Biosynthetic potential of uncultured anammox community bacteria revealed through multi-omics analysis

Microbial secondary metabolites (SMs) and their derivatives have been widely used in medicine, agriculture, and energy. Growing needs for renewable energy and the challenges posed by antibiotic resistance, cancer, and pesticides emphasize the crucial hunt for new SMs. Anaerobic ammonium-oxidation (anammox) systems harbor many uncultured or underexplored bacteria, representing potential resources for discovering novel SMs. Leveraging HiFi long-read metagenomic sequencing, 1,040 biosynthetic gene clusters (BGCs) were unearthed from the anammox microbiome with 58% being complete and showcasing rich diversity. Most of them showed distant relations to known BGCs, implying novelty. Members of the underexplored lineages (Chloroflexota and Planctomycetota) and Proteobacteria contained lots of BGCs, showcasing substantial biosynthetic potential. Metaproteomic results indicated that Planctomycetota members harbored the most active BGCs, particularly those involved in producing potential biofuel-ladderane. Overall, these findings underscore that anammox microbiomes could serve as valuable resources for mining novel BGCs and discovering new SMs for practical application.

Advance in Multi-omics Research Strategies on Cholesterol Metabolism in Psoriasis

The skin is a complex and dynamic organ where homeostasis is maintained through the intricate interplay between the immune system and metabolism, particularly cholesterol metabolism. Various factors such as cytokines, inflammatory mediators, cholesterol metabolites, and metabolic enzymes play crucial roles in facilitating these interactions. Dysregulation of this delicate balance contributes to the pathogenic pathways of inflammatory skin conditions, notably psoriasis. In this article, we provide an overview of omics biomarkers associated with psoriasis in relation to cholesterol metabolism. We explore multi-omics approaches that reveal the communication between immunometabolism and psoriatic inflammation. Additionally, we summarize the use of multi-omics strategies to uncover the complexities of multifactorial and heterogeneous inflammatory diseases. Finally, we highlight potential future perspectives related to targeted drug therapies and research areas that can advance precise medicine. This review aims to serve as a valuable resource for those investigating the role of cholesterol metabolism in psoriasis.

Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence

Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.

Advances in mass spectrometry-enabled multiomics at single-cell resolution

Biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.

Multi-omics analyses of sid2 mutant reflect the need of isochorismate synthase ICS1 to cope with sulfur limitation in Arabidopsis thaliana

The SID2 (SA INDUCTION-DEFICIENT2) gene that encodes ICS1 (isochorismate synthase), plays a central role in salicylic acid biosynthesis in Arabidopsis. The sid2 and NahG (encoding a bacterial SA hydroxylase) overexpressing mutants (NahG-OE) have currently been shown to outperform wild type, presenting delayed leaf senescence, higher plant biomass and better seed yield. When grown under sulfate-limited conditions (low-S), sid2 mutants exhibited early leaf yellowing compared to the NahG-OE, the npr1 mutant affected in SA signaling pathway, and WT. This indicated that the hypersensitivity of sid2 to sulfate limitation was independent of the canonical npr1 SA-signaling pathway. Transcriptomic and proteomic analyses revealed that major changes occurred in sid2 when cultivated under low-S, changes that were in good accordance with early senescence phenotype and showed the exacerbation of stress responses. The sid2 mutants displayed a lower sulfate uptake capacity when cultivated under low-S and lower S concentrations in their rosettes. Higher glutathione concentrations in sid2 rosettes under low-S were in good accordance with the higher abundance of proteins involved in glutathione and ascorbate redox metabolism. Amino acid and lipid metabolisms were also strongly modified in sid2 under low-S. Depletion of total fatty acids in sid2 under low-S was consistent with the fact that S-metabolism plays a central role in lipid synthesis. Altogether, our results show that functional ICS1 is important for plants to cope with S limiting conditions.

Defining the relationship between cellular and extracellular vesicle (EV) content in breast cancer via an integrative multi-omic analysis

Much recent research has been dedicated to exploring the utility of extracellular vesicles (EVs) as circulating disease biomarkers. Underpinning this work is the assumption that the molecular cargo of EVs directly reflects the originating cell. Few attempts have been made, however, to empirically validate this on the -omic level. To this end, we have performed an integrative multi-omic analysis of a panel of breast cancer cell lines and corresponding EVs. Whole transcriptome analysis validated that the cellular transcriptome remained stable when cultured cells are transitioned to low serum or serum-free medium for EV collection. Transcriptomic profiling of the isolated EVs indicated a positive correlation between transcript levels in cells and EVs, including disease-associated transcripts. Analysis of the EV proteome verified that HER2 protein is present in EVs, however neither the estrogen (ER) nor progesterone (PR) receptor proteins are detected regardless of cellular expression. Using multivariate analysis, we derived an EV protein signature to infer cellular patterns of ER and HER2 expression, though the ER protein could not be directly detected. Integrative analyses affirmed that the EV proteome and transcriptome captured key phenotypic hallmarks of the originating cells, supporting the potential of EVs for non-invasive monitoring of breast cancers.

Integrative multiomics analysis identifies molecular subtypes and potential targets of hepatocellular carcinoma

BACKGROUND

The liver is anatomically divided into eight segments based on the distribution of Glisson's triad. However, the molecular mechanisms underlying each segment and its association with hepatocellular carcinoma (HCC) heterogeneity are not well understood. In this study, our objective is to conduct a comprehensive multiomics profiling of the segmentation atlas in order to investigate potential subtypes and therapeutic approaches for HCC.

METHODS

A high throughput liquid chromatography-tandem mass spectrometer strategy was employed to comprehensively analyse proteome, lipidome and metabolome data, with a focus on segment-resolved multiomics profiling. To classify HCC subtypes, the obtained data with normal reference profiling were integrated. Additionally, potential therapeutic targets for HCC were identified using immunohistochemistry assays. The effectiveness of these targets were further validated through patient-derived organoid (PDO) assays.

RESULTS

A multiomics profiling of 8536 high-confidence proteins, 1029 polar metabolites and 3381 nonredundant lipids was performed to analyse the segmentation atlas of HCC. The analysis of the data revealed that in normal adjacent tissues, the left lobe was primarily involved in energy metabolism, while the right lobe was associated with small molecule metabolism. Based on the normal reference atlas, HCC patients with segment-resolved classification were divided into three subtypes. The C1 subtype showed enrichment in ribosome biogenesis, the C2 subtype exhibited an intermediate phenotype, while the C3 subtype was closely associated with neutrophil degranulation. Furthermore, using the PDO assay, exportin 1 (XPO1) and 5-lipoxygenase (ALOX5) were identified as potential targets for the C1 and C3 subtypes, respectively.

CONCLUSION

Our extensive analysis of the segmentation atlas in multiomics profiling defines molecular subtypes of HCC and uncovers potential therapeutic strategies that have the potential to enhance the prognosis of HCC.

Multiomics profiling of the therapeutic effect of Dan-deng-tong-nao capsule on cerebral ischemia-reperfusion injury

BACKGROUND

Stroke is a complex physiological process associated with intestinal flora dysbiosis and metabolic disorders. Dan-deng-tong-nao capsule (DDTN) is a traditional Chinese medicine used clinically to treat cerebral ischemia-reperfusion injury (CIRI) for many years. However, little is known about the effects of DDTN in the treatment of CIRI from the perspective of gut microbiota and metabolites.

PURPOSE

This study aimed to investigate the regulatory roles of DDTN in endogenous metabolism and gut microbiota in CIRI rats, thus providing a basis for clinical rational drug use and discovering natural products with potential physiological activities in DDTN for the treatment of CIRI.

METHODS

The chemical composition of DDTN in vitro and in vivo was investigated using ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLCHRMS), followed by target prediction using reverse molecular docking. Secondly, a biological evaluation of DDTN ameliorating neural damage in CIRI was performed at the whole animal level. Then, an integrated omics approach based on UHPLCHRMS and 16S rRNA sequencing was proposed to reveal the anti-CIRI effect and possible mechanism of DDTN. Finally, exploring the intrinsic link between changes in metabolite profiles, changes in the intestinal flora, and targets of components to reveal DDTN for the treatment of CIRI.

RESULTS

A total of 112 chemical components of DDTN were identified in vitro and 10 absorbed constituents in vivo. The efficacy of DDTN in the treatment of CIRI was confirmed by alleviating cerebral infarction and neurological deficits. After the DDTN intervention, 21 and 26 metabolites were significantly altered in plasma and fecal, respectively. Based on the fecal microbiome, a total of 36 genera were enriched among the different groups. Finally, the results of the network integration analysis showed that the 10 potential active ingredients of DDTN could mediate the differential expression of 24 metabolites and 6 gut microbes by targeting 25 target proteins.

CONCLUSION

This study was the first to outline the landscapes of metabolites as well as gut microbiota regulated by DDTN in CIRI rats using multi-omics data, and comprehensively revealed the systematic relationships among ingredients, targets, metabolites, and gut microbiota, thus providing new perspectives on the mechanism of DDTN in the treatment of CIRI.

The multi-omics analysis in the hepatopancreas of Eriocheir sinensis provides novel insights into the response mechanism of heat stress

Under global warming, heat stress can induce the excessive production of reactive oxygen species, causing irreversible damage to aquatic animals. It is essential to predict potentially harmful impacts on aquatic organisms under heat stress. Eriocheir sinensis, a typical crustacean crab, is widely distributed in China, American and Europe. Parent E. sinensis need migrate to the estuaries to reproduce in winter, and temperature is a key environmental factor. Herein, we performed a comprehensive transcriptomic and proteomic analysis in the hepatopancreas of E. sinensis under heat stress (20 °C and 30 °C), focusing on heat shock protein family, antioxidant system, energy metabolism and immune defense. The results revealed that parent E. sinensis generated adaptative responses to maintain physiological function under 20 °C stress via the transcriptional up-regulation of energy metabolism enzymes, mRNA synthesis and heat shock proteins. The transcriptional inhibition of key enzymes related to energy metabolism implied that 30 °C stress may lead to the dysfunction of energy metabolism in parent E. sinensis. Meanwhile, parent E. sinensis also enhanced the expression of ferritin and phospholipase D at translational level, and the glutathione s-transferase and heat shock protein 70 at both transcriptional and translational levels, speculating that parent E. sinensis can strengthen antioxidant and immune capacity to resist oxidative stress under 30 °C stress. This study elucidated the potential molecular mechanism in response to heat stress of parent E. sinensis hepatopancreas. The preliminary selection of heat tolerance genes or proteins in E. sinensis can provide a reference for the population prediction and the study of evolutionary mechanism under heat stress in crabs.

Multi-omic profiling of simultaneous ductal carcinoma in situ and invasive breast cancer

PURPOSE

The progression of ductal carcinoma in situ (DCIS) to invasive breast carcinoma (IBC) in humans is highly variable. To better understand the relationship between them, we performed a multi-omic characterization of co-occurring DCIS and IBC lesions in a cohort of individuals.

METHODS

Formalin-fixed paraffin-embedded tissue samples from 50 patients with co-occurring DCIS and IBC lesions were subjected to DNA-seq and whole transcriptome RNA-seq. Paired DCIS and IBC multi-omics profiles were then interrogated for DNA mutations, gene expression profiles and pathway analysis.

RESULTS

Most small variants and copy number variations were shared between co-occurring DCIS and IBC lesions, with IBC exhibiting on average a higher degree of additional mutations. However, 36% of co-occurring lesions shared no common mutations and 49% shared no common copy number variations. The most frequent genomic variants in both DCIS and IBC were PIK3CA, TP53, KMT2C, MAP3K1, GATA3 and SF3B1, with KMT2C being more frequent in DCIS and TP53 and MAP3K1 more frequent in IBC, though the numbers are too small for definitive conclusions. The most frequent copy number variations were seen in MCL1, CKSB1 and ERBB2. ERBB2 changes were not seen in IBC unless present in the corresponding DCIS. Transcriptional profiles were highly distinct between DCIS and IBC, with DCIS exhibiting upregulation of immune-related signatures, while IBC showed significant overexpression in genes and pathways associated with cell division and proliferation. Interestingly, DCIS and IBC exhibited significant differential expression of different components of extracellular matrix (ECM) formation and regulation, with DCIS showing overexpression of ECM-membrane interaction components while IBC showed upregulation of genes associated with fibronectin and invadopodia.

CONCLUSION

While most co-occurring DCIS and IBC were mutationally similar and suggestive of a common clonal progenitor, transcriptionally the lesions are highly distinct, with IBC expressing key pathways that facilitate invasion and proliferation. These results are suggestive of additional levels of regulation, epigenetic or other, that facilitate the acquisition of invasive properties during tumor evolution.

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.

Qijiao Shengbai Capsule alleviated leukopenia by interfering leukotriene pathway: Integrated network study of multi-omics

BACKGROUND

Leukopenia could be induced by chemotherapy, which leads to bone marrow suppression and even affects the therapeutic progression of cancer. Qijiao Shengbai Capsule (QSC) has been used for the treatment of leukopenia in clinic, but its bioactive components and mechanisms have not yet been elucidated clearly.

PURPOSE

This study aimed to elucidate the molecular mechanisms of QSC in treating leukopenia.

STUDY DESIGN

Serum pharmacochemistry, multi-omics, network pharmacology, and validation experiment were combined to study the effect of QSC in murine leukopenia model.

METHODS

First, UPLC-QTOF-MS was used to clarify the absorbed components of QSC. Then, cyclophosphamide (CTX) was used to induce mice model with leukopenia, and the therapeutic efficacy of QSC was assessed by an integrative approach of multi-omics and network pharmacology strategy. Finally, molecular mechanisms and potential therapeutic targets were identified by validated experiments.

RESULTS

121 compounds absorbed in vivo were identified. QSC significantly increase the count of white blood cells (WBCs) in peripheral blood of leukopenia mice with 15 days treatment. Multi-omics and network pharmacology revealed that leukotriene pathway and MAPK signaling pathway played crucial roles during the treatment of leukopenia with QSC. Six targets (ALOX5, LTB4R, CYSLTR1, FOS, JUN, IL-1β) and 13 prototype compounds were supposed to be the key targets and potential active components, respectively. The validation experiment further confirmed that QSC could effectively inhibit the inflammatory response induced by leukopenia. The inhibitors of ALOX5 activity can significantly increase the number of WBCs in leukopenia mice. Molecular docking of ALOX5 suggested that calycosin, daidzein, and medicarpin were the potentially active compounds of QSC.

CONCLUSION

Leukotriene pathway was found for the first time to be a key role in the development of leukopenia, and ALOX5 was conformed as the potential target. QSC may inhibit the inflammatory response and interfere the leukotriene pathway, it is able to improve hematopoiesis and achieve therapeutic effects in the mice with leukopenia.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

Multi-omics reveals that 5-O-methylvisammioside prevention acute liver injury in mice by regulating the TNF/MAPK/NF-κB/arachidonic acid pathway

BACKGROUND

The pathogenesis of acute liver injury (ALI) has been a pressing issue in the medical scientific community. We previously found that 5-O-methylvisammioside (MeV) from Saposhnikovia divaricata (Turcz.) Schischk has excellent anti-inflammatory properties. However, the mechanism by which MeV protects against ALI still needs to be deeply investigated.

PURPOSE

In the present study, we established an acetaminophen (APAP) -induced ALI mouse model and pre-protected the mice with MeV.

METHODS & RESULTS

Our findings indicate that MeV (5 and 10 mg/kg) lowered the blood levels of alanine aminotransferase and aspartate aminotransferase and reduced the infiltration of inflammatory cells in the liver. MeV initially showed an inhibitory effect on ALI. We then analyzed the molecular mechanisms underlying the effects of MeV by transcriptomic and metabolomic analyzes. Through transcriptomic analysis, we identified 4675 differentially expressed genes between the APAP+MeV group and the APAP-induced ALI group, which were mainly enriched in the MAPK pathway, the TNF pathway, and the NF-κB pathway. Through metabolomic analysis, we found that 249 metabolites in the liver were differentially regulated between the APAP+MeV group and the APAP- induced ALI group, which were mainly enriched in the arachidonic acid pathway. The mRNA expression levels of key genes (encoding TNF-α, p38, AP-1, RelB, IL-1β, and Ptges), as determined by RT-PCR analysis, were consistent with the RNA-seq data. The ELISA results indicate that MeV markedly decreased the serum levels of TNF-α and IL-1β in mice. Finally, the key proteins in the NF-κB and MAPK pathways were examined using immunoblotting. The results showed that MeV decreased IκB-α phosphorylation and inhibited the nuclear translocation of NF-κB. In addition, MeV reduced the hepatic inflammatory burst mainly by inhibiting the phosphorylation of p38 and JNK in the MAPK pathway.

CONCLUSION

The present study demonstrated (i) that MeV could ameliorate APAP-induced ALI by inhibiting arachidonic acid metabolism and the TNF, MAPK, and NF-κB pathways, and (ii) that MeV is a promising drug candidate for the prevention of ALI.

Multi-omics analysis identifies potential microbial and metabolite diagnostic biomarkers of bacterial vaginosis

BACKGROUND

Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or require skilled expertise, warranting alternate tests. Application of machine-learning tools to heterogeneous and high-dimensional multi-omics datasets finds promising potential in data integration and may aid biomarker discovery.

OBJECTIVES

The present study aimed to evaluate the potential of the microbiome and metabolome-derived biomarkers in BV diagnosis. Interpretable machine-learning algorithms were used to evaluate the utility of an integrated-omics-derived classification model.

METHODS

Vaginal samples obtained from reproductive-age group women with (n = 40) and without BV (n = 40) were subjected to 16S rRNA amplicon sequencing and LC-MS-based metabolomics. The vaginal microbiome and metabolome were characterized, and machine-learning analysis was performed to build a classification model using biomarkers with the highest diagnostic accuracy.

RESULTS

Microbiome-based diagnostic model exhibited a ROC-AUC (10-fold CV) of 0.84 ± 0.21 and accuracy of 0.79 ± 0.18, and important features were Aerococcus spp., Mycoplasma hominis, Sneathia spp., Lactobacillus spp., Prevotella spp., Gardnerella spp. and Fannyhessea vaginae. The metabolome-derived model displayed superior performance with a ROC-AUC of 0.97 ± 0.07 and an accuracy of 0.92 ± 0.08. Beta-leucine, methylimidazole acetaldehyde, dimethylethanolamine, L-arginine and beta cortol were among key predictive metabolites for BV. A predictive model combining both microbial and metabolite features exhibited a high ROC-AUC of 0.97 ± 0.07 and accuracy of 0.94 ± 0.08 with diagnostic performance only slightly superior to the metabolite-based model.

CONCLUSION

Application of machine-learning tools to multi-omics datasets aid biomarker discovery with high predictive performance. Metabolome-derived classification models were observed to have superior diagnostic performance in predicting BV than microbiome-based biomarkers.

Decreased circulating IPA levels identify subjects with metabolic comorbidities: A multi-omics study

In recent years several experimental observations demonstrated that the gut microbiome plays a role in regulating positively or negatively metabolic homeostasis. Indole-3-propionic acid (IPA), a Tryptophan catabolic product mainly produced by C. Sporogenes, has been recently shown to exert either favorable or unfavorable effects in the context of metabolic and cardiovascular diseases. We performed a study to delineate clinical and multiomics characteristics of human subjects characterized by low and high IPA levels. Subjects with low IPA blood levels showed insulin resistance, overweight, low-grade inflammation, and features of metabolic syndrome compared to those with high IPA. Metabolomics analysis revealed that IPA was negatively correlated with leucine, isoleucine, and valine metabolism. Transcriptomics analysis in colon tissue revealed the enrichment of several signaling, regulatory, and metabolic processes. Metagenomics revealed several OTU of ruminococcus, alistipes, blautia, butyrivibrio and akkermansia were significantly enriched in highIPA group while in lowIPA group Escherichia-Shigella, megasphera, and Desulfovibrio genus were more abundant. Next, we tested the hypothesis that treatment with IPA in a mouse model may recapitulate the observations of human subjects, at least in part. We found that a short treatment with IPA (4 days at 20/mg/kg) improved glucose tolerance and Akt phosphorylation in the skeletal muscle level, while regulating blood BCAA levels and gene expression in colon tissue, all consistent with results observed in human subjects stratified for IPA levels. Our results suggest that treatment with IPA may be considered a potential strategy to improve insulin resistance in subjects with dysbiosis.

Integrated multi-omics approach reveals the role of striated muscle preferentially expressed protein kinase in skeletal muscle including its relationship with myospryn complex

BACKGROUND

Autosomal-recessive mutations in SPEG (striated muscle preferentially expressed protein kinase) have been linked to centronuclear myopathy with or without dilated cardiomyopathy (CNM5). Loss of SPEG is associated with defective triad formation, abnormal excitation-contraction coupling, calcium mishandling and disruption of the focal adhesion complex in skeletal muscles. To elucidate the underlying molecular pathways, we have utilized multi-omics tools and analysis to obtain a comprehensive view of the complex biological processes and molecular functions.

METHODS

Skeletal muscles from 2-month-old SPEG-deficient (Speg-CKO) and wild-type (WT) mice were used for RNA sequencing (n = 4 per genotype) to profile transcriptomics and mass spectrometry (n = 4 for WT; n = 3 for Speg-CKO mice) to profile proteomics and phosphoproteomics. In addition, interactomics was performed using the SPEG antibody on pooled muscle lysates (quadriceps, gastrocnemius and triceps) from WT and Speg-CKO mice. Based on the multi-omics results, we performed quantitative real-time PCR, co-immunoprecipitation and immunoblot to verify the findings.

RESULTS

We identified that SPEG interacts with myospryn complex proteins CMYA5, FSD2 and RyR1, which are critical for triad formation, and that SPEG deficiency results in myospryn complex abnormalities (protein levels decreased to 22 ± 3% for CMYA5 [P < 0.05] and 18 ± 3% for FSD2 [P < 0.01]). Furthermore, SPEG phosphorylates RyR1 at S2902 (phosphorylation level decreased to 55 ± 15% at S2902 in Speg-CKO mice; P < 0.05), and its loss affects JPH2 phosphorylation at multiple sites (increased phosphorylation at T161 [1.90 ± 0.24-fold], S162 [1.61 ± 0.37-fold] and S165 [1.66 ± 0.13-fold]; decreased phosphorylation at S228 and S231 [39 ± 6%], S234 [50 ± 12%], S593 [48 ± 3%] and S613 [66 ± 10%]; P < 0.05 for S162 and P < 0.01 for other sites). On analysing the transcriptome, the most dysregulated pathways affected by SPEG deficiency included extracellular matrix-receptor interaction (P < 1e-15) and peroxisome proliferator-activated receptor signalling (P < 9e-14).

CONCLUSIONS

We have elucidated the critical role of SPEG in the triad as it works closely with myospryn complex proteins (CMYA5, FSD2 and RyR1), it regulates phosphorylation levels of various residues in JPH2 and S2902 in RyR1, and its deficiency is associated with dysregulation of several pathways. The study identifies unique SPEG-interacting proteins and their phosphorylation functions and emphasizes the importance of using a multi-omics approach to comprehensively evaluate the molecular function of proteins involved in various genetic disorders.

Integration of multiomics analyses reveals unique insights into CD24-mediated immunosuppressive tumor microenvironment of breast cancer

BACKGROUND

Tumor immunotherapy brings new light and vitality to breast cancer patients, but low response rate and limitations of therapeutic targets become major obstacles to its clinical application. Recent studies have shown that CD24 is involved in an important process of tumor immune regulation in breast cancer and is a promising target for immunotherapy.

METHODS

In this study, singleR was used to annotate each cell subpopulation after t-distributed stochastic neighbor embedding (t-SNE) methods. Pseudo-time trace analysis and cell communication were analyzed by Monocle2 package and CellChat, respectively. A prognostic model based on CD24-related genes was constructed using several machine learning methods. Multiple quantitative immunofluorescence (MQIF) was used to evaluate the spatial relationship between CD24+PANCK+cells and exhausted CD8+T cells.

RESULTS

Based on the scRNA-seq analysis, 1488 CD24-related differential genes were identified, and a risk model consisting of 15 prognostic characteristic genes was constructed by combining the bulk RNA-seq data. Patients were divided into high- and low-risk groups based on the median risk score. Immune landscape analysis showed that the low-risk group showed higher infiltration of immune-promoting cells and stronger immune reactivity. The results of cell communication demonstrated a strong interaction between CD24+epithelial cells and CD8+T cells. Subsequent MQIF demonstrated a strong interaction between CD24+PANCK+ and exhausted CD8+T cells with FOXP3+ in breast cancer. Additionally, CD24+PANCK+ and CD8+FOXP3+T cells were positively associated with lower survival rates.

CONCLUSION

This study highlights the importance of CD24+breast cancer cells in clinical prognosis and immunosuppressive microenvironment, which may provide a new direction for improving patient outcomes.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .