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

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.

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.

Monogenic diabetes in adults: A multi-ancestry study reveals strong disparities in diagnosis rates and clinical presentation

AIM

Identification of monogenic diabetes (MgD) conveys benefits for patients' care. Algorithms for selecting the patients to be genetically tested have been established in EuroCaucasians, but not in non-EuroCaucasian individuals. We assessed the diagnosis rate, the phenotype of MgD, and the relevance of selection criteria, according to ancestry in patients referred for a suspected MgD.

METHODS

Seven genes (GCK, HNF1A, HNF4A, HNF1B, ABCC8, KCNJ11, INS) were analyzed in 1975 adult probands (42% non-EuroCaucasians), selected on the absence of diabetes autoantibodies and ≥2 of the following criteria: age ≤40 years and body mass index <30 kg/m² at diagnosis, and a family history of diabetes in ≥2 generations.

RESULTS

Pathogenic/likely pathogenic variants were identified in 6.2% of non-EuroCaucasian and 23.6% of EuroCaucasian patients (OR 0.21, [0.16-0.29]). Diagnosis rate was low in all non-EuroCaucasian subgroups (4.1-11.8%). Common causes of MgD (GCK, HNF1A, HNF4A), but not rare causes, were less frequent in non-EuroCaucasians than in EuroCaucasians (4.1%, vs. 21.1%, OR 0.16 [0.11-0.23]). Using ethnicity-specific body mass index cutoffs increased the diagnosis rate in several non-EuroCaucasian subgroups.

CONCLUSION

The diagnosis rate of MgD is low in non-EuroCaucasian patients, but may be improved by tailoring selection criteria according to patients'ancestry.

Metabolomic profiling of Spathaspora passalidarum fermentations reveals mechanisms that overcome hemicellulose hydrolysate inhibitors

Understanding the mechanisms involved in tolerance to inhibitors is the first step in developing robust yeasts for industrial second-generation ethanol (E2G) production. Here, we used ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) and MetaboAnalyst 4.0 for analysis of MS data to examine the changes in the metabolic profile of the yeast Spathaspora passalidarum during early fermentation of hemicellulosic hydrolysates containing high or low levels of inhibitors (referred to as control hydrolysate or CH and strategy hydrolysate or SH, respectively). During fermentation of SH, the maximum ethanol production was 16 g L^-1 with a yield of 0.28 g g^-1 and productivity of 0.22 g L^-1 h^-1, whereas maximum ethanol production in CH fermentation was 1.74 g L^-1 with a yield of 0.11 g g^-1 and productivity of 0.01 g L^-1 h^-1. The high level of inhibitors in CH induced complex physiological and biochemical responses related to stress tolerance in S. passalidarum. This yeast converted compounds with aldehyde groups (hydroxymethylfurfural, furfural, 4-hydroxybenzaldehyde, syringaldehyde, and vanillin) into less toxic compounds, and inhibitors were found to reduce cell viability and ethanol production. Intracellularly, high levels of inhibitors altered the energy homeostasis and redox balance, resulting in lower levels of ATP and NADPH, while that of glycolytic, pentose phosphate, and tricarboxylic acid (TCA) cycle pathways were the most affected, being the catabolism of glucogenic amino acids, the main cellular response to inhibitor-induced stress. This metabolomic investigation reveals interesting targets for metabolic engineering of ethanologenic yeast strains tolerant against multiple inhibitors for E2G production. KEY POINTS: • Inhibitors in the hydrolysates affected the yeast's redox balance and energy status. • Inhibitors altered the glycolytic, pentose phosphate, TCA cycle and amino acid pathways. • S. passalidarum converted aldehyde groups into less toxic compounds.

Electroantennogram and machine learning reveal a volatile blend mediating avoidance behavior by Tuta absoluta females to a wild tomato plant

Tomato cultivation is threatened by the infestation of the nocturnal invasive tomato pinworm, Tuta absoluta. This study was based on field observations that a wild tomato plant, Solanum lycopersicum var. cerasiforme, grown in the Mount Kenya region, Kenya, is less attacked by T. absoluta, unlike the cultivated tomato plants like S. lycopersicum (var. Rambo F1). We hypothesized that the wild tomato plant may be actively avoided by gravid T. absoluta females because of the emission of repellent allelochemical constituents. Therefore, we compared infestation levels by the pest in field monocrops and intercrops of the two tomato genotypes, characterized the headspace volatiles, then determined the compounds detectable by the insect through gas chromatography-linked electroantennography (GC-EAG), and finally performed bioassays using a blend of four EAG-active compounds unique to the wild tomato. We found significant reductions in infestation levels in the monocrop of the wild tomato, and intercrops of wild and cultivated tomato plants compared to the monocrop of the cultivated tomato plant. Quantitative and qualitative differences were noted between volatiles of the wild and cultivated tomato plants, and between day and night volatile collections. The most discriminating compounds between the volatile treatments varied with the variable selection or machine learning methods used. In GC-EAG recordings, 16 compounds including hexanal, (Z)-3-hexenol, α-pinene, β-myrcene, α-phellandrene, β-phellandrene, (E)-β-ocimene, terpinolene, limonene oxide, camphor, citronellal, methyl salicylate, (E)-β-caryophyllene, and others tentatively identified as 3,7,7-Trimethyl-1,3,5-cycloheptatriene, germacrene D and cis-carvenone oxide were detected by antennae of T. absoluta females. Among these EAG-active compounds, (Z)-3-hexenol, α-pinene, α-phellandrene, limonene oxide, camphor, citronellal, (E)-β-caryophyllene and β-phellandrene are in the top 5 discriminating compounds highlighted by the machine learning methods. A blend of (Z)-3-hexenol, camphor, citronellal and limonene oxide detected only in the wild tomato showed dose-dependent repellence to T. absoluta females in wind tunnel. This study provides some groundwork for exploiting the allelochemicals of the wild tomato in the development of novel integrated pest management approaches against T. absoluta.

The AKT1E17K Allele Promotes Breast Cancer in Mice

Simple Summary

The main finding reported in this manuscript is that the gain-of-function mutation AKT1E17K is a bona fide oncogene for mammary epithelium, being able to efficiently initiate breast cancer in mice. On the basis of high-molecular-weight cytokeratins expressed by AKT1E17K-derived tumors supported by additional integrative gene expression analysis these tumors resulted similar to human basal-like cancer, phenotypically and molecularly. These results indicate that the AKTE17K strain may represent an appropriate model of human basal-like breast cancer for the identification of novel therapies specific for this type of tumor.

Abstract

The gain-of-function mutation in the pleckstrin homology domain of AKT1 (AKT1E17K) occurs in lung and breast cancer. Through the use of human cellular models and of a AKT1E17K transgenic Cre-inducible murine strain (R26-AKT1E17K mice), we have demonstrated that AKT1E17K is a bona fide oncogene for lung epithelial cells. However, the role of AKT1E17K in breast cancer remains to be determined. Here, we report the generation and the characterization of a MMTV-CRE; R26-AKT1E17K mouse strain that expresses the mutant AKT1E17K allele in the mammary epithelium. We observed that AKT1E17K stimulates the development of mammary tumors classified as ductal adenocarcinoma of medium–high grade and presented a variety of proliferative alterations classified as adenosis with low-to-high grade dysplasia in the mammary epithelium. A subsequent immunohistochemical characterization suggested they were PR⁻/HER2⁻/ER⁺, basal-like and CK8⁻/CK10⁻/CK5⁺/CK14⁺. We also observed that, in parallel with an increased proliferation rate, tumors expressing mutant AKT1E17K presented an activation of the GSK3/cyclin D1 pathway in the mammary epithelium and cluster significantly with the human basal-like tumors. In conclusion, we demonstrate AKT1E17K is a bona fide oncogene that can initiate tumors at high efficiency in murine mammary epithelium in vivo.

Standardized Whole Blood Assay and Bead-Based Cytokine Profiling Reveal Commonalities and Diversity of the Response to Bacteria and TLR Ligands in Cattle

Recent developments in multiplex technologies enable the determination of a large nu\mber of soluble proteins such as cytokines in various biological samples. More than a one-by-one determination of the concentration of immune mediators, they permit the establishment of secretion profiles for a more accurate description of conditions related to infectious diseases or vaccination. Cytokine profiling has recently been made available for bovine species with the development of a Luminex^® technology-based 15-plex assay. Independently from the manufacturer, we evaluated the bovine cytokine/chemokine multiplex assay for limits of detection, recovery rate, and reproducibility. Furthermore, we assessed cytokine secretion in blood samples from 107 cows upon stimulation with heat-killed bacteria and TLR2/4 ligands compared to a null condition. Secretion patterns were analyzed either using the absolute concentration of cytokines or using their relative concentration with respect to the overall secretion level induced by each stimulus. Using Partial Least Square-Discriminant Analysis, we show that the 15-cytokine profile is different under Escherichia coli, Staphylococcus aureus, and Streptococcus uberis conditions, and that IFN-γ, IL-1β, and TNF-α contribute the most to differentiate these conditions. LPS and E. coli induced largely overlapping biological responses, but S. aureus and S. uberis were associated with distinct cytokine profiles than their respective TLR ligands. Finally, results based on adjusted or absolute cytokine levels yielded similar discriminative power, but led to different stimuli-related signatures.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

Background

Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.

Methods

Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.

Results

In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).

Conclusion

Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02801-1.

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

The Feasibility of Leaf Reflectance-Based Taxonomic Inventories and Diversity Assessments of Species-Rich Grasslands: A Cross-Seasonal Evaluation Using Waveband Selection

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and this finding implies challenges for the application of spectral variance in the estimation of biodiversity.

Acute Leukemia Classification Using Transcriptional Profiles From Low-Cost Nanopore mRNA Sequencing

PURPOSE

Most cases of pediatric acute leukemia occur in low- and middle-income countries, where health centers lack the tools required for accurate diagnosis and disease classification. Recent research shows the robustness of using unbiased short-read RNA sequencing to classify genomic subtypes of acute leukemia. Compared with short-read sequencing, nanopore sequencing has low capital and consumable costs, making it suitable for use in locations with limited health infrastructure.

MATERIALS AND METHODS

We show the feasibility of nanopore mRNA sequencing on 134 cryopreserved acute leukemia specimens (26 acute myeloid leukemia [AML], 73 B-lineage acute lymphoblastic leukemia [B-ALL], 34 T-lineage acute lymphoblastic leukemia, and one acute undifferentiated leukemia). Using multiple library preparation approaches, we generated long-read transcripts for each sample. We developed a novel composite classification approach to predict acute leukemia lineage and major B-ALL and AML molecular subtypes directly from gene expression profiles.

RESULTS

We demonstrate accurate classification of acute leukemia samples into AML, B-ALL, or T-lineage acute lymphoblastic leukemia (96.2% of cases are classifiable with a probability of > 0.8, with 100% accuracy) and further classification into clinically actionable genomic subtypes using shallow RNA nanopore sequencing, with 96.2% accuracy for major AML subtypes and 94.1% accuracy for major B-lineage acute lymphoblastic leukemia subtypes.

CONCLUSION

Transcriptional profiling of acute leukemia samples using nanopore technology for diagnostic classification is feasible and accurate, which has the potential to improve the accuracy of cancer diagnosis in low-resource settings.

Loss of microbiota-derived protective metabolites after neutropenic fever

Neutropenic fever (NF) is a common complication of chemotherapy in patients with cancer which often prolongs hospitalization and worsens the quality of life. Although an empiric antimicrobial approach is used to prevent and treat NF, a clear etiology cannot be found in most cases. Emerging data suggest an altered microbiota-host crosstalk leading to NF. We profiled the serum metabolome and gut microbiome in longitudinal samples before and after NF in patients with acute myeloid leukemia, a prototype setting with a high incidence of NF. We identified a circulating metabolomic shift after NF, with a minimal signature containing 18 metabolites, 13 of which were associated with the gut microbiota. Among these metabolites were markers of intestinal epithelial health and bacterial metabolites of dietary tryptophan with known anti-inflammatory and gut-protective effects. The level of these metabolites decreased after NF, in parallel with biologically consistent changes in the abundance of mucolytic and butyrogenic bacteria with known effects on the intestinal epithelium. Together, our findings indicate a metabolomic shift with NF which is primarily characterized by a loss of microbiota-derived protective metabolites rather than an increase in detrimental metabolites. This analysis suggests that the current antimicrobial approach to NF may need a revision to protect the commensal microbiota.

Multi-Omics Reveals Different Strategies in the Immune and Metabolic Systems of High-Yielding Strains of Laying Hens

Lohmann Brown (LB) and Lohmann Selected Leghorn (LSL) are two commercially important laying hen strains due to their high egg production and excellent commercial suitability. The present study integrated multiple data sets along the genotype-phenotype map to better understand how the genetic background of the two strains influences their molecular pathways. In total, 71 individuals were analyzed (LB, n = 36; LSL, n = 35). Data sets include gut miRNA and mRNA transcriptome data, microbiota composition, immune cells, inositol phosphate metabolites, minerals, and hormones from different organs of the two hen strains. All complex data sets were pre-processed, normalized, and compatible with the mixOmics platform. The most discriminant features between two laying strains included 20 miRNAs, 20 mRNAs, 16 immune cells, 10 microbes, 11 phenotypic traits, and 16 metabolites. The expression of specific miRNAs and the abundance of immune cell types were related to the enrichment of immune pathways in the LSL strain. In contrast, more microbial taxa specific to the LB strain were identified, and the abundance of certain microbes strongly correlated with host gut transcripts enriched in immunological and metabolic pathways. Our findings indicate that both strains employ distinct inherent strategies to acquire and maintain their immune and metabolic systems under high-performance conditions. In addition, the study provides a new perspective on a view of the functional biodiversity that emerges during strain selection and contributes to the understanding of the role of host–gut interaction, including immune phenotype, microbiota, gut transcriptome, and metabolome.

SMSPL: Robust Multimodal Approach to Integrative Analysis of Multiomics Data

With the recent advancement of technologies, it is progressively easier to collect diverse types of genome-wide data. It is commonly expected that by analyzing these data in an integrated way, one can improve the understanding of a complex biological system. Current methods, however, are prone to overfitting heavy noise such that their applications are limited. High noise is one of the major challenges for multiomics data integration. This may be the main cause of overfitting and poor performance in generalization. A sample reweighting strategy is typically used to cope with this problem. In this article, we propose a robust multimodal data integration method, called SMSPL, which can simultaneously predict subtypes of cancers and identify potentially significant multiomics signatures. Especially, the proposed method leverages the linkages between different types of data to interactively recommend high-confidence samples, adopts a new soft weighting scheme to assign weights to the training samples of each type, and then iterates between weights recalculating and classifiers updating. Simulation and five real experiments substantiate the capability of the proposed method for classification and identification of significant multiomics signatures with heavy noise. We expect SMSPL to take a small step in the multiomics data integration and help researchers comprehensively understand the biological process.

Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets

Simple Summary

Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs.

Abstract

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins

Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins.

Distinct antibody profiles in HLA-B∗57+, HLA-B∗57- HIV controllers and chronic progressors

OBJECTIVE

Spontaneous control of HIV replication without treatment in HIV-1 controllers (HICs) was associated with the development of an efficient T-cell response. In addition, increasing data suggest that the humoral response participates in viral clearance.

DESIGN

In-depth characterization of Ab response in HICs may help to define new parameters associated with this control.

METHODS

We assessed the levels of total and HIV-specific IgA and IgG subtypes induction and their functional potencies - that is, neutralization, phagocytosis, antibody-dependent cellular cytotoxicity (ADCC), according to the individual's major histocompatibility complex class I (HLA)-B∗57 status, and compared it with nontreated chronic progressors.

RESULTS

We found that despite an undetectable viral load, HICs displayed HIV-specific IgG levels similar to those of chronic progressors. Interestingly, our compelling multifunctional analysis demonstrates that the functional Ab profile, by itself, allowed to discriminate HLA-B∗57+ HICs from HLA-B∗57- HICs and chronic progressors.

CONCLUSION

These results show that HICs display a particular HIV-specific antibody (Ab) profile that may participate in HIV control and emphasize the relevance of multifunctional Ab response analysis in future Ab-driven vaccine studies.

Microbiome function predicts amphibian chytridiomycosis disease dynamics

BACKGROUND

The fungal pathogen Batrachochytrium dendrobatidis (Bd) threatens amphibian biodiversity and ecosystem stability worldwide. Amphibian skin microbial community structure has been linked to the clinical outcome of Bd infections, yet its overall functional importance is poorly understood.

METHODS

Microbiome taxonomic and functional profiles were assessed using high-throughput bacterial 16S rRNA and fungal ITS2 gene sequencing, bacterial shotgun metagenomics and skin mucosal metabolomics. We sampled 56 wild midwife toads (Alytes obstetricans) from montane populations exhibiting Bd epizootic or enzootic disease dynamics. In addition, to assess whether disease-specific microbiome profiles were linked to microbe-mediated protection or Bd-induced perturbation, we performed a laboratory Bd challenge experiment whereby 40 young adult A. obstetricans were exposed to Bd or a control sham infection. We measured temporal changes in the microbiome as well as functional profiles of Bd-exposed and control animals at peak infection.

RESULTS

Microbiome community structure and function differed in wild populations based on infection history and in experimental control versus Bd-exposed animals. Bd exposure in the laboratory resulted in dynamic changes in microbiome community structure and functional differences, with infection clearance in all but one infected animal. Sphingobacterium, Stenotrophomonas and an unclassified Commamonadaceae were associated with wild epizootic dynamics and also had reduced abundance in laboratory Bd-exposed animals that cleared infection, indicating a negative association with Bd resistance. This was further supported by microbe-metabolite integration which identified functionally relevant taxa driving disease outcome, of which Sphingobacterium and Bd were most influential in wild epizootic dynamics. The strong correlation between microbial taxonomic community composition and skin metabolome in the laboratory and field is inconsistent with microbial functional redundancy, indicating that differences in microbial taxonomy drive functional variation. Shotgun metagenomic analyses support these findings, with similar disease-associated patterns in beta diversity. Analysis of differentially abundant bacterial genes and pathways indicated that bacterial environmental sensing and Bd resource competition are likely to be important in driving infection outcomes.

CONCLUSIONS

Bd infection drives altered microbiome taxonomic and functional profiles across laboratory and field environments. Our application of multi-omics analyses in experimental and field settings robustly predicts Bd disease dynamics and identifies novel candidate biomarkers of infection. Video Abstract.

Hybridization fuelled diversification in Spialia butterflies

The importance of hybridization and introgression is well documented in the evolution of plants but, in insects, their role is not fully understood. Given the fact that insects are the most diverse group of organisms, assessing the impact of reticulation events on their evolution may be key to comprehend the emergence of such remarkable diversity. Here, we used an insect model, the Spialia butterflies, to gather genomic evidence of hybridization as a promoter of novel diversity. By using double‐digest RADseq (ddRADseq), we explored the phylogenetic relationships between Spialia orbifer, S. rosae and S. sertorius, and documented two independent events of interspecific gene flow. Our data support that the Iberian endemism S. rosae probably received genetic material from S. orbifer in both mitochondrial and nuclear DNA, which could have contributed to a shift in the ecological preferences of S. rosae. We also show that admixture between S. sertorius and S. orbifer probably occurred in Italy. As a result, the admixed Sicilian populations of S. orbifer are differentiated from the rest of populations both genetically and morphologically, and display signatures of reproductive character displacement in the male genitalia. Additionally, our analyses indicated that genetic material from S. orbifer is present in S. sertorius along the Italian Peninsula. Our findings add to the view that hybridization is a pervasive phenomenon in nature and in butterflies in particular, with important consequences for evolution due to the emergence of novel phenotypes.

Computational identification of new potential transcriptional partners of ERRα in breast cancer cells: specific partners for specific targets

Estrogen related receptors are orphan members of the nuclear receptor superfamily acting as transcription factors (TFs). In contrast to classical nuclear receptors, the activities of the ERRs are not controlled by a natural ligand. Regulation of their activities thus relies on availability of transcriptional co-regulators. In this paper, we focus on ERRα, whose involvement in cancer progression has been broadly demonstrated. We propose a new approach to identify potential co-activators, starting from previously identified ERRα-activated genes in a breast cancer (BC) cell line. Considering mRNA gene expression from two sets of human BC cells as major endpoint, we used sparse partial least squares modeling to uncover new transcriptional regulators associated with ERRα. Among them, DDX21, MYBBP1A, NFKB1, and SETD7 are functionally relevant in MDA-MB-231 cells, specifically activating the expression of subsets of ERRα-activated genes. We studied SET7 in more details and showed its co-localization with ERRα and its ERRα-dependent transcriptional and phenotypic effects. Our results thus demonstrate the ability of a modeling approach to identify new transcriptional partners from gene expression. Finally, experimental results show that ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes, thus reinforcing the combinatorial specificity of transcription.

Obtaining Reliable RT-qPCR Results in Molecular Diagnostics—MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery

In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Heat stress-induced mucosal barrier dysfunction is potentially associated with gut microbiota dysbiosis in pigs

Heat stress (HS) can be detrimental to the gut health of swine. Many negative outcomes induced by HS are increasingly recognized as including modulation of intestinal microbiota. In turn, the intestinal microbiota is a unique ecosystem playing a critical role in mediating the host stress response. Therefore, we aimed to characterize gut microbiota of pigs’ exposure to short-term HS, to explore a possible link between the intestinal microbiota and HS-related changes, including serum cytokines, oxidation status, and intestinal epithelial barrier function. Our findings showed that HS led to intestinal morphological and integrity changes (villus height, serum diamine oxidase [DAO], serum D-lactate and the relative expressions of tight junction proteins), reduction of serum cytokines (interleukin [IL]-8, IL-12, interferon-gamma [IFN-γ]), and antioxidant activity (higher glutathione [GSH] and malondialdehyde [MDA] content, and lower superoxide dismutase [SOD]). Also, 16S rRNA sequencing analysis revealed that although there was no difference in microbial α-diversity, some HS-associated composition differences were revealed in the ileum and cecum, which partly led to an imbalance in the production of short-chain fatty acids including propionate acid and valerate acid. Relevance networks revealed that HS-derived changes in bacterial genera and microbial metabolites, such as Chlamydia, Lactobacillus, Succinivibrio, Bifidobacterium, Lachnoclostridium, and propionic acid, were correlated with oxidative stress, intestinal barrier dysfunction, and inflammation in pigs. Collectively, our observations suggest that intestinal damage induced by HS is probably partly related to the gut microbiota dysbiosis, though the underlying mechanism remains to be fully elucidated.

Metabolic Changes During Growth and Reproductive Phases in the Liver of Female Goldfish (Carassius auratus)

Hormones of the brain-pituitary-peripheral axis regulate metabolism, gonadal maturation, and growth in vertebrates. In fish, reproduction requires a significant energy investment to metabolically support the production of hundreds of eggs and billions of sperms in females and males, respectively. This study used an LC-MS-based metabolomics approach to investigate seasonally-related changes in metabolic profile and energy allocation patterns in female goldfish liver. We measured basal metabolic profile in female goldfish at three phases of the reproductive cycle, including 1) Maximum growth period in postovulatory regressed phase, 2) mid recrudescence in fish with developing follicles, and 3) late recrudescence when the ovary contains mature ovulatory follicles. We also investigated changes in the liver metabolism following acute treatments with GnRH and GnIH, known to be involved in controlling reproduction and growth in goldfish. Chemometrics combined with pathway-driven bioinformatics revealed significant changes in the basal and GnRH/GnIH-induced hepatic metabolic profile, indicating that metabolic energy allocation is regulated to support gonadal development and growth at different reproductive cycles. Overall, the findings support the hypothesis that hormonal control of reproduction involves accompanying metabolic changes to energetically support gonadotropic and somatotropic activities in goldfish and other oviparous vertebrates.

Medulloblastoma cerebrospinal fluid reveals metabolites and lipids indicative of hypoxia and cancer-specific RNAs

Medulloblastoma (MB) is the most common malignant brain tumor in children. There remains an unmet need for diagnostics to sensitively detect the disease, particularly recurrences. Cerebrospinal fluid (CSF) provides a window into the central nervous system, and liquid biopsy of CSF could provide a relatively non-invasive means for disease diagnosis. There has yet to be an integrated analysis of the transcriptomic, metabolomic, and lipidomic changes occurring in the CSF of children with MB. CSF samples from patients with (n = 40) or without (n = 11; no cancer) MB were subjected to RNA-sequencing and high-resolution mass spectrometry to identify RNA, metabolite, and lipid profiles. Differentially expressed transcripts, metabolites, and lipids were identified and their biological significance assessed by pathway analysis. The DIABLO multivariate analysis package (R package mixOmics) was used to integrate the molecular changes characterizing the CSF of MB patients. Differentially expressed transcripts, metabolites, and lipids in CSF were discriminatory for the presence of MB but not the exact molecular subtype. One hundred and ten genes and ten circular RNAs were differentially expressed in MB CSF compared with normal, representing TGF-β signaling, TNF-α signaling via NF-kB, and adipogenesis pathways. Tricarboxylic acid cycle and other metabolites (malate, fumarate, succinate, α-ketoglutarate, hydroxypyruvate, N-acetyl-aspartate) and total triacylglycerols were significantly upregulated in MB CSF compared with normal CSF. Although separating MBs into subgroups using transcriptomic, metabolomic, and lipid signatures in CSF was challenging, we were able to identify a group of omics signatures that could separate cancer from normal CSF. Metabolic and lipidomic profiles both contained indicators of tumor hypoxia. Our approach provides several candidate signatures that deserve further validation, including the novel circular RNA circ_463, and insights into the impact of MB on the CSF microenvironment.

Dysregulation of Human Somatic piRNA Expression in Parkinson's Disease Subtypes and Stages

Piwi interacting RNAs (piRNAs) are small non-coding single-stranded RNA species 20–31 nucleotides in size generated from distinct loci. In germline tissues, piRNAs are amplified via a “ping-pong cycle” to produce secondary piRNAs, which act in transposon silencing. In contrast, the role of somatic-derived piRNAs remains obscure. Here, we investigated the identity and distribution of piRNAs in human somatic tissues to determine their function and potential role in Parkinson’s disease (PD). Human datasets were curated from the Gene Expression Omnibus (GEO) database and a workflow was developed to identify piRNAs, which revealed 902 somatic piRNAs of which 527 were expressed in the brain. These were mainly derived from chromosomes 1, 11, and 19 compared to the germline tissues, which were from 15 and 19. Approximately 20% of somatic piRNAs mapped to transposon 3′ untranslated regions (UTRs), but a large proportion were sensed to the transcript in contrast to germline piRNAs. Gene set enrichment analysis suggested that somatic piRNAs function in neurodegenerative disease. piRNAs undergo dysregulation in different PD subtypes (PD and Parkinson’s disease dementia (PDD)) and stages (premotor and motor). piR-has-92056, piR-hsa-150797, piR-hsa-347751, piR-hsa-1909905, piR-hsa-2476630, and piR-hsa-2834636 from blood small extracellular vesicles were identified as novel biomarkers for PD diagnosis using a sparse partial least square discriminant analysis (sPLS-DA) (accuracy: 92%, AUC = 0.89). This study highlights a role for piRNAs in PD and provides tools for novel biomarker development.

Microbial and Chemical Profiles of Commercial Kombucha Products

Kombucha is an increasingly popular functional beverage that has gained attention for its unique combination of phytochemicals, metabolites, and microbes. Previous chemical and microbial composition analyses of kombucha have mainly focused on understanding their changes during fermentation. Very limited information is available regarding nutrient profiles of final kombucha products in the market. In this study, we compared the major chemicals (tea polyphenols, caffeine), antioxidant properties, microbial and metabolomic profiles of nine commercial kombucha products using shotgun metagenomics, internal transcribed spacer sequencing, untargeted metabolomics, and targeted chemical assays. All of the nine kombucha products showed similar acidity but great differences in chemicals, metabolites, microbes, and antioxidant activities. Most kombucha products are dominated by the probiotic Bacillus coagulans or bacteria capable of fermentation including Lactobacillus nagelii, Gluconacetobacter, Gluconobacter, and Komagataeibacter species. We found that all nine kombuchas also contained varying levels of enteric bacteria including Bacteroides thetaiotamicron, Escherischia coli, Enterococcus faecalis, Bacteroides fragilis, Enterobacter cloacae complex, and Akkermansia muciniphila. The fungal composition of kombucha products was characterized by predominance of fermenting yeast including Brettanomyces species and Cyberlindnera jadinii. Kombucha varied widely in chemical content assessed by global untargeted metabolomics, with metabolomic variation being significantly associated with metagenomic profiles. Variation in tea bases, bacteria/yeast starter cultures, and duration of fermentation may all contribute to the observed large differences in the microbial and chemical profiles of final kombucha products.

A vacuolar hexose transport is required for xylem development in the inflorescence stem

In Angiosperms, the development of the vascular system is controlled by a complex network of transcription factors. However, how nutrient availability in the vascular cells affects their development remains to be addressed. At the cellular level, cytosolic sugar availability is regulated mainly by sugar exchanges at the tonoplast through active and/or facilitated transport. In Arabidopsis (Arabidopsis thaliana), among the genes encoding tonoplastic transporters, SUGAR WILL EVENTUALLY BE EXPORTED TRANSPORTER 16 (SWEET16) and SWEET17 expression has been previously detected in the vascular system. Here, using a reverse genetics approach, we propose that sugar exchanges at the tonoplast, regulated by SWEET16, are important for xylem cell division as revealed in particular by the decreased number of xylem cells in the swt16 mutant and the accumulation of SWEET16 at the procambium-xylem boundary. In addition, we demonstrate that transport of hexoses mediated by SWEET16 and/or SWEET17 is required to sustain the formation of the xylem secondary cell wall. This result is in line with a defect in the xylem cell wall composition as measured by Fourier-transformed infrared spectroscopy in the swt16swt17 double mutant and by upregulation of several genes involved in secondary cell wall synthesis. Our work therefore supports a model in which xylem development partially depends on the exchange of hexoses at the tonoplast of xylem-forming cells.

A dysbiotic microbiome promotes head and neck squamous cell carcinoma

Recent studies have reported dysbiotic oral microbiota and tumor-resident bacteria in human head and neck squamous cell carcinoma (HNSCC). We aimed to identify and validate oral microbial signatures in treatment-naïve HNSCC patients compared with healthy control subjects. We confirm earlier reports that the relative abundances of Lactobacillus spp. and Neisseria spp. are elevated and diminished, respectively, in human HNSCC. In parallel, we examined the disease-modifying effects of microbiota in HNSCC, through both antibiotic depletion of microbiota in an induced HNSCC mouse model (4-Nitroquinoline 1-oxide, 4NQO) and reconstitution of tumor-associated microbiota in a germ-free orthotopic mouse model. We demonstrate that depletion of microbiota delays oral tumorigenesis, while microbiota transfer from mice with oral cancer accelerates tumorigenesis. Enrichment of Lactobacillus spp. was also observed in murine HNSCC, and activation of the aryl-hydrocarbon receptor was documented in both murine and human tumors. Together, our findings support the hypothesis that dysbiosis promotes HNSCC development.

The early reduction of left ventricular mass after sleeve gastrectomy depends on the fall of branched-chain amino acid circulating levels

Introduction

Body-mass index is a major determinant of left-ventricular-mass (LVM). Bariatric-metabolic surgery (BMS) reduces cardiovascular mortality. Its mechanism of action, however, often encompasses a weight-dependent effect. In this translational study, we aimed at investigating the mechanisms by which BMS leads to LVM reduction and functional improvement.

Methods

Twenty patients (45.2 ± 8.5years) were studied with echocardiography at baseline and at 1,6,12 and 48 months after sleeve-gastrectomy (SG). Ten Wistar rats aged 10-weeks received high-fat diet ad libitum for 10 weeks before and 4 weeks after SG or sham-operation. An oral-glucose-tolerance-test was performed to measure whole-body insulin-sensitivity. Plasma metabolomics was analysed in both human and rodent samples. RNA quantitative Real-Time PCR and western blots were performed in rodent heart biopsies. The best-fitted partial-least-square discriminant-analysis model was used to explore the variable importance in the projection score of all metabolites.

Findings

Echocardiographic LVM (-12%,-23%,-28% and -43% at 1,6,12 and 48 months, respectively) and epicardial fat decreased overtime after SG in humans while insulin-sensitivity improved. In rats, SG significantly reduced LVM and epicardial fat, enhanced ejection-fraction and improved insulin-sensitivity compared to sham-operation. Metabolomics showed a progressive decline of plasma branched-chain amino-acids (BCAA), alanine, lactate, 3-OH-butyrate, acetoacetate, creatine and creatinine levels in both humans and rodents.

Hearts of SG rats had a more efficient BCAA, glucose and fatty-acid metabolism and insulin signaling than sham-operation. BCAAs in cardiomyocyte culture-medium stimulated lipogenic gene transcription and reduced mRNA levels of key mitochondrial β-oxidation enzymes promoting lipid droplet accumulation and glycolysis.

Interpretation

After SG a prompt and sustained decrease of the LVM, epicardial fat and insulin resistance was found. Animal and in vitro studies showed that SG improves cardiac BCAA metabolism with consequent amelioration of fat oxidation and insulin signaling translating into decreased intra-myocytic fat accumulation and reduced lipotoxicity.

Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixOmics methods for profile discrimination: Application case on cheese

Volatile organic compounds (VOCs) emitted by food products are decisive for the perception of aroma and taste. The analysis of gaseous matrices is traditionally done by detection and quantification of few dozens of characteristic markers. Emerging direct injection mass spectrometry technologies offer rapid analysis based on a soft ionisation of VOCs without previous separation. The recent increase of selectivity offered by the use of several precursor ions coupled with untargeted analysis increases the potential power of these instruments. However, the analysis of complex gaseous matrix results in a large number of ion conflicts, making the quantification of markers difficult, and in a large volume of data. In this work, we present the exploitation of untargeted SIFT-MS volatile fingerprints of ewe PDO cheeses in a real farm model, using mixOmics methods allowing us to illustrate the typicality, the manufacturing processes reproducibility and the impact of the animals' diet on the final product.

Gene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome

Gut dysbiosis in Huntington's disease (HD) has recently been reported using microbiome profiling in R6/1 HD mice and replicated in clinical HD. In HD mice, environmental enrichment (EE) and exercise (EX) were shown to have therapeutic impacts on the brain and associated symptoms. We hypothesize that these housing interventions modulate the gut microbiome, configuring one of the mechanisms that mediate their therapeutic effects observed in HD. We exposed R6/1 mice to a protocol of either EE or EX, relative to standard-housed control conditions, before the onset of gut dysbiosis and motor deficits. We characterized gut structure and function, as well as gut microbiome profiling using 16S rRNA sequencing. Multivariate analysis identified specific orders, namely Bacteroidales, Lachnospirales and Oscillospirales, as the main bacterial signatures that discriminate between housing conditions. Our findings suggest a promising role for the gut microbiome in mediating the effects of EE and EX exposures, and possibly other environmental interventions, in HD mice.

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Gastrointestinal structure and motility are intact at an early stage in a HD mouse model

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There is sexual dimorphism in the presentation of the HD gut dysbiosis phenotype

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Bacteroidales, Lachnospirales and Oscillospirales bacteria are affected by experience

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Environmental enrichment and exercise may modulate HD via the microbiota-gut-brain axis

Classification for psychiatric disorders including schizophrenia, bipolar disorder, and major depressive disorder using machine learning

Schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD) are the most common psychiatric disorders. Because there were lots of overlaps among these disorders from genetic epidemiology and molecular genetics, it is hard to realize the diagnoses of these psychiatric disorders. Currently, plenty of studies have been conducted for contributing to the diagnoses of these diseases. However, constructing a classification model with superior performance for differentiating SCZ, BP, and MDD samples is still a great challenge. In this study, the transcriptomic data was applied for discovering key genes and constructing a classification model. In this dataset, there were 268 samples including four groups (67 SCZ patients, 40 BP patients, 57 MDD patients, and 104 healthy controls), which were applied for constructing a classification model. First, 269 probes of differentially expressed genes (DEGs) among four sample groups were identified by the feature selection method. Second, these DEGs were validated by the literature review including disease relevance with the psychiatric disorders of these DEGs, the hub genes in the PPI (protein–protein interaction) network, and GO (gene ontology) terms and pathways. Third, a classification model was constructed using the identified DEGs by machine learning method to classify different groups. The ROC (receiver operator characteristic) curve and AUC (area under the curve) value were used to assess the classification capacity of the model. In summary, this classification model might provide clues for the diagnoses of these psychiatric disorders.

Internal extractive electrospray ionization mass spectrometry for investigating the phospholipid dysregulation induced by perfluorooctanoic acid in Nile tilapia

Direct profiling of endogenous biomolecules in tissue samples is considered to be a promising approach to investigate metabolic-related toxicity in organisms induced by emerging pollutants.

Oral and Gut Microbial Carbohydrate-Active Enzymes Landscape in Health and Disease

Inter-individual variability in the microbial gene complement encoding for carbohydrate-active enzymes (CAZymes) can profoundly regulate how the host interacts with diverse carbohydrate sources thereby influencing host health. CAZy-typing, characterizing the microbiota-associated CAZyme-coding genes within a host individual, can be a useful tool to predict carbohydrate pools that the host can metabolize, or identify which CAZyme families are underrepresented requiring supplementation via microbiota transplantation or probiotics. CAZy-typing, moreover, provides a novel framework to search for disease biomarkers. As a proof of concept, we used publicly available metagenomes (935) representing 310 type strain bacterial genomes to establish the link between disease status and CAZymes in the oral and gut microbial ecosystem. The abundance and distribution of 220 recovered CAZyme families in saliva and stool samples from patients with colorectal cancer, rheumatoid arthritis, and type 1 diabetes were compared with healthy subjects. Based on the multivariate discriminant analysis, the disease phenotype did not alter the CAZyme profile suggesting a functional conservation in carbohydrate metabolism in a disease state. When disease and healthy CAZyme profiles were contrasted in differential analysis, CAZyme markers that were underrepresented in type 1 diabetes (15), colorectal cancer (12), and rheumatoid arthritis (5) were identified. Of interest, are the glycosyltransferase which can catalyze the synthesis of glycoconjugates including lipopolysaccharides with the potential to trigger inflammation, a common feature in many diseases. Our analysis has also confirmed the expansive carbohydrate metabolism in the gut as evidenced by the overrepresentation of CAZyme families in the gut compared to the oral site. Nevertheless, each site exhibited specific CAZyme markers. Taken together, our analysis provides an insight into the CAZyme landscape in health and disease and has demonstrated the diversity in carbohydrate metabolism in host-microbiota which can be a sound basis for optimizing the selection of pre, pro, and syn-biotic candidate products.

Peptide ancestry informative markers in uterine neoplasms from women of European, African, and Asian ancestry

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with disease pathogenesis. Nonsynonymous single-nucleotide polymorphisms encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis identified that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R² = 0.99). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues combined resulted in the quantitation of 62 pAIMs that correlate with patient race and genotype-confirmed ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. pAIMs will support generalized proteoancestry assessment as well as efforts investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

Identification of Repellents from Four Non-Host Asteraceae Plants for the Root Knot Nematode, Meloidogyne incognita

Olfactory cues guide plant parasitic nematodes (PPNs) to their host plants. We tested the hypothesis that non-host plant root volatiles repel PPNs. To achieve this, we compared the olfactory responses of infective juveniles (J2s) of the PPN Meloidogyne incognita to four non-host Asteraceae plants, namely, black-jack (Bidens pilosa), pyrethrum (Chrysanthemum cinerariifolium), marigold (Tagetes minuta), and sweet wormwood (Artemisia annua), traditionally used in sub-Saharan Africa for the management of PPNs. Chemical analysis by coupled gas chromatography-mass spectrometry (GC/MS) combined with random forest analysis, followed by behavioral assays, identified the repellents in the root volatiles of B. pilosa, T. minuta, and A. annua as (E)-β-farnesene and 1,8-cineole, whereas camphor was attractive. In contrast, random forest analysis predicted repellents for C. cinerariifolium and A. annua as β-patchoulene and isopropyl hexadecanoate. Our results suggested that terpenoids generally account for the repellency of non-host Asteraceae plants used in PPN management.

Flow cytometry-based study of model marine microalgal consortia revealed an ecological advantage of siderophore utilization by the dinoflagellate Amphidinium carterae

Investigations of phytoplankton responses to iron stress in seawater are complicated by the fact that iron concentrations do not necessarily reflect bioavailability. Most studies to date have been based on single species or field samples and are problematic to interpret. Here, we report results from an experimental cocultivation model system that enabled us to evaluate interspecific competition as a function of iron content and form, and to study the effect of nutritional conditions on the proteomic profiles of individual species. Our study revealed that the dinoflagellate Amphidinium carterae was able to utilize iron from a hydroxamate siderophore, a strategy that could provide an ecological advantage in environments where siderophores present an important source of iron. Additionally, proteomic analysis allowed us to identify a potential candidate protein involved in iron acquisition from hydroxamate siderophores, a strategy that is largely unknown in eukaryotic phytoplankton.

The Effect of Exogenous Bile Acids on Antioxidant Status and Gut Microbiota in Heat-Stressed Broiler Chickens

Bile acids are critical for lipid absorption, however, their new roles in maintaining or regulating systemic metabolism are irreplaceable. The negative impacts of heat stress (HS) on growth performance, lipid metabolism, and antioxidant status have been reported, but it remains unknown whether the bile acids (BA) composition of broiler chickens can be affected by HS. Therefore, this study aimed to investigate the modulating effects of the environment (HS) and whether dietary BA supplementation can benefit heat-stressed broiler chickens. A total of 216 Arbor Acres broilers were selected with a bodyweight approach average and treated with thermal neutral (TN), HS (32°C), or HS-BA (200 mg/kg BA supplementation) from 21 to 42 days. The results showed that an increase in average daily gain (P < 0.05) while GSH-Px activities (P < 0.05) in both serum and liver were restored to the normal range were observed in the HS-BA group. HS caused a drop in the primary BA (P = 0.084, 38.46%) and Tauro-conjugated BA (33.49%) in the ileum, meanwhile, the secondary BA in the liver and cecum were lower by 36.88 and 39.45% respectively. Notably, results were consistent that SBA levels were significantly increased in the serum (3-fold, P = 0.0003) and the ileum (24.89-fold, P < 0.0001). Among them, TUDCA levels (P < 0.01) were included. Besides, BA supplementation indeed increased significantly TUDCA (P = 0.0154) and THDCA (P = 0.0003) levels in the liver, while ileal TDCA (P = 0.0307), TLCA (P = 0.0453), HDCA (P = 0.0018), and THDCA (P = 0.0002) levels were also increased. Intestinal morphology of ileum was observed by hematoxylin and eosin (H&E) staining, birds fed with BA supplementation reduced (P = 0.0431) crypt depth, and the ratio of villous height to crypt depth trended higher (P = 0.0539) under the heat exposure. Quantitative RT-PCR showed that dietary supplementation with BA resulted in upregulation of FXR (P = 0.0369), ASBT (P = 0.0154), and Keap-1 (P = 0.0104) while downregulation of iNOS (P = 0.0399) expression in ileum. Moreover, 16S rRNA gene sequencing analysis and relevance networks revealed that HS-derived changes in gut microbiota and BA metabolites of broilers may affect their resistance to HS. Thus, BA supplementation can benefit broiler chickens during high ambient temperatures, serving as a new nutritional strategy against heat stress.

Lung Adenocarcinoma Transcriptomic Analysis Predicts Adenylate Kinase Signatures Contributing to Tumor Progression and Negative Patient Prognosis

The ability to detect and respond to hypoxia within a developing tumor appears to be a common feature amongst most cancers. This hypoxic response has many molecular drivers, but none as widely studied as Hypoxia-Inducible Factor 1 (HIF-1). Recent evidence suggests that HIF-1 biology within lung adenocarcinoma (LUAD) may be associated with expression levels of adenylate kinases (AKs). Using LUAD patient transcriptome data, we sought to characterize AK gene signatures related to lung cancer hallmarks, such as hypoxia and metabolic reprogramming, to identify conserved biological themes across LUAD tumor progression. Transcriptomic analysis revealed perturbation of HIF-1 targets to correlate with altered expression of most AKs, with AK4 having the strongest correlation. Enrichment analysis of LUAD tumor AK4 gene signatures predicts signatures involved in pyrimidine, and by extension, nucleotide metabolism across all LUAD tumor stages. To further discriminate potential drivers of LUAD tumor progression within AK4 gene signatures, partial least squares discriminant analysis was used at LUAD stage-stage interfaces, identifying candidate genes that may promote LUAD tumor growth or regression. Collectively, these results characterize regulatory gene networks associated with the expression of all nine human AKs that may contribute to underlying metabolic perturbations within LUAD and reveal potential mechanistic insight into the complementary role of AK4 in LUAD tumor development.

Characterization of the humoral immune response to the EBV proteome in extranodal NK/T-cell lymphoma

Extranodal natural killer/T-cell lymphoma (NKTCL) is an aggressive malignancy that has been etiologically linked to Epstein-Barr virus (EBV) infection, with EBV gene transcripts identified in almost all cases. However, the humoral immune response to EBV in NKTCL patients has not been well characterized. We examined the antibody response to EBV in plasma samples from 51 NKTCL cases and 154 controls from Hong Kong and Taiwan who were part of the multi-center, hospital-based AsiaLymph case–control study. The EBV-directed serological response was characterized using a protein microarray that measured IgG and IgA antibodies against 202 protein sequences representing the entire EBV proteome. We analyzed 157 IgG antibodies and 127 IgA antibodies that fulfilled quality control requirements. Associations between EBV serology and NKTCL status were disproportionately observed for IgG rather than IgA antibodies. Nine anti-EBV IgG responses were significantly elevated in NKTCL cases compared with controls and had ORs_{highest vs. lowest tertile} > 6.0 (Bonferroni-corrected P-values < 0.05). Among these nine elevated IgG responses in NKTCL patients, three IgG antibodies (all targeting EBNA3A) are novel and have not been observed for other EBV-associated tumors of B-cell or epithelial origin. IgG antibodies against EBNA1, which have consistently been elevated in other EBV-associated tumors, were not elevated in NKTCL cases. We characterize the antibody response against EBV for patients with NKTCL and identify IgG antibody responses against six distinct EBV proteins. Our findings suggest distinct serologic patterns of this NK/T-cell lymphoma compared with other EBV-associated tumors of B-cell or epithelial origin.

Time-series eco-metabolomics reveals extensive reshuffling in metabolome during transition from cold acclimation to de-acclimation in an alpine shrub

Recording environmentally induced variations in the metabolome in plants can be a promising approach for understanding the complex patterns of metabolic regulation and their eco-physiological consequences. Here, we studied metabolome-wide changes and eco-physiological adjustments occurring across the year at high elevation environments in the leaf tissue of Rhododendron anthopogon, an alpine evergreen shrub of the Himalaya. New leaves of R. anthopogon appear after the snow-melt and remain intact even when the plants get covered under snow (November-June). During this whole period, they may undergo several physiological and biochemical adjustments in response to fluctuating temperatures and light conditions. To understand these changes, we analyzed eco-physiological traits, that is, freezing resistance, dry matter content and % of nitrogen and the overall metabolome across 10 different time-points, from August until the snowfall in November 2017, and then from June to August 2018. As anticipated, the freezing resistance increased toward the onset of winters. The leaf tissues exhibited a complete reshuffling of the metabolome during the growth cycle and time-points segregated into four clusters directly correlating with distinct phases of acclimation: non-acclimation (August 22, 2017; August 14, 2018), early cold acclimation (September 12, September 29, October 11, 2017), late cold acclimation (October 23, November 4, 2017), and de-acclimation (June 15, June 28, July 14, 2018). Cold acclimation involved metabolic progression (101 metabolites) with an increase of up to 19.4-fold (gentiobiose), whereas de-acclimation showed regression (120 metabolites) with a decrease of up to 30-fold (sucrose). The changes in the metabolome during de-acclimation were maximum and were not just a reversal of cold acclimation. Our results provided insights into the direction and magnitude of physiological changes in Rhododendron anthopogon that occurred across the year.

Durable changes in the gut microbiome in survivors of childhood acute lymphoblastic leukemia

There are limiteddata on long-term changes in the gut microbiome after acute lymphoblastic leukemia (ALL) therapy. We compared the gut microbial composition in stool samples of nine survivors of childhood ALL with 10 healthy sibling controls using 16S rRNA gene sequencing. Analysis of beta diversity within family units demonstrated a significant difference in bacterial strains between patients and healthy siblings. A significant difference in alpha diversity between patients and their healthy siblings was noted using Pielou's evenness. The composition of the gut microbiome differs between pediatric ALL survivors and healthy sibling controls for years after completion of therapy.