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

Influence of wheat content and origin on the volatilome of craft wheat beer: An investigation by combined multivariate statistical approaches

A deeper knowledge of the effect of wheat origin on the volatile organic compounds (VOCs) profile of craft wheat beer is crucial for its quality improvement and local product valorisation. The VOCs profile of 17 craft wheat beers obtained by common and durum, heritage and modern, wheat varieties grown in different fields sited at different altitudes was analysed. Data were processed by multivariate analysis using different approaches. Partial least square (PLS) analysis evidenced that wheat concentration was the highest source of VOCs variance, followed by, wheat species, wheat ancientness, and altitude of cultivation. An insight into the effect of wheat concentration was given by sparse PLS analysis (sPLS). The effect of wheat variety was explored by linear discriminant analysis (LDA), which permitted to correctly classify craft beers made with wheat of different origin (species and variety) on the basis of their VOCs profile. sPLS regression analysis permitted to find a combination of VOCs able to predict the altitude of wheat cultivation as well as to correctly classify wheat beers made with wheat cultivated at different altitudes. A further 'one versus all' approach by Soft Independent Modelling of Class Analogies (SIMCA) permitted to correctly authenticate beers made with different cereal species. Finally, shape analysis by generalized Procrustes analysis (GPA) revealed that the differences among samples were conserved and reflected from wheat kernels to wheat beers. This study suggests a promising use of volatiles fingerprinting with a combination of different statistical approaches to authenticate beer made with wheat of different origin and cultivated at different altitudes, thus stressing out the importance of territory in craft beer production, which, until now, was a neglected topic.

Different depths of food restriction and high-fat diet refeeding in mice impact host obesity and metabolic phenotypes with correlative changes in the gut microbiota

Overweight and obesity affect almost 2 billion adults worldwide, and food restriction (FR) is commonly used to reduce body fat. Whether refeeding (Re) after FR at different ages and to different degrees leads to overweight and its possible mechanisms are uncertain. In this study, adult and young mice were both restricted to 15% and 40% of their casual food intake, and then were fed 60% high-fat chow (FR15%-Re, FR40%-Re), whereas the control groups(CON) consumed high-fat or normal food throughout, respectively. The results of the study suggest that mild FR-heavy feeding may lead to more significant abnormal fat accumulation, liver damage, and increased recruitment of intestinal inflammatory factors and immune cells in mice of different ages and involves multiple types of alterations in the gut microbiota. Further fecal transplantation experiments as well as serum and liver enzyme-linked immunosorbent assay experiments preliminarily suggest that the link between lipid metabolism and inflammatory responses and the gut microbiota may be related to the regulation of the gut and live by Lipopolysaccharides(LPS) and Peroxisome Proliferator-Activated Receptor-Alpha(PPAR-α). In addition, our study may also serve as a reference for studying obesity prevention and treatment programs at different ages.

Conjugated linoleic acid (CLA) modulates bovine peripheral blood mononuclear cells (PBMC) proteome in vitro

Conjugated linoleic acid (CLA) is a group of natural isomers of the n-6 polyunsaturated fatty acid (PUFA) linoleic acid, exerting biological effects on cow physiology. This study assessed the impact of the mixture 50:50 (vol:vol) of CLA isomers (cis-9, trans-11 and trans-10, cis-12) on bovine peripheral blood mononuclear cells (PBMC) proteome, identifying 1608 quantifiable proteins. A supervised multivariate statistical analysis, sparse variant partial least squares - discriminant analysis (sPLS-DA) for paired data identified 407 discriminant proteins (DP), allowing the clustering between the CLA and controls. The ProteINSIDE workflow found that DP with higher abundance in the CLA group included proteins related to innate immune defenses (PLIN2, CD36, C3, C4, and AGP), with antiapoptotic (SERPINF2 and ITIH4) and antioxidant effects (HMOX1). These results demonstrated that CLA modulates the bovine PBMC proteome, supports the antiapoptotic and immunomodulatory effects observed in previous in vitro studies on bovine PBMC, and suggests a cytoprotective role against oxidative stress. SIGNIFICANCE: In this study, we report for the first time that the mixture 50:50 (vol:vol) of cis-9, trans-11, and trans-10, cis-12-CLA isomers modulates the bovine PBMC proteome. Our results support the immunomodulatory and antiapoptotic effects observed in bovine PBMC in vitro. In addition, the present study proposes a cytoprotective role of CLA mixture against oxidative stress. We suggest a molecular signature of CLA treatment based on combining a multivariate sparse discriminant analysis and a clustering method. This demonstrates the great value of sPLS-DA as an alternative option to identify discriminant proteins with relevant biological significance.

High and low pathogenicity avian influenza virus discrimination and prediction based on volatile organic compounds signature by SIFT-MS: a proof-of-concept study

High and low pathogenicity avian influenza viruses (HPAIV, LPAIV) are the primary causes of poultry diseases worldwide. HPAIV and LPAIV constitute a major threat to the global poultry industry. Therefore, early detection and well-adapted surveillance strategies are of the utmost importance to control the spread of these viruses. Volatile Organic Compounds (VOCs) released from living organisms have been investigated over the last decades as a diagnostic strategy. Mass spectrometry instruments can analyze VOCs emitted upon viral infection. Selected ion flow tube mass spectrometry (SIFT-MS) enables direct analysis of cell headspace in less than 20 min. As a proof-of-concept study, we investigated the ability of a SIFT-MS coupled sparse Partial Least Square-Discriminant Analysis analytical workflow to discriminate IAV-infected cells. Supernatants of HPAIV, LPAIV, and control cells were collected from 1 to 72 h post-infection and analyzed using our analytical workflow. At each collection point, VOCs' signatures were first identified based on four independent experiments and then used to discriminate the infectious status of external samples. Our results indicate that the identified VOCs signatures successfully discriminate, as early as 1-h post-infection, infected cells from the control cells and differentiated the HPAIV from the LPAIV infection. These results suggest a virus-dependent VOCs signature. Overall, the external samples' status was identified with 96.67% sensitivity, 100% specificity, and 97.78% general accuracy.

Machine learning to identify endometrial biomarkers predictive of pregnancy success following artificial insemination in dairy cows†

The objective was to identify a set of genes whose transcript abundance is predictive of a cow's ability to become pregnant following artificial insemination. Endometrial epithelial cells from the uterine body were collected for RNA sequencing using the cytobrush method from 193 first-service Holstein cows at estrus prior to artificial insemination (day 0). A group of 253 first-service cows not used for cytobrush collection were controls. There was no effect of cytobrush collection on pregnancy outcomes at day 30 or 70 or on pregnancy loss between days 30 and 70. There were 2 upregulated and 214 downregulated genes (false discovery rate < 0.05, absolute fold change >2-fold) for cows pregnant at day 30 versus those that were not pregnant. Functional terms overrepresented in the downregulated genes included those related to immune and inflammatory responses. Machine learning for fertility biomarkers with the R package BORUTA resulted in identification of 57 biomarkers that predicted pregnancy outcome at day 30 with an average accuracy of 77%. Thus, machine learning can identify predictive biomarkers of pregnancy in endometrium with high accuracy. Moreover, sampling of endometrial epithelium using the cytobrush can help understand functional characteristics of the endometrium at artificial insemination without compromising cow fertility. Functional characteristics of the genes comprising the set of biomarkers is indicative that a major determinant of cow fertility, at least for first insemination after calving, is immune status of the uterus, which, in turn, is likely to reflect the previous history of uterine disease.

The salivary microbiome as a diagnostic biomarker of periodontitis: a 16S multi-batch study before and after the removal of batch effects

Introduction

Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs.

Methods

Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment.

Results

The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200-2.03%; training=100-1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%).

Conclusions

The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis.

Association between the oral microbiome and brain resting state connectivity in schizophrenia

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10-3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.

Integrating host and microbiome biology using holo-omics

Holo-omics is the use of omics data to study a host and its inherent microbiomes - a biological system known as a "holobiont". A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host-microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host-microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host-microbiome interplay to make meaningful interpretations and biotechnological applications.

Impaired metal perception and regulation of associated human foliate papillae tongue transcriptome in long-COVID-19

Chemosensory impairment is an outstanding symptom of SARS-CoV-2 infections. We hypothesized that measured sensory impairments are accompanied by transcriptomic changes in the foliate papillae area of the tongue. Hospital personnel with known SARS-CoV-2 immunoglobulin G (IgG) status completed questionnaires on sensory perception (n = 158). A subcohort of n = 141 participated in forced choice taste tests, and n = 43 participants consented to donate tongue swabs of the foliate papillae area for whole transcriptome analysis. The study included four groups of participants differing in IgG levels (≥ 10 AU/mL = IgG+; < 10 AU/mL = IgG-) and self-reported sensory impairment (SSI±). IgG+ subjects not detecting metallic taste had higher IgG+ levels than IgG+ participants detecting iron gluconate (p = 0.03). Smell perception was the most impaired biological process in the transcriptome data from IgG+/SSI+ participants subjected to gene ontology enrichment. IgG+/SSI+ subjects demonstrated lower expression levels of 166 olfactory receptors (OR) and 9 taste associated receptors (TAS) of which OR1A2, OR2J2, OR1A1, OR5K1 and OR1G1, as well as TAS2R7 are linked to metallic perception. The question raised by this study is whether odorant receptors on the tongue (i) might play a role in metal sensation, and (ii) are potential targets for virus-initiated sensory impairments, which needs to be investigated in future functional studies.

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN's demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.

Identification of metabolites associated with preserved muscle volume after aneurysmal subarachnoid hemorrhage due to high protein supplementation and neuromuscular electrical stimulation

The INSPIRE randomized clinical trial demonstrated that a high protein diet (HPRO) combined with neuromuscular electrical stimulation (NMES) attenuates muscle atrophy and may improve outcomes after aneurysmal subarachnoid hemorrhage We sought to identify specific metabolites mediating these effects. Blood samples were collected from subjects on admission prior to randomization to either standard of care (SOC; N = 12) or HPRO + NMES (N = 12) and at 7 days. Untargeted metabolomics were performed for each plasma sample. Sparse partial least squared discriminant analysis identified metabolites differentiating each group. Correlation coefficients were calculated between each metabolite and total protein per day and muscle volume. Multivariable models determined associations between metabolites and muscle volume. Unique metabolites (18) were identified differentiating SOC from HPRO + NMES. Of these, 9 had significant positive correlations with protein intake. In multivariable models, N-acetylleucine was significantly associated with preserved temporalis [OR 1.08 (95% CI 1.01, 1.16)] and quadricep [OR 1.08 (95% CI 1.02, 1.15)] muscle volume. Quinolinate was also significantly associated with preserved temporalis [OR 1.05 (95% CI 1.01, 1.09)] and quadricep [OR 1.04 (95% CI 1.00, 1.07)] muscle volume. N-acetylserine and β-hydroxyisovaleroylcarnitine were associated with preserved temporalis or quadricep volume. Metabolites defining HPRO + NMES had strong correlations with protein intake and were associated with preserved muscle volume.

Beneficial Effects of Synbiotics on the Gut Microbiome in Individuals with Low Fiber Intake: Secondary Analysis of a Double-Blind, Randomized Controlled Trial

Insufficient dietary fiber intake can negatively affect the intestinal microbiome and, over time, may result in gut dysbiosis, thus potentially harming overall health. This randomized controlled trial aimed to improve the gut microbiome of individuals with low dietary fiber intake (<25 g/day) during a 7-week synbiotic intervention. The metabolically healthy male participants (n = 117, 32 ± 10 y, BMI 25.66 ± 3.1 kg/m2) were divided into two groups: one receiving a synbiotic supplement (Biotic Junior, MensSana AG, Forchtenberg, Germany) and the other a placebo, without altering their dietary habits or physical activity. These groups were further stratified by their dietary fiber intake into a low fiber group (LFG) and a high fiber group (HFG). Stool samples for microbiome analysis were collected before and after intervention. Statistical analysis was performed using linear mixed effects and partial least squares models. At baseline, the microbiomes of the LFG and HFG were partially separated. After seven weeks of intervention, the abundance of SCFA-producing microbes significantly increased in the LFG, which is known to improve gut health; however, this effect was less pronounced in the HFG. Beneficial effects on the gut microbiome in participants with low fiber intake may be achieved using synbiotics, demonstrating the importance of personalized synbiotics.

Comparative genomic analysis of strain Priestia megaterium B1 reveals conserved potential for adaptation to endophytism and plant growth promotion

In our study, we aimed to explore the genomic and phenotypic traits of Priestia megaterium strain B1, which was isolated from root material of healthy apple plants, to adapt to the endophytic lifestyle and promote plant growth. We identified putative genes encoding proteins involved in chemotaxis, flagella biosynthesis, biofilm formation, secretory systems, detoxification, transporters, and transcription regulation. Furthermore, B1 exhibited both swarming and swimming motilities, along with biofilm formation. Both genomic and physiological analyses revealed the potential of B1 to promote plant growth through the production of indole-3-acetic acid and siderophores, as well as the solubilization of phosphate and zinc. To deduce potential genomic features associated with endophytism across members of P. megaterium strains, we conducted a comparative genomic analysis involving 27 and 31 genomes of strains recovered from plant and soil habitats, respectively, in addition to our strain B1. Our results indicated a closed pan genome and comparable genome size of strains from both habitats, suggesting a facultative host association and adaptive lifestyle to both habitats. Additionally, we performed a sparse Partial Least Squares Discriminant Analysis to infer the most discriminative functional features of the two habitats based on Pfam annotation. Despite the distinctive clustering of both groups, functional enrichment analysis revealed no significant enrichment of any Pfam domain in both habitats. Furthermore, when assessing genetic elements related to adaptation to endophytism in each individual strain, we observed their widespread presence among strains from both habitats. Moreover, all members displayed potential genetic elements for promoting plant growth.IMPORTANCEBoth genomic and phenotypic analyses yielded valuable insights into the capacity of P. megaterium B1 to adapt to the plant niche and enhance its growth. The comparative genomic analysis revealed that P. megaterium members, whether derived from soil or plant sources, possess the essential genetic machinery for interacting with plants and enhancing their growth. The conservation of these traits across various strains of this species extends its potential application as a bio-stimulant in diverse environments. This significance also applies to strain B1, particularly regarding its application to enhance the growth of plants facing apple replant disease conditions.

Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics

Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease

Background

Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD.

Method

The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants.

Results

Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]).

Conclusion

The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Serum metabolome analysis in hyperthyroid cats before and after radioactive iodine therapy

Hyperthyroidism is the most common feline endocrinopathy. In hyperthyroid humans, untargeted metabolomic analysis identified persistent metabolic derangements despite achieving a euthyroid state. Therefore, we sought to define the metabolome of hyperthyroid cats and identify ongoing metabolic changes after treatment. We prospectively compared privately-owned hyperthyroid cats (n = 7) admitted for radioactive iodine (I-131) treatment and euthyroid privately-owned control (CON) cats (n = 12). Serum samples were collected before (T0), 1-month (T1), and three months after (T3) I-131 therapy for untargeted metabolomic analysis by MS/MS. Hyperthyroid cats (T0) had a distinct metabolic signature with 277 significantly different metabolites than controls (70 increased, 207 decreased). After treatment, 66 (T1 vs. CON) and 64 (T3 vs. CON) metabolite differences persisted. Clustering and data reduction analysis revealed separate clustering of hyperthyroid (T0) and CON cats with intermediate phenotypes after treatment (T1 & T3). Mevalonate/mevalonolactone and creatine phosphate were candidate biomarkers with excellent discrimination between hyperthyroid and healthy cats. We found several metabolic derangements (e.g., decreased carnitine and α-tocopherol) do not entirely resolve after achieving a euthyroid state after treating hyperthyroid cats with I-131. Further investigation is warranted to determine diagnostic and therapeutic implications for candidate biomarkers and persistent metabolic abnormalities.

Multi-omics Analysis of a Fecal Microbiota Transplantation Trial Identifies Novel Aspects of Acute GVHD Pathogenesis

Acute GVHD (aGVHD) is a major complication of allogeneic hematopoietic cell transplantation (alloHCT) associated with gut microbiota disruptions. However, whether therapeutic microbiota modulation prevents aGVHD is unknown. We conducted a randomized, placebo-controlled trial of third-party fecal microbiota transplantation (FMT) administered at the peak of microbiota injury in 100 patients with acute myeloid leukemia receiving induction chemotherapy and alloHCT recipients. Despite improvements in microbiome diversity, expansion of commensals, and shrinkage of potential pathogens, aGVHD occurred more frequently after FMT than placebo. Although this unexpected finding could be explained by clinical differences between the two arms, we asked whether a microbiota explanation might be also present. To this end, we performed multi-omics analysis of preintervention and postintervention gut microbiome and serum metabolome. We found that postintervention expansion of Faecalibacterium, a commensal genus with gut-protective and anti-inflammatory properties under homeostatic conditions, predicted a higher risk for aGVHD. Faecalibacterium expansion occurred predominantly after FMT and was due to engraftment of unique donor taxa, suggesting that donor Faecalibacterium-derived antigens might have stimulated allogeneic immune cells. Faecalibacterium and ursodeoxycholic acid (an anti-inflammatory secondary bile acid) were negatively correlated, offering an alternative mechanistic explanation. In conclusion, we demonstrate context dependence of microbiota effects where a normally beneficial bacteria may become detrimental in disease. While FMT is a broad, community-level intervention, it may need precision engineering in ecologically complex settings where multiple perturbations (e.g., antibiotics, intestinal damage, alloimmunity) are concurrently in effect.

SIGNIFICANCE

Post-FMT expansion of Faecalibacterium, associated with donor microbiota engraftment, predicted a higher risk for aGVHD in alloHCT recipients. Although Faecalibacterium is a commensal genus with gut-protective and anti-inflammatory properties under homeostatic conditions, our findings suggest that it may become pathogenic in the setting of FMT after alloHCT. Our results support a future trial with precision engineering of the FMT product used as GVHD prophylaxis after alloHCT.

Early life adversity impacts alterations in brain structure and food addiction in individuals with high BMI

Obesity and food addiction are associated with distinct brain signatures related to reward processing, and early life adversity (ELA) also increases alterations in these same reward regions. However, the neural mechanisms underlying the effect of early life adversity on food addiction are unknown. Therefore, the aim of this study was to examine the interactions between ELA, food addiction, and brain morphometry in individuals with obesity. 114 participants with high body mass index (BMI) underwent structural MRIs, and completed several questionnaires (e.g., Yale Food Addiction Scale (YFAS), Brief Resilience Scale (BRS), Early Traumatic Inventory (ETI)). Freesurfer 6 was applied to generate the morphometry of brain regions. A multivariate pattern analysis was used to derive brain morphometry patterns associated with food addiction. General linear modeling and mediation analyses were conducted to examine the effects of ELA and resilience on food addiction in individuals with obesity. Statistical significance was determined at a level of p < 0.05. High levels of ELA showed a strong association between reward control brain signatures and food addiction (p = 0.03). Resilience positively mediated the effect of ELA on food addiction (B = 0.02, p = 0.038). Our findings suggest that food addiction is associated with brain signatures in motivation and reward processing regions indicative of dopaminergic dysregulation and inhibition of cognitive control regions. These mechanistic variabilities along with early life adversity suggest increased vulnerability to develop food addiction and obesity in adulthood, which can buffer by the neuroprotective effects of resilience, highlighting the value of incorporating cognitive appraisal into obesity therapeutic regimens.

Gut microbiota from B-cell-specific TLR9-deficient NOD mice promote IL-10+ Breg cells and protect against T1D

Introduction

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of insulin-producing β cells. Toll-like receptor 9 (TLR9) plays a role in autoimmune diseases, and B cell-specific TLR9 deficiency delays T1D development. Gut microbiota are implicated in T1D, although the relationship is complex. However, the impact of B cell-specific deficiency of TLR9 on intestinal microbiota and the impact of altered intestinal microbiota on the development of T1D are unclear.

Objectives

This study investigated how gut microbiota and the intestinal barrier contribute to T1D development in B cell-specific TLR9-deficient NOD mice. Additionally, this study explored the role of microbiota in immune regulation and T1D onset.

Methods

The study assessed gut permeability, gene expression related to gut barrier integrity, and gut microbiota composition. Antibiotics depleted gut microbiota, and fecal samples were transferred to germ-free mice. The study also examined IL-10 production, Breg cell differentiation, and their impact on T1D development.

Results

B cell-specific TLR9-deficient NOD mice exhibited increased gut permeability and downregulated gut barrier-related gene expression. Antibiotics restored gut permeability, suggesting microbiota influence. Altered microbiota were enriched in Lachnospiraceae, known for mucin degradation. Transferring this microbiota to germ-free mice increased gut permeability and promoted IL-10-expressing Breg cells. Rag-/- mice transplanted with fecal samples from Tlr9 fl/fl Cd19-Cre+ mice showed delayed diabetes onset, indicating microbiota's impact.

Conclusion

B cell-specific TLR9 deficiency alters gut microbiota, increasing gut permeability and promoting IL-10-expressing Breg cells, which delay T1D. This study uncovers a link between TLR9, gut microbiota, and immune regulation in T1D, with implications for microbiota-targeted T1D therapies.

Defining the relationship between cellular and extracellular vesicle (EV) content in breast cancer via an integrative multi-omic analysis

Much recent research has been dedicated to exploring the utility of extracellular vesicles (EVs) as circulating disease biomarkers. Underpinning this work is the assumption that the molecular cargo of EVs directly reflects the originating cell. Few attempts have been made, however, to empirically validate this on the -omic level. To this end, we have performed an integrative multi-omic analysis of a panel of breast cancer cell lines and corresponding EVs. Whole transcriptome analysis validated that the cellular transcriptome remained stable when cultured cells are transitioned to low serum or serum-free medium for EV collection. Transcriptomic profiling of the isolated EVs indicated a positive correlation between transcript levels in cells and EVs, including disease-associated transcripts. Analysis of the EV proteome verified that HER2 protein is present in EVs, however neither the estrogen (ER) nor progesterone (PR) receptor proteins are detected regardless of cellular expression. Using multivariate analysis, we derived an EV protein signature to infer cellular patterns of ER and HER2 expression, though the ER protein could not be directly detected. Integrative analyses affirmed that the EV proteome and transcriptome captured key phenotypic hallmarks of the originating cells, supporting the potential of EVs for non-invasive monitoring of breast cancers.

Circulating perturbation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) is associated to cardiac remodeling and NLRP3 inflammasome in cardiovascular patients with insulin resistance risk

Lipidome perturbation occurring during meta-inflammation is associated to left ventricle (LV) remodeling though the activation of the NLRP3 inflammasome, a key regulator of chronic inflammation in obesity-related disorders. Little is known about phosphatidylcholine (PC) and phosphatidylethanolamine (PE) as DAMP-induced NLRP3 inflammasome. Our study is aimed to evaluate if a systemic reduction of PC/PE molar ratio can affect NLRP3 plasma levels in cardiovascular disease (CVD) patients with insulin resistance (IR) risk. Forty patients from IRCCS Policlinico San Donato were enrolled, and their blood samples were drawn before heart surgery. LV geometry measurements were evaluated by echocardiography and clinical data associated to IR risk were collected. PC and PE were quantified by ESI-MS/MS. Circulating NLRP3 was quantified by an ELISA assay. Our results have shown that CVD patients with IR risk presented systemic lipid impairment of PC and PE species and their ratio in plasma was inversely associated to NLRP3 levels. Interestingly, CVD patients with IR risk presented LV changes directly associated to increased levels of NLRP3 and a decrease in PC/PE ratio in plasma, highlighting the systemic effect of meta-inflammation in cardiac response. In summary, PC and PE can be considered bioactive mediators associated to both the NLRP3 and LV changes in CVD patients with IR risk.

Evolution of tissue-specific expression of ancestral genes across vertebrates and insects

Regulation of gene expression is arguably the main mechanism underlying the phenotypic diversity of tissues within and between species. Here we assembled an extensive transcriptomic dataset covering 8 tissues across 20 bilaterian species and performed analyses using a symmetric phylogeny that allowed the combined and parallel investigation of gene expression evolution between vertebrates and insects. We specifically focused on widely conserved ancestral genes, identifying strong cores of pan-bilaterian tissue-specific genes and even larger groups that diverged to define vertebrate and insect tissues. Systematic inferences of tissue-specificity gains and losses show that nearly half of all ancestral genes have been recruited into tissue-specific transcriptomes. This occurred during both ancient and, especially, recent bilaterian evolution, with several gains being associated with the emergence of unique phenotypes (for example, novel cell types). Such pervasive evolution of tissue specificity was linked to gene duplication coupled with expression specialization of one of the copies, revealing an unappreciated prolonged effect of whole-genome duplications on recent vertebrate evolution.

Ten-year functional and oncological outcomes of a prospective randomized controlled trial comparing laparoscopic versus robot-assisted radical prostatectomy

BACKGROUND

Among prostate cancer (PCa) treatment options, mini-invasive surgical approaches have gained a wide diffusion in the last decades. The aim of this study was to present oncological, functional, and quality of life data after 10 years of follow-up of a prospective randomized controlled trial (RCT) (ISRCTN11552140) comparing robot-assisted radical prostatectomy (RARP) versus laparoscopic radical prostatectomy (LRP) for the treatment of PCa.

METHODS

Patients with localized PCa were randomized to undergo LRP or RARP between January 2010 and January 2011. Functional (continence and potency) and oncological (prostate-specific antigen, biochemical recurrence [BCR] and BCR-free survival [BCRFS]) variables were evaluated. BCRFS curves were estimated by the Kaplan-Meier method and compared using the log-rank test. Machine learning partial least square-discriminant analysis (PLS-DA) was used to identify the variables characterizing more the patients who underwent RARP or LRP.

RESULTS

Seventy-five of the originally enrolled 120 patients remained on follow-up for 10 years; 40 (53%) underwent RARP and 35 (47%) LRP. Continence and potency recovery rates did not show significant differences (p = 0.068 and p = 0.56, respectively), despite a Δ12% for continence and Δ8% for potency in favor of the robotic approach. However, the quality of continence (in terms of International Consultation on Incontinence Questionnaire-Short Form [ICIQ-SF] score) and erection (in terms of International Index of Erectile Function-5 [IIEF-5] score) was significantly better after 10 years in the robotic group (p = 0.02 and p < 0.001). PLS-DA revealed that LRP was characterized by the worst functional-related outcomes analyzing the entire follow-up period. Four (10%) and six (17%) patients experienced BCR in RARP and LRP groups, respectively (p = 0.36), with an overall 10-year BCR-free survival of 88% and 78% (p = 0.16).

CONCLUSIONS

Comparable continence and potency rates were observed between RARP and LRP after a 10-year follow-up. However, the RARP group exhibited superior totally dry rate and erection quality. No difference in terms of oncological outcomes was found.

A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics

Extracellular vesicles are shed by every cell type and can be found in any biofluid. They contain different molecules that can be utilized as biomarkers, including several RNA species which they protect from degradation. Here, we present a pipeline for the development and analysis of extracellular vesicle-associated transcriptomic biomarkers that our group has successfully applied multiple times. We highlight the key steps of the pipeline and give particular emphasis to the necessary quality control checkpoints, which are linked to numerous available guidelines that should be considered along the workflow. Our pipeline starts with patient recruitment and continues with blood sampling and processing. The purification and characterization of extracellular vesicles is explained in detail, as well as the isolation and quality control of extracellular vesicle-associated RNA. We point out the possible pitfalls during library preparation and RNA sequencing and present multiple bioinformatic tools to pinpoint biomarker signature candidates from the sequencing data. Finally, considerations and pitfalls during the validation of the biomarker signature using RT-qPCR will be elaborated.

Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury

Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .

Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis

BACKGROUND

Metritis is a prevalent uterine disease that affects the welfare, fertility, and survival of dairy cows. The uterine microbiome from cows that develop metritis and those that remain healthy do not differ from calving until 2 days postpartum, after which there is a dysbiosis of the uterine microbiome characterized by a shift towards opportunistic pathogens such as Fusobacteriota and Bacteroidota. Whether these opportunistic pathogens proliferate and overtake the uterine commensals could be determined by the type of substrates present in the uterus. The objective of this study was to integrate uterine microbiome and metabolome data to advance the understanding of the uterine environment in dairy cows that develop metritis. Holstein cows (n = 104) had uterine fluid collected at calving and at the day of metritis diagnosis. Cows with metritis (n = 52) were paired with cows without metritis (n = 52) based on days after calving. First, the uterine microbiome and metabolome were evaluated individually, and then integrated using network analyses.

RESULTS

The uterine microbiome did not differ at calving but differed on the day of metritis diagnosis between cows with and without metritis. The uterine metabolome differed both at calving and on the day of metritis diagnosis between cows that did and did not develop metritis. Omics integration was performed between 6 significant bacteria genera and 153 significant metabolites on the day of metritis diagnosis. Integration was not performed at calving because there were no significant differences in the uterine microbiome. A total of 3 bacteria genera (i.e. Fusobacterium, Porphyromonas, and Bacteroides) were strongly correlated with 49 metabolites on the day of metritis diagnosis. Seven of the significant metabolites at calving were among the 49 metabolites strongly correlated with opportunistic pathogenic bacteria on the day of metritis diagnosis. The main metabolites have been associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacteria overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation.

CONCLUSIONS

The data integration presented herein helps advance the understanding of the uterine environment in dairy cows with metritis. The identified metabolites may provide a competitive advantage to the main uterine pathogens Fusobacterium, Porphyromonas and Bacteroides, and may be promising targets for future interventions aiming to reduce opportunistic pathogenic bacteria growth in the uterus.

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing "danger signals" to stimulate the body's immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.

Deciphering the chemical language of inbred and wild mouse conspecific scents

In most mammals, conspecific chemosensory communication relies on semiochemical release within complex bodily secretions and subsequent stimulus detection by the vomeronasal organ (VNO). Urine, a rich source of ethologically relevant chemosignals, conveys detailed information about sex, social hierarchy, health, and reproductive state, which becomes accessible to a conspecific via vomeronasal sampling. So far, however, numerous aspects of social chemosignaling along the vomeronasal pathway remain unclear. Moreover, since virtually all research on vomeronasal physiology is based on secretions derived from inbred laboratory mice, it remains uncertain whether such stimuli provide a true representation of potentially more relevant cues found in the wild. Here, we combine a robust low-noise VNO activity assay with comparative molecular profiling of sex- and strain-specific mouse urine samples from two inbred laboratory strains as well as from wild mice. With comprehensive molecular portraits of these secretions, VNO activity analysis now enables us to (i) assess whether and, if so, how much sex/strain-selective 'raw' chemical information in urine is accessible via vomeronasal sampling; (ii) identify which chemicals exhibit sufficient discriminatory power to signal an animal's sex, strain, or both; (iii) determine the extent to which wild mouse secretions are unique; and (iv) analyze whether vomeronasal response profiles differ between strains. We report both sex- and, in particular, strain-selective VNO representations of chemical information. Within the urinary 'secretome', both volatile compounds and proteins exhibit sufficient discriminative power to provide sex- and strain-specific molecular fingerprints. While total protein amount is substantially enriched in male urine, females secrete a larger variety at overall comparatively low concentrations. Surprisingly, the molecular spectrum of wild mouse urine does not dramatically exceed that of inbred strains. Finally, vomeronasal response profiles differ between C57BL/6 and BALB/c animals, with particularly disparate representations of female semiochemicals.

Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study

Metabolomics has proven to be a sensitive tool for monitoring biochemical processes in cell culture. It enables multi-analysis, clarifying the correlation between numerous metabolic pathways. Together with other analysis, it thus provides a global view of a cell's physiological state. A comprehensive analysis of molecular changes is also required in the case of mesenchymal stem cells (MSCs), which currently represent an essential portion of cells used in regenerative medicine. Reproducibility and correct measurement are closely connected to careful metabolite extraction, and sample preparation is always a critical point. Our study aimed to compare the efficiencies of four harvesting and six extraction methods. Several organic reagents (methanol, ethanol, acetonitrile, methanol-chloroform, MTBE) and harvesting approaches (trypsinization vs. scraping) were tested. We used untargeted nuclear magnetic resonance spectroscopy (NMR) to determine the most efficient method for the extraction of metabolites from human adherent cells, specifically human dermal fibroblasts adult (HDFa) and dental pulp stem cells (DPSCs). A comprehensive dataset of 29 identified and quantified metabolites were determined to possess statistically significant differences in the abundances of several metabolites when the cells were detached mechanically to organic solvent compared to when applying enzymes mainly in the classes of amino acids and peptides for both types of cells. Direct scraping to organic solvent is a method that yields higher abundances of determined metabolites. Extraction with the use of different polar reagents, 50% and 80% methanol, or acetonitrile, mostly showed the same quality. For both HDFa and DPSC cells, the MTBE method, methanol-chloroform, and 80% ethanol extractions showed higher extraction efficiency for the most identified and quantified metabolites Thus, preparation procedures provided a cell sample processing protocol that focuses on maximizing extraction yield. Our approach may be useful for large-scale comparative metabolomic studies of human mesenchymal stem cell samples.

Hierarchical superstructure aerogels for in situ biofluid metabolomics

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation

BACKGROUND

Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).

METHODS

Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.

RESULTS

Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.

CONCLUSION

The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

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.

Responses of selected plasma metabolites to a two-day nutritional challenge of goats divergently selected for functional longevity

Understanding the extent to which genetics × environment plays a role in shaping individual strategies to environmental challenges is of considerable interest for future selection of more resilient animals. Accordingly, the objective of this study was to evaluate the metabolic responses to a nutritional challenge of goats divergently selected for functional longevity based on plasma metabolites and the repeatability of these responses across 2 experimental farms and years. We carried out 6 different experimental trials from years 2018 to 2022 (4 trials on site Bourges (2018-21) and 2 trials (2021-22) on site Grignon) in which 267 first kidding goats, daughters of Alpine bucks divergently selected for functional longevity, longevity plus (n = 137), and longevity minus (n = 130), were exposed to a 2-d nutritional challenge in early lactation. The experiments consisted of a 5 or 7-d control period (pre-challenge) on a standard lactation diet followed by a 2-d nutritional challenge with straw-only feeding and then a 7 or 10-d recovery period on a standard lactation diet, for site Bourges and Grignon, respectively. During the challenge plasma metabolite composition was recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual as a random effect and the 2x2 treatments (i.e., genetic line and year nested in site) and litter size as fixed effects. The linear mixed-effects model using a piecewise arrangement was used to analyze the response/recovery profiles to the nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of genetic line, were used in a Sparse Partial Least Square Discriminant Analysis (sPLS-DA) to compare the goat metabolism response to the challenge on a multivariate scale. The plasma metabolites, glucose, β-hydroxybutyrate (BHB), and nonesterified fatty acids (NEFA), and urea concentrations responded to the 2-d nutritional challenge. Selection for functional longevity did not affect plasma glucose, NEFA, BHB, and urea response/recoveries to a 2-d nutritional challenge. However, site, trial, and litter size affected these responses. Moreover, the plasma metabolites seem not to fully recover to prechallenge levels after the recovery phase. The sPLS-DA analysis did not discriminate between the 2 longevity lines. We observed meaningful between-individuals' variability in plasma BHB, especially on the prechallenge and rate of response and rate of recovery from the 2-d nutritional challenge (CV = 26.2%, 36.1%, and 41.2%, repeatability = 0.749, 0.322, and 0.741, respectively). Plasma NEFA recovery from challenge also demonstrated high between-individuals' variability (CV = 16.4%, repeatability = 0.323). Selection for functional longevity did not affect plasma metabolites responses to a 2-d nutritional challenge in dairy goats. Plasma NEFA and BHB response/recovery presented high between-individuals' variability, indicating individual adaptative characteristics to nutritional challenges not related to the environmental conditions but to inherent individual characteristics.

Susceptibility to febrile malaria is associated with an inflammatory gut microbiome

Malaria is a major public health problem, but many of the factors underlying the pathogenesis of this disease are not well understood. Here, we demonstrate in Malian children that susceptibility to febrile malaria following infection with Plasmodium falciparum is associated with the composition of the gut microbiome prior to the malaria season. Gnotobiotic mice colonized with the fecal samples of malaria-susceptible children had a significantly higher parasite burden following Plasmodium infection compared to gnotobiotic mice colonized with the fecal samples of malaria-resistant children. The fecal microbiome of the susceptible children was enriched for bacteria associated with inflammation, mucin degradation, gut permeability and inflammatory bowel disorders (e.g., Ruminococcus gauvreauii, Ruminococcus torques, Dorea formicigenerans, Dorea longicatena, Lachnoclostridium phocaeense and Lachnoclostridium sp. YL32). However, the susceptible children also had a greater abundance of bacteria known to produce anti-inflammatory short-chain fatty acids and those associated with favorable prognosis and remission following dysbiotic intestinal events (e.g., Anaerobutyricum hallii, Blautia producta and Sellimonas intestinalis). Metabolomics analysis of the human fecal samples corroborated the existence of inflammatory and recovery-associated features within the gut microbiome of the susceptible children. There was an enrichment of nitric oxide-derived DNA adducts (deoxyinosine and deoxyuridine) and long-chain fatty acids, the absorption of which has been shown to be inhibited by inflamed intestinal epithelial cells, and a decrease in the abundance of mucus phospholipids. Nevertheless, there were also increased levels of pseudouridine and hypoxanthine, which have been shown to be regulated in response to cellular stress and to promote recovery following injury or hypoxia. Overall, these results indicate that the gut microbiome may contribute malaria pathogenesis and suggest that therapies targeting intestinal inflammation could decrease malaria susceptibility.

Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity

There is considerable heterogeneity in the cardiometabolic abnormalities associated with obesity. We evaluated multi-organ system metabolic function in 20 adults with metabolically healthy obesity (MHO; normal fasting glucose and triglycerides, oral glucose tolerance, intrahepatic triglyceride content, and whole-body insulin sensitivity), 20 adults with metabolically unhealthy obesity (MUO; prediabetes, hepatic steatosis, and whole-body insulin resistance), and 15 adults who were metabolically healthy lean. Compared with MUO, people with MHO had (1) altered skeletal muscle biology (decreased ceramide content and increased expression of genes involved in BCAA catabolism and mitochondrial structure/function); (2) altered adipose tissue biology (decreased expression of genes involved in inflammation and extracellular matrix remodeling and increased expression of genes involved in lipogenesis); (3) lower 24-h plasma glucose, insulin, non-esterified fatty acids, and triglycerides; (4) higher plasma adiponectin and lower plasma PAI-1 concentrations; and (5) decreased oxidative stress. These findings provide a framework of potential mechanisms responsible for MHO and the metabolic heterogeneity of obesity. This study was registered at ClinicalTrials.gov (NCT02706262).

Microbiome composition as a potential predictor of longevity in rabbits

BACKGROUND

Longevity and resilience are two fundamental traits for more sustainable livestock production. These traits are closely related, as resilient animals tend to have longer lifespans. An interesting criterion for increasing longevity in rabbit could be based on the information provided by its gut microbiome. The gut microbiome is essential for regulating health and plays crucial roles in the development of the immune system. The aim of this research was to investigate if animals with different longevities have different microbial profiles. We sequenced the 16S rRNA gene from soft faeces from 95 does. First, we compared two maternal rabbit lines with different longevities; a standard longevity maternal line (A) and a maternal line (LP) that was founded based on longevity criteria: females with a minimum of 25 parities with an average prolificacy per parity of 9 or more. Second, we compared the gut microbiota of two groups of animals from line LP with different longevities: females that died/were culled with two parities or less (LLP) and females with more than 15 parities (HLP).

RESULTS

Differences in alpha and beta diversity were observed between lines A and LP, and a partial least square discriminant analysis (PLS-DA) showed a high prediction accuracy (> 91%) of classification of animals to line A versus LP (146 amplicon sequence variants (ASV)). The PLS-DA also showed a high prediction accuracy (> 94%) to classify animals to the LLP and HLP groups (53 ASV). Interestingly, some of the most important taxa identified in the PLS-DA were common to both comparisons (Akkermansia, Christensenellaceae R-7, Uncultured Eubacteriaceae, among others) and have been reported to be related to resilience and longevity.

CONCLUSIONS

Our results indicate that the first parity gut microbiome profile differs between the two rabbit maternal lines (A and LP) and, to a lesser extent, between animals of line LP with different longevities (LLP and HLP). Several genera were able to discriminate animals from the two lines and animals with different longevities, which shows that the gut microbiome could be used as a predictive factor for longevity, or as a selection criterion for these traits.

Untargeted metabolomics reveals PTI-associated metabolites

Plants employ a multilayered immune system to combat pathogens. In one layer, recognition of Pathogen- or Microbe-Associated Molecular Patterns or elicitors, triggers a cascade that leads to defence against the pathogen and Pattern Triggered Immunity. Secondary or specialised metabolites (SMs) are expected to play a role, because they are potentially anti-fungal compounds. Tomato (Solanum lycopersicum) plants inoculated with Alternaria solani s.l. show symptoms of infection after inoculation. Plants inoculated with Alternaria alternata remain symptomless. We hypothesised that pattern-triggered induction of resistance related metabolites in tomato contributes to the resistance against A. alternata. We compared the metabolomic profile (metabolome) of tomato after treatments with A. alternata, A. solani and the fungal elicitor chitin, and identified SMs involved in early defence of tomato plants. We revealed differential metabolome fingerprints. The composition of A. alternata and chitin induced metabolomes show larger overlap with each other than with the A. solani induced metabolome. We identify 65 metabolites possibly associated with PTI in tomato plants, including NAD and trigonelline. We confirm that trigonelline inhibits fungal growth in vitro at physiological concentrations. Thus, a true pattern-triggered, chemical defence is mounted against A. alternata, which contains anti-fungal compounds that could be interesting for crop protection strategies.

The combination of Fourier-transform infrared spectroscopy with pattern recognition techniques for classification and discrimination of red snapper fish oils

Fish oils are good sources for essential fatty acids such as omega-3 and omega-6 fatty acids needed to human growth. Indonesia is rich in fish species and among this, red snapper fish (Lutjanus sp.) can be extracted to get red snapper fish oils (RSFOs). The aim of this study was to classify and discriminate RSFO from different origins using Fourier-transform infrared (FTIR) spectra and pattern recognition techniques. All of the RSFO's FTIR spectra were very similar. The FTIR vibrations showed the presence of triglycerides as the main composition in fish oils. Principal component analysis (PCA) could separate the RSFO according to sample origin. Supervised pattern recognition of partial least square-discriminant analysis (PLS-DA) and sparse PLS-DA (sPLS-DA) successfully discriminated and classified different Lutjanus species of fish oils obtained from different origins. The vibration of functional groups at 1711, 1653, 1745, and 3012 per cm were considered for their important contributions in discriminating of Lutjanus species (variable importance in projection, variable importance in the projection score >1). Fish oils obtained from the same species were classified into the same class indicating similar chemical compositions. Among the three pattern recognition techniques used, sPLS-DA offers the best model for the discrimination and classification of Lutjanus fish oils. It can be concluded that FTIR spectroscopy in combination with the pattern recognition technique is the potential to be used for of fish oil authentication to verify the quality of the fish oils. It can be further developed as a rapid and effective method for fish oil authentication.

Global gene expression profile of proliferative verrucous leukoplakia and its underlying biological disease mechanisms

BACKGROUND

Proliferative verrucous leukoplakia (PVL) is a rare and enigmatic oral potentially malignant disorder which almost invariably results in oral squamous cell carcinoma (OSCC). The aims of this project were to use transcriptome profiling to characterise PVL gene expression patterns for biomarker identification and gain insight into the molecular aetiopathogenesis of PVL.

METHODS

Forty-three oral cavity mucosal biopsies from 32 patients with oral lesions clinically compatible with either PVL or non-PVL conventional oral leukoplakia (OLK) underwent transcriptome profiling by RNA sequencing. Data was analysed by hierarchical clustering, differential gene expression, functional enrichment and network analysis, sparse partial least squares discriminant analysis sPLS-DA, and immune cell phenotypic estimation.

RESULTS

We found 464 genes significantly differentially expressed at least 2-fold between PVL and non-PVL OLK (193 up and 271 down). HOX genes, including HOXA1 and HOXB7, keratin-associated proteins (KRTAPs) and olfactory receptor G proteins (OR) were significantly upregulated in PVL. Other upregulated genes in PVL included FOS, WNT16 and IFNA1. Pathway analysis showed that there was a significant downregulation of connective tissue signalling in PVL. Classifying multivariate models based upon 22 genes discriminated PVL from non-PVL OLK. Bioinformatic profiling showed that immune cell profiles in PVL and OLK were similar except that fibroblast markers were reduced in PVL.

CONCLUSION

These results demonstrate that PVL and conventional OLK are molecularly distinct with upregulation of many cancer-associated genes. They provide insight into the pathogenesis of PVL and show that biomarker based molecular diagnostics is feasible to discriminate and inform diagnosis and management.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Inhibition of anaerobic digestion by various ammonia sources resulted in subtle differences in metabolite dynamics

The impact of ammonia on anaerobic digestion performance and microbial dynamics has been extensively studied, but the concurrent effect of anions brought by ammonium salt should not be neglected. This paper studied this effect using metabolomics and a time-course statistical framework. Metabolomics provides novel perspectives to study microbial processes and facilitates a more profound understanding at the metabolic level. The advanced statistical framework enables deciphering the complexity of large metabolomics data sets. More specifically, a series of lab-scale batch reactors were set up with different ammonia sources added. Samples of nine time points over the degradation were analyzed with liquid chromatography-mass spectrometry. A filtering procedure was applied to select the promising metabolomic peaks from 1262 peaks, followed by modeling their intensities across time. The metabolomic peaks with similar time profiles were clustered, evidencing the correlation of different biological processes. Differential analysis was performed to seek the differences in metabolite dynamics caused by different anions. Finally, tandem mass spectrometry and metabolite annotation provided further information on the molecular structure and possible metabolic pathways. For example, the consumption of 5-aminovaleric acid, a short-chain fatty acid obtained from l-lysine degradation, was slowed down by phosphates. Overall, by investigating the effect of anions on anaerobic digestion, our study demonstrated the effectiveness of metabolomics in providing detailed information in a set of samples from different experimental conditions. With the statistical framework, the approach enables capturing subtle differences in metabolite dynamics between samples while accounting for the differences caused by time variations.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

Volatile Organic Compounds from Offspring of Stingless Bee Sacrificed in Hygienic Behavior Test

This study shows the profile of volatile organic compounds (VOCs) from pupae and larvae of Melipona quadrifasciata anthidioides Lepeletier subjected to three death induction techniques for hygienic behavior (HB) studies: freezing in liquid nitrogen (LN2), freezing in a freezer (FRZ) and piercing of offspring with an entomological pin (PIN). The VOCs from larvae and pupae were obtained through headspace solid-phase microextraction and characterized using gas chromatography coupled to mass spectrometry. In addition, an HB test was performed on the colonies. The main classes of VOCs were hydrocarbons, terpenes and alcohols. Multivariate analysis was applied and showed that there was a separation in the compound profiles between the different treatments. The HB test in the colonies showed that 24 hours after the application of the techniques, the bees removed more dead larvae in LN2 treatment (83.5 %), while after 48 hours more larvae were removed in the LN2 and FRZ treatments (92.3 %). When compared to pupae removal, larvae removal was significantly faster in LN2.

Investigating the impact of feed-induced, subacute ruminal acidosis on rumen epimural transcriptome and metatranscriptome in young calves at 8- and 17-week of age

Introduction

With the goal to maximize intake of high-fermentable diet needed to meet energy needs during weaning period, calves are at risk for ruminal acidosis. Using the calves from previously established model of feed-induced, ruminal acidosis in young calves, we aimed to investigate the changes in rumen epimural transcriptome and its microbial metatranscriptome at weaning (8-week) and post-weaning (17-week) in canulated (first occurred at 3 weeks of age) Holstein bull calves with feed-induced subacute ruminal acidosis.

Methods

Eight bull calves were randomly assigned to acidosis-inducing diet (Treated, n = 4; pelleted, 42.7% starch, 15.1% neutral detergent fiber [NDF], and 57.8% nonfiber carbohydrates), while texturized starter was fed as a control (Control, n = 4; 35.3% starch, 25.3% NDF, and 48.1% nonfiber carbohydrates) starting at 1 week through 17 weeks. Calves fed acidosis-inducing diet showed significantly less (p < 0.01) body weight over the course of the experiment, in addition to lower ruminal pH (p < 0.01) compared to the control group. Rumen epithelial (RE) tissues were collected at both 8 weeks (via biopsy) and 17 weeks (via euthanasia) and followed for whole transcriptome RNA sequencing analysis. Differentially expressed genes (DEGs) analysis was done using cufflinks2 (fold-change ≥2 and p < 0.05) between treated and control groups at 8-week of age, and between 8- and 17-week for the treated group.

Results

At 8-week of age, DEGs between treatment groups showed an enrichment of genes related to the response to lipopolysaccharide (LPS) (p < 0.005). The impact of prolonged, feed-induced acidosis was reflected by the decreased expression (p < 0.005) in genes involved in cell proliferation related pathways in the RE at 17-week of age in the treated group. Unique sets of discriminant microbial taxa were identified between 8-and 17-week calves in the treated group and the treatment groups at 8-week, indicating that active microbial community changes in the RE are an integral part of the ruminal acidosis development and progression.

Maternal short chain fructo-oligosaccharides supplementation during late gestation and lactation influences milk components and offspring gut metabolome: a pilot study

Breast milk composition is influenced by maternal diet. This study aimed to evaluate if supplementation of maternal diet with a prebiotic fibre, through its potential effect on milk composition, can be a leverage to orientate the gut microbiota of infants in a way that would be beneficial for their health. Twelve sows received a diet supplemented with short chain fructo-oligosaccharides or maltodextrins during the last month of gestation and the lactation. Oligosaccharidic and lipidomic profiles of colostrum and mature milk (21 days), as well as faecal microbiota composition and metabolomic profile of 21 day-old piglets were evaluated. The total porcine milk oligosaccharide concentration tended to be lower in scFOS-supplemented sows, mainly due to the significant reduction of the neutral core oligosaccharides (in particular that of a tetrahexose). Maternal scFOS supplementation affected the concentration of 31 lipids (mainly long-chain triglycerides) in mature milk. Faecal short-chain fatty acid content and that of 16 bacterial metabolites were modified by scFOS supplementation. Interestingly, the integrative data analysis gave a novel insight into the relationships between (i) maternal milk lipids and PMOs and (ii) offspring faecal bacteria and metabolites. In conclusion, scFOS-enriched maternal diet affected the composition of mature milk, and this was associated with a change in the colonisation of the offspring intestinal microbiota.

The impact of Bayesian optimization on feature selection

Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian optimization has demonstrated its advantages in automatically configuring the settings of hyper-parameters for various models. However, it remains unclear whether Bayesian optimization can benefit feature selection methods. In this research, we conducted extensive simulation studies to compare the performance of various feature selection methods, with a particular focus on the impact of Bayesian optimization on those where hyper-parameters tuning is needed. We further utilized the gene expression data obtained from the Alzheimer's Disease Neuroimaging Initiative to predict various brain imaging-related phenotypes, where various feature selection methods were employed to mine the data. We found through simulation studies that feature selection methods with hyper-parameters tuned using Bayesian optimization often yield better recall rates, and the analysis of transcriptomic data further revealed that Bayesian optimization-guided feature selection can improve the accuracy of disease risk prediction models. In conclusion, Bayesian optimization can facilitate feature selection methods when hyper-parameter tuning is needed and has the potential to substantially benefit downstream tasks.

Cell-cycle-linked growth reprogramming encodes developmental time into leaf morphogenesis

How is time encoded into organ growth and morphogenesis? We address this question by investigating heteroblasty, where leaf development and form are modified with progressing plant age. By combining morphometric analyses, fate-mapping through live-imaging, computational analyses, and genetics, we identify age-dependent changes in cell-cycle-associated growth and histogenesis that underpin leaf heteroblasty. We show that in juvenile leaves, cell proliferation competence is rapidly released in a "proliferation burst" coupled with fast growth, whereas in adult leaves, proliferative growth is sustained for longer and at a slower rate. These effects are mediated by the SPL9 transcription factor in response to inputs from both shoot age and individual leaf maturation along the proximodistal axis. SPL9 acts by activating CyclinD3 family genes, which are sufficient to bypass the requirement for SPL9 in the control of leaf shape and in heteroblastic reprogramming of cellular growth. In conclusion, we have identified a mechanism that bridges across cell, tissue, and whole-organism scales by linking cell-cycle-associated growth control to age-dependent changes in organ geometry.

Discriminative power of DNA-based, volatilome, near infrared spectroscopy, elements and stable isotopes methods for the origin authentication of typical Italian mountain cheese using sPLS-DA modeling

Origin authentication methods are pivotal in counteracting frauds and provide evidence for certification systems. For these reasons, geographical origin authentication methods are used to ensure product origin. This study focused on the origin authentication (i.e. at the producer level) of a typical mountain cheese origin using various approaches, including shotgun metagenomics, volatilome, near infrared spectroscopy, stable isotopes, and elemental analyses. DNA-based analysis revealed that viral communities achieved a higher classification accuracy rate (97.4 ± 2.6 %) than bacterial communities (96.1 ± 4.0 %). Non-starter lactic acid bacteria and phages specific to each origin were identified. Volatile organic compounds exhibited potential clusters according to cheese origin, with a classification accuracy rate of 90.0 ± 11.1 %. Near-infrared spectroscopy showed lower discriminative power for cheese authentication, yielding only a 76.0 ± 31.6 % classification accuracy rate. Model performances were influenced by specific regions of the infrared spectrum, possibly associated with fat content, lipid profile and protein characteristics. Furthermore, we analyzed the elemental composition of mountain Caciotta cheese and identified significant differences in elements related to dairy equipment, macronutrients, and rare earth elements among different origins. The combination of elements and isotopes showed a decrease in authentication performance (97.0 ± 3.1 %) compared to the original element models, which were found to achieve the best classification accuracy rate (99.0 ± 0.01 %). Overall, our findings emphasize the potential of multi-omics techniques in cheese origin authentication and highlight the complexity of factors influencing cheese composition and hence typicity.

Giardia Antagonizes Beneficial Functions of Indigenous and Therapeutic Intestinal Bacteria during Malnutrition

Undernutrition in children commonly disrupts the structure and function of the small intestinal microbial community, leading to enteropathies, compromised metabolic health, and impaired growth and development. The mechanisms by which diet and microbes mediate the balance between commensal and pathogenic intestinal flora remain elusive. In a murine model of undernutrition, we investigated the direct interactions Giardia lamblia, a prevalent small intestinal pathogen, on indigenous microbiota and specifically on Lactobacillus strains known for their mucosal and growth homeostatic properties. Our research reveals that Giardia colonization shifts the balance of lactic acid bacteria, causing a relative decrease in Lactobacillus spp . and an increase in Bifidobacterium spp . This alteration corresponds with a decrease in multiple indicators of mucosal and nutritional homeostasis. Additionally, protein-deficient conditions coupled with Giardia infection exacerbate the rise of primary bile acids and susceptibility to bile acid-induced intestinal barrier damage. In epithelial cell monolayers, Lactobacillus spp . mitigated bile acid-induced permeability, showing strain-dependent protective effects. In vivo, L. plantarum, either alone or within a Lactobacillus spp consortium, facilitated growth in protein-deficient mice, an effect attenuated by Giardia , despite not inhibiting Lactobacillus colonization. These results highlight Giardia's potential role as a disruptor of probiotic functional activity, underscoring the imperative for further research into the complex interactions between parasites and bacteria under conditions of nutritional deficiency.