Robust Gene Research Articles

Ferroptosis-Related Gene Signatures in Epilepsy: Diagnostic and Immune Insights.

Epilepsy is characterized by a multifaceted aetiology. Ferroptosis has recently been implicated in seizure pathophysiology, although its mechanistic role in epilepsy remains obscure. We examined the roles of ferroptosis-related genes (FRGs) in epilepsy cohorts from the GSE143272 dataset. We investigated the associations between gene expression and the immune response by performing CIBERSORT and MCP-counter analyses. By employing unsupervised consensus clustering and weighted gene coexpression network analysis (WGCNA), we delineated robust gene clusters across cohorts. Single-cell RNA sequencing data from the GSE201048 dataset provided insights into the interactions between pivotal ferroptosis-related genes and immune cells. Additionally, we employed qRT‒PCR technology to measure the levels of these central genes in the tissues of epileptic patients and mice. Our findings revealed seven pivotal genes (TFRC, POR, PTGS2, RELA, PGD, TRIM21, and QSOX1) at the forefront in epilepsy specimens. A diagnostic model harnessing these genes exhibited substantial efficacy (AUC = 0.913). Similarly, the qRT‒PCR analysis of samples from epileptic patients and mouse epileptic brain tissues substantiated these findings. Stratification of 91 patients with epilepsy via WGCNA, based on gene expression, revealed distinct immunological profiles. The scRNA-seq data further indicated increased expression of central genes in macrophages and microglia. Notably, these cells and those with elevated ferroptosis scores were significantly enriched in inflammation-related pathways. These findings support the strong involvement of FRGs in the pathogenesis of epilepsy, particularly neuroinflammation. These central genes hold promise as novel diagnostic biomarkers for epilepsy.

Destabilization of mRNAs enhances competence to initiate meiosis in mouse spermatogenic cells.

The specialized cell cycle of meiosis transforms diploid germ cells into haploid gametes. In mammals, diploid spermatogenic cells acquire the competence to initiate meiosis in response to retinoic acid. Previous mouse studies revealed that MEIOC interacts with RNA-binding proteins YTHDC2 and RBM46 to repress mitotic genes and to promote robust meiotic gene expression in spermatogenic cells that have initiated meiosis. Here, we have used the enhanced resolution of scRNA-seq and bulk RNA-seq of developmentally synchronized spermatogenesis to define how MEIOC molecularly supports early meiosis in spermatogenic cells. We demonstrate that MEIOC mediates transcriptomic changes before meiotic initiation, earlier than previously appreciated. MEIOC, acting with YTHDC2 and RBM46, destabilizes its mRNA targets, including the transcriptional repressors E2f6 and Mga, in mitotic spermatogonia. MEIOC thereby derepresses E2F6- and MGA-repressed genes, including Meiosin and other meiosis-associated genes. This confers on spermatogenic cells the molecular competence to, in response to retinoic acid, fully activate the transcriptional regulator STRA8-MEIOSIN, which is required for the meiotic G1/S phase transition and for meiotic gene expression. We conclude that, in mice, mRNA decay mediated by MEIOC-YTHDC2-RBM46 enhances the competence of spermatogenic cells to initiate meiosis.

All-RNA-mediated targeted gene integration in mammalian cells with rationally engineered R2 retrotransposons

All-RNA-mediated targeted gene integration methods, rendering reduced immunogenicity, effective deliverability with non-viral vehicles, and a low risk of random mutagenesis, are urgently needed for next-generation gene addition technologies. Naturally occurring R2 retrotransposons hold promise in this context due to their site-specific integration profile. Here, we systematically analyzed the biodiversity of R2 elements and screened several R2 orthologs capable of full-length gene insertion in mammalian cells. Robust R2 system gene integration efficiency was attained using combined donor RNA and protein engineering. Importantly, the all-RNA-delivered engineered R2 system showed effective integration activity, with efficiency over 60% in mouse embryos. Unbiased high-throughput sequencing demonstrated that the engineered R2 system exhibited high on-target integration specificity (99%). In conclusion, our study provides engineered R2 tools for applications based on hit-and-run targeted DNA integration and insights for further optimization of retrotransposon systems.

Precision in Action: The Role of Clustered Regularly Interspaced Short Palindromic Repeats/Cas in Gene Therapies.

Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated enzyme-CAS holds great promise for treating many uncured human diseases and illnesses by precisely correcting harmful point mutations and disrupting disease-causing genes. The recent Food and Drug Association (FDA) approval of the first CRISPR-based gene therapy for sickle cell anemia marks the beginning of a new era in gene editing. However, delivering CRISPR specifically into diseased cells in vivo is a significant challenge and an area of intense research. The identification of new CRISPR/Cas variants, particularly ultra-compact CAS systems with robust gene editing activities, paves the way for the low-capacity delivery vectors to be used in gene therapies. CRISPR/Cas technology has evolved beyond editing DNA to cover a wide spectrum of functionalities, including RNA targeting, disease diagnosis, transcriptional/epigenetic regulation, chromatin imaging, high-throughput screening, and new disease modeling. CRISPR/Cas can be used to engineer B-cells to produce potent antibodies for more effective vaccines and enhance CAR T-cells for the more precise and efficient targeting of tumor cells. However, CRISPR/Cas technology has challenges, including off-target effects, toxicity, immune responses, and inadequate tissue-specific delivery. Overcoming these challenges necessitates the development of a more effective and specific CRISPR/Cas delivery system. This entails strategically utilizing specific gRNAs in conjunction with robust CRISPR/Cas variants to mitigate off-target effects. This review seeks to delve into the intricacies of the CRISPR/Cas mechanism, explore progress in gene therapies, evaluate gene delivery systems, highlight limitations, outline necessary precautions, and scrutinize the ethical considerations associated with its application.

Lipoplexes' Structure, Preparation, and Role in Managing Different Diseases.

Lipid-based vectors are becoming promising alternatives to traditional therapies over the last 2 decades specially for managing life-threatening diseases like cancer. Cationic lipids are the most prevalent non-viral vectors utilized in gene delivery.The increasing number of clinical trials about lipoplex-based gene therapy demonstrates their potential as well-established technology that can provide robust gene transfection. In this regard, this review will summarize this important point.These vectors however have a modest transfection efficiency. This limitation can be partly addressed by using functional lipids that provide a plethora of options for investigating nucleic acid-lipid interactions as well as in vitro and in vivo nucleic acid delivery for biomedical applications. Despite their lower gene transfer efficiency, lipid-based vectors such as lipoplexes have several advantages over viral ones: they are less toxic and immunogenic, can be targeted, and are simple to produce on a large scale. Researchers are actively investigating the parameters that are essential for an effective lipoplex delivery method. These include factors that influence the structure, stability, internalization, and transfection of the lipoplex.Thorough understanding of the design principles will enable synthesis of customized lipoplex formulations for life-saving therapy.

Robust soybean leaf agroinfiltration.

A robust agroinfiltration-mediated transient gene expression method for soybean leaves was developed. Plant genotype, developmental stage and leaf age, surfactant, and Agrobacterium culture conditions are important for successful agroinfiltration. Agroinfiltration of Nicotiana benthamiana has emerged as a workhorse transient assay for plant biotechnology and synthetic biology to test the performance of gene constructs in dicot leaves. While effective, it is nonetheless often desirable to assay transgene constructs directly in crop species. To that end, we innovated a substantially robust agroinfiltration method for Glycine max (soybean), the most widely grown dicot crop plant in the world. Several factors were found to be relevant to successful soybean leaf agroinfiltration, including genotype, surfactant, developmental stage, and Agrobacterium strain and culture medium. Our optimized protocol involved a multi-step Agrobacterium culturing process with appropriate expression vectors, Silwet L-77 as the surfactant, selection of fully expanded leaves in the VC or V1 stage of growth, and 5min of vacuum at -85kPa followed by a dark incubation period before plants were returned to normal growth conditions. Using this method, young soybean leaves of two lines-V17-0799DT, and TN16-5004-were high expressors for GUS, two co-expressed fluorescent protein genes, and the RUBY reporter product, betalain. This work not only represents a new research tool for soybean biotechnology, but also indicates critical parameters for guiding agroinfiltration optimization for other crop species.We speculate that leaf developmental stage might be the most critical factor for successful agroinfiltration.

Noise Reduction in Resource-Coupled Multi-Module Gene Circuits through Antithetic Feedback Control.

Gene circuits within the same host cell often experience coupling, stemming from the competition for limited resources during transcriptional and translational processes. This resource competition introduces an additional layer of noise to gene expression. Here we present three multi-module antithetic control strategies: negatively competitive regulation (NCR) controller, alongside local and global controllers, aimed at reducing the gene expression noise within the context of resource competition. Through stochastic simulations and fluctuation-dissipation theorem (FDT) analysis, our findings highlight the superior performance of the NCR antithetic controller in reducing noise levels. Our research provides an effective control strategy for attenuating resource-driven noise and offers insight into the development of robust gene circuits.

Large language model based framework for automated extraction of genetic interactions from unstructured data.

Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the published literature is relevant to biological relation extraction, and the embedded sentences of relevant sections have complex structures, which can lead to incorrect inference of relationships. To overcome these challenges, we propose GIX, an automated and robust Gene Interaction Extraction framework, based on pre-trained Large Language models fine-tuned through extensive evaluations on various gene/protein interaction corpora including LLL and RegulonDB. GIX identifies relevant publications with minimal keywords, optimises sentence selection to reduce computational overhead, simplifies sentence structure while preserving meaning, and provides a confidence factor indicating the reliability of extracted relations. GIX's Stage-2 relation extraction method performed well on benchmark protein/gene interaction datasets, assessed using 10-fold cross-validation, surpassing state-of-the-art approaches. We demonstrated that the proposed method, although fully automated, performs as well as manual relation extraction, with enhanced robustness. We also observed GIX's capability to augment existing datasets with new sentences, incorporating newly discovered biological terms and processes. Further, we demonstrated GIX's real-world applicability in inferring E. coli gene circuits.

Exonuclease editor promotes precision of gene editing in mammalian cells

BackgroundMany efforts have been made to improve the precision of Cas9-mediated gene editing through increasing knock-in efficiency and decreasing byproducts, which proved to be challenging.ResultsHere, we have developed a human exonuclease 1-based genome-editing tool, referred to as exonuclease editor. When compared to Cas9, the exonuclease editor gave rise to increased HDR efficiency, reduced NHEJ repair frequency, and significantly elevated HDR/indel ratio. Robust gene editing precision of exonuclease editor was even superior to the fusion of Cas9 with E1B or DN1S, two previously reported precision-enhancing domains. Notably, exonuclease editor inhibited NHEJ at double strand breaks locally rather than globally, reducing indel frequency without compromising genome integrity. The replacement of Cas9 with single-strand DNA break-creating Cas9 nickase further increased the HDR/indel ratio by 453-fold than the original Cas9. In addition, exonuclease editor resulted in high microhomology-mediated end joining efficiency, allowing accurate and flexible deletion of targeted sequences with extended lengths with the aid of paired sgRNAs. Exonuclease editor was further used for correction of DMD patient-derived induced pluripotent stem cells, where 30.0% of colonies were repaired by HDR versus 11.1% in the control.ConclusionsTherefore, the exonuclease editor system provides a versatile and safe genome editing tool with high precision and holds promise for therapeutic gene correction.

Identification and genetic validation of leukemia inhibitory factor super-enhancers in acute respiratory distress syndrome and lung cancer.

Super-enhancers play prominent roles in driving robust pathological gene expression, but they are hidden in human genome at noncoding regions, making them difficult to explore. Leukemia inhibitory factor (LIF) is a multifunctional cytokine crucially involved in acute respiratory distress syndrome (ARDS) and lung cancer progression. However, the mechanisms governing LIF regulation in disease contexts remain largely unexplored. In this study, we observed elevated levels of LIF in the bronchoalveolar lavage fluid (BALF) of patients with sepsis-related ARDS compared to those with nonsepsis-related ARDS. Furthermore, both basal and LPS-induced LIF expression were under the control of super-enhancers. Through analysis of H3K27Ac ChIP-seq data, we pinpointed three potential super-enhancers (LIF-SE1, LIF-SE2, and LIF-SE3) located proximal to the LIF gene in cells. Notably, genetic deletion of any of these three super-enhancers using CRISPR-Cas9 technology led to a significant reduction in LIF expression. Moreover, in cells lacking these super-enhancers, both cell growth and invasion capabilities were substantially impaired. Our findings highlight the critical role of three specific super-enhancers in regulating LIF expression and offer new insights into the transcriptional regulation of LIF in ARDS and lung cancer.

Challenges of Robust RNAi-Mediated Gene Silencing in Aedes Mosquitoes.

In this study, we report the complexities and challenges associated with achieving robust RNA interference (RNAi)-mediated gene knockdown in the mosquitoes Aedes aegypti and Aedes albopictus, a pivotal approach for genetic analysis and vector control. Despite RNAi's potential for species-specific gene targeting, our independent efforts to establish oral delivery of RNAi for identifying genes critical for mosquito development and fitness encountered significant challenges, failing to reproduce previously reported potent RNAi effects. We independently evaluated a range of RNAi-inducing molecules (siRNAs, shRNAs, and dsRNAs) and administration methods (oral delivery, immersion, and microinjection) in three different laboratories. We also tested various mosquito strains and utilized microorganisms for RNA delivery. Our results reveal a pronounced inconsistency in RNAi efficacy, characterized by minimal effects on larval survival and gene expression levels in most instances despite strong published effects for the tested targets. One or multiple factors, including RNase activity in the gut, the cellular internalization and processing of RNA molecules, and the systemic dissemination of the RNAi signal, could be involved in this variability, all of which are barely understood in mosquitoes. The challenges identified in this study highlight the necessity for additional research into the underlying mechanisms of mosquito RNAi to develop more robust RNAi-based methodologies. Our findings emphasize the intricacies of RNAi application in mosquitoes, which present a substantial barrier to its utilization in genetic control strategies.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Development of a single transcript CRISPR/Cas9 toolkit for efficient genome editing in autotetraploid alfalfa

Alfalfa (Medicago sativa. L.) is a globally significant autotetraploid legume forage crop. However, despite its importance, establishing efficient gene editing systems for cultivated alfalfa remains a formidable challenge. In this study, we pioneered the development of a highly effective ultrasonic-assisted leaf disc transformation system for Gongnong 1 alfalfa, a variety widely cultivated in Northeast China. Subsequently, we created a single transcript CRISPR/Cas9 (CRISPR_2.0) toolkit, incorporating multiplex gRNAs, designed for gene editing in Gongnong 1. Both Cas9 and gRNA scaffolds were under the control of the Arabidopsis ubiquitin-10 promoter, a widely employed polymerase II constitutive promoter known for strong transgene expression in dicots. To assess the toolkit’s efficiency, we targeted PALM1, a gene associated with a recognizable multifoliate phenotype. Utilizing the CRISPR_2.0 toolkit, we directed PALM1 editing at two sites in the wild-type Gongnong 1. Results indicated a 35.1% occurrence of editing events all in target 2 alleles, while no mutations were detected at target 1 in the transgenic-positive lines. To explore more efficient sgRNAs, we developed a rapid, reliable screening system based on Agrobacterium rhizogenes-mediated hairy root transformation, incorporating the visible reporter MtLAP1. This screening system demonstrated that most purple visible hairy roots underwent gene editing. Notably, sgRNA3, with an 83.0% editing efficiency, was selected using the visible hairy root system. As anticipated, tetra-allelic homozygous palm1 mutations exhibited a clear multifoliate phenotype. These palm1 lines demonstrated an average crude protein yield increase of 21.5% compared to trifoliolate alfalfa. Our findings highlight the modified CRISPR_2.0 system as a highly efficient and robust gene editing tool for autotetraploid alfalfa.

SlNAC3 suppresses cold tolerance in tomatoes by enhancing ethylene biosynthesis.

Low temperature stress poses a significant challenge to the productivity of horticultural crops. The dynamic expression of cold-responsive genes plays a crucial role in plant cold tolerance. While NAC transcription factors have been extensively studied in plant growth and development, their involvement in regulating plant cold tolerance remains poorly understood. In this study, we focused on the identification and characterisation of SlNAC3 as the most rapid and robust responsive gene in tomato under low temperature conditions. Manipulating SlNAC3 through overexpression or silencing resulted in reduced or enhanced cold tolerance, respectively. Surprisingly, we discovered a negative correlation between the expression of CBF and cold tolerance in the SlNAC3 transgenic lines. These findings suggest that SlNAC3 regulates tomato cold tolerance likely through a CBF-independent pathway. Furthermore, we conducted additional investigations to identify the molecular mechanisms underlying SINAC3-mediated cold tolerance in tomatoes. Our results revealed that SlNAC3 controls the transcription of ethylene biosynthetic genes, thereby bursting ethylene release in response to cold stress. Indeed, the silencing of these genes led to an augmentation in cold tolerance. This discovery provides valuable insights into the regulatory pathways involved in ethylene-mediated cold tolerance in tomatoes, offering potential strategies for developing innovative approaches to enhance cold stress resilience in this economically important crop species.

Abstract P43: Optimization of DNA Methyltransferase Inhibitors-based Combination to Target the Epigenome of Gastric Cancer

Abstract Aberrant DNA methylation is one of the critical epigenetic modifications that has become a widespread phenotype in solid tumours. Similar to haematological malignancies, many studies have shown that the DNA methylation landscape of solid tumours exhibits a pattern of either hypermethylation of the promoter regions of tumour-suppressive genes in advanced cancer or global hypomethylation in the early stages of carcinogenesis. Additionally, dysregulated DNA methylation also seems to remodel the tumour microenvironment and affect immune cells' response towards immune checkpoint inhibitors. In gastric cancer, comprehensive DNA methylation analyses have demonstrated that gastric tumours carry the CpG island methylation phenotype (CIMP), resulting in extended molecular-based subgroups such as the extremely high-methylation epigenotype (E-HME), HME, and low-methylation epigenotype (LME). Therefore, conventional histology grading and molecular subtyping are insufficient to address the multifaceted epigenome of gastric cancer. As such, current standard-of-care (SOC) therapy is not able to overcome cancer relapse and tumour metastasis. Clearly, this shows the therapeutic potential of using DNA hypomethylating drugs like DNA methyltransferase inhibitors (DNMTi) for solid tumours such as gastric cancer. In our study, we exploited the engineering-based Quadratic Phenotypic Optimization Platform (QPOP) to identify top-ranking DNMTi-based drug combinations across gastric cancer cell lines (GC-CL) and patient-derived organoids (GC-PDO). Based on QPOP analysis, we showed that a non-SOC drug combination, Docetaxel/5-Azacytidine is a frequently top-ranking. Besides, our present findings suggest that CIMP-high gastric cancer cells are more susceptible towards DNMT inhibitors through the specific reduction of DNMT1 levels. As we surveyed the effects of Docetaxel/5-Azacytidine in a CIMP-high GC-CL via Infinium methylation array, we observed robust gene methylation changes that are involved in non-canonical Wnt Signaling and EMT. On the other hand, proteomic analysis revealed a small subset of our panel of GC lines that are positive for the cancer-testis antigen, MageA4. Interestingly, the protein expression of MageA4 in some CIMP-high lines is restored upon treatment with DNMT inhibitors. This evinces that DNMTi can subject epigenetically silenced MageA4 gastric tumours to autologous T-cell receptor therapy. Taken altogether, we plan to further interrogate the mechanisms of DNMT inhibitors in sensitizing CIMP-high gastric tumours towards Taxanes by acting on the non-canonical Wnt pathway and EMT activators. Citation Format: Lissa Hooi, Vivien Koh, Dexter Kai Hao Thng, Jasmine Goh, Lim Jhin Jieh, Lee Rui Xue, Masturah Bte Mohd Abdul Rashid, Toh Tan Boon, Wei Peng Yong, Edward Kai-Hua Chow. Optimization of DNA Methyltransferase Inhibitors-based Combination to Target the Epigenome of Gastric Cancer [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P43.

Altered Expression of B Cell Receptor Signaling Pathway Genes in Peripheral Blood of Patients with Multiple Sclerosis.

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease and has adverse implications. The exact mechanism of its pathogenesis is not fully understood and remains to be elucidated. In the current study we aimed to identify key genes that can serve as potential biomarkers and therapeutic targets for MS and shed light on pathogenesis mechanisms involved in MS. We analyzed a gene expression dataset (GES21942) and found 266 differentially expressed genes (DEGs) including 183 upregulated and 83 downregulated genes in MS patients compared to controls. Then we conducted pathway enrichment on DEGs and selected the top enriched pathway i.e., B cell receptor signaling pathway, and 5 genes of this pathway (CR2, BLK, BLNK, RASGRP3, and KRAS) for further investigation in our clinical samples. We recruited 50 MS patients and 50 controls and assessed the expression of selected genes in the circulation of patients versus controls. Expression of CR2, BLK, BLNK, and RASGRP3 were significantly higher in MS cases compared with controls. There was no significant difference in expression of KRAS between patients and controls. All of the selected genes with differential expression had noticeable diagnostic power and CR2 was the most robust gene in differentiating MS cases from controls. Additionally, a combination of genes resulted in enhanced diagnostic power. Collectively our results suggest that the B cell receptor signaling pathway and the selected genes from this pathway may be implicated in the pathogenesis of MS and each of these genes can be considered as potential diagnostic biomarkers and therapeutic targets.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Integration of 3D bioprinting and multi-algorithm machine learning identified glioma susceptibilities and microenvironment characteristics

Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs. The ensemble model superseded the performance of all individual algorithms across diverse in vitro systems, including sphere cultures, complex 3D bioprinted multicellular models, and 3D patient-derived tissues. By integrating bioprinting, the evaluative scope of the treatment expanded to T cell-related therapy and anti-angiogenesis targeted therapy. We identified promising compounds and drugs for glioma treatment and revealed distinct immunosuppressive or angiogenic myeloid-infiltrated tumor microenvironments. These insights pave the way for enhanced therapeutic development for glioma and potentially for other cancers, highlighting the broad application potential of this integrative and translational approach.

BootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Evaluation of Lentiviral Based Gene Delivery System in Adherent and Suspension in vitro Cell Models

Lentiviruses are a highly robust gene delivery system capable of in vitro and in vivo gene transfer into multiple cell types. Recent fourth-generation lentiviral systems have been designed for enhanced safety, however, the increased recombination events required to produce infective lentiviral particles may reduce production efficiency. A set protocol for all types of target cells is not recommended and optimization of conditions for gene transfer into different target cells is required. In this study, we aim to evaluate the efficiency and reproducibility of lentiviral production using a fourth-generation lentiviral packaging system and identify optimal parameters for successful transduction in two different cell models, adherent and suspension cells. Lentiviral production, effect of viral volume, sustained gene expression and transduction adjuvants on adherent and suspension gene- cell models were evaluated. Transfection and transduction efficiency of lentiviruses was evaluated by fluorescence microscopy and flow cytometry. This study demonstrates that production of green fluorescent protein (GFP)- lentiviruses using the fourth-generation lentiviral packaging is consistent and reproducible. Optimal transduction of adherent cell types is achieved at lower multiplicity of infection (MOI) compared to suspension cells and produces GFP-expressing cells with higher intensity. Expression of GFP is sustained in all cell types over multiple passages. Polycation DEAE-dextran was determined to improve transduction in suspension cells, however, provides similar transduction efficiency as polybrene in adherent cells. In conclusion, fourth generation lentiviral system reproducibly generates high titre lentiviruses capable of infecting multiple cell types, however transduction protocols for different cell types require further optimization.