Multiomics integrated with sensory evaluations to identify characteristic aromas and key genes in a novel brown navel orange (Citrus sinensis)

'Zong Cheng' navel orange (ZC) is a brown mutant of Lane Late navel orange (LL) and emits a more pleasant odor than that of LL. However, the key volatile compound of this aroma and underlying mechanism remains unclear. In this study, sensory evaluations and volatile profiling were performed throughout fruit development to identify significant differences in sensory perception and metabolites between LL and ZC. It revealed that the sesquiterpene content varied significantly between ZC and LL. Based on aroma extract dilution and gas chromatography-olfactometry analyses, the volatile compound leading to the background aroma of LL and ZC is d-limonene, the orange note in LL was mainly attributed to octanal, whilst valencene, β-myrcene, and (E)-β-ocimene presented balsamic, sweet, and herb notes in ZC. Furthermore, Cs5g12900 and six potential transcription factors were identified as responsible for valencene accumulation in ZC, which is important for enhancing the aroma of ZC.

Dinotefuran exposure alters biochemical, metabolomic, gut microbiome, and growth responses in decapoda pacific white shrimp Penaeus vannamei

Dinotefuran, a neonicotinoid insecticide, may impact nontarget organisms such as Decapoda P. vannamei shrimp with nervous systems similar to insects. Exposing shrimp to low dinotefuran concentrations (6, 60, and 600 μg/L) for 21 days affected growth, hepatosomatic index, and survival. Biomarkers erythromycin-N-demethylase, alanine aminotransferase, and catalase increased in all exposed groups, while glutathione S-transferase is the opposite; aminopyrin-N-demethylase, malondialdehyde, and aspartate aminotransferase increased at 60 and 600 μg/L. Concentration-dependent effects on gut microbiota altered the abundance of bacterial groups, increased potentially pathogenic and oxidative stress-resistant phenotypes, and decreased biofilm formation. Gram-positive/negative microbiota changed significantly. Metabolite differences between the exposed and control groups were identified using mass spectrometry and KEGG pathway enrichment. N-acetylcystathionine showed potential as a reliable dinotefuran metabolic marker. Weighted correlation network analysis (WGCNA) results indicated high connectivity of cruecdysone in the metabolite network and significant enrichment at 600 μg/L dinotefuran. The WGCNA results revealed a highly significant negative correlation between two key metabolites, caldine and indican, and the gut microbiota within co-expression modules. Overall, the risk of dinotefuran exposure to non-target organisms in aquatic environments still requires further attention.

Decline of a distinct coral reef holobiont community under ocean acidification

BACKGROUND

Microbes play vital roles across coral reefs both in the environment and inside and upon macrobes (holobionts), where they support critical functions such as nutrition and immune system modulation. These roles highlight the potential ecosystem-level importance of microbes, yet most knowledge of microbial functions on reefs is derived from a small set of holobionts such as corals and sponges. Declining seawater pH - an important global coral reef stressor - can cause ecosystem-level change on coral reefs, providing an opportunity to study the role of microbes at this scale. We use an in situ experimental approach to test the hypothesis that under such ocean acidification (OA), known shifts among macrobe trophic and functional groups may drive a general ecosystem-level response extending across macrobes and microbes, leading to reduced distinctness between the benthic holobiont community microbiome and the environmental microbiome.

RESULTS

We test this hypothesis using genetic and chemical data from benthic coral reef community holobionts sampled across a pH gradient from CO2 seeps in Papua New Guinea. We find support for our hypothesis; under OA, the microbiome and metabolome of the benthic holobiont community become less compositionally distinct from the sediment microbiome and metabolome, suggesting that benthic macrobe communities are colonised by environmental microbes to a higher degree under OA conditions. We also find a simplification and homogenisation of the benthic photosynthetic community, and an increased abundance of fleshy macroalgae, consistent with previously observed reef microbialisation.

CONCLUSIONS

We demonstrate a novel structural shift in coral reefs involving macrobes and microbes: that the microbiome of the benthic holobiont community becomes less distinct from the sediment microbiome under OA. Our findings suggest that microbialisation and the disruption of macrobe trophic networks are interwoven general responses to environmental stress, pointing towards a universal, undesirable, and measurable form of ecosystem changed. Video Abstract.

Lung Tissue Multi-Layer Network Analysis Uncovers the Molecular Heterogeneity of COPD

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition. We hypothesized that the unbiased integration of different COPD lung omics using a novel multi-layer approach may unravel mechanisms associated with clinical characteristics.

METHODS

We profiled mRNA, miRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer) we built a patient network based on molecular similarity. The three networks were used to build a multi-layer network, and optimization of multiplex-modularity was employed to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features.

RESULTS

We identified five patient communities in the multi-layer network which were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema, but were molecularly different: C#3, but not C#4, presented B and T cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort, and to validate them in an independent cohort. Finally, using spatial transcriptomics we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T/B cell homing chemokines, and bacterial response genes in C#3.

CONCLUSIONS

A novel multi-layer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.

Cancer risk and male Infertility: Unravelling predictive biomarkers and prognostic indicators

In recent years, there has been a global increase in cases of male infertility. There are about 30 million cases of male infertility worldwide and male reproductive health is showing rapid decline in last few decades. It is now recognized as a potential risk factor for developing certain types of cancer, particularly genitourinary malignancies like testicular and prostate cancer. Male infertility is considered a potential indicator of overall health and an early biomarker for cancer. Cases of unexplained male factor infertility have high levels of oxidative stress and oxidative DNA damage and this induces both denovo germ line mutations and epimutations due to build up of 8-hydroxy 2 deoxygunaosine abase which is highly mutagenic and also induces hypomethylation and genomic instability. Consequently, there is growing evidence to explore the various factors contributing to an increased cancer risk. Currently, the available prognostic and predictive biomarkers associated with semen characteristics and cancer risk are limited but gaining significant attention in clinical research for the diagnosis and treatment of elevated cancer risk in the individual and in offspring. The male germ cell being transcriptionally and translationally inert has a highly truncated repair mechanism and has minimal antioxidants and thus most vulnerable to oxidative injury due to environmental factors and unhealthy lifestyle and social habits. Therefore, advancing our understanding requires a thorough evaluation of the pathophysiologic mechanisms at the DNA, RNA, protein, and metabolite levels to identify key biomarkers that may underlie the pathogenesis of male infertility and associated cancer. Advanced methodologies such as genomics, epigenetics, proteomics, transcriptomics, and metabolomics stand at the forefront of cutting-edge approaches for discovering novel biomarkers, spanning from infertility to associated cancer types. Henceforth, in this review, we aim to assess the role and potential of recently identified predictive and prognostic biomarkers, offering insights into the success of assisted reproductive technologies, causes of azoospermia and idiopathic infertility, the impact of integrated holistic approach and lifestyle modifications, and the monitoring of cancer susceptibility, initiation and progression. Comprehending these biomarkers is crucial for providing comprehensive counselling to infertile men and cancer patients, along with their families.

Molecular mechanisms of selenite reduction by Lactiplantibacillus plantarum BSe: An integrated genomic and transcriptomic analysis

The reduction of selenite [Se(Ⅳ)] by microorganisms is a green and efficient detoxification strategy. We found that Se(Ⅳ) inhibited exopolysaccharide and protein secretion by Lactiplantibacillus plantarum BSe and compromised cell integrity. In this study, L. plantarum BSe reduced Se(Ⅳ) by increasing related enzyme activity and electron transfer. Genomic analysis demonstrated that L. plantarum BSe should be able to reduce Se(Ⅳ). Further transcriptome analysis showed that L. plantarum BSe enhanced its tolerance to Se(Ⅳ) by upregulating the expression of surface proteins and transporters, thus reducing the extracellular Se(Ⅳ) concentration through related enzymatic reactions and siderophore-mediated pathways. Lactiplantibacillus plantarum BSe was able to regulate the expression of related genes involved in quorum sensing and a two-component system and then select appropriate strategies for Se(Ⅳ) transformation in response to varying environmental Se(Ⅳ) concentrations. In addition, azo reductase was linked to the reduction of Se(Ⅳ) for the first time. The present study established a multipath model for the reduction of Se(Ⅳ) by L. plantarum, providing new insights into the biological reduction of Se(Ⅳ) and the biogeochemical cycle of selenium.

The molecular biology of sporadic acromegaly

GH-secreting tumors represent 15 % to 20 % of all pituitary neuroendocrine tumors (pitNETs), of which 95 % occur in a sporadic context, without an identifiable inherited cause. Recent multi-omic approaches have characterized the epigenomic, genomic, transcriptomic, proteomic and kynomic landscape of pituitary tumors. Transcriptomic analysis has allowed us to discover specific transcription factors driving the differentiation of pituitary tumors and gene expression patterns. GH-secreting, along with PRL- and TSH-secreting pitNETs are driven by POU1F1; ACTH-secreting tumors are determined by TBX19; and non-functioning tumors, which are predominantly of gonadotrope differentiation are conditioned by NR5A1. Upregulation of certain miRNAs, such as miR-107, is associated with tumor progression, while downregulation of others, like miR-15a and miR-16-1, correlates with tumor size reduction. Additionally, miRNA expression profiles are linked to treatment resistance and clinical outcomes, providing insights into potential therapeutic targets. Specific somatic mutations in GNAS, PTTG1, GIPR, HGMA2, MAST and somatic variants associated with cAMP, calcium signaling, and ATP pathways have also been associated with the development of acromegaly. This review focuses on the oncogenic mechanisms by which sporadic acromegaly can develop, covering a complex series of molecular alterations that ultimately alter the balance between proliferation and apoptosis, and dysregulated hormonal secretion.

Precision Oncology in Gastrointestinal and Colorectal Cancer Surgery

Precision medicine is used to treat gastrointestinal malignancies including esophageal, gastric, small bowel, colorectal, and pancreatic cancers. Cutting-edge assays to detect and treat these cancers are active areas of research and will soon become standard of care. Colorectal cancer is a prime example of precision oncology as disease site is no longer the final determinate of treatment. Here, the authors describe how leveraging an understanding of tumor biology translates to individualized patient care using evidence-based practices.

Integrated multi-omics analysis reveals gut microbiota dysbiosis and systemic disturbance in major depressive disorder

Major depressive disorder (MDD) involves systemic changes in peripheral blood and gut microbiota, but the current understanding is incomplete. Herein, we conducted a multi-omics analysis of fecal and blood samples obtained from an observational cohort including MDD patients (n = 99) and healthy control (HC, n = 50). 16S rRNA sequencing of gut microbiota showed structural alterations in MDD, as characterized by increased Enterococcus. Metagenomics sequencing of gut microbiota showed substantial functional alterations including upregulation in the superpathway of the glyoxylate cycle and fatty acid degradation and downregulation in various metabolic pathways in MDD. Plasma metabolomics revealed decreased amino acids and bile acids, together with increased sphingolipids and cholesterol esters in MDD. Notably, metabolites involved in arginine and proline metabolism were decreased while sphingolipid metabolic pathway were increased. Mass cytometry analysis of blood immune cell subtypes showed rises in proinflammatory immune subsets and declines in anti-inflammatory immune subsets in MDD. Furthermore, our findings revealed disease severity-related factors of MDD. Interestingly, we classified MDD into two immune subtypes that were highly correlated with disease relapse. Moreover, we established discriminative signatures that differentiate MDD from HC. These findings contribute to a comprehensive understanding of the MDD pathogenesis and provide valuable resources for the discovery of biomarkers.

Multiomics and blood-based biomarkers of electroconvulsive therapy in severe and treatment-resistant depression: study protocol of the DetECT study

Electroconvulsive therapy (ECT) is commonly used to treat treatment-resistant depression (TRD). However, our knowledge of the ECT-induced molecular mechanisms causing clinical improvement is limited. To address this issue, we developed the single-center, prospective observational DetECT study ("Multimodal Biomarkers of ECT in TRD"; registered 18/07/2022, www.clinicalTrials.gov , NCT05463562). Its objective is to identify molecular, psychological, socioeconomic, and clinical biomarkers of ECT response in TRD. We aim to recruit n = 134 patients in 3 years. Over the course of 12 biweekly ECT sessions (± 7 weeks), participant blood is collected before and 1 h after the first and seventh ECT and within 1 week after the twelfth session. In pilot subjects (first n = 10), additional blood draws are performed 3 and 6 h after the first ECT session to determine the optimal post-ECT blood draw interval. In blood samples, multiomic analyses are performed focusing on genotyping, epigenetics, RNA sequencing, neuron-derived exosomes, purines, and immunometabolics. To determine clinical response and side effects, participants are asked weekly to complete four standardized self-rating questionnaires on depressive and somatic symptoms. Additionally, clinician ratings are obtained three times (weeks 1, 4, and 7) within structured clinical interviews. Medical and sociodemographic data are extracted from patient records. The multimodal data collected are used to perform the conventional statistics as well as mixed linear modeling to identify clusters that link biobehavioural measures to ECT response. The DetECT study can provide important insight into the complex mechanisms of ECT in TRD and a step toward biologically informed and data-driven-based ECT biomarkers.

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.

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.

Multiomics insights into the female reproductive aging

The human female reproductive lifespan significantly diminishes with age, leading to decreased fertility, reduced fertility quality and endocrine function disorders. While many aspects of aging in general have been extensively documented, the precise mechanisms governing programmed aging in the female reproductive system remain elusive. Recent advancements in omics technologies and computational capabilities have facilitated the emergence of multiomics deep phenotyping. Through the application and refinement of various high-throughput omics methods, a substantial volume of omics data has been generated, deepening our comprehension of the pathogenesis and molecular underpinnings of reproductive aging. This review highlights current and emerging multiomics approaches for investigating female reproductive aging, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. We elucidate their influence on fundamental cell biology and translational research in the context of reproductive aging, address the limitations and current challenges associated with multiomics studies, and offer a glimpse into future prospects.

Spatial MS multiomics on clinical prostate cancer tissues

Mass spectrometry (MS) and MS imaging (MSI) are used extensively for both the spatial and bulk characterization of samples in lipidomics and proteomics workflows. These datasets are typically generated independently due to different requirements for sample preparation. However, modern omics technologies now provide higher sample throughput and deeper molecular coverage, which, in combination with more sophisticated bioinformatic and statistical pipelines, make generating multiomics data from a single sample a reality. In this workflow, we use spatial lipidomics data generated by matrix-assisted laser desorption/ionization MSI (MALDI-MSI) on prostate cancer (PCa) radical prostatectomy cores to guide the definition of tumor and benign tissue regions for laser capture microdissection (LCM) and bottom-up proteomics all on the same sample and using the same mass spectrometer. Accurate region of interest (ROI) mapping was facilitated by the SCiLS region mapper software and dissected regions were analyzed using a dia-PASEF workflow. A total of 5525 unique protein groups were identified from all dissected regions. Lysophosphatidylcholine acyltransferase 1 (LPCAT1), a lipid remodelling enzyme, was significantly enriched in the dissected regions of cancerous epithelium (CE) compared to benign epithelium (BE). The increased abundance of this protein was reflected in the lipidomics data with an increased ion intensity ratio for pairs of phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) in CE compared to BE.

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

INTRODUCTION

Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics).

METHODS AND OBJECTIVES

APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions.

CONCLUSION

APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project.

Pectin demethylation-mediated cell wall Na+ retention positively regulates salt stress tolerance in oilseed rape

KEY MESSAGE

Integrated phenomics, ionomics, genomics, transcriptomics, and functional analyses present novel insights into the role of pectin demethylation-mediated cell wall Na+ retention in positively regulating salt tolerance in oilseed rape. Genetic variations in salt stress tolerance identified in rapeseed genotypes highlight the complicated regulatory mechanisms. Westar is ubiquitously used as a transgenic receptor cultivar, while ZS11 is widely grown as a high-production and good-quality cultivar. In this study, Westar was found to outperform ZS11 under salt stress. Through cell component isolation, non-invasive micro-test, X-ray energy spectrum analysis, and ionomic profile characterization, pectin demethylation-mediated cell wall Na+ retention was proposed to be a major regulator responsible for differential salt tolerance between Westar and ZS11. Integrated analyses of genome-wide DNA variations, differential expression profiling, and gene co-expression networks identified BnaC9.PME47, encoding a pectin methylesterase, as a positive regulator conferring salt tolerance in rapeseed. BnaC9.PME47, located in two reported QTL regions for salt tolerance, was strongly induced by salt stress and localized on the cell wall. Natural variation of the promoter regions conferred higher expression of BnaC9.PME47 in Westar than in several salt-sensitive rapeseed genotypes. Loss of function of AtPME47 resulted in the hypersensitivity of Arabidopsis plants to salt stress. The integrated multiomics analyses revealed novel insights into pectin demethylation-mediated cell wall Na+ retention in regulating differential salt tolerance in allotetraploid rapeseed genotypes. Furthermore, these analyses have provided key information regarding the rapid dissection of quantitative trait genes responsible for nutrient stress tolerance in plant species with complex genomes.

Exploring the interplay of gut microbiota, inflammation, and LDL-cholesterol: a multiomics Mendelian randomization analysis of their causal relationship in acute pancreatitis and non-alcoholic fatty liver disease

BACKGROUND

Acute pancreatitis and non-alcoholic fatty liver disease are both serious diseases in the digestive system. The pathogenesis of both diseases is extremely complex closely and it related to gut microbiota, inflammation, and blood fat. There is a close relationship between gut microbiota and blood lipids.

METHODS

In this study, we used three types of exposure: 412 gut microbiota, 731 inflammatory cells, and 91 inflammatory proteins (pqtls), with LDL-C as an intermediary and acute pancreatitis and non-alcoholic fatty liver disease as outcomes. We mainly used MR-IVW, co-localization analysis, and reverse MR analysis methods for analysis.

RESULTS

7 gut microbiota, 21 inflammatory cells, and 3 inflammatory proteins can affect LDL-C levels. LDL-C is associated with acute pancreatitis and non-alcoholic fatty liver disease.

CONCLUSIONS

Three omics were used: 412 gut microbiota, 731 inflammatory cells, and 91 inflammatory proteins (pqtls). It explains the causal relationship between multiomics, LDL- cholesterol, acute pancreatitis, and non-alcoholic fatty liver disease.

Profiles of subgingival microbiomes and gingival crevicular metabolic signatures in patients with amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

The relationship between periodontitis and Alzheimer's disease (AD) has attracted more attention recently, whereas profiles of subgingival microbiomes and gingival crevicular fluid (GCF) metabolic signatures in AD patients have rarely been characterized; thus, little evidence exists to support the oral-brain axis hypothesis. Therefore, our study aimed to characterize both the microbial community of subgingival plaque and the metabolomic profiles of GCF in patients with AD and amnestic mild cognitive impairment (aMCI) for the first time.

METHODS

This was a cross-sectional study. Clinical examinations were performed on all participants. The microbial community of subgingival plaque and the metabolomic profiles of GCF were characterized using the 16S ribosomal RNA (rRNA) gene high-throughput sequencing and liquid chromatography linked to tandem mass spectrometry (LC-MS/MS) analysis, respectively.

RESULTS

Thirty-two patients with AD, 32 patients with aMCI, and 32 cognitively normal people were enrolled. The severity of periodontitis was significantly increased in AD patients compared with aMCI patients and cognitively normal people. The 16S rRNA gene sequencing results showed that the relative abundances of 16 species in subgingival plaque were significantly correlated with cognitive function, and LC-MS/MS analysis identified a total of 165 differentially abundant metabolites in GCF. Moreover, multiomics Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) analysis revealed that 19 differentially abundant metabolites were significantly correlated with Veillonella parvula, Dialister pneumosintes, Leptotrichia buccalis, Pseudoleptotrichia goodfellowii, and Actinomyces massiliensis, in which galactinol, sn-glycerol 3-phosphoethanolamine, D-mannitol, 1 h-indole-1-pentanoic acid, 3-(1-naphthalenylcarbonyl)- and L-iditol yielded satisfactory accuracy for the predictive diagnosis of AD progression.

CONCLUSIONS

This is the first combined subgingival microbiome and GCF metabolome study in patients with AD and aMCI, which revealed that periodontal microbial dysbiosis and metabolic disorders may be involved in the etiology and progression of AD, and the differential abundance of the microbiota and metabolites may be useful as potential markers for AD in the future.

Integrated metabolomic and transcriptomic dynamic profiles of endopleura coloration during fruit maturation in three walnut cultivars

BACKGROUND

The color of endopleura is a vital factor in determining the economic value and aesthetics appeal of nut. Walnuts (Juglans) are a key source of edible nuts, high in proteins, amino acids, lipids, carbohydrates. Walnut had a variety endopleura color as yellow, red, and purple. However, the regulation of walnut endopleura color remains little known.

RESULTS

To understand the process of coloration in endopleura, we performed the integrative analysis of transcriptomes and metabolomes at two developmental stages of walnut endopleura. We obtained total of 4,950 differentially expressed genes (DEGs) and 794 metabolites from walnut endopleura, which are involved in flavonoid and phenolic biosynthesis pathways. The enrichment analysis revealed that the cinnamic acid, coniferyl alcohol, naringenin, and naringenin-7-O-glucoside were important metabolites in the development process of walnut endopleura. Transcriptome and metabolome analyses revealed that the DEGs and differentially regulated metabolites (DRMs) were significantly enriched in flavonoid biosynthesis and phenolic metabolic pathways. Through co-expression analysis, CHS (chalcone synthase), CHI (chalcone isomerase), CCR (cinnamoyl CoA reductase), CAD (cinnamyl alcohol dehydrogenase), COMT (catechol-Omethyl transferase), and 4CL (4-coumaroyl: CoA-ligase) may be the key genes that potentially regulate walnut endopleura color in flavonoid biosynthesis and phenolic metabolic pathways.

CONCLUSIONS

This study illuminates the metabolic pathways and candidate genes that underlie the endopleura coloration in walnuts, lay the foundation for further study and provides insights into controlling nut's colour.

Dysregulated palmitic acid metabolism promotes the formation of renal calcium-oxalate stones through ferroptosis induced by polyunsaturated fatty acids/phosphatidic acid

The pathogenesis of renal calcium-oxalate (CaOx) stones is complex and influenced by various metabolic factors. In parallel, palmitic acid (PA) has been identified as an upregulated lipid metabolite in the urine and serum of patients with renal CaOx stones via untargeted metabolomics. Thus, this study aimed to mechanistically assess whether PA is involved in stone formation. Lipidomics analysis of PA-treated renal tubular epithelial cells compared with the control samples revealed that α-linoleic acid and α-linolenic acid were desaturated and elongated, resulting in the formation of downstream polyunsaturated fatty acids (PUFAs). In correlation, the levels of fatty acid desaturase 1 and 2 (FADS1 and FADS2) and peroxisome proliferator-activated receptor α (PPARα) in these cells treated with PA were increased relative to the control levels, suggesting that PA-induced upregulation of PPARα, which in turn upregulated these two enzymes, forming the observed PUFAs. Lipid peroxidation occurred in these downstream PUFAs under oxidative stress and Fenton Reaction. Furthermore, transcriptomics analysis revealed significant changes in the expression levels of ferroptosis-related genes in PA-treated renal tubular epithelial cells, induced by PUFA peroxides. In addition, phosphatidyl ethanolamine binding protein 1 (PEBP1) formed a complex with 15-lipoxygenase (15-LO) to exacerbate PUFA peroxidation under protein kinase C ζ (PKC ζ) phosphorylation, and PKC ζ was activated by phosphatidic acid derived from PA. In conclusion, this study found that the formation of renal CaOx stones is promoted by ferroptosis of renal tubular epithelial cells resulting from PA-induced dysregulation of PUFA and phosphatidic acid metabolism, and PA can promote the renal adhesion and deposition of CaOx crystals by injuring renal tubular epithelial cells, consequently upregulating adhesion molecules. Accordingly, this study provides a new theoretical basis for understanding the correlation between fatty acid metabolism and the formation of renal CaOx stones, offering potential targets for clinical applications.

NRAMP6c plays a key role in plant cadmium accumulation and resistance in tobacco (Nicotiana tabacum L.)

Tobacco plants (Nicotiana tabacum L.) exhibit considerable potential for phytoremediation of soil cadmium (Cd) pollutants, owing to their substantial biomass and efficient metal accumulation capabilities. The reduction of Cd accumulation in tobacco holds promise for minimizing Cd intake in individuals exposed to cigar smoking. NRAMP transporters are pivotal in the processes of Cd accumulation and resistance in plants; however, limited research has explored the functions of NRAMPs in tobacco plants. In this investigation, we focused on NtNRAMP6c, one of the three homologs of NRAMP6 in tobacco. We observed a robust induction of NtNRAMP6c expression in response to both Cd toxicity and iron (Fe) deficiency, with the highest expression levels detected in the roots. Subsequent subcellular localization and heterologous expression analyses disclosed that NtNRAMP6c functions as a plasma membrane-localized Cd transporter. Moreover, its overexpression significantly heightened the sensitivity of yeast cells to Cd toxicity. Through CRISPR-Cas9-mediated knockout of NtNRAMP6c, we achieved a reduction in Cd accumulation and an enhancement in Cd resistance in tobacco plants. Comparative transcriptomic analysis unveiled substantial alterations in the transcriptional profiles of genes associated with metal ion transport, photosynthesis, and macromolecule catabolism upon NtNRAMP6c knockout. Furthermore, our study employed plant metabolomics and rhizosphere metagenomics to demonstrate that NtNRAMP6c knockout led to changes in phytohormone homeostasis, as well as shifts in the composition and abundance of microbial communities. These findings bear significant biological implications for the utilization of tobacco in phytoremediation strategies targeting Cd pollutants in contaminated soils, and concurrently, in mitigating Cd accumulation in tobacco production destined for cigar consumption.

Application of omics technologies in cariology research: A critical review with bibliometric analysis

OBJECTIVES

To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges.

DATA & SOURCES

Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review.

STUDY SELECTION

Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %).

CONCLUSION

This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research.

CLINICAL SIGNIFICANCE

This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.

Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research

Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.

Solid-State Nanopores for Biomolecular Analysis and Detection

Advances in nanopore technology and data processing have rendered DNA sequencing highly accessible, unlocking a new realm of biotechnological opportunities. Commercially available nanopores for DNA sequencing are of biological origin and have certain disadvantages such as having specific environmental requirements to retain functionality. Solid-state nanopores have received increased attention as modular systems with controllable characteristics that enable deployment in non-physiological milieu. Thus, we focus our review on summarizing recent innovations in the field of solid-state nanopores to envision the future of this technology for biomolecular analysis and detection. We begin by introducing the physical aspects of nanopore measurements ranging from interfacial interactions at pore and electrode surfaces to mass transport of analytes and data analysis of recorded signals. Then, developments in nanopore fabrication and post-processing techniques with the pros and cons of different methodologies are examined. Subsequently, progress to facilitate DNA sequencing using solid-state nanopores is described to assess how this platform is evolving to tackle the more complex challenge of protein sequencing. Beyond sequencing, we highlight the recent developments in biosensing of nucleic acids, proteins, and sugars and conclude with an outlook on the frontiers of nanopore technologies.

Multiomics characterization of pyroptosis in the tumor microenvironment and therapeutic relevance in metastatic melanoma

BACKGROUND

Pyroptosis, mediated by gasdermins with the release of multiple inflammatory cytokines, has emerged as playing an important role in targeted therapy and immunotherapy due to its effectiveness at inhibiting tumor growth. Melanoma is one of the most commonly used models for immunotherapy development, though an inadequate immune response can occur. Moreover, the development of pyroptosis-related therapy and combinations with other therapeutic strategies is limited due to insufficient understanding of the role of pyroptosis in the context of different tumor immune microenvironments (TMEs).

METHODS

Here, we present a computational model (pyroptosis-related gene score, PScore) to assess the pyroptosis status. We applied PScore to 1388 melanoma samples in our in-house cohort and eight other publicly available independent cohorts and then calculated its prognostic power of and potential as a predictive marker of immunotherapy efficacy. Furthermore, we performed association analysis for PScore and the characteristics of the TME by using bulk, single-cell, and spatial transcriptomics and assessed the association of PScore with mutation status, which contributes to targeted therapy.

RESULTS

Pyroptosis-related genes (PRGs) showed distinct expression patterns and prognostic predictive ability in melanoma. Most PRGs were associated with better survival in metastatic melanoma. Our PScore model based on genes associated with prognosis exhibits robust performance in survival prediction in multiple metastatic melanoma cohorts. We also found PScore to be associated with BRAF mutation and correlate positively with multiple molecular signatures, such as KRAS signaling and the IFN gamma response pathway. Based on our data, melanoma with an immune-enriched TME had a higher PScore than melanoma with an immune-depleted or fibrotic TME. Additionally, monocytes had the highest PScore and malignant cells and fibroblasts the lowest PScore based on single-cell and spatial transcriptome analyses. Finally, a higher PScore was associated with better therapeutic efficacy of immune checkpoint blockade, suggesting the potential of pyroptosis to serve as a marker of immunotherapy response.

CONCLUSIONS

Collectively, our findings indicate that pyroptosis is a prognostic factor and is associated with the immune response in metastatic melanoma, as based on multiomics data. Our results provide a theoretical basis for drug combination and reveal potential immunotherapy response markers.

Combined Analysis of the Transcriptome, Proteome and Metabolome in Human Cryopreserved Sperm

PURPOSE

This study aimed to identify the altered pathways and genes associated with freezing damage in human sperm during cryopreservation by multiomics analysis.

MATERIALS AND METHODS

Fifteen fresh human semen samples were collected for transcriptomic analysis, and another 5 fresh human semen samples were obtained for metabolomic analysis. For each semen sample, 1 mL was cryopreserved, and another 1 mL was left untreated for paired design. The results were then combined with previously published proteomic results to identify key genes/pathways.

RESULTS

Cryopreservation significantly reduced sperm motility and mitochondrial structure. Transcriptomic analysis revealed altered mitochondrial function, including changes in tRNA-methyltransferase activity and adenosine tri-phosphate/adenosine di-phosphate transmembrane transporter activity. Metabolomic analysis showed that the citrate cycle in mitochondria was significantly altered. Combining transcriptomic, proteomic, and metabolomic analyses revealed 346 genes that were altered in at least two omics analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that metabolic pathways were significantly altered and strongly associated with mitochondria. Five genes were altered in all three omics analyses: COL11A1, COL18A1, LPCAT3, NME1, and NNT.

CONCLUSIONS

Five genes were identified by multiomics analysis in human cryopreserved sperm. These genes might have specific functions in cryopreservation. Explorations of the functions of these genes will be helpful for sperm cryopreservation and sperm motility improvement or even for reproduction in the future.

Towards reproducible radiomics research: introduction of a database for radiomics studies

OBJECTIVES

To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database.

METHODS

A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication.

RESULTS

Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase .

CONCLUSION

According to the results of this study, the majority of published radiomics models were not technically reproducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics.

CLINICAL RELEVANCE STATEMENT

To date, the reproducibility of radiomics research and open science practices within the radiomics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-sharing practices will facilitate faster clinical translation.

KEY POINTS

• There is a discrepancy between the number of published radiomics papers and the clinical implementation of these published radiomics models. • The main obstacle to clinical implementation is the lack of model-, code-, and data-sharing practices. • In order to translate radiomics research into clinical practice, the radiomics research community should adopt open science practices.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Combined Analysis of mRNA Expression and Open Chromatin in Microglia

The advance of single-cell RNA-sequencing technologies in the past years has enabled unprecedented insights into the complexity and heterogeneity of microglial cell states in the homeostatic and diseased brain. This includes rather complex proteomic, metabolomic, morphological, transcriptomic, and epigenetic adaptations to external stimuli and challenges resulting in a novel concept of core microglia properties and functions. To uncover the regulatory programs facilitating the rapid transcriptomic adaptation in response to changes in the local microenvironment, the accessibility of gene bodies and gene regulatory elements can be assessed. Here, we describe the application of a previously published method for simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq) on microglia nuclei isolated from frozen mouse brain tissue.

Macrophage heterogeneity in atherosclerosis: A matter of context

During atherogenesis, plaque macrophages take up and process deposited lipids, trigger inflammation, and form necrotic cores. The traditional inflammatory/anti-inflammatory paradigm has proven insufficient in explaining their complex disease-driving mechanisms. Instead, we now appreciate that macrophages exhibit remarkable heterogeneity and functional specialization in various pathological contexts, including atherosclerosis. Technical advances for studying individual cells, especially single-cell RNA sequencing, indeed allowed to identify novel macrophage subsets in both murine and human atherosclerosis, highlighting the existence of diverse macrophage activation states throughout pathogenesis. In addition, recent studies highlighted the role of the local microenvironment in shaping the macrophages' phenotype and function. However, this remains largely undescribed in the context of atherosclerosis. In this review we explore the origins of macrophages and their functional specialization, shedding light on the diverse sources of macrophage accumulation in the atherosclerotic plaque. Next, we discuss the phenotypic diversity observed in both murine and human atherosclerosis, elucidating their distinct functions and spatial distribution within plaques. Finally, we highlight the importance of the local microenvironment in both phenotypic and functional specialization of macrophages in atherosclerosis and elaborate on the need for spatial multiomics approaches to provide a better understanding of the different macrophage subsets' roles in the pathogenesis of atherosclerosis.

Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology

Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataracts. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq and CUT&RUN-seq to discover previously unreported mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Furthermore, we identify an epigenetic paradigm of cellular differentiation, defined by progressive loss of the H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.

Cytometry in High-Containment Laboratories

The emergence of new pathogens continues to fuel the need for advanced high-containment laboratories across the globe. Here we explore challenges and opportunities for integration of cytometry, a central technology for cell analysis, within high-containment laboratories. We review current applications in infectious disease, vaccine research, and biosafety. Considerations specific to cytometry within high-containment laboratories, such as biosafety requirements, and sample containment strategies are also addressed. We further tour the landscape of emerging technologies, including combination of cytometry with other omics, the application of automation, and artificial intelligence. Finally, we propose a framework to fast track the immersion of advanced technologies into the high-containment research setting to improve global preparedness for new emerging diseases.

Metabolic and Microbial Profiling of Soil Microbial Community under Per- and Polyfluoroalkyl Substance (PFAS) Stress

Per- and polyfluoroalkyl substances (PFAS) represent significant stress to organisms and are known to disrupt microbial community structure and function. Nevertheless, a detailed knowledge of the soil microbial community responding to PFAS stress at the metabolism level is required. Here we integrated UPLC-HRMS-based metabolomics data with 16S rRNA and ITS amplicon data across soil samples collected adjacent to a fluoropolymer production facility to directly identify the biochemical intermediates in microbial metabolic pathways and the interactions with microbial community structure under PFAS stress. A strong correlation between metabolite and microbial diversity was observed, which demonstrated significant variations in soil metabolite profiles and microbial community structures along with the sampling locations relative to the facility. Certain key metabolites were identified in the metabolite-PFAS co-occurrence network, functioning on microbial metabolisms including lipid metabolism, amino acid metabolism, and secondary metabolite biosynthesis. These results provide novel insights into the impacts of PFAS contamination on soil metabolomes and microbiomes. We suggest that soil metabolomics is an informative and useful tool that could be applied to reinforce the chemical evidence on the disruption of microbial ecological traits.

High-content multimodal analysis supports the IL-7/IL-7 receptor axis as a relevant therapeutic target in primary Sjögren's syndrome

OBJECTIVE

While the involvement of IL-7/IL-7R axis in pSS has been described in relation to T cells, little is known about the contribution of this pathway in relationship with other immune cells, and its implication in autoimmunity. Using high-content multiomics data, we aimed at characterizing IL-7R expressing cells and the involvement of IL-7/IL-7R pathway in pSS pathophysiology.

METHODS

An IL-7 signature established using RNA-sequencing of human PBMCs incubated with IL-7 was applied to 304 pSS patients, and on RNA-Seq datasets from tissue biopsies. High-content immunophenotyping using flow and imaging mass cytometry was developed to characterize peripheral and in situ IL-7R expression.

RESULTS

We identified a blood 4-gene IL-7 module (IKZF4, KIAA0040, PGAP1 and SOS1) associated with anti-SSA/Ro positiveness in patients as well as disease activity, and a tissue 5-gene IL-7 module (IL7R, PCED1B, TNFSF8, ADAM19, MYBL1) associated with infiltration severity. We confirmed expression of IL-7R on T cells subsets, and further observed upregulation of IL-7R on double-negative (DN) B cells, and especially DN2 B cells. IL-7R expression was increased in pSS compared to sicca patients with variations seen according to the degree of infiltration. When expressed, IL-7R was mainly found on epithelial cells, CD4+ and CD8+ T cells, switched memory B cells, DN B cells and M1 macrophages.

CONCLUSION

This exhaustive characterization of the IL-7/IL-7R pathway in pSS pathophysiology established that two IL-7 gene modules discriminate pSS patients with a high IL-7 axis involvement. Their use could guide the implementation of an anti-IL-7R targeted therapy in a precision medicine approach.

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up

BACKGROUND

The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level.

METHODS

Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82).

RESULTS

Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%).

CONCLUSIONS

This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.

Understanding the mechanobiology of phytoacoustics through molecular Lens: Mechanisms and future perspectives

BACKGROUND

How plants emit, perceive, and respond to sound vibrations (SVs) is a long-standing question in the field of plant sensory biology. In recent years, there have been numerous studies on how SVs affect plant morphological, physiological, and biochemical traits related to growth and adaptive responses. For instance, under drought SVs navigate plant roots towards water, activate their defence responses against stressors, and increase nectar sugar in response to pollinator SVs. Also, plants emit SVs during stresses which are informative in terms of ecological and adaptive perspective. However, the molecular mechanisms underlying the SV perception and emission in plants remain largely unknown. Therefore, deciphering the complexity of plant-SV interactions and identifying bonafide receptors and signaling players will be game changers overcoming the roadblocks in phytoacoustics.

AIM OF REVIEW

The aim of this review is to provide an overview of recent developments in phytoacoustics. We primarily focuss on SV signal perception and transduction with current challenges and future perspectives.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Timeline breakthroughs in phytoacoustics have constantly shaped our understanding and belief that plants may emit and respond to SVs like other species. However, unlike other plant mechanostimuli, little is known about SV perception and signal transduction. Here, we provide an update on phytoacoustics and its ecological importance. Next, we discuss the role of cell wall receptor-like kinases, mechanosensitive channels, intracellular organelle signaling, and other key players involved in plant-SV receptive pathways that connect them. We also highlight the role of calcium (Ca2+), reactive oxygen species (ROS), hormones, and other emerging signaling molecules in SV signal transduction. Further, we discuss the importance of molecular, biophysical, computational, and live cell imaging tools for decoding the molecular complexity of acoustic signaling in plants. Finally, we summarised the role of SV priming in plants and discuss how SVs could modulate plant defense and growth trade-offs during other stresses.

Large-scale single-cell RNA sequencing atlases of human immune cells across lifespan: Possibilities and challenges

Single-cell RNA sequencing technologies have successfully been leveraged for immunological insights into human prenatal, pediatric, and adult tissues. These single-cell studies have led to breakthroughs in our understanding of stem, myeloid, and lymphoid cell function. Computational analysis of fetal hematopoietic tissues has uncovered trajectories for T- and B-cell differentiation across multiple organ sites, and how these trajectories might be dysregulated in fetal and pediatric health and disease. As we enter the age of large-scale, multiomic, and integrative single-cell meta-analysis, we assess the advances and challenges of large-scale data generation, analysis, and reanalysis, and data dissemination for a broad range of scientific and clinical communities. We discuss Findable, Accessible, Interoperable, and Reusable data sharing and unified cell ontology languages as strategic areas for progress of the field in the near future. We also reflect on the trend toward deployment of multiomic and spatial genomic platforms within single-cell RNA sequencing projects, and the crucial role these data types will assume in the immediate future toward creation of comprehensive and rich single-cell atlases. We demonstrate using our recent studies of human prenatal and adult hematopoietic tissues the importance of interdisciplinary and collaborative working in science to reveal biological insights in parallel with technological and computational innovations.

Tryptophan metabolism regulates inflammatory macrophage polarization as a predictive factor for breast cancer immunotherapy

Metabolic reprogramming plays a pivotal role in regulating macrophage polarization and function. However, the impact of macrophage tryptophan metabolism on polarization within the breast cancer microenvironment remains elusive. In this study, we used single-cell transcriptome analysis and found that macrophages had the highest tryptophan metabolic activity in breast cancer, melanoma, and head and neck squamous cell carcinoma (HNSC). Further analysis revealed that the tryptophan metabolic activity of macrophages was positively correlated with the M1 macrophage scores in breast cancer. Pancancer analysis found positive correlations between tryptophan metabolism and the M1 macrophage score in almost all tumor types. Spatial transcriptome analysis revealed higher tryptophan metabolism in regions with higher M1 macrophage score in breast cancer tissues. Immune infiltration analysis revealed that the high tryptophan metabolism group exhibited a higher immune score, an increased proportion of CD8+ T cells, augmented cytolytic activity mediated by CD8+ T cells, and elevated expression of immune checkpoint molecules. Spatial immunophenotype cohort analysis exhibited that breast cancer patients expected to respond to immunotherapy had stronger tryptophan metabolism, with a 73.8 % area under the ROC curve. Single-cell transcriptome analysis of the immunotherapy cohort found that patients responding to immunotherapy had higher macrophage tryptophan metabolism prior to treatment initiation. Finally, in vitro experiments demonstrated elevated expression of tryptophan metabolic enzymes in M1 macrophages. Moreover, tryptophan facilitated the expression of M1 polarization markers, whereas inhibitors of tryptophan metabolic enzymes, such as NLG919, LM10, and Ro 61-8048, inhibited the expression of M1 polarization markers. In conclusion, this study identified a dual role for macrophage tryptophan metabolism in breast cancer; on the one hand, it promotes macrophage M1 polarization, while on the other hand, it serves as a promising predictor for the effectiveness of immunotherapy in breast cancer.

A systematic scientometric review of paternal inheritance of acquired metabolic traits

BACKGROUND

The concept of the inheritance of acquired traits, a foundational principle of Lamarck's evolutionary theory, has garnered renewed attention in recent years. Evidence for this phenomenon remained limited for decades but gained prominence with the Överkalix cohort study in 2002. This study revealed a link between cardiovascular disease incidence and the food availability experienced by individuals' grandparents during their slow growth periods, reigniting interest in the inheritance of acquired traits, particularly in the context of non-communicable diseases. This scientometric analysis and systematic review comprehensively explores the current landscape of paternally transmitted acquired metabolic traits.

RESULTS

Utilizing Scopus Advanced search and meticulous screening, we included mammalian studies that document the inheritance or modification of metabolic traits in subsequent generations of unexposed descendants. Our inclusive criteria encompass intergenerational and transgenerational studies, as well as multigenerational exposures. Predominantly, this field has been driven by a select group of researchers, potentially shaping the design and focus of existing studies. Consequently, the literature primarily comprises transgenerational rodent investigations into the effects of ancestral exposure to environmental pollutants on sperm DNA methylation. The complexity and volume of data often lead to multiple or redundant publications. This practice, while understandable, may obscure the true extent of the impact of ancestral exposures on the health of non-exposed descendants. In addition to DNA methylation, studies have illuminated the role of sperm RNAs and histone marks in paternally acquired metabolic disorders, expanding our understanding of the mechanisms underlying epigenetic inheritance.

CONCLUSIONS

This review serves as a comprehensive resource, shedding light on the current state of research in this critical area of science, and underscores the need for continued exploration to uncover the full spectrum of paternally mediated metabolic inheritance.

Overexpression of CD99 is associated with tumor adaptiveness and indicates the tumor recurrence and therapeutic responses in gliomas

Glioma undergoes adaptive changes, leading to poor prognosis and resistance to treatment. CD99 influences the migration and invasion of glioma cells and plays an oncogene role. However, whether CD99 can affect the adaptiveness of gliomas is still lacking in research, making its clinical value underestimated. Here, we enrolled our in-house and public multiomics datasets for bioinformatic analysis and conducted immunohistochemistry staining to investigate the role of CD99 in glioma adaptive response and its clinical implications. CD99 is expressed in more adaptative glioma subtypes and cell states. Under hypoxic conditions, CD99 is upregulated in glioma cells and is associated with angiogenesis and metabolic adaptations. Gliomas with over-expressed CD99 also increased the immunosuppressive tumor-associated macrophages. The relevance with tumor adaptiveness of CD99 presented clinical significance. We discovered that CD99 overexpression is associated with short-time recurrence and validated its prognostic value. Additionally, Glioma patients with high expression of CD99 were resistant to chemotherapy and radiotherapy. The CD99 expression was also related to anti-angiogenic and immune checkpoint inhibitor therapy response. Inhibitors of the PI3K-AKT pathway have therapeutic potential against CD99-overexpressing gliomas. Our study identified CD99 as a biomarker characterizing the adaptive response in glioma. Gliomas with high CD99 expression are highly tolerant to stress conditions such as hypoxia and antitumor immunity, making treatment responses dimmer and tumor progression. Therefore, for patients with CD99-overexpressing gliomas, tumor adaptiveness should be fully considered during treatment to avoid drug resistance, and closer clinical monitoring should be carried out to improve the prognosis.

Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer's Disease

BACKGROUND

Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation.

METHODS

To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF1/SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays.

RESULTS

We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1, convergent evidence indicated that 6 target genes (MTCH2, ACP2, NDUFS3, PSMC3, C1QTNF4, and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2.

CONCLUSIONS

Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD.

Identification of multivariable Boolean patterns in microbiome and microbial gene composition data

Virtually every biological system is governed by complex relations among its components. Identifying such relations requires a rigorous or heuristics-based search for patterns among variables/features of a system. Various algorithms have been developed to identify two-dimensional (involving two variables) patterns employing correlation, covariation, mutual information, etc. It seems obvious, however, that comprehensive descriptions of complex biological systems need also to include more complicated multivariable relations, which can only be described using patterns that simultaneously embrace 3, 4, and more variables. The goal of this manuscript is to (a) introduce a novel type of associations (multivariable Boolean patterns) that can be manifested between features of complex systems but cannot be identified (described) by traditional pair-vise metrics; (b) propose patterns classification method, and (c) provide a novel definition of the pattern's strength (pattern's score) able to accommodate heterogeneous multi-omics data. To demonstrate the presence of such patterns, we performed a search for all possible 2-, 3-, and 4-dimensional patterns in historical data from the Human Microbiome Project (15 body sites) and collection of H. pylori genomes associated with gastric ulcers, gastritis, and duodenal ulcers. In all datasets under consideration, we were able to identify hundreds of statistically significant multivariable patterns. These results suggest that such patterns can be common in microbial genomics/microbiomics systems.

Multiomics analyses reveal dose-dependent effects of dicofol exposure on host metabolic homeostasis and the gut microbiota in mice

BACKGROUND

Environmental exposure to dicofol (DCF), one of common organochlorine pesticides (OCPs) widely used for controlling agricultural pests, elicits a potential risk for human health due to its toxicity. However, potential physiological hazards of oral DCF exposure remain largely unknown.

METHODS

Mice were exposed to relatively chronic and subacute DCF at different doses (5, 20 and 100 mg/kg) by gavage for 2 weeks. 1H NMR-based metabolomics was used to explore alterations of metabolic profiling induced by DCF exposure. Targeted metabolomics was subsequently employed to investigate the dose-dependent effects of oral DCF exposure on lipid metabolism and the gut microbiota-derived metabolites of mice. 16S rRNA gene sequencing was further employed to evaluate the changes of gut community of mice exposed to DCF.

RESULTS

Oral exposure to DCF dose-dependently induced liver injury, manifested by hepatic lipogenesis, inflammation and liver dysfunction of mice. Typically, DCF exposure disrupted host fatty acids metabolism that were confirmed by marked alteration in the levels of related genes. DCF exposure also dose-dependently caused dysbiosis of the gut bacteria and its metabolites including altered microbial composition accompanied by inhibition of bacterial fermentation.

CONCLUSION

These results provide metabolic evidence that DCF exposure dose-dependently induces liver lipidosis and disruption of the gut microbiota in mice, which enrich our views of molecular mechanism of DCF hepatoxicity.

Systems biology of industrial oxytetracycline production in Streptomyces rimosus: the secrets of a mutagenized hyperproducer

BACKGROUND

Oxytetracycline which is derived from Streptomyces rimosus, inhibits a wide range of bacteria and is industrially important. The underlying biosynthetic processes are complex and hinder rational engineering, so industrial manufacturing currently relies on classical mutants for production. While the biochemistry underlying oxytetracycline synthesis is known to involve polyketide synthase, hyperproducing strains of S. rimosus have not been extensively studied, limiting our knowledge on fundamental mechanisms that drive production.

RESULTS

In this study, a multiomics analysis of S. rimosus is performed and wild-type and hyperproducing strains are compared. Insights into the metabolic and regulatory networks driving oxytetracycline formation were obtained. The overproducer exhibited increased acetyl-CoA and malonyl CoA supply, upregulated oxytetracycline biosynthesis, reduced competing byproduct formation, and streamlined morphology. These features were used to synthesize bhimamycin, an antibiotic, and a novel microbial chassis strain was created. A cluster deletion derivative showed enhanced bhimamycin production.

CONCLUSIONS

This study suggests that the precursor supply should be globally increased to further increase the expression of the oxytetracycline cluster while maintaining the natural cluster sequence. The mutagenized hyperproducer S. rimosus HP126 exhibited numerous mutations, including large genomic rearrangements, due to natural genetic instability, and single nucleotide changes. More complex mutations were found than those typically observed in mutagenized bacteria, impacting gene expression, and complicating rational engineering. Overall, the approach revealed key traits influencing oxytetracycline production in S. rimosus, suggesting that similar studies for other antibiotics could uncover general mechanisms to improve production.

Cysteine facilitates the lignocellulolytic response of Trichoderma guizhouense NJAU4742 by indirectly up-regulating membrane sugar transporters

BACKGROUND

Filamentous fungi possess a rich CAZymes system, which is widely studied and applied in the bio-conversion of plant biomass to alcohol chemicals. Carbon source acquisition is the fundamental driver for CAZymes-producing sustainability and secondary metabolism, therefore, a deeper insight into the regulatory network of sugar transport in filamentous fungi has become urgent.

RESULTS

This study reports an important linkage of sulfur assimilation to lignocellulose response of filamentous fungus. Inorganic sulfur addition facilitated biodegradation of rice straw by Trichoderma guizhouense NJAU4742. Cysteine and glutathione were revealed as major intracellular metabolites responsive to sulfur addition by metabolomics, cysteine content was increased in this process and glutathione increased correspondingly. Two membrane sugar transporter genes, Tgmst1 and Tgmst2, were identified as the critical response genes significantly up-regulated when intracellular cysteine increased. Tgmst1 and Tgmst2 were both positively regulated by the glucose regulation-related protein (GRP), up-regulation of both Tgmst1 and Tggrp can cause a significant increase in intracellular glucose. The transcriptional regulatory function of GRP mainly relied on GSH-induced glutathionylation, and the transcription activating efficiency was positively related to the glutathionylation level, furthermore, DTT-induced deglutathionylation resulted in the down-regulation of downstream genes.

CONCLUSIONS

Inorganic sulfur addition induces a rise in intracellular Cys content, and the conversion of cysteine to glutathione caused the increase of glutathionylation level of GRP, which in turn up-regulated Tgmst1 and Tgmst2. Subsequently, the sugar transport efficiency of single cells was improved, which facilitated the maintenance of vigorous CAZymes metabolism and the straw-to-biomass conversion.

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

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

Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids

BACKGROUND

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear.

METHODS

We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD.

RESULTS

A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another.

CONCLUSIONS

Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.

Multiomics Approach to Acromegaly: Unveiling Translational Insights for Precision Medicine

The clinical characteristics and prognoses of acromegaly vary among patients. Assessment of current and novel predictors can lead to multilevel categorization of patients, allowing integration into new clinical guidelines and a reduction in the increased morbidity and mortality associated with acromegaly. Despite advances in the diagnosis and treatment of acromegaly, its pathophysiology remains unclear. Recent advancements in multiomics technologies, including genomics, transcriptomics, proteomics, metabolomics, and radiomics, have offered new opportunities to unravel the complex pathophysiology of acromegaly. This review comprehensively explores the emerging role of multiomics approaches in elucidating the molecular landscape of acromegaly. We discuss the potential implications of multiomics data integration in the development of novel diagnostic tools, identification of therapeutic targets, and the prospects of precision medicine in acromegaly management. By integrating diverse omics datasets, these approaches can provide valuable insights into disease mechanisms, facilitate the identification of diagnostic biomarkers, and identify potential therapeutic targets for precision medicine in the management of acromegaly.