ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information

Regulatory network analysis of Dclk1 gene expression reveals a tuft cell-ILC2 axis that inhibits pancreatic tumor progression

Doublecortin-like kinase 1 (Dclk1) expression identifies cells that are rare in normal pancreas but occur with an increased frequency in pancreatic neoplasia. The identity of these cells has been a matter of debate. We employed Dclk1 reporter mouse models and single-cell RNA sequencing (scRNA-seq) to define Dclk1-expressing cells. In normal pancreas, Dclk1 identifies subsets of ductal, islet, and acinar cells. In pancreatic neoplasia, Dclk1 identifies several cell populations, among which acinar-to-ductal metaplasia (ADM)-like cells and tuft-like cells are predominant. These two populations play opposing roles, with Dclk1+ ADM-like cells sustaining and Dclk1+ tuft-like cells restraining tumor progression. The generation of Dclk1+ tuft-like cells requires the transcription factor SPIB and is sustained by a paracrine loop involving type 2 innate lymphoid cells (ILC2s) and cancer-associated fibroblasts (CAFs) that provide interleukin (IL)-13 and IL-33, respectively. Dclk1+ tuft-like cells release angiotensinogen to restrain tumor progression. Overall, our study defines pancreatic Dclk1+ cells and unveils a protective tuft cell-ILC2 axis against pancreatic neoplasia.

Identification and targeting of regulators of SARS-CoV-2-host interactions in the airway epithelium

The impact of SARS-CoV-2 in the lung has been extensively studied, yet the molecular regulators of host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. Some of the reasons include overreliance on transcriptomic profiling and use of nonprimary cell systems. Here we report a network-based analysis of single-cell transcriptomic profiles able to identify master regulator (MR) proteins controlling SARS-CoV-2-mediated reprogramming in pathophysiologically relevant human ciliated, secretory, and basal cells. This underscored chromatin remodeling, endosomal sorting, ubiquitin pathways, as well as proviral factors identified by CRISPR assays as components of the viral-host response in these cells. Large-scale drug perturbation screens revealed 11 candidate drugs able to invert the entire MR signature activated by SARS-CoV-2. Leveraging MR analysis and perturbational profiles of human primary cells represents an innovative approach to investigate pathogen-host interactions in multiple airway conditions for drug prioritization.

Identification of the core regulatory program driving NEUROD1-induced neuronal reprogramming

NEUROD1 (ND1)-induced astrocyte-to-neuron (AtN) conversion shows promise for treating neurological disorders. To gain insight into the molecular mechanisms of neuronal reprogramming, we established an in vitro system using primary cortical astrocyte cultures from postnatal rats and employed single-cell and multiomics sequencing. Our findings indicate that the initial cultures primarily consisted of immature astrocytes (ImAs), with potentially a minor presence of radial glial cells. The ImAs initially went through an intermediate state, activating both astrocyte and neural progenitor genes. Subsequently, they mimic in vivo neurogenesis to acquire mature neuronal characteristics. We show that ND1 acted as a pioneer factor that reshapes the chromatin landscape of astrocytes to that of neurons. This restructuring promotes the expression of neurogenic genes via inducing H3K27ac modification. Through integrative analysis of various ND1-induced neuronal specification systems, we identified 25 ND1 targets, including Hes6, as key regulators. Thus, our work highlights the key role of ND1 and its downstream regulators in neuronal reprogramming.

Neocortical tau propagation is a mediator of clinical heterogeneity in Alzheimer's disease

Heterogeneity in progression of clinical dementia obstructs the general therapeutic potential of current treatments for Alzheimer's disease (AD). Though the mechanisms of this heterogeneity remain unclear, the characterization of bioactive tau species and factors that regulate their seeding behavior might give valuable insight as pathological tau is well correlated with cognitive impairment. Here, we conducted an innovative investigation into the molecular basis of widespread, connectivity-based tau propagation that begins in the inferior temporal gyrus (ITG) and spreads to neocortical areas such as the prefrontal cortex (PFC). Biochemical analysis of human postmortem ITG and PFC tissues revealed individual variability in tau seeding, which correlated with cognitive decline, particularly in the ITG, a region known for promoting accelerated tau propagation. Notably, this study presents the first evidence that site-specific phosphorylation and isoform composition of both aggregation-prone high-molecular-weight (HMW) tau and the relatively unexplored, yet potentially crucial in AD progression low-molecular-weight (LMW) tau significantly contribute to tau propagation and cognitive decline. Our findings underscore the importance of comprehensively considering diverse tau forms including both HMW and LMW tau species in understanding AD progression. Additionally, these results are the first to identify distinct morphological strains within the AD brain associated with differing seeding propensity, potentially enabling patient stratification based on their tau profile. Furthermore, RNA-seq analyses of gene expression patterns in the ITG revealed molecular heterogeneity associated with tau seeding potential. Patients with higher levels of seed-competent tau displayed greater impairments in synaptic and neural plasticity, and increased neuroinflammation. This multidisciplinary study offers novel insights into various molecular mechanisms driving AD progression, suggesting potential molecular targets for early intervention and improved patient subtyping, which is critical for developing precision medicine approaches.

The regulatory architecture of the primed pluripotent cell state

Despite extensive research, the gene regulatory architecture governing mammalian cell states remains poorly understood. Here we present an integrative systems biology approach to elucidate the network architecture of primed state pluripotency. Using an unbiased methodology, we identified and experimentally confirmed 132 transcription factors as master regulators (MRs) of mouse epiblast stem cell (EpiSC) pluripotency, many of which were further validated by CRISPR-mediated functional assays. To assemble a comprehensive regulatory network, we silenced each of the 132 MRs to assess their effects on the other MRs and their transcriptional targets, yielding a network of 1273 MR → MR interactions. Network architecture analyses revealed four functionally distinct MR modules (communities), and identified key Speaker and Mediator MRs based on their hierarchical rank and centrality. Our findings elucidate the de-centralized logic of a "communal interaction" model in which the balanced activities of four MR communities maintain primed state pluripotency.

Granulosa cell-specific FOXJ2 overexpression induces premature ovarian insufficiency by triggering apoptosis via mitochondrial calcium overload

BACKGROUND

Follicle development is a complicated biological process that produces mature oocytes, and requires nutrients, growth factors, and steroids produced by ovarian granulosa cells (GCs). High fork head box J2 (FOXJ2) expression might negatively regulate ovarian function; however, the mechanism is unclear. This study aimed to investigate the effect and mechanism of FOXJ2 overexpression in GCs on regulating follicle development and fertility.

METHODS

A GC-specific conditional Foxj2 knock-in mouse model (Amh-cre; Foxj2tg/tg mouse) was generated. Reproductive phenotypes were compared between Amh-cre; Foxj2tg/tg and control mice using fertility evaluation, oocyte collection, estrus cycle analysis, hormone evaluation, and ovarian follicle assessment. Then, RNA sequencing and bioinformatic analyses were used to detect the altered transcriptome of GCs collected from the Amh-cre; Foxj2tg/tg and wild-type mice. Western blotting, transmission electron microscopy, immunofluorescence staining, and flow cytometry were used to explore apoptosis and mitochondrial calcium homeostasis. Furthermore, Chromatin immunoprecipitation-PCR and dual-luciferase reporter assays were used to detect the target gene of FOXJ2. Moreover, short hairpin RNA interference was performed on primary GCs and human ovarian granulosa-like tumor (KGN) cells to explore the relationship between FOXJ2 and its target gene in apoptosis and mitochondrial calcium overload.

RESULTS

FOXJ2 overexpression in GCs led to reduced fertility, hormonal abnormalities, and follicle atresia, starting at the initiation of sexual maturity, resulting in a premature ovarian insufficiency (POI)-like phenotype. Increased apoptosis and mitochondrial calcium overload were detected in the GCs of Amh-cre; Foxj2tg/tg mice. Mcu (encoding a mitochondrial calcium uniporter) was observed to be upregulated in the GCs of the Amh-cre; Foxj2tg/tg mice and was a direct target of FOXJ2. Moreover, Mcu knockdown restored mitochondrial calcium homeostasis and reduced the apoptosis in the GCs of the Amh-cre; Foxj2tg/tg mice and in KGN cells transfected with FOXJ2-overexpression lentivirus.

A factor-based analysis of individual human microglia uncovers regulators of an Alzheimer-related transcriptional signature

Human microglial heterogeneity is only beginning to be appreciated at the molecular level. Here, we present a large, single-cell atlas of expression signatures from 441,088 live microglia broadly sampled across a diverse set of brain regions and neurodegenerative and neuroinflammatory diseases obtained from 161 donors sampled at autopsy or during a neurosurgical procedure. Using single-cell hierarchical Poisson factorization (scHPF), we derived a 23-factor model for continuous gene expression signatures across microglia which capture specific biological processes (e.g., metabolism, phagocytosis, antigen presentation, inflammatory signaling, disease-associated states). Using external datasets, we evaluated the aspects of microglial phenotypes that are encapsulated in various in vitro and in vivo microglia models and identified and replicated the role of two factors in human postmortem tissue of Alzheimer's disease (AD). Further, we derived a complex network of transcriptional regulators for all factors, including regulators of an AD-related factor enriched for the mouse disease-associated microglia 2 (DAM2) signature: ARID5B, CEBPA, MITF, and PPARG. We replicated the role of these four regulators in the AD-related factor and then designed a multiplexed MERFISH panel to assess our microglial factors using spatial transcriptomics. We find that, unlike cells with high expression of the interferon-response factor, cells with high expression of the AD DAM2-like factor are widely distributed in neocortical tissue. We thus propose a novel analytic framework that provides a taxonomic approach for microglia that is more biologically interpretable and use it to uncover new therapeutic targets for AD.

iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment

Metabolic enzymes play a central role in cancer metabolic reprogramming, and their dysregulation creates vulnerabilities that can be exploited for therapy. However, accurately measuring metabolic enzyme activity in a high-throughput manner remains challenging due to the complex, multi-layered regulatory mechanisms involved. Here, we present iMetAct, a framework that integrates metabolic-transcription networks with an information propagation strategy to infer enzyme activity from gene expression data. iMetAct outperforms expression-based methods in predicting metabolite conversion rates by accounting for the effects of post-translational modifications. With iMetAct, we identify clinically significant subtypes of hepatocellular carcinoma with distinct metabolic preferences driven by dysregulated enzymes and metabolic regulators acting at both the transcriptional and non-transcriptional levels. Moreover, applying iMetAct to single-cell RNA sequencing data allows for the exploration of cancer cell metabolism and its interplay with immune regulation in the tumor microenvironment. An accompanying online platform further facilitates tumor metabolic analysis, patient stratification, and immune microenvironment characterization.

Morphogenesis and regeneration share a conserved core transition cell state program that controls lung epithelial cell fate

Transitional cell states are at the crossroads of crucial developmental and regenerative events, yet little is known about how these states emerge and influence outcomes. The alveolar and airway epithelia arise from distal lung multipotent progenitors, which undergo cell fate transitions to form these distinct compartments. The identification and impact of cell states in the developing lung are poorly understood. Here, we identified a population of Icam1/Nkx2-1 epithelial progenitors harboring a transitional state program remarkably conserved in humans and mice during lung morphogenesis and regeneration. Lineage-tracing and functional analyses reveal their role as progenitors to both airways and alveolar cells and the requirement of this transitional program to make distal lung progenitors competent to undergo airway cell fate specification. The identification of a common progenitor cell state in vastly distinct processes suggests a unified program reiteratively regulating outcomes in development and regeneration.

MYC Serine 62 phosphorylation promotes its binding to DNA double strand breaks to facilitate repair and cell survival under genotoxic stress

Genomic instability is a hallmark of cancer, driving oncogenic mutations that enhance tumor aggressiveness and drug resistance. MYC, a master transcription factor that is deregulated in nearly all human tumors, paradoxically induces replication stress and associated DNA damage while also increasing expression of DNA repair factors and mediating resistance to DNA-damaging therapies. Emerging evidence supports a non-transcriptional role for MYC in preserving genomic integrity at sites of active transcription and protecting stalled replication forks under stress. Understanding how MYC's genotoxic and genoprotective functions diverge may reveal new therapeutic strategies for MYC-driven cancers. Here, we identify a non-canonical role of MYC in DNA damage response (DDR) through its direct association with DNA breaks. We show that phosphorylation at serine 62 (pS62-MYC) is crucial for the efficient recruitment of MYC to damage sites, its interaction with repair factors BRCA1 and RAD51, and effective DNA repair to support cell survival under stress. Mass spectrometry analysis with MYC-BioID2 during replication stress reveals a shift in MYC's interactome, maintaining DDR associations while losing transcriptional regulators. These findings establish pS62-MYC as a key regulator of genomic stability and a potential therapeutic target in cancers.

Mapping of the T-cell Landscape of Biliary Tract Cancer Unravels Anatomic Subtype-Specific Heterogeneity

Biliary tract cancer (BTC), encompassing diseases such as intrahepatic (ICC), extrahepatic cholangiocarcinoma (ECC), and gallbladder cancer, is not only increasing but also poses a significant and urgent health threat due to its high malignancy. Genomic differences point to the possibility that these subtypes represent distinct diseases. Elucidation of the specific distribution of T-cell subsets, critical to cancer immunity, across these diseases could provide better insights into the unique biology of BTC subtypes and help identify potential precision medicine strategies. To address this, we conducted single-cell RNA sequencing and T-cell receptor sequencing on CD3+ T cells from 36 samples from 16 patients with BTC across all subtypes and analyzed 355 pathologic slides to examine the spatial distribution of T cells and tertiary lymphoid structures. Compared with ICC and gallbladder cancer, ECC possessed a unique immune profile characterized by T-cell exhaustion, elevated CXCL13 expression in CD4+ T helper-like and CD8+CXCL13+ exhausted T cells, more mature tertiary lymphoid structures, and fewer desert immunophenotypes. Conversely, ICC displayed an inflamed immunophenotype with an enrichment of IFN-related pathways and high expression of LGALS1 in activated regulatory T cells, associated with immunosuppression. Inhibition of LGALS1 reduced tumor growth and regulatory T-cell prevalence in ICC mouse models. Overall, this study unveils T-cell diversity across BTC subtypes at the single-cell and spatial level that could open paths for tailored immunotherapies. Significance: Single-cell and spatial analyses detailed the T-cell characteristics specific to anatomic subtypes of biliary tract cancer, identifying unique immunologic features that could potentially be harnessed to improve patient outcomes.

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.

Statement of significance

Network-based approaches, as shown in a study on metastatic breast cancer, can develop effective combinatorial therapies targeting complementary subpopulations. By analyzing scRNA-seq data and using clinically relevant drugs, researchers identified and depleted chemoresistant Cancer Stem-Like Cells, enhancing the efficacy of standard chemotherapies.

Transcriptional control of T cell tissue adaptation and effector function in infants and adults

The first years of life are essential for the development of memory T cells, which rapidly populate the body's diverse tissue sites during infancy. However, the degree to which tissue memory T cell responses in early life reflect those during adulthood is unclear. Here, we use single cell RNA-sequencing of resting and ex vivo activated T cells from lymphoid and mucosal tissues of infant (aged 2-9 months) and adult (aged 40-65 years) human organ donors to dissect the transcriptional programming of memory T cells over age. Infant memory T cells demonstrate a unique stem-like transcriptional profile and tissue adaptation program, yet exhibit reduced activation capacity and effector function relative to adults. Using CRISPR-Cas9 knockdown, we define Helios (IKZF2) as a critical transcriptional regulator of the infant-specific tissue adaptation program and restricted effector state. Our findings reveal key transcriptional mechanisms that control tissue T cell fate and function in early life.

AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs

BACKGROUND

Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments.

RESULTS

We present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is powered by Random Walk with Restart for multiplex-heterogeneous networks, with additional capabilities including degree bias adjustment and biased random walk for multi-objective module identification. AMEND was applied to two real-world multi-omic datasets: renal cell carcinoma data from The cancer genome atlas and an O-GlcNAc Transferase knockout study. Additional analyses investigate the performance of various subroutines of AMEND on tasks of node ranking and degree bias adjustment.

CONCLUSIONS

While the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND .

Identification of new salicylic acid signaling regulators for root development and microbiota composition in plants

Besides playing a crucial role in plant immunity via the nonexpressor of pathogenesis-related (NPR) proteins, increasing evidence shows that salicylic acid (SA) can also regulate plant root growth. However, the transcriptional regulatory network controlling this SA response in plant roots is still unclear. Here, we found that NPR1 and WRKY45, the central regulators of SA response in rice leaves, control only a reduced sector of the root SA signaling network. We demonstrated that SA attenuates root growth via a novel NPR1/WRKY45-independent pathway. Furthermore, using regulatory network analysis and mutant characterization, we identified a set of new NPR1/WRKY45-independent regulators that conservedly modulate the root development and root-associated microbiota composition in both Oryza sativa (monocot) and Arabidopsis thaliana (dicot) in response to SA. Our results established the SA signaling as a central element regulating plant root functions under ecologically relevant conditions. These results provide new insights to understand how regulatory networks control plant responses to abiotic and biotic stresses.

Exon inclusion signatures enable accurate estimation of splicing factor activity

Splicing factors control exon inclusion in messenger RNA, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on their own fall short in identifying which splicing factors underlie a phenotype. Here, we propose splicing factor activity can be estimated by interpreting changes in exon inclusion. We benchmark methods to construct splicing factor→exon networks and calculate activity. Combining RNA-seq perturbation-based networks with VIPER (virtual inference of protein activity by enriched regulon analysis) accurately captures splicing factor activation modulated by different regulatory layers. This approach consolidates splicing factor regulation into a single score derived solely from exon inclusion signatures, allowing functional interpretation of heterogeneous conditions. As a proof of concept, we identify recurrent cancer splicing programs, revealing oncogenic- and tumor suppressor-like splicing factors missed by conventional methods. These programs correlate with patient survival and key cancer hallmarks: initiation, proliferation, and immune evasion. Altogether, we show splicing factor activity can be accurately estimated from exon inclusion changes, enabling comprehensive analyses of splicing regulation with minimal data requirements.

Autism-Associated Genes and Neighboring lncRNAs Converge on Key Gene Regulatory Networks

The diversity of genes implicated in autism spectrum disorder (ASD) creates challenges for identifying core pathophysiological mechanisms. Aggregation of seven different classes of genetic variants implicated in ASD, in a database we call Consensus-ASD, reveals shared features across distinct types of ASD variants. Functional interrogation of 19 ASD genes and 9 neighboring long non-coding RNAs (lncRNAs) using CRISPR-Cas13 strikingly revealed differential gene expression profiles that were significantly enriched for other ASD genes. Furthermore, construction of a gene regulatory network (GRN) enabled the identification of central regulators that exhibit convergently altered activity upon ASD gene disruption. Thus, this study reveals how perturbing distinct ASD-associated genes can lead to shared, broad dysregulation of GRNs with critical relevance to ASD. This provides a crucial framework for understanding how diverse genes, including lncRNAs, can play convergent roles in key neurodevelopmental processes and ultimately contribute to ASD.

Causal models and prediction in cell line perturbation experiments

In cell line perturbation experiments, a collection of cells is perturbed with external agents and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational models that can predict cellular responses to perturbations in silico. A central challenge for these models is to predict the effect of new, previously untested perturbations that were not used in the training data. Here we propose causal structural equations for modeling how perturbations effect cells. From this model, we derive two estimators for predicting responses: a Linear Regression (LR) estimator and a causal structure learning estimator that we term Causal Structure Regression (CSR). The CSR estimator requires more assumptions than LR, but can predict the effects of drugs that were not applied in the training data. Next we present Cellbox, a recently proposed system of ordinary differential equations (ODEs) based model that obtained the best prediction performance on a Melanoma cell line perturbation data set (Yuan et al. in Cell Syst 12:128-140, 2021). We derive analytic results that show a close connection between CSR and Cellbox, providing a new causal interpretation for the Cellbox model. We compare LR and CSR/Cellbox in simulations, highlighting the strengths and weaknesses of the two approaches. Finally we compare the performance of LR and CSR/Cellbox on the benchmark Melanoma data set. We find that the LR model has comparable or slightly better performance than Cellbox.

Ongoing replication stress tolerance and clonal T cell responses distinguish liver and lung recurrence and outcomes in pancreatic cancer

Patients with metastatic pancreatic ductal adenocarcinoma survive longer if disease spreads to the lung but not the liver. Here we generated overlapping, multi-omic datasets to identify molecular and cellular features that distinguish patients whose disease develops liver metastasis (liver cohort) from those whose disease develops lung metastasis without liver metastases (lung cohort). Lung cohort patients survived longer than liver cohort patients, despite sharing the same tumor subtype. We developed a primary organotropism (pORG) gene set enriched in liver cohort versus lung cohort primary tumors. We identified ongoing replication stress response pathways in high pORG/liver cohort tumors, whereas low pORG/lung cohort tumors had greater densities of lymphocytes and shared T cell clonal responses. Our study demonstrates that liver-avid pancreatic ductal adenocarcinoma is associated with tolerance to ongoing replication stress, limited tumor immunity and less-favorable outcomes, whereas low replication stress, lung-avid/liver-averse tumors are associated with active tumor immunity that may account for favorable outcomes.

Comprehensive genomic characterization of early-stage bladder cancer

Understanding the molecular landscape of nonmuscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and treatment regimens. We performed a comprehensive genomic analysis of patients with NMIBC using whole-exome sequencing (n = 438), shallow whole-genome sequencing (n = 362) and total RNA sequencing (n = 414). A large genomic variation within NMIBC was observed and correlated with different molecular subtypes. Frequent loss of heterozygosity in FGFR3 and 17p (affecting TP53) was found in tumors with mutations in FGFR3 and TP53, respectively. Whole-genome doubling (WGD) was observed in 15% of the tumors and was associated with worse outcomes. Tumors with WGD were genomically unstable, with alterations in cell-cycle-related genes and an altered immune composition. Finally, integrative clustering of multi-omics data highlighted the important role of genomic instability and immune cell exhaustion in disease aggressiveness. These findings advance our understanding of genomic differences associated with disease aggressiveness in NMIBC and may ultimately improve patient stratification.

Context-dependent effects of CDKN2A and other 9p21 gene losses during the evolution of esophageal cancer

CDKN2A is a tumor suppressor located in chromosome 9p21 and frequently lost in Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC). How CDKN2A and other 9p21 gene co-deletions affect EAC evolution remains understudied. We explored the effects of 9p21 loss in EACs and cancer progressor and non-progressor BEs with matched genomic, transcriptomic and clinical data. Despite its cancer driver role, CDKN2A loss in BE prevents EAC initiation by counterselecting subsequent TP53 alterations. 9p21 gene co-deletions predict poor patient survival in EAC but not BE through context-dependent effects on cell cycle, oxidative phosphorylation and interferon response. Immune quantifications using bulk transcriptome, RNAscope and high-dimensional tissue imaging showed that IFNE loss reduces immune infiltration in BE, but not EAC. Mechanistically, CDKN2A loss suppresses the maintenance of squamous epithelium, contributing to a more aggressive phenotype. Our study demonstrates context-dependent roles of cancer genes during disease evolution, with consequences for cancer detection and patient management.

Molecular characterization of the sea lamprey retina illuminates the evolutionary origin of retinal cell types

The lamprey, a primitive jawless vertebrate whose ancestors diverged from all other vertebrates over 500 million years ago, offers a unique window into the ancient formation of the retina. Using single-cell RNA-sequencing, we characterize retinal cell types in the lamprey and compare them to those in mouse, chicken, and zebrafish. We find six cell classes and 74 distinct cell types, many shared with other vertebrate species. The conservation of cell types indicates their emergence early in vertebrate evolution, highlighting primordial designs of retinal circuits for the rod pathway, ON-OFF discrimination, and direction selectivity. The diversification of amacrine and some ganglion cell types appears, however, to be distinct in the lamprey. We further infer genetic regulators in specifying retinal cell classes and identify ancestral regulatory elements across species, noting decreased conservation in specifying amacrine cells. Altogether, our characterization of the lamprey retina illuminates the evolutionary origin of visual processing in the retina.

Cross-species regulatory network analysis identifies FOXO1 as a driver of ovarian follicular recruitment

The transcriptional regulation of gene expression in the latter stages of follicular development in laying hen ovarian follicles is not well understood. Although differentially expressed genes (DEGs) have been identified in pre-recruitment and pre-ovulatory stages, the master regulators driving these DEGs remain unknown. This study addresses this knowledge gap by utilizing Master Regulator Analysis (MRA) combined with the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) for the first time in laying hen research to identify master regulators that are controlling DEGs in pre-recruitment and pre-ovulatory phases. The constructed ARACNe network included 10,466 nodes and 292,391 edges. The ARACNe network was then used in conjunction with the Virtual Inference of Protein-activity by Enriched Regulon (VIPER) for the MRA to identify top up- and down-regulated master regulators. VIPER analysis revealed FOXO1 as a master regulator, influencing 275 DEGs and impacting pathways related to apoptosis, proliferation, and hormonal regulation. Additionally, CLOCK, known as a crucial regulator of circadian rhythm, emerged as an upregulated master regulator in the pre-ovulatory stage. These findings provide new insights into the transcriptional landscape of laying hen ovarian follicles, offering a foundation for further exploration of follicle development and enhancing reproductive efficiency in avian species.

Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma

Merkel Cell Carcinoma (MCC) is an aggressive neuroendocrine cutaneous malignancy arising from either ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. Despite extensive research, our understanding of the molecular mechanisms driving the transition from normal cells to MCC remains limited. To address this knowledge gap, we assessed the impact of inducible MCPyV T antigens on normal human fibroblasts by performing RNA sequencing. Our data uncovered changes in expression and regulation of Wnt signaling pathway members. Building on this observation, we bioinformatically evaluated various Wnt pathway perturbagens for their ability to reverse the MCC gene expression signature and identified pyrvinium pamoate, an FDA-approved anthelminthic drug known for its anti-tumor activity in other cancers. Leveraging transcriptomic, network, and molecular analyses, we found that pyrvinium targets multiple MCC vulnerabilities. Pyrvinium not only reverses the neuroendocrine features of MCC by modulating canonical and non-canonical Wnt signaling but also inhibits cancer cell growth by activating p53-mediated apoptosis, disrupting mitochondrial function, and inducing endoplasmic reticulum stress. Finally, we demonstrated that pyrvinium reduces tumor growth in an MCC mouse xenograft model. These findings offer a new understanding of the role of Wnt signaling in MCC and highlight the utility of pyrvinium as a potential treatment for MCC.

Inferring gene regulatory networks of ALS from blood transcriptome profiles

One of the most robust approaches to the prediction of causal driver genes of complex diseases is to apply reverse engineering methods to infer a gene regulatory network (GRN) from gene expression profiles (GEPs). In this work, we analysed 794 GEPs of 1117 human whole-blood samples from Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects reported in the GSE112681 dataset. GRNs for ALS and healthy individuals were reconstructed by ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks - Adaptive Partitioning). In order to examine phenotypic differences in the ALS population surveyed, several datasets were built by arranging GEPs according to sex, spinal or bulbar onset, and survival time. The designed reverse engineering methodology identified a significant number of potential ALS-promoting mechanisms and putative transcriptional biomarkers that were previously unknown. In particular, the characterization of ALS phenotypic networks by pathway enrichment analysis has identified a gender-specific disease signature, namely network activation related to the radiation damage response, reported in the networks of bulbar and female ALS patients. Also, focusing on a smaller interaction network, we selected some hub genes to investigate their inferred pathological and healthy subnetworks. The inferred GRNs revealed the interconnection of the four selected hub genes (TP53, SOD1, ALS2, VDAC3) with p53-mediated pathways, suggesting the potential neurovascular response to ALS neuroinflammation. In addition to being well consistent with literature data, our results provide a novel integrated view of ALS transcriptional regulators, expanding information on the possible mechanisms underlying ALS and also offering important insights for diagnostic purposes and for developing possible therapies for a disease yet incurable.

Identification and Targeting of Regulators of SARS-CoV-2-Host Interactions in the Airway Epithelium

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of nonprimary cell systems, overreliance on single-cell gene expression profiling that does not ultimately reflect protein activity, and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathways, as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

Dynamic Evolution of Fibroblasts Revealed by Single-Cell RNA Sequencing of Human Pancreatic Cancer

SIGNIFICANCE

Pancreatic cancer remains a high unmet medical need. Understanding the interactions between stroma and cancer cells in this disease may unveil new opportunities for therapeutic intervention.

NOVEL ACTIVE PROTEINS FOR SEPSIS PROGNOSIS REVEALED THROUGH ScRNA-seq AND QUANTITATIVE PROTEOMICS

ABSTRACT

Objective: To uncover critical active proteins influencing sepsis outcomes through multiomics analysis. Methods: This study collected peripheral blood from sepsis patients (NS = 26, SV = 27) and controls (Con = 16). Cellular heterogeneity was assessed using scRNA-seq. Cellular populations were identified through clustering and annotation. Gene set variation analysis was employed to detect pathway alterations in sepsis, while the Viper algorithm estimated protein activity at the single-cell level. Signaling networks were investigated via cell-cell communication analysis. Differentially expressed proteins were identified by DIA proteomics and confirmed through integrated analysis. Prognostic value was evaluated via meta and survival analyses. Results: scRNA-seq of 22,673 features within 34,228 cells identified five cellular clusters and 253 active proteins via Viper, validated by DIA (FC > 2, P < 0.05). Four proteins (SPI1, MEF2A, CBX3, UBTF) with prognostic significance were discovered and mapped onto the cellular landscape. Gene set variation analysis enrichment analysis revealed that the NS group exhibited significant alterations in pathways related to cellular apoptosis and inflammatory responses, while the SV group displayed increased activity in DNA repair and cellular survival pathways. Conclusion: The study's findings advance the understanding of sepsis pathophysiology by linking differentially active proteins to patient prognosis, paving the way for targeted therapeutic strategies.

Proteogenomic analysis reveals non-small cell lung cancer subtypes predicting chromosome instability, and tumor microenvironment

Non-small cell lung cancer (NSCLC) is histologically classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC). However, some tumors are histologically ambiguous and other pathophysiological features or microenvironmental factors may be more prominent. Here we report integrative multiomics analyses using data for 229 patients from a Korean NSCLC cohort and 462 patients from previous multiomics studies. Histological examination reveals five molecular subtypes, one of which is a NSCLC subtype with PI3K-Akt pathway upregulation, showing a high proportion of metastasis and poor survival outcomes regardless of any specific NSCLC histology. Proliferative subtypes are present in LUAD and LSCC, which show strong associations with whole genome doubling (WGD) events. Comprehensive characterization of the immune microenvironment reveals various immune cell compositions and neoantigen loads across molecular subtypes, which predicting different prognoses. Immunological subtypes exhibit a hot tumor-enriched state and a higher efficacy of adjuvant therapy.

Differential methylation of circulating free DNA assessed through cfMeDiP as a new tool for breast cancer diagnosis and detection of BRCA1/2 mutation

BACKGROUND

Recent studies have highlighted the importance of the cell-free DNA (cfDNA) methylation profile in detecting breast cancer (BC) and its different subtypes. We investigated whether plasma cfDNA methylation, using cell-free Methylated DNA Immunoprecipitation and High-Throughput Sequencing (cfMeDIP-seq), may be informative in characterizing breast cancer in patients with BRCA1/2 germline mutations for early cancer detection and response to therapy.

METHODS

We enrolled 23 BC patients with germline mutation of BRCA1 and BRCA2 genes, 19 healthy controls without BRCA1/2 mutation, and two healthy individuals who carried BRCA1/2 mutations. Blood samples were collected for all study subjects at the diagnosis, and plasma was isolated by centrifugation. Cell-free DNA was extracted from 1 mL of plasma, and cfMeDIP-seq was performed for each sample. Shallow whole genome sequencing was performed on the immuno-precipitated samples. Then, the differentially methylated 300-bp regions (DMRs) between 25 BRCA germline mutation carriers and 19 non-carriers were identified. DMRs were compared with tumor-specific regions from public datasets to perform an unbiased analysis. Finally, two statistical classifiers were trained based on the GLMnet and random forest model to evaluate if the identified DMRs could discriminate BRCA-positive from healthy samples.

RESULTS

We identified 7,095 hypermethylated and 212 hypomethylated regions in 25 BRCA germline mutation carriers compared to 19 controls. These regions discriminate tumors from healthy samples with high accuracy and sensitivity. We show that the circulating tumor DNA of BRCA1/2 mutant breast cancers is characterized by the hypomethylation of genes involved in DNA repair and cell cycle. We uncovered the TFs associated with these DRMs and identified that proteins of the Erythroblast Transformation Specific (ETS) family are particularly active in the hypermethylated regions. Finally, we assessed that these regions could discriminate between BRCA positives from healthy samples with an AUC of 0.95, a sensitivity of 88%, and a specificity of 94.74%.

CONCLUSIONS

Our study emphasizes the importance of tumor cell-derived DNA methylation in BC, reporting a different methylation profile between patients carrying mutations in BRCA1, BRCA2, and wild-type controls. Our minimally invasive approach could allow early cancer diagnosis, assessment of minimal residual disease, and monitoring of response to therapy.

Regulatory network analysis of Dclk1 gene expression reveals a tuft cell-ILC2 axis that inhibits pancreatic tumor progression

Dclk1 expression defines a rare population of cells in the normal pancreas whose frequency is increased at early stages of pancreatic tumorigenesis. The identity and the precise roles of Dclk1 expressing cells in pancreas have been matter of debate, although evidence suggests their involvement in a number of key functions, including regeneration and neoplasia. We employed a recently developed Dclk1 reporter mouse model and single cell RNAseq analysis to define Dclk1 expressing cells in normal pancreas and pancreatic neoplasia. In normal pancreas, Dclk1 epithelial expression identifies subsets of ductal, islet and acinar cells. In pancreatic neoplasia, Dclk1 expression identifies five epithelial cell populations, among which acinar-to-ductal metaplasia (ADM)-like cells and tuft-like cells are predominant. These two cell populations play opposing roles in pancreatic neoplasia, with Dclk1+ ADM-like cells sustaining tumor growth while Dclk1+ tuft-like cells restraining tumor progression. The differentiation of Kras mutant acinar cells into Dclk1+ tuft-like cells requires the activation of the transcription factor SPIB and is further supported by a cellular paracrine loop involving cancer group 2 innate lymphoid cells (ILC2) and cancer activated fibroblasts (CAFs) that provide IL13 and IL33, respectively. In turn, Dclk1+ tuft-like cells release angiotensinogen that plays protective roles against pancreatic neoplasia. Overall, our study provides novel insights on the biology of Dclk1+ cells in normal pancreas and unveils a protective axis against pancreatic neoplasia, involving CAFs, ILC2 and Dclk1+ tuft-like cells, which ultimately results in angiotensinogen release.

ELiAH: the atlas of E3 ligases in human tissues for targeted protein degradation with reduced off-target effect

The development of therapeutic agents has mainly focused on designing small molecules to modulate target proteins or genes which are conventionally druggable. Therefore, targeted protein degradation (TPD) for undruggable cases has emerged as promising pharmaceutical approach. TPD, often referred PROTACs (PROteolysis TArgeting Chimeras), uses a linker to degrade target proteins by hijacking the ubiquitination system. Therefore, unravel the relationship including reversal and co-expression between E3 ligands and other possible target genes in various human tissues is essential to mitigate off-target effects of TPD. Here, we developed the atlas of E3 ligases in human tissues (ELiAH), to prioritize E3 ligase-target gene pairs for TPD. Leveraging over 2900 of RNA-seq profiles consisting of 11 human tissues from the GTEx (genotype-tissue expression) consortium, users of ELiAH can identify tissue-specific genes and E3 ligases (FDR P-value of Mann-Whitney test < .05). ELiAH unravels 933 830 relationships consisting of 614 E3 ligases and 20 924 of expressed genes considering degree of tissue specificity, which are indispensable for ubiquitination based TPD development. In addition, docking properties of those relationships are also modeled using RosettaDock. Therefore, ELiAH presents comprehensive repertoire of E3 ligases for ubiquitination-based TPD drug development avoiding off-target effects. Database URL: https://eliahdb.org.

Transcriptional rewiring in CD8+ T cells: implications for CAR-T cell therapy against solid tumours

T cells engineered to express chimeric-antigen receptors (CAR-T cells) can effectively control relapsed and refractory haematological malignancies in the clinic. However, the successes of CAR-T cell therapy have not been recapitulated in solid tumours due to a range of barriers such as immunosuppression, poor infiltration, and tumour heterogeneity. Numerous strategies are being developed to overcome these barriers, which include improving culture conditions and manufacturing protocols, implementing novel CAR designs, and novel approaches to engineering the T cell phenotype. In this review, we describe the various emerging strategies to improve CAR T cell therapy for solid tumours. We specifically focus on new strategies to modulate cell function and fate that have precipitated from the growing knowledge of transcriptional circuits driving T cell differentiation, with the ultimate goal of driving more productive anti-tumour T cell immunity. Evidence shows that enrichment of particular phenotypic subsets of T cells in the initial cell product correlates to improved therapeutic responses and clinical outcomes. Furthermore, T cell exhaustion and poor persistence are major factors limiting therapeutic efficacy. The latest preclinical work shows that targeting specific master regulators and transcription factors can overcome these key barriers, resulting in superior T cell therapeutic products. This can be achieved by targeting key transcriptional circuits promoting memory-like phenotypes or sustaining key effector functions within the hostile tumour microenvironment. Additional discussion points include emerging considerations for the field such as (i) targeting permutations of transcription factors, (ii) transient expression systems, (iii) tissue specificity, and (iv) expanding this strategy beyond CAR-T cell therapy and cancer.

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

Ras-dependent activation of BMAL2 regulates hypoxic metabolism in pancreatic cancer

To identify drivers of malignancy in human pancreatic ductal adenocarcinoma (PDAC), we performed regulatory network analysis on a large collection of expression profiles from laser capture microdissected samples of PDAC and benign precursors. We discovered that BMAL2 plays a role in the initiation, progression, post resection survival, and KRAS activity in PDAC. Functional analysis of BMAL2 target genes led us to hypothesize that it plays a role in regulating the response to hypoxia, a critical but poorly understood feature of PDAC physiology. Knockout of BMAL2 in multiple human PDAC cell lines revealed effects on viability and invasion, particularly under hypoxic conditions. Loss of BMAL2 also affected glycolysis and other metabolic processes. We found that BMAL2 directly regulates hypoxia-responsive target genes. We also found that BMAL2 is necessary for the stabilization of HIF1A upon exposure to hypoxia, but destabilizes HIF2A under hypoxia. These data demonstrate that BMAL2 is a master transcriptional regulator of hypoxia responses in PDAC and may serve as a long-sought molecular switch that distinguishes HIF1A- and HIF2A-dependent modes of hypoxic metabolism.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Reverse engineering neuron type-specific and type-orthogonal splicing-regulatory networks using single-cell transcriptomes

Cell type-specific alternative splicing (AS) enables differential gene isoform expression between diverse neuron types with distinct identities and functions. Current studies linking individual RNA-binding proteins (RBPs) to AS in a few neuron types underscore the need for holistic modeling. Here, we use network reverse engineering to derive a map of the neuron type-specific AS regulatory landscape from 133 mouse neocortical cell types defined by single-cell transcriptomes. This approach reliably inferred the regulons of 350 RBPs and their cell type-specific activities. Our analysis revealed driving factors delineating neuronal identities, among which we validated Elavl2 as a key RBP for MGE-specific splicing in GABAergic interneurons using an in vitro ESC differentiation system. We also identified a module of exons and candidate regulators specific for long- and short-projection neurons across multiple neuronal classes. This study provides a resource for elucidating splicing regulatory programs that drive neuronal molecular diversity, including those that do not align with gene expression-based classifications.

Isthmus progenitor cells contribute to homeostatic cellular turnover and support regeneration following intestinal injury

The currently accepted intestinal epithelial cell organization model proposes that Lgr5+ crypt-base columnar (CBC) cells represent the sole intestinal stem cell (ISC) compartment. However, previous studies have indicated that Lgr5+ cells are dispensable for intestinal regeneration, leading to two major hypotheses: one favoring the presence of a quiescent reserve ISC and the other calling for differentiated cell plasticity. To investigate these possibilities, we studied crypt epithelial cells in an unbiased fashion via high-resolution single-cell profiling. These studies, combined with in vivo lineage tracing, show that Lgr5 is not a specific ISC marker and that stemness potential exists beyond the crypt base and resides in the isthmus region, where undifferentiated cells participate in intestinal homeostasis and regeneration following irradiation (IR) injury. Our results provide an alternative model of intestinal epithelial cell organization, suggesting that stemness potential is not restricted to CBC cells, and neither de-differentiation nor reserve ISC are drivers of intestinal regeneration.

KLF5 regulates actin remodeling to enhance the metastasis of nasopharyngeal carcinoma

Transcription factors (TFs) engage in various cellular essential processes including differentiation, growth and migration. However, the master TF involved in distant metastasis of nasopharyngeal carcinoma (NPC) remains largely unclear. Here we show that KLF5 regulates actin remodeling to enhance NPC metastasis. We analyzed the msVIPER algorithm-generated transcriptional regulatory networks and identified KLF5 as a master TF of metastatic NPC linked to poor clinical outcomes. KLF5 regulates actin remodeling and lamellipodia formation to promote the metastasis of NPC cells in vitro and in vivo. Mechanistically, KLF5 preferentially occupies distal enhancer regions of ACTN4 to activate its transcription, whereby decoding the informative DNA sequences. ACTN4, extensively localized within actin cytoskeleton, facilitates dense and branched actin networks and lamellipodia formation at the cell leading edge, empowering cells to migrate faster. Collectively, our findings reveal that KLF5 controls robust transcription program of ACTN4 to modulate actin remodeling and augment cell motility which enhances NPC metastasis, and provide new potential biomarkers and therapeutic interventions for NPC.

Transcriptomic and genomic characteristics of intrahepatic metastases of primary liver cancer

BACKGROUND

Patients with primary multifocal hepatocellular carcinoma (HCC) have a poor prognosis and often experience a high rate of treatment failure. Multifocal HCC is mainly caused by intrahepatic metastasis (IM), and though portal vein tumor thrombosis (PVTT) is considered a hallmark of IM, the molecular mechanism by which primary HCC cells invade the portal veins remains unclear. Therefore, it is necessary to recognize the early signs of metastasis of HCC to arrange better treatment for patients.

RESULTS

To determine the differential molecular features between primary HCC with and without phenotype of metastasis, we used the CIBERSORTx software to deconvolute cell types from bulk RNA-Seq based on a single-cell transcriptomic dataset. According to the relative abundance of tumorigenic and metastatic hepatoma cells, VEGFA+ macrophages, effector memory T cells, and natural killer cells, HCC samples were divided into five groups: Pro-T, Mix, Pro-Meta, NKC, and MemT, and the transcriptomic and genomic features of the first three groups were analyzed. We found that the Pro-T group appeared to retain native hepatic metabolic activity, whereas the Pro-Meta group underwent dedifferentiation. Genes highly expressed in the group Pro-Meta often signify a worse outcome.

CONCLUSIONS

The HCC cohort can be well-typed and prognosis predicted according to tumor microenvironment components. Primary hepatocellular carcinoma may have obtained corresponding molecular features before metastasis occurred.

Flexible modeling of regulatory networks improves transcription factor activity estimation

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum

BACKGROUND

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.

RESULTS

Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.

CONCLUSIONS

The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

SUPPLEMENTARY DATA

Attached.

Comprehensive data mining reveals RTK/RAS signaling pathway as a promoter of prostate cancer lineage plasticity through transcription factors and CNV

Prostate cancer lineage plasticity is a key driver in the transition to neuroendocrine prostate cancer (NEPC), and the RTK/RAS signaling pathway is a well-established cancer pathway. Nevertheless, the comprehensive link between the RTK/RAS signaling pathway and lineage plasticity has received limited investigation. In particular, the intricate regulatory network governing the interplay between RTK/RAS and lineage plasticity remains largely unexplored. The multi-omics data were clustered with the coefficient of argument and neighbor joining algorithm. Subsequently, the clustered results were analyzed utilizing the GSEA, gene sets related to stemness, multi-lineage state datasets, and canonical cancer pathway gene sets. Finally, a comprehensive exploration of the data based on the ssGSEA, WGCNA, GSEA, VIPER, prostate cancer scRNA-seq data, and the GPSAdb database was conducted. Among the six modules in the clustering results, there are 300 overlapping genes, including 3 previously unreported prostate cancer genes that were validated to be upregulated in prostate cancer through RT-qPCR. Function Module 6 shows a positive correlation with prostate cancer cell stemness, multi-lineage states, and the RTK/RAS signaling pathway. Additionally, the 19 leading-edge genes of the RTK/RAS signaling pathway promote prostate cancer lineage plasticity through a complex network of transcriptional regulation and copy number variations. In the transcriptional regulation network, TP63 and FOXO1 act as suppressors of prostate cancer lineage plasticity, whereas RORC exerts a promoting effect. This study provides a comprehensive perspective on the role of the RTK/RAS pathway in prostate cancer lineage plasticity and offers new clues for the treatment of NEPC.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Bioengineered Hydrogels Recapitulate Fibroblast Heterogeneity in Cancer

Recently mapped transcriptomic landscapes reveal the extent of heterogeneity in cancer-associated fibroblasts (CAFs) beyond previously established single-gene markers. Functional analyses of individual CAF subsets within the tumor microenvironment are critical to develop more accurate CAF-targeting therapeutic strategies. However, there is a lack of robust preclinical models that reflect this heterogeneity in vitro. In this study, single-cell RNA sequencing datasets acquired from head and neck squamous cell carcinoma tissues to predict microenvironmental and cellular features governing individual CAF subsets are leveraged. Some of these features are then incorporated into a tunable hyaluronan-based hydrogel system to culture patient-derived CAFs. Control over hydrogel degradability and integrin adhesiveness enabled derivation of the predominant myofibroblastic and inflammatory CAF subsets, as shown through changes in cell morphology and transcriptomic profiles. Last, using these hydrogel-cultured CAFs, microtubule dynamics are identified, but not actomyosin contractility, as a key mediator of CAF plasticity. The recapitulation of CAF heterogeneity in vitro using defined hydrogels presents unique opportunities for advancing the understanding of CAF biology and evaluation of CAF-targeting therapeutics.

Subtype-selective prenylated isoflavonoids disrupt regulatory drivers of MYCN-amplified cancers

Transcription factors have proven difficult to target with small molecules because they lack pockets necessary for potent binding. Disruption of protein expression can suppress targets and enable therapeutic intervention. To this end, we developed a drug discovery workflow that incorporates cell-line-selective screening and high-throughput expression profiling followed by regulatory network analysis to identify compounds that suppress regulatory drivers of disease. Applying this approach to neuroblastoma (NBL), we screened bioactive molecules in cell lines representing its MYC-dependent (MYCNA) and mesenchymal (MES) subtypes to identify selective compounds, followed by PLATESeq profiling of treated cells. This revealed compounds that disrupt a sub-network of MYCNA-specific regulatory proteins, resulting in MYCN degradation in vivo. The top hit was isopomiferin, a prenylated isoflavonoid that inhibited casein kinase 2 (CK2) in cells. Isopomiferin and its structural analogs inhibited MYC and MYCN in NBL and lung cancer cells, highlighting the general MYC-inhibiting potential of this unique scaffold.

Prediction of protein-RNA interactions from single-cell transcriptomic data

Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.

Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk

BACKGROUND

Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers.

METHODS

We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer.

RESULTS

We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier.

CONCLUSIONS

Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Transcriptional and metabolic effects of aspartate-glutamate carrier isoform 1 (AGC1) downregulation in mouse oligodendrocyte precursor cells (OPCs)

Aspartate-glutamate carrier isoform 1 (AGC1) is a carrier responsible for the export of mitochondrial aspartate in exchange for cytosolic glutamate and is part of the malate-aspartate shuttle, essential for the balance of reducing equivalents in the cells. In the brain, mutations in SLC25A12 gene, encoding for AGC1, cause an ultra-rare genetic disease, reported as a neurodevelopmental encephalopathy, whose symptoms include global hypomyelination, arrested psychomotor development, hypotonia and seizures. Among the biological components most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination processes, and their precursors [oligodendrocyte progenitor cells (OPCs)]. The AGC1 silencing in an in vitro model of OPCs was documented to cause defects of proliferation and differentiation, mediated by alterations of histone acetylation/deacetylation. Disrupting AGC1 activity could possibly reduce the availability of acetyl groups, leading to perturbation of many biological pathways, such as histone modifications and fatty acids formation for myelin production. Here, we explore the transcriptome of mouse OPCs partially silenced for AGC1, reporting results of canonical analyses (differential expression) and pathway enrichment analyses, which highlight a disruption in fatty acids synthesis from both a regulatory and enzymatic stand. We further investigate the cellular effects of AGC1 deficiency through the identification of most affected transcriptional networks and altered alternative splicing. Transcriptional data were integrated with differential metabolite abundance analysis, showing downregulation of several amino acids, including glutamine and aspartate. Taken together, our results provide a molecular foundation for the effects of AGC1 deficiency in OPCs, highlighting the molecular mechanisms affected and providing a list of actionable targets to mitigate the effects of this pathology.

Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations

Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis-whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations-including avapritinib, larotrectinib, and ruxolitinib-produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.