Single-cell omics: experimental workflow, data analyses and applications.

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Therapeutic SHPRH-146aa encoded by circ-SHPRH dynamically upregulates P21 to inhibit CDKs in neuroblastoma.

Recent research has underscored the significance of circular RNAs (circRNAs) in various cancers, including neuroblastoma (NB). Specifically, circ-SHPRH, a unique circRNA, has been revealed to inhibit tumor growth by sequestering miRNAs or producing the SHPRH-146aa protein. To explore circ-SHPRH's involvement in NB and its potential application in gene therapy, this study examined circ-SHPRH expression in 94 NB tissues and cell lines (SK-N-BE(2), SH-SY5Y) using real-time PCR and fluorescence in situ hybridization (FISH). Functional assays encompassing both overexpression and knockdown experiments in NB cell lines, as well as in vivo investigations, were conducted. RNA-seq analysis revealed a correlation between circ-SHPRH and the pathway of P21 (CDKN1A), a pivotal cell cycle regulator. Validation through PCR and other techniques confirmed that circ-SHPRH upregulated P21 expression. Furthermore, the regulatory role of circ-SHPRH in the P21-CDK pathway was corroborated through SHPRH-146aa expression analysis. Notably, adenovirus-mediated circ-SHPRH overexpression effectively curbed NB tumor growth in NSG mice, while combining circ-SHPRH with everolimus exhibited potential for NB treatment. This study elucidates the remarkable significance of circ-SHPRH in NB and its prospective utility in gene therapy, thereby paving the way for innovative therapeutic approaches.

Deciphering potential causative factors for undiagnosed Waardenburg syndrome through multi-data integration.

BACKGROUND: Waardenburg syndrome (WS) is a rare genetic disorder mainly characterized by hearing loss and pigmentary abnormalities. Currently, seven causative genes have been identified for WS, but clinical genetic testing results show that 38.9% of WS patients remain molecularly unexplained. In this study, we performed multi-data integration analysis through protein-protein interaction and phenotype-similarity to comprehensively decipher the potential causative factors of undiagnosed WS. In addition, we explored the association between genotypes and phenotypes in WS with the manually collected 443 cases from published literature. RESULTS: We predicted two possible WS pathogenic genes (KIT, CHD7) through multi-data integration analysis, which were further supported by gene expression profiles in single cells and phenotypes in gene knockout mouse. We also predicted twenty, seven, and five potential WS pathogenic variations in gene PAX3, MITF, and SOX10, respectively. Genotype-phenotype association analysis showed that white forelock and telecanthus were dominantly present in patients with PAX3 variants; skin freckles and premature graying of hair were more frequently observed in cases with MITF variants; while aganglionic megacolon and constipation occurred more often in those with SOX10 variants. Patients with variations of PAX3 and MITF were more likely to have synophrys and broad nasal root. Iris pigmentary abnormality was more common in patients with variations of PAX3 and SOX10. Moreover, we found that patients with variants of SOX10 had a higher risk of suffering from auditory system diseases and nervous system diseases, which were closely associated with the high expression abundance of SOX10 in ear tissues and brain tissues. CONCLUSIONS: Our study provides new insights into the potential causative factors of WS and an alternative way to explore clinically undiagnosed cases, which will promote clinical diagnosis and genetic counseling. However, the two potential disease-causing genes (KIT, CHD7) and 32 potential pathogenic variants (PAX3: 20, MITF: 7, SOX10: 5) predicted by multi-data integration in this study are all computational predictions and need to be further verified through experiments in follow-up research.

Landscape of global urban environmental resistome and its association with local socioeconomic and medical status.

Antimicrobial resistance (AMR) poses a critical threat to global health and development, with environmental factors-particularly in urban areas-contributing significantly to the spread of antibiotic resistance genes (ARGs). However, most research to date has been conducted at a local level, leaving significant gaps in our understanding of the global status of antibiotic resistance in urban environments. To address this issue, we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples, which were collected by the MetaSUB International Consortium involving diverse urban environments in 60 cities of 27 countries, utilizing a deep-learning based methodology. Our findings demonstrated the strong geographical specificity of urban environmental resistome, and their correlation with various local socioeconomic and medical conditions. We also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters (BGCs) across different countries, and discovered that the urban environment represents a rich source of novel antibiotics. Our study provides a comprehensive overview of the global urban environmental resistome, and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.

MOTIVATION: Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level. RESULTS: Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota. AVAILABILITY AND IMPLEMENTATION: PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

HiFun: homology independent protein function prediction by a novel protein-language self-attention model.

Protein function prediction based on amino acid sequence alone is an extremely challenging but important task, especially in metagenomics/metatranscriptomics field, in which novel proteins have been uncovered exponentially from new microorganisms. Many of them are extremely low homology to known proteins and cannot be annotated with homology-based or information integrative methods. To overcome this problem, we proposed a Homology Independent protein Function annotation method (HiFun) based on a unified deep-learning model by reassembling the sequence as protein language. The robustness of HiFun was evaluated using the benchmark datasets and metrics in the CAFA3 challenge. To navigate the utility of HiFun, we annotated 2 212 663 unknown proteins and discovered novel motifs in the UHGP-50 catalog. We proved that HiFun can extract latent function related structure features which empowers it ability to achieve function annotation for non-homology proteins. HiFun can substantially improve newly proteins annotation and expand our understanding of microorganisms' adaptation in various ecological niches. Moreover, we provided a free and accessible webservice at http://www.unimd.org/HiFun, requiring only protein sequences as input, offering researchers an efficient and practical platform for predicting protein functions.

Mapping knowledge landscapes and emerging trends of the biomarkers in melanoma: a bibliometric analysis from 2004 to 2022.

Background: Melanoma is a skin tumor with a high mortality rate, and early diagnosis and effective treatment are the key to reduce its mortality rate. Therefore, more and more attention has been paid for biomarker identification for early diagnosis, prognosis prediction and prognosis evaluation of melanoma. However, there is still a lack of a report that comprehensively and objectively evaluates the research status of melanoma biomarkers. Therefore, this study aims to intuitively analyze the research status and trend of melanoma biomarkers through the methods of bibliometrics and knowledge graph. Objective: This study uses bibliometrics to analyze research in biomarkers in melanoma, summarize the field's history and current status of research, and predict future research directions. Method: Articles and Reviews related to melanoma biomarkers were retrieved by using Web of Science core collection subject search. Bibliometric analysis was performed in Excel 365, CiteSpace, VOSviewer and Bibliometrix (R-Tool of R-Studio). Result: A total of 5584 documents from 2004 to 2022 were included in the bibliometric analysis. The results show that the number of publications and the frequency of citations in this field are increasing year by year, and the frequency of citations has increased rapidly after 2018. The United States is the most productive and influential country in this field, with the largest number of publications and institutions with high citation frequency. Caroline Robert, F. Stephen Hodi, Suzanne L. Topalian and others are authoritative authors in this field, and The New England Journal of Medicine, Journal of Clinical Oncology and Clinical Cancer Research are the most authoritative journals in this field. Biomarkers related to the diagnosis, treatment and prognosis of melanoma are hot topics and cutting-edge hotspots in this field. Conclusion: For the first time, this study used the bibliometric method to visualize the research in the field of melanoma biomarkers, revealing the trends and frontiers of melanoma biomarkers research, which provides a useful reference for scholars to find key research issues and partners.

SMPD1 expression profile and mutation landscape help decipher genotype-phenotype association and precision diagnosis for acid sphingomyelinase deficiency.

BACKGROUND: Acid sphingomyelinase deficiency (ASMD) disorder, also known as Niemann-Pick disease (NPD) is a rare genetic disease caused by mutations in SMPD1 gene, which encodes sphingomyelin phosphodiesterase (ASM). Except for liver and spleen enlargement and lung disease, two subtypes (Type A and B) of NDP have different onset times, survival times, ASM activities, and neurological abnormalities. To comprehensively explore NPD's genotype-phenotype association and pathophysiological characteristics, we collected 144 NPD cases with strict quality control through literature mining. RESULTS: The difference in ASM activity can differentiate NPD type A from other subtypes, with the ratio of ASM activity to the reference values being lower in type A (threshold 0.045 (4.45%)). Severe variations, such as deletion and insertion, can cause complete loss of ASM function, leading to type A, whereas relatively mild missense mutations generally result in type B. Among reported mutations, the p.Arg3AlafsX76 mutation is highly prevalent in the Chinese population, and the p.R608del mutation is common in Mediterranean countries. The expression profiles of SMPD1 from GTEx and single-cell RNA sequencing data of multiple fetal tissues showed that high expressions of SMPD1 can be observed in the liver, spleen, and brain tissues of adults and hepatoblasts, hematopoietic stem cells, STC2_TLX1-positive cells, mesothelial cells of the spleen, vascular endothelial cells of the cerebellum and the cerebrum of fetuses, indicating that SMPD1 dysfunction is highly likely to have a significant effect on the function of those cell types during development and the clinicians need pay attention to these organs or tissues as well during diagnosis. In addition, we also predicted 21 new pathogenic mutations in the SMPD1 gene that potentially cause the NPD, signifying that more rare cases will be detected with those mutations in SMPD1. Finally, we also analysed the function of the NPD type A cells following the extracellular milieu. CONCLUSIONS: Our study is the first to elucidate the effects of SMPD1 mutation on cell types and at the tissue level, which provides new insights into the genotype-phenotype association and can help in the precise diagnosis of NPD.

The role of CYP1A1/2 in cholesterol ester accumulation provides a new perspective for the treatment of hypercholesterolemia.

Cholesterol is an important precursor of many endogenous molecules. Disruption of cholesterol homeostasis can cause many pathological changes, leading to liver and cardiovascular diseases. CYP1A is widely involved in cholesterol metabolic network, but its exact function has not been fully elucidated. Here, we aim to explore how CYP1A regulates cholesterol homeostasis. Our data showed that CYP1A1/2 knockout (KO) rats presented cholesterol deposition in blood and liver. The serum levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and total cholesterol were significantly increased in KO rats. Further studies found that the lipogenesis pathway (LXRα-SREBP1-SCD1) of KO rats was activated, and the key protein of cholesterol ester hydrolysis (CES1) was inhibited. Importantly, lansoprazole can significantly alleviate rat hepatic lipid deposition in hypercholesterolemia models by inducing CYP1A. Our findings reveal the role of CYP1A as a potential regulator of cholesterol homeostasis and provide a new perspective for the treatment of hypercholesterolemia.

Gene expression variability across cells and species shapes the relationship between renal resident macrophages and infiltrated macrophages.

BACKGROUND: Two main subclasses of macrophages are found in almost all solid tissues: embryo-derived resident tissue macrophages and bone marrow-derived infiltrated macrophages. These macrophage subtypes show transcriptional and functional divergence, and the programs that have shaped the evolution of renal macrophages and related signaling pathways remain poorly understood. To clarify these processes, we performed data analysis based on single-cell transcriptional profiling of renal tissue-resident and infiltrated macrophages in human, mouse and rat. RESULTS: In this study, we (i) characterized the transcriptional divergence among species and (ii) illustrated variability in expression among cells of each subtype and (iii) compared the gene regulation network and (iv) ligand-receptor pairs in human and mouse. Using single-cell transcriptomics, we mapped the promoter architecture during homeostasis. CONCLUSIONS: Transcriptionally divergent genes, such as the differentially TF-encoding genes expressed in resident and infiltrated macrophages across the three species, vary among cells and include distinct promoter structures. The gene regulatory network in infiltrated macrophages shows comparatively better species-wide consistency than resident macrophages. The conserved transcriptional gene regulatory network in infiltrated macrophages among species is uniquely enriched in pathways related to kinases, and TFs associated with largely conserved regulons among species are uniquely enriched in kinase-related pathways.

MagMD: Database summarizing the metabolic action of gut microbiota to drugs.

An increasing number of studies have reported that microbiome can affect drug response by altering pharmacokinetics and pharmacodynamics of formation of toxic metabolites. With the development of metagenomic sequencing, gut microbial composition as well as the metabolic function are drawing more and more attention for the patient stratification. The established microbiota databases provide useful information about the gut microbe-drug interactions. However, these databases generally lacked the detailed effects on substance and the metabolites, which are helpful in elucidating the mechanisms underlying drug biotransformation and personalized medicine. To address these issues, in this study, we developed Metabolic action of gut Microbiota to Drugs (MagMD), a database and a web-service covering 32, 678 records of interactions between 2,146 gut microbes, 36 enzymes and 219 substrates (mainly drugs). The detailed annotations for each entry, including the taxonomic level of microbes, the molecular form and PubChem ID of drugs from PubChem Compound Database, types of microbial secreted enzymes and the original reference links can also be accessed from the web service. Availability and implementation: MagMD is a publicly available resource, constantly updated. It has an intuitive web interface and can be freely accessed at http://www.unimd.org/magmd.

Prognostic value of PNN in prostate cancer and its correlation with therapeutic significance.

Prostate cancer (PCa) is the most common malignancy. New biomarkers are in demand to facilitate the management. The role of the pinin protein (encoded by PNN gene) in PCa has not been thoroughly explored yet. Using The Cancer Genome Atlas (TCGA-PCa) dataset validated with Gene Expression Omnibus (GEO) and protein expression data retrieved from the Human Protein Atlas, the prognostic and diagnostic values of PNN were studied. Highly co-expressed genes with PNN (HCEG) were constructed for pathway enrichment analysis and drug prediction. A prognostic signature based on methylation status using HCEG was constructed. Gene set enrichment analysis (GSEA) and the TISIDB database were utilised to analyse the associations between PNN and tumour-infiltrating immune cells. The upregulated PNN expression in PCa at both transcription and protein levels suggests its potential as an independent prognostic factor of PCa. Analyses of the PNN's co-expression network indicated that PNN plays a role in RNA splicing and spliceosomes. The prognostic methylation signature demonstrated good performance for progression-free survival. Finally, our results showed that the PNN gene was involved in splicing-related pathways in PCa and identified as a potential biomarker for PCa.

A history of the MetaSUB consortium: Tracking urban microbes around the globe.

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

IL-17D-induced inhibition of DDX5 expression in keratinocytes amplifies IL-36R-mediated skin inflammation.

Aberrant RNA splicing in keratinocytes drives inflammatory skin disorders. In the present study, we found that the RNA helicase DDX5 was downregulated in keratinocytes from the inflammatory skin lesions in patients with atopic dermatitis and psoriasis, and that mice with keratinocyte-specific deletion of Ddx5 (Ddx5∆KC) were more susceptible to cutaneous inflammation. Inhibition of DDX5 expression in keratinocytes was induced by the cytokine interleukin (IL)-17D through activation of the CD93-p38 MAPK-AKT-SMAD2/3 signaling pathway and led to pre-messenger RNA splicing events that favored the production of membrane-bound, intact IL-36 receptor (IL-36R) at the expense of soluble IL-36R (sIL-36R) and to the selective amplification of IL-36R-mediated inflammatory responses and cutaneous inflammation. Restoration of sIL-36R in Ddx5∆KC mice with experimental atopic dermatitis or psoriasis suppressed skin inflammation and alleviated the disease phenotypes. These findings indicate that IL-17D modulation of DDX5 expression controls inflammation in keratinocytes during inflammatory skin diseases.

Transcriptome sequencing of hepatocellular carcinoma uncovers multiple types of dysregulated ncRNAs.

Transcriptome profiling of hepatocellular carcinoma (HCC) by next-generation sequencing (NGS) technology has been broadly performed by previous studies, which facilitate our understanding of the molecular mechanisms of HCC formation, progression, and metastasis. However, few studies jointly analyze multiple types of noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and micro-RNAs (miRNAs), and further uncover their implications in HCC. In this study, we observed that the circRNA cZRANB1 and lncRNA DUXAP10 were not only significantly upregulated in tumor tissues, but also higher expressed in blood exosomes of HCC as compared with healthy donors. From the analysis of subclass-associated dysregulated ncRNAs, we observed that DLX6-AS1, an antisense RNA of DLX6, and the sense gene DLX6 were highly expressed in S1, a subclass with a more invasive/disseminative phenotype. High correlation between DLX6-AS1 and DLX6 suggested that DLX6-AS1 may function via promoting the transcription of DLX6. Integrative analysis uncovers circRNA-miRNA, lncRNA-miRNA, and competing endogenous RNA networks (ceRNAs). Specifically, cZRANB1, LINC00501, CTD-2008L17.2, and SLC7A11-AS1 may function as ceRNAs that regulate mRNAs by competing the shared miRNAs. Further prognostic analysis demonstrated that the dysregulated ncRNAs had the potential to predict HCC patients' overall survival. In summary, we identified some novel circRNAs and miRNAs, and dysregulated ncRNAs that could participate in HCC tumorigenesis and progression by inducing transcription of their neighboring genes, increasing their derived miRNAs, or acting as miRNA sponges. Moreover, our systematic analysis provides not only rich data resources for related researchers, but also new insights into the molecular basis of how different ncRNAs coordinately or antagonistically participate in the pathogenesis process of diseases.

ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection.

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php.

Computational identification of new potential transcriptional partners of ERRα in breast cancer cells: specific partners for specific targets.

Estrogen related receptors are orphan members of the nuclear receptor superfamily acting as transcription factors (TFs). In contrast to classical nuclear receptors, the activities of the ERRs are not controlled by a natural ligand. Regulation of their activities thus relies on availability of transcriptional co-regulators. In this paper, we focus on ERRα, whose involvement in cancer progression has been broadly demonstrated. We propose a new approach to identify potential co-activators, starting from previously identified ERRα-activated genes in a breast cancer (BC) cell line. Considering mRNA gene expression from two sets of human BC cells as major endpoint, we used sparse partial least squares modeling to uncover new transcriptional regulators associated with ERRα. Among them, DDX21, MYBBP1A, NFKB1, and SETD7 are functionally relevant in MDA-MB-231 cells, specifically activating the expression of subsets of ERRα-activated genes. We studied SET7 in more details and showed its co-localization with ERRα and its ERRα-dependent transcriptional and phenotypic effects. Our results thus demonstrate the ability of a modeling approach to identify new transcriptional partners from gene expression. Finally, experimental results show that ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes, thus reinforcing the combinatorial specificity of transcription.

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing.

BACKGROUND: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. RESULTS: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. CONCLUSIONS: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.