GSEApy: a comprehensive package for performing gene set enrichment analysis in Python

Viroporin activity is necessary for intercellular calcium signals that contribute to viral pathogenesis

Viruses engage in a variety of processes to subvert host defenses and create an environment amenable to replication. Here, using rotavirus as a prototype, we show that calcium conductance out of the endoplasmic reticulum by the virus encoded ion channel, NSP4, induces intercellular calcium waves that extend beyond the infected cell and contribute to pathogenesis. Viruses that lack the ability to induce this signaling show diminished viral shedding and attenuated disease in a mouse model of rotavirus diarrhea. This implicates nonstructural protein 4 (NSP4) as a virulence factor and provides mechanistic insight into its mode of action. Critically, this signaling induces a transcriptional signature characteristic of interferon-independent innate immune activation, which is not observed in response to a mutant NSP4 that does not conduct calcium. This implicates calcium dysregulation as a means of pathogen recognition, a theme broadly applicable to calcium-altering pathogens beyond rotavirus.

The loss of dNK1/2 and EVT1 cells at the maternal-fetal interface is associated with recurrent miscarriage†

Recurrent miscarriage is a chronic and heterogeneous pregnancy disorder lacking effective treatment. Alterations at the maternal-fetal interface are commonly observed in recurrent miscarriage, with the loss of certain cell subpopulations believed to be a key cause. Through single-cell sequencing of recurrent miscarriage patients and healthy donors, we aim to identify aberrancy of cellular features in recurrent miscarriage tissues, providing new insights into the research. Natural killer cells, the most abundant immune cells in the decidua, are traditionally classified into dNK1, dNK2, and dNK3. In this study, we identified a new subset, dNK1/2, absent in recurrent miscarriage tissues. This subset was named because it expresses biomarkers of both dNK1 and dNK2. With further analysis, we discovered that dNK1/2 cells play roles in immunoregulation and cytokine secretion. On the villous side of the interface, a notable decrease of extravillous trophoblast cells was identified in recurrent miscarriage tissues. We clustered extravillous trophoblasts into EVT1 (absent in recurrent miscarriage) and EVT2 (retained in recurrent miscarriage). Pseudotime analysis revealed distinct differentiation paths, identifying CCNB1, HMGB1, and NPM1 as EVT1 biomarkers. Additionally, we found that EVT1 is involved in the regulation of cell death, while EVT2 exhibited more angiogenic activity. Cell communication analysis revealed that interaction between EVT1 and dNK1/2 mediates chemotaxis and endothelial cell regulation, crucial for spiral artery remodeling. The loss of this interaction may impair decidualization, which is associated with recurrent miscarriage. In summary, we propose that the loss of dNK1/2 and EVT1 cells is a significant pathological feature of recurrent miscarriage.

SUMMARY SENTENCE

The communication between EVT1 and dNK1/2 mediated the chemotaxis of EVT1 and facilitated regulation of endothelial cell death, initiating spiral artery remodeling. The loss of this specific cellular interaction may result in impaired decidualization, leading to recurrent miscarriage.

PRMT5 inhibition has a potent anti-tumor activity against adenoid cystic carcinoma of salivary glands

BACKGROUND

Adenoid cystic carcinoma (ACC) is a rare glandular malignancy, commonly originating in salivary glands of the head and neck. Given its protracted growth, ACC is usually diagnosed in advanced stage. Treatment of ACC is limited to surgery and/or adjuvant radiotherapy, which often fails to prevent disease recurrence, and no FDA-approved targeted therapies are currently available. As such, identification of new therapeutic targets specific to ACC is crucial for improved patients' outcomes.

METHODS

After thoroughly evaluating the gene expression and signaling patterns characterizing ACC, we applied PandaOmics (an AI-driven software platform for novel therapeutic target discovery) on the unique transcriptomic dataset of 87 primary ACCs. Identifying protein arginine methyl transferase 5 (PRMT5) as a putative candidate with the top-scored druggability, we next determined the applicability of PRMT5 inhibitors (PRT543 and PRT811) using ACC cell lines, organoids, and patient derived xenograft (PDX) models. Molecular changes associated with response to PRMT5 inhibition and anti-proliferative effect of the combination therapy with lenvatinib was then analyzed.

RESULTS

Using a comprehensive AI-powered engine for target identification, PRMT5 was predicted among potential therapeutic target candidates for ACC. Here we show that monotherapy with selective PRMT5 inhibitors induced a potent anti-tumor activity across several cellular and animal models of ACC, which was paralleled by downregulation of genes associated with ACC tumorigenesis, including MYB and MYC (the recognized drivers of ACC progression). Furthermore, as a subset of genes targeted by lenvatinib is upregulated in ACC, we demonstrate that addition of lenvatinib enhanced the growth inhibitory effect of PRMT5 blockade in vitro, suggesting a potential clinical benefit for patients expressing lenvatinib favorable molecular profile.

CONCLUSION

Taken together, our study underscores the role of PRMT5 in ACC oncogenesis and provides a strong rationale for the clinical development of PRMT5 inhibitors as a targeted monotherapy or combination therapy for treatment of patients with this rare disease, based on the analysis of their underlying molecular profile.

In silico characterization of defense system hotspots in Acinetobacter spp

The bacteria-phage arm race drives the evolution of diverse bacterial defenses. This study identifies and characterizes the defense hotspots in Acinetobacter baumannii using a reference-free approach. Among 4383 high-quality genomes, we found a total of 17,430 phage defense systems and with 54.54% concentrated in 21 hotspots. These hotspots exhibit distinct preferences for different defense systems, and co-occurrence patterns suggest synergistic interactions. Additionally, the mobile genetic elements are abundant around these hotspots, likely facilitating horizontal transfer and evolution of defense systems. The number of hotspots increases in species phylogenetically closer to Acinetobacter baumannii, but the number of defense systems per hotspot varies due to particular selective pressures. These findings provide critical insights into the genetic organization of phage defense systems, contributing to a broader understanding of bacterial immunity and the evolutionary dynamics that shape Acinetobacter genomes. This knowledge lays the foundation for developing targeted interventions  to combat antibiotic resistance Acinetobacter baumannii.

Increased local DNA methylation disorder in AMLs with DNMT3A-destabilizing variants and its clinical implication

The mechanistic link between the complex mutational landscape of de novo methyltransferase DNMT3A and the pathology of acute myeloid leukemia (AML) has not been clearly elucidated so far. Motivated by a recent discovery of the significance of DNMT3A-destabilizing mutations (DNMT3AINS) in AML, we here investigate the common characteristics of DNMT3AINS AML methylomes through computational analyses. We present that methylomes of DNMT3AINS AMLs are considerably different from those of DNMT3AR882 AMLs in that they exhibit increased intratumor DNA methylation heterogeneity in bivalent chromatin domains. This epigenetic heterogeneity was associated with the transcriptional variability of developmental and membrane-associated factors shaping stem cell niche, and also was a predictor of the response of AML cells to hypomethylating agents, implying that the survival of AML cells depends on stochastic DNA methylations at bivalent domains. Altogether, our work provides a novel mechanistic model suggesting the genomic origin of the aberrant epigenomic heterogeneity in disease conditions.

Sustained inhibition of CSF1R signaling augments antitumor immunity through inhibiting tumor-associated macrophages

Tumor-associated macrophages (TAMs) are one of the key immunosuppressive components in the tumor microenvironment (TME) and contribute to tumor development, progression, and resistance to cancer immunotherapy. Several reagents targeting TAMs have been tested in preclinical and clinical studies, but they have had limited success. Here, we show that a unique reagent, FF-10101, exhibited a sustained inhibitory effect against colony-stimulating factor 1 receptor by forming a covalent bond and reduced immunosuppressive TAMs in the TME, which led to strong antitumor immunity. In preclinical animal models, FF-10101 treatment significantly reduced immunosuppressive TAMs and increased antitumor TAMs in the TME. In addition, tumor antigen-specific CD8+ T cells were increased; consequently, tumor growth was significantly inhibited. Moreover, combination treatment with an anti-programmed cell death 1 (anti-PD-1) antibody and FF-10101 exhibited a far stronger antitumor effect than either treatment alone. In human cancer specimens, FF-10101 treatment reduced programmed cell death 1 ligand 1 (PD-L1) expression on TAMs, as observed in animal models. Thus, FF-10101 acts as an immunomodulatory agent that can reduce immunosuppressive TAMs and augment tumor antigen-specific T cell responses, thereby generating an immunostimulatory TME. We propose that FF-10101 is a potential candidate for successful combination cancer immunotherapy with immune checkpoint inhibitors, such as PD-1/PD-L1 blockade.

Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, spatially resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. Integrating spatial information from MERFISH with the more complete transcriptome produced by single-nucleus RNA sequencing (snRNA-seq), also reveals zonally enriched receptor-ligand interactions. Finally, MERFISH and snRNA-seq analysis of fibrotic liver samples identify two hepatocyte populations that expand with injury and do not have clear zonal distributions. Together these spatial maps of the healthy and fibrotic liver provide a deeper understanding of the cellular and spatial remodeling that drives disease which, in turn, could provide new avenues for intervention and further study.

Drug discovery and mechanism prediction with explainable graph neural networks

Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes. We propose the eXplainable Graph-based Drug response Prediction (XGDP) approach that achieves a precise drug response prediction and reveals the comprehensive mechanism of action between drugs and their targets. XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. Gene expression data from cancer cell lines are incorporated and processed by a Convolutional Neural Network module. A couple of deep learning attribution algorithms are leveraged to interpret interactions between drug molecular features and genes. We demonstrate that XGDP not only enhances the prediction accuracy compared to pioneering works but is also capable of capturing the salient functional groups of drugs and interactions with significant genes of cancer cells.

Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods

The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations with genetic profiles for DRP remains unclear. This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings. Our findings reveal that the inclusion of molecular representations from either PubChem fingerprints or SMILES can significantly enhance the performance of DRPs when used in conjunction with deep learning models. However, the optimal choice of drug molecular representation can vary depending on the predictive model and the specific DRP task. The insights derived from our study offer useful guidance on selecting the most suitable drug molecular representations for constructing efficient predictive models for DRPs, aiding for drug repurposing, personalized medicine, and new drug discovery.

Protein Phosphatase 2A Promotes CD8+ T Cell Effector Function through the Augmentation of CD28 Costimulation

Protein phosphatase 2A (PP2A) is one of the most abundant serine/threonine phosphatases and plays critical roles in regulating cell fate and function. We previously showed that PP2A regulates the differentiation of CD4+ T cells and the development of thymocytes. Nevertheless, its role in CD8+ T cells remains elusive. By ablating the catalytic subunit α (Cα) of PP2A in CD8+ T cells, we revealed the essential role of PP2A in promoting the effector functions of CD8+ T cells. Notably, PP2A Cα-deficient CD8+ T cells exhibit reduced proliferation and decreased cytokine production upon stimulation in vitro. In vivo, mice lacking PP2A Cα in T cells displayed defective immune responses against lymphocytic choriomeningitis virus infection, associated with reduced CD8+ T cell expansion and decreased cytokine production. Consistently, the ablation of the PP2A Cα subunit in CD8+ T cells results in attenuated antitumor activity in mice. There is a notable decrease in the infiltration of PP2A Cα-deficient CD8+ T cells within the tumor microenvironment, and the cells that do infiltrate exhibit diminished effector functions. Mechanistically, PP2A Cα deficiency impedes CD28-induced AKT Ser473 phosphorylation, thus impairing CD8+ T cell costimulation signal. Collectively, our findings underscore the critical role of phosphatase PP2A as a propeller for CD28-mediated costimulation signaling in CD8+ T cell effector function by fine-tuning T cell activation.

Progressive plasticity during colorectal cancer metastasis

As cancers progress, they become increasingly aggressive-metastatic tumours are less responsive to first-line therapies than primary tumours, they acquire resistance to successive therapies and eventually cause death1,2. Mutations are largely conserved between primary and metastatic tumours from the same patients, suggesting that non-genetic phenotypic plasticity has a major role in cancer progression and therapy resistance3-5. However, we lack an understanding of metastatic cell states and the mechanisms by which they transition. Here, in a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that, although primary tumours largely adopt LGR5+ intestinal stem-like states, metastases display progressive plasticity. Cancer cells lose intestinal cell identities and reprogram into a highly conserved fetal progenitor state before undergoing non-canonical differentiation into divergent squamous and neuroendocrine-like states, a process that is exacerbated in metastasis and by chemotherapy and is associated with poor patient survival. Using matched patient-derived organoids, we demonstrate that metastatic cells exhibit greater cell-autonomous multilineage differentiation potential in response to microenvironment cues compared with their intestinal lineage-restricted primary tumour counterparts. We identify PROX1 as a repressor of non-intestinal lineage in the fetal progenitor state, and show that downregulation of PROX1 licenses non-canonical reprogramming.

Dissociation between the expression of cGAS/STING and a senescence-associated signature in colon cancer

OBJECTIVE

The effect of the cGAS/STING pathway on antitumor immunity and its connection to senescence in vivo necessitates further investigation.

INTRODUCTION

Cellular senescence and its secretory phenotype (the SASP) are implicated in modulating the immune microenvironment of cancer possibly through the cGAS/STING pathway.

METHODS

Gene expression data from paired colon cancer and adjacent non-malignant mucosa (98 patients, n = 196 samples; 65 patients, n = 130 samples) were analyzed for cGAS/STING and a senescence signature. Immunohistochemistry assessed cGAS/STING protein expression in 124 colorectal samples.

RESULTS

Approximately one-quarter of patients displayed senescence profiles in both gene sets, yet without significantly correlating with cGAS/STING expression. Notably, cGAS expression was higher than STING in tumor tissue compared to non-malignant colonic mucosa. Protein analysis showed 83% positive cGAS expression and 39% positive STING expression, with discrepancies in expression patterns. Additionally, 15% of samples lacked both markers, while 35% exhibited positive staining for both. No significant correlations were found between cGAS/STING status and tumor stage, patient age, lymphovascular invasion, or lymph node involvement.

CONCLUSIONS

Our findings demonstrate significant senescence marker expression in colorectal cancer samples but with no correlation with cGAS/STING.

Characterizing the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER

Proliferation is a key hallmark of cancer, but whether it differs between evolutionarily distinct clones co-existing within a tumor is unknown. We introduce the Single-cell Proliferation Rate Inference in Non-homogeneous Tumors through Evolutionary Routes (SPRINTER) algorithm that uses single-cell whole-genome DNA sequencing data to enable accurate identification and clone assignment of S- and G2-phase cells, as assessed by generating accurate ground truth data. Applied to a newly generated longitudinal, primary-metastasis-matched dataset of 14,994 non-small cell lung cancer cells, SPRINTER revealed widespread clone proliferation heterogeneity, orthogonally supported by Ki-67 staining, nuclei imaging and clinical imaging. We further demonstrated that high-proliferation clones have increased metastatic seeding potential, increased circulating tumor DNA shedding and clone-specific altered replication timing in proliferation- or metastasis-related genes associated with expression changes. Applied to previously generated datasets of 61,914 breast and ovarian cancer cells, SPRINTER revealed increased single-cell rates of different genomic variants and enrichment of proliferation-related gene amplifications in high-proliferation clones.

A comparative analysis of Marburg virus-infected bat and human models from public high-throughput sequencing data

Marburg virus (MARV) disease (MVD) is an uncommon yet serious viral hemorrhagic fever that impacts humans and non-human primates. In humans, infection by the MARV is marked by rapid onset, high transmissibility, and elevated mortality rates, presenting considerable obstacles to the development of vaccines and treatments. Bats, particularly Rousettus aegyptiacus, are suspected to be natural hosts of MARV. Previous research reported asymptomatic MARV infection in bats, in stark contrast to the severe responses observed in humans and other primates. Recent MARV outbreaks highlight significant public health concerns, underscoring the need for gene expression studies during MARV progression. To investigate this, we employed two models from the Gene Expression Omnibus, including kidney cells from Rousettus aegyptiacus and primary proximal tubular cells from Homo sapiens. These models were chosen to identify changes in gene expression profiles and to examine co-regulated genes and pathways involved in MARV disease progression. Our analysis of differentially expressed genes (DEGs) revealed that these genes are mainly associated with pathways related to the complement system, innate immune response via interferons (IFNs), Wnt/β-catenin signaling, and Hedgehog signaling, which played crucial roles in MARV infection across both models. Furthermore, we also identified several potential compounds that may be useful against MARV infection. These findings offer valuable insights into the mechanisms underlying MARV's pathophysiology and suggest potential strategies for preventing transmission, managing post-infection effects, and developing future vaccines.

BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk

While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.

Gene-level alignment of single-cell trajectories

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Identification of Critical Phosphorylation Sites Enhancing Kinase Activity With a Bimodal Fusion Framework

Phosphorylation is an indispensable regulatory mechanism in cells, with specific sites on kinases that can significantly enhance their activity. Although several such critical phosphorylation sites (phos-sites) have been experimentally identified, many more remain to be explored. To date, no computational method exists to systematically identify these critical phos-sites on kinases. In this study, we introduce PhoSiteformer, a transformer-inspired foundational model designed to generate embeddings of phos-sites using phosphorylation mass spectrometry data. Recognizing the complementary insights offered by protein sequence data and phosphorylation mass spectrometry data, we developed a classification model, CSPred, which employs a bimodal fusion strategy. CSPred combines embeddings from PhoSiteformer with those from the protein language model ProtT5. Our approach successfully identified 77 critical phos-sites on 58 human kinases. Two of these sites, T517 on PKG1 and T735 on PRKD3, have been experimentally verified. This study presents the first systematic and computational approach to identify critical phos-sites that enhance kinase activity.

Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis

Ageing is associated with a decline in the number and fitness of adult stem cells1,2. Ageing-associated loss of stemness is posited to suppress tumorigenesis3,4, but this hypothesis has not been tested in vivo. Here we use physiologically aged autochthonous genetically engineered5,6 mouse models and primary cells5,6 to demonstrate that ageing suppresses lung cancer initiation and progression by degrading the stemness of the alveolar cell of origin. This phenotype is underpinned by the ageing-associated induction of the transcription factor NUPR1 and its downstream target lipocalin-2 in the cell of origin in mice and humans, which leads to functional iron insufficiency in the aged cells. Genetic inactivation of the NUPR1-lipocalin-2 axis or iron supplementation rescues stemness and promotes the tumorigenic potential of aged alveolar cells. Conversely, targeting the NUPR1-lipocalin-2 axis is detrimental to young alveolar cells through ferroptosis induction. Ageing-associated DNA hypomethylation at specific enhancer sites is associated with increased NUPR1 expression, which is recapitulated in young alveolar cells through DNA methylation inhibition. We uncover that ageing drives functional iron insufficiency that leads to loss of stemness and tumorigenesis but promotes resistance to ferroptosis. These findings have implications for the therapeutic modulation of cellular iron homeostasis in regenerative medicine and in cancer prevention. Furthermore, our findings are consistent with a model whereby most human cancers initiate at a young age, thereby highlighting the importance of directing cancer prevention efforts towards young individuals.

Repurposing large-format microarrays for scalable spatial transcriptomics

Spatiomolecular analyses are key to study tissue functions and malfunctions. However, we lack profiling tools for spatial transcriptomics that are easy to adopt, low cost and scalable in terms of sample size and number. Here, we describe a method, Array-seq, to repurpose classical oligonucleotide microarrays for spatial transcriptomics profiling. We generate Array-seq slides from microarrays carrying custom-design probes that contain common sequences flanking unique barcodes at known coordinates. Then we perform a simple, two-step reaction that produces mRNA capture probes across all spots on the microarray. We demonstrate that Array-seq yields spatial transcriptomes with high detection sensitivity and localization specificity using histological sections from mouse tissues as test systems. Moreover, we show that the large surface area of Array-seq slides yields spatial transcriptomes (i) at high throughput by profiling multi-organ sections, (ii) in three dimensions by processing serial sections from one sample, and (iii) across whole human organs. Thus, by combining classical DNA microarrays and next-generation sequencing, we have created a simple and flexible platform for spatiomolecular studies of small-to-large specimens at scale.

CEBPA repression by MECOM blocks differentiation to drive aggressive leukemias

Acute myeloid leukemias (AMLs) have an overall poor prognosis with many high-risk cases co-opting stem cell gene regulatory programs, yet the mechanisms through which this occurs remain poorly understood. Increased expression of the stem cell transcription factor, MECOM, underlies one key driver mechanism in largely incurable AMLs. How MECOM results in such aggressive AML phenotypes remains unknown. To address existing experimental limitations, we engineered and applied targeted protein degradation with functional genomic readouts to demonstrate that MECOM promotes malignant stem cell-like states by directly repressing pro-differentiation gene regulatory programs. Remarkably and unexpectedly, a single node in this network, a MECOM-bound cis-regulatory element located 42 kb downstream of the myeloid differentiation regulator CEBPA, is both necessary and sufficient for maintaining MECOM-driven leukemias. Importantly, targeted activation of this regulatory element promotes differentiation of these aggressive AMLs and reduces leukemia burden in vivo, suggesting a broadly applicable differentiation-based approach for improving therapy.

Dendritic cells type 1 control the formation, maintenance, and function of tertiary lymphoid structures in cancer

Tertiary lymphoid structures (TLS) are organized immune cell aggregates that arise in chronic inflammatory conditions. In cancer, TLS are associated with better prognosis and enhanced response to immunotherapy, making these structures attractive therapeutic targets. However, the mechanisms regulating TLS formation and maintenance in cancer are incompletely understood. Using spatial transcriptomics and multiplex imaging across various human tumors, we found an enrichment of mature dendritic cells (DC) expressing high levels of CCR7 in TLS, prompting us to investigate the role of DC in the formation and maintenance of TLS in solid tumors. To address this, we developed a novel murine model of non-small cell lung cancer (NSCLC) that forms mature TLS, containing B cell follicles with germinal centers and T cell zones with T follicular helper cells (TFH) and TCF1+PD-1+ progenitor exhausted CD8+ T cells (Tpex). Here we show that, during the early stages of tumor development, TLS formation relies on IFNγ-driven maturation of the conventional DC type 1 (cDC1) subset, their migration to tumor-draining lymph nodes (tdLN), and recruitment of activated T cells to the tumor site. As tumors progress, TLS maintenance becomes independent of T cell egress from tdLN, coinciding with a significant reduction of cDC1 migration to tdLN. Instead, mature cDC1 accumulate within intratumoral CCR7 ligand-enriched stromal hubs. Notably, timed depletion of cDC1 or disruption of their migration to these stromal hubs after TLS are formed alters TLS maintenance. Importantly, we found that cDC1-mediated antigen presentation to both CD4+ and CD8+ T cells and intact CD40 signaling, is critical for the maintenance of TLS, the preservation of the TFH cell pool, the formation of germinal center and the production of tumor-specific IgG antibodies. These findings underscore the key role of mature cDC1 in establishing and maintaining functional TLS within tumor lesions and highlight the potential for cDC1-targeting therapies as a promising strategy to enhance TLS function and improve anti-tumor immunity in patients with cancer.

The Rice Online Expression Profiles Array Database Version 2 (ROADv2): An Interactive Atlas for Rice Functional Genomics

The Rice Online expression profiles Array Database version 2 (ROADv2; https://roadv2.khu.ac.kr ), an enhanced database for rice gene expression analysis, transitions from the previous microarray platforms to RNA-Seq data for improved accuracy. It encompasses 328 datasets from diverse experimental series, including anatomy, abiotic and biotic stress, hormone response, and nutrient starvation. Key updates include gene annotation (upgraded to RGAP version 7) and functional enrichment data (utilizing recent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) versions). ROADv2 debuts protein-protein interaction (PPI) network analysis and broadens interactive visualization across all features. Gene expression data are segmented into anatomy, biotic, abiotic, nutrient, and hormone categories, with user-interactive heatmaps displaying normalized log2 expression and log2 fold change data. Coexpression correlation analysis identifies genes with similar patterns, visualized through interactive network graphs. Functional enrichment tools display GO and KEGG analyses with significant terms emphasized in various formats. PPI network analysis integrates coexpression data to enhance prediction accuracy. Validation studies affirm the database's reliability, demonstrating reproducible tissue/organ-specific expression patterns. ROADv2 provides a comprehensive resource for rice functional genomics studies.

Comprehensive profiling of small RNAs and their changes and linkages to mRNAs in schizophrenia and bipolar disorder

We investigated small non-coding RNAs (sncRNAs) from the prefrontal cortex of 93 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) and 77 controls. We uncovered recurring complex sncRNA profiles, with 98% of all sncRNAs being accounted for by miRNA isoforms (60.6%), tRNA-derived fragments (17.8%), rRNA-derived fragments (11.4%), and Y RNA-derived fragments (8.3%). In SCZ, 15% of all sncRNAs exhibit statistically significant changes in their abundance. In BD, the fold changes (FCs) are highly correlated with those in SCZ but less acute. Non-templated nucleotide additions to the 3´-ends of many miRNA isoforms determine their FC independently of miRNA identity or genomic locus of origin. In both SCZ and BD, disease- and age-associated sncRNAs and mRNAs reveal accelerated aging. Co-expression modules between sncRNAs and mRNAs align with the polarities of SCZ changes and implicate sncRNAs in critical processes, including synaptic signaling, neurogenesis, memory, behavior, and cognition.

Enteric glia regulate Paneth cell secretion and intestinal microbial ecology

Glial cells of the enteric nervous system (ENS) interact closely with the intestinal epithelium and secrete signals that influence epithelial cell proliferation and barrier formation in vitro. Whether these interactions are important in vivo, however, is unclear because previous studies reached conflicting conclusions [1]. To better define the roles of enteric glia in steady state regulation of the intestinal epithelium, we characterized the glia in closest proximity to epithelial cells and found that the majority express PLP1 in both mice and humans. To test their functions using an unbiased approach, we genetically depleted PLP1+ cells in mice and transcriptionally profiled the small and large intestines. Surprisingly, glial loss had minimal effects on transcriptional programs and the few identified changes varied along the gastrointestinal tract. In the ileum, where enteric glia had been considered most essential for epithelial integrity, glial depletion did not drastically alter epithelial gene expression but caused a modest enrichment in signatures of Paneth cells, a secretory cell type important for innate immunity. In the absence of PLP1+ glia, Paneth cell number was intact, but a subset appeared abnormal with irregular and heterogenous cytoplasmic granules, suggesting a secretory deficit. Consistent with this possibility, ileal explants from glial-depleted mice secreted less functional lysozyme than controls with corresponding effects on fecal microbial composition. Collectively, these data suggest that enteric glia do not exert broad effects on the intestinal epithelium but have an essential role in regulating Paneth cell function and gut microbial ecology.

The EstroGene2.0 database for endocrine therapy response and resistance in breast cancer

Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.0, an expanded database of its precursor 1.0 version. EstroGene2.0 focusses on response and resistance to endocrine therapies in breast cancer models. Incorporating multi-omic profiling of 361 experiments from 212 studies across 28 cell lines, a user-friendly browser offers comprehensive data visualization and metadata mining capabilities ( https://estrogeneii.web.app/ ). Taking advantage of the harmonized data collection, our follow-up meta-analysis revealed transcriptomic landscape and substantial diversity in response to different classes of ER modulators. Endocrine-resistant models exhibit a spectrum of transcriptomic alterations including a contra-directional shift in ER and interferon signalings, which is recapitulated clinically. Dissecting multiple ESR1-mutant cell models revealed the different clinical relevance of cell model engineering and identified high-confidence mutant-ER targets, such as NPY1R. These examples demonstrate how EstroGene2.0 helps investigate breast cancer's response to endocrine therapies and explore resistance mechanisms.

Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers

The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiome and host gene expressions in tumor microenvironment, as well as their concert effects on patient survival. In this study, we aimed to decode this complex relationship by developing ASD-cancer (autoencoder-based subtypes detector for cancer), a semi-supervised deep learning framework that could extract survival-related features from tumor microbiome and transcriptome data, and identify patients' survival subtypes. By using tissue samples from The Cancer Genome Atlas database, we identified two statistically distinct survival subtypes across all 20 types of cancer Our framework provided improved risk stratification (e.g., for liver hepatocellular carcinoma, [LIHC], log-rank test, P = 8.12E-6) compared to PCA (e.g., for LIHC, log-rank test, P = 0.87), predicted survival subtypes accurately, and identified biomarkers for survival subtypes. Additionally, we identified potential interactions between microbes and host genes that may play roles in survival. For instance, in LIHC, Arcobacter, Methylocella, and Isoptericola may regulate host survival through interactions with host genes enriched in the HIF-1 signaling pathway, indicating these species as potential therapy targets. Further experiments on validation data sets have also supported these patterns. Collectively, ASD-cancer has enabled accurate survival subtyping and biomarker discovery, which could facilitate personalized treatment for broad-spectrum types of cancers.IMPORTANCEUnraveling the intricate relationship between the tumor microbiome, host gene expressions, and their collective impact on cancer outcomes is paramount for advancing personalized treatment strategies. Our study introduces ASD-cancer, a cutting-edge autoencoder-based subtype detector. ASD-cancer decodes the complexities within the tumor microenvironment, successfully identifying distinct survival subtypes across 20 cancer types. Its superior risk stratification, demonstrated by significant improvements over traditional methods like principal component analysis, holds promise for refining patient prognosis. Accurate survival subtype predictions, biomarker discovery, and insights into microbe-host gene interactions elevate ASD-cancer as a powerful tool for advancing precision medicine. These findings not only contribute to a deeper understanding of the tumor microenvironment but also open avenues for personalized interventions across diverse cancer types, underscoring the transformative potential of ASD-cancer in shaping the future of cancer care.

Inhibition of DKK-1 by WAY262611 Inhibits Osteosarcoma Metastasis

Osteosarcoma (OS) is the most common primary malignant bone tumor in childhood. Patients who present with metastatic disease at diagnosis or relapse have a very poor prognosis, and this has not changed over the past four decades. The Wnt signaling pathway plays a role in regulating osteogenesis and is implicated in OS pathogenesis. DKK-1 inhibits the canonical Wnt signaling pathway, causing inhibition of osteoblast differentiation and disordered bone repair. Our lab previously demonstrated that a monoclonal antibody against DKK-1 prevented metastatic disease in a mouse model. This study expands upon those findings by demonstrating similar results with a small molecule inhibitor of DKK-1, WAY262611, both in vitro and in vivo . WAY262611 was evaluated in vitro on osteosarcoma cell lines, including proliferation, caspase activation, cell cycle analysis, and signaling pathway activation. We utilized our orthotopic implantation-amputation model of osteosarcoma metastasis in vivo to determine the impact of WAY262611 on primary tumor progression and metastatic outgrowth of disseminated tumor cells. Differentiation status was determined using single cell RNA sequencing. We show here that WAY262611 activates canonical Wnt signaling, enhances nuclear localization and transcriptional activity of beta-catenin, and slows proliferation of OS cell lines. We also show that WAY262611 induces osteoblastic differentiation of an OS patient-derived xenograft in vivo , as well as inhibiting metastasis. This work credentials DKK-1 as a therapeutic target in OS, allowing for manipulation of the Wnt signaling pathway and providing preclinical justification for the development of new biologics for prevention of osteosarcoma metastasis.

Distinct differentiation trajectories leave lasting impacts on gene regulation and function of V2a interneurons

During development, early regionalization segregates lineages and directs diverse cell fates. Sometimes, however, distinct progenitors produce analogous cell types. For example, V2a neurons, are excitatory interneurons that emerge from different anteroposterior progenitors. V2a neurons demonstrate remarkable plasticity after spinal cord injury and improve motor function, showing potential for cell therapy. To examine how lineage origins shape their properties, we differentiated V2a neurons from hPSC-derived progenitors with distinct anteroposterior identities. Single-nucleus multiomic analysis revealed lineage-specific transcription factor motifs and numerous differentially expressed genes related to axon growth and calcium handling. Bypassing lineage patterning via transcription factor-induced differentiation yielded neurons distinct from both developmentally relevant populations and human tissue, emphasizing the need to follow developmental steps to generate authentic cell identities. Using in silico and in vitro loss-of-function analyses, we identified CREB5 and TCF7L2 as regulators specific to posterior identities, underscoring the critical role of lieage origins in determining cell states and functions.

Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease

The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMB's role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.

NiCo identifies extrinsic drivers of cell state modulation by niche covariation analysis

Cell states are modulated by intrinsic driving forces such as gene expression noise and extrinsic signals from the tissue microenvironment. The distinction between intrinsic and extrinsic cell state determinants is essential for understanding the regulation of cell fate in tissues during development, homeostasis and disease. The rapidly growing availability of single-cell resolution spatial transcriptomics makes it possible to meet this challenge. However, available computational methods to infer topological tissue domains, spatially variable genes, or ligand-receptor interactions are limited in their capacity to capture cell state changes driven by crosstalk between individual cell types within the same niche. We present NiCo, a computational framework for integrating single-cell resolution spatial transcriptomics with matched single-cell RNA-sequencing reference data to infer the influence of the spatial niche on the cell state. By applying NiCo to mouse embryogenesis, adult small intestine and liver data, we demonstrate the ability to predict novel niche interactions that govern cell state variation underlying tissue development and homeostasis. In particular, NiCo predicts a feedback mechanism between Kupffer cells and neighboring stellate cells dampening stellate cell activation in the normal liver. NiCo provides a powerful tool to elucidate tissue architecture and to identify drivers of cellular states in local niches.

Beta cells are essential drivers of pancreatic ductal adenocarcinoma development

Pancreatic endocrine-exocrine crosstalk plays a key role in normal physiology and disease. For instance, endocrine islet beta (β) cell secretion of insulin or cholecystokinin (CCK) promotes progression of pancreatic adenocarcinoma (PDAC), an exocrine cell-derived tumor. However, the cellular and molecular mechanisms that govern endocrine-exocrine signaling in tumorigenesis remain incompletely understood. We find that β cell ablation impedes PDAC development in mice, arguing that the endocrine pancreas is critical for exocrine tumorigenesis. Conversely, obesity induces β cell hormone dysregulation, alters CCK-dependent peri-islet exocrine cell transcriptional states, and enhances islet proximal tumor formation. Single-cell RNA-sequencing, in silico latent-space archetypal and trajectory analysis, and genetic lineage tracing in vivo reveal that obesity stimulates postnatal immature β cell expansion and adaptation towards a pro-tumorigenic CCK+ state via JNK/cJun stress-responsive signaling. These results define endocrine-exocrine signaling as a driver of PDAC development and uncover new avenues to target the endocrine pancreas to subvert exocrine tumorigenesis.

KARR-seq reveals cellular higher-order RNA structures and RNA-RNA interactions

RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA-RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA-RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA-RNA interactions between the viruses and the host RNAs that potentially regulate viral replication.

Identification of epigenetic modifiers essential for growth and survival of AML1/ETO-positive leukemia

Aberrant gene expression patterns in acute myeloid leukemia (AML) with balanced chromosomal translocations are often associated with dysregulation of epigenetic modifiers. The AML1/ETO (RUNX1/MTG8) fusion protein, caused by the translocation (8;21)(q22;q22), leads to the epigenetic repression of its target genes. We aimed in this work to identify critical epigenetic modifiers, on which AML1/ETO-positive AML cells depend on for proliferation and survival using shRNA library screens and global transcriptomics approaches. Using shRNA library screens, we identified 41 commonly depleted genes in two AML1/ETO-positive cell lines Kasumi-1 and SKNO-1. We validated, genetically and pharmacologically, DNMT1 and ATR using several AML1/ETO-positive and negative cell lines. We also demonstrated in vivo differentiation of myeloblasts after treatment with the DNMT1 inhibitor decitabine in a patient with an AML1/ETO-positive AML. Bioinformatic analysis of global transcriptomics after AML1/ETO induction in 9/14/18-U937 cells identified 973 differentially expressed genes (DEGs). Three genes (PARP2, PRKCD, and SMARCA4) were both downregulated after AML1/ETO induction, and identified in shRNA screens. In conclusion, using unbiased shRNA library screens and global transcriptomics, we have identified several driver epigenetic regulators for proliferation in AML1/ETO-positive AML. DNMT1 and ATR were validated and are susceptible to pharmacological inhibition by small molecules showing promising preclinical and clinical efficacy.

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell-cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low-dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial-to-mesenchymal transition and PI3K/AKT signaling in specific sub-regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder-based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

ECGA: A web server to explore and analyze extrachromosomal gene in cancer

Circular extrachromosomal DNA (ecDNA) plays a crucial role in the onset, progression, and evolution of many types of cancers, with dysregulated gene expression driven by ecDNA as a key mechanism. Although database resources for ecDNA are now available, a sophisticated web application dedicated to ecDNA gene analysis remains absent. Therefore, we developed ecDNA gene analyzer (ECGA). ECGA catalogues 23,274 unique ecDNA genes of 27 cancers across 27 tissues. ECGA also offers five specialized analysis tools: (1) 'Venn analysis' looks for overlaps between a given gene list and ecDNA genes; (2) 'Enrichment analysis' performs over-representation analysis and gene set enrichment analysis of input gene list within predefined ecDNA gene sets; (3) 'Target discovery' identifies upregulated ecDNA genes as targets by comparing with reference expression in normal samples; (4) 'DE analysis' finds differentially expressed ecDNA genes; (5) 'Signature discovery' discerns ecDNA gene signatures capable of classifying samples into phenotypic groups, and it is accompanied by 'Signature validation' for model test on unseen data. In summary, ECGA emerges as an indispensable platform in cancer genetics, bridging gaps in basic research, medical reporting, and pharmaceutical development, and propelling ecDNA research forward. ECGA is freely available at https://www.zhounan.org/ecga/.

Leucine zipper-based immunomagnetic purification of CAR T cells displaying multiple receptors

Resistance to chimaeric antigen receptor (CAR) T cell therapy develops through multiple mechanisms, most notably antigen loss and tumour-induced immune suppression. It has been suggested that T cells expressing multiple CARs may overcome the resistance of tumours and that T cells expressing receptors that switch inhibitory immune-checkpoint signals into costimulatory signals may enhance the activity of the T cells in the tumour microenvironment. However, engineering multiple features into a single T cell product is difficult because of the transgene-packaging constraints of current gene-delivery vectors. Here we describe a cell-sorting method that leverages leucine zippers for the selective single-step immunomagnetic purification of cells co-transduced with two vectors. Such 'Zip sorting' facilitated the generation of T cells simultaneously expressing up to four CARs and coexpressing up to three 'switch' receptors. In syngeneic mouse models, T cells with multiple CARs and multiple switch receptors eliminated antigenically heterogeneous populations of leukaemia cells coexpressing multiple inhibitory ligands. By combining diverse therapeutic strategies, Zip-sorted multi-CAR multi-switch-receptor T cells can overcome multiple mechanisms of CAR T cell resistance.

An integrative analysis to identify pancancer epigenetic biomarkers

Integrating and analyzing the pancancer data collected from different experiments is crucial for gaining insights into the common mechanisms in the molecular level underlying the development and progression of cancers. Epigenetic study of the pancancer data can provide promising results in biomarker discovery. The genes that are epigenetically dysregulated in different cancers are powerful biomarkers for drug-related studies. This paper identifies the genes having altered expression due to aberrant methylation patterns using differential analysis of TCGA pancancer data of 12 different cancers. We identified a comprehensive set of 115 epigenetic biomarker genes out of which 106 genes having pancancer properties. The correlation analysis, gene set enrichment, protein-protein interaction analysis, pancancer characteristics analysis, and diagnostic modeling were performed on these biomarkers to illustrate the power of this signature and found to be important in different molecular operations related to cancer. An accuracy of 97.56% was obtained on TCGA pancancer gene expression dataset for predicting the binary class tumor or normal. The source code and dataset of this work are available at https://github.com/panchamisuneeth/EpiPanCan.git.

PhosX: data-driven kinase activity inference from phosphoproteomics experiments

SUMMARY

The inference of kinase activity from phosphoproteomics data can point to causal mechanisms driving signalling processes and potential drug targets. Identifying the kinases whose change in activity explains the observed phosphorylation profiles, however, remains challenging, and constrained by the manually curated knowledge of kinase-substrate associations. Recently, experimentally determined substrate sequence specificities of human kinases have become available, but robust methods to exploit this new data for kinase activity inference are still missing. We present PhosX, a method to estimate differential kinase activity from phosphoproteomics data that combines state-of-the-art statistics in enrichment analysis with kinases' substrate sequence specificity information. Using a large phosphoproteomics dataset with known differentially regulated kinases we show that our method identifies upregulated and downregulated kinases by only relying on the input phosphopeptides' sequences and intensity changes. We find that PhosX outperforms the currently available approach for the same task, and performs better or similarly to state-of-the-art methods that rely on previously known kinase-substrate associations. We therefore recommend its use for data-driven kinase activity inference.

AVAILABILITY AND IMPLEMENTATION

PhosX is implemented in Python, open-source under the Apache-2.0 licence, and distributed on the Python Package Index. The code is available on GitHub (https://github.com/alussana/phosx).

Decoding NY-ESO-1 TCR T cells: transcriptomic insights reveal dual mechanisms of tumor targeting in a melanoma murine xenograft model

The development of T cell receptor-engineered T cells (TCR-T) targeting intracellular antigens is a promising strategy for treating solid tumors; however, the mechanisms underlying their effectiveness remain poorly understood. In this study, we employed advanced techniques to investigate the functional state of T cells engineered with retroviral vectors to express a TCR specific for the NY-ESO-1 157-165 peptide in the HLA-A*02:01 context. Flow cytometry revealed a predominance of naïve T cells. Gene expression profiling using NanoString technology revealed upregulation of genes encoding chemokine receptors CCR2 and CCR5, indicating enhanced migration towards tumor sites. In the SK-Mel-37 xenograft model, these transduced T cells achieved complete tumor eradication. Furthermore, single-cell RNA sequencing (scRNA-seq) conducted 14 days post-TCR T cell infusion provided a comprehensive analysis of the in vivo adaptation of these cells, identifying a distinct subset of CD8+ effector T cells with an NK cell-like gene expression profile. Our findings indicate that NY-ESO-1 TCR-transduced T cells have the potential to mediate dual antitumor effects through both antigen-independent NK-like and antigen-specific CTL-like responses. This study underscores the potential of NY-ESO-1 TCR-T cells as potent tumor-eradicating agents, highlighting the importance of harnessing their versatile functional capabilities to refine and enhance therapeutic strategies.

A proteogenomic analysis of cervical cancer reveals therapeutic and biological insights

Although the incidence of cervical cancer (CC) has been reduced in high-income countries due to human papillomavirus (HPV) vaccination and screening strategies, it remains a significant public health issue that poses a threat to women's health in low-income countries. Here, we perform a comprehensive proteogenomic profiling of CC tumors obtained from 139 Chinese women. Integrated proteogenomic analysis links genetic aberrations to downstream pathogenesis-related pathways and reveals the landscape of HPV-associated multi-omic changes. EP300 is found to enhance the acetylation of FOSL2-K222, consequently accelerating the malignant proliferation of CC cells. Proteomic stratification identifies three patient subgroups with distinct features in prognosis, genetic alterations, immune infiltration, and post-translational modification regulations. PRKCB is further identified as a potential radioresponse-related biomarker of CC patients. This study provides a valuable public resource for researchers and clinicians to delve into the molecular basis of CC, to identify potential treatments and to ultimately advance clinical practice.

Graph contrastive learning of subcellular-resolution spatial transcriptomics improves cell type annotation and reveals critical molecular pathways

Imaging-based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type. However, current cell type annotation approaches of iST primarily utilize gene expression information while neglecting the spatial distribution of RNAs within cells. In this work, we introduce a semi-supervised graph contrastive learning method called Focus, the first method, to the best of our knowledge, that explicitly models RNA's subcellular distribution and community to improve cell type annotation. Focus demonstrates significant improvements over state-of-the-art algorithms across a range of spatial transcriptomics platforms, achieving improvements up to 27.8% in terms of accuracy and 51.9% in terms of F1-score for cell type annotation. Furthermore, Focus enjoys the advantages of intricate cell type-specific subcellular spatial gene patterns and providing interpretable subcellular gene analysis, such as defining the gene importance score. Importantly, with the importance score, Focus identifies genes harboring strong relevance to cell type-specific pathways, indicating its potential in uncovering novel regulatory programs across numerous biological systems.

Single cell and spatial transcriptomics highlight the interaction of club-like cells with immunosuppressive myeloid cells in prostate cancer

Prostate cancer treatment resistance is a significant challenge facing the field. Genomic and transcriptomic profiling have partially elucidated the mechanisms through which cancer cells escape treatment, but their relation toward the tumor microenvironment (TME) remains elusive. Here we present a comprehensive transcriptomic landscape of the prostate TME at multiple points in the standard treatment timeline employing single-cell RNA-sequencing and spatial transcriptomics data from 120 patients. We identify club-like cells as a key epithelial cell subtype that acts as an interface between the prostate and the immune system. Tissue areas enriched with club-like cells have depleted androgen signaling and upregulated expression of luminal progenitor cell markers. Club-like cells display a senescence-associated secretory phenotype and their presence is linked to increased polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) activity. Our results indicate that club-like cells are associated with myeloid inflammation previously linked to androgen deprivation therapy resistance, providing a rationale for their therapeutic targeting.

Identifying Herbal Candidates and Active Ingredients Against Postmenopausal Osteoporosis Using Biased Random Walk on a Multiscale Network

Postmenopausal osteoporosis is a major global health concern, particularly affecting aging women, and necessitates innovative treatment options. Herbal medicine, with its multi-compound, multi-target characteristics, offers a promising approach for complex diseases. In this study, we applied multiscale network and random walk-based analyses to identify candidate herbs and their active ingredients for postmenopausal osteoporosis, focusing on their underlying mechanisms. A dataset of medicinal herbs, their active ingredients, and protein targets was compiled, and diffusion profiles were calculated to assess the propagation effects. Through correlation analysis, we prioritized herbs based on their relevance to osteoporosis, identifying the top candidates like Benincasae Semen, Glehniae Radix, Corydalis Tuber, and Houttuyniae Herba. Gene Set Enrichment Analysis (GSEA) revealed that the 49 core protein targets of these herbs were significantly associated with pathways related to inflammation, osteoclast differentiation, and estrogen metabolism. Notably, compounds such as falcarindiol from Glehniae Radix and tetrahydrocoptisine from Corydalis Tuber-previously unstudied for osteoporosis-were predicted to interact with inflammation-related proteins, including IL6, IL1B, and TNF, affecting key biological processes like apoptosis and cell proliferation. This study advances the understanding of herbal therapies for osteoporosis and offers a framework for discovering novel therapeutic agents.

Digital profiling of gene expression from histology images with linearized attention

Cancer is a heterogeneous disease requiring costly genetic profiling for better understanding and management. Recent advances in deep learning have enabled cost-effective predictions of genetic alterations from whole slide images (WSIs). While transformers have driven significant progress in non-medical domains, their application to WSIs lags behind due to high model complexity and limited dataset sizes. Here, we introduce SEQUOIA, a linearized transformer model that predicts cancer transcriptomic profiles from WSIs. SEQUOIA is developed using 7584 tumor samples across 16 cancer types, with its generalization capacity validated on two independent cohorts comprising 1368 tumors. Accurately predicted genes are associated with key cancer processes, including inflammatory response, cell cycles and metabolism. Further, we demonstrate the value of SEQUOIA in stratifying the risk of breast cancer recurrence and in resolving spatial gene expression at loco-regional levels. SEQUOIA hence deciphers clinically relevant information from WSIs, opening avenues for personalized cancer management.

IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

Targeting LLT1 as a potential immunotherapy option for cancer patients non-responsive to existing checkpoint therapies in multiple solid tumors

BACKGROUND

High levels of LLT1 expression have been found in several cancers, where it interacts with CD161 on NK cells to facilitate tumor immune escape. Targeting LLT1 could potentially relieve this inhibitory signal and enhance anti-tumor responses mediated through NK cells. Using the 'The Cancer Genome Atlas' (TCGA) database, we investigated the role of LLT1 in the tumor microenvironment (TME) across various cancers. Identifying such biomarkers could create new therapeutic options for patients in addition to complementing existing immunotherapies.

METHODS

LLT1 expression was evaluated in 33 cancers using TCGA transcriptome data. Univariate Cox regression analysis was employed to assess the correlation of LLT1 expression with patient survival. The relationship between LLT1 expression with immune infiltrates, immune gene signatures, and cancer genomic biomarkers (TMB, MSI, and MMR) was also investigated. Immunofluorescence studies were conducted to validate LLT1 expression in tumors. Furthermore, using the CRI iAtlas data, we evaluated LLT1 distribution and its correlation with other immune checkpoint genes in patients non-responsive to existing immune checkpoint therapies across multiple solid cancers.

RESULTS

High expression of LLT1 was observed in 12 cancers, including BRCA, CHOL, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, STAD, SARC, and PCPG. In certain cancers like COAD, KICH, and KIRC, high LLT1 expression was associated with poor prognosis. Further analysis revealed that upregulated LLT1 was associated with an abundance of NK and T cell infiltrates in the TME, as well as exhaustive immune biomarkers, and inversely associated with pro-inflammatory and tumor suppressor signatures. High LLT1 expression is also positively correlated with genomic biomarkers in certain cancers. Immunofluorescence studies confirmed moderate to high LLT1 expression in immune-resistant prostate cancer, glioma, ovarian cancer, and immune-sensitive liver cancer cell lines. An independent assessment of clinical cohorts from CRI iAtlas showed a correlation of upregulated LLT1 with multiple immunosuppressive genes in patients non-responsive to current ICIs.

CONCLUSIONS

The biomarker analysis revealed a clear association between elevated LLT1 expression and an immunosuppressive TME in patient cohorts from TCGA and clinical databases. Therefore, this study provides a foundation for utilizing LLT1 as a potential target to improve clinical responses in ICI non-responsive patients with upregulated LLT1.

Reproducible processing of TCGA regulatory networks

Background

Technological advances in sequencing and computation have allowed deep exploration of the molecular basis of diseases. Biological networks have proven to be a useful framework for interrogating omics data and modeling regulatory gene and protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), have provided a rich resource for building and validating new computational methods resulting in a plethora of open-source software for downloading, pre-processing, and analyzing those data. However, for an end-to-end analysis of regulatory networks a coherent and reusable workflow is essential to integrate all relevant packages into a robust pipeline.

Findings

We developed tcga-data-nf, a Nextflow workflow that allows users to reproducibly infer regulatory networks from the thousands of samples in TCGA using a single command. The workflow can be divided into three main steps: multi-omics data, such as RNA-seq and methylation, are downloaded, preprocessed, and lastly used to infer regulatory network models with the netZoo software tools. The workflow is powered by the NetworkDataCompanion R package, a standalone collection of functions for managing, mapping, and filtering TCGA data. Here we show how the pipeline can be used to study the differences between colon cancer subtypes that could be explained by epigenetic mechanisms. Lastly, we provide pre-generated networks for the 10 most common cancer types that can be readily accessed.

Conclusions

tcga-data-nf is a complete yet flexible and extensible framework that enables the reproducible inference and analysis of cancer regulatory networks, bridging a gap in the current universe of software tools.

SIRT6-dependent functional switch via K494 modifications of RE-1 silencing transcription factor

RE-1 silencing transcription factor (REST) is a key repressor of neural genes. REST is upregulated under stress signals, aging and neurodegenerative diseases, but although it is upregulated, its function is lost in Alzheimer's Disease. However, why it becomes inactive remains unclear. Here, we show that the NAD-dependent deacetylase SIRT6 regulates REST expression, location and activity. In the absence of SIRT6, REST is overexpressed but mislocalized, leading to a partial loss of its activity and causing it to become toxic. SIRT6 deficiency abrogates REST and EZH2 interaction, perturbs the location of REST to the heterochromatin Lamin B ring, and leads to REST target gene overexpression. SIRT6 reintroduction or REST methyl-mimic K494M expression rescues this phenotype, while an acetyl-mimic mutant loses its function even in WT cells. Our studies define a novel regulatory switch where, depending on SIRT6 presence, the function of REST is regulated by post-translational modifications on K494 (Ac/me), affecting neuronal gene expression. In WT cells (left), REST functions as a repressor due to its methylation, which allows proper localization and interaction with EZH2. In SIRT6 KO cells (right), REST is overexpressed, but it is mislocalized and acetylated instead of methylated, impairing its interaction with EZH2. REST localizes in the cytoplasm in autophagosomes. The overall increase in REST without SIRT6 results in non-functional and toxic REST proteins. During aging, SIRT6 declines in the brain, while REST is upregulated to protect it. In pathological aging, where SIRT6 levels are very low, the increase in REST without SIRT6 results in non-functional and toxic REST.

Hallmarks of uterine receptivity predate placental mammals

Embryo implantation requires tightly coordinated signaling between the blastocyst and the endometrium, and is crucial for the establishment of a uteroplacental unit that persists until term in eutherian mammals. In contrast, marsupials, with a unique life cycle and short gestation, make only brief fetal-maternal contact and lack implantation. To better understand the evolutionary link between eutherian implantation and its ancestral equivalent in marsupials, we compare single-cell transcriptomes from the receptive and non-receptive endometrium of the mouse and guinea pig with that of the opossum, a marsupial. We identify substantial differences between rodent peri-implantation endometrium and opossum placental attachment, including differences in the diversity and abundance of stromal and epithelial cells which parallel the difference between histotrophic and hemotrophic provisioning strategies. We also identify a window of conserved epithelial gene expression between the opossum shelled blastocyst stage and rodent peri-implantation, including IHH and LIF . We find strong conservation of blastocyst proteases, steroid synthetases, Wnt and BMP signals between eutherians and the opossum despite its lack of implantation. Finally, we show that the signaling repertoire of the maternal uterine epithelium during implantation displays substantial overlap with that of the post-implantation placental trophoblast, suggesting that the fetal trophoblast can compensate for the loss of endometrial epithelium in eutherian invasive placentation. Together, our results suggest that eutherian implantation primarily involved the re-wiring of maternal signaling networks, some of which were already present in the therian ancestor, and points towards an essential role of maternal innovations in the evolution of invasive placentation.

Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach

Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI model using a generative adversarial network (GAN) method to facilitate the bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment. TransTox demonstrates robust performance, validated across independent datasets and laboratories. First, the concordance between real experimental data and synthetic data generated by TransTox was demonstrated in characterizing toxicity mechanisms compared to real experimental settings. Second, TransTox proved valuable in gene expression predictive models, where synthetic data could be used to develop gene expression predictive models or serve as "digital twins" for diagnostic applications. The TransTox approach holds the potential for multi-organ toxicity assessment with AI and advancing the field of precision toxicology.