Transcriptomic responses in the blood and sputum of cigarette smokers compared to e-cigarette vapers.

RATIONALE: Electronic (e)-cigarettes are popular among youth and cigarette smokers attempting to quit. Studies to date have focused on the utility of e-cigarettes as a smoking cessation tool, but the biological effects are largely unknown. OBJECTIVES: To identify transcriptomic differences in the blood and sputum of e-cigarette users compared to conventional cigarettes smokers and healthy controls and describe biological pathways affected by these tobacco products. METHODS: Cross-sectional analysis of whole blood and sputum RNA-sequencing data from 8 smokers, 9 e-cigarette users (e-cigs) and 4 controls. Weighted gene co-network analysis (WGCNA) identified gene module associations. Ingenuity Pathway Analysis (IPA) identified canonical pathways associated with tobacco products. MAIN RESULTS: In blood, a three-group comparison showed 16 differentially expressed genes (DEGs); pair-wise comparison showed 7 DEGs between e-cigs and controls, 35 DEGs between smokers and controls, and 13 DEGs between smokers and e-cigs. In sputum, 438 DEGs were in the three-group comparison. In pair-wise comparisons, there were 2 DEGs between e-cigs and controls, 270 DEGs between smokers and controls, and 468 DEGs between smokers and e-cigs. Only 2 genes in the smokers vs. control comparison overlapped between blood and sputum. Most gene modules identified through WGCNA associated with tobacco product exposures also were associated with cotinine and exhaled CO levels. IPA showed more canonical pathways altered by conventional cigarette smoking than by e-cigarette use. CONCLUSION: Cigarette smoking and e-cigarette use led to transcriptomic changes in both blood and sputum. However, conventional cigarettes induced much stronger transcriptomic responses in both compartments

A genome-wide CRISPR activation screen identifies

The genome is pervasively transcribed to produce a vast array of non-coding RNAs (ncRNAs). Long non-coding RNAs (lncRNAs) are transcripts of \u3e200 nucleotides and are best known for their ability to regulate gene expression. Enhancer RNAs (eRNAs) are subclass of lncRNAs that are synthesized from enhancer regions and have also been shown to coordinate gene expression. The biological function and significance of most lncRNAs and eRNAs remain to be determined. Epithelial to mesenchymal transition (EMT) is a ubiquitous cellular process that occurs during cellular migration, homeostasis, fibrosis, and cancer-cell metastasis. EMT- transcription factors, such as SNAI1 induce a complex transcriptional program that coordinates the morphological and molecular changes associated with EMT. Such complex transcriptional programs are often subject to coordination by networks of ncRNAs and thus can be leveraged to identify novel functional ncRNA loci. Here, using a genome-wide CRISPR activation (CRISPRa) screen targeting ~10,000 lncRNA loci we identified ncRNA loci that could either promote or attenuate EMT. We discovered a novel locus that we named SCREEM (SNAI1 cis-regulatory eRNAs expressed in monocytes). The SCREEM locus contained a cluster of eRNAs that when activated using CRISPRa induced expression of the neighboring gene SNAI1, driving concomitant EMT. However, the SCREEM eRNA transcripts themselves appeared dispensable for the induction of SNAI1 expression. Interestingly, the SCREEM eRNAs and SNAI1 were co- expressed in activated monocytes, where the SCREEM locus demarcated a monocyte-specific super-enhancer. These findings suggest a potential role for SNAI1 in monocytes. Exploration of the SCREEM-SNAI axis could reveal novel aspects of monocyte biology

MYC regulates a pan-cancer network of co-expressed oncogenic splicing factors.

MYC is dysregulated in \u3e50% of cancers, but direct targeting of MYC has been clinically unsuccessful. Targeting downstream MYC effector pathways represents an attractive alternative. MYC regulates alternative mRNA splicing, but the mechanistic links between MYC and the splicing machinery in cancer remain underexplored. Here, we identify a network of co-expressed splicing factors (SF-modules) in MYC-active breast tumors. Of these, one is a pan-cancer SF-module correlating with MYC activity across 33 tumor types. In mammary cell models, MYC activation leads to co-upregulation of pan-cancer module SFs and to changes in \u3e4,000 splicing events. In breast cancer organoids, co-overexpression of the pan-cancer SF-module induces MYC-regulated splicing events and increases organoid size and invasiveness, while knockdown decreases organoid size. Finally, we uncover a MYC-activity pan-cancer splicing signature correlating with survival across tumor types. Our findings provide insight into the mechanisms of MYC-regulated splicing and for the development of therapeutics for MYC-driven tumors

Harnessing large language models (LLMs) for candidate gene prioritization and selection.

BACKGROUND: Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection. METHODS: In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene\u27s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene. RESULTS: Of the four LLMs evaluated, OpenAI\u27s GPT-4 and Anthropic\u27s Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module. CONCLUSIONS: Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge

Protocol for establishing primary human lung organoid-derived air-liquid interface cultures from cryopreserved human lung tissue.

Primary human lung organoid-derived air-liquid interface (ALI) cultures serve as a physiologically relevant model to study human airway epithelium in vitro. Here, we present a protocol for establishing these cultures from cryopreserved human lung tissue. We describe steps for lung tissue cryostorage, tissue dissociation, lung epithelial organoid generation, and ALI culture differentiation. We also include quality control steps and technical readouts for monitoring virus response. This protocol demonstrates severe acute respiratory syndrome coronavirus 2 infection in these cultures as an example of their utility. For complete details on the use and execution of this protocol, please refer to Diana Cadena Castaneda et al. (2023)

A genome–wide CRISPR activation screen identifies SCREEM a novel SNAI1 super-enhancer demarcated by eRNAs

The genome is pervasively transcribed to produce a vast array of non-coding RNAs (ncRNAs). Long non-coding RNAs (lncRNAs) are transcripts of >200 nucleotides and are best known for their ability to regulate gene expression. Enhancer RNAs (eRNAs) are subclass of lncRNAs that are synthesized from enhancer regions and have also been shown to coordinate gene expression. The biological function and significance of most lncRNAs and eRNAs remain to be determined. Epithelial to mesenchymal transition (EMT) is a ubiquitous cellular process that occurs during cellular migration, homeostasis, fibrosis, and cancer-cell metastasis. EMT-transcription factors, such as SNAI1 induce a complex transcriptional program that coordinates the morphological and molecular changes associated with EMT. Such complex transcriptional programs are often subject to coordination by networks of ncRNAs and thus can be leveraged to identify novel functional ncRNA loci. Here, using a genome-wide CRISPR activation (CRISPRa) screen targeting ∼10,000 lncRNA loci we identified ncRNA loci that could either promote or attenuate EMT. We discovered a novel locus that we named SCREEM (SNAI1 cis-regulatory eRNAs expressed in monocytes). The SCREEM locus contained a cluster of eRNAs that when activated using CRISPRa induced expression of the neighboring gene SNAI1, driving concomitant EMT. However, the SCREEM eRNA transcripts themselves appeared dispensable for the induction of SNAI1 expression. Interestingly, the SCREEM eRNAs and SNAI1 were co-expressed in activated monocytes, where the SCREEM locus demarcated a monocyte-specific super-enhancer. These findings suggest a potential role for SNAI1 in monocytes. Exploration of the SCREEM-SNAI axis could reveal novel aspects of monocyte biology

Controlling the Innovative Supply Chain Activities for Improving the Competitive Ability of Companies

As part of the supposed approach to strengthen the competitive capacity of industrial companies based on the implementation of the control of innovative activities in the supply chain, the flexible evaluation system was developed, differentiated according to the strategic and tactical objectives of the existing growth of competitive capacity. The strategic management of innovation process supposes achievement of competitive advantages based upon maximal usage of the experience of innovative supply chain activity organization and accounting of new factors, which influence on their change. To solve this problem have been analyzed conditions that determine the appearance and replace the six models of innovative process, allocated based on the modern world scientific experience. The logic of their development allows us to see the important characteristics of their changes, explain the effectiveness of the change process models, through the manifestation of the cumulative and complementary effects. The authors proposed a system of indicators to monitor the degree of manifestation of these effects in the study. In the course of the calculations have been identified changes in the patterns of the innovation process in the Russian mining and metallurgical complex and at the same time as there was a cumulative and complementary effect in changing patterns, and thus the efficiency of controlling innovation. The results suggest that the implementation of controlling innovation in mining and metallurgical enterprises cannot be called successful. Sustained growth was observed on single indicators. In mass production, they have a high degree of variability, or a downward trend. Nowadays, there are a few number of Innovative companies. Keywords- companies, innovation processes, supply chain activity, indicators, competitive ability

Studying Tumor Angiogenesis and Cancer Invasion in a Three-Dimensional Vascularized Breast Cancer Micro-Environment.

Metastatic breast cancer is one of the deadliest forms of malignancy, primarily driven by its characteristic micro-environment comprising cancer cells interacting with stromal components. These interactions induce genetic and metabolic alterations creating a conducive environment for tumor growth. In this study, a physiologically relevant 3D vascularized breast cancer micro-environment is developed comprising of metastatic MDA-MB-231 cells and human umbilical vein endothelial cells loaded in human dermal fibroblasts laden fibrin, representing the tumor stroma. The matrix, as well as stromal cell density, impacts the transcriptional profile of genes involved in tumor angiogenesis and cancer invasion, which are hallmarks of cancer. Cancer-specific canonical pathways and activated upstream regulators are also identified by the differential gene expression signatures of these composite cultures. Additionally, a tumor-associated vascular bed of capillaries is established exhibiting dilated vessel diameters, representative of in vivo tumor physiology. Further, employing aspiration-assisted bioprinting, cancer-endothelial crosstalk, in the form of collective angiogenesis of tumor spheroids bioprinted at close proximity, is identified. Overall, this bottom-up approach of tumor micro-environment fabrication provides an insight into the potential of in vitro tumor models and enables the identification of novel therapeutic targets as a preclinical drug screening platform