Insights into the heterogeneity of the tumor microenvironment in lung adenocarcinoma and squamous carcinoma through single-cell transcriptomic analysis: Implications for distinct immunotherapy outcomes.

BACKGROUND: Immune checkpoint blockade has emerged as a key strategy to the therapy landscape of non-small cell lung cancer (NSCLC). However, notable differences in immunotherapeutic outcomes exist between the two primary NSCLC subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This disparity may stem from the tumor immune microenvironment's heterogeneity at the transcriptome level. METHODS: By integrative analysis of transcriptomic characterization of 38 NSCLC patients by single-cell RNA sequencing, the present study revealed a distinct tumor microenvironment (TME) between LUAD and LUSC, with relevant results further confirmed in bulk transcriptomic and multiplex immunofluorescence (mIF) validation cohort of neoadjuvant immunotherapy patients. RESULTS: LUAD exhibited a more active immune microenvironment compared to LUSC. This included highly expression of HLA I/II in cancer cells, reinforced antigen presentation potential of dendritic cells and enhanced cytotoxic activity observed in T/NK cells. In LUSC, cancer cells highly expressed genes belonging to the aldo-keto reductases, glutathione S-transferases and aldehyde dehydrogenase family, negatively correlating with immunotherapy outcomes in the validation cohort of our center. Further analysis revealed elevated infiltrated cancer-associated fibroblasts (CAFs) in LUSC, which was corroborated in The Cancer Genome Atlas cohort. Corresponding increased infiltration of ADH1B+ CAFs in major pathologic response (MPR) patients and the higher presence of FAP+ CAFs in non-MPR patients were demonstrated by multiplex mIF. Moreover, upregulating immunosuppressive extracellular matrix remodeling was identified in LUSC. CONCLUSIONS: These comprehensive analyses advance the understanding of the differences in TME between LUAD and LUSC, offering insights for patient selection and developing subtype-specific treatment strategies.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions.

Protein ubiquitination regulates a wide range of cellular processes. The degree of protein ubiquitination is determined by the delicate balance between ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase (DUB)-mediated deubiquitination. In comparison to the E3-substrate interactions, the DUB-substrate interactions (DSIs) remain insufficiently investigated. To address this challenge, we introduce a protein sequence-based ab initio method, TransDSI, which transfers proteome-scale evolutionary information to predict unknown DSIs despite inadequate training datasets. An explainable module is integrated to suggest the critical protein regions for DSIs while predicting DSIs. TransDSI outperforms multiple machine learning strategies against both cross-validation and independent test. Two predicted DUBs (USP11 and USP20) for FOXP3 are validated by "wet lab" experiments, along with two predicted substrates (AR and p53) for USP22. TransDSI provides new functional perspective on proteins by identifying regulatory DSIs, and offers clues for potential tumor drug target discovery and precision drug application.

Identification and validation of hub genes in drug induced acute kidney injury basing on integrated transcriptomic analysis.

Background: Drug-induced acute kidney damage (DI-AKI) is a clinical phenomenon of rapid loss of kidney function over a brief period of time as a consequence of the using of medicines. The lack of a specialized treatment and the instability of traditional kidney injury markers to detect DI-AKI frequently result in the development of chronic kidney disease. Thus, it is crucial to continue screening for DI-AKI hub genes and specific biomarkers. Methods: Differentially expressed genes (DEGs) of group iohexol, cisplatin, and vancomycin's were analyzed using Limma package, and the intersection was calculated. DEGs were then put into String database to create a network of protein-protein interactions (PPI). Ten algorithms are used in the Cytohubba plugin to find the common hub genes. Three DI-AKI models' hub gene expression was verified in vivo and in vitro using PCR and western blot. To investigate the hub gene's potential as a biomarker, protein levels of mouse serum and urine were measured by ELISA kits. The UUO, IRI and aristolochic acid I-induced nephrotoxicity (AAN) datasets in the GEO database were utilized for external data verification by WGCNA and Limma package. Finally, the Elisa kit was used to identify DI-AKI patient samples. Results: 95 up-regulated common DEGs and 32 down-regulated common DEGs were obtained using Limma package. A PPI network with 84 nodes and 24 edges was built with confidence >0.4. Four hub genes were obtained by Algorithms of Cytohubba plugin, including TLR4, AOC3, IRF4 and TNFAIP6. Then, we discovered that the protein and mRNA levels of four hub genes were significantly changed in the DI-AKI model in vivo and in vitro. External data validation revealed that only the AAN model, which also belonged to DI-AKI model, had significant difference in these hub genes, whereas IRI and UUO did not. Finally, we found that plasma TLR4 levels were higher in patients with DI-AKI, especially in vancomycin-induced AKI. Conclusion: The immune system and inflammation are key factors in DI-AKI. We discovered the immunological and inflammatory-related genes TLR4, AOC3, IRF4, and TNFAIP6, which may be promising specific biomarkers and essential hub genes for the prevention and identification of DI-AKI.

Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing.

BACKGROUND: Immunotherapy has revolutionized cancer treatment, but most patients are refractory to immunotherapy or acquire resistance, with the underlying mechanisms remaining to be explored. METHODS: We characterized the transcriptomes of ~92,000 single cells from 3 pre-treatment and 12 post-treatment patients with non-small cell lung cancer (NSCLC) who received neoadjuvant PD-1 blockade combined with chemotherapy. The 12 post-treatment samples were categorized into two groups based on pathologic response: major pathologic response (MPR; n = 4) and non-MPR (NMPR; n = 8). RESULTS: Distinct therapy-induced cancer cell transcriptomes were associated with clinical response. Cancer cells from MPR patients exhibited a signature of activated antigen presentation via major histocompatibility complex class II (MHC-II). Further, the transcriptional signatures of FCRL4+FCRL5+ memory B cells and CD16+CX3CR1+ monocytes were enriched in MPR patients and are predictors of immunotherapy response. Cancer cells from NMPR patients exhibited overexpression of estrogen metabolism enzymes and elevated serum estradiol. In all patients, therapy promoted expansion and activation of cytotoxic T cells and CD16+ NK cells, reduction of immunosuppressive Tregs, and activation of memory CD8+T cells into an effector phenotype. Tissue-resident macrophages were expanded after therapy, and tumor-associated macrophages (TAMs) were remodeled into a neutral instead of an anti-tumor phenotype. We revealed the heterogeneity of neutrophils during immunotherapy and identified an aged CCL3+ neutrophil subset was decreased in MPR patients. The aged CCL3+ neutrophils were predicted to interact with SPP1+ TAMs through a positive feedback loop to contribute to a poor therapy response. CONCLUSIONS: Neoadjuvant PD-1 blockade combined with chemotherapy led to distinct NSCLC tumor microenvironment transcriptomes that correlated with therapy response. Although limited by a small patient sample size subjected to combination therapy, this study provides novel biomarkers to predict therapy response and suggests potential strategies to overcome immunotherapy resistance.

Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma.

BACKGROUND: The diversity of histologic composition reflects the inter- and intra-tumor heterogeneity of lung adenocarcinomas (LUADs) macroscopically. Insights into the oncological characteristics and tumor microenvironment (TME) of different histologic subtypes of LUAD at the single-cell level can help identify potential therapeutic vulnerabilities and combinational approaches to improve the survival of LUAD patients. METHODS: Through comparative profiling of cell communities defined by scRNA-seq data, we characterized the TME of LUAD samples of distinct histologic subtypes, with relevant results further confirmed in multiple bulk transcriptomic, proteomic datasets and an independent immunohistochemical validation cohort. RESULTS: We find that the hypoxic and acidic situation is the worst in the TME of solid LUADs compared to other histologic subtypes. Besides, the tumor metabolic preferences vary across histologic subtypes and may correspondingly impinge on the metabolism and function of immune cells. Remarkably, tumor cells from solid LUADs upregulate energy and substance metabolic activities, particularly the folate-mediated one-carbon metabolism and the key gene MTHFD2, which could serve as a potential therapeutic target. Additionally, ubiquitination modifications may also be involved in the progression of histologic patterns. Immunologically, solid LUADs are characterized by a predominance of exhausted T cells and immunosuppressive myeloid cells, where the hypoxic, acidified and nutrient-deprived TME has a non-negligible impact. Discrepancies in stromal cell function, evidenced by varying degrees of stromal remodeling and fibrosis, may also contribute to the specific immune phenotype of solid LUADs. CONCLUSIONS: Overall, our research proposes several potential entry points to improve the immunosuppressive TME of solid LUADs, thereby synergistically potentiating their immunotherapeutic efficacy, and may provide precise therapeutic strategies for LUAD patients of distinct histologic subtype constitution.

Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations.

Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein-protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013-2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets.

Sleeve resection after neoadjuvant chemoimmunotherapy in the treatment of locally advanced non-small cell lung cancer.

Background: We aimed to characterize the outcomes of sleeve resection after neoadjuvant chemoimmunotherapy for the treatment of non-small cell lung cancer (NSCLC), including perioperative and oncologic outcomes, and to identify any impact of operative approach on resultant findings. Methods: We identified patients with NSCLC who underwent sleeve resection after ≥2 cycles of neoadjuvant chemoimmunotherapy between May 2019 and April 2021 and retrospectively reviewed clinical records. Perioperative data were collected and compared between video-assisted thoracoscopic surgery (VATS) (n=8) and thoracotomy (n=15) groups. Immunohistochemistry (IHC) scores were compared between tumors with and without major pathological response (MPR). Results: Twenty-three patients met inclusion criteria, with clinical stages as follows: IB, 2 (8.7%); IIIA, 14 (60.9%); and IIIB, 7 (30.4%). Treatment-related adverse events (TRAE) were recorded in 17 patients (73.9%), including anemia and neutropenia, with no patients exhibiting serious TRAE. Radiological evaluation revealed 5 (21.7%) patients with complete response (CR), 14 (60.9%) with partial response (PR), and 4 (17.4%) with stable disease (SD). Complete resection was accomplished for all patients. One VATS procedure was converted to thoracotomy due to extensive pleural adhesions. There were no significant differences in intraoperative blood loss (87.5±51.8 vs. 193.9±145.3 mL), operative time (198.8±79.7 vs. 225.5±55.0 min), number of lymph node examined (16.9±6.6 vs. 18.2±6.5), and hospital stay (5.5±2.8 vs. 9.2±11.2 days) between the VATS and thoracotomy groups (all P>0.05). Postoperative complications occurred in 3 patients, and 1 patient died of bronchopleural fistula (BPF) in the thoracotomy group. Complete pathological response (CPR) and MPR were achieved in seven (30.4%) and 13 (56.5%) patients, respectively. Both preoperative histopathology (P=0.024) and radiological response (P=0.002) were significantly associated with MPR. In postoperative specimens with MPR, the IHC scores of cluster of differentiation (CD)4, CD8, and CD20 were modestly higher, while programmed cell death receptor 1 (PD-1), lymphocyte-activation gene 3 (LAG3) and T cell immunoglobulin and ITIM domain (TIGIT) were lower compared with non-MPR specimens, albeit insignificantly. Conclusions: Sleeve resection after neoadjuvant chemoimmunotherapy was feasible in patients with locally advanced NSCLC. Perioperative outcomes were comparable between the VATS and thoracotomy groups.

Single-Cell Transcriptome Identifies Drug-Resistance Signature and Immunosuppressive Microenvironment in Metastatic Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a deadly neuroendocrine malignancy with high metastasis. However, the heterogeneity of metastatic SCLC at the single-cell level remains elusive. The single-cell transcriptome of a total of 24 081 cells in metastatic lymph node samples from seven SCLC patients via endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is examined. Genomic alterations are also examined by whole exome sequencing (WES) and the immune infiltration between SCLC and non-SCLC (NSCLC) is compared using public single-cell RNA sequencing (scRNA-seq) data. It is identified that malignant cells in lymph-node metastatic SCLC have inter-patient and intra-tumor heterogeneity characterized by distinct ASCL1 and NEUROD1 expression patterns. High expression of genes such as FZD8 in WNT pathway is associated with drug resistance in malignant cells. Compared to NSCLC, SCLC harbors a unique immunosuppressive tumor microenvironment. Malignant cells exhibit a pattern of attenuated MHC-I antigen presentation-related gene expression, which is associated with relatively low proportion of exhausted T cells. Natural killer (NK) cells display impaired antitumor function with high expression of TGFBR2. This work characterizes the inter-patient and intra-tumor heterogeneity of metastatic SCLC and uncovers the exhaustion signatures in NK cells, which may pave the way for novel treatments for SCLC including immune checkpoint blockade-based immunotherapy.

Single-Cell Transcriptome Identifies Drug-Resistance Signature and Immunosuppressive Microenvironment in Metastatic Small Cell Lung Cancer (Advanced Genetics 2/03).

Single-Cell RNA Sequencing This cover illustrates the work of Xujun Wang, Xianmin Zhu, Peng Zhang, and co-workers in article number 2100060 which reveals the drug-resistance signature and immunosuppressive microenvironment in small cell lung cancer (SCLC) by single-cell RNA-sequencing. "Wu Song Fought the Tiger" comes from the famous Chinese novel: Outlaws of the Marsh. In the cover, the warrior Wu Song stands for the doctors and researchers. The tiger bearing "SCLC" on its face is dangerous for its sharp teeth and claws (early metastasis and drug resistance) and the surrounding water bubbles (immune infiltration). In addition, 2022 is the Year of the Tiger.

Regulation of the Golgi apparatus via GOLPH3-mediated new selective autophagy.

AIMS: Although previous studies elaborated that selective autophagy was involved in quality control of some organelles, including nucleus, mitochondria, the endoplasmic reticulum and peroxisomes, it remained unclear whether the selective autophagy of the Golgi apparatus (Golgiphagy) existed or not. MAIN METHODS: In this study, H9c2 cells, HUVECs, HA-VSMCs and HEK293T cells were treated with autophagy inducers, Golgi stress inducers and cardiomyocytes hypertrophy stimulators. The Golgiphagy was evaluated by analysing the co-localization of Golgi markers and LC3B. Furthermore, the transmission electron microscope was used to observe the occurrence of Golgiphagy. The co-immunoprecipitation assay was used to evaluate the interaction of GOLPH3 and LC3B. KEY FINDINGS: Results showed that starvation promoted the co-localization of both GM130-positive and TGN46-positive Golgi fragments with LC3B-positive autophagosomes in H9c2 cells, HUVECs, HA-VSMCs and HEK293T cells. Transmission electron microscopy images showed that Golgi apparatus was sequestered into the autophagosomes in the starvation group. Moreover, Golgi stress inducers also facilitated the co-localization of Golgi markers and LC3B in H9c2 cells, HUVECs, HA-VSMCs and HEK293T cells. Furthermore, cardiomyocyte hypertrophy stimulators also triggered the appearance of Golgiphagy in H9c2 cells. Importantly, the co-immunoprecipitation assay indicated endogenous GOLPH3 interacted with LC3B in H9c2 cells, HUVECs, HA-VSMCs. However, knocking down GOLPH3 inhibited the Golgiphagy. SIGNIFICANCE: This study unveiled a new selective autophagy of the Golgi apparatus (Golgiphagy). In addition, GOLPH3 might act as a novel cargo receptor to regulate Golgiphagy. Maintaining homeostasis of the Golgi apparatus via GOLPH3-mediated autophagy was indispensable for cell survival.