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

Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction

The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation. Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ T cells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.

Coordinated wound responses in a regenerative animal-algal holobiont

Animal regeneration involves coordinated responses across cell types throughout the animal body. In endosymbiotic animals, whether and how symbionts react to host injury and how cellular responses are integrated across species remain unexplored. Here, we study the acoel Convolutriloba longifissura, which hosts symbiotic Tetraselmis sp. green algae and can regenerate entire bodies from tissue fragments. We show that animal injury causes a decline in the photosynthetic efficiency of the symbiotic algae, alongside two distinct, sequential waves of transcriptional responses in acoel and algal cells. The initial algal response is characterized by the upregulation of a cohort of photosynthesis-related genes, though photosynthesis is not necessary for regeneration. A conserved animal transcription factor, runt, is induced after injury and required for acoel regeneration. Knockdown of Cl-runt dampens transcriptional responses in both species and further reduces algal photosynthetic efficiency post-injury. Our results suggest that the holobiont functions as an integrated unit of biological organization by coordinating molecular networks across species through the runt-dependent animal regeneration program.

Viroporin activity from rotavirus nonstructural protein 4 induces intercellular calcium waves that contribute to pathogenesis

Acute gastroenteritis remains the second leading cause of death among children under the age of 5 worldwide. While enteric viruses are the most common etiology, the drivers of their virulence remain incompletely understood. We recently found that cells infected with rotavirus, the most prevalent enteric virus in infants and young children, initiate hundreds of intercellular calcium waves that enhance both fluid secretion and viral spread. Understanding how rotavirus triggers intercellular calcium waves may allow us to design safer, more effective vaccines and therapeutics, but we still lack a mechanistic understanding of this process. In this study, we used existing virulent and attenuated rotavirus strains, as well as reverse engineered recombinants, to investigate the role of rotavirus nonstructural protein 4 (NSP4) in intercellular calcium wave induction using in vitro , organoid, and in vivo model systems. We found that the capacity to induce purinergic intercellular calcium waves (ICWs) segregated with NSP4 in both simian and murine-like rotavirus backgrounds, and NSP4 expression alone was sufficient to induce ICWs. NSP4's ability to function as a viroporin, which conducts calcium out of the endoplasmic reticulum, was necessary for ICW induction. Furthermore, viroporin activity and the resulting ICWs drove transcriptional changes indicative of innate immune activation, which were lost upon attenuation of viroporin function. Multiple aspects of RV disease severity in vivo correlated with the generation of ICWs, identifying a critical link between viroporin function, intercellular calcium waves, and enteric viral virulence.

danRerLib: a Python package for zebrafish transcriptomics

Summary

Understanding the pathways and biological processes underlying differential gene expression is fundamental for characterizing gene expression changes in response to an experimental condition. Zebrafish, with a transcriptome closely mirroring that of humans, are frequently utilized as a model for human development and disease. However, a challenge arises due to the incomplete annotations of zebrafish pathways and biological processes, with more comprehensive annotations existing in humans. This incompleteness may result in biased functional enrichment findings and loss of knowledge. danRerLib, a versatile Python package for zebrafish transcriptomics researchers, overcomes this challenge and provides a suite of tools to be executed in Python including gene ID mapping, orthology mapping for the zebrafish and human taxonomy, and functional enrichment analysis utilizing the latest updated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. danRerLib enables functional enrichment analysis for GO and KEGG pathways, even when they lack direct zebrafish annotations through the orthology of human-annotated functional annotations. This approach enables researchers to extend their analysis to a wider range of pathways, elucidating additional mechanisms of interest and greater insight into experimental results.

Availability and implementation

danRerLib, along with comprehensive documentation and tutorials, is freely available. The source code is available at https://github.com/sdsucomptox/danrerlib/ with associated documentation and tutorials at https://sdsucomptox.github.io/danrerlib/. The package has been developed with Python 3.9 and is available for installation on the package management systems PIP (https://pypi.org/project/danrerlib/) and Conda (https://anaconda.org/sdsu_comptox/danrerlib) with additional installation instructions on the documentation website.

Scalable integration of multiomic single-cell data using generative adversarial networks

MOTIVATION

Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable.

RESULTS

We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated.

AVAILABILITY AND IMPLEMENTATION

Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

Integrative multi-omics profiling in human decedents receiving pig heart xenografts

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.

The prognostic value of MEK pathway-associated estrogen receptor signaling activity for female cancers

BACKGROUND

Other than for breast cancer, endocrine therapy has not been highly effective for gynecologic cancers. Endocrine therapy resistance in estrogen receptor positive gynecologic cancers is still poorly understood. In this retrospective study, we examined the estrogen receptor (ER) signaling pathway activities of breast, ovarian, endometrial, and cervical cancers to identify those that may predict endocrine therapy responsiveness.

METHODS

Clinical and genomic data of women with breast and gynecological cancers were downloaded from cBioPortal for Cancer Genomics. Estrogen receptor alpha (ESR1) expression level and sample-level pathway enrichment scores (EERES) were calculated to classify patients into four groups (low/high ESR1 and low/high EERES). Correlation between ESR1/EERES score and survival was further validated with RNAseq data from low-grade serous ovarian cancer. Pathway analyses were performed among different ESR1/EERES groups to identify genes that correlate with endocrine resistance, which are validated using Cancer Cell Line Encyclopedia gene expression and Genomics of Drug Sensitivity in Cancer data.

RESULTS

We identified a novel combined prognostic value of ESR1 expression and the corresponding estrogen response signaling (EERES score) for breast cancer. The combined prognostic value (ESR1/EERES) may be applicable to other gynecologic cancers. More importantly, we discovered that ER signaling can cross-regulate MEK pathway activation. We identified downstream genes in the MEK pathway (EPHA2, INAVA, MALL, MPZL2, PCDH1, and TNFRSF21) that are potential endocrine therapy response biomarkers.

CONCLUSION

This study demonstrated that targeting both the ER and the ER signaling activity related MEK pathway may aid the development of endocrine therapy strategies for personalized medicine.

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. Several studies have identified predictive gene lists for patient prognosis in breast cancer, but these lists are unstable and have only a few genes in common. Instability of feature selection impedes biological interpretability: genes that are relevant for cancer pathology should be members of any predictive gene list obtained for the same clinical type of patients. Stability and interpretability of selected features can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are methods to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to construct feature sets by aggregating individual explanations. We suggest a methodology to systematically and quantitatively analyze the stability, the impact on the classification performance, and the interpretability of the selected feature sets. We used this methodology to compare GCNN+LRP to GCNN+SHAP and to more classical ML-based feature selection approaches. Utilizing a large breast cancer gene expression dataset we show that, while feature selection with SHAP is useful in applications where selected features have to be impactful for classification performance, among all studied methods GCNN+LRP delivers the most stable (reproducible) and interpretable gene lists.

A cell atlas of thoracic aortic perivascular adipose tissue: a focus on mechanotransducers

Perivascular adipose tissue (PVAT) is increasingly recognized for its function in mechanotransduction. However, major gaps remain in our understanding of the cells present in PVAT, as well as how different cells contribute to mechanotransduction. We hypothesized that snRNA-seq would reveal the expression of mechanotransducers, and test one (PIEZO1) to illustrate the expression and functional agreement between single-nuclei RNA sequencing (snRNA-seq) and physiological measurements. To contrast two brown tissues, subscapular brown adipose tissue (BAT) was also examined. We used snRNA-seq of the thoracic aorta PVAT (taPVAT) and BAT from male Dahl salt-sensitive (Dahl SS) rats to investigate cell-specific expression mechanotransducers. Localization and function of the mechanostransducer PIEZO1 were further examined using immunohistochemistry (IHC) and RNAscope, as well as pharmacological antagonism. Approximately 30,000 nuclei from taPVAT and BAT each were characterized by snRNA-seq, identifying eight major cell types expected and one unexpected (nuclei with oligodendrocyte marker genes). Cell-specific differential gene expression analysis between taPVAT and BAT identified up to 511 genes (adipocytes) with many (≥20%) being unique to individual cell types. Piezo1 was the most highly, widely expressed mechanotransducer. The presence of PIEZO1 in the PVAT but not the adventitia was confirmed by RNAscope and IHC in male and female rats. Importantly, antagonism of PIEZO1 by GsMTX4 impaired the PVAT's ability to hold tension. Collectively, the cell compositions of taPVAT and BAT are highly similar, and PIEZO1 is likely a mechanotransducer in taPVAT.NEW & NOTEWORTHY This study describes the atlas of cells in the thoracic aorta perivascular adipose tissue (taPVAT) of the Dahl-SS rat, an important hypertension model. We show that mechanotransducers are widely expressed in these cells. Moreover, PIEZO1 expression is shown to be restricted to the taPVAT and is functionally implicated in stress relaxation. These data will serve as the foundation for future studies investigating the role of taPVAT in this model of hypertensive disease.

SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations

Background

The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and context-dependent networks. This limitation constrains the applicability of current methods.

Results

We introduced SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis.

Conclusions

SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Finally, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients.

Effect of Poria cocos Terpenes: Verifying Modes of Action Using Molecular Docking, Drug-Induced Transcriptomes, and Diffusion Network Analyses

We characterized the therapeutic biological modes of action of several terpenes in Poria cocos F.A Wolf (PC) and proposed a broad therapeutic mode of action for PC. Molecular docking and drug-induced transcriptome analysis were performed to confirm the pharmacological mechanism of PC terpene, and a new analysis method, namely diffusion network analysis, was proposed to verify the mechanism of action against Alzheimer's disease. We confirmed that the compound that exists only in PC has a unique mechanism through statistical-based docking analysis. Also, docking and transcriptomic analysis results could reflect results in clinical practice when used complementarily. The detailed pharmacological mechanism of PC was confirmed by constructing and analyzing the Alzheimer's disease diffusion network, and the antioxidant activity based on microglial cells was verified. In this study, we used two bioinformatics approaches to reveal PC's broad mode of action while also using diffusion networks to identify its detailed pharmacological mechanisms of action. The results of this study provide evidence that future pharmacological mechanism analysis should simultaneously consider complementary docking and transcriptomics and suggest diffusion network analysis, a new method to derive pharmacological mechanisms based on natural complex compounds.

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Dysregulation of CD4+ and CD8+ resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

BACKGROUND

Colitis caused by checkpoint inhibitors (CPI) is frequent and is treated with empiric steroids, but CPI colitis mechanisms in steroid-experienced or refractory disease are unclear.

METHODS

Using colon biopsies and blood from predominantly steroid-experienced CPI colitis patients, we performed multiplexed single-cell transcriptomics and proteomics to nominate contributing populations.

RESULTS

CPI colitis biopsies showed enrichment of CD4+resident memory (RM) T cells in addition to CD8+ RM and cytotoxic CD8+ T cells. Matching T cell receptor (TCR) clonotypes suggested that both RMs are progenitors that yield cytotoxic effectors. Activated, CD38+ HLA-DR+ CD4+ RM and cytotoxic CD8+ T cells were enriched in steroid-experienced and a validation data set of steroid-naïve CPI colitis, underscoring their pathogenic potential across steroid exposure. Distinct from ulcerative colitis, CPI colitis exhibited perturbed stromal metabolism (NAD+, tryptophan) impacting epithelial survival and inflammation. Endothelial cells in CPI colitis after anti-TNF and anti-cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) upregulated the integrin α4β7 ligand molecular vascular addressin cell adhesion molecule 1 (MAdCAM-1), which may preferentially respond to vedolizumab (anti-α4β7).

CONCLUSIONS

These findings nominate CD4+ RM and MAdCAM-1+ endothelial cells for targeting in specific subsets of CPI colitis patients.

scRNA-seq characterizing the heterogeneity of fibroblasts in breast cancer reveals a novel subtype SFRP4+ CAF that inhibits migration and predicts prognosis

Introduction

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly impact the tumor microenvironment and therapeutic responses in breast cancer (BC). Despite their importance, the comprehensive profile of CAFs in BC remains to be fully elucidated.

Methods

To address this gap, we utilized single-cell RNA sequencing (scRNA-seq) to delineate the CAF landscape within 14 BC normal-tumor paired samples. We further corroborated our findings by analyzing several public datasets, thereby validating the newly identified CAF subtype. Additionally, we conducted coculture experiments with BC cells to assess the functional implications of this CAF subtype.

Results

Our scRNA-seq analysis unveiled eight distinct CAF subtypes across five tumor and six adjacent normal tissue samples. Notably, we discovered a novel subtype, designated as SFRP4+ CAFs, which was predominantly observed in normal tissues. The presence of SFRP4+ CAFs was substantiated by two independent scRNA-seq datasets and a spatial transcriptomics dataset. Functionally, SFRP4+ CAFs were found to impede BC cell migration and the epithelial-mesenchymal transition (EMT) process by secreting SFRP4, thereby modulating the WNT signaling pathway. Furthermore, we established that elevated expression levels of SFRP4+ CAF markers correlate with improved survival outcomes in BC patients, yet paradoxically, they predict a diminished response to neoadjuvant chemotherapy in cases of triple-negative breast cancer.

Conclusion

This investigation sheds light on the heterogeneity of CAFs in BC and introduces a novel SFRP4+ CAF subtype that hinders BC cell migration. This discovery holds promise as a potential biomarker for refined prognostic assessment and therapeutic intervention in BC.

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will only be maintained if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74-94% of the variance in aneuploid strains' growth rates is explained by the additive cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of snoRNAs and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics

Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide CRISPR library to systematically identify host cell regulators of Ebola virus infection in 39,085,093 million single cells. Measuring viral RNA and protein levels together with their localization in cells identified over 998 related host factors and provided detailed information about the role of each gene across the virus replication cycle. We trained a deep learning model on single-cell images to associate each host factor with predicted replication steps, and confirmed the predicted relationship for select host factors. Among the findings, we showed that the mitochondrial complex III subunit UQCRB is a post-entry regulator of Ebola virus RNA replication, and demonstrated that UQCRB inhibition with a small molecule reduced overall Ebola virus infection with an IC50 of 5 μM. Using a random forest model, we also identified perturbations that reduced infection by disrupting the equilibrium between viral RNA and protein. One such protein, STRAP, is a spliceosome-associated factor that was found to be closely associated with VP35, a viral protein required for RNA processing. Loss of STRAP expression resulted in a reduction in full-length viral genome production and subsequent production of non-infectious virus particles. Overall, the data produced in this genome-wide high-content single-cell screen and secondary screens in additional cell lines and related filoviruses (MARV and SUDV) revealed new insights about the role of host factors in virus replication and potential new targets for therapeutic intervention.

Evaluation of large language models for discovery of gene set function

Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete. Here we evaluate five Large Language Models (LLMs) for their ability to discover the common biological functions represented by a gene set, substantiated by supporting rationale, citations and a confidence assessment. Benchmarking against canonical gene sets from the Gene Ontology, GPT-4 confidently recovered the curated name or a more general concept (73% of cases), while benchmarking against random gene sets correctly yielded zero confidence. Gemini-Pro and Mixtral-Instruct showed ability in naming but were falsely confident for random sets, whereas Llama2-70b had poor performance overall. In gene sets derived from 'omics data, GPT-4 identified novel functions not reported by classical functional enrichment (32% of cases), which independent review indicated were largely verifiable and not hallucinations. The ability to rapidly synthesize common gene functions positions LLMs as valuable 'omics assistants.

A method for predicting drugs that can boost the efficacy of immune checkpoint blockade

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

B-cell infiltration is associated with survival outcomes following programmed cell death protein 1 inhibition in head and neck squamous cell carcinoma

BACKGROUND

Programmed cell death protein 1 (PD-1) axis blockade has become the mainstay in the treatment of recurrent and/or metastatic (R/M) head and neck squamous cell cancer (HNSCC). Programmed death-ligand 1 (PD-L1) is the only approved biomarker for patient selection; however, response rate is limited even among high expressors. Our primary objective was to investigate the association of immune cell-related biomarkers in the tumor and tumor microenvironment with PD-1 checkpoint inhibitors' outcomes in patients with R/M HNSCC.

PATIENTS AND METHODS

NCT03652142 was a prospective study in nivolumab-treated platinum-refractory R/M HNSCC, aiming to evaluate biomarkers of response to treatment. Tumor biopsies and blood samples were collected from 60 patients at baseline, post-treatment, and at progression. Immune cells in the tumor and stromal compartments were quantified by immunofluorescence using a five-protein panel (CD3, CD8, CD20, FoxP3, cytokeratin). Tertiary lymphoid structures (TLSs), PD-L1 expression, and peripheral blood immune cell composition were also evaluated for associations with outcome. Our findings were validated by gene set enrichment analysis (GSEA) messenger RNA in situ expression data from the same patients, for B-cell- and TLS-associated genes.

RESULTS

High pre-treatment density of stromal B cells was associated with prolonged progression-free survival (PFS) (P = 0.011). This result was validated by GSEA, as stromal enrichment with B-cell-associated genes showed association with response to nivolumab. PD-L1 positivity combined with high B-cell counts in stroma defined a subgroup with significantly longer PFS and overall survival (P = 0.013 and P = 0.0028, respectively).

CONCLUSIONS

Increased B cells in pre-treatment HNSCC biopsy samples correlate with prolonged benefit from PD-1-based immunotherapy and could further enhance the predictive value of PD-L1 expression.

Pan-cancer analyses suggest kindlin-associated global mechanochemical alterations

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial-mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment.

Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy

A central problem in cancer immunotherapy with immune checkpoint blockade (ICB) is the development of resistance, which affects 50% of patients with metastatic melanoma1,2. T cell exhaustion, resulting from chronic antigen exposure in the tumour microenvironment, is a major driver of ICB resistance3. Here, we show that CD38, an ecto-enzyme involved in nicotinamide adenine dinucleotide (NAD+) catabolism, is highly expressed in exhausted CD8+ T cells in melanoma and is associated with ICB resistance. Tumour-derived CD38hiCD8+ T cells are dysfunctional, characterised by impaired proliferative capacity, effector function, and dysregulated mitochondrial bioenergetics. Genetic and pharmacological blockade of CD38 in murine and patient-derived organotypic tumour models (MDOTS/PDOTS) enhanced tumour immunity and overcame ICB resistance. Mechanistically, disrupting CD38 activity in T cells restored cellular NAD+ pools, improved mitochondrial function, increased proliferation, augmented effector function, and restored ICB sensitivity. Taken together, these data demonstrate a role for the CD38-NAD+ axis in promoting T cell exhaustion and ICB resistance, and establish the efficacy of CD38 directed therapeutic strategies to overcome ICB resistance using clinically relevant, patient-derived 3D tumour models.

Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma

The aggressive nature of glioblastoma (GBM) - one of the deadliest forms of brain tumors - is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts.

Whole organism snRNA-seq reveals systemic peripheral changes in Alzheimer's Disease fly models

Peripheral tissues become disrupted in Alzheimer's Disease (AD). However, a comprehensive understanding of how the expression of AD-associated toxic proteins, Aβ42 and Tau, in neurons impacts the periphery is lacking. Using Drosophila, a prime model organism for studying aging and neurodegeneration, we generated the Alzheimer's Disease Fly Cell Atlas (AD-FCA): whole-organism single-nucleus transcriptomes of 219 cell types from adult flies neuronally expressing human Aβ42 or Tau. In-depth analyses and functional data reveal impacts on peripheral sensory neurons by Aβ42 and on various non-neuronal peripheral tissues by Tau, including the gut, fat body, and reproductive system. This novel AD atlas provides valuable insights into potential biomarkers and the intricate interplay between the nervous system and peripheral tissues in response to AD-associated proteins.

SIRT3 Expression Predicts Overall Survival and Neoadjuvant Chemosensitivity in Triple-Negative Breast Cancer

Background

The Sirtuin (SIRT) family consists of seven evolutionary conserved NAD-dependent deacetylases that play important roles in various cancers, including breast cancer (BC). SIRTs expression has been reported to have prognostic value in BC, but these studies used limited sample size and yielded inconsistent conclusions. This study evaluated the association of SIRT3 and other SIRT family members with survival and neoadjuvant chemotherapy outcomes.

Methods

BC patients' data was obtained from the TCGA-BRCA, METABRIC and GEO databases, comprising 4336 samples. SIRTs expression and overall survival (OS) were analyzed using Kaplan-Meier analysis and Cox proportional hazards regression. SIRT3 expression levels were compared between pathologic complete response (pCR) and non-pCR groups after neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). Protein-protein interaction networks were constructed using the STRING database. Gene set enrichment analysis (GSEA) was performed to explore potential functions of SIRT3.

Results

Through systematic analysis of SIRTs expression and OS of BC using three independent cohorts: TCGA-BRCA, METABRIC and GSE16446, we found that high SIRT3 expression was significantly associated with worse OS in TNBC in the TCGA-BRCA cohort, which was validated in the METABRIC and GSE16446 cohorts. SIRT3 expression was correlated with BC subtypes and American Joint Committee on Cancer (AJCC) T stage, but not with age-at-diagnosis, race, or tumor stage. Moreover, TNBC patients with higher SIRT3 expression had lower pCR rates after neoadjuvant chemotherapy (p = 6.40e-03) and SIRT3 expression was significantly lower in the pCR group than in the non-pCR group in TNBC (p = 4.2e-03). GSEA indicated that SIRT3 was involved in drug-related pathways such as oxidative phosphorylation, metabolism of xenobiotics by cytochrome P450, and drug metabolism.

Conclusion

Our study suggests that SIRT3 is a potential biomarker for both OS and neoadjuvant chemosensitivity in TNBC. It may also assist in selecting suitable candidates and treatment options for TNBC patients.

Endocrine islet β-cell subtypes with differential function are derived from biochemically distinct embryonic endocrine islet progenitors that are regulated by maternal nutrients

Endocrine islet b cells comprise heterogenous cell subsets. Yet when/how these subsets are produced and how stable they are remain unknown. Addressing these questions is important for preventing/curing diabetes, because lower numbers of b cells with better secretory function is a high risk of this disease. Using combinatorial cell lineage tracing, scRNA-seq, and DNA methylation analysis, we show here that embryonic islet progenitors with distinct gene expression and DNA methylation produce b-cell subtypes of different function and viability in adult mice. The subtype with better function is enriched for genes involved in vesicular production/trafficking, stress response, and Ca2+-secretion coupling, which further correspond to differential DNA methylation in putative enhancers of these genes. Maternal overnutrition, a major diabetes risk factor, reduces the proportion of endocrine progenitors of the b-cell subtype with better-function via deregulating DNA methyl transferase 3a. Intriguingly, the gene signature that defines mouse b-cell subtypes can reliably divide human cells into two sub-populations while the proportion of b cells with better-function is reduced in diabetic donors. The implication of these results is that modulating DNA methylation in islet progenitors using maternal food supplements can be explored to improve b-cell function in the prevention and therapy of diabetes.

O-GlcNAcylation of FOXK1 orchestrates the E2F pathway and promotes oncogenesis

Gene transcription is a highly regulated process, and deregulation of transcription factors activity underlies numerous pathologies including cancer. Albeit near four decades of studies have established that the E2F pathway is a core transcriptional network that govern cell division in multi-cellular organisms1,2, the molecular mechanisms that underlie the functions of E2F transcription factors remain incompletely understood. FOXK1 and FOXK2 transcription factors have recently emerged as important regulators of cell metabolism, autophagy and cell differentiation3-6. While both FOXK1 and FOXK2 interact with the histone H2AK119ub deubiquitinase BAP1 and possess many overlapping functions in normal biology, their specific functions as well as deregulation of their transcriptional activity in cancer is less clear and sometimes contradictory7-13. Here, we show that elevated expression of FOXK1, but not FOXK2, in primary normal cells promotes transcription of E2F target genes associated with increased proliferation and delayed entry into cellular senescence. FOXK1 expressing cells are highly prone to cellular transformation revealing important oncogenic properties of FOXK1 in tumor initiation. High expression of FOXK1 in patient tumors is also highly correlated with E2F gene expression. Mechanistically, we demonstrate that FOXK1, but not FOXK2, is specifically modified by O-GlcNAcylation. FOXK1 O-GlcNAcylation is modulated during the cell cycle with the highest levels occurring during the time of E2F pathway activation at G1/S. Moreover, loss of FOXK1 O-GlcNAcylation impairs FOXK1 ability to promote cell proliferation, cellular transformation and tumor growth. Mechanistically, expression of FOXK1 O-GlcNAcylation-defective mutants results in reduced recruitment of BAP1 to gene regulatory regions. This event is associated with a concomitant increase in the levels of histone H2AK119ub and a decrease in the levels of H3K4me1, resulting in a transcriptional repressive chromatin environment. Our results define an essential role of O-GlcNAcylation in modulating the functions of FOXK1 in controlling the cell cycle of normal and cancer cells through orchestration of the E2F pathway.

Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging

Human life expectancy is constantly increasing and aging has become a major risk factor for many diseases, although the underlying gene regulatory mechanisms are still unclear. Using transcriptomic and chromosomal conformation capture (Hi-C) data from human skin fibroblasts from individuals across different age groups, we identified a tight coupling between the changes in co-regulation and co-localization of genes. We obtained transcription factors, cofactors, and chromatin regulators that could drive the cellular aging process by developing a time-course prize-collecting Steiner tree algorithm. In particular, by combining RNA-Seq data from different age groups and protein-protein interaction data we determined the key transcription regulators and gene regulatory changes at different life stage transitions. We then mapped these transcription regulators to the 3D reorganization of chromatin in young and old skin fibroblasts. Collectively, we identified key transcription regulators whose target genes are spatially rearranged and correlate with changes in their expression, thereby providing potential targets for reverting cellular aging.

SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data

Multi-omic single-cell technologies, which simultaneously measure the transcriptional and epigenomic state of the same cell, enable understanding epigenetic mechanisms of gene regulation. However, noisy and sparse data pose fundamental statistical challenges to extract biological knowledge from complex datasets. SHARE-Topic, a Bayesian generative model of multi-omic single cell data using topic models, aims to address these challenges. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the data complexity. Tested on data from different technological platforms, SHARE-Topic provides low dimensional representations recapitulating known biology and defines associations between genes and distal regulators in individual cells.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. By analyzing publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones.

Statement of Significance

Tumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.

A spatiotemporal molecular atlas of the ovulating mouse ovary

Ovulation is essential for reproductive success, yet the underlying cellular and molecular mechanisms are far from clear. Here, we applied high-resolution spatiotemporal transcriptomics to map out cell type- and ovulation stage-specific molecular programs as function of time during follicle maturation and ovulation in mice. Our analysis revealed dynamic molecular transitions within granulosa cell types that occur in tight coordination with mesenchymal cell proliferation. We identified molecular markers for the emerging cumulus cell fate during the preantral-to-antral transition. We describe transcriptional programs that respond rapidly to ovulation stimulation and those associated with follicle rupture, highlighting the prominent roles of apoptotic and metabolic pathways during the final stages of follicle maturation. We further report stage-specific oocyte-cumulus cell interactions and diverging molecular differentiation in follicles approaching ovulation. Collectively, this study provides insights into the cellular and molecular processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.

Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

Exercise training has tremendous systemic tissue-specific health benefits, but the molecular adaptations to long-term exercise training are not completely understood. We investigated the skeletal muscle proteome of highly endurance-trained, strength-trained, and untrained individuals and performed exercise- and sex-specific analyses. Of the 6,000+ proteins identified, >650 were differentially expressed in endurance-trained individuals compared with controls. Strikingly, 92% of the shared proteins with higher expression in both the male and female endurance groups were known mitochondrial. In contrast to the findings in endurance-trained individuals, minimal differences were found in strength-trained individuals and between females and males. Lastly, a co-expression network and comparative literature analysis revealed key proteins and pathways related to the health benefits of exercise, which were primarily related to differences in mitochondrial proteins. This network is available as an interactive database resource where investigators can correlate clinical data with global gene and protein expression data for hypothesis generation.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project

BACKGROUND

Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes.

METHODS

We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging.

FINDINGS

In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations.

CONCLUSIONS

The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes.

FUNDING

E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

Joint clinical and molecular subtyping of COPD with variational autoencoders

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, resulting in classifications that do not fully capture the disease's complexity. Here, we bridge this gap by introducing a subtyping pipeline that integrates clinical and gene expression data with variational autoencoders. We apply this methodology to the COPDGene study, a large study of current and former smoking individuals with and without COPD. Our approach generates a set of vector embeddings, called Personalized Integrated Profiles (PIPs), that recapitulate the joint clinical and molecular state of the subjects in the study. Prediction experiments show that the PIPs have a predictive accuracy comparable to or better than other embedding approaches. Using trajectory learning approaches, we analyze the main trajectories of variation in the PIP space and identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and disease outcomes. Notably, these subtypes are more robust to data resampling compared to those identified using traditional clustering approaches. Overall, our findings provide new avenues to establish fine-grained associations between the clinical characteristics, molecular processes, and disease outcomes of COPD.

EndoQuad: a comprehensive genome-wide experimentally validated endogenous G-quadruplex database

G-quadruplexes (G4s) are non-canonical four-stranded structures and are emerging as novel genetic regulatory elements. However, a comprehensive genomic annotation of endogenous G4s (eG4s) and systematic characterization of their regulatory network are still lacking, posing major challenges for eG4 research. Here, we present EndoQuad (https://EndoQuad.chenzxlab.cn/) to address these pressing issues by integrating high-throughput experimental data. First, based on high-quality genome-wide eG4s mapping datasets (human: 1181; mouse: 24; chicken: 2) generated by G4 ChIP-seq/CUT&Tag, we generate a reference set of genome-wide eG4s. Our multi-omics analyses show that most eG4s are identified in one or a few cell types. The eG4s with higher occurrences across samples are more structurally stable, evolutionarily conserved, enriched in promoter regions, mark highly expressed genes and associate with complex regulatory programs, demonstrating higher confidence level for further experiments. Finally, we integrate millions of functional genomic variants and prioritize eG4s with regulatory functions in disease and cancer contexts. These efforts have culminated in the comprehensive and interactive database of experimentally validated DNA eG4s. As such, EndoQuad enables users to easily access, download and repurpose these data for their own research. EndoQuad will become a one-stop resource for eG4 research and lay the foundation for future functional studies.

WikiPathways 2024: next generation pathway database

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.

scMAE: a masked autoencoder for single-cell RNA-seq clustering

MOTIVATION

Single-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq data analysis, facilitating the identification of cell types and exploration of cellular heterogeneity. Despite the recent development of many deep learning-based single-cell clustering methods, few have effectively exploited the correlations among genes, resulting in suboptimal clustering outcomes.

RESULTS

Here, we propose a novel masked autoencoder-based method, scMAE, for cell clustering. scMAE perturbs gene expression and employs a masked autoencoder to reconstruct the original data, learning robust and informative cell representations. The masked autoencoder introduces a masking predictor, which captures relationships among genes by predicting whether gene expression values are masked. By integrating this masking mechanism, scMAE effectively captures latent structures and dependencies in the data, enhancing clustering performance. We conducted extensive comparative experiments using various clustering evaluation metrics on 15 scRNA-seq datasets from different sequencing platforms. Experimental results indicate that scMAE outperforms other state-of-the-art methods on these datasets. In addition, scMAE accurately identifies rare cell types, which are challenging to detect due to their low abundance. Furthermore, biological analyses confirm the biological significance of the identified cell subpopulations.

AVAILABILITY AND IMPLEMENTATION

The source code of scMAE is available at: https://zenodo.org/records/10465991.

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor-node-metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.

PGF2α signaling drives fibrotic remodeling and fibroblast population dynamics in mice

Idiopathic pulmonary fibrosis (IPF) is a chronic parenchymal lung disease characterized by repetitive alveolar cell injury, myofibroblast proliferation, and excessive extracellular matrix deposition for which unmet need persists for effective therapeutics. The bioactive eicosanoid, prostaglandin F2α, and its cognate receptor FPr (Ptgfr) are implicated as a TGF-β1-independent signaling hub for IPF. To assess this, we leveraged our published murine PF model (IER-SftpcI73T) expressing a disease-associated missense mutation in the surfactant protein C (Sftpc) gene. Tamoxifen-treated IER-SftpcI73T mice developed an early multiphasic alveolitis and transition to spontaneous fibrotic remodeling by 28 days. IER-SftpcI73T mice crossed to a Ptgfr-null (FPr-/-) line showed attenuated weight loss and gene dosage-dependent rescue of mortality compared with FPr+/+ cohorts. IER-SftpcI73T/FPr-/- mice also showed reductions in multiple fibrotic endpoints for which administration of nintedanib was not additive. Single-cell RNA-Seq, pseudotime analysis, and in vitro assays demonstrated Ptgfr expression predominantly within adventitial fibroblasts, which were reprogrammed to an "inflammatory/transitional" cell state in a PGF2α /FPr-dependent manner. Collectively, the findings provide evidence for a role for PGF2α signaling in IPF, mechanistically identify a susceptible fibroblast subpopulation, and establish a benchmark effect size for disruption of this pathway in mitigating fibrotic lung remodeling.

Testosterone affects type I/type II interferon response of neutrophils during hepatic amebiasis

Differences in immune response between men and women may influence the outcome of infectious diseases. Intestinal infection with Entamoeba histolytica leads to hepatic amebiasis, which is more common in males. Previously, we reported that innate immune cells contribute to liver damage in males in the murine model for hepatic amebiasis. Here, we focused on the influences of sex and androgens on neutrophils in particular. Infection associated with neutrophil accumulation in the liver was higher in male than in female mice and further increased after testosterone treatment in both sexes. Compared with female neutrophils, male neutrophils exhibit a more immature and less activated status, as evidenced by a lower proinflammatory N1-like phenotype and deconvolution, decreased gene expression of type I and type II interferon stimulated genes (ISGs) as well as downregulation of signaling pathways related to neutrophil activation. Neutrophils from females showed higher protein expression of the type I ISG viperin/RSAD2 during infection, which decreased by testosterone substitution. Moreover, ex vivo stimulation of human neutrophils revealed lower production of RSAD2 in neutrophils from men compared with women. These findings indicate that sex-specific effects on neutrophil physiology associated with maturation and type I IFN responsiveness might be important in the outcome of hepatic amebiasis.

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

Research on cell-cell communication (CCC) is crucial for understanding biology and diseases. Many existing CCC inference tools neglect potential confounders, such as batch and demographic variables, when analyzing multi-sample, multi-condition scRNA-seq datasets. To address this significant gap, we introduce STACCato, a Supervised Tensor Analysis tool for studying Cell-cell Communication, that identifies CCC events and estimates the effects of biological conditions (e.g., disease status, tissue types) on such events, while adjusting for potential confounders. Application of STACCato to both simulated data and real scRNA-seq data of lupus and autism studies demonstrate that incorporating sample-level variables into CCC inference consistently provides more accurate estimations of disease effects and cell type activity patterns than existing methods that ignore sample-level variables. A computational tool implementing the STACCato framework is available on GitHub.

Vinorelbine Alters lncRNA Expression in Association with EGFR Mutational Status and Potentiates Tumor Progression Depending on NSCLC Cell Lines' Genetic Profile

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most common type. In addition, NSCLC has a high mortality rate and an overall adverse patient outcome. Although significant improvements have been made in therapeutic options, effectiveness is still limited in late stages, so the need for a better understanding of the genomics events underlying the current therapies is crucial to aid future drug development. Vinorelbine (VRB) is an anti-mitotic chemotherapy drug (third-generation vinca alkaloid) used to treat several malignancies, including NSCLC. However, despite its widespread clinical use, very little is known about VRB-associated genomic alterations in different subtypes of NSCLC. This article is an in vitro investigation of the cytotoxic effects of VRB on three different types of NSCLC cell lines, A549, Calu-6, and H1792, with a closer focus on post-treatment genetic alterations. Based on the obtained results, VRB cytotoxicity produces modifications on a cellular level, altering biological processes such as apoptosis, autophagy, cellular motility, cellular adhesion, and cell cycle, but also at a genomic level, dysregulating the expression of some coding genes, such as EGFR, and long non-coding RNAs (lncRNAs), including CCAT1, CCAT2, GAS5, MALAT1, NEAT1, NORAD, XIST, and HOTAIR, that are implicated in the mitogen-activated protein kinase (MAPK) signaling pathway. Therefore, although extensive validation is required, these results pave the way towards a better understanding of the cellular and genomic alterations underlying the cytotoxicity of VRB.

Single-cell transcriptomics stratifies organoid models of metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing cause of morbidity with limited treatment options. Thus, accurate in vitro systems to test new therapies are indispensable. While recently, human liver organoid models have emerged to assess steatotic liver disease, a systematic evaluation of their translational potential is still missing. Here, we evaluated human liver organoid models of MASLD, comparatively testing disease induction in three conditions: oleic acid, palmitic acid, and TGF-β1. Through single-cell analyses, we find that all three models induce inflammatory signatures, but only TGF-β1 promotes collagen production, fibrosis, and hepatic stellate cell expansion. In striking contrast, oleic acid ameliorates fibrotic signatures and reduces the hepatic stellate cell population. Linking data from each model to gene expression signatures associated with MASLD disease progression further demonstrates that palmitic acid and TGF-β1 more robustly model inflammation and fibrosis. Our findings highlight the importance of stratifying MASLD organoid models by signatures of clinical disease progression, provide a single-cell reference to benchmark future organoid injury models, and allow us to study evolving steatohepatitis, fibrosis, and HSC susceptibility to injury in a dynamic, multi-lineage human in vitro system.

Knockouts of TNFRSF1A and TNFRSF1B Genes in K562 Cell Line Lead to Diverse Long-Lasting Responses to TNF-α

This research delves into the intricate landscape of tumor necrosis factor-alpha (TNF-α) signaling, a multi-functional cytokine known for its diverse cellular effects. Specifically, we investigate the roles of two TNF receptors, TNFR1 and TNFR2, in mediating TNF-α-induced transcriptional responses. Using human K562 cell lines with TNFR1 and TNFR2 knockouts, we explore changes in gene expression patterns following TNF-α stimulation. Our findings reveal distinct transcriptional profiles in TNFR1 and TNFR2 knockout cells, shedding light on the unique contributions of these receptors to TNF-α signaling. Notably, several key pathways associated with inflammation, apoptosis, and cell proliferation exhibit altered regulation in the absence of TNFR1 or TNFR2. This study provides valuable insights into the intricate mechanisms governing TNF-α signaling and its diverse cellular effects, with potential implications for targeted therapeutic strategies.

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with single-cell RNA sequencing and induced a multilineage circuit of fibrogenic cell states in hPCLS. We showed that these cell states were highly similar to the in vivo cell circuit in a multicohort lung cell atlas from patients with pulmonary fibrosis. Using micro-CT-staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state, and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the hPCLS model provided evidence that the basaloid cell state was derived from alveolar type 2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in hPCLS, and antifibrotic drug treatments showed highly cell type-specific effects. Our work provides an experimental framework for perturbational single-cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration, and pathology. We further demonstrate that hPCLS offer an avenue for scalable, high-resolution drug testing to accelerate antifibrotic drug development and translation.

CD8+ lymphocytes are critical for early control of tuberculosis in macaques

The functional role of CD8+ lymphocytes in tuberculosis remains poorly understood. We depleted innate and/or adaptive CD8+ lymphocytes in macaques and showed that loss of all CD8α+ cells (using anti-CD8α antibody) significantly impaired early control of Mycobacterium tuberculosis (Mtb) infection, leading to increased granulomas, lung inflammation, and bacterial burden. Analysis of barcoded Mtb from infected macaques demonstrated that depletion of all CD8+ lymphocytes allowed increased establishment of Mtb in lungs and dissemination within lungs and to lymph nodes, while depletion of only adaptive CD8+ T cells (with anti-CD8β antibody) worsened bacterial control in lymph nodes. Flow cytometry and single-cell RNA sequencing revealed polyfunctional cytotoxic CD8+ lymphocytes in control granulomas, while CD8-depleted animals were unexpectedly enriched in CD4 and γδ T cells adopting incomplete cytotoxic signatures. Ligand-receptor analyses identified IL-15 signaling in granulomas as a driver of cytotoxic T cells. These data support that CD8+ lymphocytes are required for early protection against Mtb and suggest polyfunctional cytotoxic responses as a vaccine target.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Global and cell type-specific immunological hallmarks of severe dengue progression identified via a systems immunology approach

Severe dengue (SD) is a major cause of morbidity and mortality. To define dengue virus (DENV) target cells and immunological hallmarks of SD progression in children's blood, we integrated two single-cell approaches capturing cellular and viral elements: virus-inclusive single-cell RNA sequencing (viscRNA-Seq 2) and targeted proteomics with secretome analysis and functional assays. Beyond myeloid cells, in natural infection, B cells harbor replicating DENV capable of infecting permissive cells. Alterations in cell type abundance, gene and protein expression and secretion as well as cell-cell communications point towards increased immune cell migration and inflammation in SD progressors. Concurrently, antigen-presenting cells from SD progressors demonstrate intact uptake yet impaired interferon response and antigen processing and presentation signatures, which are partly modulated by DENV. Increased activation, regulation and exhaustion of effector responses and expansion of HLA-DR-expressing adaptive-like NK cells also characterize SD progressors. These findings reveal DENV target cells in human blood and provide insight into SD pathogenesis beyond antibody-mediated enhancement.

Conserved and divergent gene regulatory programs of the mammalian neocortex

Divergence of cis-regulatory elements drives species-specific traits1, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains unclear. Here we investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset and mouse using single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome and chromosomal conformation profiles from a total of over 200,000 cells. From these data, we show evidence that divergence of transcription factor expression corresponds to species-specific epigenome landscapes. We find that conserved and divergent gene regulatory features are reflected in the evolution of the three-dimensional genome. Transposable elements contribute to nearly 80% of the human-specific candidate cis-regulatory elements in cortical cells. Through machine learning, we develop sequence-based predictors of candidate cis-regulatory elements in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Finally, we show that epigenetic conservation combined with sequence similarity helps to uncover functional cis-regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.