Uncoupling of macrophage inflammation from self-renewal modulates host recovery from respiratory viral infection.

Tissue macrophages self-renew during homeostasis and produce inflammatory mediators upon microbial infection. We examined the relationship between proliferative and inflammatory properties of tissue macrophages by defining the impact of the Wnt/ß-catenin pathway, a central regulator of self-renewal, in alveolar macrophages (AMs). Activation of ß-catenin by Wnt ligand inhibited AM proliferation and stemness, but promoted inflammatory activity. In a murine influenza viral pneumonia model, ß-catenin-mediated AM inflammatory activity promoted acute host morbidity; in contrast, AM proliferation enabled repopulation of reparative AMs and tissue recovery following viral clearance. Mechanistically, Wnt treatment promoted ß-catenin-HIF-1α interaction and glycolysis-dependent inflammation while suppressing mitochondrial metabolism and thereby, AM proliferation. Differential HIF-1α activities distinguished proliferative and inflammatory AMs in vivo. This ß-catenin-HIF-1α axis was conserved in human AMs and enhanced HIF-1α expression associated with macrophage inflammation in COVID-19 patients. Thus, inflammatory and reparative activities of lung macrophages are regulated by ß-catenin-HIF-1α signaling, with implications for the treatment of severe respiratory diseases.

Energy status dictates PD-L1 protein abundance and anti-tumor immunity to enable checkpoint blockade.

Aberrant energy status contributes to multiple metabolic diseases, including obesity, diabetes, and cancer, but the underlying mechanism remains elusive. Here, we report that ketogenic-diet-induced changes in energy status enhance the efficacy of anti-CTLA-4 immunotherapy by decreasing PD-L1 protein levels and increasing expression of type-I interferon (IFN) and antigen presentation genes. Mechanistically, energy deprivation activates AMP-activated protein kinase (AMPK), which in turn, phosphorylates PD-L1 on Ser283, thereby disrupting its interaction with CMTM4 and subsequently triggering PD-L1 degradation. In addition, AMPK phosphorylates EZH2, which disrupts PRC2 function, leading to enhanced IFNs and antigen presentation gene expression. Through these mechanisms, AMPK agonists or ketogenic diets enhance the efficacy of anti-CTLA-4 immunotherapy and improve the overall survival rate in syngeneic mouse tumor models. Our findings reveal a pivotal role for AMPK in regulating the immune response to immune-checkpoint blockade and advocate for combining ketogenic diets or AMPK agonists with anti-CTLA4 immunotherapy to combat cancer.

Study of prognostic splicing factors in cancer using machine learning approaches.

Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing the generation of oncogenic proteins involved in cancer hallmarks. In this study, we investigated the genes that encode RNA-binding proteins and identified potential splicing factors that contribute to the aberrant splicing applying a random forest classification model. The result suggested 56 splicing factors were related to the prognosis of 13 cancers, two SF complexes in liver hepatocellular carcinoma, and one SF complex in esophageal carcinoma. Further systematic bioinformatics studies on these cancer prognostic splicing factors and their related alternative splicing events revealed the potential regulations in a cancer-specific manner. Our analysis found high ILF2-ILF3 expression correlates with poor prognosis in LIHC through alternative splicing. These findings emphasize the importance of SFs as potential indicators for prognosis or targets for therapeutic interventions. Their roles in cancer exhibit complexity and are contingent upon the specific context in which they operate. This recognition further underscores the need for a comprehensive understanding and exploration of the role of SFs in different types of cancer, paving the way for their potential utilization in prognostic assessments and the development of targeted therapies.

The COPD GWAS gene ADGRG6 instructs function and injury response in human iPSC-derived type II alveolar epithelial cells.

Emphysema and chronic obstructive pulmonary disease (COPD) most commonly result from the effects of environmental exposures in genetically susceptible individuals. Genome-wide association studies have implicated ADGRG6 in COPD and reduced lung function, and a limited number of studies have examined the role of ADGRG6 in cells representative of the airway. However, the ADGRG6 locus is also associated with DLCO/VA, an indicator of gas exchange efficiency and alveolar function. Here, we sought to evaluate the mechanistic contributions of ADGRG6 to homeostatic function and disease in type 2 alveolar epithelial cells. We applied an inducible CRISPR interference (CRISPRi) human induced pluripotent stem cell (iPSC) platform to explore ADGRG6 function in iPSC-derived AT2s (iAT2s). We demonstrate that ADGRG6 exerts pleiotropic effects on iAT2s including regulation of focal adhesions, cytoskeleton, tight junctions, and proliferation. Moreover, we find that ADGRG6 knockdown in cigarette smoke-exposed iAT2s alters cellular responses to injury, downregulating apical complexes in favor of proliferation. Our work functionally characterizes the COPD GWAS gene ADGRG6 in human alveolar epithelium.

AntiFormer: graph enhanced large language model for binding affinity prediction.

Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.

FusionNeoAntigen: a resource of fusion gene-specific neoantigens.

Among the diverse sources of neoantigens (i.e. single-nucleotide variants (SNVs), insertions or deletions (Indels) and fusion genes), fusion gene-derived neoantigens are generally more immunogenic, have multiple targets per mutation and are more widely distributed across various cancer types. Therefore, fusion gene-derived neoantigens are a potential source of highly immunogenic neoantigens and hold great promise for cancer immunotherapy. However, the lack of fusion protein sequence resources and knowledge prevents this application. We introduce 'FusionNeoAntigen', a dedicated resource for fusion-specific neoantigens, accessible at https://compbio.uth.edu/FusionNeoAntigen. In this resource, we provide fusion gene breakpoint crossing neoantigens focused on ∼43K fusion proteins of ∼16K in-frame fusion genes from FusionGDB2.0. FusionNeoAntigen provides fusion gene information, corresponding fusion protein sequences, fusion breakpoint peptide sequences, fusion gene-derived neoantigen prediction, virtual screening between fusion breakpoint peptides having potential fusion neoantigens and human leucocyte antigens (HLAs), fusion breakpoint RNA/protein sequences for developing vaccines, information on samples with fusion-specific neoantigen, potential CAR-T targetable cell-surface fusion proteins and literature curation. FusionNeoAntigen will help to develop fusion gene-based immunotherapies. We will report all potential fusion-specific neoantigens from all possible open reading frames of ∼120K human fusion genes in future versions.

CircaKB: a comprehensive knowledgebase of circadian genes across multiple species.

Circadian rhythms, which are the natural cycles that dictate various physiological processes over a 24-h period, have been increasingly recognized as important in the management and treatment of various human diseases. However, the lack of sufficient data and reliable analysis methods have been a major obstacle to understanding the bidirectional interaction between circadian variation and human health. We have developed CircaKB, a comprehensive knowledgebase of circadian genes across multiple species. CircaKB is the first knowledgebase that provides systematic annotations of the oscillatory patterns of gene expression at a genome-wide level for 15 representative species. Currently, CircaKB contains 226 time-course transcriptome datasets, covering a wide variety of tissues, organs, and cell lines. In addition, CircaKB integrates 12 computational models to facilitate reliable data analysis and identify oscillatory patterns and their variations in gene expression. CircaKB also offers powerful functionalities to its users, including easy search, fast browsing, strong visualization, and custom upload. We believe that CircaKB will be a valuable tool and resource for the circadian research community, contributing to the identification of new targets for disease prevention and treatment. We have made CircaKB freely accessible at https://cdsic.njau.edu.cn/CircaKB.

COV2Var, a function annotation database of SARS-CoV-2 genetic variation.

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.

metsDB: a knowledgebase of cancer metastasis at bulk, single-cell and spatial levels.

Cancer metastasis, the process by which tumour cells migrate and colonize distant organs from a primary site, is responsible for the majority of cancer-related deaths. Understanding the cellular and molecular mechanisms underlying this complex process is essential for developing effective metastasis prevention and therapy strategies. To this end, we systematically analysed 1786 bulk tissue samples from 13 cancer types, 988 463 single cells from 17 cancer types, and 40 252 spots from 45 spatial slides across 10 cancer types. The results of these analyses are compiled in the metsDB database, accessible at https://relab.xidian.edu.cn/metsDB/. This database provides insights into alterations in cell constitutions, cell relationships, biological pathways, molecular biomarkers, and drug responses during cancer metastasis at bulk, single-cell, and spatial levels. Users can perform cell or gene searches to obtain multi-view and multi-scale metastasis-related data. This comprehensive resource is invaluable for understanding the metastasis process and for designing molecular therapies.

AgeAnnoMO: a knowledgebase of multi-omics annotation for animal aging.

Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denominators of aging on the molecular, cellular and systemic levels across multiple species. Thus, understanding the function of aging hallmarks and their relationships across species can facilitate the translation of anti-aging drug development from model organisms to humans. Here, we built AgeAnnoMO (https://relab.xidian.edu.cn/AgeAnnoMO/#/), a knowledgebase of multi-omics annotation for animal aging. AgeAnnoMO encompasses an extensive collection of 136 datasets from eight modalities, encompassing 8596 samples from 50 representative species, making it a comprehensive resource for aging and longevity research. AgeAnnoMO characterizes multiple aging regulators across species via multi-omics data, comprehensively annotating aging-related genes, proteins, metabolites, mitochondrial genes, microbiotas and age-specific TCR and BCR sequences tied to aging hallmarks for these species and tissues. AgeAnnoMO not only facilitates a deeper and more generalizable understanding of aging mechanisms, but also provides potential insights of the specificity across tissues and species in aging process, which is important to develop the effective anti-aging interventions for diverse populations. We anticipate that AgeAnnoMO will provide a valuable resource for comprehending and integrating the conserved driving hallmarks in aging biology and identifying the targetable biomarkers for aging research.

StemDriver: a knowledgebase of gene functions for hematopoietic stem cell fate determination.

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.

DRMref: comprehensive reference map of drug resistance mechanisms in human cancer.

Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.

Trophoblast PR-SET7 dysfunction induces viral mimicry response and necroptosis associated with recurrent miscarriage.

Recurrent miscarriage (RM) is a distressing pregnancy complication. While the etiology of RM remains unclear, growing evidence has indicated the relevance of trophoblast impairment to the pathogenesis of RM. PR-SET7 is the sole enzyme catalyzing monomethylation of H4K20 (H4K20me1) and has been implicated in many pathophysiological processes. However, how PR-SET7 functions in trophoblasts and its relevance to RM remain unknown. Here, we found that trophoblast-specific loss of Pr-set7 in mice led to defective trophoblasts, resulting in early embryonic loss. Mechanistic analysis revealed that PR-SET7 deficiency in trophoblasts derepressed endogenous retroviruses (ERVs), leading to double-stranded RNA stress and subsequent viral mimicry, which drove overwhelming interferon response and necroptosis. Further examination discovered that H4K20me1 and H4K20me3 mediated the inhibition of cell-intrinsic expression of ERVs. Importantly, dysregulation of PR-SET7 expression and the corresponding aberrant epigenetic modifications were observed in the placentas of RM. Collectively, our results demonstrate that PR-SET7 acts as an epigenetic transcriptional modulator essential for repressing ERVs in trophoblasts, ensuring normal pregnancy and fetal survival, which sheds new light on potential epigenetic causes contributing to RM.

FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies.

BACKGROUND: Asthma is a heterogeneous common respiratory disease that remains poorly understood. The established genetic associations fail to explain the high estimated heritability, and the prevalence of asthma differs between populations and geographic regions. Robust association analyses incorporating different genetic ancestries and whole-genome sequencing data may identify novel genetic associations. METHODS: We performed family-based genome-wide association analyses of childhood-onset asthma based on whole-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GACRS) and the Childhood Asthma Management Program (CAMP). Based on parent-child trios with children diagnosed with asthma, we performed a single variant analysis using an additive and a recessive genetic model and a region-based association analysis of low-frequency and rare variants. RESULTS: Based on 1180 asthmatic trios (894 GACRS trios and 286 CAMP trios, a total of 3540 samples with WGS data), we identified three novel genetic loci associated with childhood-onset asthma: rs4832738 on 4p14 ($P=1.72\ast{10}^{-9}$, recessive model), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only). Integrative analyses suggested potential novel candidate genes underlying these associations: PGM2 on 4p14 and FGF20 on 8p22. CONCLUSION: Our family-based whole-genome sequencing analysis identified three novel genetic loci for childhood-onset asthma. Gene expression data and integrative analyses point to PGM2 on 4p14 and FGF20 on 8p22 as linked genes. Furthermore, region-based analyses suggest independent potential low-frequency/rare variant associations on 8p22. Follow-up analyses are needed to understand the functional mechanisms and generalizability of these associations.

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.

Genetic control of RNA editing in neurodegenerative disease.

A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variants. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership-Alzheimer's Disease and Parkinson's Progression Markers Initiative. The large-scale and genome-wide identification of 95 198 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA-binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56 and DHX9, since their regulations on multiple RNA-editing events were probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of 3 proteins, expressions of 277 genes and splicing of 449 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.

Integrated mRNA sequence optimization using deep learning.

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics.

Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ.

Microhomology-mediated end joining (MMEJ), an error-prone DNA damage repair mechanism, frequently leads to chromosomal rearrangements due to its ability to engage in promiscuous end joining of genomic instability and also leads to increasing mutational load at the sequences flanking the breakpoints (BPs). In this study, we systematically investigated the homology sequences around the genomic breakpoint area of human fusion genes, which were formed by the chromosomal rearrangements initiated by DNA double-strand breakage. Since the RNA-seq data is the typical data set to check the fusion genes, for the known exon junction fusion breakpoints identified from RNA-seq data, we have to infer the high chance of genomic breakpoint regions. For this, we utilized the high feature importance score area calculated from our recently developed fusion BP prediction model, FusionAI and identified 151 K microhomologies among ~24 K fusion BPs in 20 K fusion genes. From our multiple bioinformatics studies, we found a relationship between sequence homologies and the immune system. This in-silico study will provide novel knowledge on the sequence homologies around the coded structural variants.

Enhanced Ca2+-Dependent SK-Channel Gating and Membrane Trafficking in Human Atrial Fibrillation.

BACKGROUND: Small-conductance Ca2+-activated K+ (SK)-channel inhibitors have antiarrhythmic effects in animal models of atrial fibrillation (AF), presenting a potential novel antiarrhythmic option. However, the regulation of SK-channels in human atrial cardiomyocytes and its modification in patients with AF are poorly understood and were the object of this study. METHODS: Apamin-sensitive SK-channel current (ISK) and action potentials were recorded in human right-atrial cardiomyocytes from sinus rhythm control (Ctl) patients or patients with (long-standing persistent) chronic AF (cAF). RESULTS: ISK was significantly higher, and apamin caused larger action potential prolongation in cAF- versus Ctl-cardiomyocytes. Sensitivity analyses in an in silico human atrial cardiomyocyte model identified IK1 and ISK as major regulators of repolarization. Increased ISK in cAF was not associated with increases in mRNA/protein levels of SK-channel subunits in either right- or left-atrial tissue homogenates or right-atrial cardiomyocytes, but the abundance of SK2 at the sarcolemma was larger in cAF versus Ctl in both tissue-slices and cardiomyocytes. Latrunculin-A and primaquine (anterograde and retrograde protein-trafficking inhibitors) eliminated the differences in SK2 membrane levels and ISK between Ctl- and cAF-cardiomyocytes. In addition, the phosphatase-inhibitor okadaic acid reduced ISK amplitude and abolished the difference between Ctl- and cAF-cardiomyocytes, indicating that reduced calmodulin-Thr80 phosphorylation due to increased protein phosphatase-2A levels in the SK-channel complex likely contribute to the greater ISK in cAF-cardiomyocytes. Finally, rapid electrical activation (5 Hz, 10 minutes) of Ctl-cardiomyocytes promoted SK2 membrane-localization, increased ISK and reduced action potential duration, effects greatly attenuated by apamin. Latrunculin-A or primaquine prevented the 5-Hz-induced ISK-upregulation. CONCLUSIONS: ISK is upregulated in patients with cAF due to enhanced channel function, mediated by phosphatase-2A-dependent calmodulin-Thr80 dephosphorylation and tachycardia-dependent enhanced trafficking and targeting of SK-channel subunits to the sarcolemma. The observed AF-associated increases in ISK, which promote reentry-stabilizing action potential duration shortening, suggest an important role for SK-channels in AF auto-promotion and provide a rationale for pursuing the antiarrhythmic effects of SK-channel inhibition in humans.

SPASCER: spatial transcriptomics annotation at single-cell resolution.

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.