PANEV: an R package for a pathway-based network visualization

One path, two solutions: Network-based analysis identifies targetable pathways for the treatment of comorbid type II diabetes and neuropsychiatric disorders

Comorbid diseases complicate patient outcomes and escalate healthcare costs, necessitating the need for a deeper mechanistic understanding. Neuropsychiatric disorders (NPDs) such as Neurotic Disorder, Major Depression, Bipolar Disorder, Anxiety Disorder, and Schizophrenia significantly exacerbate Type 2 Diabetes Mellitus (DM2), often leading to suboptimal treatment outcomes. The neurobiological mechanisms underlying this comorbidity remain poorly understood. To address this gap, we developed a novel pathway-based network computational framework to identify critical shared disease mechanisms between DM2 and these five prevalent comorbid NPDs. Our approach involves reconstructing an integrated DM2 ∩ NPDs KEGG pathway-pathway network and employs two complementary analytical methods, including the "minimum path to comorbidity" method to identify the shortest path fostering comorbid development. This analysis uncovered shared pathways like the PI3K-Akt signaling pathway and highlighted key nodes such as calcium signaling, MAPK, estrogen signaling, and apoptosis pathways. Dysregulation of these pathways likely contributes to the development of DM2-NPDs comorbidity. These findings have significant clinical implications, as they identify promising therapeutic targets that could lead to more effective treatments addressing both DM2 and NPDs simultaneously. Our model not only elucidates the intricate molecular interactions driving this comorbidity but also identifies promising therapeutic targets, paving the way for innovative treatment strategies. Additionally, the framework developed in this study can be adapted to study other complex comorbid conditions, advancing personalized medicine for comorbidities and improving patient care.

Integrating network pharmacology and computational biology to propose Yiqi Sanjie formula's mechanisms in treating NSCLC: molecular docking, ADMET, and molecular dynamics simulation

Background

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths globally. Current treatments often do not fully meet efficacy and quality of life expectations. Traditional Chinese medicine (TCM), particularly the Yiqi Sanjie formula, shows promise but lacks clear mechanistic understanding. This study addresses this gap by investigating the therapeutic effects and underlying mechanisms of Yiqi Sanjie formula in NSCLC.

Methods

We utilized network pharmacology to identify potential NSCLC drug targets of the Yiqi Sanjie formula via the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Compounds with favorable oral bioavailability and drug-likeness scores were selected. Molecular docking was conducted using AutoDock Vina with structural data from the Protein Data Bank and PubChem. Molecular dynamics (MD) simulations were performed with Desmond Molecular Dynamics System, analyzing interactions up to 500 nanoseconds using the OPLS4 force field. ADMET predictions were executed using SwissADME and ADMETlab 2.0, assessing pharmacokinetic properties.

Results

Using network pharmacology tools, we performed Search Tool for the Retrieval of Interaction Genes/Proteins (STRING) analysis for protein-protein interaction, Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway enrichment, and gene ontology (GO) for functional enrichment, identifying crucial signaling pathways and biological processes influenced by the hit compounds bifendate, xambioona, and hederagenin. STRING analysis indicated substantial connectivity among the targets, suggesting significant interactions within the cell cycle regulation and growth factor signaling pathways as outlined in our KEGG results. The GO analysis highlighted their involvement in critical biological processes such as cell cycle control, apoptosis, and drug response. Molecular docking simulations quantified the binding efficiencies of the identified compounds with their targets-CCND1, CDK4, and EGFR-selected based on high docking scores that suggest strong potential interactions crucial for NSCLC inhibition. Subsequent MD simulations validated the stability of these complexes, supporting their potential as therapeutic interventions. Additionally, the novel identification of ADH1B as a target underscores its prospective significance in NSCLC therapy, further expanded by our comprehensive bioinformatics approach.

Conclusions

Our research demonstrates the potential of integrating network pharmacology and computational biology to elucidate the mechanisms of the Yiqi Sanjie formula in NSCLC treatment. The identified compounds could lead to novel targeted therapies, especially for patients with overexpressed targets. The discovery of ADH1B as a therapeutic target adds a new dimension to NSCLC treatment strategies. Further studies, both in vitro and in vivo, are needed to confirm these computational findings and advance these compounds towards clinical trials.

Hepatic transcript profiling in beef cattle: Effects of feeding endophyte-infected tall fescue seeds

The objective of our study was to evaluate the effect of endophyte-infected tall fescue (E+) seeds intake on liver tissue transcriptome in growing Angus × Simmental steers and heifers through RNA-seq analysis. Normal weaned calves (~8 months old) received either endophyte-free tall fescue (E-; n = 3) or infected tall fescue (E+; n = 6) seeds for a 30-d period. The diet offered was ad libitum bermudagrass (Cynodon dactylon) hay combined with a nutritional supplement of 1.61 kg (DM basis) of E+ or E- tall fescue seeds, and 1.61 kg (DM basis) of energy/protein supplement pellets for a 30-d period. Dietary E+ tall fescue seeds were included in a rate of 20 μg of ergovaline/kg BW/day. Liver tissue was individually obtained through biopsy at d 30. After preparation and processing of the liver samples for RNA sequencing, we detected that several metabolic pathways were activated (i.e., upregulated) by the consumption of E+ tall fescue. Among them, oxidative phosphorylation, ribosome biogenesis, protein processing in endoplasmic reticulum and apoptosis, suggesting an active mechanism to cope against impairment in normal liver function. Interestingly, hepatic protein synthesis might increase due to E+ consumption. In addition, there was upregulation of "thermogenesis" KEGG pathway, showing a possible increase in energy expenditure in liver tissue due to consumption of E+ diet. Therefore, results from our study expand the current knowledge related to liver metabolism of growing beef cattle under tall fescue toxicosis.

Hepatic transcript profiling in beef cattle: Effects of rumen-protected niacin supplementation

The objective of our study was to assess the effect of rumen-protected niacin supplementation on the transcriptome of liver tissue in growing Angus × Simmental steers and heifers through RNA-seq analysis. Consequently, we wanted to assess the known role of niacin in the physiological processes of vasodilation, detoxification, and immune function in beef hepatic tissue. Normal weaned calves (~8 months old) were provided either a control diet or a diet supplemented with rumen-protected niacin (6 g/hd/d) for a 30-day period, followed by a liver biopsy. We observed a significant list of changes at the transcriptome level due to rumen-protected niacin supplementation. Several metabolic pathways revealed potential positive effects to the animal's liver metabolism due to administration of rumen-protected niacin; for example, a decrease in lipolysis, apoptosis, inflammatory responses, atherosclerosis, oxidative stress, fibrosis, and vasodilation-related pathways. Therefore, results from our study showed that the liver transcriptional machinery switched several metabolic pathways to a condition that could potentially benefit the health status of animals supplemented with rumen-protected niacin. In conclusion, based on the results of our study, we can suggest the utilization of rumen-protected niacin supplementation as a nutritional strategy could improve the health status of growing beef cattle in different beef production stages, such as backgrounding operations or new arrivals to a feedlot.

The network pharmacology study and molecular docking to investigate the potential mechanism of Acoritataninowii Rhizoma against Alzheimer's Disease

Alzheimer's Disease is considered as an insidious neurodegenerative progressive disease but its pathogenesis has not been elucidated. Acoritataninowii Rhizoma exhibits anti-dementia effects as a traditional Chinese medicine (TCM), which is linked to its anti- Alzheimer's Disease mechanism. In this study, network pharmacology and molecular docking were used to examine the potential of Acoritataninowii Rhizoma for Alzheimer's Disease. In order to construct PPI networks and drug-component-target-disease networks, disease-related genes and proteins were gathered from the database. Gene ontology (GO), pathway enrichment (KEGG), and molecular docking were used to forecast the potential mechanism of Acoritataninowii Rhizoma on Alzheimer's disease. Therefore, 4 active ingredients and 81 target genes were screened from Acoritataninowii Rhizoma, 6765 specific target genes were screened from Alzheimer's Disease, and 61 drug-disease cross genes were validated. GO analysis showed that Acoritataninowii Rhizoma can regulate processes such as the protein serine/threonine kinase associated with MAPK. KeGG pathway analysis showed that the signaling pathways affected by Acoritataninowii Rhizoma were fluid shear stress and atherosclerosis, AGE-RAGE and other pathways. Molecular docking implied that the pharmacological influences of the bioactive constituents of Acoritataninowii Rhizoma (Cycloaartenol and kaempferol) on Alzheimer's Disease may related to ESR1 and AKT1, respectively. AKT1 and ESR1 may be the core target genes of the treatment for Alzheimer's disease. Kaempferol and Cycloartenol might be core bioactive constituents for treatment.

The Novel Effector Ue943 Is Essential for Host Plant Colonization by Ustilago esculenta

The smut fungus Ustilago esculenta obligately parasitizes Zizania latifolia and induces smut galls at the stem tips of host plants. Previous research identified a putative secreted protein, Ue943, which is required for the biotrophic phase of U. esculenta but not for the saprophytic phase. Here, we studied the role of Ue943 during the infection process. Conserved homologs of Ue943 were found in smut fungi. Ue943 can be secreted by U. esculenta and localized to the biotrophic interface between fungi and plants. It is required at the early stage of colonization. The Ue943 deletion mutant caused reactive oxygen species (ROS) production and callose deposition in the host plant at 1 and 5 days post inoculation, which led to failed colonization. The virulence deficiency was restored by overexpressing gene Ue943 or Ue943:GFP. Transcriptome analysis further showed a series of changes in plant hormones following ROS production when the host plant was exposed to ΔUe943. We hypothesize that Ue943 might be responsible for ROS suppression or avoidance of recognition by the plant immune system. The mechanism underlying Ue943 requires further study to provide more insights into the virulence of smut fungi.

Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

Background

There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a PaO2/FiO2 < 200 to those with a PaO2/FiO2 ≥ 200.

Results

We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a PaO2/FiO2 < 200 vs. PaO2/FiO2 ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

Conclusions

Cellular energetics and metabolic pathways are important mechanisms to consider to further our understanding of the heterogeneity and underlying pathobiology of moderate and severe pediatric acute respiratory distress syndrome. Our findings are hypothesis generating and support the study of metabolic pathways and cellular energetics to understand heterogeneity and underlying pathobiology of moderate and severe acute hypoxemic respiratory failure in children.

PathIN: an integrated tool for the visualization of pathway interaction networks

PathIN is a web-service that provides an easy and flexible way for rapidly creating pathway-based networks at several functional biological levels: genes, compounds and reactions. The tool is supported by a database repository of reference pathway networks across a large set of species, developed through the freely available information included in the KEGG, Reactome and Wiki Pathways database repositories. PathIN provides networks by means of five diverse methodologies: (a) direct connections between pathways of interest, (b) direct connections as well as the first neighbours of the given pathways, (c) direct connections, the first neighbours and the connections in between them, and (d) two additional methodologies for creating complementary pathway-to-pathway networks that involve additional (missing) pathways that interfere in-between pathways of interest. PathIN is expected to be used as a simple yet informative reference tool for understanding networks of molecular mechanisms related to specific diseases.

Survey for Computer-Aided Tools and Databases in Metabolomics

Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.

Identification of Ancestry Informative Markers in Mediterranean Trout Populations of Molise (Italy): A Multi-Methodological Approach with Machine Learning

Brown trout (Salmo trutta), like many other freshwater species, is threated by the release in its natural environment of alien species and the restocking with allochthonous conspecific stocks. Many conservation projects are ongoing and several morphological and genetic tools have been proposed to support activities aimed to restore genetic integrity status of native populations. Nevertheless, due to the complexity of degree of introgression reached up after many generations of crossing, the use of dichotomous key and molecular markers, such as mtDNA, LDH-C1* and microsatellites, are often not sufficient to discriminate native and admixed specimens at individual level. Here we propose a reduced panel of ancestry-informative SNP markers (AIMs) to support on field activities for Mediterranean trout management and conservation purpose. Starting from the genotypes data obtained on specimens sampled in the main two Molise’s rivers (Central-Southern Italy), a 47 AIMs panel was identified and validated on simulated and real hybrid population datasets, mainly through a Machine Learning approach based on Random Forest classifier. The AIMs panel proposed may represent an interesting and cost-effective tool for monitoring the level of introgression between native and allochthonous trout population for conservation purpose and this methodology could be also applied in other species.

The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis

Purpose

Heart failure (HF) is a clinical syndrome caused by ventricular insufficiency. In order to further explore the biomarkers related to HF, we apply the high-throughput database.

Materials and Methods

The GSE21610 was applied for the differentially expressed gene (DEG) analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The Gene Set Enrichment Analysis (GSEA) was used for gene expression profile GSE21610. The Protein-Protein Interaction (PPI) network and modules were also constructed for research. These hub gene function pathways were estimated in HF progression.

Result

We have identified 434 DEGs in total, including 304 downregulated DEGs and 130 upregulated DEGs. GO and KEGG illustrated that DEGs in HF were significantly enriched in G protein-coupled receptor binding, peroxisome, and cAMP signaling pathway. GSEA results showed gene set GSE21610 was gathered in lipid digestion, defense response to fungus, and intestinal lipid absorption. Finally, through analyzing the PPI network, we screened hub genes CDH1, TFRC, CCL2, BUB1B, and CD19 by the Cytoscape software.

Conclusion

This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to HF. These analysis results provide us with new ideas for finding biomarkers and treatment methods for HF.

A network pharmacology study with molecular docking to investigate the possibility of licorice against posttraumatic stress disorder

Post traumatic stress disorder (PTSD) is a mental health condition that has a debilitating effect on a person's quality of life and leads to a high socioeconomic burden. Licorice has been demonstrated to have neuroprotective and antidepressant-like effects, but little is known about its effects for the treatment of PTSD. The present study aimed to explore the potential of licorice for PTSD therapy using a network pharmacology approach with molecular docking studies. The compounds of licorice were obtained from databases with screening by absorption, distribution, metabolism and excretion (ADME) evaluation. Genes associated with compounds or PTSD were obtained from public databases, and the genes overlapping between licorice compounds and PTSD were compared by Venn diagram. A network of medicine-ingredients-targets-disease was constructed, visualized, and analyzed using cytoscape software. Protein-protein interactions, gene ontology, pathway enrichment and molecular docking were performed to evaluate the effect of licorice for the treatment of PTSD. 69 potential compounds were screened after ADME evaluation. A total of 81 compound-related genes and 566 PTSD-related genes were identified in the databases with 27 overlapping genes. Licorice compounds (e.g., medicarpin, 7-methoxy-2-methyl isoflavone, shinpterocarpin, formononetin, licochalcone a) and target proteins (e.g., ESR1, PTGS2, NOS2, and ADRB2) with high degree in the network were involved in G protein-coupled receptor signaling pathways at the postsynaptic/synaptic membrane. Moreover, neuroactive ligand-receptor interactions, calcium signaling, cholinergic synapse, serotonergic synapse and adrenergic signaling in cardiomyocytes may play important roles in the treatment of PTSD by licorice. This study provides molecular evidence of the beneficial effects of licorice for the treatment of PTSD.

Machine Learning-Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome

OBJECTIVES

To identify differentially expressed genes and networks from the airway cells within 72 hours of intubation of children with and without pediatric acute respiratory distress syndrome. To test the use of a neutrophil transcription reporter assay to identify immunogenic responses to airway fluid from children with and without pediatric acute respiratory distress syndrome.

DESIGN

Prospective cohort study.

SETTING

Thirty-six bed academic PICU.

PATIENTS

Fifty-four immunocompetent children, 28 with pediatric acute respiratory distress syndrome, who were between 2 days to 18 years old within 72 hours of intubation for acute hypoxemic respiratory failure.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We applied machine learning methods to a Nanostring transcriptomics on primary airway cells and a neutrophil reporter assay to discover gene networks differentiating pediatric acute respiratory distress syndrome from no pediatric acute respiratory distress syndrome. An analysis of moderate or severe pediatric acute respiratory distress syndrome versus no or mild pediatric acute respiratory distress syndrome was performed. Pathway network visualization was used to map pathways from 62 genes selected by ElasticNet associated with pediatric acute respiratory distress syndrome. The Janus kinase/signal transducer and activator of transcription pathway emerged. Support vector machine performed best for the primary airway cells and the neutrophil reporter assay using a leave-one-out cross-validation with an area under the operating curve and 95% CI of 0.75 (0.63-0.87) and 0.80 (0.70-1.0), respectively.

CONCLUSIONS

We identified gene networks important to the pediatric acute respiratory distress syndrome airway immune response using semitargeted transcriptomics from primary airway cells and a neutrophil reporter assay. These pathways will drive mechanistic investigations into pediatric acute respiratory distress syndrome. Further studies are needed to validate our findings and to test our models.

Single-Step Genome Wide Association Study Identifies QTL Signals for Untrimmed and Trimmed Thigh Weight in Italian Crossbred Pigs for Dry-Cured Ham Production

Simple Summary

Along with the traditional traits, swine breeding programs for Italian dry-cured ham production have recently aimed to include novel phenotypes. The identification of the genomic regions underlying such new traits helps to untangle their genetic architecture and may provide useful information to be integrated in genetic selection. With this aim, we estimated genetic parameters and conducted a single step genome wide association studies (GWAS) on untrimmed and trimmed thigh weight considering two pig crossbred lines approved for Italian Protected Designation of Origin ham production. Quantitative trait loci (QTLs) were characterized based on the variance of 10-SNP sliding windows genomic estimated breeding values. In particular, we identified interesting QTL signals on several chromosomes, notably on chromosome 4, 6, 7 and 15. A high heritability and genetic correlation were observed for the two traits under investigation and although independent studies including other pig populations are required to disentangle the possible effects of specific linkage disequilibrium in our population, our findings suggest that such QTL could be investigated in future pig breeding programs to improve the reliability of genomic estimated breeding values for the dry-cured ham production.

Abstract

Protected Designation of Origin (PDO) dry-cured ham is the most important product in the Italian pig breeding industry, mainly oriented to produce heavy pig carcasses to obtain hams of the right weight and maturity. Recently, along with the traditional traits swine breeding programs have aimed to include novel carcass traits. The identification at the genome level of quantitative trait loci (QTLs) affecting such new traits helps to reveal their genetic determinism and may provide information to be integrated in prediction models in order to improve prediction accuracy as well as to identify candidate genes underlying such traits. This study aimed to estimate genetic parameters and perform a single step genome wide association studies (ssGWAS) on novel carcass traits such as untrimmed (UTW) and trimmed thigh weight (TTW) in two pig crossbred lines approved for the ham production of the Italian PDO. With this purpose, phenotypes were collected from ~1800 animals and 240 pigs were genotyped with Illumina PorcineSNP60 Beadchip. The single-step genomic BLUP procedure was used for the heritability estimation and to implement the ssGWAS. QTL were characterized based on the variance of 10-SNP sliding window genomic estimated breeding values. Moderate heritabilities were detected and QTL signals were identified on chromosome 1, 4, 6, 7, 11 and 15 for both traits. As expected, the genetic correlation among the two traits was very high (~0.99). The QTL regions encompassed a total of 249 unique candidate genes, some of which were already reported in association with growth, carcass or ham weight traits in pigs. Although independent studies are required to further verify our findings and disentangle the possible effects of specific linkage disequilibrium in our population, our results support the potential use of such new QTL information in future breeding programs to improve the reliability of genomic prediction.

Unique adaptations in neonatal hepatic transcriptome, nutrient signaling, and one-carbon metabolism in response to feeding ethyl cellulose rumen-protected methionine during late-gestation in Holstein cows

BACKGROUND

Methionine (Met) supply during late-pregnancy enhances fetal development in utero and leads to greater rates of growth during the neonatal period. Due to its central role in coordinating nutrient and one-carbon metabolism along with immune responses of the newborn, the liver could be a key target of the programming effects induced by dietary methyl donors such as Met. To address this hypothesis, liver biopsies from 4-day old calves (n = 6/group) born to Holstein cows fed a control or the control plus ethyl-cellulose rumen-protected Met for the last 28 days prepartum were used for DNA methylation, transcriptome, metabolome, proteome, and one-carbon metabolism enzyme activities.

RESULTS

Although greater withers and hip height at birth in Met calves indicated better development in utero, there were no differences in plasma systemic physiological indicators. RNA-seq along with bioinformatics and transcription factor regulator analyses revealed broad alterations in 'Glucose metabolism', 'Lipid metabolism, 'Glutathione', and 'Immune System' metabolism due to enhanced maternal Met supply. Greater insulin sensitivity assessed via proteomics, and efficiency of transsulfuration pathway activity suggested beneficial effects on nutrient metabolism and metabolic-related stress. Maternal Met supply contributed to greater phosphatidylcholine synthesis in calf liver, with a role in very low density lipoprotein secretion as a mechanism to balance metabolic fates of fatty acids arising from the diet or adipose-depot lipolysis. Despite a lack of effect on hepatic amino acid (AA) transport, a reduction in metabolism of essential AA within the liver indicated an AA 'sparing effect' induced by maternal Met.

CONCLUSIONS

Despite greater global DNA methylation, maternal Met supply resulted in distinct alterations of hepatic transcriptome, proteome, and metabolome profiles after birth. Data underscored an effect on maintenance of calf hepatic Met homeostasis, glutathione, phosphatidylcholine and taurine synthesis along with greater efficiency of nutrient metabolism and immune responses. Transcription regulators such as FOXO1, PPARG, E2F1, and CREB1 appeared central in the coordination of effects induced by maternal Met. Overall, maternal Met supply induced better immunometabolic status of the newborn liver, conferring the calf a physiologic advantage during a period of metabolic stress and suboptimal immunocompetence.

Ranking Metabolite Sets by Their Activity Levels

Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site.

How insects protect themselves against combined starvation and pathogen challenges, and the implications for reductionism

An explosion of data has provided detailed information about organisms at the molecular level. For some traits, this information can accurately predict phenotype. However, knowledge of the underlying molecular networks often cannot be used to accurately predict higher order phenomena, such as the response to multiple stressors. This failure raises the question of whether methodological reductionism is sufficient to uncover predictable connections between molecules and phenotype. This question is explored in this paper by examining whether our understanding of the molecular responses to food limitation and pathogens in insects can be used to predict their combined effects. The molecular pathways underlying the response to starvation and pathogen attack in insects demonstrates the complexity of real-world physiological networks. Although known intracellular signaling pathways suggest that food restriction should enhance immune function, a reduction in food availability leads to an increase in some immune components, a decrease in others, and a complex effect on disease resistance in insects such as the caterpillar Manduca sexta. However, our inability to predict the effects of food restriction on disease resistance is likely due to our incomplete knowledge of the intra- and extracellular signaling pathways mediating the response to single or multiple stressors. Moving from molecules to organisms will require novel quantitative, integrative and experimental approaches (e.g. single cell RNAseq). Physiological networks are non-linear, dynamic, highly interconnected and replete with alternative pathways. However, that does not make them impossible to predict, given the appropriate experimental and analytical tools. Such tools are still under development. Therefore, given that molecular data sets are incomplete and analytical tools are still under development, it is premature to conclude that methodological reductionism cannot be used to predict phenotype.