Sparse Partial Least Squares Discriminant Analysis Research Articles

Laser-irradiating infrared attenuated total reflection spectroscopy of articular cartilage: Potential and challenges for diagnosing osteoarthritis

ObjectiveA prototype infrared attenuated total reflection (IR-ATR) laser spectroscopic system designed for in vivo classification of human cartilage tissue according to its histological health status during arthroscopic surgery is presented. Prior to real-world in vivo applications, this so-called osteoarthritis (OA) scanner has been tested at in vitro conditions revealing the challenges associated with complex sample matrices and the accordingly obtained sparse spectral datasets. MethodsIn vitro studies on human knee cartilage samples at different contact pressures (i.e., 0.2–0.5 ​MPa) allowed recording cartilage degeneration characteristic IR signatures comparable to in vivo conditions with high temporal resolution. Afterwards, the cartilage samples were assessed based on the clinically acknowledged osteoarthritis cartilage histopathology assessment (OARSI) system and correlated with the obtained sparse IR data. ResultsAmide and carbohydrate signal behavior was observed to be almost identical between the obtained sparse IR data and previously measured FTIR data used for sparse partial least squares discriminant analysis (SPLSDA) to identify the spectral regions relevant to cartilage condition. Contact pressures between 0.3 and 0.4 ​MPa seem to provide the best sparse IR spectra for cylindrical (d ​= ​3 ​mm) probe tips. ConclusionLaser-irradiating IR-ATR spectroscopy is a promising analytical technique for future arthroscopic applications to differentiate healthy and osteoarthritic cartilage tissue. However, this study also revealed that the flexible connection between the laser-based analyzer and the arthroscopic ATR-probe via IR-transparent fiberoptic cables may affect the robustness of the obtained IR data and requires further improvements.

Prediction of Deoxynivalenol Contamination in Wheat via Infrared Attenuated Total Reflection Spectroscopy and Multivariate Data Analysis.

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 μg/kg but also provided valuable insights into the relevant parameters shaping these models.

Proteomic Analysis Identifies Distinct Protein Patterns for High Ovulation in FecB Mutant Small Tail Han Sheep Granulosa Cells.

The Booroola fecundity (FecB) mutation in the bone morphogenetic protein receptor type 1B (BMPR1B) gene increases ovulation in sheep. However, its effect on follicular maturation is not fully understood. Therefore, we collected granulosa cells (GCs) at a critical stage of follicle maturation from nine wild-type (WW), nine heterozygous FecB mutant (WB), and nine homozygous FecB mutant (BB) Small Tail Han sheep. The GCs of three ewes were selected at random from each genotype and consolidated into a single group, yielding a total of nine groups (three groups per genotype) for proteomic analysis. The tandem mass tag technique was utilized to ascertain the specific proteins linked to multiple ovulation in the various FecB genotypes. Using a general linear model, we identified 199 proteins significantly affected by the FecB mutation with the LIMMA package (p < 0.05). The differential abundance of proteins was enriched in pathways related to cholesterol metabolism, carbohydrate metabolism, amino acid biosynthesis, and glutathione metabolism. These pathways are involved in important processes for GC-regulated 'conservation' of oocyte maturation. Further, the sparse partial least-squares discriminant analysis and the Fuzzy-C-mean clustering method were combined to estimate weights and cluster differential abundance proteins according to ovulation to screen important ovulation-related proteins. Among them, ZP2 and ZP3 were found to be enriched in the cellular component catalog term "egg coat", as well as some apolipoproteins, such as APOA1, APOA2, and APOA4, enriched in several Gene Ontology terms related to cholesterol metabolism and lipoprotein transport. A higher abundance of these essential proteins for oocyte maturation was observed in BB and WB genotypes compared with WW ewes. These proteins had a high weight in the model for discriminating sheep with different FecB genotypes. These findings provide new insight that the FecB mutant in GCs improves nutrient metabolism, leading to better oocyte maturation by altering the abundance of important proteins (ZP2, ZP3, and APOA1) in favor of increased ovulation or better oocyte quality.

FRI032 Cellular Insights Into Metabolically Healthy And Unhealthy Obesity

Abstract Disclosure: M.C. Petersen: None. G.I. Smith: None. J. Yu: None. R.A. Barve: Other; Self; Royalties; PercayAI. J. Yoshino: None. G.I. Shulman: None. S. Klein: Advisory Board Member; Self; Merck, Altimmune. Some people with obesity are resistant to the adverse metabolic effects of excess adiposity and are considered to have “metabolically healthy obesity” (MHO). However, the mechanisms responsible for MHO are not clear. We evaluated whole-body insulin sensitivity (glucose infusion rate/plasma insulin during a hyperinsulinemic-euglycemic clamp) and several putative factors that regulate insulin action, including skeletal muscle diacylglycerol (DAG) and ceramide content and transcriptomic profiles in abdominal subcutaneous adipose tissue (ASAT) and muscle, in three groups separated by adiposity and metabolic function: i) 20 adults with MHO (BMI = 38.6 ± 1.2 kg/m2, normal fasting glucose, oral glucose tolerance, hemoglobin A1c, and intrahepatic triglyceride content); ii) 20 adults with MUO (BMI= 39.5 ± 1.1 kg/m2, prediabetes and hepatic steatosis); and iii) 15 adults who were metabolically-healthy and lean (MHL, BMI= 22.7 ± 0.4 kg/m2). Insulin sensitivity progressively decreased from MHL to MHO to MUO [109 ± 7 to 72 ± 7 to 30 ± 2 (μg glucose/kg FFM/min)/(μU/mL), respectively]. Muscle plasma membrane and total cellular sn-1,2-DAG contents were greater in the MHO and MUO groups compared with the MHL group, with no difference between MHO and MUO groups. In contrast, muscle ceramide content in multiple compartments progressively increased from MHL to MHO to MUO and were inversely correlated with insulin sensitivity among all subjects (plasma membrane, r = -0.61; mitochondria, r = -0.56; endoplasmic reticulum, r = -0.51, all P &amp;lt; 0.001). In ASAT, the expression of genes involved in extracellular matrix (ECM) remodeling and inflammation were greater in MUO than MHO, whereas the expression of genes involved in lipogenesis was greater in MHO than MUO. Sparse partial least-squares discriminant analysis, a classification algorithm, was used to identify the features that best discriminated the MHO and MUO groups. Of 125 variables, fasting plasma C-peptide concentration and skeletal muscle mitochondrial ceramide content were the best discriminants of MHO and MUO. Muscle mitochondrial ceramide content was negatively correlated with muscle transcripts involved in mitochondrial structure/function (e.g., ATP5MG [r = -0.69, P &amp;lt; 0.0001] and MPC1 [r = -0.67, P &amp;lt; 0.0001]) and PAQR4 (r = -0.71, P &amp;lt; 0.0001), which encodes a ceramidase in the adiponectin receptor family. In addition, the muscle transcripts that positively correlated with muscle mitochondrial ceramide content were strongly enriched for ECM and TGFβ-related pathways. Conclusions: Greater whole-body insulin sensitivity in people with MHO than MUO is associated with alterations in adipose tissue and skeletal muscle biology, namely decreased markers of ASAT ECM remodeling and inflammation, decreased skeletal muscle ceramide content, and increased skeletal muscle expression of PAQR4 and genes associated with mitochondrial content/function. Presentation: Friday, June 16, 2023

THU208 Predicting Treatment Quality Assessment Of Children With Congenital Adrenal Hyperplasia Using 24h Urine Metabolomics Profiling And A Machine Learning-assisted Approach

Abstract Disclosure: O. Abawi: None. G. Sommer: None. M. Groessl: None. U. Halbsguth: None. S.E. Hannema: None. C. de Bruin: None. E. Charmandari: None. E.L. van den Akker: None. A.B. Leichtle: None. C.E. Flueck: None. Introduction: Current treatment monitoring of children with congenital adrenal hyperplasia (CAH) relies on specialist’s interpretation of clinical and biochemical parameters, but remains dissatisfactory. Comprehensive 24h urine steroid profiling provides detailed insight into adrenal steroid pathways, but its merit in routine treatment monitoring of CAH is not yet established. Aim: To investigate whether 24h urine steroid profiling can predict treatment quality assessment in children with CAH using machine learning (ML). Methods: This prospective observational cohort study included children with genetically confirmed 21-hydroxylase deficiency. Children collected 24h urine at 2 outpatient clinic visits (mean 4.1 ± 0.7 months apart). Using gas chromatography-mass spectrometry, 40 adrenal steroids and metabolites from the classic, backdoor and 11-oxygenated pathways were analysed. Patients were classified as undertreated, optimally treated or overtreated by the pediatric endocrinologist based on detailed clinical and endocrinological evaluation including serum 17-hydroxyprogesterone and androstenedione. We used sparse partial least-squares discriminant analysis (sPLS-DA) to investigate optimal prediction of treatment quality assessment. This ML method computes components (combinations of all input variables) and selects the most discriminative parameters to classify samples (in our case optimally treated vs undertreated) by maximizing between-class variance. We computed area under the ROC-curve (AUC) of two sPLS-DA models: 1. using only 24h urine metabolites; 2. adding also clinical variables age, sex, pubertal status, CAH subtype (classic vs non-classic), medication (hydrocortisone [HC] vs prednisolone), daily HC-equivalent dose, Δbone age minus chronological age, ΔBMI-z, and Δheight-z. Results: We included 112 visits (68 [61%] optimally treated, 44 [39%] undertreated) of 59 patients: 27 (46%) girls, 46 (78%) classic CAH, 19 (32%) prepubertal. Mean age at first visit was 11.9 ± 4.0 years and mean BMI SDS 0.6 ± 1.1. SPLS-DA using 24h urine metabolites showed clear clustering of optimally treated patients on two components, while undertreated patients were more heterogenous (AUC 0.88; 95% CI 0.81-0.94). The model selected pregnanetriol and hydroxypregnanolon contributing to excluding undertreatment and 7 metabolites contributing to excluding optimal treatment: estradiol, cortison, tetrahydroaldosterone, androstenetriol, etiocholanolone, androstenediol, and α-dihydrocortison. Addition of clinical variables did not improve classification (AUC 0.90, 95% CI 0.84-0.96, P=0.59). Discussion: Using ML on 24h urine steroid profiling predicted treatment quality assessment in children with CAH even in absence of clinical data, suggesting that comprehensive 24h urine steroid profiling could improve treatment monitoring in children with CAH. Presentation: Thursday, June 15, 2023

Development of a metabolite calculator for diagnosis of pancreatic cancer.

Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy. A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses. Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively. We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.

OA40 Predicting cardiovascular disease risk in children and adolescents with juvenile-onset systemic lupus erythematosus using the APPLE (Atherosclerosis Prevention in Paediatric Lupus Erythematosus) clinical trial cohort

Abstract Background/Aims The risk of developing cardiovascular disease (CVD) through atherosclerosis in juvenile-onset systemic lupus erythematosus (JSLE) patients is significantly increased. This study aimed to stratify and characterise JSLE patients at elevated CVD risk using patient/disease-related factors and metabolomic data from patients recruited to the APPLE (Atherosclerosis Prevention in Paediatric Lupus Erythematosus) clinical trial, designed to assess atherosclerosis development. Methods Unsupervised hierarchical clustering was performed to stratify patients by arterial intima-media thickness (IMT) measurements at baseline (N = 151) and carotid (c)IMT progression over 36 months (placebo arm only, N = 60). Baseline metabolomic profiles (∼250 serum metabolites) were compared between clusters using conventional statistics, univariate logistic regression, sparse Partial Least-Squares Discriminant Analysis (sPLS-DA) and random forest classifier. An independent cohort (UCL-JSLE cohort, N = 89) with matching metabolomics, immunophenotyping and proteomics, was used to validate the discovered CVD risk-related signatures from the APPLE cohort. Results Baseline IMT stratification identified 3 clusters with high, intermediate, and low baseline IMT measurements and progression trajectories over 36 months, each having distinct racial/BMI/household education/income characteristics. Analysis of cIMT progression over 36 months identified 2 patient groups with high and low IMT progression. Unique metabolomic profiles differentiated high and low cIMT progression groups, with a good discriminatory ability (0.81 AUC in ROC analysis) using the top 6 metabolites (total cholesterol esters, total cholesterol, phospholipids in small LDL particles, total cholesterol in small LDL particles, free cholesterol in medium LDL particles, and total lipids in small LDL particles) selected from the analysis. cIMT progression over 36 months in the placebo group correlated positively with baseline disease activity (SLEDAI), damage score (SLICC), white blood cell count, serum complement C3, blood pressure (both systolic and diastolic) and BMI. Metabolomics signatures discovered from the APPLE cohort were applied to stratify JSLE patients in the validation cohort (UCL-JSLE), where 3 groups were identified with distinct metabolomics profiles indicating JSLE patients with high risk (N = 20), intermediate risk (N = 43) and low risk (N = 26) CVD-risk. Significant differences were observed in the frequency of classical monocytes (p = 0.015) and nonclassical monocytes (p = 0.005) when comparing the high and low CVD risk groups in the UCL-JSLE cohort. Conclusion Complex analysis of IMT patterns and progression in the APPLE trial cohort identified novel key determinants that could guide further research for CVD-risk stratification in JSLE. Disclosure J. Peng: None. G.A. Robinson: None. S.P. Ardoin: None. L.E. Schanberg: None. O. Carra: None. E. Jury: None. C. Ciurtin: None.

P266 Potential oral microbial markers for differential diagnosis of Crohn’s disease and ulcerative colitis using machine learning model

Abstract Background Although the gut microbiome dysbiosis have independently been shown to be associated with inflammatory bowel disease (IBD), less is known about the relationship between oral microbiota and IBD. This study aimed to elucidate unique microbiome patterns in saliva from patients with IBD and investigate potential oral microbial markers for differentiating Crohn’s disease (CD) and ulcerative colitis (UC). Methods A multicenter, prospective cohort study recruited patients with IBD (UC, n=175, CD, n=127) and unrelated healthy controls (HC, n=100) to examine microbiota within the oral microenvironments. We used 16S rRNA gene sequencing data as features in training machine learning models (sPLS-DA, Sparse Partial Least-Squares Discriminant Analysis) to classify CD and UC. Results The V3-V4 amplicon reads of the saliva 16S rRNA sequencing data were taxonomically classified to a total of 2839 taxa (2270 genera) using Kraken2 based on Silva 138.1 reference. The sequences that were not classifiable down to family level were removed, and the samples having sequence depth less than 30000 were also removed, resulting in 2616 taxa for 390 samples (UC, n=168, CD, n=124, HC, n=98). The alpha diversity analysis revealed that the microbiome in IBD patients were significantly less rich than the healthy controls, while CD samples were slightly richer then UC samples (Figure 1, Observed, P = 0.01, Shannon index, p=0.02, Chao index, P=0.0001). An sPLS-DA model with 470 taxa as features was able to distinguish IBD vs control with high performance (AUROC=0.9774), while a separate sPLS-DA model with 130 features classified CD vs UC with an AUROC of 0.8755 (Figure 2,3). Conclusion Collectively, oral microbial profiles can serve as a diagnostic marker to discriminate patients with IBD from HC, and patients with CD from UC. As obtaining oral samples is relatively easier than obtaining stool or intestinal biopsies, an opportunity exists to perform oral microbiome-based studies in larger cohort sizes, preferentially in a longitudinal fashion.

Metabolomic Profiles Associated with Obesity and Periodontitis during Pregnancy: Cross-Sectional Study with Proton Nuclear Magnetic Resonance (1H-NMR)-Based Analysis.

This study aimed to elucidate the metabolomic signature associated with obesity and periodontitis during pregnancy in plasma and saliva biofluids. Ninety-eight pregnant women were divided into: with obesity and periodontitis (OP = 20), with obesity but without periodontitis (OWP = 27), with normal BMI but with periodontitis (NP = 21), with normal BMI and without periodontitis (NWP = 30). Saliva and plasma were analyzed by 1H-NMR for metabolites identification. Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA (sPLS-DA), and Variable Importance of Projection (VIP) were performed. ANOVA and Pearson's correlation were applied (p &lt; 0.05). Plasmatic analysis indicated the levels of glucose (p = 0.041) and phenylalanine (p = 0.015) were positively correlated with periodontal parameters and BMI, respectively. In saliva, periodontitis was mainly associated with high levels of acetic acid (p = 0.024), isovaleric acid, butyric acid, leucine, valine, isoleucine, and propionic acid (p &lt; 0.001). High salivary concentrations of glycine (p = 0.015), succinic acid (p = 0.015), and lactate (p = 0.026) were associated with obesity. Saliva demonstrated a more elucidative difference than plasma, indicating the glucose-alanine cycle, alanine metabolism, valine, leucine and isoleucine degradation, glutamate metabolism, and Warburg effect as the main metabolic pathways.

Associating changes in the bacterial community of rumen and faeces and milk fatty acid profiles in dairy cows fed high-starch or starch and oil-supplemented diets.

The experiment reported in this research paper aimed to evaluate the effects of high-starch or starch and oil-supplemented diets on rumen and faecal bacteria, and explore links between the structure of bacterial communities and milk fatty acid (FA) profiles. We used four Holstein dairy cows in a 4 × 4 Latin square design. Cows were fed a diet rich in cereals (high-starch diet with 23% starch content on dry matter (DM) basis), a diet supplemented with saturated FA from Ca salts of palm oil + 18% DM starch, a diet with high content of monounsaturated FA (from extruded rapeseeds) + 18% DM starch or a diet rich in polyunsaturated FA (from extruded sunflower seeds) + 17% DM starch. At the end of each experimental period, cows were sampled for rumen and faecal contents, which were used for DNA extraction and amplicon sequencing. Partial least squares (PLS) regression analysis highlighted diet-related changes in both rumen and faecal bacterial structures. Sparse PLS discriminant analysis was further employed to identify biologically relevant operational taxonomical units (OTUs) driving these differences. Our results show that Butyrivibrio discriminated the high-starch diet and linked positively with higher concentrations of milk odd- and branched-chain FA. YS2-related OTUs were key taxa distinguishing diets supplemented with Ca salts of palm oil or sunflower seeds and correlated positively with linoleic acid in milk. Similarly, diets modulated faecal bacterial composition. However, correlations between changes in faecal and rumen bacteria were poor. With this work, we demonstrated that high-starch or lipid-supplemented diets affect rumen and faecal bacterial community structure, and these changes could have a knock-on effect on milk FA profiles.

1331-P: Associations between Insulin Resistance and Plasma Lipids and Metabolomics in Women with and without Type 1 Diabetes

Insulin resistance (IR) increases risk for cardiovascular disease and mortality in people with type 1 diabetes (T1D) . We previously found associations between plasma lipids, metabolites and IR; whether these associations differ in people with and without T1D is not well understood. We analyzed targeted plasma lipidomics and untargeted plasma metabolomics in 25 premenopausal women aged 18-45 yrs (12 with T1D and 13 without diabetes [non-DM]) who had a hyperinsulinemic euglycemic clamp to measure glucose infusion rate (GIR) . After stratifying by diabetes, women were classified by tertiles of GIR. We used sparse partial least-squares discriminant analysis to predict and moderated t-tests to compare most IR vs. most insulin sensitive (IS) tertile. Among women with T1D, 3 lipids, 4 amino acids, 3 peptides, 3 xenobiotics, 1 carbohydrate, and 1 nucleotide were lower and 1 lipid and 1 fatty acid were higher in the most IS tertile (Table) . Among non-DM women, 4 lipids, 1 cofactor, 1 fatty acid, and 1 phosphatidylethanolamine were lower and 1 nucleotide and 1 lipid were higher in the most IS tertile. None of the top features were the same in the T1D and non-DM groups. Plasma lipids and metabolites were differentially associated with IR in women with and without T1D. Future work should validate these findings in a larger sample of women and men, and explore how these biomarkers relate to IR and metabolism in T1D. Disclosure T.B.Vigers: None. L.Pyle: None. I.E.Schauer: None. B.C.Bergman: Research Support; Eli Lilly and Company. J.K.Snell-bergeon: Stock/Shareholder; GlaxoSmithKline plc. Funding American Diabetes Association (7-13-CD-10) ; National Heart, Lung, and Blood Institute (grant HL61753 and HL113029) , JDRF Diabetes Foundation (grant 17-2013-313) , DERC Clinical Investigation Core (grant P30 DK57516) , NIH-M01-RR00051, NIH P30-DK116073

Remote sensing of invasive alien wattle using image texture ratios in the low-lying Midlands of KwaZulu-Natal, South Africa

The presence of invasive alien plants (IAPs) cause dynamic structural and ecological change, since they encroach on the productivity of ecosystems with their high-water consumption, ability to rapidly spread and reduce natural biodiversity. Remote sensing techniques utilised for early detection and mapping of invasive alien wattle (Acacia mearnsii) are pivotal towards understanding the spatial distribution of IAPs for control and management. In this study, we assessed the utility of image texture ratios computed from a 6 m SPOT-6 image combined with the sparse partial least squares discriminant analysis (SPLS-DA) algorithm to detect and map invasive alien wattle, based on field information related to the presence of these invasive species. We specifically tested the performance of single band and multi-band texture metrices, as well as vegetation indices in detecting and mapping invasive alien wattle. The results revealed that the image texture ratio model (overall accuracy = 74%; kappa statistic = 0.70) outcompeted the single image texture model (overall accuracy = 68%; kappa statistic = 0.65) and vegetation indices model (overall accuracy = 62%; kappa statistic = 0.59). The most significant texture filters selected by the SPLS-DA model were correlation, second moment and homogeneity, which were predominantly computed from the NIR and red bands. In addition, the 5 × 5 moving window was the most optimal window-size for detecting the alien invasive. Moreover, the SPLS-DA model outcompeted the partial least squares linear discriminant analysis (PLS-LDA) model in detecting invasive alien wattle over the entire study area. Overall, this study underscores the potential of image texture ratios in concert with SPLS-DA in effectively detecting and mapping invasive alien wattle.

Identification of High and Low Branched-Chain Fatty Acid–Producing Phenotypes in Holstein Cows following High-Forage and Low-Forage Diets in a Crossover Designed Trial

Branched-chain fatty acids (BCFAs) are rumen-derived fatty acids comprising ∼2% of bovine-milk fatty acids. BCFAs possess anti-inflammatory properties and enriching the BCFA content of bovine milk may provide human health benefits. We determined whether forage content impacts the BCFA content of milk from Holstein cows and identified fatty acid phenotypes in high vs. low BCFA-containing milks. Holstein cows (n = 62), fed for 67 d in a crossover design, consumed a diet with high forage and low concentrate (HF:C) and a diet with low forage and high concentrate (LF:C). Milk samples were collected at the end of each treatment period and fatty acid content determined. Paired t-tests, 1-factor ANOVA, sparse partial least-squares discriminant analysis (sPLSDA), and Pearson's correlation analysis were used to analyze the data. The total milk fatty acid concentration for cows fed the HF:C diet was greater than that of cows fed the LF:C diet (4.2 ± 0.7 g/100 mL vs. 3.9 ± 0.9 g/100 mL). sPLSDA demonstrated separation of the dietary treatments, with BCFAs and odd-chain fatty acids as primary determinants. Total BCFA content in milk fat was elevated by HF:C intake compared with LF:C intake (1.80 vs. 1.68%). Quintile separation of high vs. low BCFA milks resulted in 4 groups: HF:C /low BCFAs, HF:C /high BCFAs; LF:C /low BCFAs, and LF:C /high BCFAs. Milks from the high BCFA quintiles had lower palmitic acid content (29.6% vs. 34.4%) but higher oleic acid content than milks from the low BCFA quintiles (19.7% vs. 17.0%). Some cows were identified as high BCFA producers or low BCFA producers regardless of diet. BCFA content of milk is diet-sensitive but variation in responses exists. The potential to produce milk with high BCFA content and lower SFA content needs further study.

Raspberry consumption: identification of distinct immune-metabolic response profiles by whole blood transcriptome profiling

Numerous studies have reported that diets rich in phenolic compounds are beneficial to immune-metabolic health, yet these effects are heterogeneous and the underlying mechanisms are poorly understood. To investigate the inter-individual variability of the immune-metabolic response to raspberry consumption, whole-blood RNAseq data from 24 participants receiving 280 g/d of raspberries for 8 weeks were used for the identification of responsiveness subgroups by using partial least squares-discriminant analysis (PLSDA) and hierarchical clustering. Transcriptomic-based clustering regrouped participants into two distinct subgroups of 13 and 11 participants, so-called responders and non-responders, respectively. Following raspberry consumption, a significant decrease in triglycerides, cholesterol and C-reactive protein levels were found in responders, as compared to non-responders. Two major gene expression components of 100 and 220 genes were identified by sparse PLSDA as those better discriminating responders from non-responders, and functional analysis identified pathways related to cytokine production, leukocyte activation and immune response as significantly enriched with most discriminant genes. As compared to non-responders, the plasma lipidomic profile of responders was characterized by a significant decrease in triglycerides and an increase in phosphatidylcholines following raspberry consumption. Prior to the intervention, a distinct metagenomic profile was identified by PLSDA between responsiveness subgroups, and the Firmicutes-to-Bacteroidota ratio was found significantly lower in responders, as compared to non-responders. Findings point to this transcriptomic-based clustering approach as a suitable tool to identify distinct responsiveness subgroups to raspberry consumption. This approach represents a promising framework to tackle the issue of inter-individual variability in the understanding of the impact of foods on immune-metabolic health.

Azoxymethane Alters the Plasma Metabolome to a Greater Extent in Mice Fed a High-Fat Diet Compared to an AIN-93 Diet.

Consumption of a high-fat diet (HFD) links obesity to colon cancer in humans. Our data show that a HFD (45% energy fat versus 16% energy fat in an AIN-93 diet (AIN)) promotes azoxymethane (AOM)-induced colonic aberrant crypt foci (ACF) formation in a mouse cancer model. However, the underlying metabolic basis remains to be determined. In the present study, we hypothesize that AOM treatment results in different plasma metabolomic responses in diet-induced obese mice. An untargeted metabolomic analysis was performed on the plasma samples by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). We found that 53 of 144 identified metabolites were different between the 4 groups of mice (AIN, AIN + AOM, HFD, HFD + AOM), and sparse partial least-squares discriminant analysis showed a separation between the HFD and HFD + AOM groups but not the AIN and AIN + AOM groups. Moreover, the concentrations of dihydrocholesterol and cholesterol were inversely associated with AOM-induced colonic ACF formation. Functional pathway analyses indicated that diets and AOM-induced colonic ACF modulated five metabolic pathways. Collectively, in addition to differential plasma metabolomic responses, AOM treatment decreases dihydrocholesterol and cholesterol levels and alters the composition of plasma metabolome to a greater extent in mice fed a HFD compared to the AIN.

Utility of texture combinations computed from fused WorldView-2 imagery in discriminating commercial forest species

Commercial forest species discrimination is valuable for optimal management of commercial forests. Therefore, second-order image texture combinations computed from a 0.5 m WorldView-2 pan-sharpened image integrated with sparse partial least squares discriminant analysis (SPLS-DA) and partial least squares discriminant analysis (PLS-DA) were used to discriminate commercial forest species. The findings show that the SPLS-DA model, which is characterised by concurrent variable selection and reduction of data dimensionality, produced an overall classification accuracy of 86%, with an allocation disagreement of 9 and a quantity disagreement of 5. Conversely, the PLS-DA model with variable importance in projection (VIP) produced an overall classification accuracy of 81%, with an allocation disagreement of 12 and a quantity disagreement of 7. Overall, this study demonstrates the value of second-order image texture combinations in discriminating commercial forest species and presents an opportunity for improved commercial forest species delineation.

Cockroach-induced IL9, IL13, and IL31 expression and the development of allergic asthma in urban children

Cockroach-induced IL9, IL13, and IL31 expression and the development of allergic asthma in urban children

Fasting plasma metabolomic profiles are altered by three days of standardized diet and restricted physical activity

ObjectiveFew studies have examined the effects of participants’ diet and activity prior to sample collection on metabolomics profiles, and results have been conflicting. We compared the effects of overnight fasting with or without 3 days of standardized diet and restricted physical activity on the human blood metabolome, and examined the effects of these protocols on our ability to detect differences in metabolomics profiles in adolescent girls with obesity and polycystic ovary syndrome (PCOS) vs. sex and BMI-matched controls. MethodsThis was a cross-sectional study of 16 adolescent girls with obesity and PCOS and 5 sex and BMI-matched controls. Fasting plasma metabolomic profiles were measured twice in each participant: once without preceding restriction of physical activity or control of macronutrient content (“typical fasting visit”), and again after 12 h of monitored inpatient fasting with 3 days of standardized diet and avoidance of vigorous exercise (“controlled fasting visit”). Moderated paired t-tests with FDR correction for multiple testing and multilevel sparse partial least-squares discriminant analysis (sPLS-DA) were used to examine differences between the 2 visits and to compare the PCOS and control groups with the 2 visits combined and again after stratifying by visit. ResultsTwenty-three known metabolites were significantly different between the controlled fasting and typical fasting visits. Hypoxanthine and glycochenodeoxycholic acid had the largest increases in relative abundance at the controlled fasting visit compared to the typical fasting visit, while oleoyl-glycerol and oleamide had the largest increases in relative abundance at the typical fasting visit compared to the controlled fasting visit. sPLS-DA showed excellent discrimination between the 2 visits; however, when the samples from the 2 visits were combined, differences between the PCOS and control groups could not be detected. After stratifying by visit, discrimination of PCOS status was improved. ConclusionsThere were differences in fasting metabolomic profiles following typical fasting vs monitored fasting with preceding restriction of physical activity and control of macronutrient content, and combining samples from the two visits obscured differences by PCOS status. In studies performing metabolomics analysis, careful attention should be paid to acute diet and activity history. Depending on the sample size of the study and the expected effect size of the outcomes of interest, control of diet and physical activity beyond typical outpatient fasting may not be required.

Untargeted Chemometrics Evaluation of the Effect of Juicing Technique on Phytochemical Profiles and Antioxidant Activities in Common Vegetables

This study used ultra-high-performance liquid chromatography combined with ion-trap time-of-flight mass spectrometry (UHPLC-QTOF-MS) and complementary metabolomics approaches to investigate the effects of three processing techniques (blending, high-speed centrifugal juicing, and low-speed juicing) on the phytochemical profiles and antioxidant activities of 19 vegetables, including kale and beets. UHPLC-QTOF-MS combined with chemometric sparse partial least-squares discriminant analysis and heat map approaches identified 85 different metabolites. Kaempferol, quercetin glycosides, and amino acid-attached betaxanthins were identified as potential markers that differentiate the processing techniques, with higher relative abundances in kale juices produced in a low-speed juicer, followed by beet juices produced in a high-speed centrifugal juicer. The antioxidant activities were significantly different in juices produced by the three processing techniques and in different vegetable varieties, with the green kale juice produced by the low-speed juicer having the highest level of total phenolics (1201.31 μg/g of gallic acid equivalents) and DPPH value (1235.53 μg/g of ascorbic acid equivalents).

In Situ Metabolomics Expands the Spectrum of Renal Tumours Positive on 99mTc-sestamibi Single Photon Emission Computed Tomography/Computed Tomography Examination

BackgroundDefinite noninvasive characterisation of renal tumours positive on 99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) examination including renal oncocytomas (ROs), hybrid oncocytic chromophobe tumours (HOCTs), and chromophobe renal cell carcinoma (chRCC) is currently not feasible. ObjectiveTo investigate whether combined 99mTc-sestamibi SPECT/CT and in situ metabolomic profiling can accurately characterise renal tumours exhibiting 99mTc-sestamibi uptake. Design, setting, and participantsA tissue microarray analysis of 33 tumour samples from 28 patients was used to investigate whether their in situ metabolomic status correlates with their features on 99mTc-sestamibi SPECT/CT examination. In order to validate emerging data, an independent cohort comprising 117 tumours was subjected to matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI). Outcome measurements and statistical analysisMALDI MSI data analysis and image generation were facilitated by FlexImaging v. 4.2, while k-means analysis by SCiLS Lab software followed by R-package CARRoT analysis was used for assessing the highest predictive power in the differential of RO versus chRCC. Heatmap-based clustering, sparse partial least-squares discriminant analysis, and volcano plots were created with MetaboAnalyst 3.0. Results and limitationsWe identified a discriminatory metabolomic signature for 99mTc-sestamibi SPECT/CT–positive Birt-Hogg-Dubè–associated HOCTs versus other renal oncocytic tumours. Metabolomic differences were also evident between 99mTc-sestamibi–positive and 99mTc-sestamibi–negative chRCCs, prompting additional expert review; two of three 99mTc-sestamibi–positive chRCCs were reclassified as low-grade oncocytic tumours (LOTs). Differences were identified between distal-derived tumours from those of proximal tubule origin, including differences between ROs and chRCCs. ConclusionsThe current study expands the spectrum of 99mTc-sestamibi SPECT/CT–positive renal tumours, encompassing ROs, HOCTs, LOTs, and chRCCs, and supports the feasibility of in situ metabolomic profiling in the diagnostics and classification of renal tumours. Patient summaryFor preoperative evaluation of solid renal tumours, 99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) is a novel examination method. To increase diagnostic accuracy, we propose that 99mTc-sestamibi–positive renal tumours should be biopsied and followed by a combined histometabolomic analysis.