Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Deciphering molecular mechanisms of SARS-CoV-2 pathogenesis and drug repurposing through GRN motifs: a comprehensive systems biology study

The world has recently been plagued by a new coronavirus infection called SARS-CoV-2. This virus may lead to severe acute respiratory syndrome followed by multiple organ failure. SARS-CoV-2 has approximately 80-90% genetic similarity to SARS-CoV. Given the limited omics data available for host response to the viruses (more limited data for SARS-CoV-2), we attempted to unveil the crucial molecular mechanisms underlying the SARS-CoV-2 pathogenesis by comparing its regulatory network motifs with SARS-CoV. We also attempted to identify the non-shared crucial molecules and their functions to predict the specific mechanisms for each infection and the processes responsible for their different manifestations. Deciphering the crucial shared and non-shared mechanisms at the molecular level and signaling pathways underlying both diseases may help shed light on their pathogenesis and pave the way for other new drug repurposing against COVID-19. We constructed the GRNs for host response to SARS-CoV and SARS-CoV-2 pathogens (in vitro) and identified the significant 3-node regulatory motifs by analyzing them topologically and functionally. We attempted to identify the shared and non-shared regulatory elements and signaling pathways between their host responses. Interestingly, our findings indicated that NFKB1, JUN, STAT1, FOS, KLF4, and EGR1 were the critical shared TFs between motif-related subnetworks in both SARS and COVID-1, which are considered genes with specific functions in the immune response. Enrichment analysis revealed that the NOD-like receptor signaling, TNF signaling, and influenza A pathway were among the first significant pathways shared between SARS and COVID-19 up-regulated DEGs networks, and the term "metabolic pathways" (hsa01100) among the down-regulated DEGs networks. WEE1, PMAIP1, and TSC22D2 were identified as the top three hubs specific to SARS. However, MYPN, SPRY4, and APOL6 were the tops specific to COVID-19 in vitro. The term "Complement and coagulation cascades" pathway was identified as the first top non-shared pathway for COVID-19 and the MAPK signaling pathway for SARS. We used the identified crucial DEGs to construct a drug-gene interaction network to propose some drug candidates. Zinc chloride, Fostamatinib, Copper, Tirofiban, Tretinoin, and Levocarnitine were the six drugs with higher scores in our drug-gene network analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03518-x.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Integrated analysis of mRNA and extrachromosomal circular DNA profiles to identify the potential mRNA biomarkers in breast cancer

Extrachromosomal circular DNAs (eccDNAs) have been proved an inseparable relationship with cancer, based on the biological mechanisms of its biogenesis and impact on tumorigenesis, but still lacked of methods to analyze its function on the pathogenesis and progression of breast cancer (BC). The mRNA and eccDNA from BC cell samples (MDA-MB-453 and MCF-12A) were extracted with the removal of rRNA and linear DNA, respectively. High-throughput sequencing and bioinformatics analysis were performed to explore their expression level and molecular characterization of eccDNA. A total number of 161,062 eccDNA ranging from 33 bp to 54229 bp were detected with a median size of 1143 bp, distributed on all chromosomes and enriched on chromosome 20 the most. EccDNAs located in exons, upstream and downstream 2 kb regions were significantly increased compared with background. Analysis of eccDNA-related differentially expressed genes (eccDEGs) showed that FAT2 properly separated the two cells. CTNNB1, CACNA2D2 and CACNA1D were the hub genes with higher degrees in critical modules. All these four genes were significantly differentially expressed between breast invasive carcinoma (BRCA) tissues and normal ones. FAT2 and CTNNB1 correlated with significantly different overall survival (OS) when differentially expressed. The four genes showed a strong correlation with each other significantly and changed between tumor and normal samples. The results showed the potential of FAT2, CTNNB1, CACNA2D2 and CACNA1D as biomarkers with analysis of both DEGs and eccDEGs, which might assist in clinical medical treatment.

Integrated proteomic and phosphoproteomic analysis for characterization of colorectal cancer

Proteins and translationally modified proteins like phosphoproteins have essential regulatory roles in tumorigenesis. This study attempts to elucidate the dysregulated proteins driving colorectal cancer (CRC). To explore the differential proteins, we performed iTRAQ labeling proteomics and TMT labeling phosphoproteomics analysis of CRC tissues and adjacent non-cancerous tissues. The functions of quantified proteins were analyzed using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Subcellular localization analysis. Depending on the results, we identified 330 differential proteins and 82 phosphoproteins in CRC. GO and KEGG analyses demonstrated that protein changes were primarily associated with regulating biological and metabolic processes through binding to other molecules. Co-expression relationships between proteomic and phosphoproteomic analysis revealed that TMC5, SMC4, SLBP, VSIG2, and NDRG2 were significantly dysregulated differential proteins. Additionally, based on the predicted co-expression proteins, we identified that the stem-loop binding protein (SLBP) was up-regulated in CRC cells and promoted the proliferation and migration of CRC. This study reports an integrated proteomic and phosphoproteomic analysis of CRC to discern the functional impact of protein alterations and provides a candidate diagnostic biomarker or therapeutic target for CRC. SIGNIFICANCE: Combining one or more high-throughput omics technologies with bioinformatics to analyze biological samples and explore the links between biomolecules and their functions can provide more comprehensive and multi-level insights for disease mechanism research. Proteomics, phosphoproteomics, metabolomics and their combined analysis play an important role in the auxiliary diagnosis, the discovery of biomarkers and novel therapeutic targets for colorectal cancer. In this integrated proteomic and phosphoproteomic analysis, we identified proteins and phosphoproteins in colorectal cancer tissue and analyzed potential mechanisms contributing to progression in colorectal cancer. The results of this study provide a foundation to focus future experiments on the contribution of altered protein and phosphorylation patterns to prevention and treatment of colorectal cancer.

Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma

BACKGROUND

Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage.

METHODS

The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis (p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage.

RESULTS

Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity).

CONCLUSIONS

Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.

Metabolomics in Corneal Diseases: A Narrative Review from Clinical Aspects

Corneal pathologies may have subtle manifestations in the initial stages, delaying diagnosis and timely treatment. This can lead to irreversible visual loss. Metabolomics is a rapidly developing field that allows the study of metabolites in a system, providing a complementary tool in the early diagnosis and management of corneal diseases. Early identification of biomarkers is key to prevent disease progression. The advancement of nuclear magnetic resonance and mass spectrometry allows the identification of new biomarkers in the analysis of tear, cornea, and aqueous humor. Novel perspectives on disease mechanisms are identified, which provide vital information for potential targeted therapies in the future. Current treatments are analyzed at a molecular level to offer further information regarding their efficacy. In this article, we provide a comprehensive review of the metabolomic studies undertaken in the cornea and various pathologies such as dry eye disease, Sjogren’s syndrome, keratoconus, post-refractive surgery, contact lens wearers, and diabetic corneas. Lastly, we discuss the exciting future that metabolomics plays in cornea research.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

A classification method of gastric cancer subtype based on residual graph convolution network

Background: Clinical diagnosis and treatment of tumors are greatly complicated by their heterogeneity, and the subtype classification of cancer frequently plays a significant role in the subsequent treatment of tumors. Presently, the majority of studies rely far too heavily on gene expression data, omitting the enormous power of multi-omics fusion data and the potential for patient similarities. Method: In this study, we created a gastric cancer subtype classification model called RRGCN based on residual graph convolutional network (GCN) using multi-omics fusion data and patient similarity network. Given the multi-omics data's high dimensionality, we built an artificial neural network Autoencoder (AE) to reduce the dimensionality of the data and extract hidden layer features. The model is then built using the feature data. In addition, we computed the correlation between patients using the Pearson correlation coefficient, and this relationship between patients forms the edge of the graph structure. Four graph convolutional network layers and two residual networks with skip connections make up RRGCN, which reduces the amount of information lost during transmission between layers and prevents model degradation. Results: The results show that RRGCN significantly outperforms other classification methods with an accuracy as high as 0.87 when compared to four other traditional machine learning methods and deep learning models. Conclusion: In terms of subtype classification, RRGCN excels in all areas and has the potential to offer fresh perspectives on disease mechanisms and disease progression. It has the potential to be used for a broader range of disorders and to aid in clinical diagnosis.

Untargeted Metabolomics for Disease-Specific Signatures

Human diseases account for complex traits that usually exhibit markedly diverse clinical manifestations coming from a series of pathogenic processes that shape heterogeneous phenotypes. Considering that correlation does not imply causation as well as population differences and/or inter-individual variability, disease-specific signatures are becoming critical for biomarker discovery. Untargeted metabolomics is deemed to be a powerful approach to delineate molecular pathways of prime interest. Metabotypes capture the interplay of genomics and environmental influences per se. Untargeted metabolomics share the charm of being not only hypothesis-driven but also hypothesis-generating. Notwithstanding, the applicability of untargeted metabolomics toward clinically relevant outcomes depend on wet- and dry-lab procedures in the context of elegant study designs with clear rationale. As ideal may be far from feasible, herein we provide recommendations to combat sample mishandling that adversely affect data outcomes and if so, deal with imbalanced datasets toward data integrity.

Integration of metabolomics and proteomics analysis to explore the mechanism of neurotoxicity induced by receipt of isoniazid and rifampicin in mice

Isoniazid (INH) and rifampicin (RIF) are co-administered in tuberculosis treatment but can cause neurotoxicity, and the mechanism is not known. To explore this mechanism, we employed an integrated approach using metabolomics analysis (MA) and proteomics analysis (PA). Male mice were divided into three groups and administered vehicle (control group), or co-administered INH (120 mg/kg) and RIF (240 mg/kg), for 7 or 14 days. Mice brains were collected for mass spectrometry-based PA and MA plus lipidomics analysis. Measurement of brain levels of malondialdehyde and superoxide dismutase revealed time-dependent brain injury after exposure to INH+RIF for 7 and 14 days. Also, 422 proteins, 35 metabolites, and 21 lipids were dysregulated and identified. MA demonstrated "purine metabolism," "phenylalanine, tyrosine and tryptophan biosynthesis," "biosynthesis of unsaturated fatty acids," "phenylalanine metabolism," and "arginine biosynthesis" to be disturbed significantly. PA demonstrated pathways such as "lipids," "amino acids," and "energy metabolism" to be disrupted. Peroxisome proliferator-activated receptor (PPAR) pathways were changed in energy metabolism, which led to the neurotoxicity induced by INH+RIF. Immunohistochemical analyses of PPARs in mice brains verified that PPAR-α and -γ expression was downregulated. PPAR-α and -γ activation might be a key target for alleviating INH+RIF-induced neurotoxicity.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

Endotoxin in Sepsis: Methods for LPS Detection and the Use of Omics Techniques

Lipopolysaccharide (LPS) or endotoxin, the major cell wall component of Gram-negative bacteria, plays a pivotal role in the pathogenesis of sepsis. It is able to activate the host defense system through interaction with Toll-like receptor 4, thus triggering pro-inflammatory mechanisms. A large amount of LPS induces inappropriate activation of the immune system, triggering an exaggerated inflammatory response and consequent extensive organ injury, providing the basis of sepsis damage. In this review, we will briefly describe endotoxin's molecular structure and its main pathogenetic action during sepsis. In addition, we will summarize the main different available methods for endotoxin detection with a special focus on the wider spectrum offered by omics technologies (genomics, transcriptomics, proteomics, and metabolomics) and promising applications of these in the identification of specific biomarkers for sepsis.

Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine?

Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.

NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways

SUMMARY

Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/.

AVAILABILITY AND IMPLEMENTATION

NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated proteomics and metabolomics analysis of lumbar in a rat model of osteoporosis treated with Gushukang capsules

BACKGROUND

Gushukang (GSK) capsules are a Chinese patented medicine that is widely used in clinics for the treatment of osteoporosis (OP). Animal experiments have revealed that the bone mineral density of osteoporotic rats increase after treatment with GSK capsules. However, the specific mechanism and target of GSK in the treatment of osteoporosis are unclear. Further studies are needed.

METHODS

Metabolomics (GC/MS) and proteomics (TMT-LC-MC/MC) with bioinformatics (KEGG pathway enrichment), correlation analysis (Pearson correlation matrix), and joint pathway analysis (MetaboAnalyst) were employed to determine the underlying mechanisms of GSK. The differential expression proteins were verified by WB experiment.

RESULTS

The regulation of proteins, i.e., Cant1, Gstz1, Aldh3b1, Bid, and Slc1a3, in the common metabolic pathway of differential proteins and metabolites between GSK/OP and OP/SHAM was corrected in the GSK group. The regulation of 12 metabolites (tyramine, thymidine, deoxycytidine, cytosine, L-Aspartate, etc.) were differential in the common enrichment metabolic pathway between GSK /OP and OP/SHAM. Differential proteins and metabolites jointly regulate 11 metabolic pathways, such as purine metabolism, pyrimidine metabolism, histidine metabolism, beta-alanine metabolism, and so on.

CONCLUSION

GSK may protect bone metabolism in osteoporotic rats by affecting nucleotide metabolism, amino acid metabolism, and the immune system.

The uses of transcriptomics and lipidomics indicated that direct contact with graphene oxide altered lipid homeostasis through ER stress in 3D human brain organoids

The potential uses of graphene-based nanomaterials (NMs) in various fields lead to the concern about their neurotoxicity, considering that graphene-based NMs are capable to cross blood brain barrier (BBB) and enter central nervous system (CNS). Although previous studies reported the possibility of graphene-based NM exposure to alter lipid homeostasis in animals or cultured neurons, recent studies suggested the need to use 3D human brain organoids for mechanism-based toxicological studies as this model might better recapitulate the complex human brains. Herein, we used multi-omics techniques to investigate the mechanisms of graphene oxide (GO) on lipid homeostasis in a novel 3D brain organoid model. We found that 50 μg/mL GO induced cytotoxicity but not superoxide. RNA-sequencing data showed that 50 μg/mL GO significantly up-regulated and down-regulated 80 and 121 genes, respectively. Furthermore, we found that GO exposure altered biological molecule metabolism pathways including lipid metabolism. Consistently, lipidomics data supported dose-dependent alteration of lipid profiles by GO in 3D brain organoids. Interestingly, co-exposure to GO and endoplasmic reticulum (ER) stress inhibitor 4-phenylbutyric acid (4-PBA) decreased most of the lipid classes compared with the exposure of GO only. We further verified that exposure to GO promoted ER stress marker GRP78 proteins, which in turn activated IRE1α/XBP-1 axis, and these changes were partially or completely inhibited by 4-PBA. These results proved that direct contact with GO disrupted lipid homeostasis through the activation of ER stress. As 3D brain organoids resemble human brains, these data might be better extrapolated to humans.

Sex as a biological variable in determining the metabolic changes influencing acute ischemic stroke outcomes-Where is the data: A systematic review

Women continue to face a greater lifetime morbidity and mortality from stroke and have been shown to respond differently to stroke treatments compared to men. Since 2016, updated National Institutes of Health (NIH) policies require research studies to consider sex as a biological variable. However, the way in which this policy affects study design, analysis, and reporting is variable, with few studies performing and reporting a subgroup analysis based on biological sex. In acute ischemic stroke, the underlying biological explanation for sex-based differences in patient outcomes and response to treatments remains understudied. We performed a systematic review of preclinical and clinical research studies that explored sex differences in the metabolic response to acute ischemic stroke as it relates to neurological outcomes. Through a literature search in Ovid Medline, Embase, and Web of Science, 1,004 potential references were identified for screening. After abstract and full-text review, we identified only two studies which assessed metabolic response to acute ischemic stroke (within 72 h of last known well) and neurological outcome [Barthel Index, modified Rankin Scale (mRS) or an equivalent in preclinical models] and reported results based on biological sex. One article was a preclinical rat model and the other a clinical cohort study. In both studies, metabolites involved in amino acid metabolism, energy metabolism, fat metabolism, or oxidative stress were identified. We review these results and link to additional articles that use metabolomics to identify metabolites differentially expressed by sex or regulated based on stroke outcomes, but not both. The results of this systematic review should not only help identify targets in need of further investigation to improve the understanding of sex differences in the pathophysiology of acute ischemic stroke, but also highlight the critical need to expand the incorporation of sex as a biological variable in acute stroke research beyond simply including both sexes and reporting the proportion of males/females in each population studied.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Big Data for Tiny Patients: A Precision Medicine Approach to Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease of extreme prematurity. Despite more than 50 years of research, current treatments are ineffective, and clinicians are largely unable to accurately predict which neonates the condition will develop in. A deeper understanding of the molecular mechanisms underlying the characteristic arrest in lung development are warranted. Integrating high-fidelity technology from precision medicine approaches may fill this gap and provide the tools necessary to identify biomarkers and targetable pathways. In this review, we describe insights garnered from current studies using omics for BPD prediction and stratification. We conclude by describing novel programs that will integrate multi-omics in efforts to better understand and treat the pathogenesis of BPD. [Pediatr Ann. 2022;51(10):e396-e404.].

SurvMaximin: Robust federated approach to transporting survival risk prediction models

OBJECTIVE

For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.

MATERIALS AND METHODS

For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning.

RESULTS

Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.

CONCLUSIONS

The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Biomarkers of Fruit Intake Using a Targeted Metabolomics Approach: an Observational Cross-Sectional Analysis of the ELSA-Brasil Study

BACKGROUND

Advances in technology have led to the identification of a greater number of metabolites related to diet. Although fruit intake biomarkers have been reported in some studies, these findings require further replication, considering the relevance of fruits for diet quality and health.

OBJECTIVES

The aim of this study was to explore the associations of a set of potential urinary biomarkers of diet, assessed using a targeted metabolomics approach, with self-reported fruit intake data in participants of a computer-assisted 24-h dietary recall (GloboDiet software) validation study.

METHODS

A total of 93 individuals aged 43-72 y, 54% female, participated in this study. The subjects were a subsample of the Longitudinal Study of Adult Health (ELSA-Brasil). A 24-h dietary recall was obtained with the aid of GloboDiet software matching a 24-h urine sample from each participant. Candidate biomarkers were selected in a literature search and identified in urine by LC coupled to high-resolution MS. Spearman correlation analyses were performed between fruit intake and each biomarker.

RESULTS

Spearman correlation analysis showed that total fruits intake was significantly correlated with citric acid (ρ = 0.213, P = 0.041), ferulic acid sulfate I (ρ = 0.240, P = 0.020), hesperetin glucuronide/homoeriodictyol glucuronide (ρ = 0.303, P = 0.003), hydroxyhippuric acid (ρ = 0.239, P = 0.021), homovanillic alcohol sulfate (ρ = 0.339, P = 0.001), methylgallic acid sulfate (ρ = 0.268, P = 0.009), naringenin glucuronide (NG; ρ = 0.278, P = 0.007), proline betaine (PB; ρ = 0.305, P = 0.003), syringic acid sulfate (ρ = 0.210, P = 0.044), and sinapic acid sulfate (ρ = 0.412, P < 0.001). Among them, 3 have been described in literature as promising biomarkers for intake of total fruit, oranges, and citrus fruit: NG, hesperetin glucuronide, and PB.

CONCLUSIONS

Associations of total fruits intake with urinary measurements indicate the potential usefulness of dietary biomarkers in the Brazilian population as a complement to self-reported dietary assessments.

Soluble CD14-associated DNA methylation sites predict mortality among men with HIV infection

OBJECTIVES

Elevated plasma levels of sCD14 predict all-cause mortality in people with HIV (PWH). Epigenetic regulation plays a key role in infection and inflammation. To reveal the epigenetic relationships between sCD14, immune function and disease progression among PWH, we conducted an epigenome-wide association study (EWAS) of sCD14 and investigated the relationship with mortality.

DESIGN AND METHODS

DNA methylation (DNAm) levels of peripheral blood samples from PWH in the Veterans Aging Cohort Study (VACS) were measured using the Illumina Infinium Methylation 450K (n = 549) and EPIC (850K) BeadChip (n = 526). Adjusted for covariates and multiple testing, we conducted an epigenome-wide discovery, replication, and meta-analysis to identify significant associations with sCD14. We then examined and replicated the relationship between the principal epigenetic sites and survival using Cox regression models.

FINDINGS

We identified 118 DNAm sites significantly associated with sCD14 in the meta-analysis of 1075 PWH. The principal associated DNAm sites mapped to genes (e.g. STAT1, PARP9, IFITM1, MX1, and IFIT1) related to inflammation and antiviral response. Adjusting for multiple testing, 10 of 118 sCD14-associated DNAm sites significantly predicted survival time conditional on sCD14 levels.

CONCLUSION

The identification of DNAm sites independently predicting survival may improve our understanding of prognosis and potential therapeutic targets among PWH.

Metabolomics and Biomarkers in Retinal and Choroidal Vascular Diseases

The retina is one of the most important structures in the eye, and the vascular health of the retina and choroid is critical to visual function. Metabolomics provides an analytical approach to endogenous small molecule metabolites in organisms, summarizes the results of “gene-environment interactions”, and is an ideal analytical tool to obtain “biomarkers” related to disease information. This study discusses the metabolic changes in neovascular diseases involving the retina and discusses the progress of the study from the perspective of metabolomics design and analysis. This study advocates a comparative strategy based on existing studies, which encompasses optimization of the performance of newly identified biomarkers and the consideration of the basis of existing studies, which facilitates quality control of newly discovered biomarkers and is recommended as an additional reference strategy for new biomarker discovery. Finally, by describing the metabolic mechanisms of retinal and choroidal neovascularization, based on the results of existing studies, this study provides potential opportunities to find new therapeutic approaches.

Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity

The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.

The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data

BACKGROUND

Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer.

METHODS

The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method.

RESULTS

The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients.

CONCLUSIONS

Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.

Metabolomics: A New Approach in the Evaluation of Effects in Human Beings and Wildlife Associated with Environmental Exposition to POPs

Human beings and wild organisms are exposed daily to a broad range of environmental stressors. Among them are the persistent organic pollutants that can trigger adverse effects on these organisms due to their toxicity properties. There is evidence that metabolomics can be used to identify biomarkers of effect by altering the profiles of endogenous metabolites in biological fluids or tissues. This approach is relatively new and has been used in vitro studies mainly. Therefore, this review addresses those that have used metabolomics as a key tool to identify metabolites associated with environmental exposure to POPs in wildlife and human populations and that can be used as biomarkers of effect. The published results suggest that the metabolic pathways that produce energy, fatty acids, and amino acids are commonly affected by POPs. Furthermore, these pathways can be promoters of additional effects. In the future, metabolomics combined with other omics will improve understanding of the origin, development, and progression of the effects caused by environmental exposure.

Effects of Storage Temperature on Indica-Japonica Hybrid Rice Metabolites, Analyzed Using Liquid Chromatography and Mass Spectrometry

The Yongyou series of indica-japonica hybrid rice has excellent production potential and storage performance. However, little is known about the underlying mechanism of its storage resistance. In this study, Yongyou 1540 rice (Oryza sativa cv. yongyou 1540) was stored at different temperatures, and the storability was validated though measuring nutritional components and apparent change. In addition, a broad-targeted metabolomic approach coupled with liquid chromatography-mass spectrometry was applied to analyze the metabolite changes. The study found that under high temperature storage conditions (35 °C), Yongyou 1540 was not significantly worse in terms of fatty acid value, whiteness value, and changes in electron microscope profile. A total of 19 key differential metabolites were screened, and lipid metabolites related to palmitoleic acid were found to affect the aging of rice. At the same time, two substances, guanosine 3′,5′-cyclophosphate and pipecolic acid, were beneficial to enhance the resistance of rice under harsh storage conditions, thereby delaying the deterioration of its quality and maintaining its quality. Significant regulation of galactose metabolism, alanine, aspartate and glutamate metabolism, butyrate metabolism, and arginine and proline metabolism pathways were probably responsible for the good storage capacity of Yongyou 1540.

Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19

Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.

The Role of CEP55 Expression in Tumor Immune Response and Prognosis of Patients with Non-small Cell lung Cancer

Background: With the continuous advancement of diagnostic methods, more and more early-stage Non-small cell lung cancer (NSCLC) patients are diagnosed. Although many scholars have devoted substantial efforts to investigate the pathogenesis and prognosis of NSCLC, its molecular mechanism is still not well explained. Methods: We retrieved three gene datasets GSE10072, GSE19188 and GSE40791 from the Gene Expression Omnibus (GEO) database and screened and identified differentially expressed genes (DEGs). Then, we performed KEGG and GO functional enrichment analysis, survival analysis, risk analysis and prognosis analysis on the selected hub genes. We constructed a protein-protein interaction (PPI) network, and used the STRING database and Cytoscape software. Results: The biological process analysis showed that these genes were mainly enriched in cell division and nuclear division. Survival analysis showed that the genes of CEP55 (centrosomal protein 55), NMU (neuromedin U), CAV1 (Caveolin 1), TBX3 (T-box transcription factor 3), FBLN1 (fibulin 1) and SYNM (synemin) may be involved in the development, invasion or metastasis of NSCLC (P<0.05, logFC>1). Prognostic analysis and independent prognostic analysis showed that the expression of these hub gene-related mRNAs was related to the prognostic risk of NSCLC. Risk analysis showed that the selected hub genes were closely related to the overall survival time of patients with NSCLC. Conclusion: The DEGs and hub genes screened and identified in this study will help us to understand the molecular mechanisms of NSCLC, and CEP55 expression affects the survival and prognosis of patients with NSCLC, and participates in tumor immune response.

Integrated proteomics, phosphoproteomics and metabolomics analyses reveal similarities amongst giant cell granulomas of the jaws with different genetic mutations

BACKGROUND

Giant cell granuloma of the jaws are benign osteolytic lesions of the jaws. These lesions are genetically characterized by mutually exclusive somatic mutations at TRPV4, KRAS, and FGFR1, and a fourth molecular subgroup which is wild-type for the three mutations. Irrespective of the molecular background, giant cell granulomas show MAPK/ERK activation. However, it remains unclear if these mutations lead to differences in their molecular signaling in giant cell granulomas.

METHODS

Metabolomics, proteomics and phosphoproteomics analyses were carried out in formalin-fixed paraffin-embedded samples of giant cell granuloma of the jaws. The study cohort consisted of five lesions harboring mutations in FGFR1, six in KRAS, five in TRPV4 and five that were wild-type for these mutations.

RESULTS

Lesions harboring KRAS or FGFR1 mutations showed overall similar proteomics and metabolomics profiles. In all four groups, metabolic pathways showed similarity in apoptosis, cell signaling, gene expression, cell differentiation and erythrocyte activity. Lesions harboring TRPV4 mutations showed a greater number of enriched pathways related to tissue architecture. On the other hand, the wild-type group presented increased number of enriched pathways related to protein metabolism compared to the other groups.

CONCLUSION

Despite some minor differences, our results revealed an overall similar molecular profile among the groups with different mutational profile at the metabolic, proteic and phosphopeptidic levels. This article is protected by copyright. All rights reserved.

Identification of novel biomarkers for acute radiation injury using multi-omics approach and nonhuman primate model

PURPOSE

The availability of validated biomarkers to assess radiation exposure and to assist in developing medical countermeasures remains an unmet need.

METHODS AND MATERIALS

We used a cobalt-60 gamma-irradiated nonhuman primate (NHP) model to delineate a multi-omics-based serum probability index of radiation exposure. Both male and female NHPs were irradiated with different doses ranging from 6.0 to 8.5 Gy, with 0.5 Gy increments between doses. We leveraged high-resolution mass spectrometry for analysis of metabolites, lipids, and proteins at 1, 2, and 6 days post-irradiation in NHP serum.

RESULTS

A logistic regression model was implemented to develop a 4-analyte panel to stratify irradiated NHPs from unirradiated with high accuracy that was agnostic for all doses of gamma-rays tested in the study, up to six days after exposure. This panel was comprised of Serpin Family A9, acetylcarnitine, PC (16:0/22:6), and suberylglycine, which showed 2 - 4-fold elevation in serum abundance upon irradiation in NHPs and can potentially be translated as a molecular diagnostic for human use following larger validation studies.

CONCLUSIONS

Taken together, this study, for the first time, demonstrates the utility of a combinatorial molecular characterization approach using an NHP model for developing minimally invasive assays from small volumes of blood that can be effectively used for radiation exposure assessments.

Abplatin(IV) inhibited tumor growth on a patient derived cancer model of hepatocellular carcinoma and its comparative multi-omics study with cisplatin

BACKGROUND

Cisplatin, the alkylating agent of platinum(II) (Pt(II)), is the most common antitumor drug in clinic; however, it has many side effects, therefore it is higly desired to develop low toxicity platinum(IV) (Pt(IV)) drugs. Multi-omics analysis, as a powerful tool, has been frequently employed for the mechanism study of a certain therapy at the molecular level, which might be helpful for elucidating the mechanism of platinum drugs and facilitating their clinical application.

METHODS

Strating form cisplatin, a hydrophobic Pt(IV) prodrug (CisPt(IV)) with two hydrophobic aliphatic chains was synthesized, and further encapsulated with a drug carrier, human serum albumin (HSA), to form nanoparticles, namely AbPlatin(IV). The anticancer effect of AbPlatin(IV) was investigated in vitro and in vivo. Moreover, transcriptomics, metabolomics and lipidomics were performed to explore the mechanism of AbPlatin(IV).

RESULTS

Compared with cisplatin, Abplatin(IV) exhibited better tumor-targeting effect and greater tumor inhibition rate. Lipidomics study showed that Abplatin(IV) might induce the changes of BEL-7404 cell membrane, and cause the disorder of glycerophospholipids and sphingolipids. In addition, transcriptomics and metabolomics study showed that Abplatin(IV) significantly disturbed the purine metabolism pathway.

CONCLUSIONS

This research highlighted the development of Abplatin(IV) and the use of multi-omics for the mechanism elucidation of prodrug, which is the key to the clinical translation of prodrug.

Metabolomic signatures after bariatric surgery - a systematic review

Metabolomics emerged as an important tool to gain insights on how the body responds to therapeutic interventions. Bariatric surgery is the most effective treatment for severe obesity and obesity-related co-morbidities. Our aim was to conduct a systematic review of the available data on metabolomics profiles that characterize patients submitted to different bariatric surgery procedures, which could be useful to predict clinical outcomes including weight loss and type 2 diabetes remission. For that, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - PRISMA guidelines were followed. Data from forty-seven original study reports addressing metabolomics profiles induced by bariatric surgery that met eligibility criteria were compiled and summarized. Amino acids, lipids, energy-related and gut microbiota-related were the metabolite classes most influenced by bariatric surgery. Among these, higher pre-operative levels of specific lipids including phospholipids, long-chain fatty acids and bile acids were associated with post-operative T2D remission. As conclusion, metabolite profiling could become a useful tool to predict long term response to different bariatric surgery procedures, allowing more personalized interventions and improved healthcare resources allocation.

The Mechanism of Interaction Between Gold Nanoparticles and Human Dermal Fibroblasts Based on Integrative Analysis of Transcriptomics and Metabolomics Data

The aim of this paper was to combine transcriptomics and metabolomics to analyze the mechanism of gold nanoparticles (GNPs) on human dermal fibroblasts (HDFs). First, 20-nm GNPs were prepared, and the differentially expressed genes in HDFs were subsequently screened by transcriptome sequencing technology after 4, 8, and 24 h of treatment with GNPs. By comparing the metabolic pathways in which the metabolites obtained in a previous study were involved, the pathways involving both genes and metabolites were filtered, and the differentially expressed genes and metabolites with upstream and downstream relationships were screened out. The gene–metabolite–metabolic pathway network was further constructed, and the functions of metabolic pathways, genes and metabolites in the important network were analyzed and experimentally verified. The results of transcriptome sequencing experiments showed that 1904, 1216 and 489 genes were differentially expressed in HDFs after 4, 8 and 24 h of treatment with GNPs, and these genes were involved in 270, 235 and 163 biological pathways, respectively. Through the comparison and analysis of the metabolic pathways affected by the metabolites, 7, 3 and 2 metabolic pathways with genes and metabolites exhibiting upstream and downstream relationships were identified. Through analysis of the gene–metabolite–metabolic pathway network, 4 important metabolic pathways, 9 genes and 7 metabolites were identified. Combined with the results of verification experiments on oxidative stress, apoptosis, the cell cycle, the cytoskeleton and cell adhesion, it was found that GNPs regulated the synthesis of downstream metabolites through upstream genes in important metabolic pathways. GNPs inhibited oxidative stress and thus did not induce significant apoptosis, but they exerted effects on several cellular functions, including arresting the cell cycle and affecting the cytoskeleton and cell adhesion.

Majorbio Cloud: A one-stop, comprehensive bioinformatic platform for multiomics analyses

The platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.

The Gut Microbiota of Healthy and Flavobacterium psychrophilum-Infected Rainbow Trout Fry Is Shaped by Antibiotics and Phage Therapies

In the aquaculture sector, there is an increased interest in developing environmentally friendly alternatives to antibiotics in the treatment and prevention of bacterial infections. This requires an understanding of the effects of different treatments on the fish microbiota as a measure for improving the fish health status. In this study, we focused on the freshwater pathogen Flavobacterium psychrophilum and investigated the effects of antibiotics (florfenicol) and phage therapies on the gut microbiota of healthy and infected rainbow trout fry (1–2 g). Florfenicol-coated feed was administered for 10 days, starting two days after the infection procedure. A two-component mix of phage targeting F. psychrophilum (FpV4 and FPSV-D22) was continuously delivered by feed with a prophylactic period of 12 days. Samples of the distal intestine were collected over time (day -1 and 1, 8, and 33 days post-infection) and analyzed by community analysis targeting the 16S rRNA gene (V3–V4 region). Results showed the dysbiosis effect caused both by the infection and by florfenicol administration. Shifts in the overall composition were detected by β-diversity analysis, and changes in specific populations were observed during taxonomic mapping. Measures of α-diversity were only affected in infected fish (large variation observed 1 and 8 dpi). These community alterations disappeared again when fish recovered from the infection and the antibiotic treatment was terminated (33 dpi). Interestingly, phage addition altered the microbiota of the fish independently of the presence of their target bacterium. The overall gut bacterial community in fish fed phage-treated feed was different from the controls at each time point as revealed by β-diversity analysis. However, it was not possible to identify specific bacterial populations responsible for these changes except for an increase of lactic acid bacteria 33 dpi. Overall, the results indicate that the administered phages might affect the complex network of phage-bacteria interactions in the fish gut. Nevertheless, we did not observe negative effects on fish health or growth, and further studies should be directed in understanding if these changes are beneficial or not for the fish health with an additional focus on the host immune response.

Multiomics Studies Investigating Recurrent Pregnancy Loss: An Effective Tool for Mechanism Exploration

Patients with recurrent pregnancy loss (RPL) account for approximately 1%-5% of women aiming to achieve childbirth. Although studies have shown that RPL is associated with failure of endometrial decidualization, placental dysfunction, and immune microenvironment disorder at the maternal-fetal interface, the exact pathogenesis remains unknown. With the development of high-throughput technology, more studies have focused on the genomics, transcriptomics, proteomics and metabolomics of RPL, and new gene mutations and new biomarkers of RPL have been discovered, providing an opportunity to explore the pathogenesis of RPL from different biological processes. Bioinformatics analyses of these differentially expressed genes, proteins and metabolites also reflect the biological pathways involved in RPL, laying a foundation for further research. In this review, we summarize the findings of omics studies investigating decidual tissue, villous tissue and blood from patients with RPL and identify some possible limitations of current studies.

Genetics of atrial fibrillation-an update of recent findings

Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. AF has a strong genetic predisposition. This review highlights the recent findings on the genetics of AF from genome-wide association studies (GWAS) and high-throughput sequencing studies. The consensus from GWAS implies that AF is both polygenic and pleiotropic in nature. With the advent of whole-genome sequencing and whole-exome sequencing, rare variants associated with AF pathogenesis have been identified. The recent studies have contributed towards better understanding of AF pathogenesis.

AGBE: a dual deaminase-mediated base editor by fusing CGBE with ABE for creating a saturated mutant population with multiple editing patterns

Establishing saturated mutagenesis in a specific gene through gene editing is an efficient approach for identifying the relationships between mutations and the corresponding phenotypes. CRISPR/Cas9-based sgRNA library screening often creates indel mutations with multiple nucleotides. Single base editors and dual deaminase-mediated base editors can achieve only one and two types of base substitutions, respectively. A new glycosylase base editor (CGBE) system, in which the uracil glycosylase inhibitor (UGI) is replaced with uracil-DNA glycosylase (UNG), was recently reported to efficiently induce multiple base conversions, including C-to-G, C-to-T and C-to-A. In this study, we fused a CGBE with ABE to develop a new type of dual deaminase-mediated base editing system, the AGBE system, that can simultaneously introduce 4 types of base conversions (C-to-G, C-to-T, C-to-A and A-to-G) as well as indels with a single sgRNA in mammalian cells. AGBEs can be used to establish saturated mutant populations for verification of the functions and consequences of multiple gene mutation patterns, including single-nucleotide variants (SNVs) and indels, through high-throughput screening.

Regulatory network identified by pulmonary transcriptome and proteome profiling reveals extensive change of tumor-related genes in microRNA-21 knockout mice

PURPOSE

MicroRNA-21 (miR-21) is a well-known oncomiR and plays key roles in regulating various biological processes related to pulmonary diseases, especially lung carcinoma. The regulatory roles and downstream targets of miR-21 remain far from well understood. We aimed to identify miR-21-gene regulatory network in lung tissue.

METHODS

Transcriptome and proteome analyses were performed on lung tissues from miR-21 knockout (KO) mice and their wildtype (WT) littermates. Differentially expressed genes (DEGs) and proteins (DEPs) between miR-21KO and WT were analyzed, and correlation analysis was performed between transcriptional and translational level. DEPs were used for prediction of miR-21 target genes and construction of co-expression network.

RESULTS

Comparing with WT mice, 820 DEGs and 623 DEPs were identified in lung tissues of miR-21KO mice. Upregulated DEGs and DEPs were both significantly enriched in pathways of metabolism of xenobiotics by cytochrome P450, drug metabolism, and chemical carcinogenesis. Of the 31 molecules commonly identified in DEGs and DEPs, 9 upregulated genes were tumor suppressor genes while 8 downregulated genes were oncogenes, and 12 genes showed closely positive correlation between mRNA and protein expression. Real-time PCR validation results were consistent with the omics data. Among the upregulated DEPs in miR-21KO mice, 21 genes were predicted as miR-21 targets. The miR-21 regulatory network was constructed by target genes and their highly co-expressed proteins, which identified the miR-21 target Itih4 as a hub gene.

CONCLUSION

MiR-21-gene regulatory network was constructed in mouse lung tissue. MiR-21KO resulted in extensive upregulation of tumor suppressor genes and downregulation of oncogenes.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

Non-targeted metabolomics reveals differences in the gut metabolic profile of the fall armyworm strains when feeding different food sources

Spodoptera frugiperda (fall armyworm - FAW) is an important polyphagous agricultural pest feeding on nearly 350 host plants. FAW is undergoing incipient speciation with two well-characterized host-adapted strains, the "corn" (CS) and "rice" (RS) strains, which are morphologically identical but carry several genes under positive selection for host adaptation. We used non-targeted metabolomics based on gas chromatography/mass spectrometry to identify differences in metabolite profiles of the larval gut of CS and RS feeding on different host plants. Larvae were fed on artificial diet, maize, rice, or cotton leaves from eclosion to the sixth instar, when they had their midgut dissected for analysis. This study revealed that the midgut metabolome of FAW varied due to larval diet and differed between the FAW host-adapted strains. Additionally, we identified several candidate metabolites that may be involved in the adaptation of CS and RS to their host plants. Our findings provide clues toward the gut metabolic activities of the FAW strains.

Integrative clustering methods for multi-omics data

Integrative analysis of multi-omics data has drawn much attention from the scientific community due to the technological advancements which have generated various omics data. Leveraging these multi-omics data potentially provides a more comprehensive view of the disease mechanism or biological processes. Integrative multi-omics clustering is an unsupervised integrative method specifically used to find coherent groups of samples or features by utilizing information across multi-omics data. It aims to better stratify diseases and to suggest biological mechanisms and potential targeted therapies for the diseases. However, applying integrative multi-omics clustering is both statistically and computationally challenging due to various reasons such as high dimensionality and heterogeneity. In this review, we summarized integrative multi-omics clustering methods into three general categories: concatenated clustering, clustering of clusters, and interactive clustering based on when and how the multi-omics data are processed for clustering. We further classified the methods into different approaches under each category based on the main statistical strategy used during clustering. In addition, we have provided recommended practices tailored to four real-life scenarios to help researchers to strategize their selection in integrative multi-omics clustering methods for their future studies.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

BACKGROUND

Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.

AIM OF REVIEW

To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation

Omics data address key issues in liver transplantation (LT) as the most effective therapeutic means for end-stage liver disease. The purpose of this study was to review the current application and future direction for omics in LT. We reviewed the use of multiomics to elucidate the pathogenesis leading to LT and prognostication. Future directions with respect to the use of omics in LT are also described based on perspectives of surgeons with experience in omics. Significant molecules were identified and summarized based on omics, with a focus on post-transplant liver fibrosis, early allograft dysfunction, tumor recurrence, and graft failure. We emphasized the importance omics for clinicians who perform LTs and prioritized the directions that should be established. We also outlined the ideal workflow for omics in LT. In step with advances in technology, the quality of omics data can be guaranteed using an improved algorithm at a lower price. Concerns should be addressed on the translational value of omics for better therapeutic effects in patients undergoing LT.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Molecular characterization of chronic liver disease dynamics: from liver fibrosis to acute-on-chronic liver failure

Background & Aims

The molecular mechanisms driving the progression from early-chronic liver disease (CLD) to cirrhosis and, finally, acute-on-chronic liver failure (ACLF) are largely unknown. Our aim was to develop a protein network-based approach to investigate molecular pathways driving progression from early-CLD to ACLF.

Methods

Transcriptome analysis was performed on liver biopsies from patients at different liver disease stages, including fibrosis, compensated cirrhosis, decompensated cirrhosis and ACLF, and control healthy livers. We created 9 liver-specific disease-related protein-protein interaction networks capturing key pathophysiological processes potentially related to CLD. We used these networks as a framework and performed gene set-enrichment analysis (GSEA) to identify dynamic gene profiles of disease progression.

Results

Principal component analyses revealed that samples clustered according to the disease stage. GSEA of the defined processes showed an upregulation of inflammation, fibrosis and apoptosis networks throughout disease progression. Interestingly, we did not find significant gene expression differences between compensated and decompensated cirrhosis, while ACLF showed acute expression changes in all the defined liver disease-related networks. The analyses of disease progression patterns identified ascending and descending expression profiles associated with ACLF onset. Functional analyses showed that ascending profiles were associated with inflammation, fibrosis, apoptosis, senescence and carcinogenesis networks, while descending profiles were mainly related to oxidative stress and genetic factors. We confirmed by qPCR the upregulation of genes of the ascending profile and validated our findings in an independent patient cohort.

Conclusion

ACLF is characterized by a specific hepatic gene expression pattern related to inflammation, fibrosis, apoptosis, senescence and carcinogenesis. Moreover, the observed profile is significantly different from that of compensated and decompensated cirrhosis, supporting the hypothesis that ACLF should be considered a distinct entity.

Lay summary

By using transjugular biopsies obtained from patients at different stages of chronic liver disease, we unveil the molecular pathogenic mechanisms implicated in the progression of chronic liver disease to cirrhosis and acute-on-chronic liver failure. The most relevant finding in this study is that patients with acute-on-chronic liver failure present a specific hepatic gene expression pattern distinct from that of patients at earlier disease stages. This gene expression pattern is mostly related to inflammation, fibrosis, angiogenesis, and senescence and apoptosis pathways in the liver.

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We unveiled the molecular pathogenic mechanisms implicated in the progression of chronic liver disease to cirrhosis and ACLF.

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ACLF presents a specific hepatic gene expression pattern distinct from that of patients at earlier disease stages.

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Gene expression pattern of ACLF is mostly related to inflammation, fibrosis, angiogenesis, senescence and apoptosis pathways in the liver.