Gene: a gene-centered information resource at NCBI

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Background and objective

Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns.

Methodology

This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method.

Results

The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies-Bouldin clustering index and a ∼ 60 % increase in the classification specificity score.

Conclusion

A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

Exploring biomarkers in trigeminal neuralgia patients operated with microvascular decompression: A comparison with multiple sclerosis patients and non-neurological controls

BACKGROUND

Trigeminal neuralgia (TN) is a severe facial pain condition often associated with a neurovascular conflict. However, neuroinflammation has also been implicated in TN, as it frequently co-occurs with multiple sclerosis (MS).

METHODS

We analysed protein expression levels of TN patients compared to MS patients and controls. Proximity Extension Assay technology was used to analyse the levels of 92 proteins with the Multiplex Neuro-Exploratory panel provided by SciLifeLab, Uppsala, Sweden. Serum and CSF samples were collected from TN patients before (n = 33 and n = 27, respectively) and after (n = 28 and n = 8, respectively) microvascular decompression surgery. Additionally, we included samples from MS patients (n = 20) and controls (n = 20) for comparison.

RESULTS

In both serum and CSF, several proteins were found increased in TN patients compared to either MS patients, controls, or both, including EIF4B, PTPN1, EREG, TBCB, PMVK, FKBP5, CD63, CRADD, BST2, CD302, CRIP2, CCL27, PPP3R1, WWP2, KLB, PLA2G10, TDGF1, SMOC1, RBKS, LTBP3, CLSTN1, NXPH1, SFRP1, HMOX2, and GGT5. The overall expression of the 92 proteins in postoperative TN samples seems to shift towards the levels of MS patients and controls in both serum and CSF, as compared to preoperative samples. Interestingly, there was no difference in protein levels between MS patients and controls.

CONCLUSIONS

We conclude that TN patients showed increased serum and CSF levels of specific proteins and that successful surgery normalizes these protein levels, highlighting its potential as an effective treatment. However, the similarity between MS and controls challenges the idea of shared pathophysiology with TN, suggesting distinct underlying mechanisms in these conditions.

SIGNIFICANCE

This study advances our understanding of trigeminal neuralgia (TN) and its association with multiple sclerosis (MS). By analysing 92 protein biomarkers, we identified distinctive molecular profiles in TN patients, shedding light on potential pathophysiological mechanisms. The observation that successful surgery normalizes many protein levels suggests a promising avenue for TN treatment. Furthermore, the contrasting protein patterns between TN and MS challenge prevailing assumptions of similarity between the two conditions and point to distinct pathophysiological mechanisms.

Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation

MOTIVATION

Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process.

RESULTS

We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.

This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge.

AVAILABILITY AND IMPLEMENTATION

https://github.com/jiyuc/de-inconsistency.

Expanding and Enriching the LncRNA Gene-Disease Landscape Using the GeneCaRNA Database

The GeneCaRNA human gene database is a member of the GeneCards Suite. It presents ~280,000 human non-coding RNA genes, identified algorithmically from ~690,000 RNAcentral transcripts. This expands by ~tenfold the ncRNA gene count relative to other sources. GeneCaRNA thus contains ~120,000 long non-coding RNAs (LncRNAs, >200 bases long), including ~100,000 novel genes. The latter have sparse functional information, a vast terra incognita for future research. LncRNA genes are uniformly represented on all nuclear chromosomes, with 10 genes on mitochondrial DNA. Data obtained from MalaCards, another GeneCards Suite member, finds 1547 genes associated with 1 to 50 diseases. About 15% of the associations portray experimental evidence, with cancers tending to be multigenic. Preliminary text mining within GeneCaRNA discovers interactions of lncRNA transcripts with target gene products, with 25% being ncRNAs and 75% proteins. GeneCaRNA has a biological pathways section, which at present shows 131 pathways for 38 lncRNA genes, a basis for future expansion. Finally, our GeneHancer database provides regulatory elements for ~110,000 lncRNA genes, offering pointers for co-regulated genes and genetic linkages from enhancers to diseases. We anticipate that the broad vista provided by GeneCaRNA will serve as an essential guide for further lncRNA research in disease decipherment.

Virological characteristics of a SARS-CoV-2-related bat coronavirus, BANAL-20-236

BACKGROUND

Although several SARS-CoV-2-related coronaviruses (SC2r-CoVs) were discovered in bats and pangolins, the differences in virological characteristics between SARS-CoV-2 and SC2r-CoVs remain poorly understood. Recently, BANAL-20-236 (B236) was isolated from a rectal swab of Malayan horseshoe bat and was found to lack a furin cleavage site (FCS) in the spike (S) protein. The comparison of its virological characteristics with FCS-deleted SARS-CoV-2 (SC2ΔFCS) has not been conducted yet.

METHODS

We prepared human induced pluripotent stem cell (iPSC)-derived airway and lung epithelial cells and colon organoids as human organ-relevant models. B236, SARS-CoV-2, and artificially generated SC2ΔFCS were used for viral experiments. To investigate the pathogenicity of B236 in vivo, we conducted intranasal infection experiments in hamsters.

FINDINGS

In human iPSC-derived airway epithelial cells, the growth of B236 was significantly lower than that of the SC2ΔFCS. A fusion assay showed that the B236 and SC2ΔFCS S proteins were less fusogenic than the SARS-CoV-2 S protein. The infection experiment in hamsters showed that B236 was less pathogenic than SARS-CoV-2 and even SC2ΔFCS. Interestingly, in human colon organoids, the growth of B236 was significantly greater than that of SARS-CoV-2.

INTERPRETATION

Compared to SARS-CoV-2, we demonstrated that B236 exhibited a tropism toward intestinal cells rather than respiratory cells. Our results are consistent with a previous report showing that B236 is enterotropic in macaques. Altogether, our report strengthens the assumption that SC2r-CoVs in horseshoe bats replicate primarily in the intestinal tissues rather than respiratory tissues.

FUNDING

This study was supported in part by AMED ASPIRE (JP23jf0126002, to Keita Matsuno, Kazuo Takayama, and Kei Sato); AMED SCARDA Japan Initiative for World-leading Vaccine Research and Development Centers "UTOPIA" (JP223fa627001, to Kei Sato), AMED SCARDA Program on R&D of new generation vaccine including new modality application (JP223fa727002, to Kei Sato); AMED SCARDA Hokkaido University Institute for Vaccine Research and Development (HU-IVReD) (JP223fa627005h0001, to Takasuke Fukuhara, and Keita Matsuno); AMED Research Program on Emerging and Re-emerging Infectious Diseases (JP21fk0108574, to Hesham Nasser; JP21fk0108493, to Takasuke Fukuhara; JP22fk0108617 to Takasuke Fukuhara; JP22fk0108146, to Kei Sato; JP21fk0108494 to G2P-Japan Consortium, Keita Matsuno, Shinya Tanaka, Terumasa Ikeda, Takasuke Fukuhara, and Kei Sato; JP21fk0108425, to Kazuo Takayama and Kei Sato; JP21fk0108432, to Kazuo Takayama, Takasuke Fukuhara and Kei Sato; JP22fk0108534, Terumasa Ikeda, and Kei Sato; JP22fk0108511, to Yuki Yamamoto, Terumasa Ikeda, Keita Matsuno, Shinya Tanaka, Kazuo Takayama, Takasuke Fukuhara, and Kei Sato; JP22fk0108506, to Kazuo Takayama and Kei Sato); AMED Research Program on HIV/AIDS (JP22fk0410055, to Terumasa Ikeda; and JP22fk0410039, to Kei Sato); AMED Japan Program for Infectious Diseases Research and Infrastructure (JP22wm0125008 to Keita Matsuno); AMED CREST (JP21gm1610005, to Kazuo Takayama; JP22gm1610008, to Takasuke Fukuhara; JST PRESTO (JPMJPR22R1, to Jumpei Ito); JST CREST (JPMJCR20H4, to Kei Sato); JSPS KAKENHI Fund for the Promotion of Joint International Research (International Leading Research) (JP23K20041, to G2P-Japan Consortium, Keita Matsuno, Takasuke Fukuhara and Kei Sato); JST SPRING (JPMJSP2108 to Shigeru Fujita); JSPS KAKENHI Grant-in-Aid for Scientific Research C (22K07103, to Terumasa Ikeda); JSPS KAKENHI Grant-in-Aid for Scientific Research B (21H02736, to Takasuke Fukuhara); JSPS KAKENHI Grant-in-Aid for Early-Career Scientists (22K16375, to Hesham Nasser; 20K15767, to Jumpei Ito); JSPS Core-to-Core Program (A. Advanced Research Networks) (JPJSCCA20190008, to Kei Sato); JSPS Research Fellow DC2 (22J11578, to Keiya Uriu); JSPS Research Fellow DC1 (23KJ0710, to Yusuke Kosugi); JSPS Leading Initiative for Excellent Young Researchers (LEADER) (to Terumasa Ikeda); World-leading Innovative and Smart Education (WISE) Program 1801 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) (to Naganori Nao); Ministry of Health, Labour and Welfare (MHLW) under grant 23HA2010 (to Naganori Nao and Keita Matsuno); The Cooperative Research Program (Joint Usage/Research Center program) of Institute for Life and Medical Sciences, Kyoto University (to Kei Sato); International Joint Research Project of the Institute of Medical Science, the University of Tokyo (to Terumasa Ikeda and Takasuke Fukuhara); The Tokyo Biochemical Research Foundation (to Kei Sato); Takeda Science Foundation (to Terumasa Ikeda and Takasuke Fukuhara); Mochida Memorial Foundation for Medical and Pharmaceutical Research (to Terumasa Ikeda); The Naito Foundation (to Terumasa Ikeda); Hokuto Foundation for Bioscience (to Tomokazu Tamura); Hirose Foundation (to Tomokazu Tamura); and Mitsubishi Foundation (to Kei Sato).

Computational analysis and expression profiling of two-component system (TCS) gene family members in mango (Mangifera indica) indicated their roles in stress response

The two-component system (TCS) gene family is among the most important signal transduction families in plants and is involved in the regulation of various abiotic stresses, cell growth and division. To understand the role of TCS genes in mango (Mangifera indica ), a comprehensive analysis of TCS gene family was carried out in mango leading to identification of 65 MiTCS genes. Phylogenetic analysis divided MiTCSs into three groups (histidine kinases, histidine-containing phosphotransfer proteins, and response regulators) and 11 subgroups. One tandem duplication and 23 pairs of segmental duplicates were found within the MiTCSs . Promoter analysis revealed that MiTCSs contain a large number of cis -elements associated with environmental stresses, hormone response, light signalling, and plant development. Gene ontology analysis showed their involvement in various biological processes and molecular functions, particularly signal transduction. Protein-protein interaction analysis showed that MiTCS proteins interacted with each other. The expression pattern in various tissues and under many stresses (drought, cold, and disease) showed that expression levels varied among various genes in different conditions. MiTCSs 3D structure predictions showed structural conservation among members of the same groups. This information can be further used to develop improved cultivars and will serve as a foundation for gaining more functional insights into the TCS gene family.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

An IFNγ-dependent immune-endocrine circuit lowers blood glucose to potentiate the innate antiviral immune response

Viral infection makes us feel sick as the immune system alters systemic metabolism to better fight the pathogen. The extent of these changes is relative to the severity of disease. Whether blood glucose is subject to infection-induced modulation is mostly unknown. Here we show that strong, nonlethal infection restricts systemic glucose availability, which promotes the antiviral type I interferon (IFN-I) response. Following viral infection, we find that IFNγ produced by γδ T cells stimulates pancreatic β cells to increase glucose-induced insulin release. Subsequently, hyperinsulinemia lessens hepatic glucose output. Glucose restriction enhances IFN-I production by curtailing lactate-mediated inhibition of IRF3 and NF-κB signaling. Induced hyperglycemia constrained IFN-I production and increased mortality upon infection. Our findings identify glucose restriction as a physiological mechanism to bring the body into a heightened state of responsiveness to viral pathogens. This immune-endocrine circuit is disrupted in hyperglycemia, possibly explaining why patients with diabetes are more susceptible to viral infection.

CEBPB-mediated upregulation of SERPINA1 promotes colorectal cancer progression by enhancing STAT3 signaling

Colorectal cancer (CRC) is a highly malignant carcinoma associated with poor prognosis, and metastasis is one of the most common causes of death in CRC. Serpin Family A Member 1 (SERPINA1) is a serine protease inhibitor from the Serpin family. Till now, the function and mechanism of SERPINA1 in CRC progression have not been fully illustrated. We established highly metastatic colorectal cancer cells named as RKO-H and Caco2-H by mice liver metastasis model. By integrative bioinformatic approaches, we analyzed the prognostic value and clinical significance of SERPINA1 in CRC, and predicted potential transcription factors. Colony formation, EDU, MTS, Transwell and wound healing assay were performed to evaluate the biological functions of SERPINA1 in CRC in vitro. Experiments in vivo were conducted to explore the effects of SERPINA1 on liver metastasis of CRC. ChIP and luciferase reporter gene assays were performed to identify the transcriptional regulatory mechanism of SERPINA1 by CEBPB. Our results show that SERPINA1 is highly expressed in CRC and correlated with poor clinical outcomes. SERPINA1 promotes the proliferation, migration by activating STAT3 pathway. Mechanistically, CEBPB binds SERPINA1 gene promoter sequence and promotes the transcription of SERPINA1. SERPINA1 drives CEBPB-induced tumor cell growth and migration via augmenting STAT3 signaling. Our results suggest that SERPINA1 is a potential prognostic marker and may serve as a novel treatment target for CRC.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Danggui Shaoyao San protects cyclophosphamide-induced premature ovarian failure by inhibiting apoptosis and oxidative stress through the regulation of the SIRT1/p53 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

It has been reported that apoptosis and oxidative stress are related to cyclophosphamide (CYC)-induced premature ovarian failure (POF). Therefore, anti-apoptotic and anti-oxidative stress treatments exhibit therapeutic efficacy in CYC-induced POF. Danggui Shaoyao San (DSS), which has been extensively used to treat gynecologic diseases, is found to inhibit apoptosis and reduce oxidative stress. However, the roles of DSS in regulating apoptosis and oxidative stress during CYC-induced POF, and its associated mechanisms are still unknown.

AIM OF THE STUDY

This work aimed to investigate the roles and mechanisms of DSS in inhibiting apoptosis and oxidative stress in CYC-induced POF.

MATERIALS AND METHODS

CYC (75 mg/kg) was intraperitoneally injected in mice to construct the POF mouse model for in vivo study. Thereafter, alterations of body weight, ovary morphology and estrous cycle were monitored to assess the ovarian protective properties of DSS. Serum LH and E2 levels were analyzed by enzyme-linked immunosorbent assay (ELISA). Hematoxylin-eosin (HE) staining was employed for examining ovarian pathological morphology and quantifying follicles in various stages. Meanwhile, TUNEL staining and apoptosis-related proteins were adopted for evaluating apoptosis. Oxidative stress was measured by the levels of ROS, MDA, and 4-HNE. Western blot (WB) assay was performed to detect proteins related to the SIRT1/p53 pathway. KGN cells were used for in vitro experiment. TBHP stimulation was carried out for establishing the oxidative stress-induced apoptosis cell model. Furthermore, MTT assay was employed for evaluating the protection of DSS from TBHP-induced oxidative stress. The anti-apoptotic ability of DSS was evaluated by hoechst/PI staining, JC-1 staining, and apoptosis-related proteins. Additionally, the anti-oxidative stress ability of DSS was measured by detecting the levels of ROS, MDA, and 4-HNE. Proteins related to SIRT1/p53 signaling pathway were also measured using WB and immunofluorescence (IF) staining. Besides, SIRT1 expression was suppressed by EX527 to further investigate the role of SIRT1 in the effects of DSS against apoptosis and oxidative stress.

RESULTS

In the in vivo experiment, DSS dose-dependently exerted its anti-apoptotic, anti-oxidative stress, and ovarian protective effects. In addition, apoptosis, apoptosis-related protein and oxidative stress levels were inhibited by DSS treatment. DSS treatment up-regulated SIRT1 and down-regulated p53 expression. From in vitro experiment, it was found that DSS treatment protected KGN cells from TBHP-induced oxidative stress injury. Besides, DSS administration suppressed the apoptosis ratio, apoptosis-related protein levels, mitochondrial membrane potential damage, and oxidative stress. SIRT1 suppression by EX527 abolished the anti-apoptotic, anti-oxidative stress, and ovarian protective effects, as discovered from in vivo and in vitro experiments.

CONCLUSIONS

DSS exerts the anti-apoptotic, anti-oxidative stress, and ovarian protective effects in POF mice, and suppresses the apoptosis and oxidative stress of KGN cells through activating SIRT1 and suppressing p53 pathway.

The deleterious variants of N-acetylgalactosamine-6-sulfatase (GalN6S) enzyme trigger Morquio a syndrome by disrupting protein foldings

Lysosomal enzymes degrade cellular macromolecules, while their inactivation causes human hereditary metabolic disorders. Mucopolysaccharidosis IVA (MPS IVA; Moquio A syndrome) is one of the lysosomal storage disorders caused by a defective Galactosamine-6-sulfatase (GalN6S) enzyme. In several populations, disease incidence is elevated due to missense mutations brought on by non-synonymous allelic variation in the GalN6S enzyme. Here, we studied the effect of non-synonymous single nucleotide polymorphism (nsSNPs) on the structural dynamics of the GalN6S enzyme and its binding with N-acetylgalactosamine (GalNAc) using all-atom molecular dynamics simulation and an essential dynamics approach. Consequently, in this study, we have identified three functionally disruptive mutations in domain-I and domain-II, that is, S80L, R90W, and S162F, which presumably contribute to post-translational modifications. The study delineated that both domains work cooperatively, and alteration in domain II (S80L, R90W) leads to conformational changes in the catalytic site in domain-I, while mutation S162F mainly provokes higher residual flexibility of domain II. These results show that these mutations impair the hydrophobic core, implying that Morquio A syndrome is caused by misfolding of the GalN6S enzyme. The results also show the instability of the GalN6S-GalNAc complex upon substitution. Overall, the structural dynamics resulting from point mutations give the molecular rationale for Moquio A syndrome and, more importantly, the Mucopolysaccharidoses (MPS) family of diseases, re-establishing MPS IVA as a protein-folding disease.Communicated by Ramaswamy H. Sarma.

GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT

There has been significant recent progress in leveraging large-scale gene expression data to develop foundation models for single-cell biology. Models such as Geneformer and scGPT implicitly learn gene and cellular functions from the gene expression profiles of millions of cells, which requires extensive data curation and resource-intensive training. Here we explore a much simpler alternative by leveraging ChatGPT embeddings of genes based on literature. Our proposal, GenePT, uses NCBI text descriptions of individual genes with GPT-3.5 to generate gene embeddings. From there, GenePT generates single-cell embeddings in two ways: (i) by averaging the gene embeddings, weighted by each gene's expression level; or (ii) by creating a sentence embedding for each cell, using gene names ordered by the expression level. Without the need for dataset curation and additional pretraining, GenePT is efficient and easy to use. On many downstream tasks used to evaluate recent single-cell foundation models - e.g., classifying gene properties and cell types - GenePT achieves comparable, and often better, performance than Geneformer and other models. GenePT demonstrates that large language model embedding of literature is a simple and effective path for biological foundation models.

StoneMod 2.0: Database and prediction of kidney stone modulatory proteins

Stone modulators are various kinds of molecules that play crucial roles in promoting/inhibiting kidney stone formation. Several recent studies have extensively characterized the stone modulatory proteins with the ultimate goal of preventing kidney stone formation. Herein, we introduce the StoneMod 2.0 database (https://www.stonemod.org), which has been dramatically improved from the previous version by expanding the number of the modulatory proteins in the list (from 32 in the initial version to 17,130 in this updated version). The stone modulatory proteins were recruited from solid experimental evidence (via PubMed) and/or predicted evidence (via UniProtKB, QuickGO, ProRule, STITCH and OxaBIND to retrieve calcium-binding and oxalate-binding proteins). Additionally, StoneMod 2.0 has implemented a scoring system that can be used to determine the likelihood and to classify the potential stone modulatory proteins as either "solid" (modulator score ≥ 50) or "weak" (modulator score < 50) modulators. Furthermore, the updated version has been designed with more user-friendly interfaces and advanced visualization tools. In addition to the monthly scheduled update, the users can directly submit their experimental evidence online anytime. Therefore, StoneMod 2.0 is a powerful database with prediction scores that will be very useful for many future studies on the stone modulatory proteins.

SJPedPanel: A pan-cancer gene panel for childhood malignancies

Background

Large scale genomics projects have identified driver alterations for most childhood cancers that provide reliable biomarkers for clinical diagnosis and disease monitoring using targeted sequencing. However, there is lack of a comprehensive panel that matches the list of known driver genes. Here we fill this gap by developing SJPedPanel for childhood cancers.

Results

SJPedPanel covers 5,275 coding exons of 357 driver genes, 297 introns frequently involved in rearrangements that generate fusion oncoproteins, commonly amplified/deleted regions (e.g., MYCN for neuroblastoma, CDKN2A and PAX5 for B-/T-ALL, and SMARCB1 for AT/RT), and 7,590 polymorphism sites for interrogating tumors with aneuploidy, such as hyperdiploid and hypodiploid B-ALL or 17q gain neuroblastoma. We used driver alterations reported from an established real-time clinical genomics cohort (n=253) to validate this gene panel. Among the 485 pathogenic variants reported, our panel covered 417 variants (86%). For 90 rearrangements responsible for oncogenic fusions, our panel covered 74 events (82%). We re-sequenced 113 previously characterized clinical specimens at an average depth of 2,500X using SJPedPanel and recovered 354 (91%) of the 389 reported pathogenic variants. We then investigated the power of this panel in detecting mutations from specimens with low tumor purity (as low as 0.1%) using cell line-based dilution experiments and discovered that this gene panel enabled us to detect ∼80% variants with allele fraction of 0.2%, while the detection rate decreases to ∼50% when the allele fraction is 0.1%. We finally demonstrate its utility in disease monitoring on clinical specimens collected from AML patients in morphologic remission.

Conclusions

SJPedPanel enables the detection of clinically relevant genetic alterations including rearrangements responsible for subtype-defining fusions for childhood cancers by targeted sequencing of ∼0.15% of human genome. It will enhance the analysis of specimens with low tumor burdens for cancer monitoring and early detection.

Sperm DNA methylation dynamics after chemotherapy: a longitudinal study of a patient with testicular germ cell tumor treatment

BACKGROUND

An important issue for young men affected by testicular germ cell tumor (TGCT) is how TGCT and its treatment will affect, transiently or permanently, their future reproductive health. Previous studies have reported that xenobiotics can induce changes on human sperm epigenome and have the potential to promote epigenetic alterations in the offspring.

OBJECTIVES

Here, we report the first longitudinal DNA methylation profiling of frozen sperm from a TGCT patient before and up to 2 years after a bleomycin, etoposide, and cisplatin (BEP) chemotherapy.

MATERIALS AND METHODS

A TGCT was diagnosed in a 30-year-old patient. A cryopreservation of spermatozoa was proposed before adjuvant BEP treatment. Semen samples were collected before and after chemotherapy at 6, 9, 12, and 24 months. The DNA methylation status was determined by RRBS to detect DNA differentially methylated regions (DMRs).

RESULTS

The analysis revealed that among the 74 DMRs showing modified methylation status 6 months after therapy, 17 remained altered 24 months after treatment. We next associated DMRs with differentially methylated genes (DMGs), which were subsequently intersected with loci known to be important or expressed during early development.

DISCUSSION AND CONCLUSION

The consequences of the cancer treatment on the sperm epigenome during the recovery periods are topical issues of increasing significance as epigenetic modifications to the paternal genome may have deleterious effects on the offspring. The altered methylated status of these DMGs important for early development might modify their expression pattern and thus affect their function during key stages of embryogenesis, potentially leading to developmental disorders or miscarriages.

RVvictor: Virus RNA-directed molecular interactions for RNA virus infection

RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.

Molecular Insights into the Synergistic Effects of Putrescine and Ammonium on Dinoflagellates

Ammonium and polyamines are essential nitrogen metabolites in all living organisms. Crosstalk between ammonium and polyamines through their metabolic pathways has been demonstrated in plants and animals, while no research has been directed to explore this relationship in algae or to investigate the underlying molecular mechanisms. Previous research demonstrated that high concentrations of ammonium and putrescine were among the active substances in bacteria-derived algicide targeting dinoflagellates, suggesting that the biochemical inter-connection and/or interaction of these nitrogen compounds play an essential role in controlling these ecologically important algal species. In this research, putrescine, ammonium, or a combination of putrescine and ammonium was added to cultures of three dinoflagellate species to explore their effects. The results demonstrated the dose-dependent and species-specific synergistic effects of putrescine and ammonium on these species. To further explore the molecular mechanisms behind the synergistic effects, transcriptome analysis was conducted on dinoflagellate Karlodinium veneficum treated with putrescine or ammonium vs. a combination of putrescine and ammonium. The results suggested that the synergistic effects of putrescine and ammonium disrupted polyamine homeostasis and reduced ammonium tolerance, which may have contributed to the cell death of K. veneficum. There was also transcriptomic evidence of damage to chloroplasts and impaired photosynthesis of K. veneficum. This research illustrates the molecular mechanisms underlying the synergistic effects of the major nitrogen metabolites, ammonium and putrescine, in dinoflagellates and provides direction for future studies on polyamine biology in algal species.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures

Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.

Unraveling the anti-primary dysmenorrhea mechanism of Ainsliaea fragrans Champ. extract by the integrative approach of network pharmacology and experimental verification

BACKGROUND

The plant Ainsliaea fragrans Champ. (A. fragrans) named "Xingxiang Tuerfeng", is a traditional herb with a long history of therapeutic practice in southern China in the treatment of gynecological diseases.

PURPOSE

The anti-inflammatory extract of Ainsliaea fragrans Champ. (AF-ext) exhibited anti-primary dysmenorrhea (PD) activity in oxytocin-induced mice. This study aimed to unravel the underlying mechanisms of AF-ext on PD by the integrative approach of network pharmacology and experimental verification.

METHODS

First, the therapeutic targets of AF-ext are predicted using network pharmacology and molecular docking methods. Second, activity screening and immunoblotting methods were used for target validation. Then, the therapeutic effect of AF-ext on PD was evaluated using oxytocin-induced mice and uterine strips model.

RESULTS

AF-p1, and AF-p2, the active ingredients of AF-ext, showed inhibitory effects on COX1/2 and EGFR, and all five active components showed antagonistic activity on TRPV1. AF-ext (25, 50, 100 mg/kg) could significantly reduce the number of writhing times and prolong writhing latencies in a dose-dependent manner. AF-ext inhibited spasmolytic activity in uterine strips induced by oxytocin and Ca2+ stimulation. AF-ext inhibited NF-κB/COX-2/PG pathway and activation of the NLRP3 inflammasome in PD mice. It significantly downregulated the PD-induced overexpression of p-p65/p65, p-IκBα, and COX-2 by inhibiting the NF-κB pathway. Moreover, the overexpression of NLRP3, p20/pro-Caspase 1, and p17/pro-IL-1β was greatly downregulated.

CONCLUSIONS

AF-ext demonstrated anti-inflammatory, analgesic, and spasmolytic activity in the treatment of PD. It inhibited the NF-κB/COX-2/PG pathway and NLRP3 inflammasome activation in PD mice with a multi-target approach.

Joint representation of molecular networks from multiple species improves gene classification

Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts.

Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher's exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications.Communicated by Ramaswamy H. Sarma.

NTHL1 is a recessive cancer susceptibility gene

In search of novel breast cancer (BC) risk variants, we performed a whole-exome sequencing and variant analysis of 69 Finnish BC patients as well as analysed loss-of-function variants identified in DNA repair genes in the Finns from the Genome Aggregation Database. Additionally, we carried out a validation study of SERPINA3 c.918-1G>C, recently suggested for BC predisposition. We estimated the frequencies of 41 rare candidate variants in 38 genes by genotyping them in 2482-4101 BC patients and in 1273-3985 controls. We further evaluated all coding variants in the candidate genes in a dataset of 18,786 BC patients and 182,927 controls from FinnGen. None of the variants associated significantly with cancer risk in the primary BC series; however, in the FinnGen data, NTHL1 c.244C>T p.(Gln82Ter) associated with BC with a high risk for homozygous (OR = 44.7 [95% CI 6.90-290], P = 6.7 × 10-5) and a low risk for heterozygous women (OR = 1.39 [1.18-1.64], P = 7.8 × 10-5). Furthermore, the results suggested a high risk of colorectal, urinary tract, and basal-cell skin cancer for homozygous individuals, supporting NTHL1 as a recessive multi-tumour susceptibility gene. No significant association with BC risk was detected for SERPINA3 or any other evaluated gene.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

C8ORF88: A Novel eIF4E-Binding Protein

Translation initiation in eukaryotes is regulated at several steps, one of which involves the availability of the cap binding protein to participate in cap-dependent protein synthesis. Binding of eIF4E to translational repressors (eIF4E-binding proteins [4E-BPs]) suppresses translation and is used by cells to link extra- and intracellular cues to protein synthetic rates. The best studied of these interactions involves repression of translation by 4E-BP1 upon inhibition of the PI3K/mTOR signaling pathway. Herein, we characterize a novel 4E-BP, C8ORF88, whose expression is predominantly restricted to early spermatids. C8ORF88:eIF4E interaction is dependent on the canonical eIF4E binding motif (4E-BM) present in other 4E-BPs. Whereas 4E-BP1:eIF4E interaction is dependent on the phosphorylation of 4E-BP1, these sites are not conserved in C8ORF88 indicating a different mode of regulation.

Identification and characterization of Glycolate oxidase gene family in garden lettuce (Lactuca sativa cv. 'Salinas') and its response under various biotic, abiotic, and developmental stresses

Glycolate oxidase (GLO) is an FMN-containing enzyme localized in peroxisomes and performs in various molecular and biochemical mechanisms. It is a key player in plant glycolate and glyoxylate accumulation pathways. The role of GLO in disease and stress resistance is well-documented in various plant species. Although studies have been conducted regarding the role of GLO genes from spinach on a microbial level, the direct response of GLO genes to various stresses in short-season and leafy plants like lettuce has not been published yet. The genome of Lactuca sativa cultivar 'Salinas' (v8) was used to identify GLO gene members in lettuce by performing various computational analysis. Dual synteny, protein-protein interactions, and targeted miRNA analyses were conducted to understand the function of GLO genes. The identified GLO genes showed further clustering into two groups i.e., glycolate oxidase (GOX) and hydroxyacid oxidase (HAOX). Genes were observed to be distributed unevenly on three chromosomes, and syntenic analysis revealed that segmental duplication was prevalent. Thus, it might be the main reason for GLO gene diversity in lettuce. Almost all LsGLO genes showed syntenic blocks in respective plant genomes under study. Protein-protein interactions of LsGLO genes revealed various functional enrichments, mainly photorespiration, and lactate oxidation, and among biological processes oxidative photosynthetic carbon pathway was highly significant. Results of in-depth analyses disclosed the interaction of GLO genes with other members of the glycolate pathway and the activity of GLO genes in various organs and developmental stages in lettuce. The extensive genome evaluation of GLO gene family in garden lettuce is believed to be a reference for cloning and studying functional analyses of GLO genes and characterizing other members of glycolate/glyoxylate biosynthesis pathway in various plant species.

BELB: a biomedical entity linking benchmark

MOTIVATION

Biomedical entity linking (BEL) is the task of grounding entity mentions to a knowledge base (KB). It plays a vital role in information extraction pipelines for the life sciences literature. We review recent work in the field and find that, as the task is absent from existing benchmarks for biomedical text mining, different studies adopt different experimental setups making comparisons based on published numbers problematic. Furthermore, neural systems are tested primarily on instances linked to the broad coverage KB UMLS, leaving their performance to more specialized ones, e.g. genes or variants, understudied.

RESULTS

We therefore developed BELB, a biomedical entity linking benchmark, providing access in a unified format to 11 corpora linked to 7 KBs and spanning six entity types: gene, disease, chemical, species, cell line, and variant. BELB greatly reduces preprocessing overhead in testing BEL systems on multiple corpora offering a standardized testbed for reproducible experiments. Using BELB, we perform an extensive evaluation of six rule-based entity-specific systems and three recent neural approaches leveraging pre-trained language models. Our results reveal a mixed picture showing that neural approaches fail to perform consistently across entity types, highlighting the need of further studies towards entity-agnostic models.

AVAILABILITY AND IMPLEMENTATION

The source code of BELB is available at: https://github.com/sg-wbi/belb. The code to reproduce our experiments can be found at: https://github.com/sg-wbi/belb-exp.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

Identifying potential anti-metastasis drugs for prostate cancer through integrative bioinformatics analysis and compound library screening

BACKGROUND

Metastasis poses the greatest threat to the lives of individuals with prostate cancer. Therefore, it is imperative to identify the underlying mechanism driving metastasis. Doing so would facilitate the detection of new diagnostic biomarkers and the advancement of treatment options for patients.

METHODS

Metastasis-related modules were identified through weighted gene co-expression network analysis based on microarray GSE6919. Hub genes were confirmed by quantitative real-time PCR across different prostate cell lines and clinic samples. Pivotal genes were determined through integration of RNA and transcription factor-target associated interactions. To predict drugs with potential to suppress tumor metastasis, we applied molecular networks using the DrugBank database. Drug repositioning analysis and confirmation of drug screen were conducted using the compound library. Confirmation of selective cytotoxicity of cupric oxide was carried out via invasion, transwell and apoptosis assays.

RESULTS

We identified five metastasis-related modules. Of these modules, two were identified to represent core dysfunction modules in which five hub genes were determined for each module. Five of these 10 genes correlating with prostate cancer progression. Furthermore, our analysis revealed that there are 36 drugs with the potential to be active against tumor metastasis. Finally, we identified four compounds that have not previously been reported to have any association with cancer therapy. Of these, cupric oxide was determined to have the best chemotherapeutic potential in treating prostate cancer metastasis.

CONCLUSIONS

By combining bioinformatics methods with compound library screening, this study proposes a valuable approach to drug discovery. Cupric oxide showed the potential in the treatment of prostate cancer metastasis and deserves further study.

Artificial general intelligence for radiation oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.

Molecular Mechanisms for Changing Brain Connectivity in Mice and Humans

The goal of this study was to examine commonalities in the molecular basis of learning in mice and humans. In previous work we have demonstrated that the anterior cingulate cortex (ACC) and hippocampus (HC) are involved in learning a two-choice visuospatial discrimination task. Here, we began by looking for candidate genes upregulated in mouse ACC and HC with learning. We then determined which of these were also upregulated in mouse blood. Finally, we used RT-PCR to compare candidate gene expression in mouse blood with that from humans following one of two forms of learning: a working memory task (network training) or meditation (a generalized training shown to change many networks). Two genes were upregulated in mice following learning: caspase recruitment domain-containing protein 6 (Card6) and inosine monophosphate dehydrogenase 2 (Impdh2). The Impdh2 gene product catalyzes the first committed step of guanine nucleotide synthesis and is tightly linked to cell proliferation. The Card6 gene product positively modulates signal transduction. In humans, Card6 was significantly upregulated, and Impdh2 trended toward upregulation with training. These genes have been shown to regulate pathways that influence nuclear factor kappa B (NF-κB), a factor previously found to be related to enhanced synaptic function and learning.

Brain Gene Co-Expression Network Analysis Identifies 22q13 Region Genes Associated with Autism, Intellectual Disability, Seizures, Language Impairment, and Hypotonia

Phelan-McDermid syndrome (PMS) is a rare genetic neurodevelopmental disorder caused by 22q13 region deletions or SHANK3 gene variants. Deletions vary in size and can affect other genes in addition to SHANK3. PMS is characterized by autism spectrum disorder (ASD), intellectual disability (ID), developmental delays, seizures, speech delay, hypotonia, and minor dysmorphic features. It is challenging to determine individual gene contributions due to variability in deletion sizes and clinical features. We implemented a genomic data mining approach for identifying and prioritizing the candidate genes in the 22q13 region for five phenotypes: ASD, ID, seizures, language impairment, and hypotonia. Weighted gene co-expression networks were constructed using the BrainSpan transcriptome dataset of a human brain. Bioinformatic analyses of the co-expression modules allowed us to select specific candidate genes, including EP300, TCF20, RBX1, XPNPEP3, PMM1, SCO2, BRD1, and SHANK3, for the common neurological phenotypes of PMS. The findings help understand the disease mechanisms and may provide novel therapeutic targets for the precise treatment of PMS.

Prenatal exposure to triclosan assessed in multiple urine samples and placental DNA methylation

A previous study reported positive associations of maternal urinary concentrations of triclosan, a synthetic phenol with widespread exposure in the general population, with placental DNA methylation of male fetuses. Given the high number of comparisons performed in -omic research, further studies were needed to validate and extend on these findings. Using a cohort of male and female fetuses with repeated maternal urine samples to assess exposure, we studied the associations between triclosan and placental DNA methylation. We assessed triclosan concentrations in two pools of 21 urine samples collected among 395 women from the SEPAGES cohort. We used Infinium Methylation EPIC arrays to measure DNA methylation in placental biopsies collected at delivery. We performed a candidate study restricted to a set of candidate CpGs (n = 500) identified in a previous work as well as an exploratory epigenome-wide association study to investigate the associations between triclosan and differentially methylated probes and regions. Analyses were conducted on the whole population and stratified by child's sex. Mediation analysis was performed to test whether heterogeneity of placental tissue may mediate the observed associations. In the candidate approach, we confirmed 18 triclosan-associated genes when both sexes were considered. After stratification for child's sex, triclosan was associated with 72 genes in females and three in males. Most of the associations were positive and several CpGs mapped to imprinted genes: FBRSL1, KCNQ1, RHOBTB3, and SMOC1. A mediation effect by placental tissue heterogeneity was identified for most of the observed associations. In the exploratory analysis, we identified a few isolated associations in the sex-stratified analysis. In line with a previous study on male placentas, our approach revealed several positive associations between triclosan exposure and placental DNA methylation. Several identified loci mapped to imprinted genes.

CanISO: a database of genomic and transcriptomic variations in domestic dog (Canis lupus familiaris)

BACKGROUND

The domestic dog, Canis lupus familiaris, is a companion animal for humans as well as an animal model in cancer research due to similar spontaneous occurrence of cancers as humans. Despite the social and biological importance of dogs, the catalogue of genomic variations and transcripts for dogs is relatively incomplete.

RESULTS

We developed CanISO, a new database to hold a large collection of transcriptome profiles and genomic variations for domestic dogs. CanISO provides 87,692 novel transcript isoforms and 60,992 known isoforms from whole transcriptome sequencing of canine tumors (N = 157) and their matched normal tissues (N = 64). CanISO also provides genomic variation information for 210,444 unique germline single nucleotide polymorphisms (SNPs) from the whole exome sequencing of 183 dogs, with a query system that searches gene- and transcript-level information as well as covered SNPs. Transcriptome profiles can be compared with corresponding human transcript isoforms at a tissue level, or between sample groups to identify tumor-specific gene expression and alternative splicing patterns.

CONCLUSIONS

CanISO is expected to increase understanding of the dog genome and transcriptome, as well as its functional associations with humans, such as shared/distinct mechanisms of cancer. CanISO is publicly available at https://www.kobic.re.kr/caniso/ .

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

GNorm2: an improved gene name recognition and normalization system

MOTIVATION

Gene name normalization is an important yet highly complex task in biomedical text mining research, as gene names can be highly ambiguous and may refer to different genes in different species or share similar names with other bioconcepts. This poses a challenge for accurately identifying and linking gene mentions to their corresponding entries in databases such as NCBI Gene or UniProt. While there has been a body of literature on the gene normalization task, few have addressed all of these challenges or make their solutions publicly available to the scientific community.

RESULTS

Building on the success of GNormPlus, we have created GNorm2: a more advanced tool with optimized functions and improved performance. GNorm2 integrates a range of advanced deep learning-based methods, resulting in the highest levels of accuracy and efficiency for gene recognition and normalization to date. Our tool is freely available for download.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ncbi/GNorm2.

Associations between epigenome-wide DNA methylation and height-related traits among Sub-Saharan Africans: the RODAM study

Human height and related traits are highly complex, and extensively research has shown that these traits are determined by both genetic and environmental factors. Such factors may partially affect these traits through epigenetic programing. Epigenetic programing is dynamic and plays an important role in controlling gene expression and cell differentiation during (early) development. DNA methylation (DNAm) is the most commonly studied epigenetic feature. In this study we conducted an epigenome-wide DNAm association analysis on height-related traits in a Sub-Saharan African population, in order to detect DNAm biomarkers across four height-related traits. DNAm profiles were acquired in whole blood samples of 704 Ghanaians, sourced from the Research on Obesity and Diabetes among African Migrants study, using the Illumina Infinium HumanMethylation450 BeadChip. Linear models were fitted to detect differentially methylated positions (DMPs) and regions (DMRs) associated with height, leg-to-height ratio (LHR), leg length, and sitting height. No epigenome-wide significant DMPs were recorded. However we did observe among our top DMPs five informative probes associated with the height-related traits: cg26905768 (leg length), cg13268132 (leg length), cg19776793 (height), cg23072383 (LHR), and cg24625894 (sitting height). All five DMPs are annotated to genes whose functions were linked to bone cell regulation and development. DMR analysis identified overlapping DMRs within the gene body of HLA-DPB1 gene, and the HOXA gene cluster. In this first epigenome-wide association studies of these traits, our findings suggest DNAm associations with height-related heights, and might influence development and maintenance of these traits. Further studies are needed to replicate our findings, and to elucidate the molecular mechanism underlying human height-related traits.

Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient

Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe's culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy.

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

Complete genome sequence analysis of plant growth-promoting bacterium, Isoptericola sp. AK164 isolated from the rhizosphere of Avicennia marina growing at the Red Sea coast

Isoptericola sp. AK164 is a Gram-positive, aerobic bacterial genus from the family Promicromonosporaceae, isolated from the root rhizosphere of Avicennia marina. AK164 significantly enhanced the growth of the Arabidopsis thaliana plant under normal and saline conditions. These bacteria can produce ACC deaminase and several enzymes playing a role in carbohydrate hydrolyses, such as cellulose, hemicellulose, and chitin degradation, which may contribute to plant growth, salt tolerance, and stress elevation. The genome sequence AK164 has a single circular chromosome of approximately 3.57 Mbp with a GC content of 73.53%. A whole genome sequence comparison of AK164 with type strains from the same genus, using digital DNA-DNA hybridization and average nucleotide identity calculations, revealed that AK164 might potentially belong to a new species of Isoptericola. Genome data and biochemical analyses indicate that AK164 could be a potential biostimulant for improving agriculture in submerged saline land.

The expanding universe of NUTM1 fusions in pediatric cancer

NUT midline carcinoma family member 1 (NUTM1) fusions were originally identified in poorly differentiated and clinically aggressive carcinomas typically located in the midline structures of children and young adults, and collectively known as NUT (midline) carcinomas. Next-generation sequencing later uncovered NUTM1 fusions in a variety of other pediatric and adult cancers of diverse location and type, including hematologic malignancies, cutaneous adnexal tumors, and sarcomas. A vast array of NUTM1 fusions with bromodomain containing 4 (BRD4) or bromodomain containing 3 (BRD3), which are characteristic of NUT carcinoma, and with several other fusion partners have been identified and associated with variable prognosis. These non-kinase fusions are thought to cause epigenetic reprogramming, thereby promoting proliferation, and hindering the differentiation of cancer cells. Many questions about both the function of the naïve NUTM1 protein, which is mostly restricted to the germ cells of the testis and is related to spermatogenesis and the oncogenic mechanisms of the various NUTM1 fusions in both adult and pediatric cancer, are still unanswered. Moreover, whether there is a relationship defined by the presence of NUTM1 fusions between conventional NUT carcinoma and other NUTM1-rearranged neoplasms remains to be elucidated. This review will focus on recent discoveries of NUTM1 fusions found in pediatric cancers, their prognostic impact, and emergence as novel oncogenic drivers.

MyGeneset.info: an interactive and programmatic platform for community-curated and user-created collections of genes

Gene definitions and identifiers can be painful to manage-more so when trying to include gene function annotations as this can be highly context-dependent. Creating groups of genes or gene sets can help provide such context, but it compounds the issue as each gene within the gene set can map to multiple identifiers and have annotations derived from multiple sources. We developed MyGeneset.info to provide an API for integrated annotations for gene sets suitable for use in analytical pipelines or web servers. Leveraging our previous work with MyGene.info (a server that provides gene-centric annotations and identifiers), MyGeneset.info addresses the challenge of managing gene sets from multiple resources. With our API, users readily have read-only access to gene sets imported from commonly-used resources such as Wikipathways, CTD, Reactome, SMPDB, MSigDB, GO, and DO. In addition to supporting the access and reuse of approximately 180k gene sets from humans, common model organisms (mice, yeast, etc.), and less-common ones (e.g. black cottonwood tree), MyGeneset.info supports user-created gene sets, providing an important means for making gene sets more FAIR. User-created gene sets can serve as a way to store and manage collections for analysis or easy dissemination through a consistent API.

Integrated serum pharmacochemistry, 16S rRNA sequencing and metabolomics to reveal the material basis and mechanism of Yinzhihuang granule against non-alcoholic fatty liver disease

ETHNOPHARMACOLOGICAL RELEVANCE

Yinzhihuang granule (YZHG) has liver protective effect and can be used for clinical treatment of non-alcoholic fatty liver disease (NAFLD), but its material basis and mechanism need to be further clarified.

AIM OF THE STUDY

This study aims to reveal the material basis and mechanism of YZHG treating NAFLD.

MATERIALS AND METHODS

Serum pharmacochemistry were employed to identify the components from YZHG. The potential targets of YZHG against NAFLD were predicted by system biology and then preliminarily verified by molecular docking. Furthermore, the functional mechanism of YZHG in NAFLD mice was elucidated by 16S rRNA sequencing and untargeted metabolomics.

RESULTS

From YZHG, 52 compounds were identified, of which 42 were absorbed into the blood. Network pharmacology and molecular docking showed that YZHG treats NAFLD with multi-components and multi-targets. YZHG can improve the levels of blood lipids, liver enzymes, lipopolysaccharide (LPS), and inflammatory factors in NAFLD mice. YZHG can also significantly improve the diversity and richness of intestinal flora and regulate glycerophospholipid and sphingolipid metabolism. Moreover, Western Blot experiment showed that YZHG can regulate liver lipid metabolism and enhance intestinal barrier function.

CONCLUSIONS

YZHG may treat NAFLD by improving the disruption of intestinal flora and enhancing the intestinal barrier. This will reduce the invasion of LPS into the liver subsequently regulate liver lipid metabolism and reduce liver inflammation.

Sertoli Cells Express Accommodation, Survival, and Immunoregulatory Factors When Exposed to Normal Human Serum

Transplantation is a clinical procedure that treats a variety of diseases yet is unattainable for many patients due to a nationwide organ shortage and the harsh side effects of chronic immune suppression. Xenografted pig organs are an attractive alternative to traditional allografts and would provide an endless supply of transplantable tissue, but transplants risk rejection by the recipient's immune system. An essential component of the rejection immune response is the complement system. Sertoli cells, an immunoregulatory testicular cell, survive complement as xenografts long term without any immune suppressants. We hypothesized that exposure to the xenogeneic complement influences Sertoli cell gene expression of other accommodation factors that contribute to their survival; thus, the purpose of this study was to describe these potential changes in gene expression. RNA sequencing of baseline neonatal pig Sertoli cells (NPSC) as compared to NPSC after exposure to normal human serum (NHS, containing complement) revealed 62 significantly differentially expressed genes (DEG) that affect over 30 pathways involved in immune regulation, cell survival, and transplant accommodation. Twelve genes of interest were selected for further study, and Sertoli cell protein expression of CCL2 and the accommodation factor A20 were confirmed for the first time. Functional pathway analyses were conducted in NPSC and three biological clusters were revealed as being considerably affected by NHS exposure: innate immune signaling, cytokine signaling, and T cell regulation. Better understanding of the interaction of Sertoli cells with complement in a xenograft environment may reveal the mechanisms behind immune-privileged systems to increase graft viability.

IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on single-cell sequencing data

MOTIVATION

Single-cell sequencing enables exploring the pathways and processes of cells, and cell populations. However, there is a paucity of pathway enrichment methods designed to tolerate the high noise and low gene coverage of this technology. When gene expression data are noisy and signals are sparse, testing pathway enrichment based on the genes expression may not yield statistically significant results, which is particularly problematic when detecting the pathways enriched in less abundant cells that are vulnerable to disturbances.

RESULTS

In this project, we developed a Weighted Concept Signature Enrichment Analysis specialized for pathway enrichment analysis from single-cell transcriptomics (scRNA-seq). Weighted Concept Signature Enrichment Analysis took a broader approach for assessing the functional relations of pathway gene sets to differentially expressed genes, and leverage the cumulative signature of molecular concepts characteristic of the highly differentially expressed genes, which we termed as the universal concept signature, to tolerate the high noise and low coverage of this technology. We then incorporated Weighted Concept Signature Enrichment Analysis into an R package called "IndepthPathway" for biologists to broadly leverage this method for pathway analysis based on bulk and single-cell sequencing data. Through simulating technical variability and dropouts in gene expression characteristic of scRNA-seq as well as benchmarking on a real dataset of matched single-cell and bulk RNAseq data, we demonstrate that IndepthPathway presents outstanding stability and depth in pathway enrichment results under stochasticity of the data, thus will substantially improve the scientific rigor of the pathway analysis for single-cell sequencing data.

AVAILABILITY AND IMPLEMENTATION

The IndepthPathway R package is available through: https://github.com/wangxlab/IndepthPathway.

Xihuang Pill-destabilized CD133/EGFR/Akt/mTOR cascade reduces stemness enrichment of glioblastoma via the down-regulation of SOX2

BACKGROUND

Our previous study found that XHP could induce GBM cells to undergo apoptosis. A lot of evidence suggests that glioma stem-like cells (GSCs) are key factors that contribute to disease progression and poor prognosis of glioblastoma multiforme (GBM). Traditional Chinese medicine has been applied in clinical practice as a complementary and alternative therapy for glioma.

PURPOSE

To evaluate the effect and the potential molecular mechanism of Xihuang pill (XHP) on GSCs.

METHODS

UPLC-QTOF-MS analysis was used for constituent analysis of XHP. Using network pharmacology and bioinformatics methods, a molecular network targeting GSCs by the active ingredients in XHP was constructed. Cell viability, self-renewal ability, apoptosis, and GSC markers were detected by CCK-8 assay, tumor sphere formation assay and flow cytometry, respectively. The interrelationship between GSC markers (CD133 and SOX2) and key proteins of the EGFR/Akt/mTOR signaling pathway was evaluated using GEPIA and verified by western blot. A GBM cell line stably overexpressing Akt was constructed using lentivirus to evaluate the role of Akt signaling in the regulation of glioma stemness. The effect of XHP on glioma growth was analyzed by a subcutaneously transplanted glioma cell model in nude mice, hematoxylin-eosin staining was used to examine pathological changes, TUNEL staining was used to detect apoptosis in tumor tissues, and the expression of GSC markers in tumor tissues was identified by western blot and immunofluorescence.

RESULTS

Bioinformatics analysis showed that 55 matched targets were related to XHP targets and glioma stem cell targets. In addition to causing apoptosis, XHP could diminish the number of GBM 3D spheroids, the proportion of CD133-positive cells and the expression level of GSC markers (CD133 and SOX2) in vitro. Furthermore, XHP could attenuate the expression of CD133, EGFR, p-Akt, p-mTOR and SOX2 in GBM spheres. Overexpression of Akt significantly increased the expression level of SOX2, which was prohibited in the presence of XHP. XHP reduced GSC markers including CD133 and SOX2, and impeded the development of glioma growth in xenograft mouse models in vivo.

CONCLUSION

We demonstrate for the first time that XHP down-regulates stemness, restrains self-renewal and induces apoptosis in GSCs and impedes glioma growth by down-regulating SOX2 through destabilizing the CD133/EGFR/Akt/mTOR cascade.

Functional roles of sphingolipids in immunity and their implication in disease

Sphingolipids, which are components of cellular membranes and organ tissues, can be synthesized or degraded to modulate cellular responses according to environmental cues, and the balance among the different sphingolipids is important for directing immune responses, regardless of whether they originate, as intra- or extracellular immune events. Recent progress in multiomics-based analyses and methodological approaches has revealed that human health and diseases are closely related to the homeostasis of sphingolipid metabolism, and disease-specific alterations in sphingolipids and related enzymes can be prognostic markers of human disease progression. Accumulating human clinical data from genome-wide association studies and preclinical data from disease models provide support for the notion that sphingolipids are the missing pieces that supplement our understanding of immune responses and diseases in which the functions of the involved proteins and nucleotides have been established. In this review, we analyze sphingolipid-related enzymes and reported human diseases to understand the important roles of sphingolipid metabolism. We discuss the defects and alterations in sphingolipid metabolism in human disease, along with functional roles in immune cells. We also introduce several methodological approaches and provide summaries of research on sphingolipid modulators in this review that should be helpful in studying the roles of sphingolipids in preclinical studies for the investigation of experimental and molecular medicines.