The Reactome Pathway Knowledgebase

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

De novo transcriptome analysis of the Indian squid Uroteuthis duvaucelii (Orbigny, 1848) from the Indian Ocean

Cephalopods have dominated the oceans for hundreds of millions of years and are unquestionably at the peak of molluscan evolution. The development of the large brain and a well-sophisticated sensory system contributed significantly to its success. Therefore, it is considered the best example of convergent evolution and attracted the attention of scientists from various disciplines of biology. The aim of the present study is to construct a reference transcriptome in the Indian squid Uroteuthis duvaucelii to gain insights into cephalopod evolution and enrich the existing cephalopod database. Around 72 million short Illumina reads were generated from five different tissues, including the brain, eye, gill, heart and gonads, and assembled using the Trinity assembler. About 26230 protein-coding sequences were annotated from the assembled transcripts. The BUSCO completeness of the assembly was 71.71% compared to the Mollusca_Odb10 gene set. KEGG and REACTOME pathway analyzes revealed that U. duvaucelii shares many genes and pathways with higher vertebrates.

dbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally validated from 48 databases and over 80 000 research articles. This version integrates proteomic data from 13 cancer types, with a particular focus on phosphoproteomic data and kinase activity profiles, allowing the exploration of personalized phosphorylation patterns in tumor samples. Integrating kinase-substrate phosphorylations with E3 ligase-substrate interactions, dbPTM 2025 provides a detailed map of PTM regulatory networks, offering insights into cancer-specific post-translational regulations. This update also includes advanced search capabilities, enabling users to efficiently query PTM data across species, PTM types and modified residues. The platform's new features-interactive visualization tools and streamlined data downloads-allow researchers to access and analyze PTM data easily. dbPTM 2025 also enhances functional annotations, regulatory networks and disease associations, broadening its application for cancer research and the study of disease-associated PTMs. Through these enhancements, dbPTM 2025 is a comprehensive, user-friendly resource, facilitating the study of PTMs and their roles in cancer research. The database is now freely accessible at https://biomics.lab.nycu.edu.tw/dbPTM/.

Protocol to discover actionable cancer vulnerabilities enabled by neomorph protein-protein interactions with the AVERON Notebook

While some tumor driver mutations inhibit existing protein-protein interactions (PPIs), others can create neomorph interactions (neoPPIs) not characteristic of the wild-type counterparts. Such tumor-specific neoPPIs may represent targets for therapeutic interventions. Here, we present a protocol to computationally uncover neoPPI-enabled druggable tumor dependencies using the AVERON Notebook environment. We describe steps for determining PPI levels, identifying clinically significant neoPPIs, and determining neoPPI-regulated pathways. We then detail procedures for determining neoPPI-regulated therapeutically actionable targets. For complete details on the use and execution of this protocol, please refer to Chen et al.1.

Circadian transcriptome oscillations in human adipose tissue depend on napping status and link to metabolic and inflammatory pathways

STUDY OBJECTIVES

Napping is a common habit in many countries. Nevertheless, studies about the chronic effects of napping on obesity are contradictory, and the molecular link between napping and metabolic alterations has yet to be studied. We aim to identify molecular mechanisms in adipose tissue (AT) that may connect napping and abdominal obesity.

METHODS

In this cross-sectional study, we extracted the RNA repeatedly across 24 hours from cultured AT explants and performed RNA sequencing. Circadian rhythms were analyzed using six consecutive time points across 24 hours. We also assessed global gene expression in each group (nappers vs. non-nappers).

RESULTS

With napping, there was an 88% decrease in the number of rhythmic genes compared to that in non-nappers, a reduction in rhythm amplitudes of 29%, and significant phase changes from a coherent unimodal acrophase in non-nappers, towards a scattered and bimodal acrophase in nappers. Those genes that lost rhythmicity with napping were mainly involved in pathways of glucose and lipid metabolism, and of the circadian clock. Additionally, we found differential global gene expression between nappers and non-nappers with 34 genes down- and 32 genes upregulated in nappers. The top upregulated gene (IER3) and top down-regulated pseudogene (VDAC2P2) in nappers have been previously shown to be involved in inflammation.

CONCLUSIONS

These new findings have implications for our understanding of napping's relationship with obesity and metabolic disorders.

EphA2 regulates vascular permeability and prostate cancer metastasis via modulation of cell junction protein phosphorylation

Prostate cancer morbidity and mortality demonstrate a need for more effective targeted therapies. One potential target is EphA2, although paradoxically, pro- and anti-oncogenic effects have been shown to be mediated by EphA2. We demonstrate that unique activating and blocking EphA2-targeting monoclonal antibodies display opposing tumor-suppressive and oncogenic properties in vivo. To further explore this complexity, we performed detailed phosphoproteomic analysis following ligand-induced EphA2 activation. Our analysis identified altered phosphorylation of 73 downstream proteins related to the PI3K/AKT/mTOR and ERK/MAPK pathways, with the majority implicated in cell junction and cytoskeletal organization, cell motility, and tumor metastasis. We demonstrate that the adapter protein SHB is an essential component in mediating the inhibition of the ERK/MAPK pathway in response to EphA2 receptor activation. Furthermore, we identify the adherence junction protein afadin as an EphA2-regulated phosphoprotein which is involved in prostate cancer migration and invasion.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

Using clusterProfiler to characterize multiomics data

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

On the interface of aging, cancer, and neurodegeneration with SIRT6 and L1 retrotransposon protein interaction network

Roles of the sirtuins in aging and longevity appear related to their evolutionarily conserved functions as retroviral-restriction factors. Retrotransposons also promote the aging process, which can be reversed by the inhibition of their activity. SIRT6 can functionally limit the mutation activity of LINE-1 (L1), a retrotransposon causing cancerogenesis-linked mutations accumulating during aging. Here, an overview of the molecular mechanisms of the controlling effects was created by the pathway enrichment and gene function prediction analysis of a protein interaction network of SIRT6 and L1 retrotransposon proteins L1 ORF1p, and L1 ORF2p. The L1-SIRT6 interaction network is enriched in pathways and nodes associated with RNA quality control, DNA damage response, tumor-related and retrotransposon activity-suppressing functions. The analysis also highlighted sumoylation, which controls protein-protein interactions, subcellular localization, and other post-translational modifications; DNA IR Damage and Cellular Response via ATR, and Hallmark Myc Targets V1, which scores are a measure of tumor aggressiveness. The protein node prioritization analysis emphasized the functions of tumor suppressors p53, PARP1, BRCA1, and BRCA2 having L1 retrotransposon limiting activity; tumor promoters EIF4A3, HNRNPA1, HNRNPH1, DDX5; and antiviral innate immunity regulators DDX39A and DDX23. The outline of the regulatory mechanisms involved in L1 retrotransposition with a focus on the prioritized nodes is here demonstrated in detail. Furthermore, a model establishing functional links between HIV infection, L1 retrotransposition, SIRT6, and cancer development is also presented. Finally, L1-SIRT6 subnetwork SIRT6-PARP1-BRCA1/BRCA2-TRIM28-PIN1-p53 was constructed, where all nodes possess L1 retrotransposon activity-limiting activity and together represent candidates for multitarget control.

Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.

Unique and Shared Molecular Mechanisms of Alcoholic and Non-Alcoholic Liver Cirrhosis Identified Through Transcriptomics Data Integration

Liver cirrhosis is a severe chronic disease that results from various etiological factors and leads to substantial morbidity and mortality. Alcoholic cirrhosis (AC) and non-AC (NAC) arise from prolonged and excessive consumption of alcohol and metabolic syndromes, respectively. Precise molecular mechanisms of AC and NAC are yet to be comprehensively understood for diagnostics and therapeutic advances to materialize. This study reports novel findings to this end by utilizing high-throughput RNA sequencing and microarray data from the Gene Expression Omnibus (GEO). We performed a meta-analysis of transcriptomics data to identify the differentially expressed genes specific to AC and NAC. Functional enrichment and protein-protein interaction network analyses uncovered novel hub genes and transcription factors (TFs) critical to AC and NAC. We found that AC is primarily driven by metabolic dysregulation and oxidative stress, with key TFs such as RELA, NFKB1, and STAT3. NAC was characterized by fibrosis and tissue remodeling associated with metabolic dysfunction, with TFs including USF1, MYCN, and HIF1A. Key hub genes such as ESR1, JUN, FOS, and PKM in AC, and CD8A, MAPK3, CCND1, and CXCR4 in NAC were identified, along with their associated TFs, pointing to potential therapeutic targets. Our results underscore the unique and shared molecular mechanisms that underlie AC and NAC and inform the efforts toward precision medicine and improved patient outcomes in liver cirrhosis.

GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely focused on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven methodologies for precision genomics.

Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study

Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, underscoring the critical need for early detection methods and more effective therapeutic approaches. This research rigorously investigates the molecular mechanisms underlying PDAC, with a focus on the identification of pivotal genes and pathways that may hold therapeutic relevance and prognostic value. Through the construction of a protein-protein interaction (PPI) network and the examination of differentially expressed genes (DEGs), the study uncovers key hub genes such as CDK1, KIF11, and BUB1, demonstrating their substantial role in the pathogenesis of PDAC. Notably, the dysregulation of these genes is consistent across a spectrum of cancers, positing them as potential targets for wide-ranging cancer therapeutics. This study also brings to the fore significant genes encoding intrinsically disordered proteins, in particular GPRC5A and KRT7, unveiling promising new pathways for therapeutic intervention. Advanced machine learning techniques were harnessed to classify PDAC patients with high accuracy, utilizing the key genetic markers as a dataset. The Support Vector Machine (SVM) model leveraged the hub genes to achieve a sensitivity of 91 % and a specificity of 85 %, while the RandomForest model notched a sensitivity of 91 % and specificity of 92.5 %. Crucially, when the identified genes were cross-referenced with TCGA-PAAD clinical datasets, a tangible correlation with patient survival rates was discovered, reinforcing the potential of these genes as prognostic biomarkers and their viability as targets for therapeutic intervention. This study's findings serve as a potent testament to the value of molecular analysis in enhancing the understanding of PDAC and in advancing the pursuit for more effective diagnostic and treatment strategies.

Toden-E: Topology-Based and Density-Based Ensembled Clustering for the Development of Super-PAG in Functional Genomics using PAG Network and LLM

The integrative analysis of gene sets, networks, and pathways is pivotal for deciphering omics data in translational biomedical research. To significantly increase gene coverage and enhance the utility of pathways, annotated gene lists, and gene signatures from diverse sources, we introduced pathways, annotated gene lists, and gene signatures (PAGs) enriched with metadata to represent biological functions. Furthermore, we established PAG-PAG networks by leveraging gene member similarity and gene regulations. However, in practice, high similarity in functional descriptions or gene membership often leads to redundant PAGs, hindering the interpretation from a fuzzy enriched PAG list. In this study, we developed todenE (topology-based and density-based ensemble) clustering, pioneering in integrating topology-based and density-based clustering methods to detect PAG communities leveraging the PAG network and Large Language Models (LLM). In computational genomics annotation, the genes can be grouped/clustered through the gene relationships and gene functions via guilt by association. Similarly, PAGs can be grouped into higher-level clusters, forming concise functional representations called Super-PAGs. TodenE captures PAG-PAG similarity and encapsulates functional information through LLM, in characterizing network-based functional Super-PAGs. In synthetic data, we introduced a metric called the Disparity Index (DI), measuring the connectivity of gene neighbors to gauge clusterability. We compared multiple clustering algorithms to identify the best method for generating performance-driven clusters. In non-simulated data (Gene Ontology), by leveraging transfer learning and LLM, we formed a language-based similarity embedding. TodenE utilizes this embedding together with the topology-based embedding to generate putative Super-PAGs with superior performance in semantic and gene member inclusiveness.

Proteomic and transcriptomic analyses identify apo-transcobalamin-II as a biomarker of overall survival in osteosarcoma

Background

The large-scale proteomic platform known as the SomaScan® assay is capable of simultaneously measuring thousands of proteins in patient specimens through next-generation aptamer-based multiplexed technology. While previous studies have utilized patient peripheral blood to suggest serum biomarkers of prognostic or diagnostic value in osteosarcoma (OSA), the most common primary pediatric bone cancer, they have ultimately been limited in the robustness of their analyses. We propose utilizing this aptamer-based technology to describe the systemic proteomic milieu in patients diagnosed with this disease.

Methods

To determine novel biomarkers associated with overall survival in OSA, we deployed the SomaLogic SomaScan® 7k assay to investigate the plasma proteomic profile of naive primary, recurrent, and metastatic OSA patients. Following identification of differentially expressed proteins (DEPs) between 2-year deceased and survivor cohorts, publicly available databases including Survival Genie, TIGER, and KM Plotter Immunotherapy, among others, were utilized to investigate the significance of our proteomic findings.

Results

Apo-transcobalamin-II (APO-TCN2) was identified as the most DEP between 2-year deceased and survivor cohorts (Log2 fold change = 6.8, P-value = 0.0017). Survival analysis using the Survival Genie web-based platform indicated that increased intratumoral TCN2 expression was associated with better overall survival in both OSA (TARGET-OS) and sarcoma (TCGA-SARC) datasets. Cell-cell communication analysis using the TIGER database suggested that TCN2+ Myeloid cells likely interact with marginal zone and immunoglobin-producing B lymphocytes expressing the TCN2 receptor (CD320) to promote their proliferation and survival in both non-small cell lung cancer and melanoma tumors. Analysis of publicly available OSA scRNA-sequencing datasets identified similar populations in naive primary tumors. Furthermore, circulating APO-TCN2 levels in OSA were then associated with a plasma proteomic profile likely necessary for robust B lymphocyte proliferation, infiltration, and formation of intratumoral tertiary lymphoid structures for improved anti-tumor immunity.

Conclusions

Overall, APO-TCN2, a circulatory protein previously described in various lymphoproliferative disorders, was associated with 2-year survival status in patients diagnosed with OSA. The relevance of this protein and apparent immunological function (anti-tumor B lymphocyte responses) was suggested using publicly available solid tumor RNA-sequencing datasets. Further studies characterizing the biological function of APO-TCN2 and its relevance in these diseases is warranted.

The clinical and genetic spectrum of paediatric speech and language disorders

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Accurate identification of locally aneuploid cells by incorporating cytogenetic information in single cell data analysis

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.

A living organoid biobank of patients with Crohn's disease reveals molecular subtypes for personalized therapeutics

Crohn's disease (CD) is a complex and heterogeneous condition with no perfect preclinical model or cure. To address this, we explore adult stem cell-derived organoids that retain their tissue identity and disease-driving traits. We prospectively create a biobank of CD patient-derived organoid cultures (PDOs) from colonic biopsies of 53 subjects across all clinical subtypes and healthy subjects. Gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and identified two major molecular subtypes: immune-deficient infectious CD (IDICD) and stress and senescence-induced fibrostenotic CD (S2FCD). Each subtype shows internal consistency in the transcriptome, genome, and phenome. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. Drug screens reverse subtype-specific phenotypes, suggesting phenotyped-genotyped CD PDOs can bridge basic biology and patient trials by enabling preclinical phase "0" human trials for personalized therapeutics.

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells

Misfolded species of superoxide dismutase 1 (SOD1) are associated with increased death in amyotrophic lateral sclerosis (ALS) models compared to insoluble protein aggregates. The mechanism by which structurally independent SOD1 trimers cause cellular toxicity is unknown but may drive disease pathology. Here, we uncovered the SOD1 trimer interactome-a map of potential tissue-selective protein-binding partners in the brain, spinal cord, and skeletal muscle. We identified binding partners and key pathways associated with SOD1 trimers and found that trimers may affect normal cellular functions such as dendritic spine morphogenesis and synaptic function in the central nervous system and cellular metabolism in skeletal muscle. We discovered SOD1 trimer-selective enrichment of genes. We performed detailed computational and biochemical characterization of SOD1 trimer protein binding for septin-7. Our investigation highlights key proteins and pathways within distinct tissues, revealing a plausible intersection of genetic and pathophysiological mechanisms in ALS through interactions involving SOD1 trimers.

Oncogenic plasmid DNA and liver injury agent dictates liver cancer development in a mouse model

Primary liver cancer is an increasing problem worldwide and is associated with significant mortality. A popular method of modeling liver cancer in mice is plasmid hydrodynamic tail vein injection (HTVI). However, plasmid-HTVI models rarely recapitulate the chronic liver injury which precedes the development of most human liver cancer. We sought to investigate how liver injury using thioacetamide contributes to the pathogenesis and progression of liver cancer in two oncogenic plasmid-HTVI-induced mouse liver cancer models. Fourteen-week-old male mice received double-oncogene plasmid-HTVI (SB/AKT/c-Met and SB/AKT/NRas) and then twice-weekly intraperitoneal injections of thioacetamide for 6 weeks. Liver tissue was examined for histopathological changes, including fibrosis and steatosis. Further characterization of fibrosis and inflammation was performed with immunostaining and real-time quantitative PCR. RNA sequencing with pathway analysis was used to explore novel pathways altered in the cancer models. Hepatocellular and cholangiocellular tumors were observed in mice injected with double-oncogene plasmid-HTVI models (SB/AKT/c-Met and SB/AKT/NRas). Thioacetamide induced mild fibrosis and increased alpha smooth muscle actin-expressing cells. However, the combination of plasmids and thioacetamide did not significantly increase tumor size, but increased multiplicity of small neoplastic lesions. Cancer and/or liver injury up-regulated profibrotic and proinflammatory genes while metabolic pathway genes were mostly down-regulated. We conclude that the liver injury microenvironment can interact with liver cancer and alter its presentation. However, the effects on cancer development vary depending on the genetic drivers with differing active oncogenic pathways. Therefore, the choice of plasmid-HTVI model and injury agent may influence the extent to which injury promotes liver cancer development.

Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers

Background/Objectives: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment. Methods: We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC. Results: Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjusted p-value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings. Conclusions: The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC.

Insights into the radiation and oxidative stress mechanisms in genus Deinococcus

Deinococcus species, noted for their exceptional resistance to DNA-damaging environmental stresses, have piqued scientists' interest for decades. This study dives into the complex mechanisms underpinning radiation resistance in the Deinococcus genus. We have examined the genomes of 82 Deinococcus species and classified radiation-resistance proteins manually into five unique curated categories: DNA repair, oxidative stress defense, Ddr and Ppr proteins, regulatory proteins, and miscellaneous resistance components. This classification reveals important information about the various molecular mechanisms used by these extremophiles which have been less explored so far. We also investigated the presence or lack of these proteins in the context of phylogenetic relationships, core, and pan-genomes, which offered light on the evolutionary dynamics of radiation resistance. This comprehensive study provides a deeper understanding of the genetic underpinnings of radiation resistance in the Deinococcus genus, with potential implications for understanding similar mechanisms in other organisms using an interactomics approach. Finally, this study reveals the complexities of radiation resistance mechanisms, providing a comprehensive understanding of the genetic components that allow Deinococcus species to flourish under harsh environments. The findings add to our understanding of the larger spectrum of stress adaption techniques in bacteria and may have applications in sectors ranging from biotechnology to environmental research.

TGFβ signalling pathway impacts brain metastases profiles in locally advanced colorectal cancer

RATIONALE

Colorectal Cancer (CRC) represents the third most common type of cancer in Germany and the second most common cancer-related cause of death worldwide. Distant metastases are still the main limit for patient survival. While liver metastases as well as peritoneal carcinomatosis can often either be resected or treated with systemic therapy, little options remain for brain metastases. Additionally, a number of studies has already investigated hepatic, peritoneal, pulmonary as well as continuing distant metastases in colorectal cancer. Yet, with respect to tumor biology and brain metastases, little is known so far.

MATERIAL AND METHODS

Two cohorts, M0 without distant spread and BRA with brain metastases were build. RNA was isolated from paraffin embedded specimen. Gene expression was performed by an RNA NanoString-Analysis using the nCounter® PanCancer Progression Panel by NanoString-Technologies (Hamburg, Germany). Results were analysed by principal component analysis, gene expression and pathway analysis using commonly available databases such as KEGG as benchmark for comparison.

RESULTS

We were able to determine a gene signature that provides a sophisticated group separation between M0 and BRA using principal component analysis. All genes with strong loading characteristics on principal component 1 were cross-referenced with the subsequently performed accurate gene set enrichment analysis (GSEA). The GSEA revealed a clear dysregulation of the TGFβ pathway in compared cohorts M0 and BRA. Interestingly, the targeted pathways analysis of the identified genes confirmed that in fact almost all strong loading genes of PC1 play a role in the TGFβ pathway.

CONCLUSION

Our results suggest the TGFβ pathway as a crucial player in the development of brain metastases in primary CRC. In some types of colorectal cancer, downregulation of the TGFβ pathway might hinder primary colorectal cancer to metastasize to the nervous system. While the paradoxical functioning of the TGFβ pathway is still not fully understood, these shed light on yet another clinical implication of this complex pathway.

Subclinical peripheral inflammation has systemic effects impacting central nervous system proteome in budgerigars

Regulation of neuroimmune interactions varies across avian species. Little is presently known about the interplay between periphery and central nervous system (CNS) in parrots, birds sensitive to neuroinflammation. Here we investigated the systemic and CNS responses to dextran sulphate sodium (DSS)- and lipopolysaccharide (LPS)-induced subclinical acute peripheral inflammation in budgerigar (Melopsittacus undulatus). Three experimental treatment groups differing in DSS and LPS stimulation were compared to controls. Individuals treated with DSS showed significant histological intestinal damage. Through quantitative proteomics we described changes in plasma (PL) and cerebrospinal fluid (CSF) composition. In total, we identified 180 proteins in PL and 978 proteins in CSF, with moderate co-structure between the proteomes. Between treatments we detected differences in immune, coagulation and metabolic pathways. Proteomic variation was associated with the levels of pro-inflammatory cytokine mRNA expression in intestine and brain. Our findings shed light on systemic impacts of peripheral low-grade inflammation in birds.

Tuning Immune-Cold Tumor by Suppressing USP10/B7-H4 Proteolytic Axis Reinvigorates Therapeutic Efficacy of ADCs

Tuning immune-cold tumor hot has largely attracted attention to improve cancer treatment, including immunotherapy and antibody-drug conjugates (ADCs). Utilizing multiomic analyses and experimental validation, this work identifies a pivotal role for the USP10/B7-H4 proteolytic axis in mediating the interplay between tumor immune responses and ADC efficacy, particularly for sacituzumab govitecan (SG) in treating triple negative breast cancers (TNBCs). Mechanistically, the inhibition of autocrine motility factor receptor (AMFR)-mediated ubiquitylation of B7-H4 by the deubiquitinase USP10 leads to the stabilization of B7-H4, which suppresses tumor immune activity and reduces SG treatment effectiveness. Pharmacological inhibition of USP10 promotes the degradation of B7-H4, enhancing tumor immunogenicity and consequently improving the tumor-killing efficacy of SG. In preclinical TNBC models, suppression of USP10/B7-H4 proteolytic axis is effective in increasing SG killing efficacy and reducing tumor growth, especially for the tumors with the USP10high/B7-H7high signature. Collectively, these findings uncover a novel strategy for targeting the immunosuppressive molecule B7-H4 for cancer therapy.

RNA Sequencing Analysis of Monocytes Exposed to Airway Fluid From Children With Pediatric Acute Respiratory Distress Syndrome

OBJECTIVES

Monocytes are plastic cells that assume different polarization states that can either promote inflammation or tissue repair and inflammation resolution. Polarized monocytes are partially defined by their transcriptional profiles that are influenced by environmental stimuli. The airway monocyte response in pediatric acute respiratory distress syndrome (PARDS) is undefined. To identify differentially expressed genes and networks using a novel transcriptomic reporter assay with donor monocytes exposed to the airway fluid of intubated children with and at-risk for PARDS. To determine differences in gene expression at two time points using the donor monocyte assay exposed to airway fluid from intubated children with PARDS obtained 48-96 hours following initial tracheal aspirate sampling.

DESIGN

In vitro pilot study carried out using airway fluid supernatant.

SETTING

Academic 40-bed PICU.

PARTICIPANTS

Fifty-seven children: 44 children with PARDS and 13 children at-risk for PARDS.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We performed bulk RNA sequencing using a transcriptomic reporter assay of monocytes exposed to airway fluid from intubated children to discover gene networks differentiating PARDS from at-risk for PARDS and those differentiating mild/moderate from severe PARDS. We also report differences in gene expression in children with PARDS 48-96 hours following initial tracheal aspirate sampling. We found that interleukin (IL)-10, IL-4, and IL-13, cytokine/chemokine signaling, and the senescence-associated secretory phenotype are upregulated in monocytes exposed to airway fluid from intubated children with PARDS compared with those at-risk for PARDS. Signaling by NOTCH, histone deacetylation/acetylation, DNA methylation, chromatin modifications (B-WICH complex), and RNA polymerase I transcription and its associated regulatory apparatus were upregulated in children with PARDS 48-96 hours following initial tracheal aspirate sampling.

CONCLUSIONS

We identified gene networks important to the PARDS airway immune response using bulk RNA sequencing from a monocyte reporter assay that exposed monocytes to airway fluid from intubated children with and at-risk for PARDS. Mechanistic investigations are needed to validate our findings.

Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma

High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer.

Modelling cell type-specific lncRNA regulatory network in autism with Cycle

BACKGROUND

Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types.

RESULTS

Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation.

CONCLUSION

Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.

Longitudinal association of peripheral blood DNA methylation with liver fat content: distinguishing between predictors and biomarkers

BACKGROUND

Alterations in DNA methylation (DNAm) have been observed in patients with fatty liver, but whether they are cause or consequence remains unknown. The study aimed to investigate longitudinal association of epigenome-wide DNAm with liver fat content (LFC) in Chinese participants, and explore their temporal relationships.

METHODS

Data were obtained from 2 waves over a four-year time period of the Shanghai Changfeng Study (discovery, n = 407 and replication, n = 126). LFC and peripheral blood DNAm were repeatedly measured using quantitative hepatic ultrasonography and the 850 K Illumina EPIC BeadChip, respectively. Longitudinal and cross-sectional epigenome-wide association studies (EWASs) were conducted with linear mixed model and linear regression model, respectively. Meta-analysis was performed using METAL. Cross-lagged panel analysis (CLPA) was carried out to infer temporal relationships between the significant CpGs and LFC.

RESULTS

Longitudinal EWAS identified cg11024682 (SREBF1), cg06500161 (ABCG1), cg16740586 (ABCG1), cg15659943 (ABCA1) and cg00163198 (SNX19) significantly associated with LFC with P < 1e-7. Another 6 of the 22 previously reported CpGs were replicated in the present longitudinal EWAS. CLPA showed longitudinal effects of cg11024682 (SREBF1) (β = 0.14 [0.06, 0.23]), cg16740586 (ABCG1) (β = 0.17 [0.08, 0.25]), cg06500161 (ABCG1) (β = 0.12 [0.03, 0.20]), cg17901584 (DHCR24) (β = -0.10 [-0.18, -0.02]), cg00574958 (CPT1A) (β = -0.09 [-0.17, -0.01]), cg08309687 (LINC00649) (β = -0.11 [-0.19, -0.03]), and cg27243685 (ABCG1) (β = 0.09 [0.01, 0.18]) on subsequent LFC. The effects were attenuated when further adjusting for body mass index. High levels of LFC led to alterations in DNAm of cg15659943 (ABCA1) (β = 0.13 [0.04, 0.21]), cg07162647 (β = -0.11 [-0.19, -0.03]), cg06500161 (ABCG1) (β = 0.10 [0.02, 0.18]), and cg27243685 (ABCG1) (β = 0.10 [0.02, 0.18]).

CONCLUSIONS

Blood DNAm at SREBF1, ABCG1, DHCR24, CPT1A, and LINC00649 may be predictors of subsequent LFC change. The effects of DNAm at SREBF1 and ABCG1 on LFC were partially influenced by obesity. The findings have potential implications in understanding disease pathogenesis and highlight the potential of DNAm for early detection or intervention of fatty liver.

Serum-deprivation response of ARPE-19 cells; expression patterns relevant to age-related macular degeneration

ARPE-19 cells are derived from adult human retinal pigment epithelium (RPE). The response of these cells to the stress of serum deprivation mimics some important processes relevant to age-related macular degeneration (AMD). Here we extend the characterization of this response using RNASeq and EGSEA gene set analysis of ARPE-19 cells over nine days of serum deprivation. This experiment confirmed the up-regulation of cholesterol and lipid-associated pathways that increase cholesterol levels in these cells. The gene expression analysis also identified other pathways relevant to AMD progression. There were significant changes in extracellular matrix gene expression, notably a switch from expression of collagen IV, a key component of Bruch's membrane (part of the blood-retina barrier), to expression of a fibrosis-like collagen type I matrix. Changes in the expression profile of the extracellular matrix led to the discovery that amelotin is induced in AMD and is associated with the development of the calcium deposits seen in late-stage geographic atrophy. The transcriptional profiles of other pathways, including inflammation, complement, and coagulation, were also modified, consistent with immune response patterns seen in AMD. As previously noted, the cells resist apoptosis and autophagy but instead initiate a gene expression pattern characteristic of senescence, consistent with the maintenance of barrier function even as other aspects of RPE function are compromised. Other differentially regulated genes were identified that open new avenues for investigation. Our results suggest that ARPE-19 cells maintain significant stress responses characteristic of native RPE that are informative for AMD. As such, they provide a convenient system for discovery and for testing potential therapeutic interventions.

Deep learning in template-free de novo biosynthetic pathway design of natural products

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.

Distinct Genomic Expression Signatures after Low-Force Electrically Induced Exercises in Persons with Spinal Cord Injury

People with a spinal cord injury are at an increased risk of metabolic dysfunction due to skeletal muscle atrophy and the transition of paralyzed muscle to a glycolytic, insulin-resistant phenotype. Providing doses of exercise through electrical muscle stimulation may provide a therapeutic intervention to help restore metabolic function for people with a spinal cord injury, but high-frequency and high-force electrically induced muscle contractions increase fracture risk for the underlying osteoporotic skeletal system. Therefore, we investigated the acute molecular responses after a session of either a 3 Hz or 1 Hz electrically induced exercise program. Ten people with a complete spinal cord injury completed a 1 h (3 Hz) or 3 h (1 Hz) unilateral electrically induced exercise session prior to a skeletal muscle biopsy of the vastus lateralis. The number of pulses was held constant. Tissue samples were analyzed for genomic and epigenomic expression profiles. There was a strong acute response after the 3 Hz exercise leading to the upregulation of early response genes (NR4A3, PGC-1α, ABRA, IRS2, EGR1, ANKRD1, and MYC), which have prominent roles in regulating molecular pathways that control mitochondrial biogenesis, contractile protein synthesis, and metabolism. Additionally, these genes, and others, contributed to the enrichment of pathways associated with signal transduction, cellular response to stimuli, gene expression, and metabolism. While there were similar trends observed after the 1 Hz exercise, the magnitude of gene expression changes did not reach our significance thresholds, despite a constant number of stimuli delivered. There were also no robust acute changes in muscle methylation after either form of exercise. Taken together, this study supports that a dose of low-force electrically induced exercise for 1 h using a 3 Hz stimulation frequency is suitable to trigger an acute genomic response in people with chronic paralysis, consistent with an expression signature thought to improve the metabolic and contractile phenotype of paralyzed muscle, if performed on a regular basis.

Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets

BACKGROUND/OBJECTIVES

Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence.

METHODS

We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets.

RESULTS

We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms.

CONCLUSIONS

We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.

Whole-exome sequencing study of opioid dependence offers novel insights into the contributions of exome variants

Opioid dependence (OD) is epidemic in the United States and it is associated with a variety of adverse health effects. Its estimated heritability is ∼50%, and recent genome-wide association studies have identified more than a dozen common risk variants. However, there are no published studies of rare OD risk variants. In this study, we analyzed whole-exome sequencing data from the Yale-Penn cohort, comprising 2,100 participants of European ancestry (EUR; 1,321 OD cases) and 1,790 of African ancestry (AFR; 864 cases). A novel low-frequency variant (rs746301110) in the RUVBL2 gene was identified in EUR ( p =6.59×10 -10 ). Suggestive associations ( p <1×10 -5 ) were observed in TMCO3 in EUR, in NEIL2 and CFAP44 in AFR, and in FAM210B in the cross-ancestry meta-analysis. Gene-based collapsing tests identified SLC22A10 , TMCO3 , FAM90A1 , DHX58 , CHRND , GLDN , PLAT , H1-4 , COL3A1 , GPHB5 and QPCTL as top genes ( p <1×10 -4 ) with most associations attributable to rare variants and driven by the burden of predicted loss-of-function and missense variants. This study begins to fill the gap in our understanding of the genetic architecture of OD, providing insights into the contribution of rare coding variants and potential targets for future functional studies and drug development.

PerturbDB for unraveling gene functions and regulatory networks

Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-based genetic screens with single-cell RNA sequencing readouts for high-content phenotypic screens. Despite the rapid accumulation of Perturb-Seq datasets, there remains a lack of a user-friendly platform for their efficient reuse. Here, we developed PerturbDB (http://research.gzsys.org.cn/perturbdb), a platform to help users unveil gene functions using Perturb-Seq datasets. PerturbDB hosts 66 Perturb-Seq datasets, which encompass 4 518 521 single-cell transcriptomes derived from the knockdown of 10 194 genes across 19 different cell lines. All datasets were uniformly processed using the Mixscape algorithm. Genes were clustered by their perturbed transcriptomic phenotypes derived from Perturb-Seq data, resulting in 421 gene clusters, 157 of which were stable across different cellular contexts. Through integrating chemically perturbed transcriptomes with Perturb-Seq data, we identified 552 potential inhibitors targeting 1409 genes, including an mammalian target of rapamycin (mTOR) signaling inhibitor, retinol, which was experimentally verified. Moreover, we developed a 'Cancer' module to facilitate the understanding of the regulatory role of genes in cancer using Perturb-Seq data. An interactive web interface has also been developed, enabling users to visualize, analyze and download all the comprehensive datasets available in PerturbDB. PerturbDB will greatly drive gene functional studies and enhance our understanding of the regulatory roles of genes in diseases such as cancer.

A primer on single-cell RNA-seq analysis using dendritic cells as a case study

Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.

Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug-target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery.

Evaluation of the Immune Response within the Tumor Microenvironment in African American and Non-Hispanic White Patients with Non-Small Cell Lung Cancer

BACKGROUND

African Americans have higher incidence and mortality from lung cancer than non-Hispanic Whites, but investigations into differences in immune response have been minimal. Therefore, we compared components of the tumor microenvironment among African Americans and non-Hispanic Whites diagnosed with non-small cell lung cancer based on PDL1 or tertiary lymphoid structure (TLS) status to identify differences of translational relevance.

METHODS

Using a cohort of 280 patients with non-small cell lung cancer from the Inflammation, Health, Ancestry, and Lung Epidemiology study (non-Hispanic White: n = 155; African American: n = 125), we evaluated PDL1 tumor proportion score (<1% vs. ≥1%) and TLS status (presence/absence), comparing differences within the tumor microenvironment based on immune cell distribution and differential expression of genes.

RESULTS

Tumors from African Americans had a higher proportion of plasma cell signatures within the tumor microenvironment than non-Hispanic Whites. In addition, gene expression patterns in African American PDL1-positive samples suggest that these tumors contained greater numbers of γδ T cells and resting dendritic cells, along with fewer CD8+ T cells after adjusting for age, sex, pack-years, stage, and histology. Investigation of differential expression of B cell/plasma cell-related genes between the two patient populations revealed that two immunoglobulin genes (IGKV2-29 and IGLL5) were associated with decreased mortality risk in African Americans.

CONCLUSIONS

In the first known race-stratified analysis of tumor microenvironment components in lung cancer based on PDL1 expression or TLS status, differences within the immune cell composition and transcriptomic signature were identified that may have therapeutic implications.

IMPACT

Future investigation of racial variation within the tumor microenvironment may help direct the use of immunotherapy.

Integrative study of chicken lung transcriptome to understand the host immune response during Newcastle disease virus challenge

Introduction

Newcastle disease is one of the significant issues in the poultry industry, having catastrophic effects worldwide. The lung is one of the essential organs which harbours Bronchus-associated lymphoid tissue and plays a vital role in the immune response. Leghorn and Fayoumi breeds are known to have differences in resistance to Newcastle disease. Along with genes and long non-coding RNAs (lncRNAs) are also known to regulate various biological pathways through gene regulation.

Methods

This study analysed the lung transcriptome data and identified the role of genes and long non-coding RNAs in differential immune resistance. The computational pipeline, FHSpipe, as used in our previous studies on analysis of harderian gland and trachea transcriptome was used to identify genes and lncRNAs. This was followed by differential expression analysis, functional annotation of genes and lncRNAs, identification of transcription factors, microRNAs and finally validation using qRT-PCR.

Results and discussion

A total of 8219 novel lncRNAs were identified. Of them, 1263 lncRNAs and 281 genes were differentially expressed. About 66 genes were annotated with either an immune-related GO term or pathway, and 12 were annotated with both. In challenge and breed-based analysis, most of these genes were upregulated in Fayoumi compared to Leghorn, and in timepoint-based analysis, Leghorn challenge chicken showed downregulation between time points. A similar trend was observed in the expression of lncRNAs. Co-expression analysis has revealed several lncRNAs co-expressing with immune genes with a positive correlation. Several genes annotated with non-immune pathways, including metabolism, signal transduction, transport of small molecules, extracellular matrix organization, developmental biology and cellular processes, were also impacted. With this, we can understand that Fayoumi chicken showed upregulated immune genes and positive cis-lncRNAs during both the non-challenged and NDV-challenge conditions, even without viral transcripts in the tissue. This finding shows that these immune-annotated genes and coexpressing cis-lncRNAs play a significant role in Fayoumi being comparatively resistant to NDV compared to Leghorn. Our study affirms and expands upon the outcomes of previous studies and highlights the crucial role of lncRNAs during the immune response to NDV.

Conclusion

This analysis clearly shows the differences in the gene expression patterns and lncRNA co-expression with the genes between Leghorn and Fayoumi, indicating that the lncRNAs and co-expressing genes might potentially have a role in differentiating these breeds. We hypothesise that these genes and lncRNAs play a vital role in the higher resistance of Fayoumi to NDV than Leghorn. This study can pave the way for future studies to unravel the biological mechanism behind the regulation of immune-related genes.

Atypical cellular responses mediated by intracellular constitutive active TrkB (NTRK2) kinase domains and a solely intracellular NTRK2-fusion oncogene

Trk (NTRK) receptor and NTRK gene fusions are oncogenic drivers of a wide variety of tumors. Although Trk receptors are typically activated at the cell surface, signaling of constitutive active Trk and diverse intracellular NTRK fusion oncogenes is barely investigated. Here, we show that a high intracellular abundance is sufficient for neurotrophin-independent, constitutive activation of TrkB kinase domains. In HEK293 cells, constitutive active TrkB kinase and an intracellular NTRK2-fusion oncogene (SQSTM1-NTRK2) reduced actin filopodia dynamics, phosphorylated FAK, and altered the cell morphology. Atypical cellular responses could be mimicked with the intracellular kinase domain, which did not activate the Trk-associated MAPK/ERK pathway. In glioblastoma-like U87MG cells, expression of TrkB or SQSTM1-NTRK2 reduced cell motility and caused drastic changes in the transcriptome. Clinically approved Trk inhibitors or mutating Y705 in the kinase domain, blocked the cellular effects and transcriptome changes. Atypical signaling was also seen for TrkA and TrkC. Moreover, hallmarks of atypical pTrk kinase were found in biopsies of Nestin-positive glioblastoma. Therefore, we suggest Western blot-like immunoassay screening of NTRK-related (brain) tumor biopsies to identify patients with atypical panTrk or phosphoTrk signals. Such patients could be candidates for treatment with NTRK inhibitors such as Larotrectinhib or Entrectinhib.

Characterization of a novel mitophagy-related 5-genes signature for diagnosis of acute myocardial infarction

Myocardial infarction (MI) and the ensuing heart failure (HF) remain the main cause of morbidity and mortality worldwide. One of the strategies to combat MI and HF lies in the ability to accurately predict the onset of these disorders. Alterations in mitochondrial homeostasis have been reported to be involved in the pathogenesis of various cardiovascular diseases (CVDs). In this regard, perturbations to mitochondrial dynamics leading to impaired clearance of dysfunctional mitochondria have been previously established to be a crucial trigger for MI/HF. In this study, we found that MI patients could be classified into three clusters based on the expression levels of mitophagy-related genes and consensus clustering. We identified a mitophagy-related diagnostic 5-genes signature for MI using support vector machines-Recursive Feature Elimination (SVM-RFE) and random forest, with the area under the ROC curve (AUC) value of the predictive model at 0.813. Additionally, the single-cell transcriptome and pseudo-time analyses showed that the mitoscore was significantly upregulated in macrophages, endothelial cells, pericytes, fibroblasts and monocytes in patients with ischemic cardiomyopathy, while sequestosome 1 (SQSTM1) exhibited remarkable increase in the infarcted (ICM) and non-infarcted (ICMN) myocardium samples dissected from the left ventricle compared with control samples. Lastly, through analysis of peripheral blood from MI patients, we found that the expression of SQSTM1 is positively correlated with troponin-T (P < 0.0001, R = 0.4195, R2 = 0.1759). Therefore, this study provides the rationale for a cell-specific mitophagy-related gene signature as an additional supporting diagnostic for CVDs.

Transcription factor-based transdifferentiation of human embryonic to trophoblast stem cells

During the first week of development, human embryos form a blastocyst composed of an inner cell mass and trophectoderm (TE) cells, the latter of which are progenitors of placental trophoblast. Here, we investigated the expression of transcripts in the human TE from early to late blastocyst stages. We identified enrichment of the transcription factors GATA2, GATA3, TFAP2C and KLF5 and characterised their protein expression dynamics across TE development. By inducible overexpression and mRNA transfection, we determined that these factors, together with MYC, are sufficient to establish induced trophoblast stem cells (iTSCs) from primed human embryonic stem cells. These iTSCs self-renew and recapitulate morphological characteristics, gene expression profiles, and directed differentiation potential, similar to existing human TSCs. Systematic omission of each, or combinations of factors, revealed the crucial importance of GATA2 and GATA3 for iTSC transdifferentiation. Altogether, these findings provide insights into the transcription factor network that may be operational in the human TE and broaden the methods for establishing cellular models of early human placental progenitor cells, which may be useful in the future to model placental-associated diseases.

A genome-wide arrayed CRISPR screen identifies PLSCR1 as an intrinsic barrier to SARS-CoV-2 entry that recent virus variants have evolved to resist

Interferons (IFNs) play a crucial role in the regulation and evolution of host-virus interactions. Here, we conducted a genome-wide arrayed CRISPR knockout screen in the presence and absence of IFN to identify human genes that influence Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. We then performed an integrated analysis of genes interacting with SARS-CoV-2, drawing from a selection of 67 large-scale studies, including our own. We identified 28 genes of high relevance in both human genetic studies of Coronavirus Disease 2019 (COVID-19) patients and functional genetic screens in cell culture, with many related to the IFN pathway. Among these was the IFN-stimulated gene PLSCR1. PLSCR1 did not require IFN induction to restrict SARS-CoV-2 and did not contribute to IFN signaling. Instead, PLSCR1 specifically restricted spike-mediated SARS-CoV-2 entry. The PLSCR1-mediated restriction was alleviated by TMPRSS2 overexpression, suggesting that PLSCR1 primarily restricts the endocytic entry route. In addition, recent SARS-CoV-2 variants have adapted to circumvent the PLSCR1 barrier via currently undetermined mechanisms. Finally, we investigate the functional effects of PLSCR1 variants present in humans and discuss an association between PLSCR1 and severe COVID-19 reported recently.

Schizophrenia and Rheumatoid Arthritis Genetic Scenery: Potential Non-HLA Genes Involved in Both Diseases Relationship

Background: The link between rheumatoid arthritis (RA) and schizophrenia (SZ) has long been a hot topic of deliberation among scientists from various fields. Especially when it comes to genetics, the connection between RA and SZ is still up for discussion, as can be observed in this study. The HLA genes are the most disputed in identifying a connection between the two diseases, but a more thorough investigation of other genes that may be ignored could yield something even more interesting. Thus, finding the genes responsible for this long-sought relationship will necessitate looking for them. Materials and Methods: Shared and overlapped associated genes involved between SZ and RA were extracted from four databases. The overlapping genes were examined using Database for Annotation, Visualization and Integrated Discovery (DAVID) and InnateDB to search the pertinent genes that concatenate between these two disorders. Results: A total of 91 overlapped genes were discovered, and that 13 genes, divided into two clusters, showed a similarity in function, suggesting that they may serve as an important meeting point. FCGR2A, IL18R, BTNL2, AGER, and CTLA4 are five non-HLA genes related to the immune system, which could lead to new discoveries about the connection between these two disorders. Conclusion: An in-depth investigation of these functionally comparable non-HLA genes that overlap could reveal new interesting information in both diseases. Understanding the molecular and immune-related aspects of RA and SZ may shed light on their etiology and inform future research on targeted treatment strategies.

A computational framework to in silico screen for drug-induced hepatocellular toxicity

Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the leading cause of attrition in drug development. In this study, we developed an in silico framework to screen drug-induced hepatocellular toxicity (INSIGHT) by integrating the post-treatment transcriptomic data from both rodent models and primary human hepatocytes. We first built an early prediction model using logistic regression with elastic net regularization for 123 compounds and established the INSIGHT framework that can screen for drug-induced hepatotoxicity. The 235 signature genes identified by INSIGHT were involved in metabolism, bile acid synthesis, and stress response pathways. Applying the INSIGHT to an independent transcriptomic dataset treated by 185 compounds predicted that 27 compounds show a high DILI risk, including zoxazolamine and emetine. Further integration with cell image data revealed that predicted compounds with high DILI risk can induce abnormal morphological changes in the endoplasmic reticulum and mitochondrion. Clustering analysis of the treatment-induced transcriptomic changes delineated distinct DILI mechanisms induced by these compounds. Our study presents a computational framework for a mechanistic understanding of long-term liver injury and the prospective prediction of DILI risk.

Investigation of lactotransferrin messenger RNA expression levels as an anti-type 2 asthma biomarker

BACKGROUND

Lactotransferrin (LTF) has an immunomodulatory function, and its expression levels are associated with asthma susceptibility.

OBJECTIVES

We sought to investigate LTF messenger RNA (mRNA) expression levels in human bronchial epithelial cells (BECs) as an anti-type 2 (T2) asthma biomarker.

METHODS

Association analyses between LTF mRNA expression levels in BECs and asthma-related phenotypes were performed in the Severe Asthma Research Program (SARP) cross-sectional (n = 155) and longitudinal (n = 156) cohorts using a generalized linear model. Correlation analyses of mRNA expression levels between LTF and all other genes were performed by Spearman correlation.

RESULTS

Low LTF mRNA expression levels were associated with asthma susceptibility and severity (P < .025), retrospective and prospective asthma exacerbations, and low lung function (P < 8.3 × 10-3). Low LTF mRNA expression levels were associated with high airway T2 inflammation biomarkers (sputum eosinophils and fractional exhaled nitric oxide; P < 8.3 × 10-3) but were not associated with blood eosinophils or total serum IgE. LTF mRNA expression levels were negatively correlated with expression levels of TH2 or asthma-associated genes (POSTN, NOS2, and MUC5AC) and eosinophil-related genes (IL1RL1, CCL26, and IKZF2) and positively correlated with expression levels of TH1 and inflammation genes (IL12A, MUC5B, and CC16) and TH17-driven cytokines or chemokines for neutrophils (CXCL1, CXCL6, and CSF3) (P < 3.5 × 10-6).

CONCLUSIONS

Low LTF mRNA expression levels in BECs are associated with asthma susceptibility, severity, and exacerbations through upregulation of airway T2 inflammation. LTF is a potential anti-T2 biomarker, and its expression levels may help determine the balance of eosinophilic and neutrophilic asthma.

Limosilactobacillus reuteri promotes the expression and secretion of enteroendocrine- and enterocyte-derived hormones

Observations that intestinal microbes can beneficially impact host physiology have prompted investigations into the therapeutic usage of such microbes in a range of diseases. For example, the human intestinal microbe Limosilactobacillus reuteri strains ATCC PTA 6475 and DSM 17938 are being considered for use for intestinal ailments including colic, infection, and inflammation as well as non-intestinal ailments including osteoporosis, wound healing, and autism spectrum disorder. While many of their beneficial properties are attributed to suppressing inflammatory responses in the gut, we postulated that L. reuteri may also regulate hormones of the gastrointestinal tract to affect physiology within and outside of the gut. To determine if L. reuteri secreted factors impact the secretion of enteric hormones, we treated an engineered jejunal organoid line, NGN3-HIO, which can be induced to be enriched in enteroendocrine cells, with L. reuteri 6475 or 17938 conditioned medium and performed transcriptomics. Our data suggest that these L. reuteri strains affect the transcription of many gut hormones, including vasopressin and luteinizing hormone subunit beta, which have not been previously recognized as being produced in the gut epithelium. Moreover, we find that these hormones appear to be produced in enterocytes, in contrast to canonical gut hormones which are produced in enteroendocrine cells. Finally, we show that L. reuteri conditioned media promotes the secretion of several enteric hormones including serotonin, GIP, PYY, vasopressin, and luteinizing hormone subunit beta. These results support L. reuteri affecting host physiology through intestinal hormone secretion, thereby expanding our understanding of the mechanistic actions of this microbe.