Advances and Trends in Omics Technology Development

Sample transformation in online separations: how chemical conversion advances analytical technology

While the advent of modern analytical technology has allowed scientists to determine the complexity of mixtures, it also spurred the demand to understand these sophisticated mixtures better. Chemical transformation can be used to provide insights into properties of complex samples such as degradation pathways or molecular heterogeneity that are otherwise unaccessible. In this article, we explore how sample transformation is exploited across different application fields to empower analytical methods. Transformation mechanisms include molecular-weight reduction, controlled degradation, and derivatization. Both offline and online transformation methods have been explored. The covered studies show that sample transformation facilitates faster reactions (e.g. several hours to minutes), reduces sample complexity, unlocks new sample dimensions (e.g. functional groups), provides correlations between multiple sample dimensions, and improves detectability. The article highlights the state-of-the-art and future prospects, focusing in particular on the characterization of protein and nucleic-acid therapeutics, nanoparticles, synthetic polymers, and small molecules.

Implications of oral dysbiosis and HPV infection in head and neck cancer: from molecular and cellular mechanisms to early diagnosis and therapy

Head and neck cancer (HNC) is the sixth most common type of cancer, with more than half a million new cases annually. This review focuses on the role of oral dysbiosis and HPV infection in HNCs, presenting the involved taxons, molecular effectors and pathways, as well as the HPV-associated particularities of genetic and epigenetic changes and of the tumor microenvironment occurred in different stages of tumor development. Oral dysbiosis is associated with the evolution of HNCs, through multiple mechanisms such as inflammation, genotoxins release, modulation of the innate and acquired immune response, carcinogens and anticarcinogens production, generation of oxidative stress, induction of mutations. Thus, novel microbiome-derived biomarkers and interventions could significantly contribute to achieving the desideratum of personalized management of oncologic patients, regarding both early diagnosis and treatment. The results reported by different studies are not always congruent regarding the variations in the abundance of different taxons in HNCs. However, there is a consistent reporting of a higher abundance of Gram-negative species such as Fusobacterium, Leptotrichia, Treponema, Porphyromonas gingivalis, Prevotella, Bacteroidetes, Haemophilus, Veillonella, Pseudomonas, Enterobacterales, which are probably responsible of chronic inflammation and modulation of tumor microenvironment. Candida albicans is the dominant fungi found in oral carcinoma being also associated with shorter survival rate. Specific microbial signatures (e.g., F. nucleatum, Bacteroidetes and Peptostreptococcus) have been associated with later stages and larger tumor, suggesting their potential to be used as biomarkers for tumor stratification and prognosis. On the other hand, increased abundance of Corynebacterium, Kingella, Abiotrophia is associated with a reduced risk of HNC. Microbiome could also provide biomarkers for differentiating between oropharyngeal and hypopharyngeal cancers as well as between HPV-positive and HPV-negative tumors. Ongoing clinical trials aim to validate non-invasive tests for microbiome-derived biomarkers detection in oral and throat cancers, especially within high-risk populations. Oro-pharyngeal dysbiosis could also impact the HNCs therapy and associated side-effects of radiotherapy, chemotherapy, and immunotherapy. HPV-positive tumors harbor fewer mutations, as well as different DNA methylation pattern and tumor microenvironment. Therefore, elucidation of the molecular mechanisms by which oral microbiota and HPV infection influence the HNC initiation and progression, screening for HPV infection and vaccination against HPV, adopting a good oral hygiene, and preventing oral dysbiosis are important tools for advancing in the battle with this public health global challenge.

Towards the crux of sex-dependent variability in red cell concentrates

Donor sex can alter the RBC 'storage lesion' progression, contributing to dissimilarities in blood product quality, and thus adverse post-transfusion reactions. The mechanisms underlying the reduced sensitivity of female RBCs to storage-induced stress are partially ascribed to the differential effects of testosterone, progesterone, and estrogen on hemolytic propensity. Contributing to this is the increased proportion of more robust, biologically 'young' subpopulations of RBCs in females. Herein, we discuss the impact of sex hormones on RBCs and the relevance of these biological subpopulations to provide further insight into sex-dependent blood product variability.

Understanding anthelmintic resistance in livestock using "omics" approaches

Widespread and improper use of various anthelmintics, genetic, and epidemiological factors has resulted in anthelmintic-resistant (AR) helminth populations in livestock. This is currently quite common globally in different livestock animals including sheep, goats, and cattle to gastrointestinal nematode (GIN) infections. Therefore, the mechanisms underlying AR in parasitic worm species have been the subject of ample research to tackle this challenge. Current and emerging technologies in the disciplines of genomics, transcriptomics, metabolomics, and proteomics in livestock species have advanced the understanding of the intricate molecular AR mechanisms in many major parasites. The technologies have improved the identification of possible biomarkers of resistant parasites, the ability to find actual causative genes, regulatory networks, and pathways of parasites governing the AR development including the dynamics of helminth infection and host-parasite infections. In this review, various "omics"-driven technologies including genome scan, candidate gene, quantitative trait loci, transcriptomic, proteomic, and metabolomic approaches have been described to understand AR of parasites of veterinary importance. Also, challenges and future prospects of these "omics" approaches are also discussed.

Investigation of the response of Platycodongrandiflorus (Jacq.) A. DC to salt stress using combined transcriptomics and metabolomics

BACKGROUND

Platycodon grandiflorus (Jacq.) A. DC is a famous traditional Chinese medicine in China and an authentic medicine in Inner Mongolia. It has been traditionally used as an expectorant in cough and also has anti-inflammatory and other pharmacological effects. As a homologous plant of medicine and food, P. grandiflorus is widely planted in Northeast China. Soil salinity isa limiting factor for its cultivation. In this study, we comprehensively described the physiological characteristics of P. grandiflorus and combined transcriptomics and metabolomics to study the response of roots of P. grandiflorus to salt stress.

RESULTS

Overall, 8,988 differentially expressed genes were activated and significantly altered the metabolic processes. In total, 428 differentially abundant metabolites were affected by salt stress. After moderate and severe salt stress, most of the differentially abundant metabolites were enriched in the L-phenylalanine metabolic pathway. Through the comprehensive analysis of the interaction between key genes and metabolites, the main pathways such as lignin compound biosynthesis and triterpene saponin biosynthesis were completed. The relative content of compounds related to lignin biosynthesis, such as caffeic acid, coniferin, and syringing, increased under salt stress, and the related genes such as PAL, C4H, and the key enzyme gene UGT72E2 were activated to adapt to the salt stress. Platycodon saponin is one of the major triterpene saponins in P. grandiflorus, and Platycodin D is its most abundant major bioactive component. Under severe salt stress, Platycodin D level increased by nearly 1.77-fold compared with the control group. Most of the genes involved insynthetic pathway of Platycodin D, such as HMGCR, GGPS, SE, and LUP, were upregulated under salt stress.

CONCLUSION

Salt stress led to a decrease in the biomass and affected the activities of antioxidant enzymes and contents of osmotic regulators in the plant. These results provided not only novel insights into the underlying mechanisms of response of P. grandiflorus to salt stress but also a foundation for future studies on the function of genes related to salt tolerance in the triterpenoid saponin biosynthesis pathway.

Protein Expression and Bioinformatics Study of Granulosa Cells of Polycystic Ovary Syndrome Expressed Under the Influence of DHEA

Background

The reproductive system is heavily dependent on ovarian follicles, which are made up of germ cells (oocytes) and granulosa cells (GCs), including cumulus granulosa cells (CGCs) and mural granulosa cells (MGCs). Understanding their normal and steroid-induced functions is the key to understanding the pathophysiology of endocrinal diseases in women.

Objective

This study investigated the differentially expressed proteins by CGCs and MGCs of patients with polycystic ovarian syndrome (PCOS) and without subsequent exposure to dehydroepiandrosterone sulfate (DHEAS) and functional differentiation.

Design

The present study was observational and experimental study carried out in hospital involving 80 female patients undergoing IVF for infertility.

Methods

In this study, we isolated CGCs and MGCs from the follicular fluid of both PCOS and non-PCOS patients undergoing in vitro fertilization (IVF). The cells were cultured and treated with DHEAS for 48 hours, and these cells were extracted, digested, and analyzed by tandem mass spectrometry followed by processing of the results using open-source bioinformatics tools.

Results

The present investigation discovered 276 and 341 proteins in CGCs and MGCs, respectively. DHEAS reduced the number of proteins expressed by CGCs and MGCs to 34 and 57 from 91 and 94, respectively. Venn results of CGCs revealed 49, 53, 36, and 21 proteins in normal CGCs, PCOS-CGCs, post-DHEAS, and PCOS-CGCs, respectively. Venn analysis of MGCs showed 51 proteins specific to PCOS and 29 shared by normal and PCOS samples after DHEAS therapy. MGCs express the most binding and catalytic proteins, whereas CGCs express transporter-related proteins. A protein pathway study demonstrated considerable differences between normal and PCOS samples, while DHEAS-treated samples of both cell lines showed distinct pathways. String findings identified important network route components such as albumin, actin, apolipoprotein, complement component C3, and heat shock protein.

Conclusion

This is the first study to show how DHEAS-induced stress affects the expression of proteins by MGCs and CGCs isolated from normal and PCOS patients. Further studies are recommended to identify PCOS biomarkers from CGCs and MGCs expressed under the influence of DHEAS.

N6-Methyladenosine mRNA Modification: From Modification Site Selectivity to Neurological Functions

The development of various chemical methods has enabled scientists to decipher the distribution features and biological functions of RNA modifications in the past decade. In addition to modifying noncoding RNAs such as tRNAs and rRNAs, N6-methyladenosine (m6A) has been proven to be the most abundant internal chemical modification on mRNAs in eukaryotic cells and is also the most widely studied mRNA modification to date. Extensive studies have repeatedly demonstrated the important functions of m6A in various biological conditions, ranging from embryonic organ development to adult organ function and pathogenesis. Unlike DNA methylation which is relatively stable, the reversible m6A modification on mRNA is highly dynamic and easily influenced by various internal or external factors, such as cell type, developmental stage, nutrient supply, circadian rhythm, and environmental stresses.In this Account, we review our previous findings on the site selectivity mechanisms regulating m6A formation, as well as the physiological roles of m6A modification in cerebellum development and long-term memory consolidation. In our initial efforts to profile m6A in various types of mouse and human cells, we surprisingly found that the sequence motifs surrounding m6A sites were often complementary with the seed sequences of miRNAs. By manipulating the abundance of the miRNA biogenesis enzyme Dicer or individual miRNAs or mutating miRNA sequences, we were able to reveal a new role of nucleus localized miRNAs, which is to guide the m6A methyltransferase METTL3 to bind to mRNAs and to promote m6A formation. As a result, we partially answered the question of why only a small proportion of m6A motifs within an mRNA could have m6A modification at a certain time point. We further explored the functions of m6A modification in regulating brain development and brain functions. We found that cerebellum had the most severe defects when Mettl3 was knocked out in developing mouse embryonic brain and revealed that the underlying mechanisms could be attributed to aberrant mRNA splicing and enhanced cell apoptosis under m6A deficit conditions. On the other hand, knocking out Mettl3 in postnatal hippocampus did not cause morphological defects in the mouse brain but impaired the efficacy of long-term memory consolidation. Under learning stimuli, formation of m6A modifications could be detected on transcripts encoding proteins related to dendrite growth, synapse formation, and other memory related functions. Loss of m6A modifications on these transcripts would result in translation deficiency and reduced protein production, particularly in the translation of early response genes, and therefore would compromise the efficacy of long-term memory consolidation. Interestingly, excessive training sessions or increased training intensity could overcome such m6A deficiency related memory defects, which is likely due to the longer turnover cycle and the cumulative abundance of proteins throughout the training process. In addition to revealing the roles of m6A modification in regulating long-term memory formation, our work also demonstrated an effective method for studying memory formation efficacy. As the lack of an appropriate model for studying memory formation efficacy has been a long-lasting problem in the field of neural science, our hippocampus-specific postnatal m6A knockout model could also be utilized to study other questions related to memory formation efficacy.

A roadmap for delivering a human musculoskeletal cell atlas

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims to create a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

Insights into Grape Ripe Rot: A Focus on the Colletotrichum gloeosporioides Species Complex and Its Management Strategies

Grape ripe rot, which is predominantly caused by the Colletotrichum species, presents a growing threat to global grape cultivation. This threat is amplified by the increasing populations of the Colletotrichum species in response to warmer climates. In this review, we investigate the wide-ranging spectrum of grape ripe rot, specifically highlighting the role and characteristics of the C. gloeosporioides species complex (CGSC). We incorporate this understanding as we explore the diverse symptoms that lead to infected grapevines, their intricate life cycle and epidemiology, and the escalating prevalence of C. viniferum in Asia and globally. Furthermore, we delve into numerous disease management strategies, both conventional and emerging, such as prevention and mitigation measures. These strategies include the examination of host resistances, beneficial cultivation practices, sanitation measures, microbiome health maintenance, fungicide choice and resistance, as well as integrated management approaches. This review seeks to enhance our understanding of this globally significant disease, aspiring to assist in the development and improvement of effective prevention and control strategies.

A Tale of Three Proteomes: Visualizing Protein and Transcript Abundance Relationships in the Breast Cancer Proteome Portal

Molecular characterization is transforming research on novel therapeutics in breast cancer. High-throughput methodologies are unbiased to hypotheses; thus, data produced are relevant to address unlimited questions and provide resources for the experimental design process. However, the opportunity is often overlooked because data are not readily accessed or analyzed. Herein, the Breast Cancer Proteome Portal, the only online tool for analyzing protein and transcript abundances across the three breast cancer proteomics studies, is presented. The tool is applied to demonstrate that cofunctioning protein abundances are highly correlated and, conversely, high abundance correlation may be an indicator of cofunction. Furthermore, the cofunction-correlation relationship is less resolved at the transcript level. By applying analysis and visualization tools within the Breast Cancer Proteome Portal, insights are garnered about serine synthesis and the compartmentalization of one-carbon metabolism in breast cancer, and a transcription factor tumorigenic regulatory network of glutamine deamination and oxidation is proposed, illustrating that the Breast Cancer Proteome Portal provides an interface for garnering insights from the information-rich studies of the breast cancer proteome.

Genomic Medicine in Canine Periodontal Disease: A Systematic Review

Genomic medicine has become a growing reality; however, it is still taking its first steps in veterinary medicine. Through this approach, it will be possible to trace the genetic profile of a given individual and thus know their susceptibility to certain diseases, namely periodontal disease. This condition is one of the most frequently diagnosed in companion animal clinics, especially in dogs. Due to the limited existing information and the lack of comprehensive studies, the objective of the present study was to systematically review the existing scientific literature regarding genomic medicine in canine periodontal disease and determine which genes have already been studied and their probable potential. This study followed the recommendations of the PRISMA 2020 methodology. Canine periodontal disease allied to genomic medicine were the subjects of this systematic review. Only six articles met all of the inclusion criteria, and these were analyzed in detail. These studies described genetic variations in the following genes: interleukin-6, interleukin-10, interleukin-1, lactotransferrin, toll-like receptor 9, and receptor activator of nuclear factor-kappa B. Only in two of them, namely interleukin-1 and toll-like receptor 9 genes, may the identified genetic variations explain the susceptibility that certain individuals have to the development of periodontal disease. It is necessary to expand the studies on the existing polymorphic variations in genes and their relationship with the development of periodontal disease. Only then will it be possible to fully understand the biological mechanisms that are involved in this disease and that determine the susceptibility to its development.

Comparison of the gut microbiota and untargeted gut tissue metabolome of Chinese mitten crabs (Eriocheir sinensis) with different shell colors

Introduction

The Chinese mitten crab (Eriocheir sinensis) is a highly valued freshwater crustacean in China. While the natural shell color of E. sinensis is greenish brown (GH), we found a variety with a brownish-orange shell color (RH). Although RH is more expensive, it exhibits a lower molting frequency and growth rate compared with GH, which significantly reduces its yield and hinders large-scale farming. The growth and development of animals are closely related to their gut microbiota and gut tissue metabolic profiles.

Methods

In this study, we compared the gut microbiome communities and metabolic profiles of juvenile RH and GH crabs using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS), respectively.

Results

Our findings indicated that the intestinal microbial composition and metabolic characteristics of E. sinensis differed significantly between RH and GH. At the operational taxonomic unit (OTU) level, the α-diversity of the gut microbiota did not differ significantly between RH and GH, while the β-diversity of the RH gut microbiota was higher than that of the GH gut microbiota. At the species level, the richness of unclassified_c_Alphaproteobacteria was significantly higher in the GH group, while the RH group had a significantly higher richness of three low-abundance species, Flavobacteria bacterium BAL38, Paraburkholderia ferrariae, and uncultured_bacterium_g__Legionella. In the current study, 598 gut tissue metabolites were identified, and 159 metabolites were significantly different between GH and RH. The metabolite profile of RH was characteristic of a low level of most amino acids and lipid metabolites and a high level of several pigments compared with that of GH. These metabolites were enriched in 102 KEGG pathways. Four pathways, including (1) Central carbon metabolism in cancer, (2) protein digestion and absorption, (3) alanine, aspartate and glutamate metabolism, and (4) aminoacyl-tRNA biosynthesis, were significantly enriched. The correlation analysis between metabolites and microbiotas indicated that most key differential metabolites were positively correlated with the abundance of Shewanella_sp_MR-7.

Discussion

This research provided a greater understanding of the physiological conditions of E. sinensis varieties with different shell colors by comparing the gut microbiota and gut tissue metabolome.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

Metabolomics in Acute Kidney Injury: The Clinical Perspective

BACKGROUND

Acute kidney injury (AKI) affects increasing numbers of hospitalized patients worldwide. The diagnosis of AKI is made too late in most individuals since it is still based on dynamic changes in serum creatinine. In recent years, new AKI biomarkers have been identified; however, none of these can reliably replace serum creatinine yet. Metabolomic profiling (metabolomics) allows the concomitant detection and quantification of large numbers of metabolites from biological specimens. The current article aims to summarize clinical studies on metabolomics in AKI diagnosis and risk prediction.

METHODS

The following databases were searched for references: PubMed, Web of Science, Cochrane Library, and Scopus, and the period lasted from 1940 until 2022. The following terms were utilized: 'AKI' OR 'Acute Kidney Injury' OR 'Acute Renal Failure' AND 'metabolomics' OR 'metabolic profiling' OR 'omics' AND 'risk' OR 'death' OR 'survival' OR 'dialysis' OR 'KRT' OR 'kidney replacement therapy' OR 'RRT' OR 'renal replacement therapy' OR 'recovery of kidney function' OR 'renal recovery' OR 'kidney recovery' OR 'outcome'. Studies on AKI risk prediction were only selected if metabolomic profiling allowed differentiation between subjects that fulfilled a risk category (death or KRT or recovery of kidney function) and those who did not. Experimental (animal-based) studies were not included.

RESULTS

In total, eight studies were identified. Six studies were related to the diagnosis of AKI; two studies were performed on metabolic analysis in AKI risk (death) prediction. Metabolomics studies in AKI already helped to identify new biomarkers for AKI diagnosis. The data on metabolomics for AKI risk prediction (death, KRT, recovery of kidney function), however, are very limited.

CONCLUSIONS

Both the heterogenous etiology and the high degree of pathogenetic complexity of AKI most likely require integrated approaches such as metabolomics and/or additional types of '-omics' studies to improve clinical outcomes in AKI.

Untargeted Metabolomics Identifies Biomarkers for MCADD Neonates in Dried Blood Spots

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common inherited mitochondrial metabolic disease of fatty acid β-oxidation, especially in newborns. MCADD is clinically diagnosed using Newborn Bloodspot Screening (NBS) and genetic testing. Still, these methods have limitations, such as false negatives or positives in NBS and the variants of uncertain significance in genetic testing. Thus, complementary diagnostic approaches for MCADD are needed. Recently, untargeted metabolomics has been proposed as a diagnostic approach for inherited metabolic diseases (IMDs) due to its ability to detect a wide range of metabolic alterations. We performed an untargeted metabolic profiling of dried blood spots (DBS) from MCADD newborns (n = 14) and healthy controls (n = 14) to discover potential metabolic biomarkers/pathways associated with MCADD. Extracted metabolites from DBS samples were analyzed using UPLC-QToF-MS for untargeted metabolomics analyses. Multivariate and univariate analyses were used to analyze the metabolomics data, and pathway and biomarker analyses were also performed on the significantly identified endogenous metabolites. The MCADD newborns had 1034 significantly dysregulated metabolites compared to healthy newborns (moderated t-test, no correction, p-value ≤ 0.05, FC 1.5). A total of 23 endogenous metabolites were up-regulated, while 84 endogenous metabolites were down-regulated. Pathway analyses showed phenylalanine, tyrosine, and tryptophan biosynthesis as the most affected pathways. Potential metabolic biomarkers for MCADD were PGP (a21:0/PG/F1alpha) and glutathione, with an area under the curve (AUC) of 0.949 and 0.898, respectively. PGP (a21:0/PG/F1alpha) was the first oxidized lipid in the top 15 biomarker list affected by MCADD. Additionally, glutathione was chosen to indicate oxidative stress events that could happen during fatty acid oxidation defects. Our findings suggest that MCADD newborns may have oxidative stress events as signs of the disease. However, further validations of these biomarkers are needed in future studies to ensure their accuracy and reliability as complementary markers with established MCADD markers for clinical diagnosis.

Metabolomics in Acute Kidney Injury: The Experimental Perspective

Acute kidney injury (AKI) affects increasing numbers of in-hospital patients in Central Europe and the USA, the prognosis remains poor. Although substantial progress has been achieved in the identification of molecular/cellular processes that induce and perpetuate AKI, more integrated pathophysiological perspectives are missing. Metabolomics enables the identification of low-molecular-weight (< 1.5 kD) substances from biological specimens such as certain types of fluid or tissue. The aim of the article was to review the literature on metabolic profiling in experimental AKI and to answer the question if metabolomics allows the integration of distinct pathophysiological events such as tubulopathy and microvasculopathy in ischemic and toxic AKI. The following databases were searched for references: PubMed, Web of Science, Cochrane Library, Scopus. The period lasted from 1940 until 2022. The following terms were utilized: "acute kidney injury" OR "acute renal failure" OR "AKI" AND "metabolomics" OR "metabolic profiling" OR "omics" AND "ischemic" OR "toxic" OR "drug-induced" OR "sepsis" OR "LPS" OR "cisplatin" OR "cardiorenal" OR "CRS" AND "mouse" OR "mice" OR "murine" OR "rats" OR "rat". Additional search terms were "cardiac surgery", "cardiopulmonary bypass", "pig", "dog", and "swine". In total, 13 studies were identified. Five studies were related to ischemic, seven studies to toxic (lipopolysaccharide (LPS), cisplatin), and one study to heat shock-associated AKI. Only one study, related to cisplatin-induced AKI, was performed as a targeted analysis. The majority of the studies identified multiple metabolic deteriorations upon ischemia/the administration of LPS or cisplatin (e.g., amino acid, glucose, lipid metabolism). Particularly, abnormalities in the lipid homeostasis were shown under almost all experimental conditions. LPS-induced AKI most likely depends on the alterations in the tryptophan metabolism. Metabolomics studies provide a deeper understanding of pathophysiological links between distinct processes that are responsible for functional impairment/structural damage in ischemic or toxic or other types of AKI.

Imaging biofilms using fluorescence in situ hybridization: seeing is believing

Biofilms are complex structures with an intricate relationship between the resident microorganisms, the extracellular matrix, and the surrounding environment. Interest in biofilms is growing exponentially given its ubiquity in so diverse fields such as healthcare, environmental and industry. Molecular techniques (e.g., next-generation sequencing, RNA-seq) have been used to study biofilm properties. However, these techniques disrupt the spatial structure of biofilms; therefore, they do not allow to observe the location/position of biofilm components (e.g., cells, genes, metabolites), which is particularly relevant to explore and study the interactions and functions of microorganisms. Fluorescence in situ hybridization (FISH) has been arguably the most widely used method for an in situ analysis of spatial distribution of biofilms. In this review, an overview on different FISH variants already applied on biofilm studies (e.g., CLASI-FISH, BONCAT-FISH, HiPR-FISH, seq-FISH) will be explored. In combination with confocal laser scanning microscopy, these variants emerged as a powerful approach to visualize, quantify and locate microorganisms, genes, and metabolites inside biofilms. Finally, we discuss new possible research directions for the development of robust and accurate FISH-based approaches that will allow to dig deeper into the biofilm structure and function.

Current advances for omics-guided process optimization of microbial manufacturing

Currently, microbial manufacturing is widely used in various fields, such as food, medicine and energy, for its advantages of greenness and sustainable development. Process optimization is the committed step enabling the commercialization of microbial manufacturing products. However, the present optimization processes mainly rely on experience or trial-and-error method ignoring the intrinsic connection between cellular physiological requirement and production performance, so in many cases the productivity of microbial manufacturing could not been fully exploited at economically feasible cost. Recently, the rapid development of omics technologies facilitates the comprehensive analysis of microbial metabolism and fermentation performance from multi-levels of molecules, cells and microenvironment. The use of omics technologies makes the process optimization more explicit, boosting microbial manufacturing performance and bringing significant economic benefits and social value. In this paper, the traditional and omics technologies-guided process optimization of microbial manufacturing are systematically reviewed, and the future trend of process optimization is prospected.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

A Central Asian Food Dataset for Personalized Dietary Interventions

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.

Epigenetics Approaches toward Precision Medicine for Idiopathic Pulmonary Fibrosis: Focus on DNA Methylation

Genetic information is not transmitted solely by DNA but by the epigenetics process. Epigenetics describes molecular missing link pathways that could bridge the gap between the genetic background and environmental risk factors that contribute to the pathogenesis of pulmonary fibrosis. Specific epigenetic patterns, especially DNA methylation, histone modifications, long non-coding, and microRNA (miRNAs), affect the endophenotypes underlying the development of idiopathic pulmonary fibrosis (IPF). Among all the epigenetic marks, DNA methylation modifications have been the most widely studied in IPF. This review summarizes the current knowledge concerning DNA methylation changes in pulmonary fibrosis and demonstrates a promising novel epigenetics-based precision medicine.

A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.

CustOmics: A versatile deep-learning based strategy for multi-omics integration

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

Lyme Borreliosis in Dogs: Background, Epidemiology, Diagnostics, Treatment and Prevention

Lyme borreliosis (LB) is a multisystemic tick-borne disease that can affect many organs and have various clinical manifestations in dogs. We attempted to summarise various aspects of Lyme disease: i. e., pathogenesis, epidemiology, benefits and risks of diagnostic approaches, treatment options, and prevention in dogs. Several diagnostic bottlenecks for LB in dogs and humans are compared. Because the occurrence of LB in both humans and dogs is closely related, monitoring its prevalence in dogs as sentinel animals is an excellent aid in assessing the risk of Lyme disease in a given geographic area. Although clinical symptoms in humans help clinicians diagnose LB, they are ineffective in dogs because canines rarely exhibit LB symptoms. Despite significant differences in sensitivity and specificity, sero-logical two-step detection of antibodies against Borrelia spp. (ELISA and Western blot) is the most commonly used method in humans and dogs. The limitations of the assay highlight the need for further research to develop new clinical markers and more accurate diagnostic tests. Due to the lack of a specific all-encompassing LB test, a definitive diagnosis of LB remains a difficult and time-consuming process in human and veterinary medicine. Understanding the disease prevalence and diagnostics, as well as preventing its spread with effective and timely treatment, are fundamental principles of good disease management.

Sex dimorphism and metabolic profiles in management of metabolic-associated fatty liver disease

Metabolic-associated fatty liver disease (MAFLD) refers to the build-up of fat in the liver associated with metabolic dysfunction and has been estimated to affect a quarter of the population worldwide. Although metabolism is highly influenced by the effects of sex hormones, studies of sex differences in the incidence and progression of MAFLD are scarce. Metabolomics represents a powerful approach to studying these differences and identifying potential biomarkers and putative mechanisms. First, metabolomics makes it possible to obtain the molecular phenotype of the individual at a given time. Second, metabolomics may be a helpful tool for classifying patients according to the severity of the disease and obtaining diagnostic biomarkers. Some studies demonstrate associations between circulating metabolites and early and established MAFLD, but little is known about how metabolites relate to and encompass sex differences in disease progression and risk management. In this review, we will discuss the epidemiological metabolomic studies for sex differences in the development and progression of MAFLD, the role of metabolic profiles in understanding mechanisms and identifying sex-dependent biomarkers, and how this evidence may help in the future management of the disease.

Exploring the "gene-protein-metabolite" network of coronary heart disease with phlegm and blood stasis syndrome by integrated multi-omics strategy

Background: According to the theory of traditional Chinese medicine, phlegm and blood stasis (PBS) is the pathological basis for coronary heart disease (CHD). This study aimed to explore the biological basis of PBS syndrome in CHD. Methods: Using a strategy that integrated RNA-seq, DIA-based proteomics, and untargeted metabolomics on 90 clinic samples, we constructed a "gene-protein-metabolite" network for CHD-PBS syndrome. We expanded the sample size and validated the differential genes and metabolites in the network through enzyme-linked immunosorbent assay. Results: Our findings revealed that the "gene-protein-metabolite" network of CHD-PBS syndrome included 33 mRNAs, four proteins, and 25 metabolites. JNK1, FOS, CCL2, CXCL8, PTGS2, and CSF1 were all poorly expressed in the PBS group during the sequencing stage, whereas arachidonic acid (AA) was highly expressed. During the validation stage, JNK1, AP-1, CCL2, and CXCL8 were poorly expressed, whereas PTGS2, CSF1, and AA were highly expressed. The area under the receiver operating curve was as follows: CSF1 [0.9635, 95%CI (0.9295, 0.9976)] >JNK1 [0.9361, 95% CI (0.8749, 0.9972)] >CXCL8 [0.8953, 95% CI (0.8222, 0.9684)] > CCL2 [0.8458, 95% CI (0.7676, 0.9241)] >AP-1 [0.7884, 95%CI (0.6869, 0.8899)]. The logistic regression model composed of CSF1 and JNK1 showed the greatest diagnostic value and significance for PBS syndrome. Conclusion: PBS syndrome is characterized by low levels of FOS, AP-1, CCL2, CXCL8, and JNK1 and elevated levels of PTGS2 and CSF1, implying that the AA metabolism is abnormal and that the JNK/AP-1 pathway is inhibited. PBS syndromes, as a subtype of CHD, may have unique molecular changes. Background. Globally, coronary heart disease (CHD) is the leading cause of death, and this would likely continue until 2030 (Mirzaei et al., 2009, 95, 740-746). According to the disease course, CHD can be classified as chronic stable CHD (or chronic coronary syndrome) and acute coronary syndrome (ACS) (Katus et al., 2017; Knuuti, 2019). Although stable CHD is not as lethal as ACS, it has a varied incidence range and patients with CHD have prolonged angina. Some symptoms of stable angina are alleviated with pharmacological therapy, but it cannot eliminate recurrent angina (Rousan et al., 2017). The clinical outcomes were not significantly improved in patients who underwent revascularization compared with those who received optimal pharmacological therapy (Shaw et al., 2008; Antman and Braunwald, 2020). A bottleneck appears to exist in CHD treatment, and traditional Chinese medicine (TCM) can act as a favorable complement. Because of its individualized treatment approach, TCM is widely practiced in eastern civilizations (Teng et al., 2016). TCM has become a principal complement in western countries (Wieland et al., 2013). Like "disease" is used in western medicine, "syndrome" is used in TCM to comprehend anomalous human conditions on the basis of patients' symptoms, tongue, and pulse (Li et al., 2012). On the basis of disease-syndrome diagnose, a TCM doctor can subclassify CHD patients into various categories, such as phlegm and blood stasis (PBS) syndrome, cold congealing and Qi stagnation syndrome, and Qi stagnation and blood stasis syndrome. PBS syndrome has recently emerged as a hot research topic in the TCM field. Objective diagnosis, expert consultations, and efficacy evaluation scales have been developed for PBS syndrome (Ren et al., 2020; Liu et al., 2021; Zheng et al., 2022). The concept of "omics" originates from the genome. It refers to the vocabulary generated by biological molecules at different levels to describe high-sequence molecular biological data resources (Dai and Shen, 2022). RNA, protein, and metabolites decipher the essence of complex etiologies, and the integration of transcriptomics, proteomics, and metabolomics are becoming a promising research mode (Pan et al., 2022). Multi-omics studies have revealed the biological characteristics of APOE transgenic mice, bronchopulmonary dysplasia, and plant tolerant to heavy metals (Singh et al., 2016; Lal et al., 2018; Mohler et al., 2020). Over the past few years, many academic achievements related to CHD-PBS syndrome have been accrued in the single-omic area. For example, Zhou identified the differential metabolites between PBS syndrome and Qi and Yin deficiency syndrome by using the urine samples of 1072 volunteers. Some of the specific metabolites of PBS syndrome are pyroglutamic acid, glutaric acid, glucose, mannitol, and xanthine (Zhou et al., 2019). Li's metabolomic study suggested that valine, leucine, isoleucine, and glycerol phospholipid metabolism could represent PBS syndrome (Zheng et al., 2022). Although some progress has been made in the understanding of PBS syndrome in CHD through the studies conducted, some issues still exist, such as a single-omics level, a lack of in-depth research, an inability to verify each other's research results, and a lack of validation of research conclusions. Overall, a systematic description of the biological foundation of PBS syndrome is lacking. Thus, the present study utilizes system biology methodologies and constructs a multi-omics network by integrating differential genes, proteins, and metabolites to systematically and comprehensively reveal the biological basis of CHD-PBS syndrome. The current study explored 1) the characteristics of the transcriptome, proteome, and metabolome for CHD-PBS syndrome; 2) the "gene-protein-metabolite" network based on differential genes (DGs), differential proteins (DPs), and differential metabolites (DMs); 3) the key biological process and metabolic pathway most related to PBS syndrome; and 4) quantitative results and the diagnostic potential of biomarkers for PSB syndrome. Materials and methods. Multi-omics sequencing, bioinformatics analysis, and clinical validation research strategy. We collected the blood samples from healthy subjects as well as CHD patients with PBS and non-phlegm and blood stasis (NPBS) syndrome to compare the differences between them by subjecting the samples to the transcriptome, proteome, and metabolomics analyses. Bioinformatics analysis identified differential molecules as well as related biological processes and pathways. Next, the "gene-protein-metabolite" network was constructed using the MetaboAnalyst database, String database, and Cytoscape software. We selected molecules with strong centrality and biological association as potential PBS syndrome biomarkers and recruited more volunteers for further validation by enzyme-linked immunosorbent assay (ELISA). Finally, the ROC curve was utilized to assess the level and diagnostic efficacy of various molecules (Figure 1).

The Promises, Challenges, and Opportunities of Omics for Studying the Plant Holobiont

Microorganisms are critical drivers of biological processes that contribute significantly to plant sustainability and productivity. In recent years, emerging research on plant holobiont theory and microbial invasion ecology has radically transformed how we study plant–microbe interactions. Over the last few years, we have witnessed an accelerating pace of advancements and breadth of questions answered using omic technologies. Herein, we discuss how current state-of-the-art genomics, transcriptomics, proteomics, and metabolomics techniques reliably transcend the task of studying plant–microbe interactions while acknowledging existing limitations impeding our understanding of plant holobionts.