Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in a cost-effective way. In this study, we developed mirTools 2.0, an updated version of mirTools 1.0, which includes the following new features. (1) From miRNA discovery in mirTools 1.0, mirTools 2.0 allows users to detect and profile various types of ncRNAs, such as miRNA, tRNA, snRNA, snoRNA, rRNA, and piRNA. (2) From miRNA profiling in mirTools 1.0, mirTools 2.0 allows users to identify miRNA-targeted genes and performs detailed functional annotation of miRNA targets, including Gene Ontology, KEGG pathway and protein-protein interaction. (3) From comparison of two samples for differentially expressed miRNAs in mirTools 1.0, mirTools 2.0 allows users to detect differentially expressed ncRNAs between two experimental groups or among multiple samples. (4) Other significant improvements include strategies used to detect novel miRNAs and piRNAs, more taxonomy categories to discover more known miRNAs and a stand-alone version of mirTools 2.0. In conclusion, we believe that mirTools 2.0 (122.228.158.106/mr2_dev and centre.bioinformatics.zj.cn/mr2_dev) will provide researchers with more detailed insight into small RNA transcriptomes.

Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of ∼1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.

Background: Melanoma patients who have detectable serum soluble NKG2D ligands either at the baseline or post-treatment of PD1/PDL1 blockade exhibit poor overall survival. Among families of soluble human NKG2D ligands, the soluble human MHC I chain-related molecule (sMIC) was found to be elevated in melanoma patients and mostly associated with poor response to PD1/PDL1 blockade therapy. Methods: In this study, we aim to investigate whether co-targeting tumor-released sMIC enhances the therapeutic outcome of PD1/PDL1 blockade therapy for melanoma. We implanted sMIC-expressing B16F10 melanoma tumors into syngeneic host and evaluated therapeutic efficacy of anti-sMIC antibody and anti-PDL1 antibody combination therapy in comparison with monotherapy. We analyzed associated effector mechanism. We also assessed sMIC/MIC prevalence in metastatic human melanoma tumors. Results: We found that the combination therapy of the anti-PDL1 antibody with an antibody targeting sMIC significantly improved animal survival as compared to monotherapies and that the effect of combination therapy depends significantly on NK cells. We show that combination therapy significantly increased IL-2Rα (CD25) on NK cells which sensitizes NK cells to low dose IL-2 for survival. We demonstrate that sMIC negatively reprograms gene expression related to NK cell homeostatic survival and proliferation and that antibody clearing sMIC reverses the effect of sMIC and reprograms NK cell for survival. We further show that sMIC/MIC is abundantly present in metastatic human melanoma tumors. Conclusions: Our findings provide a pre-clinical proof-of-concept and a new mechanistic understanding to underscore the significance of antibody targeting sMIC to improve therapeutic efficacy of anti-PD1/PDL1 antibody for MIC/sMIC⁺ metastatic melanoma patients.

RRAD (Ras-related associated with diabetes) is a small Ras-related GTPase that is frequently inactivated by DNA methylation of the CpG island in its promoter region in cancer tissues. However, the role of the methylation-induced RRAD inactivation in tumorigenesis remains unclear. In this study, the Ras-regulated transcriptome and epigenome were profiled by comparing T29H (a Ras^V12-transformed human ovarian epithelial cell line) with T29 (an immortalized but non-transformed cell line) through reduced representation bisulfite sequencing and digital gene expression. We found that Ras^V12-mediated oncogenic transformation was accompanied by RRAD promoter hypermethylation and a concomitant loss of RRAD expression. In addition, we found that the RRAD promoter was hypermethylated, and its transcription was reduced in ovarian cancer versus normal ovarian tissues. Treatment with the DNA methyltransferase inhibitor 5-aza-2′-deoxycytidine resulted in demethylation in the RRAD promoter and restored RRAD expression in T29H cells. Additionally, treatment with farnesyltransferase inhibitor FTI277 resulted in restored RRAD expression and inhibited DNA methytransferase expression and activity in T29H cells. By employing knockdown and overexpression techniques in T29 and T29H, respectively, we found that RRAD inhibited glucose uptake and lactate production by repressing the expression of glucose transporters. Finally, RRAD overexpression in T29H cells inhibited tumor formation in nude mice, suggesting that RRAD is a tumor suppressor gene. Our results indicate that Ras^V12-mediated oncogenic transformation induces RRAD epigenetic inactivation, which in turn promotes glucose uptake and may contribute to ovarian cancer tumorigenesis.

The benefits of adult stem cells for repair of the heart have been attributed to the repertoire of salutary paracrine activities they appear to exert. We previously isolated human W8B2⁺cardiac stem cells (CSCs) and found they powerfully influence cardiomyocytes and endothelial cells to collectively promote cardiac repair and regeneration. Here, the complexity of the W8B2⁺CSC secretomes was characterised and examined in more detail. Using ion exchange chromatography to separate soluble proteins based on their net surface charge, the secreted factors responsible for the pro-survival activity of W8B2⁺CSCs were found within the low and medium cation fractions. In addition to the soluble proteins, extracellular vesicles generated from W8B2⁺CSCs not only exhibited pro-survival and pro-angiogenic activities, but also promoted proliferation of neonatal cardiomyocytes. These extracellular vesicles contain a cargo of proteins, mRNA and primary microRNA precursors that are enriched in exosomes and are capable of modulating collectively many of the cellular pathways involved in protein metabolism, cell growth, as well as cellular responses to stress and organisation of the extracellular matrix. Thus the W8B2⁺CSC secretome contains a multitude of bioactive paracrine factors we have now characterised, that might well be harnessed for therapeutic application for cardiac repair and regeneration.

Background: Nanobodies, also known as VHH or single-domain antibodies, are unique antibody fragments derived solely from heavy chains. They offer advantages of small molecules and conventional antibodies, making them promising therapeutics. The paratope is the specific region on an antibody that binds to an antigen. Paratope prediction involves the identification and characterization of the antigen-binding site on an antibody. This process is crucial for understanding the specificity and affinity of antibody-antigen interactions. Various computational methods and experimental approaches have been developed to predict and analyze paratopes, contributing to advancements in antibody engineering, drug development, and immunotherapy. However, existing predictive models trained on traditional antibodies may not be suitable for nanobodies. Additionally, the limited availability of nanobody datasets poses challenges in constructing accurate models. Methods: To address these challenges, we have developed a novel nanobody prediction model, named NanoBERTa-ASP (Antibody Specificity Prediction), which is specifically designed for predicting nanobody-antigen binding sites. The model adopts a training strategy more suitable for nanobodies, based on an advanced natural language processing (NLP) model called BERT (Bidirectional Encoder Representations from Transformers). To be more specific, the model utilizes a masked language modeling approach named RoBERTa (Robustly Optimized BERT Pretraining Approach) to learn the contextual information of the nanobody sequence and predict its binding site. Results: NanoBERTa-ASP achieved exceptional performance in predicting nanobody binding sites, outperforming existing methods, indicating its proficiency in capturing sequence information specific to nanobodies and accurately identifying their binding sites. Furthermore, NanoBERTa-ASP provides insights into the interaction mechanisms between nanobodies and antigens, contributing to a better understanding of nanobodies and facilitating the design and development of nanobodies with therapeutic potential. Conclusion: NanoBERTa-ASP represents a significant advancement in nanobody paratope prediction. Its superior performance highlights the potential of deep learning approaches in nanobody research. By leveraging the increasing volume of nanobody data, NanoBERTa-ASP can further refine its predictions, enhance its performance, and contribute to the development of novel nanobody-based therapeutics. Github repository: https://github.com/WangLabforComputationalBiology/NanoBERTa-ASP