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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.

To elucidate the currently unknown molecular mechanisms responsible for the aberrant expression of recoverin (Rec) within cancerous cells, we examined two-dimensional (2D) and three-dimensional (3D) cultures of Rec-negative lung adenocarcinoma A549 cells which had been transfected with a plasmid containing human recoverin cDNA (A549 Rec) or an empty plasmid as a mock control (A549 MOCK). Using these cells, we measured cytotoxicity by several anti-tumor agents (2D), cellular metabolism including mitochondrial and glycolytic functions by a Seahorse bio-analyzer (2D), the physical properties, size and stiffness of the 3D spheroids, trypsin sensitivities (2D and 3D), and RNA sequencing analysis (2D). Compared with the A549 MOCK, the A549 Rec cells showed (1) more sensitivity toward anti-tumor agents (2D) and a 0.25% solution of trypsin (3D); (2) a metabolic shift from glycolysis to oxidative phosphorylation; and (3) the formation of larger and stiffer 3D spheroids. RNA sequencing analysis and bioinformatic analyses of the differentially expressed genes (DEGs) using Gene Ontology (GO) enrichment analysis suggested that aberrantly expressed Rec is most likely associated with several canonical pathways including G-protein-coupled receptor (GPCR)-mediated signaling and signaling by the cAMP response element binding protein (CREB). The findings reported here indicate that the aberrantly expressed Rec-induced modulation of the cell viability and drug sensitivity may be GPCR mediated.

Nerve injury is a common and difficult clinical problem worldwide with a high disability rate. Different from the central nervous system, the peripheral nervous system is able to regenerate after injury. Wallerian degeneration in the distal nerve stump contributes to the construction of a permissible microenvironment for peripheral nerve regeneration. To gain new molecular insights into Wallerian degeneration, this study aimed to identify differentially expressed genes and elucidate significantly involved pathways and cellular functions in the distal nerve stump following nerve injury. Microarray analysis showed that a few genes were differentially expressed at 0.5 and 1 h post nerve injury and later on a relatively larger number of genes were up-regulated or down-regulated. Ingenuity pathway analysis indicated that inflammation and immune response, cytokine signaling, cellular growth and movement, as well as tissue development and function were significantly activated following sciatic nerve injury. Notably, a cellular function highly related to nerve regeneration, which is called Nervous System Development and Function, was continuously activated from 4 days until 4 weeks post injury. Our results may provide further understanding of Wallerian degeneration from a genetic perspective, thus aiding the development of potential therapies for peripheral nerve injury.

Genome scans provide a comprehensive method to explore genome‐wide variation associated with traits under study. However, linking individual genes to broader functional groupings and pathways is often challenging, yet crucial for understanding the evolutionary mechanisms underlying these traits. This task is particularly relevant for multi‐trait processes such as domestication, which are influenced by complex interactions between numerous genetic and non‐genetic factors, including epigenetic regulation. As various traits within the broader spectrum of domestication are selected in concert over time, this process offers an opportunity to identify broader functional overlaps and understand the integrated genetic architecture underlying these traits. In this study, we analyzed approximately 600,000 SNPs from a Pool‐Seq experiment comparing eight natural‐origin and 12 farmed populations of European seabass in the Mediterranean Sea region. We implemented two genome scan approaches and focused on genomic regions supported by both methods, resulting in the identification of 96 candidate genes, including nine CpG islands, which highligt potential epigenetic influences. Many of these genes and CpG islands are in linkage groups previously associated with domestication‐related traits. The most significantly overrepresented molecular function was “oxidoreductase activity”. Furthermore, a dense network of interactions was identified, connecting 22 of the candidate genes. Within this network, the most significantly enriched pathways and central genes were involved in “chromatin organization”, highlighting another potential epigenetic mechanism. Altogether, our findings underscore the utility of interactome‐assisted pathway analysis in elucidating the genomic architecture of polygenic traits and suggest that epigenetic regulation may play a crucial role in the domestication of European seabass. Our study identified 96 candidate genes and nine CpG islands potentially linked to domestication in European seabass, many of which are associated with traits influenced by both genetic and epigenetic factors. The most enriched molecular functions were “oxidoreductase activity” and “chromatin organization”, indicating that epigenetic regulation may play a critical role in shaping domestication processes. Overall, our findings demonstrate the value of combining genome scans with interactome‐assisted pathway analysis to uncover the complex genetic architecture underlying polygenic traits like domestication.

To elucidate the currently unknown mechanisms responsible for the diverse biological aspects between two-dimensional (2D) and three-dimensional (3D) cultured 3T3-L1 preadipocytes, RNA-sequencing analyses were performed. During a 7-day culture period, 2D- and 3D-cultured 3T3-L1 cells were subjected to lipid staining by BODIPY, qPCR for adipogenesis related genes, including peroxisome proliferator-activated receptor γ (Pparγ), CCAAT/enhancer-binding protein alpha (Cebpa), Ap2 (fatty acid-binding protein 4; Fabp4), leptin, and AdipoQ (adiponectin), and RNA-sequencing analysis. Differentially expressed genes (DEGs) were detected by next-generation RNA sequencing (RNA-seq) and validated by a quantitative reverse transcription–polymerase chain reaction (qRT–PCR). Bioinformatic analyses were performed on DEGs using a Gene Ontology (GO) enrichment analysis and an Ingenuity Pathway Analysis (IPA). Significant spontaneous adipogenesis was observed in 3D 3T3-L1 spheroids, but not in 2D-cultured cells. The mRNA expression of Pparγ, Cebpa, and Ap2 among the five genes tested were significantly higher in 3D spheroids than in 2D-cultured cells, thus providing support for this conclusion. RNA analysis demonstrated that a total of 826 upregulated and 725 downregulated genes were identified as DEGs. GO enrichment analysis and IPA found 50 possible upstream regulators, and among these, 6 regulators—transforming growth factor β1 (TGFβ1), signal transducer and activator of transcription 3 (STAT3), interleukin 6 (IL6), angiotensinogen (AGT), FOS, and MYC—were, in fact, significantly upregulated. Further analyses of these regulators by causal networks of the top 14 predicted diseases and functions networks (IPA network score indicated more than 30), suggesting that STAT3 was the most critical upstream regulator. The findings presented herein suggest that STAT3 has a critical role in regulating the unique biological properties of 3D spheroids that are produced from 3T3-L1 preadipocytes.

Age is a significant contributing factor to the occurrence and progression of cardiovascular disease (CVD). Pharmacological treatment can effectively alleviate CVD symptoms caused by aging. However, 90% of the drugs have failed in clinics because of the loss of drug effects or the occurrence of the side effects. One of the reasons is the disparity between animal models used and the actual physiological levels in humans. Therefore, we integrated multiple datasets from single‐cell and bulk‐seq RNA‐sequencing data in rats, monkeys, and humans to identify genes and pathways with consistent/differential expression patterns across these three species. An approach called “Cross‐species signaling pathway analysis” was developed to select suitable animal models for drug screening. The effectiveness of this method was validated through the analysis of the pharmacological predictions of four known anti‐vascular aging drugs used in animal/clinical experiments. The effectiveness of drugs was consistently observed between the models and clinics when they targeted pathways with the same trend in our analysis. However, drugs might have exhibited adverse effects if they targeted pathways with opposite trends between the models and the clinics. Additionally, through our approach, we discovered four targets for anti‐vascular aging drugs, which were consistent with their pharmaceutical effects in literatures, showing the value of this approach. In the end, software was established to facilitate the use of “Cross‐species signaling pathway analysis.” In sum, our study suggests utilizing bioinformatics analysis based on disease characteristics can help in choosing more appropriate animal models.

The pathological mechanisms that lead to the onset and reactivation of celiac disease (CD) remain largely unknown. While gluten free diet (GFD) improves the intestinal damage and associated clinical symptoms in majority of cases, it falls short of providing full recovery. Additionally, late or misdiagnosis is also common as CD presents with a wide range of symptoms. Clear understanding of CD pathogenesis is thus critical to address both diagnostic and treatment concerns. We aimed to study the molecular impact of short gluten exposure in GFD treated CD patients, as well as identify biological pathways that remain altered constitutively in CD regardless of treatment. Using RNAseq profiling of PBMC samples collected from treated CD patients and gluten challenged patient and healthy controls, we explored the peripheral transcriptome in CD patients following a short gluten exposure. Short gluten exposure of just three days was enough to alter the genome-wide PBMC transcriptome of patients. Pathway analysis revealed gluten-induced upregulation of mainly immune response related pathways, both innate and adaptive, in CD patients. We evaluated the perturbation of biological pathways in sample-specific manner. Compared to gluten exposed healthy controls, pathways related to tight junction, olfactory transduction, metabolism of unsaturated fatty acids (such as arachidonic acid), metabolism of amino acids (such as cysteine and glutamate), and microbial infection were constitutively altered in CD patients regardless of treatment, while GFD treatment appears to mostly normalize immune response pathways to “healthy” state. Upstream regulator prediction analysis using differentially expressed genes identified constitutively activated regulators relatively proximal to previously reported CD associated loci, particularly SMARCA4 on 19p13.2 and CSF2 on 5q31. We also found constitutively upregulated genes in CD that are in CD associated genetic loci such as MEF2BNB-MEF2B (BORCS8-MEF2B) on 19p13.11 and CSTB on 21q22.3. RNAseq revealed strong effects of short oral gluten challenge on whole PBMC fraction and constitutively altered pathways in CD PBMC suggesting important factors other than gluten in CD pathogenesis.

The sequencing of the human genome in 2003 marked a transformative shift from a one-size-fits-all approach to personalized medicine, emphasizing patient-specific molecular and physiological characteristics. Advances in sequencing technologies, from Sanger methods to Next-Generation Sequencing (NGS), have generated vast genomic datasets, enabling the development of tailored therapeutic strategies. Pharmacogenomics (PGx) has played a pivotal role in elucidating how the genetic make-up influences inter-individual variability in drug efficacy and toxicity discovering predictive and prognostic biomarkers. However, challenges persist in interpreting polymorphic variants and translating findings into clinical practice. Multi-omics data integration and bioinformatics tools are essential for addressing these complexities, uncovering novel molecular insights, and advancing precision medicine. In this review, starting from our experience in PGx studies performed by DMET microarray platform, we propose a guideline combining machine learning, statistical, and network-based approaches to simplify and better understand complex DMET PGx data analysis which can be adapted for broader PGx applications, fostering accessibility to high-performance bioinformatics, also for non-specialists. Moreover, we describe an example of how bioinformatic tools can be used for a comprehensive integrative analysis which could allow the translation of genetic insights into personalized therapeutic strategies.

The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task.

The regenerative capacity of peripheral nerves is limited after nerve injury. A number of growth factors modulate many cellular behaviors, such as proliferation and migration, and may contribute to nerve repair and regeneration. Our previous study observed the dynamic changes of genes in L4-6 dorsal root ganglion after rat sciatic nerve crush using transcriptome sequencing. Our current study focused on upstream growth factors and found that a total of 19 upstream growth factors were dysregulated in dorsal root ganglions at 3, 9 hours, 1, 4, or 7 days after nerve crush, compared with the 0 hour control. Thirty-six rat models of sciatic nerve crush injury were prepared as described previously. Then, they were divided into six groups to measure the expression changes of representative genes at 0, 3, 9 hours, 1, 4 or 7 days post crush. Our current study measured the expression levels of representative upstream growth factors, including nerve growth factor, brain-derived neurotrophic factor, fibroblast growth factor 2 and amphiregulin genes, and explored critical signaling pathways and biological process through bioinformatic analysis. Our data revealed that many of these dysregulated upstream growth factors, including nerve growth factor, brain-derived neurotrophic factor, fibroblast growth factor 2 and amphiregulin, participated in tissue remodeling and axon growth-related biological processes Therefore, the experiment described the expression pattern of upstream growth factors in the dorsal root ganglia after peripheral nerve injury. Bioinformatic analysis revealed growth factors that may promote repair and regeneration of damaged peripheral nerves. All animal surgery procedures were performed in accordance with Institutional Animal Care Guidelines of Nantong University and ethically approved by the Administration Committee of Experimental Animals, China (approval No. 20170302-017) on March 2, 2017.