cellular status at both transcriptional and translation levels. In this chapter, we will provide a background on the approaches of the transcriptomic assembly along with the development of tools and algorithms that can be used for building prokaryotic transcriptomes.
In the last several years, accumulating evidence indicates that noncoding RNAs, especially long-noncoding RNAs (lncRNAs) and microRNAs, play essential roles in regulating angiogenesis. However, the contribution of lncRNA-mediated competing-endogenous RNA (ceRNA) activity in the control of capillary sprouting from the pre-existing ones has not been described so far. Here, by exploiting the transcriptomic profile of VEGF-A-activated endothelial cells in a consolidate three-dimensional culture system, we identified a list of lncRNAs whose expression was modified during the sprouting process. By crossing the lncRNAs with a higher expression level and the highest fold change value between unstimulated and VEGF-A-stimulated endothelial cells, we identified the unknown LINC02802 as the best candidate to take part in sprouting regulation. LINC02802 was upregulated after VEGF-A stimulation and its knockdown resulted in a significant reduction in sprouting activity. Mechanistically, we demonstrated that LINC02802 acts as a ceRNA in the post-transcriptional regulation of Mastermind-like-3 (MAML3) gene expression through a competitive binding with miR-486-5p. Taken together, these results suggest that LINC02802 plays a critical role in preventing the miR-486-5p anti-angiogenic effect and that this inhibitory effect results from the reduction in MAML3 expression.
Abstract Introduction: Non-small cell lung cancer (NSCLC) is a highly heterogenous disease with the largest number of cancer-related mortality worldwide, one of the reasons for this is the complex and diverse tumor microenvironment (TME) comprising of numerous cell types. Several studies have already highlighted the importance of TME in dictating progression steps and response to therapies; however, a transcriptome-based molecular subtyping of patients in lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) can further determine the distinct tumor immune microenvironment (TiME), which can eventually provide a systematic overview to improve the diagnosis and prognosis of patients. Material and method: To elucidate such nature of interactions between tumor cells and cells comprising the TME, we exploited the transcriptome of 300 early stages (Ib-IIIa) NSCLC recruited in the prospective observational clinical trial PROMOLE. With the help of a clustering approach, initially we performed a molecular-based virtual stratification/dissection on the NSCLC patients. Next, to elucidate the relative cell-type abundance, a deconvolution approach was applied to identify the possibility of tumor infiltrating immune cells within these subgroups. Immunohistochemistry (IHC) was then used to substantiate these predictions on tumor cells. Results and discussion: The resulting subgroups of LUADs and LUSCs are biologically well-characterized by mutational and gene expression profiles. Cell-type abundance approach identified samples which are enriched with tumor infiltrating immune cells like Neutrophils, Tcells, macrophages, etc. These findings were positively confirmed by IHC with multiple cell markers such as MPO, CD4, CD8, CD68, etc. Integrating these two results highlighted the proportion of TiME in the two different sub-populations along with shedding some light on the crosstalk happening between different cancer-/immune- cell lines. Conclusion: The in-silico predictions on bulk RNA data by virtual micro-dissection, distinguished the two distinct NSCLC subtypes, each associated with clinical and molecular features. Furthermore, the immune cells infiltration suggests a possible role of infiltrating tumor immune cells with the prognosis of patients. Our analysis successfully performed an intra-sample and inter-sample comparison, which can unveil new prognostic markers that can provide relevant information for cancer immunotherapy. Citation Format: Sushant Parab, Francesca Napoli, Davide Corà, Gabriella Doronzo, Valentina Communanza, Luisella Righi, Luca Primo, Valentina Monica, Lorenzo Manganaro, Bianco Selene, Paolo Bironzo, Giorgio Scagliotti, Federico Bussolino. Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach [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 1536.
For the last several decades, Actinobacteria have been considered as the major storehouse for the discovery and production of natural products as well as of many valuable secondary metabolites, thus being recognized as one of the most important industrial bacteria. With the advancements in high-throughput genome sequencing methods, there has been a rapid increase in the discovery of novel compounds produced by actinomycetes. Hence, genome mining with various awakening strategies may further enable the identification of new and weakly produced compounds. In this chapter, we discuss the recent advancements in Actinobacteria genome sequencing and the applications of genome mining approaches to identify and characterize biosynthetic gene clusters (BGCs). Furthermore, we discuss several challenges that need to be overcome to accelerate the genome mining process and support the discovery of novel bioactive compounds.
In the past few decades, there has been a significant development in the sequencing technologies, which has accelerated the research to study complex microbiomes. Although 16S rRNA analysis and diversity analysis at species level are most commonly used, researchers are making a shift towards using more comprehensive sequencing methods such as metagenomics (sequencing of total DNA—revealing which microbes are present) and metatranscriptomics (sequencing of total RNA—capturing all gene expression). Integrating these two levels of information provides a more robust method for understanding the mechanisms of a microbial community. However, such multi-omics data poses new challenges and create new scientific questions that are still to be answered. In this chapter, we will try to summarize the recently updated databases, which host these sequencing data from the microbial populations. We will also provide a survey of algorithms and tools used for building genome/transcriptome assembly along with its annotation. We will then conclude with a discussion on specific challenges posed by multi-omics data and outline some of the strategies recently developed to address these complexities.
Various human diseases are triggered by molecular alterations influencing the fine-tuned expression and activity of transcription factors, usually due to imbalances in targets including protein-coding genes and non-coding RNAs, such as microRNAs (miRNAs). The transcription factor EB (TFEB) modulates human cellular networks, overseeing lysosomal biogenesis and function, plasma–membrane trafficking, autophagic flux, and cell cycle progression. In endothelial cells (ECs), TFEB is essential for the maintenance of endothelial integrity and function, ensuring vascular health. However, the comprehensive regulatory network orchestrated by TFEB remains poorly understood. Here, we provide novel mechanistic insights into how TFEB regulates the transcriptional landscape in primary human umbilical vein ECs (HUVECs), using an integrated approach combining high-throughput experimental data with dedicated bioinformatics analysis. By analyzing HUVECs ectopically expressing TFEB using ChIP-seq and examining both polyadenylated mRNA and small RNA sequencing data from TFEB-silenced HUVECs, we have developed a bioinformatics pipeline mapping the different gene regulatory interactions driven by TFEB. We show that TFEB directly regulates multiple miRNAs, which in turn post-transcriptionally modulate a broad network of target genes, significantly expanding the repertoire of gene programs influenced by this transcription factor. These insights may have significant implications for vascular biology and the development of novel therapeutics for vascular disease.
Malignant melanoma is an aggressive cancer, with a high risk of metastasis and mortality rates, characterized by cancer cell heterogeneity and complex tumor microenvironment (TME). Single cell biology is an ideal and powerful tool to address these features at a molecular level. However, this approach requires enzymatic cell dissociation that can influence cellular coverage. By contrast, single nucleus RNA sequencing (snRNA-seq) has substantial advantages including compatibility with frozen samples and the elimination of a dissociation-induced, transcriptional stress response. To better profile and understand the functional diversity of different cellular components in melanoma progression, we performed snRNA-seq of 16,839 nuclei obtained from tumor samples along the growth of murine syngeneic melanoma model carrying a BRAFV600E mutation and collected 9 days or 23 days after subcutaneous cell injection. We defined 11 different subtypes of functional cell clusters among malignant cells and 5 different subsets of myeloid cells that display distinct global transcriptional program and different enrichment in early or advanced stage of tumor growth, confirming that this approach was useful to accurately identify intratumor heterogeneity and dynamics during tumor evolution. The current study offers a deep insight into the biology of melanoma highlighting TME reprogramming through tumor initiation and progression, underlying further discovery of new TME biomarkers which may be potentially druggable.
