rNMPID: a database for riboNucleoside MonoPhosphates in DNA.

Motivation: Ribonucleoside monophosphates (rNMPs) are the most abundant non-standard nucleotides embedded in genomic DNA. If the presence of rNMP in DNA cannot be controlled, it can lead to genome instability. The actual regulatory functions of rNMPs in DNA remain mainly unknown. Considering the association between rNMP embedment and various diseases and cancer, the phenomenon of rNMP embedment in DNA has become a prominent area of research in recent years. Results: We introduce the rNMPID database, which is the first database revealing rNMP-embedment characteristics, strand bias, and preferred incorporation patterns in the genomic DNA of samples from bacterial to human cells of different genetic backgrounds. The rNMPID database uses datasets generated by different rNMP-mapping techniques. It provides the researchers with a solid foundation to explore the features of rNMP embedded in the genomic DNA of multiple sources, and their association with cellular functions, and, in future, disease. It also significantly benefits researchers in the fields of genetics and genomics who aim to integrate their studies with the rNMP-embedment data. Availability and implementation: rNMPID is freely accessible on the web at https://www.rnmpid.org.

miR-29a-5p rescues depressive-like behaviors in a CUMS-induced mouse model by facilitating microglia M2-polarization in the prefrontal cortex via TMEM33 suppression.

BACKGROUND: Depression accounts for a high proportion of neuropsychiatric disorders and is associated with abnormal states of neurons in specific brain regions. Microglia play a pivotal role in the inflammatory state during depression development; however, the exact mechanism underlying chronic mood states remains unknown. Thus, the present study aimed to determine whether microRNAs (miRNAs) alleviate stress-induced depression-like behavior in mice by regulating the expression levels of their target genes, explore the role of neuroinflammation induced by microglial activation in the pathogenesis and progression of depression, and determine whether the role of the miR-29a-5p/transmembrane protein 33 (TMEM33) axis. METHODS: In this study, chronic unpredictable mild stress (CUMS) mouse depression model, various behavioral tests, western blotting, dual-luciferase reporter assay, enzyme-linked immunosorbent assay, real-time quantitative reverse transcription PCR, immunofluorescence and lentivirus-mediated gene transfer were used. RESULTS: After exposure to the CUMS paradigm, miR-29a-5p was significantly down-regulated. This downregulation subsequently promoted the polarization of microglia M1 by upregulating the expression of TMEM33, resulting in enhanced inflammatory chemokines affecting neurons. Conversely, the upregulation of miR-29a-5p within the prefrontal cortex (PFC) suppressed TMEM33 expression, facilitated microglia M2-polarization, and ameliorated depressive-like behavior. LIMITATIONS: Only rodent models of depression were used, and human samples were not included. CONCLUSIONS: The results of this study suggest that miR-29a-5p deficits within the PFC mediate microglial anomalies and contribute to depressive-like behaviors. miR-29a-5p and TMEM33 may, therefore, serve as potential therapeutic targets for the treatment of depression.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Mechanism of miR-98-5p in gastric cancer cell proliferation, migration, and invasion through the USP44/CTCFL axis.

Objectives: Gastric cancer (GC) is the leading digestive malignancy with high incidence and mortality rate. microRNAs (miRs) play an important role in GC progresssion. This study aimed to investigate the effect of miR-98-5p on proliferation, migration, and invasion of GC cells. Methods: The expression levels of miR-98-5p, ubiquitin specific peptidase 44 (USP44), and CCCTCbinding factor-like (CTCFL) in GC tissues and cells were identified using reversetranscription quantitative polymerase chain reaction and Western blot assay. The relationship between miR-98-5p expression/USP44 and the clinicopathological features in GC patients was analyzed. GC cell proliferation, invasion, and migration were evaluated by cell counting kit-8 and clone formation assays and Transwell assays. The bindings of miR-98-5p to USP44 and USP44 to CTCFL were examined using dualluciferase assay and co-immunoprecipitation. GC cells were treated with MG132 and the ubiquitination level of CTCFL was examined using ubiquitination assay. Rescue experiments were performed to verify the roles of USP44 and CTCFL in GC cells. Results: miR-98-5p was downregulated in GC. miR-98-5p overexpression inhibited the proliferation, migration, and invasion of GC cells. miR-98-5p inhibited USP44 expression. USP44 bound to CTCFL and limited ubiquitination degradation of CTCFL. Overexpression of USP44 and CTCFL attenuated the inhibitory effects of miR-98-5p overexpression on GC cell progression. Conclusion: miR-98-5p overexpression limited USP44-mediated CTCFL deubiquitination, and suppressed CTCFL expression, mitigating GC cell proliferation, migration, and invasion.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities.

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling.

BACKGROUND: Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets. RESULTS: As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols. CONCLUSIONS: Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Repurposing fluphenazine as an autophagy modulator for treating liver cancer.

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system with a low early diagnosis rate. Owing to the side effects, tolerance, and patient contraindications of existing therapies, effective drug treatments for HCC remain a major clinical challenge. However, using approved or investigational drugs not initially intended for cancer therapy is a promising strategy for resolving this problem because their safety have been tested in clinic. Therefore, this study evaluated differentially expressed genes between liver cancer and normal tissues in a cohort of patients with HCC from The Cancer Genome Atlas and applied them to query a connectivity map to identify candidate anti-HCC drugs. As a result, fluphenazine was identified as a candidate for anti-HCC therapy in vitro and in vivo. Fluphenazine suppressed HCC cell proliferation and migration and induced cell cycle arrest and apoptosis, possibly owing to disrupted lysosomal function, blocking autophagy flux. Additionally, in vivo studies demonstrated that fluphenazine suppresses HCC subcutaneous xenografts growth without causing severe side effects. Strikingly, fluphenazine could be used as an analgesic to alleviate oxaliplatin-induced pain as well as pain related anxiety-like behavior. Therefore, fluphenazine could be a novel liver cancer treatment candidate.

Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance.

BACKGROUND: Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. RESULTS: We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. CONCLUSIONS: The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control.

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

Prediction of base editor off-targets by deep learning.

Due to the tolerance of mismatches between gRNA and targeting sequence, base editors frequently induce unwanted Cas9-dependent off-target mutations. Here, to develop models to predict such off-targets, we design gRNA-off- target pairs for adenine base editors (ABEs) and cytosine base editors (CBEs) and stably integrate them into the human cells. After five days of editing, we obtain valid efficiency datasets of 54,663 and 55,727 off-targets for ABEs and CBEs, respectively. We use the datasets to train deep learning models, resulting in ABEdeepoff and CBEdeepoff, which can predict off-target sites. We use these tools to predict off-targets for a panel of endogenous loci and achieve Spearman correlation values varying from 0.710 to 0.859. Finally, we develop an integrated tool that is freely accessible via an online web server http://www.deephf.com/#/bedeep/bedeepoff . These tools could facilitate minimizing the off-target effects of base editing.

Identification of CCDC115 as an adverse prognostic biomarker in liver cancer based on bioinformatics and experimental analyses.

Liver hepatocellular carcinoma (LIHC) is a major malignant tumor of the digestive system with a high incidence rate and poor early diagnosis. Coiled-coil domain-containing protein 115 (CCDC115), an accessory component of vacuolar-ATPase with dramatically abnormal expression, is associated with survival outcomes of cancer patients. However, the role of CCDC115 in LIHC remains unclear. In this study, we aimed to determine the functional role of CCDC115 in LIHC by examining CCDC115 expression, and its influence on LIHC prognosis. Through extensive statistical analyses, using LIHC patient databases, we observed that CCDC115 expression significantly increased in tumor tissues of LIHC patients. In addition, CCDC115 expression correlated with the poor prognosis. Additionally, CCDC115 was found to be involved in several cancer-related pathways, specifically the PI3K-Akt pathway. The expression of CCDC115 was positively correlated with human leukocyte antigen molecules as well as with immune checkpoint molecules in LIHC patients. We performed in vitro experiments and confirmed that the expression of CCDC115 significantly affects the proliferation potential, metastasis and sorafenib resistance of liver cancer cells, as well as some key protein expression in PI3K-Akt pathway. These results indicate that CCDC115 could serve as a diagnostic and prognostic biomarker of LIHC, and targeting CCDC115 may provide a potential strategy to enhance the efficacy of liver cancer therapy.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

BACKGROUND: Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. RESULTS: As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. CONCLUSIONS: Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Differences of molecular events driving pathological and radiological progression of lung adenocarcinoma.

BACKGROUND: Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD. METHODS: Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we validated the abundance of immune cells and activity of cell proliferation. FINDINGS: Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an independent predictor of patients' prognosis in 11 external LUAD datasets. INTERPRETATION: Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for the treatment of GGO-like LUAD. FUNDING: This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).

Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph.

The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.

Molecular Profiling Provides Clinical Insights Into Targeted and Immunotherapies as Well as Colorectal Cancer Prognosis.

BACKGROUND & AIMS: Tumor genetic testing is indispensable in the management of primary and metastatic colorectal cancer (CRC), yet the indications for genomics-guided precision medicine and immunotherapy must be better understood and defined. METHODS: We prospectively sequenced tumors from 869 Chinese patients with CRC by a large panel and evaluated the clinical significance of single-gene somatic mutations and co-occurring events in metastatic CRC, as well as their functional effects and tumorigenic mechanisms. We systematically assessed the heterogeneity of the tumor immune microenvironment in different genomic contexts through the combined analysis of Immunoscore, multiplex immunostaining, whole-exome sequencing, transcriptome, and single-cell sequencing. RESULTS: Single-gene somatic mutations in BRAF or RBM10 were associated with shorter progression-free survival in patients with metastatic CRC. Functional studies suggested RBM10 acts as a tumor suppressor in CRC development. Co-mutations of KRAS/AMER1 or KRAS/APC were enriched in the metastatic cohort, which had poor progression-free survival and did not benefit from bevacizumab due to accelerated drug metabolism. Forty patients (4.6%) carried pathogenic or likely pathogenic germline alterations in the DNA damage repair pathway and 37.5% of these tumors had secondary-hit events with loss of heterozygosity or biallelic alterations. A high tumor insertion or deletion burden with high microsatellite instability suggested immunogenicity with numerous activated tumor-infiltrating lymphocytes, whereas polymerase epsilon exonuclease mutation with ultrahigh tumor mutation burden indicated a relatively quiescent immunophenotype. The heterogeneous genomic-immunologic interactions were reflected in the divergent neoantigen presentation and depletion, immune checkpoint expression, PD-1/PD-L1 interaction, and T-cell responsiveness to pembrolizumab. CONCLUSIONS: Our integrated analysis provides insights into CRC prognostic stratification, drug response, and personalized genomics-guided targeted and immunotherapies.

Caffeine ameliorates the metabolic syndrome in diet-induced obese mice through regulating the gut microbiota and serum metabolism.

OBJECTIVE: Obesity is associated with gut microbiota disorders, which has been related to developing metabolic syndromes. The research aims to investigate the effects of caffeine treatment on insulin resistance, intestinal microbiota composition and serum metabolomic changes in high-fat diet (HFD)-induced obesity mice. METHODS: Eight-week-old male C57BL/6 J mice were fed a normal chow diet (NCD) or HFD with or without different concentrations of caffeine. After 12 weeks of treatment, body weight, insulin resistance, serum lipid profiles, gut microbiota and serum metabolomic profiles were assessed. RESULTS: Caffeine intervention improved the metabolic syndrome in HFD-fed mice, such as serum lipid disorders and insulin resistance. 16S rRNA Sequencing analysis revealed that caffeine increased the relative abundance of Dubosiella, Bifidobacterium and Desulfovibrio and decreased that of Bacteroides, Lactobacillus and Lactococcus to reverse HFD-fed obesity in mice. Additionally, Caffeine Supplementation also altered serum metabolomics, mainly focusing on lipid metabolism, bile acid metabolism and energy metabolism. Caffeine increased its metabolite 1,7-Dimethylxanthine, which was positively correlated with Dubosiella. CONCLUSIONS: Caffeine exerts a beneficial effect on insulin resistance in HFD-mice, and the underlying mechanism may be partly related to altered gut microbiota and bile acid metabolism.