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

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.

Multiplexed MRM-based proteomics for identification of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) increases the risk of coronary heart disease (CHD) by 2-4 fold, and is associated with endothelial dysfunction, dyslipidaemia, insulin resistance, and chronic hyperglycaemia. The aim of this investigation was to assess, by a multimarker mass spectrometry approach, the predictive role of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus.

METHODS

The study considered 34 patients with both T2DM and CHD, 31 patients with T2DM and without CHD, and 30 patients without diabetes with a diagnosis of CHD. Plasma samples of subjects were analysed through a multiplexed targeted liquid chromatography mass spectrometry (LC-MS)-based assay, namely Multiple Reaction Monitoring (MRM), allowing the simultaneous detection of peptides derived from a protein of interest. Gene Ontology (GO) Analysis was employed to identify enriched GO terms in the biological process, molecular function, or cellular component categories. Non-parametric multivariate methods were used to classify samples from patients and evaluate the relevance of the analysed proteins' panel.

RESULTS

A total of 81 proteins were successfully quantified in the human plasma samples. Gene Ontology analysis assessed terms related to blood microparticles, extracellular exosomes and collagen-containing extracellular matrix. Preliminary evaluation using analysis of variance (ANOVA) of the differences in the proteomic profile among patient groups identified 13 out of the 81 proteins as significantly different. Multivariate analysis, including cluster analysis and principal component analysis, identified relevant grouping of the 13 proteins. The first main cluster comprises apolipoprotein C-III, apolipoprotein C-II, apolipoprotein A-IV, retinol-binding protein 4, lysozyme C and cystatin-C; the second one includes, albeit with sub-grouping, alpha 2 macroglobulin, afamin, kininogen 1, vitronectin, vitamin K-dependent protein S, complement factor B and mannan-binding lectin serine protease 2. Receiver operating characteristic (ROC) curves obtained with the 13 selected proteins using a nominal logistic regression indicated a significant overall distinction (p < 0.001) among the three groups of subjects, with area under the ROC curve (AUC) ranging 0.91-0.97, and sensitivity and specificity ranging from 85 to 100%.

CONCLUSIONS

Targeted mass spectrometry approach indicated 13 multiple circulating proteins as possible biomarkers of cardiovascular damage progression associated with T2DM, with excellent classification results in terms of sensitivity and specificity.

Identifying distinctive brain regions related to consumer choice behaviors on branded foods using activation likelihood estimation and machine learning

Introduction

Brand equity plays a crucial role in a brand's commercial success; however, research on the brain regions associated with brand equity has had mixed results. This study aimed to investigate key brain regions associated with the decision-making of branded and unbranded foods using quantitative neuroimaging meta-analysis and machine learning.

Methods

Quantitative neuroimaging meta-analysis was performed using the activation likelihood method. Activation of the ventral medial prefrontal cortex (VMPFC) overlapped between branded and unbranded foods. The lingual and parahippocampal gyri (PHG) were activated in the case of branded foods, whereas no brain regions were characteristically activated in response to unbranded foods. We proposed a novel predictive method based on the reported foci data, referencing the multi-voxel pattern analysis (MVPA) results. This approach is referred to as the multi-coordinate pattern analysis (MCPA). We conducted the MCPA, adopting the sparse partial least squares discriminant analysis (sPLS-DA) to detect unique brain regions associated with branded and unbranded foods based on coordinate data. The sPLS-DA is an extended PLS method that enables the processing of categorical data as outcome variables.

Results

We found that the lingual gyrus is a distinct brain region in branded foods. Thus, the VMPFC might be a core brain region in food categories in consumer behavior, regardless of whether they are branded foods. Moreover, the connection between the PHG and lingual gyrus might be a unique neural mechanism in branded foods.

Discussion

As this mechanism engages in imaging the feature-self based on emotionally subjective contextual associative memories, brand managers should create future-oriented relevancies between brands and consumers to build valuable brands.

Association of gut microbiome with COPD in Japanese male residents: the SESSA study

Background

Altered gut microbiota may contribute to COPD development or progression. Herein, we investigated the association of gut microorganisms with COPD, taking into account the impact of smoking status.

Methods

This cross-sectional observational study was a part of the Shiga Epidemiological Study of Subclinical Atherosclerosis, a population-based cohort study of Japanese men aged 46-76 years, conducted from 2010 to 2016. The gut microbiome, determined using 16S rRNA gene sequencing, was compared among 99 never-smokers, 306 non-COPD ever-smokers and 76 patients with COPD while adjusting for age, body mass index, ethanol consumption and treatment for type 2 diabetes mellitus.

Results

The abundance of phylum Firmicutes was comparable between patients with COPD and non-COPD ever-smokers but tended to be higher in never-smokers. Similarly, the α- and β-diversity analysis showed similarity between patients with COPD and non-COPD ever-smokers, which tended to differ from never-smokers. Discriminant analysis identified the genus [Prevotella] to be more prevalent in patients with COPD than in never-smokers or non-COPD ever-smokers. Post hoc analysis confirmed similarity of gut microbiome between COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) I and non-COPD ever-smokers, which was different from GOLD II.

Conclusion

Smoking may alter the overall gut microbial composition, but gut microbial composition itself may not play a role in the development of COPD. Rather, specific gut bacteria, such as [Prevotella], could be a risk factor for the development of COPD; this may be a potential therapeutic target.

Deep subsurface microbial life in impact-altered Late Paleozoic granitoid rocks from the Chicxulub impact crater

In 2016, IODP-ICDP Expedition 364 recovered an 829-meter-long core within the peak ring of the Chicxulub impact crater (Yucatán, Mexico), allowing us to investigate the post-impact recovery of the heat-sterilized deep continental microbial biosphere at the impact site. We recently reported increased cell biomass in the impact suevite, which was deposited within the first few hours of the Cenozoic, and that the overall microbial communities differed significantly between the suevite and the other main core lithologies (i.e., the granitic basement and the overlying Early Eocene marine sediments; Cockell et al., 2021). However, only seven rock intervals were previously analyzed from the geologically heterogenic and impact-deformed 587-m-long granitic core section below the suevite interval. Here, we used 16S rRNA gene profiling to study the microbial community composition in 45 intervals including (a) 31 impact-shocked granites, (b) 7 non-granitic rocks (i.e., consisting of suevite and impact melt rocks intercalated into the granites during crater formation and strongly serpentinized pre-impact sub-volcanic, ultramafic basanite/dolerite), and (c) 7 cross-cut mineral veins of anhydride and silica. Most recovered microbial taxa resemble those found in hydrothermal systems. Spearman correlation analysis confirmed that the borehole temperature, which gradually increased from 47 to 69°C with core depth, significantly shaped a subset of the vertically stratified modern microbial community composition in the granitic basement rocks. However, bacterial communities differed significantly between the impoverished shattered granites and nutrient-enriched non-granite rocks, even though both lithologies were at similar depths and temperatures. Furthermore, Spearman analysis revealed a strong correlation between the microbial communities and bioavailable chemical compounds and suggests the presence of chemolithoautotrophs, which most likely still play an active role in metal and sulfur cycling. These results indicate that post-impact microbial niche separation has also occurred in the granitic basement lithologies, as previously shown for the newly formed lithologies. Moreover, our data suggest that the impact-induced geochemical boundaries continue to shape the modern-day deep biosphere in the granitic basement underlying the Chicxulub crater.

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 (HC) 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 H 2 S 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 HC with strong performance in classifying SZ from HC, 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.

A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn's disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD (n = 671) and UC (n = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center. A streamlined pipeline based on HmmUFOTU was used. After a series of filtering steps, 1517 phylotypes and 1846 samples were retained for subsequent analysis. After 100 rounds of downsampling with age, sex, and sample size matching, and division into training and test sets, we constructed two binary prediction models to distinguish between IBD and HC and CD and UC using the training set. The binary prediction models exhibited high accuracy and area under the curve (for differentiating IBD from HC (mean accuracy, 0.950; AUC, 0.992) and CD from UC (mean accuracy, 0.945; AUC, 0.988)), respectively, in the test set. This study underscores the diagnostic potential of an ML model based on sPLS-DA, utilizing fecal microbiome analysis, highlighting its ability to differentiate between IBD and HC and distinguish CD from UC.

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

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

Rapid intestinal and systemic metabolic reprogramming in an immunosuppressed environment

Intrinsic metabolism shapes the immune environment associated with immune suppression and tolerance in settings such as organ transplantation and cancer. However, little is known about the metabolic activities in an immunosuppressive environment. In this study, we employed metagenomic, metabolomic, and immunological approaches to profile the early effects of the immunosuppressant drug tacrolimus, antibiotics, or both in gut lumen and circulation using a murine model. Tacrolimus induced rapid and profound alterations in metabolic activities within two days of treatment, prior to alterations in gut microbiota composition and structure. The metabolic profile and gut microbiome after seven days of treatment was distinct from that after two days of treatment, indicating continuous drug effects on both gut microbial ecosystem and host metabolism. The most affected taxonomic groups are Clostriales and Verrucomicrobiae (i.e., Akkermansia muciniphila), and the most affected metabolic pathways included a group of interconnected amino acids, bile acid conjugation, glucose homeostasis, and energy production. Highly correlated metabolic changes were observed between lumen and serum metabolism, supporting their significant interactions. Despite a small sample size, this study explored the largely uncharacterized microbial and metabolic events in an immunosuppressed environment and demonstrated that early changes in metabolic activities can have significant implications that may serve as antecedent biomarkers of immune activation or quiescence. To understand the intricate relationships among gut microbiome, metabolic activities, and immune cells in an immune suppressed environment is a prerequisite for developing strategies to monitor and optimize alloimmune responses that determine transplant outcomes.

Human milk miRNAs associate to maternal dietary nutrients, milk microbiota, infant gut microbiota and growth

BACKGROUND

Maternal diet influences the milk composition, yet little information is available on the impact of maternal diet on milk miRNAs expression. Further, the association of human milk miRNAs to maternal diet and milk microbiota is not explored. In addition, the role of milk miRNAs on the infant gut microbiota, infant growth and development has not been investigated.

METHODS

Milk samples were collected from 60 healthy lactating women at ≤15d post-partum, HTG transcriptome assay was performed to examine milk miRNA profile. Maternal clinical and dietary clusters information were available and infant anthropometric measures were followed up to one year of age. Milk and infant microbiota were analyzed by 16S rRNA gene sequencing and integrative multi-omics data analysis was performed to identify potential association between microRNA, maternal dietary nutrients and microbiota.

RESULTS

Discriminant analysis revealed that the milk miRNAs were clustered into groups according to the maternal protein source. Interestingly, 31 miRNAs were differentially expressed (P adj < 0.05) between maternal dietary clusters (Cluster 1: enriched in plant protein and fibers and Cluster 2: enriched in animal protein), with 30 miRNAs downregulated in the plant protein group relative to animal protein group. Pathway analysis revealed that the top enriched pathways (P adj < 0.01) were involved in cell growth and proliferation processes. Furthermore, significant features contributing to the clustering were associated with maternal dietary nutrients and milk microbiota (r > 0.70). Further, miR-378 and 320 family miRNAs involved in adipogenesis were positively correlated to the infant BMI-z-scores, weight, and weight for length-z-scores at 6 months of age.

CONCLUSIONS

Maternal dietary source impacts the milk miRNA expression profile. Further, miRNAs were associated with maternal dietary nutrients, milk microbiota and to the infant gut microbiota and infant growth and development.

CLINICAL TRIAL

The study is registered in ClinicalTrials.gov. The identification number is NCT03552939.

Inflammatory characteristics of central compartment atopic disease

BACKGROUND

Central compartment atopic disease (CCAD) is an emerging phenotype of chronic rhinosinusitis with nasal polyposis (CRSwNP) characterized by prominent central nasal inflammatory changes. This study compares the inflammatory characteristics of CCAD relative to other phenotypes of CRSwNP.

METHODS

A cross-sectional analysis of data from a prospective clinical study was performed on patients with CRSwNP who were undergoing endoscopic sinus surgery (ESS). Patients with CCAD, aspirin-exacerbated respiratory disease (AERD), allergic fungal rhinosinusitis (AFRS), and non-typed CRSwNP (CRSwNP NOS) were included and mucus cytokine levels and demographic data were analyzed for each group. Chi-squared/Mann-Whitney U tests and partial least squares discriminant analysis (PLS-DA) were performed for comparison and classification.

RESULTS

A total of 253 patients were analyzed (CRSwNP, n = 137; AFRS, n = 50; AERD, n = 42; CCAD, n = 24). Patients with CCAD were the least likely to have comorbid asthma (p = 0.0004). The incidence of allergic rhinitis in CCAD patients did not vary significantly compared to patients with AFRS and AERD, but was higher compared to patients with CRSwNP NOS (p = 0.04). On univariate analysis, CCAD was characterized by less inflammatory burden, with reduced levels of interleukin 6 (IL-6), IL-8, interferon gamma (IFN-γ), and eotaxin relative to other groups and significantly lower type 2 cytokines (IL-5, IL-13) relative to both AERD and AFRS. These findings were supported by multivariate PLS-DA, which clustered CCAD patients into a relatively homogenous low-inflammatory cytokine profile.

CONCLUSIONS

CCAD has unique endotypic features compared to other patients with CRSwNP. The lower inflammatory burden may be reflective of a less severe variant of CRSwNP.

Characterization of circulating microRNA profiles of postpartum dairy cows with persistent subclinical endometritis

Subclinical endometritis (SCE) is an unresolved inflammation of the endometrium of postpartum dairy cows, seriously affecting fertility. Current diagnosis, which relies on uterine cytology or even more invasive biopsy sampling, would benefit from the identification of blood-based diagnostic biomarkers. Due to the known role of microRNAs (miRNAs) in other diseases, this case-control study evaluated the cell-free circulating miRNA profiles of SCE cows, and the network of transcripts predicted to interact with those miRNAs, previously identified as differentially expressed genes (DEG) in the endometrium of the same cows. Healthy (H, n = 6) and persistent SCE (n = 11) cows characterized by endometrial cytology and biopsy were blood sampled at 21 and 44 d postpartum (DPP). Following extraction of cell-free plasma miRNAs and RNA-seq analysis, differential abundance analysis of miRNAs was performed with the DESeq2 R package (adjusted p-value of 0.05), and in silico prediction of miRNA-interacting genes on a sequence complementary basis was conducted using the miRWalk database. The principal component analysis showed a clear clustering between groups of uterine health phenotypes (H vs. SCE), although the clustering between groups was less pronounced at 44 DPP than at 21 DPP. No effect of the stage (21 vs. 44 DPP) was observed. A total of 799 known circulating miRNAs were identified, from which 34 demonstrated differential abundance between H and SCE cows (12 less abundant and 22 more abundant in SCE than in H cows). These 34 miRNAs are predicted to interact with 10,104 transcripts, among which 43, 81, and 147 were previously identified as differentially expressed in, respectively, endometrial luminal epithelial, glandular epithelial, and stromal cells of the same cows. This accounts for approximately half of the DEG identified between those H and SCE cows, including genes involved in endometrial cell proliferation, angiogenesis and immune response, whose dysregulation in SCE cows may impair pregnancy establishment. From 219 miRNAs with mean normalized read counts above 100, the presence and abundance of miR-425-3p and miR-2285z had the highest discriminatory level to differentiate SCE from H cows. In conclusion, despite apparent confinement to the endometrium, SCE is associated with a distinct circulating miRNA profile, which may represent a link between the systemic changes associated with disease and the endometrial immune response. The validation of a miRNA panel consisting of circulating cell-free miR-425-3p and miR-2285z may prove a relevant advancement for the noninvasive diagnosis of persistent SCE.

Detection of early prognostic biomarkers for metastasis of Ewing's sarcoma in pediatric patients

AIMS

Ewing's Sarcoma is an extremely aggressive tumor in children. The disease is associated with highly metastatic rate, especially at the time of diagnosis, contributing to a lower survival rate and poor prognosis. The study aimed to identify predictive biomarkers for metastatic Ewing's sarcoma through in-depth analysis of the plasma proteome profile of pediatric Ewing's sarcoma patients.

MAIN METHODS

Plasma samples from Ewing's sarcoma patients and control individuals were profiled using both shotgun and dimethyl-labeled proteomics analysis. Subsequently, Ewing's sarcoma patients were further stratified according to their metastatic state and chemotherapy response. Western blot was used for validation. Univariate and multivariate analyses were performed to determine proteome metastasis predictors. Receiver operating characteristic (ROC) analysis was done to assess the diagnostic significance of the potential plasma Ewing's sarcoma biomarkers.

KEY FINDINGS

Our results revealed a set of proteins significantly associated with the metastatic Ewing's sarcoma disease profile. These proteins include ceruloplasmin and several immunoglobulins. Additionally, our study disclosed significant differentially expressed proteins in pediatric Ewing's sarcoma, including CD5 antigen-like, clusterin, and dermcidin. Stable isotope dimethyl labeling and western blot further confirmed our results, strengthening the impact of such proteins in disease development. Furthermore, an unbiased ROC curve evaluated and confirmed the predictive power of these biomarker candidates.

SIGNIFICANCE

This study presented potential empirical predictive circulating biomarkers for determining the disease status of pediatric Ewing's sarcoma, which is vital for early prediction.

Logistic regression with sparse common and distinctive covariates

Having large sets of predictor variables from multiple sources concerning the same individuals is becoming increasingly common in behavioral research. On top of the variable selection problem, predicting a categorical outcome using such data gives rise to an additional challenge of identifying the processes at play underneath the predictors. These processes are of particular interest in the setting of multi-source data because they can either be associated individually with a single data source or jointly with multiple sources. Although many methods have addressed the classification problem in high dimensionality, the additional challenge of distinguishing such underlying predictor processes from multi-source data has not received sufficient attention. To this end, we propose the method of Sparse Common and Distinctive Covariates Logistic Regression (SCD-Cov-logR). The method is a multi-source extension of principal covariates regression that combines with generalized linear modeling framework to allow classification of a categorical outcome. In a simulation study, SCD-Cov-logR resulted in outperformance compared to related methods commonly used in behavioral sciences. We also demonstrate the practical usage of the method under an empirical dataset.

High-Protein Supplementation and Neuromuscular Electric Stimulation after Aneurysmal Subarachnoid Hemorrhage Increases Systemic Amino Acid and Oxidative Metabolism: A Plasma Metabolomics Approach

Background The INSPIRE randomized clinical trial demonstrated that a high protein diet (HPRO) combined with neuromuscular electrical stimulation (NMES) attenuates muscle atrophy and may improve functional outcomes after aSAH. Using an untargeted metabolomics approach, we sought to identify specific metabolites mediating these effects. Methods 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 as part of the INSPIRE protocol. Untargeted metabolomics were performed for each plasma sample. Paired fold changes were calculated for each metabolite among subjects in the HPRO+NMES group at baseline and 7 days after intervention. Changes in metabolites from baseline to 7 days were compared for the HPRO+NMES and SOC groups. Sparse partial least squared discriminant analysis (sPLS-DA) identified metabolites discriminating each group. Pearson's correlation coefficients were calculated between each metabolite and total protein per day, nitrogen balance, and muscle volume Multivariable models were developed to determine associations between each metabolite and muscle volume. Results A total of 18 unique metabolites were identified including pre and post treatment and differentiating SOC vs HPRO+NMES. Of these, 9 had significant positive correlations with protein intake: N-acetylserine (ρ=0.61, P =1.56x10 -3 ), N-acetylleucine (ρ=0.58, P =2.97x10 -3 ), β-hydroxyisovaleroylcarnitine (ρ=0.53, P =8.35x10 -3 ), tiglyl carnitine (ρ=0.48, P =0.0168), N-acetylisoleucine (ρ=0.48, P =0.0183), N-acetylthreonine (ρ=0.47, P =0.0218), N-acetylkynurenine (ρ=0.45, P =0.0263), N-acetylvaline (ρ=0.44, P =0.0306), and urea (ρ=0.43, P =0.0381). In multivariable regression 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, N-acetylcitrulline, and b-hydroxyisovaleroylcarnitine were also associated with preserved temporalis or quadricep volume. Conclusions Metabolites defining the HPRO+NMES intervention mainly consisted of amino acid derivatives. These metabolites had strong correlations with protein intake and were associated with preserved muscle volume.

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.

Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes

In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher β-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.

Delayed metabolic disturbances in the myocardium after exertional heat stroke: contrasting effects of exertion and thermal load

Epidemiological studies report higher risks of cardiovascular disease in humans exposed to heat stroke earlier in life. Previously, we explored mechanistic links between heat stroke and developing cardiac abnormalities using a preclinical mouse model of exertional heat stroke (EHS). Profound metabolic abnormalities developed in the ventricles of females but not males after 2 wk of recovery. Here we tested whether this lack of response in males could be attributed to the lower exercise performances or reduced thermal loads they experienced with the same running protocol. We systematically altered environmental temperature (Te) during EHS to manipulate heat exposure and exercise performance in the males. Three groups of adult C57BL/6 male mice were studied: "EHS-34" (Te = 34°C), "EHS-41" (Te = 41°C), and "EHS-39.5" (Te = 39.5°C). Mice ran until symptom limitation (unconsciousness), reaching max core temperature (Tc,max). After a 2-wk recovery, the mice were euthanized, and the ventricles were removed for untargeted metabolomics. Results were compared against age-matched nonexercise controls. The EHS-34 mice greatly elevated their exercise performance but reached lower Tc,max and lower thermal loads. The EHS-41 mice exhibited equivalent thermal loads, exercise times, and Tc,max compared with EHS-39.5. The ventricles from EHS-34 mice exhibited the greatest metabolic disturbances in the heart, characterized by shifts toward glucose metabolism, reductions in acylcarnitines, increased amino acid metabolites, elevations in antioxidants, altered TCA cycle flux, and increased xenobiotics. In conclusion, delayed metabolic disturbances following EHS in male myocardium appear to be greatly amplified by higher levels of exertion in the heat, even with lower thermal loads and max core temperatures.NEW & NOTEWORTHY Epidemiological data demonstrate greater cardiovascular risk in patients with previous heat stroke exposure. Using a preclinical mouse model of exertional heat stroke, male mice were exposed to one of three environmental temperatures (Te) during exercise. Paradoxically, after 2 wk, the mice in the lowest Te, exhibiting the largest exercise response and lowest heat load, had the greatest ventricular metabolic disturbances. Metabolic outcomes resemble developing left ventricular hypertrophy or stress-induced heart disease.

Adaptor protein complex 2 in the orbitofrontal cortex predicts alcohol use disorder

Alcohol use disorder (AUD) is a life-threatening disease characterized by compulsive drinking, cognitive deficits, and social impairment that continue despite negative consequences. The inability of individuals with AUD to regulate drinking may involve functional deficits in cortical areas that normally balance actions that have aspects of both reward and risk. Among these, the orbitofrontal cortex (OFC) is critically involved in goal-directed behavior and is thought to maintain a representation of reward value that guides decision making. In the present study, we analyzed post-mortem OFC brain samples collected from age- and sex-matched control subjects and those with AUD using proteomics, bioinformatics, machine learning, and reverse genetics approaches. Of the 4,500+ total unique proteins identified in the proteomics screen, there were 47 proteins that differed significantly by sex that were enriched in processes regulating extracellular matrix and axonal structure. Gene ontology enrichment analysis revealed that proteins differentially expressed in AUD cases were involved in synaptic and mitochondrial function, as well as transmembrane transporter activity. Alcohol-sensitive OFC proteins also mapped to abnormal social behaviors and social interactions. Machine learning analysis of the post-mortem OFC proteome revealed dysregulation of presynaptic (e.g., AP2A1) and mitochondrial proteins that predicted the occurrence and severity of AUD. Using a reverse genetics approach to validate a target protein, we found that prefrontal Ap2a1 expression significantly correlated with voluntary alcohol drinking in male and female genetically diverse mouse strains. Moreover, recombinant inbred strains that inherited the C57BL/6J allele at the Ap2a1 interval consumed higher amounts of alcohol than those that inherited the DBA/2J allele. Together, these findings highlight the impact of excessive alcohol consumption on the human OFC proteome and identify important cross-species cortical mechanisms and proteins that control drinking in individuals with AUD.

Sexual dimorphism in peripheral blood cell characteristics linked to recanalization success of endovascular thrombectomy in acute ischemic stroke

Endovascular thrombectomy (EVT) success to treat acute ischemic stroke varies with factors like stroke etiology and clot composition, which can differ between sexes. We studied if sex-specific blood cell characteristics (BCCs) are related to recanalization success. We analyzed electronic health records of 333 EVT patients from a single intervention center, and extracted 71 BCCs from the Sapphire flow cytometry analyzer. Through Sparse Partial Least Squares Discriminant Analysis, incorporating cross-validation and stability selection, we identified BCCs associated with successful recanalization (TICI 3) in both sexes. Stroke etiology was considered, while controlling for cardiovascular risk factors. Of the patients, successful recanalization was achieved in 51% of women and 49% of men. 21 of the 71 BCCs showed significant differences between sexes  (pFDR-corrected < 0.05). The female-focused recanalization model had lower error rates than both combined [t(192.4) = 5.9, p < 0.001] and male-only models [t(182.6) = - 15.6, p < 0.001]. In women, successful recanalization and cardioembolism were associated with a higher number of reticulocytes, while unsuccessful recanalization and large artery atherosclerosis (LAA) as cause of stroke were associated with a higher mean corpuscular hemoglobin concentration. In men, unsuccessful recanalization and LAA as cause of stroke were associated with a higher coefficient of variance of lymphocyte complexity of the intracellular structure. Sex-specific BCCs related to recanalization success varied and were linked to stroke etiology. This enhanced understanding may facilitate personalized treatment for acute ischemic stroke.

A glimmer of hope - ash genotypes with increased resistance to ash dieback pathogen show cross-resistance to emerald ash borer

Plants rely on cross-resistance traits to defend against multiple, phylogenetically distinct enemies. These traits are often the result of long co-evolutionary histories. Biological invasions can force naïve plants to cope with novel, coincident pests, and pathogens. For example, European ash (Fraxinus excelsior) is substantially threatened by the emerald ash borer (EAB), Agrilus planipennis, a wood-boring beetle, and the ash dieback (ADB) pathogen, Hymenoscyphus fraxineus. Yet, plant cross-resistance traits against novel enemies are poorly explored and it is unknown whether naïve ash trees can defend against novel enemy complexes via cross-resistance mechanisms. To gain mechanistic insights, we quantified EAB performance on grafted replicates of ash genotypes varying in ADB resistance and characterized ash phloem chemistry with targeted and untargeted metabolomics. Emerald ash borer performed better on ADB-susceptible than on ADB-resistant genotypes. Moreover, changes in EAB performance aligned with differences in phloem chemical profiles between ADB-susceptible and ADB-resistant genotypes. We show that intraspecific variation in phloem chemistry in European ash can confer increased cross-resistance to invasive antagonists from different taxonomic kingdoms. Our study suggests that promotion of ADB-resistant ash genotypes may simultaneously help to control the ADB disease and reduce EAB-caused ash losses, which may be critical for the long-term stability of this keystone tree species.

Influence of organic plant breeding on the rhizosphere microbiome of common bean (Phaseolus vulgaris L.)

Introduction

We now recognize that plant genotype affects the assembly of its microbiome, which in turn, affects essential plant functions. The production system for crop plants also influences the microbiome composition, and as a result, we would expect to find differences between conventional and organic production systems. Plant genotypes selected in an organic regime may host different microbiome assemblages than those selected in conventional environments. We aimed to address these questions using recombinant inbred populations of snap bean that differed in breeding history.

Methods

Rhizosphere microbiomes of conventional and organic common beans (Phaseolus vulgaris L.) were characterized within a long-term organic research site. The fungal and bacterial communities were distinguished using pooled replications of 16S and ITS amplicon sequences, which originated from rhizosphere samples collected between flowering and pod set.

Results

Bacterial communities significantly varied between organic and conventional breeding histories, while fungal communities varied between breeding histories and parentage. Within the organically-bred populations, a higher abundance of a plant-growth-promoting bacteria, Arthrobacter pokkalii, was identified. Conventionally-bred beans hosted a higher abundance of nitrogen-fixing bacteria that normally do not form functional nodules with common beans. Fungal communities in the organically derived beans included more arbuscular mycorrhizae, as well as several plant pathogens.

Discussion

The results confirm that the breeding environment of crops can significantly alter the microbiome community composition of progeny. Characterizing changes in microbiome communities and the plant genes instrumental to these changes will provide essential information about how future breeding efforts may pursue microbiome manipulation.

Liver microbial community and associated host transcriptome in calves with feed induced acidosis

Introduction

In the dairy industry, calves are typically fed diets rich in highly fermentable carbohydrates and low in fibrous feeds to maximize ruminal papillae and tissue development. Calves on such diets are vulnerable at developing ruminal acidosis. Prevalent in cattle, liver abscess (LA) is considered a sequela to ruminal acidosis. LAs can cause significant liver function condemnation and decreased growth and production. Currently, we know little about the liver microbiome in calves with feed-induced acidosis.

Methods

Using our established model of ruminal acidosis, where young calves were fed an acidosis-inducing (AC) or -blunting (control) diet starting at birth until 17-week of age, we investigated microbial community changes in the liver resultant from ruminal acidosis. Eight calves were randomly assigned to each diet, with four animals per treatment. Rumen epithelium and liver tissues were collected at 17 weeks of age right after euthanasia. Total RNAs were extracted and followed by whole transcriptome sequencing. Microbial RNA reads were enriched bioinformatically and used for microbial taxonomy classification using Kraken2.

Results

AC Calves showed significantly less weight gain over the course of the experiment, in addition to significantly lower ruminal pH, and rumen degradation comparison to the control group (p < 0.05). In the liver, a total of 29 genera showed a significant (p < 0.05) abundance change (> 2-fold) between the treatments at 17-week of age. Among these, Fibrobacter, Treponema, Lactobacillus, and Olsenella have been reported in abscessed liver in cattle. Concurrent abundance changes in 9 of the genera were observed in both the liver and rumen tissues collected at 17-week of age, indicating potential crosstalk between the liver and rumen epithelial microbial communities. Significant association was identified between host liver gene and its embedded microbial taxa. Aside from identifying previously reported microbial taxa in cattle abscessed liver, new repertoire of actively transcribed microbial taxa was identified in this study.

Discussion

By employing metatranscriptome sequencing, our study painted a picture of liver microbiome in young calves with or without feed induced acidosis. Our study suggested that liver microbiome may have a critical impact on host liver physiology. Novel findings of this study emphasize the need for further in-depth analysis to uncover the functional roles of liver resident microbiome in liver metabolic acidosis resultant from feed-related ruminal acidosis.

Imaging and multi-omics datasets converge to define different neural progenitor origins for ATRT-SHH subgroups

Atypical teratoid rhabdoid tumors (ATRT) are divided into MYC, TYR and SHH subgroups, suggesting diverse lineages of origin. Here, we investigate the imaging of human ATRT at diagnosis and the precise anatomic origin of brain tumors in the Rosa26-CreERT2::Smarcb1flox/flox model. This cross-species analysis points to an extra-cerebral origin for MYC tumors. Additionally, we clearly distinguish SHH ATRT emerging from the cerebellar anterior lobe (CAL) from those emerging from the basal ganglia (BG) and intra-ventricular (IV) regions. Molecular characteristics point to the midbrain-hindbrain boundary as the origin of CAL SHH ATRT, and to the ganglionic eminence as the origin of BG/IV SHH ATRT. Single-cell RNA sequencing on SHH ATRT supports these hypotheses. Trajectory analyses suggest that SMARCB1 loss induces a de-differentiation process mediated by repressors of the neuronal program such as REST, ID and the NOTCH pathway.

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Metabolite profiling identifies chemical markers associated with the cytotoxic properties of roasted fermented avocado seeds

Studies have demonstrated avocado seeds are a good source of bioactive compounds. This study investigated the effects of roasting on the metabolites and anticancer activities of fermented avocado seeds. All three anti-cancer activities of fermented avocado seeds were higher at lower roasting temperature and time. The best inhibition effect was found against Hep G2 followed by the MDA-MB-231 and MCF-7 cancer cell lines. Untargeted metabolite profiling using gas chromatography-mass spectrometry resulted in identification of 208 metabolites. In total, 41 metabolites identified had VIP values more than 1 using PLS-R that were related to anticancer activities. All amino acids and most sugars were higher at lower roasting temperature and positively correlated to anticancer activity. The roasting conditions for optimal antioxidant and anticancer activities were determined to be 121 °C for 9 min. Findings showed that fermented avocado seed powder has the potential to become a functional food ingredient with beneficial bioctive properties.

Methylome and proteome integration in human skeletal muscle uncover group and individual responses to high-intensity interval training

Exercise is a major beneficial contributor to muscle metabolism, and health benefits acquired by exercise are a result of molecular shifts occurring across multiple molecular layers (i.e., epigenome, transcriptome, and proteome). Identifying robust, across-molecular level targets associated with exercise response, at both group and individual levels, is paramount to develop health guidelines and targeted health interventions. Sixteen, apparently healthy, moderately trained (VO2 max = 51.0 ± 10.6 mL min-1  kg-1 ) males (age range = 18-45 years) from the Gene SMART (Skeletal Muscle Adaptive Responses to Training) study completed a longitudinal study composed of 12-week high-intensity interval training (HIIT) intervention. Vastus lateralis muscle biopsies were collected at baseline and after 4, 8, and 12 weeks of HIIT. DNA methylation (~850 CpG sites) and proteomic (~3000 proteins) analyses were conducted at all time points. Mixed models were applied to estimate group and individual changes, and methylome and proteome integration was conducted using a holistic multilevel approach with the mixOmics package. A total of 461 proteins significantly changed over time (at 4, 8, and 12 weeks), whilst methylome overall shifted with training only one differentially methylated position (DMP) was significant (adj.p-value < .05). K-means analysis revealed cumulative protein changes by clusters of proteins that presented similar changes over time. Individual responses to training were observed in 101 proteins. Seven proteins had large effect-sizes >0.5, among them are two novel exercise-related proteins, LYRM7 and EPN1. Integration analysis showed bidirectional relationships between the methylome and proteome. We showed a significant influence of HIIT on the epigenome and more so on the proteome in human muscle, and uncovered groups of proteins clustering according to similar patterns across the exercise intervention. Individual responses to exercise were observed in the proteome with novel mitochondrial and metabolic proteins consistently changed across individuals. Future work is required to elucidate the role of these proteins in response to exercise.

Multidimensional quantitative phenotypic and molecular analysis reveals neomorphic behaviors of p53 missense mutants

Mutations in the TP53 tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specific TP53 missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position-R273C vs. R273H-has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.

Rapid intestinal and systemic metabolic reprogramming in an immunosuppressed environment

Intrinsic metabolism shapes the immune environment associated with immune suppression and tolerance in settings such as organ transplantation and cancer. However, little is known about the metabolic activities in an immunosuppressive environment. In this study, we employed metagenomic, metabolomic, and immunological approaches to profile the early effects of the immunosuppressant drug tacrolimus, antibiotics, or both in gut lumen and circulation using a murine model. Tacrolimus induced rapid and profound alterations in metabolic activities within two days of treatment, prior to alterations in gut microbiota composition and structure. The metabolic profile and gut microbiome after seven days of treatment was distinct from that after two days of treatment, indicating continuous drug effects on both gut microbial ecosystem and host metabolism. The most affected taxonomic groups are Clostriales and Verrucomicrobiae (i.e., Akkermansia muciniphila), and the most affected metabolic pathways included a group of interconnected amino acids, bile acid conjugation, glucose homeostasis, and energy production. Highly correlated metabolic changes were observed between lumen and serum metabolism, supporting their significant interactions. Despite a small sample size, this study explored the largely uncharacterized microbial and metabolic events in an immunosuppressed environment and demonstrated that early changes in metabolic activities can have significant implications that may serve as antecedent biomarkers of immune activation or quiescence. To understand the intricate relationships among gut microbiome, metabolic activities, and immune cells in an immune suppressed environment is a prerequisite for developing strategies to monitor and optimize alloimmune responses that determine transplant outcomes.

CD39 and LDHA affects the prognostic role of NLR in metastatic melanoma patients treated with immunotherapy

BACKGROUND

Identifying response markers is highly needed to guide the treatment strategy in patients with metastatic melanoma.

METHODS

A retrospective study was carried out in patients with unresectable/metastatic melanoma (stage IIIb-IV), treated with anti-PD-1 in the first line setting, to better explore the role and the timing of neutrophil/lymphocyte ratio (NLR) as potential biomarker of response. The relationship of NLR with inflammation-immune mediators and the underlying negative effect of raising NLR during immunotherapy, have been investigated with transcriptomic gene analysis.

RESULTS

The results confirmed previous findings that a high baseline NLR is associated with a poorer prognosis and with higher serum level of lactate dehydrogenase (LDH), regardless of the presence of brain metastases. The transcriptomic analysis showed that high baseline NLR is associated with a characteristic gene signature CCNA1, LDHA and IL18R1, which correlates with inflammation and tumorigenesis. Conversely, low baseline NLR is associated with the signature CD3, SH2D1A, ZAP70 and CD45RA, linked to the immune-activation. The genes positively associated with NLR (CD39 (ENTPD1), PTEN, MYD88, MMP9 and LDH) are involved in processes of immunosuppression, inflammation and tumor-promoting activity. Increased expression of CD39 correlated with TGFβ2, a marker of the N2 neutrophils with immunosuppressive activity.

CONCLUSIONS

These results suggest that increasing NLR is associated with an increased neutrophil population, with polarization to the N2 phenotype, and this process may be the basis for the negatively prognostic role of NLR.

eoPred: predicting the placental phenotype of early-onset preeclampsia using public DNA methylation data

Background: A growing body of literature has reported molecular and histological changes in the human placenta in association with preeclampsia (PE). Placental DNA methylation (DNAme) and transcriptomic patterns have revealed molecular subgroups of PE that are associated with placental histopathology and clinical phenotypes of the disease. However, the clinical and molecular heterogeneity of PE both across and within subtypes complicates the study of this disease. PE is most strongly associated with placental pathology and adverse fetal and maternal outcomes when it develops early in pregnancy. We focused on placentae from pregnancies affected by preeclampsia that were delivered before 34 weeks of gestation to develop eoPred, a predictor of the DNAme signature associated with the placental phenotype of early-onset preeclampsia (EOPE). Results: Public data from 83 placental samples (HM450K), consisting of 42 EOPE and 41 normotensive preterm birth (nPTB) cases, was used to develop eoPred-a supervised model that relies on a highly discriminative 45 CpG DNAme signature of EOPE in the placenta. The performance of eoPred was assessed using cross-validation (AUC = 0.95) and tested in an independent validation cohort (n = 49, AUC = 0.725). A subset of fetal growth restriction (FGR) and late-PE cases showed a similar DNAme profile at the 45 predictive CpGs, consistent with the overlap in placental pathology between these conditions. The relationship between the EOPE probability generated by eoPred and various phenotypic variables was also assessed, revealing that it is associated with gestational age, and it is not driven by cell composition differences. Conclusion: eoPred relies on a 45-CpG DNAme signature to predict a homogeneous placental phenotype of EOPE in a discrete or continuous manner. Using this classifier should 1) aid in the study of placental insufficiency and improve the consistency of future placental DNAme studies of PE, 2) facilitate identifying the placental phenotype of EOPE in public data sets and 3) importantly, standardize the placental diagnosis of EOPE to allow better cross-cohort comparisons. Lastly, classification of cases with eoPred will be useful for investigating the relationship between placental pathology and genetic or environmental variables.

Biomolecular alterations detected in multiple sclerosis skin fibroblasts using Fourier transform infrared spectroscopy

Multiple sclerosis (MS) is the leading cause of non-traumatic disability in young adults. New avenues are needed to help predict individuals at risk for developing MS and aid in diagnosis, prognosis, and outcome of therapeutic treatments. Previously, we showed that skin fibroblasts derived from patients with MS have altered signatures of cell stress and bioenergetics, which likely reflects changes in their protein, lipid, and biochemical profiles. Here, we used Fourier transform infrared (FTIR) spectroscopy to determine if the biochemical landscape of MS skin fibroblasts were altered when compared to age- and sex-matched controls (CTRL). More so, we sought to determine if FTIR spectroscopic signatures detected in MS skin fibroblasts are disease specific by comparing them to amyotrophic lateral sclerosis (ALS) skin fibroblasts. Spectral profiling of skin fibroblasts from MS individuals suggests significant alterations in lipid and protein organization and homeostasis, which may be affecting metabolic processes, cellular organization, and oxidation status. Sparse partial least squares-discriminant analysis of spectral profiles show that CTRL skin fibroblasts segregate well from diseased cells and that changes in MS and ALS may be unique. Differential changes in the spectral profile of CTRL, MS, and ALS cells support the development of FTIR spectroscopy to detect biomolecular modifications in patient-derived skin fibroblasts, which may eventually help establish novel peripheral biomarkers.

Human milk oligosaccharides modulate the intestinal microbiome of healthy adults

Human milk contains over 200 distinct oligosaccharides, which are critical to shaping the developing neonatal gut microbiome. To investigate whether a complex mixture of human milk oligosaccharides (HMOs) would similarly modulate the adult gut microbiome, HMO-Concentrate derived from pooled donor breast milk was administered orally to 32 healthy adults for 7 days followed by 21 days of monitoring. Fecal samples were collected for 16S rRNA gene sequencing, shotgun metagenomics, and metabolomics analyses. HMO-Concentrate induced dose-dependent Bifidobacterium expansion, reduced microbial diversity, and altered microbial gene content. Following HMO cessation, a microbial succession occurred with diverse taxonomic changes-including Bacteroides expansion-that persisted through day 28. This was associated with altered microbial gene content, shifts in serum metabolite levels, and increased circulating TGFβ and IL-10. Incubation of cultured adult microbiota with HMO-Concentrate induced dose-dependent compositional shifts that were not recapitulated by individual HMOs or defined mixtures of the 10 most abundant HMOs in HMO-Concentrate at their measured concentrations. These findings support that pooled donor HMOs can exert direct effects on adult gut microbiota and that complex mixtures including low abundance HMOs present in donor milk may be required for maximum effect.Registration: ClinicalTrials.gov NCT05516225.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

Adaptor protein complex 2 in the orbitofrontal cortex predicts alcohol use disorder

Alcohol use disorder (AUD) is a life-threatening disease characterized by compulsive drinking, cognitive deficits, and social impairment that continue despite negative consequences. The inability of individuals with AUD to regulate drinking may involve functional deficits in cortical areas that normally balance actions that have aspects of both reward and risk. Among these, the orbitofrontal cortex (OFC) is critically involved in goal-directed behavior and is thought to maintain a representation of reward value that guides decision making. In the present study, we analyzed post-mortem OFC brain samples collected from age- and sex-matched control subjects and those with AUD using proteomics, bioinformatics, machine learning, and reverse genetics approaches. Of the 4,500+ total unique proteins identified in the proteomics screen, there were 47 proteins that differed significantly by sex that were enriched in processes regulating extracellular matrix and axonal structure. Gene ontology enrichment analysis revealed that proteins differentially expressed in AUD cases were involved in synaptic and mitochondrial function, as well as transmembrane transporter activity. Alcohol-sensitive OFC proteins also mapped to abnormal social behaviors and social interactions. Machine learning analysis of the post-mortem OFC proteome revealed dysregulation of presynaptic (e.g., AP2A1) and mitochondrial proteins that predicted the occurrence and severity of AUD. Using a reverse genetics approach to validate a target protein, we found that prefrontal Ap2a1 expression significantly correlated with voluntary alcohol drinking in male and female genetically diverse mouse strains. Moreover, recombinant inbred strains that inherited the C57BL/6J allele at the Ap2a1 interval consumed higher amounts of alcohol than those that inherited the DBA/2J allele. Together, these findings highlight the impact of excessive alcohol consumption on the human OFC proteome and identify important cross-species cortical mechanisms and proteins that control drinking in individuals with AUD.

Evaluation of Gut Microbiota in Healthy Persons and Type 1 Diabetes Mellitus Patients in North-Western Russia

Bacterial microbiota in stool may vary over a wide range, depending on age, nutrition, etc. The purpose of our work was to discriminate phyla and genera of intestinal bacteria and their biodiversity within a healthy population (North-Western Russia) compared to the patients with type 1 diabetes mellitus (T1DM). The study group included 183 healthy persons 2 to 53 years old (a mean of 26.5±1.0 years old), and 41 T1DM patients (mean age 18.2±1.8 years old). The disease onset was at 11±1.5 years, with a T1DM experience of 7±1.5 years. Total DNA was isolated from the stool samples, and sequencing libraries were prepared by amplifying the V3-V4 region of the 16S rRNA gene sequenced by Illumina MiSeq. Bioinformatic processing of NGS databases was adapted for microbiota evalutaion. Despite the broad scatter, the biological diversity for bacterial microbiota expressed as the Shannon index was significantly increased from younger to older ages in the comparison group, higher in adult healthy persons, with a trend for decrease in the Actinomycetota phylum which includes Bifidobacterium longum species. Similar but non-significant age trends were noted in the T1DM group. Concordant with the Bacillota prevalence in stool samples of diabetic patients, some anaerobic bacteria (Faecalibacteria, Lachnospira and Ruminococcae, Roseburia) were enriched in the T1DM microbiome against controls. Hence, correction of microbiota for Ruminococcus and Lachnospiraceae requires future search for new probiotics. Lower abundance of Actinomycetota and Bifidobacter in T1DM suggests potential usage of Bifidobacter-based probiotics in this cohort.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

Subgingival microbiome in periodontal health, gingivitis and different stages of periodontitis

AIM

To characterize the subgingival microbiome in subjects with different periodontal health statuses.

MATERIALS AND METHODS

In this cross-sectional observational study, subgingival samples were harvested from Spanish subjects with different periodontal health statuses, based on the 2018 Classification of Periodontal and Peri-Implant Diseases and Conditions. Samples were processed using high-throughput sequencing technologies (Illumina MiSeq). Taxa differentially abundant were identified using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). α- and β-diversity metrics were calculated using q2-diversity in QIIME2. The analyses were adjusted for age, gender and smoking status.

RESULTS

The identified subgingival microbiome showed statistically significant differences among subjects, categorized into periodontal health, gingivitis and stages I-II and III-IV periodontitis (p < .05). In patients with severe (stages III-IV) periodontitis, the genera Filifactor and Fretibacterium were detected 24 times more frequently than in periodontally healthy subjects. Similarly, the genera Porphyromonas, Prevotella and Tannerella were detected four times more frequently (p < .05). The genera Granulicatella, Streptococcus, Paracoccus, Pseudomonas, Haemophilus, Actinobacteria, Bergeyella and Capnocytophaga were significantly associated with healthier periodontal status (p < .05).

CONCLUSIONS

Significant differences were detected in the subgingival microbiome among periodontal health, gingivitis and stages I-II or III-IV periodontitis, suggesting overlapping, yet distinguishable microbial profiles.

The skin microbiome in the first year of life and its association with atopic dermatitis

BACKGROUND

Early-life microbial colonization of the skin may modulate the immune system and impact the development of atopic dermatitis (AD) and allergic diseases later in life. To address this question, we assessed the association between the skin microbiome and AD, skin barrier integrity and allergic diseases in the first year of life. We further explored the evolution of the skin microbiome with age and its possible determinants, including delivery mode.

METHODS

Skin microbiome was sampled from the lateral upper arm on the first day of life, and at 3, 6, and 12 months of age. Bacterial communities were assessed by 16S rRNA gene amplicon sequencing in 346 infants from the PreventADALL population-based birth cohort study, representing 970 samples. Clinical investigations included skin examination and skin barrier function measured as trans-epidermal water loss (TEWL) at the site and time of microbiome sampling at 3, 6, and 12 months. Parental background information was recorded in electronic questionnaires, and delivery mode (including vaginal delivery (VD), VD in water, elective caesarean section (CS) and emergency CS) was obtained from maternal hospital charts.

RESULTS

Strong temporal variations in skin bacterial community composition were found in the first year of life, with distinct patterns associated with different ages. Confirming our hypothesis, skin bacterial community composition in the first year of life was associated with skin barrier integrity and later onsets of AD. Delivery mode had a strong impact on the microbiome composition at birth, with each mode leading to distinct patterns of colonization. Other possible determinants of the skin microbiome were identified, including environmental and parental factors as well as breastfeeding.

CONCLUSION

Skin microbiome composition during infancy is defined by age, transiently influenced by delivery mode as well as environmental, parental factors and breastfeeding. The microbiome is also associated with skin barrier integrity and the onset of AD.

Potential Oral Microbial Markers for Differential Diagnosis of Crohn's Disease and Ulcerative Colitis Using Machine Learning Models

Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn's disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy for Forensic Screening of Long-Term Alcohol Consumption from Human Nails

Fourier transform infrared (FT-IR) spectroscopy is used throughout forensic laboratories for many applications. FT-IR spectroscopy can be useful with ATR accessories in forensic analysis for several reasons. It provides excellent data quality combined with high reproducibility, with minimal user-induced variations and no sample preparation. Spectra from heterogeneous biological systems, including the integumentary system, can be associated with hundreds or thousands of biomolecules. The nail matrix of keratin possesses a complicated structure with captured circulating metabolites whose presence may vary in space and time depending on context and history. We developed a new approach by using machine-learning (ML) tools to leverage the potential and enhance the selectivity of the instrument, create classification models, and provide invaluable information saved in human nails with statistical confidence. Here, we report chemometric analysis of ATR FT-IR spectra for the classification and prediction of long-term alcohol consumption from nail clippings in 63 donors. A partial least squares discriminant analysis (PLS-DA) was used to create a classification model that was validated against an independent data set which resulted in 91% correctly classified spectra. However, when considering the prediction results at the donor level, 100% accuracy was achieved, and all donors were correctly classified. To the best of our knowledge, this proof-of-concept study demonstrates for the first time the ability of ATR FT-IR spectroscopy to discriminate donors who do not drink alcohol from those who drink alcohol on a regular basis.

Mechanisms of systemic low-grade inflammation in HIV patients on long-term suppressive antiretroviral therapy: the inflammasome hypothesis

OBJECTIVE

We aimed to determine the contribution of inflammasome activation in chronic low-grade systemic inflammation observed in patients with HIV (PWH) on long-term suppressive antiretroviral therapy (ART) and to explore mechanisms of such activation.

DESIGN

Forty-two PWH on long-term suppressive ART (HIV-RNA < 40 copies/ml) were compared with 10 HIV-negative healthy controls (HC).

METHODS

Inflammasome activation was measured by dosing mature interleukin (IL)-1β and IL-18 cytokines in patient serum. We explored inflammasome pathways through ex vivo stimulation of PWH primary monocytes with inflammasome activators; expression of inflammasome components by transcriptomic analysis; and metabolomics analysis of patient sera.

RESULTS

Median (Q1; Q3) age, ART and viral suppression duration in PWH were 54 (48; 60), 15 (9; 20) and 7.5 (5; 12) years, respectively. Higher serum IL-18 was measured in PWH than in HC (61 (42; 77) vs. 36 (27-48 pg/ml), P = 0.009); IL-1β was detected in 10/42 PWH (0.5 (0.34; 0.80) pg/ml) but not in HC. Monocytes from PWH did not produce more inflammatory cytokines in vitro , but secretion of IL-1β in response to NOD like receptor family, pyrin domain containing 3 (NLRP3) inflammasome stimulation was higher than in HC. This was not explained at the transcriptional level. We found an oxidative stress molecular profile in PWH sera.

CONCLUSION

HIV infection with long-term effective ART is associated with a serum inflammatory signature, including markers of inflammasome activation, and an increased activation of monocytes upon inflammasome stimulation. Other cells should be investigated as sources of inflammatory cytokines in PWH. Oxidative stress might contribute to this chronic low-grade inflammation.

Significant Microbial Changes Are Evident in the Reproductive Tract of Pregnant Rhesus Monkeys at Mid-Gestation but Their Gut Microbiome Does Not Shift until Late Gestation

Vaginal and rectal specimens were obtained from cycling, pregnant, and nursing rhesus monkeys to assess pregnancy-related changes in the commensal bacteria in their reproductive and intestinal tracts. Using 16S rRNA gene amplicon sequencing, significant differences were found only in the vagina at mid-gestation, not in the hindgut. To verify the apparent stability in gut bacterial composition at mid-gestation, the experiment was repeated with additional monkeys, and similar results were found with both 16S rRNA gene amplicon and metagenomic sequencing. A follow-up study investigated if bacterial changes in the hindgut might occur later in pregnancy. Gravid females were assessed closer to term and compared to nonpregnant females. By late pregnancy, significant differences in bacterial composition, including an increased abundance of 4 species of Lactobacillus and Bifidobacterium adolescentis, were detected, but without a shift in the overall community structure. Progesterone levels were assessed as a possible hormone mediator of bacterial change. The relative abundance of only some taxa (e.g., Bifidobacteriaceae) were specifically associated with progesterone. In summary, pregnancy changes the microbial profiles in monkeys, but the bacterial diversity in their lower reproductive tract is different from women, and the composition of their intestinal symbionts remains stable until late gestation when several Firmicutes become more prominent.

Comprehensive Transcriptomics Profiling of MicroRNA Reveals Plasma Circulating Biomarkers of Hypertrophic Cardiomyopathy and Dysregulated Signaling Pathways

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in genes coding for proteins essential for myocardial contraction. However, it remains unclear through which signaling pathways these gene mutations mediate HCM pathogenesis. Growing evidence indicates that microRNAs (miRNAs) play an important role in the regulation of gene expression. We hypothesized that transcriptomics profiling of plasma miRNAs would reveal circulating biomarkers and dysregulated signaling pathways in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma transcriptomics profiling of miRNAs using RNA sequencing. We developed a transcriptomics-based discrimination model using samples retrieved during the first two-thirds of the study period at one institution (training set). We prospectively tested its discriminative ability in samples collected thereafter from the same institution (prospective test set). We also externally validated the model by applying it to samples collected from the other institutions (external test set). We executed pathway analysis of dysregulated miRNAs with univariable P<0.05.

RESULTS

This study included 555 patients (392 cases and 163 controls). One thousand one hundred forty-one miRNAs passed our quality control filters. The area under the receiver operating characteristic curve of the transcriptomics-based model derived from the training set was 0.86 (95% CI, 0.79-0.93) in the prospective test set and 0.94 (95% CI, 0.90-0.97) in the external test set. Pathway analysis revealed dysregulation of the Ras-MAPK (mitogen-activated protein kinase) pathway and pathways related to inflammation in HCM.

CONCLUSIONS

This study utilized comprehensive transcriptomics profiling with RNA sequencing in HCM, revealing circulating miRNA biomarkers and dysregulated pathways.

Anti-staphylococcus aureus adaptive immunity is impaired in end-stage renal disease patients on hemodialysis: one-year longitudinal study

Introduction

Patients with end-stage renal disease (ESRD) display defects in adaptive and innate immunity, increasing susceptibility to infection. Staphylococcus aureus (S. aureus) is a major cause of bacteraemia in this population and is associated with increased mortality. More information on the immune response to S. aureus in these patients is needed to inform effective vaccine development.

Methods

A longitudinal prospective study was carried out at two medical centers and included 48 ESRD patients who started chronic hemodialysis (HD) treatment ≤3 months before inclusion. Control samples were taken from 62 consenting healthy blood donors. Blood samples were obtained from ESRD patients at each visit, on month (M) 0 (beginning of HD), M6 and M12. Around 50 immunological markers of adaptive and innate immunity were assessed to compare immune responses to S. aureus in ESRD patients versus controls to document the changes on their immune profile during HD.

Results

S. aureus survival in whole blood was significantly higher in ESRD patients than in controls at M0 (P=0.049), while impaired oxidative burst activity was observed in ESRD patients at all timepoints (P<0.001). S. aureus-specific immunoglobulin G (IgG) responses to iron surface determinant B (IsdB) and S. aureus α hemolysin (Hla) antigens were lower in ESRD patients than in healthy donors at M0 (P=0.003 and P=0.007, respectively) and M6 (P=0.05 and P=0.03, respectively), but were restored to control levels at M12. Moreover, S. aureus-specific T-helper cell responses were comparable to controls for IsdB but were impaired for Hla antigen at all timepoints: 10% of ESRD patients responded to Hla at M0, increasing to 30% at M12, compared with 45% of healthy donors. B-cell and T-cell concentrations in blood were significantly reduced (by 60% and 40%, respectively) compared with healthy controls. Finally, upregulation of Human Leucocyte Antigen-DR (HLA-DR) and C-C chemokine Receptor type 2 (CCR2) was impaired at M0 but was restored during the first year of HD.

Conclusion

All together, these results show that adaptive immunity was largely impaired in ESRD patients, whereas innate immunity was less impacted and tended to be restored by HD.

Metabolite abundance in bovine preovulatory follicular fluid is influenced by follicle developmental progression post estrous onset in cattle

Introduction: Preovulatory follicle response to the luteinizing hormone (LH) surge leads to metabolic, molecular, and functional changes in the oocyte and somatic follicular cells from the onset of estrus to ovulation. Follicular fluid contains metabolites, miRNAs, proteins, and hormones that are byproducts of follicular metabolism and support cellular processes of oocyte, cumulus, and granulosa constituents. Numerous studies have highlighted the importance of follicular fluid composition to support fertility, but critical gaps exist toward understanding dynamic modifications in the follicular fluid metabolome from estrous onset to ovulation. The hypothesis was that abundance of follicular fluid metabolites is dependent on follicle progression post LH surge and variability in follicular fluid metabolome profiles indicate key processes required for preparation of the follicle and oocyte for optimal fertility. The objective was to generate preovulatory follicular fluid metabolome profiles and discern differences in the metabolome of preovulatory follicular fluid samples collected at onset of estrus, 11 h post estrous onset, and 18 h post estrous onset. Methods: Estrus was synchronized in non-lactating Jersey cows (n=40) and follicular fluid was collected immediately after the first observed standing mount (hr 0) or at approximately h 11 or 18 after the first standing mount. Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry was performed on preovulatory follicular fluid samples (n = 9 collected at hr 0, 9 at h 11, and 10 at h 18) and a multiple linear model was performed to determine if time post estrous onset impacted metabolite abundance. Results: Metabolites influenced by time post estrous onset were tested for enrichment in KEGG pathways. Ninety metabolites were identified in follicular fluid samples. Twenty metabolites differed in abundance among timepoints post estrous onset (p ≤ 0.05). Pathways corresponding to amino acid and energy metabolism were enriched with metabolites impacted by time post estrous onset (FDR ≤ 0.10). Discussion: Results from the current study indicate early response to the LH surge to increase bioavailability of amino acids and metabolites used by the cumulus and granulosa cells for energy production and shuttled into the oocyte to support meiotic maturation. Such metabolites may later be used by the ovulatory follicle for protein production.