Identifying significantly impacted pathways: a comprehensive review and assessment

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Gene expression in the dorsal root ganglion and the cerebrospinal fluid metabolome in polyneuropathy and opioid tolerance in rats

First-line pharmacotherapy for peripheral neuropathic pain (NP) of diverse pathophysiology consists of antidepressants and gabapentinoids, but only a minority achieve sufficient analgesia with these drugs. Opioids are considered third-line analgesics in NP due to potential severe and unpredictable adverse effects in long-term use. Also, opioid tolerance and NP may have shared mechanisms, raising further concerns about opioid use in NP. We set out to further elucidate possible shared and separate mechanisms after chronic morphine treatment and oxaliplatin-induced and diabetic polyneuropathies, and to identify potential diagnostic markers and therapeutic targets. We analysed thermal nociceptive behaviour, the transcriptome of dorsal root ganglia (DRG) and the metabolome of cerebrospinal fluid (CSF) in these three conditions, in rats. Several genes were differentially expressed, most following oxaliplatin and least after chronic morphine treatment, compared with saline-treated rats. A few genes were differentially expressed in the DRGs in all three models (e.g. Csf3r and Fkbp5). Some, e.g. Alox15 and Slc12a5, were differentially expressed in both diabetic and oxaliplatin models. Other differentially expressed genes were associated with nociception, inflammation, and glial cells. The CSF metabolome was most significantly affected in the diabetic rats. Interestingly, we saw changes in nicotinamide metabolism, which has been associated with opioid addiction and withdrawal, in the CSF of morphine-tolerant rats. Our results offer new hypotheses for the pathophysiology and treatment of NP and opioid tolerance. In particular, the role of nicotinamide metabolism in opioid addiction deserves further study.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

BACKGROUND

The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.

RESULTS

To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA.

CONCLUSION

The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

Tear Proteins Altered in Patients with Persistent Eye Pain after Refractive Surgery: Biomarker Candidate Discovery

Some patients develop persistent eye pain after refractive surgery, but factors that cause or sustain pain are unknown. We tested whether tear proteins of patients with pain 3 months after surgery differ from those of patients without pain. Patients undergoing refractive surgery (laser in situ keratomileusis or photorefractive keratectomy ) were recruited from 2 clinics, and tears were collected 3 months after surgery. Participants rated their eye pain using a numerical rating scale (NRS, 0-10; no pain-worst pain) at baseline, 1 day, and 3 months after surgery. Using tandem mass tag proteomic analysis, we examined tears from patients with pain [NRS ≥ 3 at 3 months (n = 16)] and patients with no pain [NRS ≤ 1 at 3 months (n = 32)] after surgery. A subset of proteins (83 of 2748 detected, 3.0%) were associated with pain 3 months after surgery. High-dimensional statistical models showed that the magnitude of differential expression was not the only important factor in classifying tear samples from pain patients. Models utilizing 3 or 4 proteins had better classification performance than single proteins and represented differences in both directions (higher or lower in pain). Thus, patterns of protein differences may serve as biomarkers of postsurgical eye pain as well as potential therapeutic targets.

Archetypal clustering reveals physiological mechanisms linking milk yield and fertility in dairy cattle

Fertility in dairy cattle has declined as an unintended consequence of single-trait selection for high milk yield. The unfavorable genetic correlation between milk yield and fertility is now well documented; however, the underlying physiological mechanisms are still uncertain. To understand the relationship between these traits, we developed a method that clusters variants with similar patterns of effects and, after the integration of gene expression data, identifies the genes through which they are likely to act. Biological processes that are enriched in the genes of each cluster were then identified. We identified several clusters with unique patterns of effects. One of the clusters included variants associated with increased milk yield and decreased fertility, where the "archetypal" variant (i.e., the one with the largest effect) was associated with the GC gene, whereas others were associated with TRIM32, LRRK2, and U6-associated snRNA. These genes have been linked to transcription and alternative splicing, suggesting that these processes are likely contributors to the unfavorable relationship between the 2 traits. Another cluster, with archetypal variant near DGAT1 and including variants associated with CDH2, BTRC, SFRP2, ZFHX3, and SLITRK5, appeared to affect milk yield but have little effect on fertility. These genes have been linked to insulin, adipose tissue, and energy metabolism. A third cluster with archetypal variant near ZNF613 and including variants associated with ROBO1, EFNA5, PALLD, GPC6, and PTPRT were associated with fertility but not milk yield. These genes have been linked to GnRH neuronal migration, embryonic development, or ovarian function. The use of archetypal clustering to group variants with similar patterns of effects may assist in identifying the biological processes underlying correlated traits. The method is hypothesis generating and requires experimental confirmation. However, we have uncovered several novel mechanisms potentially affecting milk production and fertility such as GnRH neuronal migration. We anticipate our method to be a starting point for experimental research into novel pathways, which have been previously unexplored within the context of dairy production.

Transcriptomic characterization of lung pericytes in systemic sclerosis-associated pulmonary fibrosis

Systemic sclerosis (SSc) is a chronic disease characterized by fibrosis and vascular abnormalities in the skin and internal organs, including the lung. SSc-associated pulmonary fibrosis (SSc-PF) is the leading cause of death in SSc patients. Pericytes are key regulators of vascular integrity and endothelial function. The role that pericytes play in SSc-PF remains unclear. We compared the transcriptome of pericytes from SSc-PF lungs (SScL) to pericytes from normal lungs (NORML). We identified 1,179 differentially expressed genes in SScL pericytes. Pathways enriched in SScL pericytes included prostaglandin, PI3K-AKT, calcium, and vascular remodeling signaling. Decreased cyclic AMP production and altered phosphorylation of AKT in response to prostaglandin E2 in SScL pericytes demonstrate the functional consequence of changes in the prostaglandin pathway that may contribute to fibrosis. The transcriptomic signature of SSc lung pericytes suggests that they promote vascular dysfunction and contribute to the loss of protection against lung inflammation and fibrosis.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Generalized reporter score-based enrichment analysis for omics data

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

Predicting transcription factor activity using prior biological information

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Autoimmunity-associated allele of tyrosine phosphatase gene PTPN22 enhances anti-viral immunity

The 1858C>T allele of the tyrosine phosphatase PTPN22 is present in 5-10% of the North American population and is strongly associated with numerous autoimmune diseases. Although research has been done to define how this allele potentiates autoimmunity, the influence PTPN22 and its pro-autoimmune allele has in anti-viral immunity remains poorly defined. Here, we use single cell RNA-sequencing and functional studies to interrogate the impact of this pro-autoimmune allele on anti-viral immunity during Lymphocytic Choriomeningitis Virus clone 13 (LCMV-cl13) infection. Mice homozygous for this allele (PEP-619WW) clear the LCMV-cl13 virus whereas wildtype (PEP-WT) mice cannot. This is associated with enhanced anti-viral CD4 T cell responses and a more immunostimulatory CD8α- cDC phenotype. Adoptive transfer studies demonstrated that PEP-619WW enhanced anti-viral CD4 T cell function through virus-specific CD4 T cell intrinsic and extrinsic mechanisms. Taken together, our data show that the pro-autoimmune allele of Ptpn22 drives a beneficial anti-viral immune response thereby preventing what is normally a chronic virus infection.

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

Whole Blood Transcriptome Analysis in Patients with Trigeminal Neuralgia: a Prospective Clinical Study

Trigeminal neuralgia (TN) brings a huge burden to patients, without long-term effective treatment. This study aimed to explore the differentially expressed genes (DEGs) and related enrichment pathways in patients with TN. This was a study of transcriptome sequencing and bioinformatics analysis of human samples. Whole blood samples were collected from the TN patients and pain-free controls. RNA was extracted to conduct the RNA-sequencing and the subsequent bioinformatics analysis. DEGs between the two groups were derived. Kyoto encyclopedia of genes and genomes (KEGG) and Gene ontology (GO) was used to find the enrichment pathways of DEGs. Protein protein interaction (PPI) network was used to depict the interaction between DEGs and find the most important gene, hub gene. Compared with the control group, there were 117 up-regulated DEGs and 103 down-regulated DEGs in the whole blood of patients in the TN group. Pathway enrichment analysis showed that DEGs were mainly enriched in the neuroimmune and metabolic pathways. The PPI network demonstrated that colony stimulating factor 2 (CSF2) was the most important hub gene in the whole blood of TN patients. This study shows the expression of the transcriptome in the whole blood samples of TN patients. The neuroimmune responses and key hub gene CSF2 in the whole blood cells play a vital role in the occurrence of TN. Our research provides a theoretical basis for the diagnosis and treatments of TN. This study was registered at clinicaltrials.gov in June 2021 (No. NCT04923399).

Maternal Western diet programs cardiometabolic dysfunction and hypothalamic inflammation via epigenetic mechanisms predominantly in the male offspring

OBJECTIVE

Maternal exposure during pregnancy is a strong determinant of offspring health outcomes. Such exposure induces changes in the offspring epigenome resulting in gene expression and functional changes. In this study, we investigated the effect of maternal Western hypercaloric diet (HCD) programming during the perinatal period on neuronal plasticity and cardiometabolic health in adult offspring.

METHODS

C57BL/6J dams were fed HCD for 1 month prior to mating with regular diet (RD) sires and kept on the same diet throughout pregnancy and lactation. At weaning, offspring were maintained on either HCD or RD for 3 months resulting in 4 treatment groups that underwent cardiometabolic assessments. DNA and RNA were extracted from the hypothalamus to perform whole genome methylation, mRNA, and miRNA sequencing followed by bioinformatic analyses.

RESULTS

Maternal programming resulted in male-specific hypertension and hyperglycemia, with both males and females showing increased sympathetic tone to the vasculature. Surprisingly, programmed male offspring fed HCD in adulthood exhibited lower glucose levels, less insulin resistance, and leptin levels compared to non-programmed HCD-fed male mice. Hypothalamic genes involved in inflammation and type 2 diabetes were targeted by differentially expressed miRNA, while genes involved in glial and astrocytic differentiation were differentially methylated in programmed male offspring. These data were supported by our findings of astrogliosis, microgliosis and increased microglial activation in programmed males in the paraventricular nucleus (PVN). Programming induced a protective effect in male mice fed HCD in adulthood, resulting in lower protein levels of hypothalamic TGFβ2, NF-κB2, NF-κBp65, Ser-pIRS1, and GLP1R compared to non-programmed HCD-fed males. Although TGFβ2 was upregulated in male mice exposed to HCD pre- or post-natally, only blockade of the brain TGFβ receptor in RD-HCD mice improved glucose tolerance and a trend to weight loss.

CONCLUSIONS

Our study shows that maternal HCD programs neuronal plasticity in the offspring and results in male-specific hypertension and hyperglycemia associated with hypothalamic inflammation in mechanisms and pathways distinct from post-natal HCD exposure. Together, our data unmask a compensatory role of HCD programming, likely via priming of metabolic pathways to handle excess nutrients in a more efficient way.

Benchmarking enrichment analysis methods with the disease pathway network

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

Plasma-Derived Exosome Proteins as Novel Diagnostic and Prognostic Biomarkers in Neuroblastoma Patients

Neuroblastoma (NB) is the most common extracranial solid tumor during infancy, causing up to 10% of mortality in children; thus, identifying novel early and accurate diagnostic and prognostic biomarkers is mandatory. NB-derived exosomes carry proteins (Exo-prots) reflecting the status of the tumor cell of origin. The purpose of this study was to characterize, for the first time, the Exo-prots specifically expressed in NB patients associated with tumor phenotype and disease stage. We isolated exosomes from plasma specimens of 24 HR-NB patients and 24 low-risk (LR-NB) patients at diagnosis and of 24 age-matched healthy controls (CTRL). Exo-prot expression was measured by liquid chromatography-mass spectrometry. The data are available via ProteomeXchange (PXD042422). The NB patients had a different Exo-prot expression profile compared to the CTRL. The deregulated Exo-prots in the NB specimens acted mainly in the tumor-associated pathways. The HR-NB patients showed a different Exo-prot expression profile compared to the LR-NB patients, with the modulation of proteins involved in cell migration, proliferation and metastasis. NCAM, NCL, LUM and VASP demonstrated a diagnostic value in discriminating the NB patients from the CTRL; meanwhile, MYH9, FN1, CALR, AKAP12 and LTBP1 were able to differentiate between the HR-NB and LR-NB patients with high accuracy. Therefore, Exo-prots contribute to NB tumor development and to the aggressive metastatic NB phenotype.

RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.

Circulatory extracellular vesicle derived miR-195-5p promotes cellular apoptosis and suppresses cell proliferation in the buffalo endometrial primary cell culture

In pregnant animals, communication between the mother and conceptus occurs via extracellular vesicles (EVs) that carry several biomolecules such as nucleic acids (miRNAs, mRNAs), proteins, and lipids. At the time of implantation, the endometrium undergoes several morphological and physiological changes, such as angiogenesis, apoptosis, and cell proliferation regulation at the implantation site, to attain a receptive state. This study was conducted to detect pregnancy-specific miRNAs derived from extracellular vesicles in the systemic circulation of Bubalus bubalis (water buffalo) and to assess their functional significance in the modulation of endometrial primary cells. The extracellular vesicles were isolated from the blood plasma using a precipitation-based method and further characterized by various methods such as Differential light scattering, Nanoparticle tracking assay, Western blot, and transmission electron microscopy. The relative expression of the selected extracellular vesicles associated miRNAs (EV-miRNA) at different intervals (days 15, 19, 25, and 30) post artificial insemination (AI) was analyzed using RT-qPCR, and expression of miR-195-5p was found to be significantly higher (P < 0.01) in pregnant animals on day 19 post AI (implantation window) as compared to day 15 post AI. The elevated expression might indicate the involvement of this miRNA in the maternal-conceptus cross-talk occurring during the implantation period. The KEGG pathway enrichment and Gene Ontology analyses of the miR-195-5p target genes revealed that these were mostly involved in the PI3-Akt, MAPK, cell cycle, ubiquitin-mediated proteolysis, and mTOR signaling pathways, which are related to the regulation of cell proliferation. Transfecting the in vitro cultured cells with miR-195-5p mimic significantly suppressed (P < 0.05) the expression of its target genes such as YWHAQ, CDC27, AKT-3, FGF-7, MAPK8, SGK1, VEGFA, CACAND1, CUL2, MKNK1, and CACAN2D1. Furthermore, the downregulation of the miR-195-5p target genes was positively correlated with a significant increase in the apoptotic rate and a decrease in the proliferation. In conclusion, the current findings provide vital information on the presence of EV miR-195-5p in maternal circulation during the implantation window indicating its important role in the modulation of buffalo endometrium epithelial cells via promoting cell death. Altogether, the milieu of miR-195-5p may serve as a novel and potential molecular factor facilitating the implantation of the early embryo during the establishment of pregnancy in buffaloes. Thus, miR-195-5p may be identified as a unique circulatory EV biomarker related to establishing pregnancy in buffaloes as early as day 19 post-AI.

Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.

The rat Achilles and patellar tendons have similar increases in mechanical properties but become transcriptionally divergent during postnatal development

The molecular events that drive post-natal tendon development are poorly characterized. In this study, we profiled morphological, mechanical, and transcriptional changes in the rat Achilles and patellar tendon before walking (P7), shortly after onset of walking (P14), and at motor maturity (P28). The Achilles and patellar tendons increased collagen content and mechanical strength similarly throughout post-natal development. However, at P28 the patellar tendon tended to display a higher maximal tensile load (MTL) (P = 0.0524) than the Achilles tendon, but a similar ultimate tensile strength (UTS; load relative to cross-sectional area) probably due to its increased cross-sectional area during development. The tendons started transcriptionally similar, with overlapping PCA clusters at P7 and P14, before becoming transcriptionally distinct at P28. In both tendons, there was an increase in extracellular matrix (ECM) gene expression and a concomitant decrease in cell cycle and mitochondrial gene expression. The transcriptional divergence at P28 suggested that STAT signalling was lower in the patellar tendon where MTL increased the most. Treating engineered human ligaments with the STAT inhibitor itacitinib increased collagen content and MTL. Our results suggest that during post-natal development, cellular resources are initially allocated towards cell proliferation before shifting towards extracellular matrix development following the onset of mechanical load and provide potential targets for improving tendon function. KEY POINTS: Little is known about mechanisms of post-natal tendon growth. We characterized morphological, mechanical, and transcriptional changes that occur before (P7), and early (P14) and late after (P28) rats begin to walk. From P7 to P28, the Achilles tendon increased in length, whereas the patellar tendon increased in cross-sectional area. Mechanical and material properties of the Achilles and patellar tendon increased from P7 to P28. From P7 to P28, the Achilles and patellar tendons increased expression of ECM genes and decreased mitochondrial and cell cycle gene expression. Ribosomal gene expression also significantly decreased in the Achilles and tended to decrease in the patellar tendon. At P28, STAT1 signalling tended to be lower in the patellar tendon which had grown by increasing cross-sectional area and inhibiting STAT activation in vitro improved mechanical properties in engineered human ligaments.

Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

The impact of sex and physical activity on the local immune response to muscle pain

Induction of muscle pain triggers a local immune response to produce pain and this mechanism may be sex and activity level dependent. The purpose of this study was to measure the immune system response in the muscle following induction of pain in sedentary and physically active mice. Muscle pain was produced via an activity-induced pain model using acidic saline combined with fatiguing muscle contractions. Prior to induction of muscle pain, mice (C57/BL6) were sedentary or physically active (24hr access to running wheel) for 8 weeks. The ipsilateral gastrocnemius was harvested 24hr after induction of muscle pain for RNA sequencing or flow cytometry. RNA sequencing revealed activation of several immune pathways in both sexes after induction of muscle pain, and these pathways were attenuated in physically active females. Uniquely in females, the antigen processing and presentation pathway with MHC II signaling was activated after induction of muscle pain; activation of this pathway was blocked by physical activity. Blockade of MHC II attenuated development of muscle hyperalgesia exclusively in females. Induction of muscle pain increased the number of macrophages and T-cells in the muscle in both sexes, measured by flow cytometry. In both sexes, the phenotype of macrophages shifted toward a pro-inflammatory state after induction of muscle pain in sedentary mice (M1 + M1/2) but toward an anti-inflammatory state in physically active mice (M2 + M0). Thus, induction of muscle pain activates the immune system with sex-specific differences in the transcriptome while physical activity attenuates immune response in females and alters macrophage phenotype in both sexes.

Metabolomic Profiles Associated With Blood Pressure Reduction in Response to the DASH and DASH-Sodium Dietary Interventions

BACKGROUND

The DASH (Dietary Approaches to Stop Hypertension) diets reduced blood pressure (BP) in the DASH and DASH-Sodium trials, but the underlying mechanisms are unclear. We identified metabolites associated with systolic BP or diastolic BP (DBP) changes induced by dietary interventions (DASH versus control arms) in 2 randomized controlled feeding studies-the DASH and DASH-Sodium trials.

METHODS

Metabolomic profiling was conducted in serum and urine samples collected at the end of diet interventions: DASH (n=219) and DASH-Sodium (n=395). Using multivariable linear regression models, associations were examined between metabolites and change in systolic BP and DBP. Tested for interactions between diet interventions and metabolites were the following comparisons: (1) DASH versus control diets in the DASH trial (serum), (2) DASH high-sodium versus control high-sodium diets in the DASH-Sodium trial (urine), and (3) DASH low-sodium versus control high-sodium diets in the DASH-Sodium trial (urine).

RESULTS

Sixty-five significant interactions were identified (DASH trial [serum], 12; DASH high sodium [urine], 35; DASH low sodium [urine], 18) between metabolites and systolic BP or DBP. In the DASH trial, serum tryptophan betaine was associated with reductions in DBP in participants consuming the DASH diets but not control diets (P interaction, 0.023). In the DASH-Sodium trial, urine levels of N-methylglutamate and proline derivatives (eg, stachydrine, 3-hydroxystachydrine, N-methylproline, and N-methylhydroxyproline) were associated with reductions in systolic BP or DBP in participants consuming the DASH diets but not control diets (P interaction, <0.05 for all tests).

CONCLUSIONS

We identified metabolites that were associated with BP lowering in response to dietary interventions.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov/ct2/show/NCT03403166; Unique identifier: NCT03403166 (DASH trial). URL: https://www.

CLINICALTRIALS

gov/ct2/show/NCT00000608; Unique identifier: NCT00000608 (DASH-Sodium trial).

Hydroxylation markedly alters how the polychlorinated biphenyl (PCB) congener, PCB52, affects gene expression in human preadipocytes

Polychlorinated biphenyls (PCBs) accumulate in adipose tissue and are linked to obesity and diabetes. The congener, PCB52 (2,2',5,5'-tetrachorobiphenyl), is found at high levels in school air. Hydroxylation of PCB52 to 4-OH-PCB52 (4-hydroxy-2,2',5,5'-tetrachorobiphenyl) may increase its toxicity. To understand PCB52's role in causing adipose dysfunction, we exposed human preadipocytes to PCB52 or 4-OH-PCB52 across a time course and assessed transcript changes using RNAseq. 4-OH-PCB52 caused considerably more changes in the number of differentially expressed genes as compared to PCB52. Both PCB52 and 4-OH-PCB52 upregulated transcript levels of the sulfotransferase SULT1E1 at early time points, but cytochrome P450 genes were generally not affected. A set of genes known to be transcriptionally regulated by PPARα were consistently downregulated by PCB52 at all time points. In contrast, 4-OH-PCB52 affected a variety of pathways, including those involving cytokine responses, hormone responses, focal adhesion, Hippo, and Wnt signaling. Sets of genes known to be transcriptionally regulated by IL17A or parathyroid hormone (PTH) were found to be consistently downregulated by 4-OH-PCB52. Most of the genes affected by PCB52 and 4-OH-PCB52 were different and, of those that were the same, many were changed in an opposite direction. These studies provide insight into how PCB52 or its metabolites may cause adipose dysfunction to cause disease.

Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.

Bioinformatics analysis reveals the landscape of immune cell infiltration and novel immune-related biomarkers in moyamoya disease

Objective: This study aimed to identify immune infiltration characteristics and new immunological diagnostic biomarkers in the cerebrovascular tissue of moyamoya disease (MMD) using bioinformatics analysis. Methods: GSE189993 and GSE141022 were downloaded from the GEO database. Differentially expressed gene and PPI analysis were performed. After performing WGCNA, the most significant module associated with MMD was obtained. Next, functional pathways according to GSEA, GO, and KEGG were enriched for the aforementioned core genes obtained from PPI and WGCNA. Additionally, immune infiltration, using the CIBERSORT deconvolution algorithm, immune-related biomarkers, and the relationship between these genes, was further explored. Finally, diagnostic accuracy was verified with ROC curves in the validation dataset GSE157628. Results: A total of 348 DEGs were screened, including 89 downregulated and 259 upregulated genes. The thistle^l module was detected as the most significant module associated with MMD. Functional analysis of the core genes was chiefly involved in the immune response, immune system process, protein tyrosine kinase activity, secretory granule, and so on. Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity. Meanwhile, a higher proportion of eosinophils, not T cells or B cells, was demonstrated in the specific immune infiltration landscape of MMD. Conclusion: Immune activities and immune cells were actively involved in the progression of MMD. BTK, FGR, PTPN11, and SYK were identified as potential immune diagnostic biomarkers. These immune-related genes and cells may provide novel insights for immunotherapy in the future.

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype-phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC.

METHODS

Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model.

RESULTS

The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort.

CONCLUSION

Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34^-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.

Age-related matrix stiffening epigenetically regulates α-Klotho expression and compromises chondrocyte integrity

Extracellular matrix stiffening is a quintessential feature of cartilage aging, a leading cause of knee osteoarthritis. Yet, the downstream molecular and cellular consequences of age-related biophysical alterations are poorly understood. Here, we show that epigenetic regulation of α-Klotho represents a novel mechanosensitive mechanism by which the aged extracellular matrix influences chondrocyte physiology. Using mass spectrometry proteomics followed by a series of genetic and pharmacological manipulations, we discovered that increased matrix stiffness drove Klotho promoter methylation, downregulated Klotho gene expression, and accelerated chondrocyte senescence in vitro. In contrast, exposing aged chondrocytes to a soft matrix restored a more youthful phenotype in vitro and enhanced cartilage integrity in vivo. Our findings demonstrate that age-related alterations in extracellular matrix biophysical properties initiate pathogenic mechanotransductive signaling that promotes Klotho promoter methylation and compromises cellular health. These findings are likely to have broad implications even beyond cartilage for the field of aging research.

PathwayKO: An integrated platform for deciphering the systems-level signaling pathways

Systems characterization of immune landscapes in health, disease and clinical intervention cases is a priority in modern medicine. High-throughput transcriptomes accumulated from gene-knockout (KO) experiments are crucial for deciphering target KO signaling pathways that are impaired by KO genes at the systems-level. There is a demand for integrative platforms. This article describes the PathwayKO platform, which has integrated state-of-the-art methods of pathway enrichment analysis, statistics analysis, and visualizing analysis to conduct cutting-edge integrative pathway analysis in a pipeline fashion and decipher target KO signaling pathways at the systems-level. We focus on describing the methodology, principles and application features of PathwayKO. First, we demonstrate that the PathwayKO platform can be utilized to comprehensively analyze real-world mouse KO transcriptomes (GSE22873 and GSE24327), which reveal systemic mechanisms underlying the innate immune responses triggered by non-infectious extensive hepatectomy (2 hours after 85% liver resection surgery) and infectious CASP-model sepsis (12 hours after CASP-model surgery). Strikingly, our results indicate that both cases hit the same core set of 21 KO MyD88-associated signaling pathways, including the Toll-like receptor signaling pathway, the NFκB signaling pathway, the MAPK signaling pathway, and the PD-L1 expression and PD-1 checkpoint pathway in cancer, alongside the pathways of bacterial, viral and parasitic infections. These findings suggest common fundamental mechanisms between these immune responses and offer informative cues that warrant future experimental validation. Such mechanisms in mice may serve as models for humans and ultimately guide formulating the research paradigms and composite strategies to reduce the high mortality rates of patients in intensive care units who have undergone successful traumatic surgical treatments. Second, we demonstrate that the PathwayKO platform model-based assessments can effectively evaluate the performance difference of pathway analysis methods when benchmarked with a collection of proper transcriptomes. Together, such advances in methods for deciphering biological insights at the systems-level may benefit the fields of bioinformatics, systems immunology and beyond.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

Single sample pathway analysis in metabolomics: performance evaluation and application

BACKGROUND

Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures.

RESULTS

While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/ ), providing implementations of all the methods benchmarked in this study.

CONCLUSION

This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.

Quantitative Framework for Bench-to-Bedside Cancer Research

Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Altered methylation pattern in EXOC4 is associated with stroke outcome: an epigenome-wide association study

BACKGROUND AND PURPOSE

The neurological course after stroke is highly variable and is determined by demographic, clinical and genetic factors. However, other heritable factors such as epigenetic DNA methylation could play a role in neurological changes after stroke.

METHODS

We performed a three-stage epigenome-wide association study to evaluate DNA methylation associated with the difference between the National Institutes of Health Stroke Scale (NIHSS) at baseline and at discharge (ΔNIHSS) in ischaemic stroke patients. DNA methylation data in the Discovery (n = 643) and Replication (n = 62) Cohorts were interrogated with the 450 K and EPIC BeadChip. Nominal CpG sites from the Discovery (p value < 10-06) were also evaluated in a meta-analysis of the Discovery and Replication cohorts, using a random-fixed effect model. Metabolic pathway enrichment was calculated with methylGSA. We integrated the methylation data with 1305 plasma protein expression levels measured by SOMAscan in 46 subjects and measured RNA expression with RT-PCR in a subgroup of 13 subjects. Specific cell-type methylation was assessed using EpiDISH.

RESULTS

The meta-analysis revealed an epigenome-wide significant association in EXOC4 (p value = 8.4 × 10-08) and in MERTK (p value = 1.56 × 10-07). Only the methylation in EXOC4 was also associated in the Discovery and in the Replication Cohorts (p value = 1.14 × 10-06 and p value = 1.3 × 10-02, respectively). EXOC4 methylation negatively correlated with the long-term outcome (coefficient = - 4.91) and showed a tendency towards a decrease in EXOC4 expression (rho = - 0.469, p value = 0.091). Pathway enrichment from the meta-analysis revealed significant associations related to the endocytosis and deubiquitination processes. Seventy-nine plasma proteins were differentially expressed in association with EXOC4 methylation. Pathway analysis of these proteins showed an enrichment in natural killer (NK) cell activation. The cell-type methylation analysis in blood also revealed a differential methylation in NK cells.

CONCLUSIONS

DNA methylation of EXOC4 is associated with a worse neurological course after stroke. The results indicate a potential modulation of pathways involving endocytosis and NK cells regulation.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field. For that purpose, we have reviewed the recent scientific literature from PubMed, Web of Science, EMBASE, and PubTator using the terms “metabolomics”, “DILI”, and “humans”. Despite the undoubted contribution of metabolomics to our understanding of the underlying mechanisms of DILI and the identification of promising novel metabolite biomarkers, there are still some inconsistencies and limitations that hinder the translation of these research findings into general clinical practice, probably due to the variability of the methods used as well to the different mechanisms elicited by the DILI causing agent.

CASP-Model Sepsis Triggers Systemic Innate Immune Responses Revealed by the Systems-Level Signaling Pathways

Colon ascendens stent peritonitis (CASP) surgery induces a leakage of intestinal contents which may cause polymicrobial sepsis related to post-operative failure of remote multi-organs (including kidney, liver, lung and heart) and possible death from systemic syndromes. Mechanisms underlying such phenomena remain unclear. This article aims to elucidate the mechanisms underlying the CASP-model sepsis by analyzing real-world GEO data (GSE24327_A, B and C) generated from mice spleen 12 hours after a CASP-surgery in septic MyD88-deficient and wildtype mice, compared with untreated wildtype mice. Firstly, we identify and characterize 21 KO MyD88-associated signaling pathways, on which true key regulators (including ligands, receptors, adaptors, transducers, transcriptional factors and cytokines) are marked, which were coordinately, significantly, and differentially expressed at the systems-level, thus providing massive potential biomarkers that warrant experimental validations in the future. Secondly, we observe the full range of polymicrobial (viral, bacterial, and parasitic) sepsis triggered by the CASP-surgery by comparing the coordinated up- or down-regulations of true regulators among the experimental treatments born by the three data under study. Finally, we discuss the observed phenomena of “systemic syndrome”, “cytokine storm” and “KO MyD88 attenuation”, as well as the proposed hypothesis of “spleen-mediated immune-cell infiltration”. Together, our results provide novel insights into a better understanding of innate immune responses triggered by the CASP-model sepsis in both wildtype and MyD88-deficient mice at the systems-level in a broader vision. This may serve as a model for humans and ultimately guide formulating the research paradigms and composite strategies for the early diagnosis and prevention of sepsis.

Transcriptome sequencing of 3,3',4,4',5-Pentachlorobiphenyl (PCB126)-treated human preadipocytes demonstrates progressive changes in pathways associated with inflammation and diabetes

Polychlorinated biphenyls (PCBs) are persistent organic pollutants that accumulate in adipose tissue and have been associated with cardiometabolic disease. We have previously demonstrated that exposure of human preadipocytes to the dioxin-like PCB126 disrupts adipogenesis via the aryl hydrocarbon receptor (AhR). To further understand how PCB126 disrupts adipose tissue cells, we performed RNAseq analysis of PCB126-treated human preadipocytes over a 3-day time course. The most significant predicted upstream regulator affected by PCB126 exposure at the early time point of 9 h was the AhR. Progressive changes occurred in the number and magnitude of transcript levels of genes associated with inflammation, most closely fitting the pathways of cytokine-cytokine-receptor signaling and the AGE-RAGE diabetic complications pathway. Transcript levels of genes involved in the IL-17A, IL-1β, MAP kinase, and NF-κB signaling pathways were increasingly dysregulated by PCB126 over time. Our results illustrate the progressive time-dependent nature of transcriptional changes caused by toxicants such as PCB126, point to important pathways affected by PCB126 exposure, and provide a rich dataset for further studies to address how PCB126 and other AhR agonists disrupt preadipocyte function. These findings have implications for understanding how dioxin-like PCBs and other dioxin-like compounds are involved in the development of obesity and diabetes.

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients

The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Central Nervous System Metabolism in Autism, Epilepsy and Developmental Delays: A Cerebrospinal Fluid Analysis

Neurodevelopmental disorders are associated with metabolic pathway imbalances; however, most metabolic measurements are made peripherally, leaving central metabolic disturbances under-investigated. Cerebrospinal fluid obtained intraoperatively from children with autism spectrum disorder (ASD, n = 34), developmental delays (DD, n = 20), and those without known DD/ASD (n = 34) was analyzed using large-scale targeted mass spectrometry. Eighteen also had epilepsy (EPI). Metabolites significantly related to ASD, DD and EPI were identified by linear models and entered into metabolite–metabolite network pathway analysis. Common disrupted pathways were analyzed for each group of interest. Central metabolites most involved in metabolic pathways were L-cysteine, adenine, and dodecanoic acid for ASD; nicotinamide adenine dinucleotide phosphate, L-aspartic acid, and glycine for EPI; and adenosine triphosphate, L-glutamine, ornithine, L-arginine, L-lysine, citrulline, and L-homoserine for DD. Amino acid and energy metabolism pathways were most disrupted in all disorders, but the source of the disruption was different for each disorder. Disruption in vitamin and one-carbon metabolism was associated with DD and EPI, lipid pathway disruption was associated with EPI and redox metabolism disruption was related to ASD. Two microbiome metabolites were also detected in the CSF: shikimic and cis-cis-muconic acid. Overall, this study provides increased insight into unique metabolic disruptions in distinct but overlapping neurodevelopmental disorders.