Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

Machine-learning methods applied to integrated transcriptomic data from bovine blastocysts and elongating conceptuses to identify genes predictive of embryonic competence

Early pregnancy loss markedly impacts reproductive efficiency in cattle. The objectives were to model a biologically relevant gene signature predicting embryonic competence for survival after integrating transcriptomic data from blastocysts and elongating conceptuses with different developmental capacities and to validate the potential biomarkers with independent embryonic data sets through the application of machine-learning algorithms. First, two data sets from in vivo-produced blastocysts competent or not to sustain a pregnancy were integrated with a data set from long and short day-15 conceptuses. A statistical contrast determined differentially expressed genes (DEG) increasing in expression from a competent blastocyst to a long conceptus and vice versa; these were enriched for KEGG pathways related to glycolysis/gluconeogenesis and RNA processing, respectively. Next, the most discriminative DEG between blastocysts that resulted or did not in pregnancy were selected by linear discriminant analysis. These eight putative biomarker genes were validated by modeling their expression in competent or noncompetent blastocysts through Bayesian logistic regression or neural networks and predicting embryo developmental fate in four external data sets consisting of in vitro-produced blastocysts (i) competent or not, or (ii) exposed or not to detrimental conditions during culture, and elongated conceptuses (iii) of different length, or (iv) developed in the uteri of high- or subfertile heifers. Predictions for each data set were more than 85% accurate, suggesting that these genes play a key role in embryo development and pregnancy establishment. In conclusion, this study integrated transcriptomic data from seven independent experiments to identify a small set of genes capable of predicting embryonic competence for survival.

A multi-omic brain gut microbiome signature differs between IBS subjects with different bowel habits

Alterations of the brain-gut-microbiome system (BGM) have been implicated in the pathophysiology of irritable bowel syndrome (IBS), yet bowel habit-specific alterations have not been elucidated. In this cross-sectional study, we apply a systems biology approach to characterize BGM patterns related to predominant bowel habit. Fecal samples and resting state fMRI were obtained from 102 premenopausal women (36 constipation-predominant IBS (IBS-C), 27 diarrhea-predominant IBS (IBS-D), 39 healthy controls (HCs)). Data integration analysis using latent components (DIABLO) was used to integrate data from the phenome, microbiome, metabolome, and resting-state connectome to predict HCs vs IBS-C vs IBS-D. Bloating and visceral sensitivity, distinguishing IBS from HC, were negatively associated with beneficial microbes and connectivity involving the orbitofrontal cortex. This suggests that gut interactions may generate aberrant central autonomic and descending pain pathways in IBS. The connection between IBS symptom duration, key microbes, and caudate connectivity may provide mechanistic insight to the chronicity of pain in IBS. Compared to IBS-C and HCs, IBS-D had higher levels of many key metabolites including tryptophan and phenylalanine, and increased connectivity between the sensorimotor and default mode networks; thus, suggestingan influence on diarrhea, self-related thoughts, and pain perception in IBS-D ('bottom-up' mechanism). IBS-C's microbiome and metabolome resembled HCs, but IBS-C had increased connectivity in the default mode and salience networks compared to IBS-D, which may indicate importance of visceral signals, suggesting a more 'top-down' BGM pathophysiology. These BGM characteristics highlight possible mechanistic differences for variations in the IBS bowel habit phenome. This article is part of the Special Issue on 'Microbiome & the Brain: Mechanisms & Maladies'.

Sex-Specific Response of the Brain Free Oxylipin Profile to Soluble Epoxide Hydrolase Inhibition

Oxylipins are the oxidation products of polyunsaturated fatty acids and have been implicated in neurodegenerative disorders, including dementia. Soluble epoxide hydrolase (sEH) converts epoxy-fatty acids to their corresponding diols, is found in the brain, and its inhibition is a treatment target for dementia. In this study, male and female C57Bl/6J mice were treated with an sEH inhibitor (sEHI), trans-4-[4-(3-adamantan-1-yl-ureido)-cyclohexyloxy]-benzoic acid (t-AUCB), for 12 weeks to comprehensively study the effect of sEH inhibition on the brain oxylipin profile, and modulation by sex. Ultra-high-performance liquid chromatography-tandem mass spectrometry was used to measure the profile of 53 free oxylipins in the brain. More oxylipins were modified by the inhibitor in males than in females (19 versus 3, respectively) and favored a more neuroprotective profile. Most were downstream of lipoxygenase and cytochrome p450 in males, and cyclooxygenase and lipoxygenase in females. The inhibitor-associated oxylipin changes were unrelated to serum insulin, glucose, cholesterol, or female estrous cycle. The inhibitor affected behavior and cognitive function as measured by open field and Y-maze tests in males, but not females. These findings are novel and important to our understanding of sexual dimorphism in the brain's response to sEHI and may help inform sex-specific treatment targets.

Plasma Apolipoprotein Concentrations Are Highly Altered in Severe Intensive Care Unit COVID-19 Patients: Preliminary Results from the LIPICOR Cohort Study

SARS-CoV-2 infection goes beyond acute pneumonia, as it also impacts lipid metabolism. Decreased HDL-C and LDL-C levels have been reported in patients with COVID-19. The lipid profile is a less robust biochemical marker than apolipoproteins, components of lipoproteins. However, the association of apolipoprotein levels during COVID-19 is not well described and understood. The objective of our study is to measure plasma levels of 14 apolipoproteins in patients with COVID-19 and to evaluate the relationships between apolipoprotein levels, severity factors and patient outcomes. From November to March 2021, 44 patients were recruited on admission to the intensive care unit because of COVID-19. Fourteen apolipoproteins and LCAT were measured by LC-MS/MS in plasma of 44 COVID-19 patients on admission to the ICU and 44 healthy control subjects. Absolute apolipoprotein concentrations were compared between COVID-19 patients and controls. Plasma apolipoproteins (Apo) A (I, II, IV), C(I, II), D, H, J and M and LCAT were lower in COVID-19 patients, whereas Apo E was higher. COVID-19 severity factors such as PaO2/FiO2 ratio, SO-FA score and CRP were correlated with certain apolipoproteins. Lower Apo B100 and LCAT levels were observed in non-survivors of COVID-19 versus survivors. To conclude, in this study, lipid and apolipoprotein profiles are altered in COVID-19 patients. Low Apo B100 and LCAT levels may be predictive of non-survival in COVID-19 patients.

Deafness gene screening based on a multilevel cascaded BPNN model

Sudden sensorineural hearing loss is a common and frequently occurring condition in otolaryngology. Existing studies have shown that sudden sensorineural hearing loss is closely associated with mutations in genes for inherited deafness. To identify these genes associated with deafness, researchers have mostly used biological experiments, which are accurate but time-consuming and laborious. In this paper, we proposed a computational method based on machine learning to predict deafness-associated genes. The model is based on several basic backpropagation neural networks (BPNNs), which were cascaded as multiple-level BPNN models. The cascaded BPNN model showed a stronger ability for screening deafness-associated genes than the conventional BPNN. A total of 211 of 214 deafness-associated genes from the deafness variant database (DVD v9.0) were used as positive data, and 2110 genes extracted from chromosomes were used as negative data to train our model. The test achieved a mean AUC higher than 0.98. Furthermore, to illustrate the predictive performance of the model for suspected deafness-associated genes, we analyzed the remaining 17,711 genes in the human genome and screened the 20 genes with the highest scores as highly suspected deafness-associated genes. Among these 20 predicted genes, three genes were mentioned as deafness-associated genes in the literature. The analysis showed that our approach has the potential to screen out highly suspected deafness-associated genes from a large number of genes, and our predictions could be valuable for future research and discovery of deafness-associated genes.

A novel approach to interrogating the effects of chemical warfare agent exposure using organ-on-a-chip technology and multiomic analysis

Organ-on-a-chip platforms are utilized in global bioanalytical and toxicological studies as a way to reduce materials and increase throughput as compared to in vivo based experiments. These platforms bridge the infrastructure and regulatory gaps between in vivo animal work and human systems, with models that exemplify active biological pathways. In conjunction with the advent of increased capabilities associated with next generation sequencing and mass spectrometry based '-omic' technologies, organ-on-a-chip platforms provide an excellent opportunity to investigate the global changes at multiple biological levels, including the transcriptome, proteome and metabolome. When investigated concurrently, a complete profile of cellular and regulatory perturbations can be characterized following treatment with specific agonists. In this study, global effects were observed and analyzed following liver chip exposure to the chemical warfare agent, VX. Even though the primary mechanism of action of VX (i.e. acetylcholinesterase inhibition) is well characterized, recent in vivo studies suggest additional protein binding partners that are implicated in metabolism and cellular energetic pathways. In addition, secondary toxicity associated with peripheral organ systems, especially in human tissues, is not well defined. Our results demonstrate the potential of utilizing an organ-on-a-chip platform as a surrogate system to traditional in vivo studies. This is realized by specifically indicating significant dysregulation of several cellular processes in response to VX exposure including but not limited to amino acid synthesis, drug metabolism, and energetics pathways.

Global Lipidome Profiling Revealed Multifaceted Role of Lipid Species in Hepatitis C Virus Replication, Assembly, and Host Antiviral Response

Hepatitis C virus (HCV) is a major human pathogen that requires a better understanding of its interaction with host cells. There is a close association of HCV life cycle with host lipid metabolism. Lipid droplets (LDs) have been found to be crucial organelles that support HCV replication and virion assembly. In addition to their role in replication, LDs also have protein-mediated antiviral properties that are activated during HCV infection. Studies have shown that HCV replicates well in cholesterol and sphingolipid-rich membranes, but the ways in which HCV alters host cell lipid dynamics are not yet known. In this study, we performed a kinetic study to check the enrichment of LDs at different time points of HCV infection. Based on the LD enrichment results, we selected early and later time points of HCV infection for global lipidomic study. Early infection represents the window period for HCV sensing and host immune response while later infection represents the establishment of viral RNA replication, virion assembly, and egress. We identified the dynamic profile of lipid species at early and later time points of HCV infection by global lipidomic study using mass spectrometry. At early HCV infection, phosphatidylinositol phospholipids (PIPs), lysophosphatidic acid (LPA), triacyl glycerols (TAG), phosphatidylcholine (PC), and trihexosylceramides (Hex3Cer) were observed to be enriched. Similarly, free fatty acids (FFA), phosphatidylethanolamine (PE), N-acylphosphatidylethanolamines (NAPE), and tri acylglycerols were enriched at later time points of HCV infection. Lipids enriched at early time of infection may have role in HCV sensing, viral attachment, and immune response as LPA and PIPs are important for immune response and viral attachment, respectively. Moreover, lipid species observed at later infection may contribute to HCV replication and virion assembly as PE, FFA, and triacylglycerols are known for the similar function. In conclusion, we identified lipid species that exhibited dynamic profile across early and later time points of HCV infection compared to mock cells, which could be therapeutically relevant in the design of more specific and effective anti-viral therapies.

Responses of the fall armyworm (Spodoptera frugiperda) to different host plants: Implications for its management strategy

BACKGROUND

The selection of suitable host plants for oviposition is critical for herbivorous insects to maximise survival of their offspring. Olfaction plays an important role in this process. However, little is known about how olfaction shapes the interaction between the fall armyworm (Spodoptera frugiperda) and host plants. In this study, we tested the hypothesis that olfaction guides the host selection process in the fall armyworm using oviposition and wind tunnel bioassays.

RESULTS

In no-choice and dual-choice assays, female moths oviposited on all seven host plants that were tested (maize, sorghum, wheat, bean, cowpea, tomato and cabbage). However, in multiple-choice assays, no eggs were deposited on cowpea and cabbage. We found that maize, sorghum and wheat were most preferred for oviposition, whereas cowpea was least preferred. Wind tunnel assays confirmed these divergent oviposition preferences, with maize, sorghum and wheat odours being the most attractive. Gas chromatography-mass spectrometry analysis followed by random forest classification identified terpenes as the potential host-plant attractants.

CONCLUSION

Our results improve our understanding of the chemical ecology of the fall armyworm and suggest that some of these host plants could offer potential for use in an intercropping strategy to manage S. frugiperda. © 2022 Society of Chemical Industry.

E. coli catheter-associated urinary tract infections are associated with distinctive virulence and biofilm gene determinants

Urinary catheterization facilitates urinary tract colonization by E. coli and increases infection risk. Here, we aimed to identify strain-specific characteristics associated with the transition from colonization to infection in catheterized patients. In a single-site study population, we compared E. coli isolates from patients with catheter-associated asymptomatic bacteriuria (CAASB) to those with catheter-associated urinary tract infection (CAUTI). CAUTI isolates were dominated by a phylotype B2 subclade containing the multidrug-resistant ST131 lineage relative to CAASB isolates, which were phylogenetically more diverse. A distinctive combination of virulence-associated genes was present in the CAUTI-associated B2 subclade. Catheter-associated biofilm formation was widespread among isolates and did not distinguish CAUTI from CAASB strains. Preincubation with CAASB strains could inhibit catheter colonization by multiple ST131 CAUTI isolates. Comparative genomic analysis identified a group of variable genes associated with high catheter biofilm formation present in both CAUTI and CAASB strains. Among these, ferric citrate transport (Fec) system genes were experimentally associated with enhanced catheter biofilm formation using reporter and fecA deletion strains. These results are consistent with a variable role for catheter biofilm formation in promoting CAUTI by ST131-like strains or resisting CAUTI by lower-risk strains that engage in niche exclusion.

Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection

Purpose

In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria.

Experimental design

Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier.

Results

Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy.

Conclusion

Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.

Hemoglobin normalization outperforms other methods for standardizing dried blood spot metabolomics: A comparative study

Dried blood spot (DBS) metabolomics has numerous applications in newborn health screening, exposomics, and biomonitoring of environmental chemicals in pregnant women and the elderly. However, accurate metabolite quantification is hindered by several challenges: notably the "hematocrit effect" and unknown blood-spotting volumes. Different techniques have been employed to overcome these issues but there is no consensus on the optimal normalization method for DBS metabolomics, and in some cases no normalization is used. We compared five normalization methods (hemoglobin (Hb), specific gravity (SG), protein, spot weight, potassium (K+)) to unnormalized data, and assessed sex-related differences in the DBS metabolome in 21 adults (group 1, n = 10 males, n = 11 females). The performance of each normalization method was evaluated using multiple criteria: (a) reduction of intragroup variation (pooled median absolute deviation, pooled estimate of variance, pooled coefficient of variation, NMDS and principal component analysis), (b) effect on differential metabolic analysis (dendrogram, heatmap, p-value distribution), and (c) influence on classification accuracy (partial least squares discriminant analysis, sparse partial least squares discriminant analysis error rates, receiver operating curve, random forest out of bag error rate). Our results revealed that Hb normalization outperformed all the other methods based on the three criteria and 13 different parameters; the performance of Hb was further demonstrated in an independent group of DBS from 18 neonates (group 2, n = 9 males, n = 9 females). Furthermore, we showed that SG and Hb are correlated in adults (rs = 0.86, p < 0.001), and validated this relationship in an independent group of 18 neonates and infants (group 3) (rs = 0.84, p < 0.001). Using the equation, SG = -0.4814Hb2 + 2.44Hb + 0.005, SG can be used as a surrogate for normalization by Hb. This is the first comparative study to concurrently evaluate multiple normalization methods for DBS metabolomics which will serve as a robust methodological platform for future environmental epidemiological studies.

Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer's Disease

BACKGROUND

DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction.

OBJECTIVE

Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers.

METHODS

EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points.

RESULTS

EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOEɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Effect of two milk supplements and two ways of administration on growth performance, welfare and fecal microbial ecology of suckling piglets

Introduction

The aim of this study was to evaluate the effect of two MS formulas, DanMilk™ (AB Neo, Denmark) (MS1) and Neopigg^® RescueMilk (Provimi, Netherlands) (MS2) administered manually and to compare two ways of administration (manual vs automatic) of MS1 on growth performance, health, fecal microbial profile, behavior, and skin lesions of piglets during suckling and post-weaning.

Methods

Forty litters (528 piglets) were divided into 4 groups: 1) Control group receiving no MS (CON); 2) MS1 administered automatically (A-MS1); 3) MS1 administered manually (Ma-MS1) 4) MS2 administered manually (Ma-MS2). All groups had access to sow milk and creep feed. On day 5 after birth (d0), litters were equalized (13.2 piglets/litter ± 0.8 SD), thereafter no cross-fostering was allowed. Piglets were weighed at day 5 after birth (d0), at the end of milk supplementation (d14), at weaning (d21 of the trial, 26 days of age) and ten days post-weaning (d31). Piglet welfare was assessed using behavioral and lesion measures at d4 and d10. Feces were collected at d14 and d21.

Results and discussion

During the suckling period, A-MS1 had lowest mortality (p < 0.05), while Ma-MS1 had lower mortality compared with CON and Ma-MS2 (p < 0.05). Negative social behavior at d4, was more frequent in MS groups (A-MS1, Ma-MS1, Ma-MS2) compared to CON group (p = 0.03). Growth performance and lesion prevalence were not affected by MS provision. During lactation, Ma-MS2 group had a higher percentage of piglets not eating during suckling at d18 compared with Ma-MS1 (p = 0.03). MS1 increased microbial diversity compared with CON at d14 (Chao1, p = 0.02; Shannon, p = 0.03) and compared with CON (Shannon, p < 0.05; InvSimpson, p = 0.01) and Ma-MS2 (Chao1, p < 0.05; Shannon, p = 0.05, InvSimpson p = 0.01) at d21. Groups that received MS1 were characterized by genera producing short-chain fatty acids (SCFAs), i.e., Lachnospiraceae (A-MS1) and Oscillospiraceae (Ma-MS1). MS composition and availability can contribute to reduce piglet's mortality during the suckling phase and can also affect intestinal microbiota by favoring the presence of SCFAs producing bacteria.

Complementarity determining regions in SARS-CoV-2 hybrid immunity

Pre-vaccination SARS-CoV-2 infection can boost protection elicited by COVID-19 vaccination and post-vaccination breakthrough SARS-CoV-2 infection can boost existing immunity conferred by COVID-19 vaccination. Such 'hybrid immunity' is effective against SARS-CoV-2 variants. In order to understand 'hybrid immunity' at the molecular level we studied the complementarity determining regions (CDR) of anti-RBD (receptor binding domain) antibodies isolated from individuals with 'hybrid immunity' as well as from 'naive' (not SARS-CoV-2 infected) vaccinated individuals. CDR analysis was done by liquid chromatography/mass spectrometry-mass spectrometry. Principal component analysis and partial least square differential analysis showed that COVID-19 vaccinated people share CDR profiles and that pre-vaccination SARS-CoV-2 infection or breakthrough infection further shape the CDR profile, with a CDR profile in hybrid immunity that clustered away from the CDR profile in vaccinated people without infection. Thus, our results show a CDR profile in hybrid immunity that is distinct from the vaccination-induced CDR profile.

Multivariate Analysis with the R Package mixOmics

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Machine learning and analysis of genomic diversity of "Candidatus Liberibacter asiaticus" strains from 20 citrus production states in Mexico

BACKGROUND

Huanglongbing (HLB, yellow shoot disease) is a highly destructive citrus disease associated with a nonculturable bacterium, "Candidatus Liberibacter asiaticus" (CLas), which is transmitted by Asian citrus psyllid (ACP, Diaphorina citri). In Mexico, HLB was first reported in Tizimin, Yucatán, in 2009 and is now endemic in 351 municipalities of 25 states. Understanding the population diversity of CLas is critical for HLB management. Current CLas diversity research is exclusively based on analysis of the bacterial genome, which composed two regions, chromosome (> 1,000 genes) and prophage (about 40 genes).

METHODS AND RESULTS

In this study, 40 CLas-infected ACP samples from 20 states in Mexico were collected. CLas was detected and confirmed by PCR assays. A prophage gene(terL)-based typing system (TTS) divided the Mexican CLas strains into two groups: Term-G including four strains from Yucatán and Chiapas, as well as strain psy62 from Florida, USA, and Term-A included all other 36 Mexican strains, as well as strain AHCA1 from California, USA. CLas diversity was further evaluated to include all chromosomal and prophage genes assisted by using machine learning (ML) tools to resolve multidimensional data handling issues. A Term-G strain (YTMX) and a Term-A strain (BCSMX) were sequenced and analyzed. The two Mexican genome sequences along with the CLas genome sequences available in GenBank were studied. An unsupervised ML was implemented through principal component analysis (PCA) on average nucleotide identities (ANIs) of CLas whole genome sequences; And a supervised ML was implemented through sparse partial least squares discriminant analysis (sPLS-DA) on single nucleotide polymorphisms (SNPs) of coding genes of CLas guided by the TTS. Two CLas Geno-groups, Geno-group 1 that extended Term-A and Geno-group 2 that extended Term-G, were established.

CONCLUSIONS

This study concluded that: 1) there were at least two different introductions of CLas into Mexico; 2) CLas strains between Mexico and USA are closely related; and 3) The two Geno-groups provide the basis for future CLas subspecies research.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling

BACKGROUND

Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.

METHODS

In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.

RESULTS

The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.

A guide to multi-omics data collection and integration for translational medicine

The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.

Maternal obesity alters offspring liver and skeletal muscle metabolism in early post-puberty despite maintaining a normal post-weaning dietary lifestyle

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age-related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well-established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post-pubertal offspring (3-6.5 y) of MO mothers (n = 19) and from control animals born to mothers fed a standard diet (CON, n = 13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography-mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue-specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue-specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age-related metabolic diseases later in life.

Multi-omics to predict changes during cold pressor test

Background

The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each ‘omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least squares [PLS] regression analysis) and all ‘omic levels were combined using an integrative multi-omics approach, all using the paired-sample design.

Results

We showed that PCA was not able to discriminate time points, while PLS did significantly distinguish time points using metabolomics and/or transcriptomic data, but not using conventional physiological measures. Transcriptomic and metabolomic data revealed at feature-, systems- and integrative- level biologically relevant processes involved during CPT, e.g. lipid metabolism and stress response.

Conclusion

Multi-omics strategies have a great potential in pain research, both at feature- and systems- level. Therefore, they should be exploited in intervention studies, such as pain provocation tests, to gain knowledge on the biological mechanisms involved in complex traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08981-z.

Increased Rice Susceptibility to Rice Blast Is Related to Post-Flowering Nitrogen Assimilation Efficiency

Reducing nitrogen leaching and nitrous oxide emissions with the goal of more sustainability in agriculture implies better identification and characterization of the different patterns in nitrogen use efficiency by crops. However, a change in the ability of varieties to use nitrogen resources could also change the access to nutrient resources for a foliar pathogen such as rice blast and lead to an increase in the susceptibility of these varieties. This study focuses on the pre- and post-floral biomass accumulation and nitrogen uptake and utilization of ten temperate japonica rice genotypes grown in controlled conditions, and the relationship of these traits with molecular markers and susceptibility to rice blast disease. After flowering, the ten varieties displayed diversity in nitrogen uptake and remobilization. Surprisingly, post-floral nitrogen uptake was correlated with higher susceptibility to rice blast, particularly in plants fertilized with nitrogen. This increase in susceptibility is associated with a particular metabolite profile in the upper leavers of these varieties.

Plasma Metabolomic and Lipidomic Profiling of Metabolic Dysfunction-Associated Fatty Liver Disease in Humans Using an Untargeted Multiplatform Approach

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies.

Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.

Metabolic Characteristics of Porcine LA-MRSA CC398 and CC9 Isolates from Germany and China via Biolog Phenotype MicroArrayTM

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is an important zoonotic pathogen, often multi-resistant to antimicrobial agents. Among swine, LA-MRSA of clonal complex (CC) 398 dominates in Europe, Australia and the Americas, while LA-MRSA-CC9 is the main epidemic lineage in Asia. Here, we comparatively investigated the metabolic properties of rare and widespread porcine LA-MRSA isolates from Germany and China using Biolog Phenotype MicroArray technology to evaluate if metabolic variations could have played a role in the development of two different epidemic LA-MRSA clones in swine. Overall, we were able to characterize the isolates' metabolic profiles and show their tolerance to varying environmental conditions. Sparse partial least squares discriminant analysis (sPLS-DA) supported the detection of the most informative substrates and/or conditions that revealed metabolic differences between the LA-MRSA lineages. The Chinese LA-MRSA-CC9 isolates displayed unique characteristics, such as a consistently delayed onset of cellular respiration, and increased, reduced or absent usage of several nutrients. These possibly unfavorable metabolic properties might promote the ongoing gradual replacement of the current epidemic LA-MRSA-CC9 clone in China with the emerging LA-MRSA-CC398 lineage through livestock trade and occupational exposure. Due to the enhanced pathogenicity of the LA-MRSA-CC398 clone, the public health risk posed by LA-MRSA from swine might increase further.

Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples

Fecal samples are frequently used to characterize bacterial populations of the gastrointestinal tract. A protocol is provided to profile gut bacterial populations using rodent fecal samples. We describe the optimal procedures for collecting rodent fecal samples, isolating genomic DNA, 16S rRNA gene V4 region sequencing, and bioinformatic analyses. This protocol includes detailed instructions and example outputs to ensure accurate, reproducible results and data visualization. Comprehensive troubleshooting and limitation sections address technical and statistical issues that may arise when profiling microbiota. For complete details on the use and execution of this protocol, please refer to Gubert et al. (2022).

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.

Daphnia as a Sentinel Species for Environmental Health Protection: A Perspective on Biomonitoring and Bioremediation of Chemical Pollution

Despite available technology and the knowledge that chemical pollution damages human and ecosystem health, chemical pollution remains rampant, ineffectively monitored, rarely prevented, and only occasionally mitigated. We present a framework that helps address current major challenges in the monitoring and assessment of chemical pollution by broadening the use of the sentinel species Daphnia as a diagnostic agent of water pollution. And where prevention has failed, we propose the application of Daphnia as a bioremediation agent to help reduce hazards from chemical mixtures in the environment. By applying "omics" technologies to Daphnia exposed to real-world ambient chemical mixtures, we show improvements at detecting bioactive components of chemical mixtures, determining the potential effects of untested chemicals within mixtures, and identifying targets of toxicity. We also show that using Daphnia strains that naturally adapted to chemical pollution as removal agents of ambient chemical mixtures can sustainably improve environmental health protection. Expanding the use of Daphnia beyond its current applications in regulatory toxicology has the potential to improve both the assessment and the remediation of environmental pollution.

Unique Metabolic Profiles Associate with Gestational Diabetes and Ethnicity in Low- and High-Risk Women Living in the UK

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common global pregnancy complication; however, prevalence varies substantially between ethnicities, with South Asians (SAs) experiencing up to 3 times the risk of the disease compared with white Europeans (WEs). Factors driving this discrepancy are unclear, although the metabolome is of great interest as GDM is known to be characterized by metabolic dysregulation.

OBJECTIVES

The primary aim was to characterize and compare the metabolic profiles of GDM in SA and WE women (at <28 wk of gestation) from the Born in Bradford (BIB) prospective birth cohort in the United Kingdom.

METHODS

In total, 146 fasting serum metabolites, from 2,668 pregnant WE and 2,671 pregnant SA women (average BMI 26.2 kg/m2, average age 27.3 y) were analyzed using partial least squares discriminatory analyses to characterize GDM status. Linear associations between metabolite values and post-oral glucose tolerance test measures of dysglycemia (fasting glucose and 2 h postglucose) were also examined.

RESULTS

Seven metabolites associated with GDM status in both ethnicities (variable importance in projection ≥1), whereas 6 additional metabolites associated with GDM only in WE women. Unique metabolic profiles were observed in healthy-weight women who later developed GDM, with distinct metabolite patterns identified by ethnicity and BMI status. Of the metabolite values analyzed in relation to dysglycemia, lactate, histidine, apolipoprotein A1, HDL cholesterol, and HDL2 cholesterol associated with decreased glucose concentration, whereas DHA and the diameter of very low-density lipoprotein particles (nm) associated with increased glucose concertation in WE women, and in SAs, albumin alone associated with decreased glucose concentration.

CONCLUSIONS

This study shows that the metabolic risk profile for GDM differs between WE and SA women enrolled in BiB in the United Kingdom. This suggests that etiology of the disease differs between ethnic groups and that ethnic-appropriate prevention strategies may be beneficial.

Alterations to the duodenal microbiota are linked to gastric emptying and symptoms in functional dyspepsia

OBJECTIVE

Functional dyspepsia (FD) is a complex disorder, with debilitating epigastric symptoms. Evidence suggests alterations in gastrointestinal (GI) motility, visceral hypersensitivity, permeability and low-level immune activation in the duodenum may play a role. However, we still have a relatively poor understanding of how these factors interact to precipitate the onset of FD symptoms which are frequently meal related. The duodenal microbiota, in combination with specific dietary substrates, may be important mediators in disease pathophysiology; however, these interlinked factors have not been thoroughly investigated in FD.

DESIGN

Eighty-six individuals (56 FD, 30 controls) undergoing endoscopy were consecutively recruited and underwent detailed clinical assessment, including upper GI symptoms, gastric emptying and dietary assessment. Duodenal biopsies were obtained aseptically, and the mucosa-associated microbiota (MAM) analysed via 16S rRNA gene amplicon sequencing.

RESULTS

The relative abundances of predominant members of the Firmicutes, Bacteroidota and Fusobacteriota phyla were linked to symptom burden in FD. Inverse relationships between the relative abundances of Streptococcus and Prevotella, and the relative abundance of Veillonella spp with gastric emptying time, were also observed. No significant differences in long-term nutrient intake or diet quality were found between FD and controls, and there appeared to be limited association between habitual diet and duodenal MAM profiles.

CONCLUSION

This study suggests a link between the duodenal MAM, gastric emptying and FD symptoms, and this is largely independent of long-term dietary intake.

Quantitative modeling of human liver reveals dysregulation of glycosphingolipid pathways in nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent disease that is associated with multiple metabolic disturbances, yet the metabolic pathways underlying its progression are poorly understood. Here, we studied metabolic pathways of the human liver across the full histological spectrum of NAFLD. We analyzed whole liver tissue transcriptomics and serum metabolomics data obtained from a large, prospectively enrolled cohort of 206 histologically characterized patients derived from the European NAFLD Registry and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. We identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids, and their link with complex glycosaminoglycans in advanced fibrosis. Furthermore, we derived GEMs and identified metabolic signatures of three common NAFLD-associated gene variants (PNPLA3, TM6SF2, and HSD17B13). The study demonstrates dysregulated liver metabolic pathways which may contribute to the progression of NAFLD.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

Multiscale study of the oral and gut environments in children with high- and low-threshold peanut allergy

BACKGROUND

The oral and gut microbiomes have each been associated with food allergy status. Within food allergy, they may also influence reaction thresholds.

OBJECTIVE

Our aim was to identify oral and gut microbiota associated with reaction thresholds in peanut allergy.

METHODS

A total of 59 children aged 4 to 14 years with suspected peanut allergy underwent double-blind, placebo-controlled food challenge to peanut. Those children who reacted at the 300-mg or higher dose of peanut were classified as high-threshold (HT), those who reacted to lower doses were classified as low-threshold (LT), and those children who did not react were classified as not peanut allergic (NPA). Saliva and stool samples collected before challenge underwent DNA isolation followed by 16S rRNA sequencing and short-chain fatty acid measurement.

RESULTS

The 59 participants included 38 HT children and 13 LT children. Saliva microbiome α-diversity (Shannon index) was higher in LT children (P = .017). We identified saliva and stool microbiota that distinguished HT children from LT children, including oral Veillonella nakazawae (amplicon sequence variant 1979), which was more abundant in the HT group than in the LT group (false discovery rate [FDR] = 0.025), and gut Bacteroides thetaiotaomicron (amplicon sequence variant 6829), which was less abundant in HT children than in LT children (FDR = 0.039). Comparison with NPA children revealed consistent ordinal trends between these discriminating species and reaction thresholds. Importantly, many of these threshold-associated species were also correlated with short-chain fatty acid levels at the respective body sites, including between oral V nakazawae and oral butyrate (r = 0.57; FDR = 0.049).

CONCLUSION

Findings from this multiscale study raise the possibility of microbial therapeutics to increase reaction thresholds in children with food allergy.

Urinary metabolomics to develop predictors for pediatric acute kidney injury

BACKGROUND

Acute kidney injury (AKI) is characterized by an abrupt decline in glomerular filtration rate (GFR). We sought to identify separate early urinary metabolomic signatures at AKI onset (with-AKI) and prior to onset of functional impairment (pre-AKI).

METHODS

Pre-AKI (n=15), AKI (n=22), and respective controls (n=30) from two prospective PICU cohort studies provided urine samples which were analyzed by GC-MS and DI-MS mass spectrometry (193 metabolites). The cohort (n=58) was 8.7±6.4 years old and 66% male. AKI patients had longer PICU stays, higher PRISM scores, vasopressors requirement, and respiratory diagnosis and less commonly had trauma or post-operative diagnosis. Urine was collected within 2-3 days after admission and daily until day 5 or 14.

RESULTS

The metabolite classifiers for pre-AKI samples (1.5±1.1 days prior to AKI onset) had a cross-validated area under receiver operator curve (AUC)=0.93 (95%CI 0.85-1.0); with-AKI samples had an AUC=0.94 (95%CI 0.87-1.0). A parsimonious pre-AKI classifier with 13 metabolites was similarly robust (AUC=0.96, 95%CI 0.89-1.0). Both classifiers were similar and showed modest correlation of high-ranking metabolites (tau=0.47, p<0.001).

CONCLUSIONS

This exploratory study demonstrates the potential of a urine metabolite classifier to detect AKI-risk in pediatric populations earlier than the current standard of diagnosis with the need for external validation. A higher resolution version of the Graphical abstract is available as Supplementary information with inner reference to ESM for GA.

Influence of sire fertility status on conceptus-induced transcriptomic response of the bovine endometrium

The aim was to examine the effect of sire fertility status on conceptus-induced changes in the bovine endometrial transcriptome. To generate elongated conceptuses, Day 7 blastocysts produced in vitro using frozen-thawed sperm from Holstein Friesian bulls (3 High fertility, HF and 3 Low fertility, LF) were transferred in groups of 5–10 into synchronized heifers (n = 7 heifers per bull) and recovered following slaughter on Day 15. Day 15 endometrial explants recovered from the uterine horn ipsilateral to the corpus luteum were recovered from synchronized cyclic heifers (n = 4). Explants from each heifer were co-cultured for 6 h in RPMI medium alone (Control) or with 100 ng/ml ovine recombinant interferon tau (IFNT) or with a single conceptus from each HF or LF bull. After 6 h, explants were snap frozen and stored at −80°C. Extracted mRNA was subjected to RNA-seq and the resulting data were analyzed with R software. The numbers of differentially expressed genes (DEG; FDR&lt;0.05) were: HF vs. Control: 956; LF vs. Control: 1021; IFNT vs. Control: 1301; HF vs. LF: 2. Unsurprisingly, the majority of DEG (658) were common to all comparisons and were related to IFNT-induced changes in the endometrium. Prior to applying the adjusted p-value, there were 700 DEG between HF and LF, with 191 and 509 genes more expressed in HF or LF, respectively (p &lt; 0.05). Overrepresentation analysis of KEGG pathways (FDR&lt;0.05), revealed that DEG with higher expression in LF were involved in cell cycle and proteolysis, while those upregulated DEG by HF conceptuses were strongly associated with immune process pathways, such as TNF, NF-kappa B, cytokine-cytokine receptor interaction, and TLR signaling. These pathways were also enriched by DEG upregulated by IFNT compared to the Control. Furthermore, only the HF, and not the LF group, affected the expression of most genes in these pathways (p &lt; 0.05) according to a negative binomial regression model. Finally, a weighted gene co-expression network analysis revealed two clusters of co-expressed genes associated with the HF conceptuses (p &lt; 0.05), which were also enriched for the aforementioned pathways. In conclusion, HF conceptuses, similar to IFNT treatment, stimulated multiple pathways involved in immune response, which were apparently not affected by LF conceptuses.

MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning

Human microbiome consists of trillions of microorganisms. Microbiota can modulate the host physiology through molecule and metabolite interactions. Integrating microbiome and metabolomics data have the potential to predict different diseases more accurately. Yet, most datasets only measure microbiome data but without paired metabolome data. Here, we propose a novel integrative modeling framework, Microbiome-based Supervised Contrastive Learning Framework (MB-SupCon). MB-SupCon integrates microbiome and metabolome data to generate microbiome embeddings, which can be used to improve the prediction accuracy in datasets that only measure microbiome data. As a proof of concept, we applied MB-SupCon on 720 samples with paired 16S microbiome data and metabolomics data from patients with type 2 diabetes. MB-SupCon outperformed existing prediction methods and achieved high average prediction accuracies for insulin resistance status (84.62%), sex (78.98%), and race (80.04%). Moreover, the microbiome embeddings form separable clusters for different covariate groups in the lower-dimensional space, which enhances data visualization. We also applied MB-SupCon on a large inflammatory bowel disease study and observed similar advantages. Thus, MB-SupCon could be broadly applicable to improve microbiome prediction models in multi-omics disease studies.

diaPASEF Proteomics and Feature Selection for the Description of Sputum Proteome Profiles in a Cohort of Different Subtypes of Lung Cancer Patients and Controls

The high mortality, the presence of an initial asymptomatic stage and the fact that diagnosis in early stages reduces mortality justify the implementation of screening programs in the populations at risk of lung cancer. It is imperative to develop less aggressive methods that can complement existing diagnosis technologies. In this study, we aimed to identify lung cancer protein biomarkers and pathways affected in sputum samples, using the recently developed diaPASEF mass spectrometry (MS) acquisition mode. The sputum proteome of lung cancer cases and controls was analyzed through nano-HPLC–MS using the diaPASEF mode. For functional analysis, the results from differential expression analysis were further analyzed in the STRING platform, and feature selection was performed using sparse partial least squares discriminant analysis (sPLS-DA). Our results showed an activation of inflammation, with an alteration of pathways and processes related to acute-phase, complement, and immune responses. The resulting sPLS-DA model separated between case and control groups with high levels of sensitivity and specificity. In conclusion, we showed how new-generation proteomics can be used to detect potential biomarkers in sputum samples, and ultimately to discriminate patients from controls and even to help to differentiate between different cancer subtypes.

Mucosal Immune Profiles Associated with Diarrheal Disease Severity in Shigella- and Enteropathogenic Escherichia coli-Infected Children Enrolled in the Global Enteric Multicenter Study

Enteropathogenic Escherichia coli (EPEC) and Shigella are etiologic agents of diarrhea in children <5 years old living in resource-poor countries. Repeated bouts of infection lead to lifelong morbidity and even death. The goal of this study was to characterize local mucosal immune responses in Shigella- and EPEC-infected children <5 years of age with moderate to severe diarrhea (MSD) enrolled in the Global Enteric Multicenter Study (GEMS). We hypothesized that infection with each of these pathogens would induce distinct gut mucosal immune profiles indicative of disease etiology and severity. To test this hypothesis, innate and adaptive immune markers were measured in stools from children with diarrhea due to EPEC, Shigella, or other organisms and in children who had no diarrhea. Shigella-positive diarrhea evoked robust proinflammatory and T_H1/T_H2 cytokine responses compared to diarrhea caused by EPEC or other organisms, with the exception of interleukin 5 (IL-5), which was associated with EPEC infection. The presence of IL-1β, IL-4, IL-16, and tumor necrosis factor beta (TNF-β) was associated with the absence of dysentery. EPEC-positive diarrhea evoked high levels of IL-1β, vascular endothelial growth factor (VEGF), and IL-10. Granulocyte-macrophage colony-stimulating factor (GM-CSF) had opposing roles in disease severity, being associated with absence of diarrhea in EPEC-infected children and with dysenteric Shigella infection. High levels of antigen-specific antibodies were detected in the controls and children with Shigella without dysentery, which suggests a protective role against severe disease. In summary, this study identified distinct local immune responses associated with two clinically relevant diarrheagenic pathogens, Shigella and EPEC, in children and identified protective immune phenotypes that can inform the development of preventive measures.

Low-dose interleukin-2 shapes a tolerogenic gut microbiota that improves autoimmunity and gut inflammation

Gut microbiota dysbiosis is associated with inflammatory bowel diseases and with cardiometabolic, neurological, and autoimmune diseases. Gut microbiota composition has a direct effect on the immune system, and vice versa, and it has a particular effect on Treg homeostasis. Low-dose IL-2 (IL-2_LD) stimulates Tregs and is a promising treatment for autoimmune and inflammatory diseases. We aimed to evaluate the impact of IL-2_LD on gut microbiota and correlatively on the immune system. We used 16S ribosomal RNA profiling and metagenomics to characterize gut microbiota of mice and humans treated or not with IL-2_LD. We performed fecal microbiota transplantation (FMT) from IL-2_LD–treated to naive recipient mice and evaluated its effects in models of gut inflammation and diabetes. IL-2_LD markedly affected gut microbiota composition in mice and humans. Transfer of an IL-2–tuned microbiota by FMT protected C57BL/6J mice from dextran sulfate sodium–induced colitis and prevented diabetes in NOD mice. Metagenomic analyses highlighted a role for several species affected by IL-2_LD and for microbial pathways involved in the biosynthesis of amino acids, short-chain fatty acids, and L-arginine. Our results demonstrate that IL-2_LD induced changes in gut microbiota that are involved in the immunoregulatory effects of IL-2_LD and suggest a crosstalk between Tregs and gut microbiota. These results provide potentially novel insight for understanding the mode of action of Treg-directed therapies.

Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

A Lactobacilli diet that confers MRSA resistance causes amino acid depletion and increased antioxidant levels in the C. elegans host

Probiotic bacteria are increasingly popular as dietary supplements and have the potential as alternatives to traditional antibiotics. We have recently shown that pretreatment with Lactobacillus spp. Lb21 increases the life span of C. elegans and results in resistance toward pathogenic methicillin-resistant Staphylococcus aureus (MRSA). The Lb21-mediated MRSA resistance is dependent on the DBL-1 ligand of the TGF-β signaling pathway. However, the underlying changes at the metabolite level are not understood which limits the application of probiotic bacteria as timely alternatives to traditional antibiotics. In this study, we have performed untargeted nuclear magnetic resonance-based metabolic profiling. We report the metabolomes of Lactobacillus spp. Lb21 and control E. coli OP50 bacteria as well as the nematode-host metabolomes after feeding with these diets. We identify 48 metabolites in the bacteria samples and 51 metabolites in the nematode samples and 63 across all samples. Compared to the control diet, the Lactobacilli pretreatment significantly alters the metabolic profile of the worms. Through sparse Partial Least Squares discriminant analyses, we identify the 20 most important metabolites distinguishing probiotics from the regular OP50 food and worms fed the two different bacterial diets, respectively. Among the changed metabolites, we find lower levels of essential amino acids as well as increased levels of the antioxidants, ascorbate, and glutathione. Since the probiotic diet offers significant protection against MRSA, these metabolites could provide novel ways of combatting MRSA infections.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

A diagnostic model for overweight and obesity from untargeted urine metabolomics of soldiers

Soldiers in active military service need optimal physical fitness for successfully carrying out their operations. Therefore, their health status is regularly checked by army doctors. These inspections include physical parameters such as the body-mass index (BMI), functional tests, and biochemical studies. If a medical exam reveals an individual's excess weight, further examinations are made, and corrective actions for weight lowering are initiated. The collection of urine is non-invasive and therefore attractive for frequent metabolic screening. We compared the chemical profiles of urinary samples of 146 normal weight, excess weight, and obese soldiers of the Mexican Army, using untargeted metabolomics with liquid chromatography coupled to high-resolution mass spectrometry (LC-MS). In combination with data mining, statistical and metabolic pathway analyses suggest increased S-adenosyl-L-methionine (SAM) levels and changes of amino acid metabolites as important variables for overfeeding. We will use these potential biomarkers for the ongoing metabolic monitoring of soldiers in active service. In addition, after validation of our results, we will develop biochemical screening tests that are also suitable for civil applications.

Cutaneous and acral melanoma cross-OMICs reveals prognostic cancer drivers associated with pathobiology and ultraviolet exposure

Ultraviolet radiation (UV) is causally linked to cutaneous melanoma, yet the underlying epigenetic mechanisms, known as molecular sensors of exposure, have not been characterized in clinical biospecimens. Here, we integrate clinical, epigenome (DNA methylome), genome and transcriptome profiling of 112 cutaneous melanoma from two multi-ethnic cohorts. We identify UV-related alterations in regulatory regions and immunological pathways, with multi-OMICs cancer driver potential affecting patient survival. TAPBP, the top gene, is critically involved in immune function and encompasses several UV-altered methylation sites that were validated by targeted sequencing, providing cost-effective opportunities for clinical application. The DNA methylome also reveals non UV-related aberrations underlying pathological differences between the cutaneous and 17 acral melanomas. Unsupervised epigenomic mapping demonstrated that non UV-mutant cutaneous melanoma more closely resembles acral rather than UV-exposed cutaneous melanoma, with the latter showing better patient prognosis than the other two forms. These gene-environment interactions reveal translationally impactful mechanisms in melanomagenesis.

TNFR2 Costimulation Differentially Impacts Regulatory and Conventional CD4+ T-Cell Metabolism

CD4⁺ conventional T cells (Tconvs) mediate adaptive immune responses, whereas regulatory T cells (Tregs) suppress those responses to safeguard the body from autoimmunity and inflammatory diseases. The opposing activities of Tconvs and Tregs depend on the stage of the immune response and their environment, with an orchestrating role for cytokine- and costimulatory receptors. Nutrient availability also impacts T-cell functionality via metabolic and biosynthetic processes that are largely unexplored. Many data argue that costimulation by Tumor Necrosis Factor Receptor 2 (TNFR2) favors support of Treg over Tconv responses and therefore TNFR2 is a key clinical target. Here, we review the pertinent literature on this topic and highlight the newly identified role of TNFR2 as a metabolic regulator for thymus-derived (t)Tregs. We present novel transcriptomic and metabolomic data that show the differential impact of TNFR2 on Tconv and tTreg gene expression and reveal distinct metabolic impact on both cell types.

Slightly different metabolomic profiles are associated with high or low weight duck foie gras

Understanding the evolution of fatty liver metabolism of ducks is a recurrent issue for researchers and industry. Indeed, the increase in weight during the overfeeding period leads to an important change in the liver metabolism. However, liver weight is highly variable at the end of overfeeding within a batch of animals reared, force-fed and slaughtered in the same way. For this study, we performed a proton nuclear magnetic resonance (¹H-NMR) analysis on two classes of fatty liver samples, called low-weight liver (weights between 550 and 599 g) and high-weight liver (weights above 700 g). The aim of this study was to identify the differences in metabolism between two classes of liver weight (low and high). Firstly, the results suggested that increased liver weight is associated with higher glucose uptake leading to greater lipid synthesis. Secondly, this increase is probably also due to a decline in the level of export of triglycerides from the liver by maintaining them at high hepatic concentration levels, but also of hepatic cholesterol. Finally, the increase in liver weight could lead to a significant decrease in the efficiency of aerobic energy metabolism associated with a significant increase in the level of oxidative stress. However, all these hypotheses will have to be confirmed in the future, by studies on plasma levels and specific assays to validate these results.

Alzheimer’s Disease-Related Genes Identified by Linking Spatial Patterns of Pathology and Gene Expression

Background

Alzheimer’s Disease (AD) is an age-related neurodegenerative disease with a poorly understood etiology, shown to be partly genetic. Glucose hypometabolism, extracellular Amyloid-beta (Aβ) deposition, and intracellular Tau deposition are cardinal features of AD and display characteristic spatial patterns in the brain. We hypothesize that regional differences in underlying gene expression confer either resistance or susceptibility to AD pathogenic processes and are associated with these spatial patterns. Data-driven methods for the identification of genes involved in AD pathogenesis complement hypothesis-driven approaches that reflect current theories about the disease. Here we present a data driven method for the identification of genes involved in AD pathogenesis based on comparing spatial patterns of normal gene expression to Positron Emission Tomography (PET) images of glucose hypometabolism, Aβ deposition, and Tau deposition.

Methods

We performed correlations between the cerebral cortex microarray samples from the six cognitively normal (CN) post-mortem Allen Human Brain Atlas (AHBA) specimens and PET FDG-18, AV-45, and AV-1451 tracer images from AD and CN participants in the Alzheimer’s Disease and Neuroimaging Initiative (ADNI) database. Correlation coefficients for each gene by each ADNI subject were then entered into a partial least squares discriminant analysis (PLS-DA) to determine sets that best classified the AD and CN groups. Pathway analysis via BioPlanet 2019 was then used to infer the function of implicated genes.

Results

We identified distinct sets of genes strongly associated with each PET modality. Pathway analyses implicated novel genes involved in mitochondrial function, and Notch signaling, as well as genes previously associated with AD.

Conclusion

Using an unbiased approach, we derived sets of genes with expression patterns spatially associated with FDG hypometabolism, Aβ deposition, and Tau deposition in AD. This methodology may complement population-based approaches for identifying the genetic underpinnings of AD.