Functional characterization of somatic mutations in cancer using network-based inference of protein activity

On the identification of differentially-active transcription factors from ATAC-seq data

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (chromVAR-limma with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs - in our case NFkB - at the protein level.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Noninvasive Detection of Neuroendocrine Prostate Cancer through Targeted Cell-free DNA Methylation

Castration-resistant prostate cancer (CRPC) is a heterogeneous disease associated with phenotypic subtypes that drive therapy response and outcome differences. Histologic transformation to castration-resistant neuroendocrine prostate cancer (CRPC-NE) is associated with distinct epigenetic alterations, including changes in DNA methylation. The current diagnosis of CRPC-NE is challenging and relies on metastatic biopsy. We developed a targeted DNA methylation assay to detect CRPC-NE using plasma cell-free DNA (cfDNA). The assay quantifies tumor content and provides a phenotype evidence score that captures diverse CRPC phenotypes, leveraging regions to inform transcriptional state. We tested the design in independent clinical cohorts (n = 222 plasma samples) and qualified it achieving an AUC > 0.93 for detecting pathology-confirmed CRPC-NE (n = 136). Methylation-defined cfDNA tumor content was associated with clinical outcomes in two prospective phase II clinical trials geared towards aggressive variant CRPC and CRPC-NE. These data support the application of targeted DNA methylation for CRPC-NE detection and patient stratification.

SIGNIFICANCE

Neuroendocrine prostate cancer is an aggressive subtype of treatment-resistant prostate cancer. Early detection is important, but the diagnosis currently relies on metastatic biopsy. We describe the development and validation of a plasma cell-free DNA targeted methylation panel that can quantify tumor fraction and identify patients with neuroendocrine prostate cancer noninvasively. This article is featured in Selected Articles from This Issue, p. 384.

Surface CD52, CD84, and PTGER2 mark mature PMN-MDSCs from cancer patients and G-CSF-treated donors

Precise molecular characterization of circulating polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) is hampered by their mixed composition of mature and immature cells and lack of specific markers. Here, we focus on mature CD66b+CD10+CD16+CD11b+ PMN-MDSCs (mPMN-MDSCs) from either cancer patients or healthy donors receiving G-CSF for stem cell mobilization (GDs). By RNA sequencing (RNA-seq) experiments, we report the identification of a distinct gene signature shared by the different mPMN-MDSC populations under investigation, also validated in mPMN-MDSCs from GDs and tumor-associated neutrophils (TANs) by single-cell RNA-seq (scRNA-seq) experiments. Analysis of such a gene signature uncovers a specific transcriptional program associated with mPMN-MDSC differentiation and allows us to identify that, in patients with either solid or hematologic tumors and in GDs, CD52, CD84, and prostaglandin E receptor 2 (PTGER2) represent potential mPMN-MDSC-associated markers. Altogether, our findings indicate that mature PMN-MDSCs distinctively undergo specific reprogramming during differentiation and lay the groundwork for selective immunomonitoring, and eventually targeting, of mature PMN-MDSCs.

Cellular hierarchy insights reveal leukemic stem-like cells and early death risk in acute promyelocytic leukemia

Acute promyelocytic leukemia (APL) represents a paradigm for targeted differentiation therapy, with a minority of patients experiencing treatment failure and even early death. We here report a comprehensive single-cell analysis of 16 APL patients, uncovering cellular compositions and their impact on all-trans retinoic acid (ATRA) response in vivo and early death. We unveil a cellular differentiation hierarchy within APL blasts, rooted in leukemic stem-like cells. The oncogenic PML/RARα fusion protein exerts branch-specific regulation in the APL trajectory, including stem-like cells. APL cohort analysis establishes an association of leukemic stemness with elevated white blood cell counts and FLT3-ITD mutations. Furthermore, we construct an APL-specific stemness score, which proves effective in assessing early death risk. Finally, we show that ATRA induces differentiation of primitive blasts and patients with early death exhibit distinct stemness-associated transcriptional programs. Our work provides a thorough survey of APL cellular hierarchies, offering insights into cellular dynamics during targeted therapy.

Evaluation of single-sample network inference methods for precision oncology

A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method.

SMARCAL1 is a dual regulator of innate immune signaling and PD-L1 expression that promotes tumor immune evasion

Genomic instability can trigger cancer-intrinsic innate immune responses that promote tumor rejection. However, cancer cells often evade these responses by overexpressing immune checkpoint regulators, such as PD-L1. Here, we identify the SNF2-family DNA translocase SMARCAL1 as a factor that favors tumor immune evasion by a dual mechanism involving both the suppression of innate immune signaling and the induction of PD-L1-mediated immune checkpoint responses. Mechanistically, SMARCAL1 limits endogenous DNA damage, thereby suppressing cGAS-STING-dependent signaling during cancer cell growth. Simultaneously, it cooperates with the AP-1 family member JUN to maintain chromatin accessibility at a PD-L1 transcriptional regulatory element, thereby promoting PD-L1 expression in cancer cells. SMARCAL1 loss hinders the ability of tumor cells to induce PD-L1 in response to genomic instability, enhances anti-tumor immune responses and sensitizes tumors to immune checkpoint blockade in a mouse melanoma model. Collectively, these studies uncover SMARCAL1 as a promising target for cancer immunotherapy.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods

Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.

Results

Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.

Discussion

The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Amphiregulin from regulatory T cells promotes liver fibrosis and insulin resistance in non-alcoholic steatohepatitis

Production of amphiregulin (Areg) by regulatory T (Treg) cells promotes repair after acute tissue injury. Here, we examined the function of Treg cells in non-alcoholic steatohepatitis (NASH), a setting of chronic liver injury. Areg-producing Treg cells were enriched in the livers of mice and humans with NASH. Deletion of Areg in Treg cells, but not in myeloid cells, reduced NASH-induced liver fibrosis. Chronic liver damage induced transcriptional changes associated with Treg cell activation. Mechanistically, Treg cell-derived Areg activated pro-fibrotic transcriptional programs in hepatic stellate cells via epidermal growth factor receptor (EGFR) signaling. Deletion of Areg in Treg cells protected mice from NASH-dependent glucose intolerance, which also was dependent on EGFR signaling on hepatic stellate cells. Areg from Treg cells promoted hepatocyte gluconeogenesis through hepatocyte detection of hepatic stellate cell-derived interleukin-6. Our findings reveal a maladaptive role for Treg cell-mediated tissue repair functions in chronic liver disease and link liver damage to NASH-dependent glucose intolerance.

Identification of a gene network driving the attenuated response to lipopolysaccharide of monocytes from hypertensive coronary artery disease patients

Introduction

The impact of cardiovascular disease (CVD) risk factors, encompassing various biological determinants and unhealthy lifestyles, on the functional dynamics of circulating monocytes-a pivotal cell type in CVD pathophysiology remains elusive. In this study, we aimed to elucidate the influence of CVD risk factors on monocyte transcriptional responses to an infectious stimulus.

Methods

We conducted a comparative analysis of monocyte gene expression profiles from the CTMM - CIRCULATING CELLS Cohort of coronary artery disease (CAD) patients, at baseline and after lipopolysaccharide (LPS) stimulation. Gene co-expression analysis was used to identify gene modules and their correlations with CVD risk factors, while pivotal transcription factors controlling the hub genes in these modules were identified by regulatory network analyses. The identified gene module was subjected to a drug repurposing screen, utilizing the LINCS L1000 database.

Results

Monocyte responsiveness to LPS showed a highly significant, negative correlation with blood pressure levels (ρ< -0.4; P<10-80). We identified a ZNF12/ZBTB43-driven gene module closely linked to diastolic blood pressure, suggesting that monocyte responses to infectious stimuli, such as LPS, are attenuated in CAD patients with elevated diastolic blood pressure. This attenuation appears associated with a dampening of the LPS-induced suppression of oxidative phosphorylation. Finally, we identified the serine-threonine inhibitor MW-STK33-97 as a drug candidate capable of reversing this aberrant LPS response.

Conclusions

Monocyte responses to infectious stimuli may be hampered in CAD patients with high diastolic blood pressure and this attenuated inflammatory response may be reversed by the serine-threonine inhibitor MW-STK33-97. Whether the identified gene module is a mere indicator of, or causal factor in diastolic blood pressure and the associated dampened LPS responses remains to be determined.

The 29th Annual Prostate Cancer Foundation Scientific Retreat Report

BACKGROUND

The 29th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held from October 27 to 29, 2022, at the Omni La Costa Resort in Carlsbad, CA. This was the first-ever hybrid PCF Retreat.

METHODS

The Annual PCF Scientific Retreat is a prominent international scientific gathering centered on groundbreaking, unpublished, and influential studies in basic, translational, and clinical prostate cancer research. It also covers research from related fields with a strong potential for influencing prostate cancer research and patient care.

RESULTS

Key areas of research that were focused on at the 2022 PCF Retreat included: (i) the contributions of molecular and genomic factors to prostate cancer disparities; (ii) novel clinical trial updates; (iii) lessons from primary prostate cancer; (iv) lessons from single-cell studies; (v) genetic, epigenetic, epitranscriptomic and posttranslational mechanisms and clinical heterogeneity in prostate cancer; (vi) biology of neuroendocrine and lineage-plastic prostate cancer; (vii) next generation prostate cancer theranostics and combination therapies; (viii) the biology and therapeutic potential of targeting phosphoinositide 3-kinases pathways; (ix) combining immunomodulatory treatments for prostate cancer; (x) novel gamma delta (γδ) T-cell therapy platforms for oncology; and (xi) lessons from other cancers.

CONCLUSIONS

This article provides a summary of the presentations from the 2022 PCF Scientific Retreat. By disseminating this knowledge, we hope to enhance our understanding of the present research landscape and guide future strides in both prostate cancer research and patient care.

Regulation of respiratory complex I assembly by FMN cofactor targeting

Respiratory complex I plays a crucial role in the mitochondrial electron transport chain and shows promise as a therapeutic target for various human diseases. While most studies focus on inhibiting complex I at the Q-site, little is known about inhibitors targeting other sites within the complex. In this study, we demonstrate that diphenyleneiodonium (DPI), a N-site inhibitor, uniquely affects the stability of complex I by reacting with its flavin cofactor FMN. Treatment with DPI blocks the final stage of complex I assembly, leading to the complete and reversible degradation of complex I in different cellular models. Growing cells in medium lacking the FMN precursor riboflavin or knocking out the mitochondrial flavin carrier gene SLC25A32 results in a similar complex I degradation. Overall, our findings establish a direct connection between mitochondrial flavin homeostasis and complex I stability and assembly, paving the way for novel pharmacological strategies to regulate respiratory complex I.

Transcriptional and phenotypic heterogeneity underpinning venetoclax resistance in AML

The venetoclax BCL2 inhibitor in combination with hypomethylating agents represents a cornerstone of induction therapy for older AML patients, unfit for intensive chemotherapy. Like other targeted therapies, venetoclax-based therapies suffer from innate and acquired resistance. While several mechanisms of resistance have been identified, the heterogeneity of resistance mechanism across patient populations is poorly understood. Here we utilized integrative analysis of transcriptomic and ex-vivo drug response data in AML patients to identify four transcriptionally distinct VEN resistant clusters (VR_C1-4), with distinct phenotypic, genetic and drug response patterns. VR_C1 was characterized by enrichment for differentiated monocytic- and cDC-like blasts, transcriptional activation of PI3K-AKT-mTOR signaling axis, and energy metabolism pathways. They showed sensitivity to mTOR and CDK inhibition. VR_C2 was enriched for NRAS mutations and associated with distinctive transcriptional suppression of HOX expression. VR_C3 was characterized by enrichment for TP53 mutations and higher infiltration by cytotoxic T cells. This cluster showed transcriptional expression of erythroid markers, suggesting tumor cells mimicking erythroid differentiation, activation of JAK-STAT signaling, and sensitivity to JAK inhibition, which in a subset of cases synergized with venetoclax. VR_C4 shared transcriptional similarities with venetoclax-sensitive patients, with modest over-expression of interferon signaling. They were also characterized by high rates of DNMT3A mutations. Finally, we projected venetoclax-resistance states onto single cells profiled from a patient who relapsed under venetoclax therapy capturing multiple resistance states in the tumor and shifts in their abundance under venetoclax selection, suggesting that single tumors may consist of cells mimicking multiple VR_Cs contributing to intra-tumor heterogeneity. Taken together, our results provide a strategy to evaluate inter- and intra-tumor heterogeneity of venetoclax resistance mechanisms and provide insights into approaches to navigate further management of patients who failed therapy with BCL2 inhibitors.

Lack of imbalance between the master regulators TTF1/NKX2-1 and ΔNp63/p40 implies adverse prognosis in non-small cell lung cancer

The transcription factors TTF1/NKX2-1 and ΔNp63/p40 are the counterposed molecular markers associated with the main Non-Small Cell Lung Cancer subtypes: TTF1 for adenocarcinoma, p40 for squamous cell carcinoma. Although they generally display a mutually exclusive expression, some exceptions exist simultaneously lacking or (very rarely) expressing both markers, either pattern being associated to poor prognosis. Hence, we quantitatively analyzed the relationship between their coordinated activity and prognosis. By analyzing the respective downstream transcriptional programs of the two genes, we defined a simple quantitative index summarizing the amount of mutual exclusivity between their activities, called Mean Absolute Activity (MAA). Systematic analysis of the MAA index in a dataset of 1018 NSCLC samples replicated on a validation dataset of 275 showed that the loss of imbalance between TTF-1 and p40 corresponds to a steady, progressive reduction in both overall and recurrence-free survival. Coherently, samples correspondent to more balanced activities were enriched for pathways related to increased malignancy and invasiveness. Importantly, multivariate analysis showed that the prognostic significance of the proposed index MAA is independent of other clinical variables including stage, sex, age and smoke exposure. These results hold irrespectively of tumor morphology across NSCLC subtypes, providing a unifying description of different expression patterns.

AutoTransOP: translating omics signatures without orthologue requirements using deep learning

The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.

Representation and quantification of module activity from omics data with rROMA

The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.

Cross-species comparative hippocampal transcriptomics in Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial pathology, with most cases having a sporadic origin. Recently, knock-in (KI) mouse models, such as the novel humanized amyloid-β (hAβ)-KI, have been developed to better resemble sporadic human AD.

METHODS

Here, we compared hippocampal publicly available transcriptomic profiles of transgenic (5xFAD and APP/PS1) and KI (hAβ-KI) mouse models with early- (EOAD) and late- (LOAD) onset AD patients.

RESULTS

The three mouse models presented more Gene Ontology biological processes terms and enriched signaling pathways in common with LOAD than with EOAD individuals. Experimental validation of consistently dysregulated genes revealed five altered in mice (SLC11A1, S100A6, CD14, CD33, and C1QB) and three in humans (S100A6, SLC11A1, and KCNK). Finally, we identified 17 transcription factors potentially acting as master regulators of AD.

CONCLUSION

Our cross-species analyses revealed that the three mouse models presented a remarkable similarity to LOAD, with the hAβ-KI being the more specific one.

Immunomodulatory response to neoadjuvant nivolumab in non-metastatic clear cell renal cell carcinoma

Novel perioperative strategies are needed to reduce recurrence rates in patients undergoing nephrectomy for high-risk, non-metastatic clear cell renal cell carcinoma (ccRCC). We conducted a prospective, phase I trial of neoadjuvant nivolumab prior to nephrectomy in 15 evaluable patients with non-metastatic ccRCC. We leveraged tissue from that cohort to elucidate the effects of PD-1 inhibition on immune cell populations in ccRCC and correlate the evolving immune milieu with anti-PD-1 response. We found that nivolumab durably induces a pro-inflammatory state within the primary tumor, and baseline immune infiltration within the primary tumor correlates with nivolumab responsiveness. Nivolumab increases CTLA-4 expression in the primary tumor, and subsequent nephrectomy increases circulating concentrations of sPD-L1, sPD-L3 (sB7-H3), and s4-1BB. These findings form the basis to consider neoadjuvant immune checkpoint inhibition (ICI) for high-risk ccRCC while the tumor remains in situ and provide the rationale for perioperative strategies of novel ICI combinations.

Single-cell sequencing analysis within biologically relevant dimensions

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics

The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.

Single-cell RNA sequencing reveals characteristics of myeloid cells in post-acute sequelae of SARS-CoV-2 patients with persistent respiratory symptoms

Background

Although our understanding of the immunopathology and subsequent risk and severity of COVID-19 disease is evolving, a detailed account of immune responses that contribute to the long-term consequences of pulmonary complications in COVID-19 infection remains unclear. Few studies have detailed the immune and cytokine profiles associated with post-acute sequelae of SARS-CoV-2 infection (PASC) with persistent pulmonary symptoms. The dysregulation of the immune system that drives pulmonary sequelae in COVID-19 survivors and PASC sufferers remains largely unknown.

Results

To characterize the immunological features of pulmonary PASC (PPASC), we performed droplet-based single-cell RNA sequencing (scRNA-seq) to study the transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) from a participant naïve to SARS-CoV-2 (Control) (n=1) and infected with SARS-CoV-2 with chronic pulmonary symptoms (PPASC) (n=2). After integrating scRNA-seq data with a naïve participant from a published dataset, 11 distinct cell populations were identified based on the expression of canonical markers. The proportion of myeloid-lineage cells ([MLCs]; CD14+/CD16+monocytes, and dendritic cells) was increased in PPASC (n=2) compared to controls (n=2). MLCs from PPASC displayed up-regulation of genes associated with pulmonary symptoms/fibrosis, while glycolysis metabolism-related genes were downregulated. Similarly, pathway analysis showed that fibrosis-related (VEGF, WNT, and SMAD) and cell death pathways were up-regulated, but immune pathways were down-regulated in PPASC. Further comparison of PPASC with scRNA-seq data with Severe COVID-19 (n=4) data demonstrated enrichment of fibrotic transcriptional signatures. In PPASC, we observed interactive VEGF ligand-receptor pairs among MLCs, and network modules in CD14+ (cluster 4) and CD16+ (Cluster 5) monocytes displayed a significant enrichment for biological pathways linked to adverse COVID-19 outcomes, fibrosis, and angiogenesis. Further analysis revealed a distinct metabolic alteration in MLCs with a down-regulation of glycolysis/gluconeogenesis in PPASC compared to SARS-CoV-2 naïve samples.

Conclusion

Analysis of a small scRNA-seq dataset demonstrated alterations in the immune response and cellular landscape in PPASC. The presence of elevated MLC levels and their corresponding gene signatures associated with fibrosis, immune response suppression, and altered metabolic states suggests a potential role in PPASC development.

Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC

Heterogeneous response to Enzalutamide, a second-generation androgen receptor signaling inhibitor, is a central problem in castration-resistant prostate cancer (CRPC) management. Genome-wide systems investigation of mechanisms that govern Enzalutamide resistance promise to elucidate markers of heterogeneous treatment response and salvage therapies for CRPC patients. Focusing on the de novo role of MYC as a marker of Enzalutamide resistance, here we reconstruct a CRPC-specific mechanism-centric regulatory network, connecting molecular pathways with their upstream transcriptional regulatory programs. Mining this network with signatures of Enzalutamide response identifies NME2 as an upstream regulatory partner of MYC in CRPC and demonstrates that NME2-MYC increased activities can predict patients at risk of resistance to Enzalutamide, independent of co-variates. Furthermore, our experimental investigations demonstrate that targeting MYC and its partner NME2 is beneficial in Enzalutamide-resistant conditions and could provide an effective strategy for patients at risk of Enzalutamide resistance and/or for patients who failed Enzalutamide treatment.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

Plasticity of intragraft alloreactive T cell clones in human gut correlates with transplant outcomes

The site of transition between tissue-resident memory (TRM) and circulating phenotypes of T cells is unknown. We integrated clonotype, alloreactivity, and gene expression profiles of graft-repopulating recipient T cells in the intestinal mucosa at the single-cell level after human intestinal transplantation. Host-versus-graft (HvG)-reactive T cells were mainly distributed to TRM, effector T (Teff)/TRM, and T follicular helper compartments. RNA velocity analysis demonstrated a trajectory from TRM to Teff/TRM clusters in association with rejection. By integrating pre- and post-transplantation (Tx) mixed lymphocyte reaction-determined alloreactive repertoires, we observed that pre-existing HvG-reactive T cells that demonstrated tolerance in the circulation were dominated by TRM profiles in quiescent allografts. Putative de novo HvG-reactive clones showed a transcriptional profile skewed to cytotoxic effectors in rejecting grafts. Inferred protein regulon network analysis revealed upstream regulators that accounted for the effector and tolerant T cell states. We demonstrate Teff/TRM interchangeability for individual T cell clones with known (allo)recognition in the human gut, providing novel insight into TRM biology.

Emerging Treatment Approaches for COVID-19 Infection: A Critical Review

In the present scenario, the SARS-CoV-2 virus has imposed enormous damage on human survival and the global financial system. It has been estimated that around 111 million people all around the world have been infected, and about 2.47 million people died due to this pandemic. The major symptoms were sneezing, coughing, cold, difficulty breathing, pneumonia, and multi-organ failure associated 1with SARS-CoV-2. Currently, two key problems, namely insufficient attempts to develop drugs against SARSCoV-2 and the lack of any biological regulating process, are mostly responsible for the havoc caused by this virus. Henceforth, developing a few novel drugs is urgently required to cure this pandemic. It has been noticed that the pathogenesis of COVID-19 is caused by two main events: infection and immune deficiency, that occur during the pathological process. Antiviral medication can treat both the virus and the host cells. Therefore, in the present review, the major approaches for the treatment have been divided into "target virus" and "target host" groups. These two mechanisms primarily rely on drug repositioning, novel approaches, and possible targets. Initially, we discussed the traditional drugs per the physicians' recommendations. Moreover, such therapeutics have no potential to fight against COVID-19. After that, detailed investigation and analysis were conducted to find some novel vaccines and monoclonal antibodies and conduct a few clinical trials to check their effectiveness against SARSCoV- 2 and mutant strains. Additionally, this study presents the most successful methods for its treatment, including combinatorial therapy. Nanotechnology was studied to build efficient nanocarriers to overcome the traditional constraints of antiviral and biological therapies.

Integrated analysis of single-cell and bulk transcriptomics develops a robust neuroendocrine cell-intrinsic signature to predict prostate cancer progression

Neuroendocrine prostate cancer (NEPC) typically implies severe lethality and limited treatment options. The precise identification of NEPC cells holds paramount significance for both research and clinical applications, yet valid NEPC biomarker remains to be defined. Methods: Leveraging 11 published NE-related gene sets, 11 single-cell RNA-sequencing (scRNA-seq) cohorts, 15 bulk transcriptomic cohorts, and 13 experimental models of prostate cancer (PCa), we employed multiple advanced algorithms to construct and validate a robust NEPC risk prediction model. Results: Through the compilation of a comprehensive scRNA-seq reference atlas (comprising a total of 210,879 single cells, including 66 tumor samples) from 9 multicenter datasets of PCa, we observed inconsistent and inefficient performance among the 11 published NE gene sets. Therefore, we developed an integrative analysis pipeline, identifying 762 high-quality NE markers. Subsequently, we derived the NE cell-intrinsic gene signature, and developed an R package named NEPAL, to predict NEPC risk scores. By applying to multiple independent validation datasets, NEPAL consistently and accurately assigned NE feature and delineated PCa progression. Intriguingly, NEPAL demonstrated predictive capabilities for prognosis and therapy responsiveness, as well as the identification of potential epigenetic drivers of NEPC. Conclusion: The present study furnishes a valuable tool for the identification of NEPC and the monitoring of PCa progression through transcriptomic profiles obtained from both bulk and single-cell sources.

In situ lipid-loading activates peripheral dendritic cell subsets characterized by cellular ROS accumulation but compromises their capacity to prime naïve T cells

BACKGROUND AND AIMS

Dendritic cells (DCs), professional antigen-presenting cells, play an important role in pathologies by controlling adaptive immune responses. However, their adaptation to and functionality in hypercholesterolemia, a driving factor in disease onset and progression of atherosclerosis remains to be established.

METHODS

In this study, we addressed the immediate impact of high fat diet-induced hypercholesterolemia in low-density lipoprotein receptor deficient (Ldlr-/-) mice on separate DC subsets, their compartmentalization and functionality.

RESULTS

While hypercholesterolemia induced a significant rise in bone marrow myeloid and dendritic cell progenitor (MDP) frequency and proliferation rate after high fat diet feeding, it did not affect DC subset numbers in lymphoid tissue. Hypercholesterolemia led to almost immediate and persistent augmentation in granularity of conventional DCs (cDCs), in particular cDC2, reflecting progressive lipid accumulation by these subsets. Plasmacytoid DCs were only marginally and transiently affected. Lipid loading increased co-stimulatory molecule expression and ROS accumulation by cDC2. Despite this hyperactivation, lipid-laden cDC2 displayed a profoundly reduced capacity to stimulate naïve CD4+ T cells.

CONCLUSION

Our data provide evidence that in hypercholesterolemic conditions, peripheral cDC2 subsets engulf lipids in situ, leading to a more activated status characterized by cellular ROS accumulation while, paradoxically, compromising their T cell priming ability. These findings will have repercussions not only for lipid driven cardiometabolic disorders like atherosclerosis, but also for adaptive immune responses to pathogens and/or endogenous (neo) antigens under conditions of hyperlipidemia.

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.

Protocol for generation of a time-resolved cellular interactome during tissue remodeling in adult mice

Efficient skeletal muscle regeneration necessitates fine-tuned coordination among multiple cell types through an intricate network of intercellular communication. We present a protocol for generation of a time-resolved cellular interactome during tissue remodeling. We describe steps for isolating distinct cell populations from skeletal muscle of adult mice after acute damage and extracting RNA from purified cells prior to the generation of RNA sequencing data. We then detail procedures for generating and deciphering a time- and lineage-resolved model of intercellular crosstalk. For complete details on the use and execution of this protocol, please refer to Groppa et al. (2023).1.

SOS1 inhibition enhances the efficacy of and delays resistance to G12C inhibitors in lung adenocarcinoma

Clinical effectiveness of KRAS G12C inhibitors (G12Cis) is limited both by intrinsic and acquired resistance, necessitating the development of combination approaches. We found that targeting proximal receptor tyrosine kinase (RTK) signaling using the SOS1 inhibitor (SOS1i) BI-3406 both enhanced the potency of and delayed resistance to G12Ci treatment, but the extent of SOS1i effectiveness was modulated by both SOS2 expression and the specific mutational landscape. SOS1i enhanced the efficacy of G12Ci and limited rebound RTK/ERK signaling to overcome intrinsic/adaptive resistance, but this effect was modulated by SOS2 protein levels. Survival of drug-tolerant persister (DTP) cells within the heterogeneous tumor population and/or acquired mutations that reactivate RTK/RAS signaling can lead to outgrowth of tumor initiating cells (TICs) that drive therapeutic resistance. G12Ci drug tolerant persister cells showed a 2-3-fold enrichment of TICs, suggesting that these could be a sanctuary population of G12Ci resistant cells. SOS1i re-sensitized DTPs to G12Ci and inhibited G12C-induced TIC enrichment. Co-mutation of the tumor suppressor KEAP1 limits the clinical effectiveness of G12Cis, and KEAP1 and STK11 deletion increased TIC frequency and accelerated the development of acquired resistance to G12Ci in situ. SOS1i both delayed acquired G12Ci resistance and limited the total number of resistant colonies regardless of KEAP1 and STK11 mutational status. These data suggest that SOS1i could be an effective strategy to both enhance G12Ci efficacy and prevent G12Ci resistance regardless of co-mutations.

Sperm chromatin accessibility's involvement in the intergenerational effects of stress hormone receptor activation

Dexamethasone is a stress hormone receptor agonist used widely in clinics. We and others previously showed that paternal administration of dexamethasone in mice affects the phenotype of their offspring. The substrate of intergenerational transmission of environmentally induced effects often involves changes in sperm RNA, yet other epigenetic modifications in the germline can be affected and are also plausible candidates. First, we tested the involvement of altered sperm RNAs in the transmission of dexamethasone induced phenotypes across generations. We did this by injecting sperm RNA into naïve fertilized oocytes, before performing metabolic and behavioral phenotyping of the offspring. We observed phenotypic changes in discordance with those found in offspring generated by in vitro fertilization using sperm from dexamethasone exposed males. Second, we investigated the effect of dexamethasone on chromatin accessibility using ATAC sequencing and found significant changes at specific genomic features and gene regulatory loci. Employing q-RT-PCR, we show altered expression of a gene in the tissue of offspring affected by accessibility changes in sperm. Third, we establish a correlation between specific DNA modifications and stress hormone receptor activity as a likely contributing factor influencing sperm accessibility. Finally, we independently investigated this dependency by genetically reducing thymine-DNA glycosylase levels and observing concomitant changes at the level of chromatin accessibility and stress hormone receptor activity.

Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity

Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.

Single-Cell RNA Sequencing Reveals That Adaptation of Human Aortic Endothelial Cells to Antiproliferative Therapies Is Modulated by Flow-Induced Shear Stress

BACKGROUND

Endothelial cells (ECs) are capable of quickly responding in a coordinated manner to a wide array of stresses to maintain vascular homeostasis. Loss of EC cellular adaptation may be a potential marker for cardiovascular disease and a predictor of poor response to endovascular pharmacological interventions such as drug-eluting stents. Here, we report single-cell transcriptional profiling of ECs exposed to multiple stimulus classes to evaluate EC adaptation.

METHODS

Human aortic ECs were costimulated with both pathophysiological flows mimicking shear stress levels found in the human aorta (laminar and turbulent, ranging from 2.5 to 30 dynes/cm2) and clinically relevant antiproliferative drugs, namely paclitaxel and rapamycin. EC state in response to these stimuli was defined using single-cell RNA sequencing.

RESULTS

We identified differentially expressed genes and inferred the TF (transcription factor) landscape modulated by flow shear stress using single-cell RNA sequencing. These flow-sensitive markers differentiated previously identified spatially distinct subpopulations of ECs in the murine aorta. Moreover, distinct transcriptional modules defined flow- and drug-responsive EC adaptation singly and in combination. Flow shear stress was the dominant driver of EC state, altering their response to pharmacological therapies.

CONCLUSIONS

We showed that flow shear stress modulates the cellular capacity of ECs to respond to paclitaxel and rapamycin administration, suggesting that while responding to different flow patterns, ECs experience an impairment in their transcriptional adaptation to other stimuli.

A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis

Coronary artery disease (CAD) is characterized by atherosclerotic plaque formation in the arterial wall. CAD progression involves complex interactions and phenotypic plasticity among vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but human cell phenotypes remain controversial. Here, we perform an integrated meta-analysis of 22 scRNA-seq libraries to generate a comprehensive map of human atherosclerosis with 118,578 cells. Besides characterizing granular cell-type diversity and communication, we leverage this atlas to provide insights into smooth muscle cell (SMC) modulation. We integrate genome-wide association study data and uncover a critical role for modulated SMC phenotypes in CAD, myocardial infarction, and coronary calcification. Finally, we identify fibromyocyte/fibrochondrogenic SMC markers (LTBP1 and CRTAC1) as proxies of atherosclerosis progression and validate these through omics and spatial imaging analyses. Altogether, we create a unified atlas of human atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.

Elevated stress response marks deeply quiescent reserve cells of gastric chief cells

Gastrointestinal tract organs harbor reserve cells, which are endowed with cellular plasticity and regenerate functional units in response to tissue damage. However, whether the reserve cells in gastrointestinal tract exist as long-term quiescent cells remain incompletely understood. In the present study, we systematically examine H2b-GFP label-retaining cells and identify a long-term slow-cycling population in the gastric corpus but not in other gastrointestinal organs. The label-retaining cells, which reside near the basal layers of the corpus, comprise a subpopulation of chief cells. The identified quiescent cells exhibit induction of Atf4 and its target genes including Atf3, a marker of paligenosis, and activation of the unfolded protein response, but do not show elevated expression of Troy, Lgr5, or Mist. External damage to the gastric mucosa induced by indomethacin treatment triggers proliferation of the quiescent Atf4+ population, indicating that the gastric corpus harbors a specific cell population that is primed to facilitate stomach regeneration.

Anti-PD-1 immunotherapy with androgen deprivation therapy induces robust immune infiltration in metastatic castration-sensitive prostate cancer

When compared to other malignancies, the tumor microenvironment (TME) of primary and castration-resistant prostate cancer (CRPC) is relatively devoid of immune infiltrates. While androgen deprivation therapy (ADT) induces a complex immune infiltrate in localized prostate cancer, the composition of the TME in metastatic castration-sensitive prostate cancer (mCSPC), and the effects of ADT and other treatments in this context are poorly understood. Here, we perform a comprehensive single-cell RNA sequencing (scRNA-seq) profiling of metastatic sites from patients participating in a phase 2 clinical trial (NCT03951831) that evaluated standard-of-care chemo-hormonal therapy combined with anti-PD-1 immunotherapy. We perform a longitudinal, protein activity-based analysis of TME subpopulations, revealing immune subpopulations conserved across multiple metastatic sites. We also observe dynamic changes in these immune subpopulations in response to treatment and a correlation with clinical outcomes. Our study uncovers a therapy-resistant, transcriptionally distinct tumor subpopulation that expands in cell number in treatment-refractory patients.

Comparative analysis of syngeneic mouse models of high-grade serous ovarian cancer

Ovarian cancers exhibit high rates of recurrence and poor treatment response. Preclinical models that recapitulate human disease are critical to develop new therapeutic approaches. Syngeneic mouse models allow for the generation of tumours comprising the full repertoire of non-malignant cell types but have expanded in number, varying in the cell type of origin, method for transformation, and ultimately, the properties of the tumours they produce. Here we have performed a comparative analysis of high-grade serous ovarian cancer models based on transcriptomic profiling of 22 cell line models, and intrabursal and intraperitoneal tumours from 12. Among cell lines, we identify distinct signalling activity, such as elevated inflammatory signalling in STOSE and OVE16 models, and MAPK/ERK signalling in ID8 and OVE4 models; metabolic differences, such as reduced glycolysis-associated expression in several engineered ID8 subclones; and relevant functional properties, including differences in EMT activation, PD-L1 and MHC class I expression, and predicted chemosensitivity. Among tumour samples, we observe increased variability and stromal content among intrabursal tumours. Finally, we predict differences in the microenvironment of ID8 models engineered with clinically relevant mutations. We anticipate that this work will serve as a valuable resource, providing new insight to help select models for specific experimental objectives.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

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

Merkel Cell Carcinoma (MCC) is a highly aggressive neuroendocrine cutaneous malignancy arising from either ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. It is the only known neuroendocrine tumor (NET) with a virus etiology. Despite extensive research, our understanding of the molecular mechanisms driving the transition from normal cells to MCC remains limited. To address this knowledge gap, we assessed the impact of inducible MCPyV T antigens into normal human fibroblasts by performing RNA sequencing. Our findings suggested that the WNT signaling pathway plays a critical role in the development of MCC. To test this model, we bioinformatically evaluated various perturbagens for their ability to reverse the MCC gene expression signature and identified pyrvinium pamoate, an FDA-approved anthelminthic drug known for its anti-tumor potential in multiple cancers. Leveraging transcriptomic, network, and molecular analyses, we found that pyrvinium effectively targets multiple MCC vulnerabilities. Specifically, pyrvinium not only reverses the neuroendocrine features of MCC by modulating canonical and non-canonical WNT signaling pathways but also inhibits cancer cell growth by activating the p53-mediated apoptosis pathway, disrupting mitochondrial function, and inducing endoplasmic reticulum (ER) stress. Pyrvinium also effectively inhibits tumor growth in an MCC mouse xenograft model. These findings offer new avenues for the development of therapeutic strategies for neuroendocrine cancer and highlight the utility of pyrvinium as a potential treatment for MCC.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

FSHR-mTOR-HIF1 signaling alleviates mouse follicles from AMPK-induced atresia

The majority of activated ovarian follicles undergo atresia during reproductive life in mammals, and only a small number of follicles are ovulated. Though hormone treatment has been widely used to promote folliculogenesis, the molecular mechanism behind follicle selection and atresia remains under debate due to inconsistency among investigation models. Using a high-throughput molecular pathology strategy, we depicted a transcriptional atlas of mouse follicular granulosa cells (GCs) under physiological condition and obtained molecular signatures in healthy and atresia GCs during development. Functional results revealed hypoxia-inducible factor 1 (HIF1) as a major effector downstream of follicle-stimulating hormone (FSH), and HIF1 activation is essential for follicle growth. Energy shortage leads to prevalent AMP-activated protein kinase (AMPK) activation and drives follicular atresia. FSHR-mTOR-HIF1 signaling helps follicles escape from the atresia fate, while energy stress persists. Our work provides a comprehensive understanding of the molecular network behind follicle selection and atresia under physiological condition.

Isoform-resolved transcriptome of the human preimplantation embryo

Human preimplantation development involves extensive remodeling of RNA expression and splicing. However, its transcriptome has been compiled using short-read sequencing data, which fails to capture most full-length mRNAs. Here, we generate an isoform-resolved transcriptome of early human development by performing long- and short-read RNA sequencing on 73 embryos spanning the zygote to blastocyst stages. We identify 110,212 unannotated isoforms transcribed from known genes, including highly conserved protein-coding loci and key developmental regulators. We further identify 17,964 isoforms from 5,239 unannotated genes, which are largely non-coding, primate-specific, and highly associated with transposable elements. These isoforms are widely supported by the integration of published multi-omics datasets, including single-cell 8CLC and blastoid studies. Alternative splicing and gene co-expression network analyses further reveal that embryonic genome activation is associated with splicing disruption and transient upregulation of gene modules. Together, these findings show that the human embryo transcriptome is far more complex than currently known, and will act as a valuable resource to empower future studies exploring development.

Androgen blockade primes NLRP3 in macrophages to induce tumor phagocytosis

Immune-based therapies induce durable remissions in subsets of patients across multiple malignancies. However, there is limited efficacy of immunotherapy in metastatic castrate-resistant prostate cancer (mCRPC), manifested by an enrichment of immunosuppressive (M2) tumor- associated macrophages (TAM) in the tumor immune microenvironment (TME). Therefore, therapeutic strategies to overcome TAM-mediated immunosuppression are critically needed in mCRPC. Here we discovered that NLR family pyrin domain containing 3 (NLRP3), an innate immune sensing protein, is highly expressed in TAM from metastatic PC patients treated with standard-of-care androgen deprivation therapy (ADT). Importantly, ex vivo studies revealed that androgen receptor (AR) blockade in TAM upregulates NLRP3 expression, but not inflammasome activity, and concurrent AR blockade/NLRP3 agonist (NLRP3a) treatment promotes cancer cell phagocytosis by immunosuppressive M2 TAM. In contrast, NLRP3a monotherapy was sufficient to enhance phagocytosis of cancer cells in anti-tumor (M1) TAM, which exhibit high de novo NLRP3 expression. Critically, combinatorial treatment with ADT/NLRP3a in a murine model of advanced PC resulted in significant tumor control, with tumor clearance in 55% of mice via TAM phagocytosis. Collectively, our results demonstrate NLRP3 as an AR-regulated "macrophage phagocytic checkpoint", inducibly expressed in TAM by ADT and activated by NLRP3a treatment, the combination resulting in TAM-mediated phagocytosis and tumor control.

Anti-inflammatory effects of cold atmospheric plasma irradiation on the THP-1 human acute monocytic leukemia cell line

Cold atmospheric plasma (CAP) has been studied and clinically applied to treat chronic wounds, cancer, periodontitis, and other diseases. CAP exerts cytotoxic, bactericidal, cell-proliferative, and anti-inflammatory effects on living tissues by generating reactive species. Therefore, CAP holds promise as a treatment for diseases involving chronic inflammation and bacterial infections. However, the cellular mechanisms underlying these anti-inflammatory effects of CAP are still unclear. Thus, this study aimed to elucidate the anti-inflammatory mechanisms of CAP in vitro. The human acute monocytic leukemia cell line, THP-1, was stimulated with lipopolysaccharide and irradiated with CAP, and the cytotoxic effects of CAP were evaluated. Time-course differentiation of gene expression was analyzed, and key transcription factors were identified via transcriptome analysis. Additionally, the nuclear localization of the CAP-induced transcription factor was examined using western blotting. The results indicated that CAP showed no cytotoxic effects after less than 70 s of irradiation and significantly inhibited interleukin 6 (IL6) expression after more than 40 s of irradiation. Transcriptome analysis revealed many differentially expressed genes (DEGs) following CAP irradiation at all time points. Cluster analysis classified the DEGs into four distinct groups, each with time-dependent characteristics. Gene ontology and gene set enrichment analyses revealed CAP-induced suppression of IL6 production, other inflammatory responses, and the expression of genes related to major histocompatibility complex (MHC) class II. Transcription factor analysis suggested that nuclear factor erythroid 2-related factor 2 (NRF2), which suppresses intracellular oxidative stress, is the most activated transcription factor. Contrarily, regulatory factor X5, which regulates MHC class II expression, is the most suppressed transcription factor. Western blotting revealed the nuclear localization of NRF2 following CAP irradiation. These data suggest that CAP suppresses the inflammatory response, possibly by promoting NRF2 nuclear translocation.

Melatonin improves the vitrification of sheep morulae by modulating transcriptome

Embryo vitrification technology is widely used in livestock production, but freezing injury has been a key factor hindering the efficiency of embryo production. There is an urgent need to further analyze the molecular mechanism of embryo damage by the vitrification process. In the study, morulae were collected from Hu sheep uterine horns after superovulation and sperm transfusion. Morulae were Cryotop vitrified and warmed. Nine morulae were in the vitrified control group (frozen), and seven morulae were vitrified and warmed with 10-5 M melatonin (melatonin). Eleven non-frozen morulae were used as controls (fresh). After warming, each embryo was sequenced separately for library construction and gene expression analysis. p < 0.05 was used to differentiate differentially expressed genes (DEG). The results showed that differentiated differentially expressed genes (DEG) in vitrified morulae were mainly enriched in protein kinase activity, adhesion processes, calcium signaling pathways and Wnt, PI3K/AKT, Ras, ErbB, and MAPK signaling pathways compared to controls. Importantly, melatonin treatment upregulated the expression of key pathways that increase the resistance of morulae against vitrification induced damage. These pathways include kinase activity pathway, ErbB, and PI3K/Akt signaling pathway. It is worth mentioning that melatonin upregulates the expression of XPA, which is a key transcription factor for DNA repair. In conclusion, vitrification affected the transcriptome of in vivo-derived Hu sheep morulae, and melatonin had a protective effect on the vitrification process. For the first time, the transcriptome profiles caused by vitrification and melatonin in sheep morulae were analyzed in single embryo level. These data obtained from the single embryo level provide an important molecular mechanism for further optimizing the cryopreservation of embryos or other cells.

ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that the HOX/CUT transcription factor ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 targets include the glucocorticoid receptor and the NE splicing factor SRRM4, among others. OC2 regulates gene expression by promoter binding, enhancement of chromatin accessibility, and formation of novel super-enhancers. OC2 also activates glucuronidation genes that irreversibly disable androgen, thereby evoking phenotypic heterogeneity indirectly by hormone depletion. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC. Our findings support enhanced efforts to therapeutically target this protein as a means of suppressing treatment-resistant disease.

Identification of ACHE as the hub gene targeting solasonine associated with non-small cell lung cancer (NSCLC) using integrated bioinformatics analysis

Background

Solasonine, as a major biological component of Solanum nigrum L., has demonstrated anticancer effects against several malignancies. However, little is understood regarding its biological target and mechanism in non-small cell lung cancer (NSCLC).

Methods

We conducted an analysis on transcriptomic data to identify differentially expressed genes (DEGs), and employed an artificial intelligence (AI) strategy to predict the target protein for solasonine. Subsequently, genetic dependency analysis and molecular docking were performed, with Acetylcholinesterase (ACHE) selected as a pivotal marker for solasonine. We then employed a range of bioinformatic approaches to explore the relationship between ACHE and solasonine. Furthermore, we investigated the impact of solasonine on A549 cells, a human lung cancer cell line. Cell inhibition of A549 cells following solasonine treatment was analyzed using the CCK8 assay. Additionally, we assessed the protein expression of ACHE, as well as markers associated with apoptosis and inflammation, using western blotting. To investigate their functions, we employed a plasmid-based ACHE overexpression system. Finally, we performed dynamics simulations to simulate the interaction mode between solasonine and ACHE.

Results

The results of the genetic dependency analysis revealed that ACHE could be identified as the pivotal target with the highest docking affinity. The cell experiments yielded significant findings, as evidenced by the negative regulatory effect of solasonine treatment on tumor cells, as demonstrated by the CCK8 assay. Western blotting analysis revealed that solasonine treatment resulted in the downregulation of the Bcl-2/Bax ratio and upregulation of cleaved caspase-3 protein expression levels. Moreover, we observed that ACHE overexpression promoted the expression of the Bcl-2/Bax ratio and decreased cleaved caspase-3 expression in the OE-ACHE group. Notably, solasonine treatment rescued the Bcl-2/Bax ratio and cleaved caspase-3 expression in OE-ACHE cells compared to OE-ACHE cells without solasonine treatment, suggesting that solasonine induces apoptosis. Besides, solasonine exhibited its anti-inflammatory effects by inhibiting P38 MAPK. This was supported by the decline in protein levels of IL-1β and TNF-α, as well as the phosphorylated forms of JNK and P38 MAPK. The results from the molecular docking and dynamics simulations further confirmed the potent binding affinity and effective inhibitory action between solasonine and ACHE.

Conclusions

The findings of the current investigation show that solasonine exerts its pro-apoptosis and anti-inflammatory effects by suppressing the expression of ACHE.