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

Probiotics Strains Modulate Gut Microbiota and Lipid Metabolism in Mule Ducks

Background:

Livestock production should respond to societal, environmental and economic changes. Since 2006 and the ban on antibiotics as growth factors in European Union, the use of probiotics has become widespread and has demonstrated the effect of intestinal microbiota on the performance of farm animals.

Objective:

The aim of this study was to investigate the effect of supplementation with Lactobacillus salivarius (as a probiotics strain or combined with other strains) on zootechnical performance, metabolic and immune gene expression and intestinal microbiota diversity in mule ducks using high-throughput sequencing and real-time PCR.

Method:

The mule ducks were reared for 79 days and overfed for 12 days with or without probiotics. Samples were collected at 14 (starting period) and 91 days (end of overfeeding period), 3 hours post feeding.

Results:

Irrespective of digestive content, age, level of feed intake or supplementation with probiotics, Firmicutes, Proteobacteria and Bacteroidetes were the dominant phyla in the bacterial community in mule ducks. At 14 days, both the ileal and cecal samples were dominated by Firmicutes (in particular the Clostridiales order). Overfeeding induced a shift between Clostridiales and Lactobacillales in the ileal samples whereas in the cecal samples, the relative abundance of Firmicutes decreased. Overfeeding also induced hepatic over-expression of Fatty Acid Synthase (FAS) and of the lipid transporter gene Fatty Acid Binding Protein 4 (FABP4). This increase in lipid metabolism genes is associated with a decrease in inflammatory response.

Conclusion:

Finally, probiotic supplementation had only a slight impact on gene expression and microbiota diversity, both at 14 days and after overfeeding.

Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process

In pigs, the perinatal period is the most critical time for survival. Piglet maturation, which occurs at the end of gestation, is an important determinant of early survival. Skeletal muscle plays a key role in adaptation to extra-uterine life, e.g. motor function and thermoregulation. Progeny from two breeds with extreme neonatal mortality rates were analyzed at 90 and 110 days of gestation (dg). The Large White breed is a highly selected breed for lean growth and exhibits a high rate of neonatal mortality, whereas the Meishan breed is fatter and more robust and has a low neonatal mortality. Our aim was to identify molecular signatures underlying late fetal longissimus muscle development. First, integrated analysis was used to explore relationships between co-expression network models built from a proteomic data set (bi-dimensional electrophoresis) and biological phenotypes. Second, correlations with a transcriptomic data set (microarrays) were investigated to combine different layers of expression with a focus on transcriptional regulation. Muscle glycogen content and myosin heavy chain polymorphism were good descriptors of muscle maturity and were used for further data integration analysis. Using 89 identified unique proteins, network inference, correlation with biological phenotypes and functional enrichment revealed that mitochondrial oxidative metabolism was a key determinant of neonatal muscle maturity. Some proteins, including ATP5A1 and CKMT2, were important nodes in the network related to muscle metabolism. Transcriptomic data suggest that overexpression of mitochondrial PCK2 was involved in the greater glycogen content of Meishan fetuses at 110 dg. GPD1, an enzyme involved in the mitochondrial oxidation of cytosolic NADH, was overexpressed in Meishan. Thirty-one proteins exhibited a positive correlation between mRNA and protein levels in both extreme fetal genotypes, suggesting transcriptional regulation. Gene ontology enrichment and Ingenuity analyses identified PPARGC1A and ESR1 as possible transcriptional factors positively involved in late fetal muscle maturation.

Shifts in the Gut Metabolome and Clostridium difficile Transcriptome throughout Colonization and Infection in a Mouse Model

Clostridium difficile is a bacterial pathogen of global significance that is a major cause of antibiotic-associated diarrhea. Antibiotics deplete the indigenous gut microbiota and change the metabolic environment in the gut to one favoring C. difficile growth. Here we used metabolomics and transcriptomics to define the gut environment after antibiotics and during the initial stages of C. difficile colonization and infection. We show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time and that C. difficile gene expression is consistent with their utilization by the bacterium in vivo. We employed an integrated approach to analyze the metabolome and transcriptome to identify associations between metabolites and transcripts. This highlighted the importance of key nutrients in the early stages of colonization, and the data provide a rationale for the development of therapies based on the use of bacteria that specifically compete for nutrients that are essential for C. difficile colonization and disease.

Antibiotics alter the gut microbiota and decrease resistance to Clostridium difficile colonization; however, the mechanisms driving colonization resistance are not well understood. Loss of resistance to C. difficile colonization due to antibiotic treatment is associated with alterations in the gut metabolome, specifically, with increases in levels of nutrients that C. difficile can utilize for growth in vitro. To define the nutrients that C. difficile requires for colonization and pathogenesis in vivo, we used a combination of mass spectrometry and RNA sequencing (RNA Seq) to model the gut metabolome and C. difficile transcriptome throughout an acute infection in a mouse model at the following time points: 0, 12, 24, and 30 h. We also performed multivariate-based integration of the omics data to define the signatures that were most important throughout colonization and infection. Here we show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time in the mouse cecum and that C. difficile gene expression is consistent with their utilization in vivo. This was also reinforced by the multivariate-based integration of the omics data where we were able to discriminate the metabolites and transcripts that support C. difficile physiology between the different time points throughout colonization and infection. This report illustrates how important the availability of amino acids and other nutrients is for the initial stages of C. difficile colonization and progression of disease. Future studies identifying the source of the nutrients and engineering bacteria capable of outcompeting C. difficile in the gut will be important for developing new targeted bacterial therapeutics.

IMPORTANCEClostridium difficile is a bacterial pathogen of global significance that is a major cause of antibiotic-associated diarrhea. Antibiotics deplete the indigenous gut microbiota and change the metabolic environment in the gut to one favoring C. difficile growth. Here we used metabolomics and transcriptomics to define the gut environment after antibiotics and during the initial stages of C. difficile colonization and infection. We show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time and that C. difficile gene expression is consistent with their utilization by the bacterium in vivo. We employed an integrated approach to analyze the metabolome and transcriptome to identify associations between metabolites and transcripts. This highlighted the importance of key nutrients in the early stages of colonization, and the data provide a rationale for the development of therapies based on the use of bacteria that specifically compete for nutrients that are essential for C. difficile colonization and disease.

Rumen Biohydrogenation and Microbial Community Changes Upon Early Life Supplementation of 22:6n-3 Enriched Microalgae to Goats

Dietary supplementation of docosahexaenoic acid (DHA)-enriched products inhibits the final step of biohydrogenation in the adult rumen, resulting in the accumulation of 18:1 isomers, particularly of trans(t)-11 18:1. Occasionally, a shift toward the formation of t10 intermediates at the expense of t11 intermediates can be triggered. However, whether similar impact would occur when supplementing DHA-enriched products during pregnancy or early life remains unknown. Therefore, the current in vivo study aimed to investigate the effect of a nutritional intervention with DHA in the early life of goat kids on rumen biohydrogenation and microbial community. Delivery of DHA was achieved by supplementing DHA-enriched microalgae (DHA Gold) either to the maternal diet during pregnancy (prenatal) or to the diet of the young offspring (postnatal). At the age of 12 weeks, rumen fluid was sampled for analysis of long-chain fatty acids and microbial community based on bacterial 16S rRNA amplicon sequencing. Postnatal supplementation with DHA-enriched microalgae inhibited the final biohydrogenation step, as observed in adult animals. This resulted particularly in increased ruminal proportions of t11 18:1 rather than a shift to t10 intermediates, suggesting that both young and adult goats might be less prone to dietary induced shifts toward the formation of t10 intermediates, in comparison with cows. Although Butyrivibrio species have been identified as the most important biohydrogenating bacteria, this genus was more abundant when complete biohydrogenation, i.e. 18:0 formation, was inhibited. Blautia abundance was positively correlated with 18:0 accumulation, whereas Lactobacillus spp. Dialister spp. and Bifidobacterium spp. were more abundant in situations with greater t10 accumulation. Extensive comparisons made between current results and literature data indicate that current associations between biohydrogenation intermediates and rumen bacteria in young goats align with former observations in adult ruminants.

Differentiation of Rums Produced from Sugar Cane Juice (Rhum Agricole) from Rums Manufactured from Sugar Cane Molasses by a Metabolomics Approach

A large set of volatiles (a metabolome) was isolated by SAFE distillation from 25 high priced rums prepared from sugar cane juice (SCJ) and 26 high priced rums manufactured from sugar cane molasses (SCM). The volatile fractions were first analyzed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF-MS), and the "comprehensive template matching fingerprinting" was used to extract the entire features present in the respective set of volatile compounds. After raw data pretreatment, chemometrics was used to locate marker compounds. Following, a sparse-partial-least-squares discriminant analysis ( sPLS-DA) and a partial-least-squares discriminant analysis (PLS-DA) were applied to a training data set for creating a model. The model was validated using leave-one-out cross validation and tested over an independent data set to evaluate its predictive power. The characteristic fingerprint resulted in a 100% correct classification of sugar cane juice rums, thus achieving the first aim of locating markers for these higher quality rums. Then, past-processing identification within the discriminant features was done to characterize 12 significant marker compounds as 1-decanol, γ-dodecalactone, ethyl 3-methylbutanoate, ethyl nonanoate, 3-furancarboxaldehyde, 1-hexanol, β-ionone, 2- and 3-methylbutanol, methyl decanoate, 3-octanol, and 2-undecanone. Quantitation of eight selected markers by stable isotope dilution assays confirmed higher concentrations in SCJ compared to SCM and served as the final proof to differentiate both types of spirits.

A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America

HIV infection has a tremendous impact on the immune system's proper functioning. The mucosa-associated lymphoid tissue (MALT) is significantly disarrayed during HIV infection. Compositional changes in the gut microbiota might contribute to the mucosal barrier disruption, and consequently to microbial translocation. We performed an observational, cross-sectional study aimed at evaluating changes in the fecal microbiota of HIV-infected individuals from Colombia. We analyzed the fecal microbiota of 37 individuals via 16S rRNA gene sequencing; 25 HIV-infected patients and 12 control (non-infected) individuals, which were similar in body mass index, age, gender balance and socioeconomic status. To the best of our knowledge, no such studies have been conducted in Latin American countries. Given its compositional nature, microbiota data were normalized and transformed using Aitchison's Centered Log-Ratio. Overall, a change in the network structure in HIV-infected patients was revealed by using the SPIEC-EASI MB tool. Genera such as Blautia, Dorea, Yersinia, Escherichia-Shigella complex, Staphylococcus, and Bacteroides were highly relevant in HIV-infected individuals. Differential abundance analysis by both sparse Partial Least Square-Discriminant Analysis and Random Forest identified a greater abundance of Lachnospiraceae-OTU69, Blautia, Dorea, Roseburia, and Erysipelotrichaceae in HIV-infected individuals. We show here, for the first time, a predominantly Lachnospiraceae-based signature in HIV-infected individuals.

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling

Background

Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer.

Results

A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miRNAs were enriched for HER-2 and estrogen-dependent breast cancer signaling.

Materials and Methods

MiRNAs were assessed in two cross-sectional studies of women without breast cancer and a nested case-control study of breast cancer. Using breast tissues, a multivariate analysis was used to model women with high and low breast cancer risk (based upon Gail risk model) in a discovery study of women without breast cancer (n=90), and applied to an independent replication study (n=71). The model was then assessed using serum samples from the nested case-control study (n=410).

Conclusions

Studying breast tissues of women without breast cancer revealed miRNAs correlated with breast cancer risk, which were then found to be altered in the serum of women who later developed breast cancer. These results serve as proof-of-principle that miRNAs in women without breast cancer may be useful for predicting breast cancer risk and/or as an adjunct for breast cancer early detection. The miRNAs identified herein may be involved in breast carcinogenic pathways because they were first identified in the breast tissues of healthy women.

Type 1 diabetes susceptibility alleles are associated with distinct alterations in the gut microbiota

BACKGROUND

Dysbiosis of the gut microbiota has been implicated in the pathogenesis of many autoimmune conditions including type 1 diabetes (T1D). It is unknown whether changes in the gut microbiota observed in T1D are due to environmental drivers, genetic risk factors, or both. Here, we have performed an analysis of associations between the gut microbiota and T1D genetic risk using the non-obese diabetic (NOD) mouse model of T1D and the TwinsUK cohort.

RESULTS

Through the analysis of five separate colonies of T1D susceptible NOD mice, we identified similarities in NOD microbiome that were independent of animal facility. Introduction of disease protective alleles at the Idd3 and Idd5 loci (IL2, Ctla4, Slc11a1, and Acadl) resulted in significant alterations in the NOD microbiome. Disease-protected strains exhibited a restoration of immune regulatory pathways within the gut which could also be reestablished using IL-2 therapy. Increased T1D disease risk from IL-2 pathway loci in the TwinsUK cohort of human subjects resulted in some similar microbiota changes to those observed in the NOD mouse.

CONCLUSIONS

These findings demonstrate for the first time that type 1 diabetes-associated genetic variants that restore immune tolerance to islet antigens also result in functional changes in the gut immune system and resultant changes in the microbiota.

Value of Circulating Cytokine Profiling During Submaximal Exercise Testing in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Myalgic Encephalomyelitis or Chronic Fatigue Syndrome (ME/CFS) is a heterogeneous syndrome in which patients often experience severe fatigue and malaise following exertion. Immune and cardiovascular dysfunction have been postulated to play a role in the pathophysiology. We therefore, examined whether cytokine profiling or cardiovascular testing following exercise would differentiate patients with ME/CFS. Twenty-four ME/CFS patients were matched to 24 sedentary controls and underwent cardiovascular and circulating immune profiling. Cardiovascular analysis included echocardiography, cardiopulmonary exercise and endothelial function testing. Cytokine and growth factor profiles were analyzed using a 51-plex Luminex bead kit at baseline and 18 hours following exercise. Cardiac structure and exercise capacity were similar between groups. Sparse partial least square discriminant analyses of cytokine profiles 18 hours post exercise offered the most reliable discrimination between ME/CFS and controls (κ = 0.62(0.34,0.84)). The most discriminatory cytokines post exercise were CD40L, platelet activator inhibitor, interleukin 1-β, interferon-α and CXCL1. In conclusion, cytokine profiling following exercise may help differentiate patients with ME/CFS from sedentary controls.

Homeolog expression analysis in an allotriploid non-model crop via integration of transcriptomics and proteomics

The fate of doubled genes, from allopolyploid or autopolyploid origin, is controlled at multiple levels, resulting in the modern day cultivars. We studied the root growth of 3 different triploid banana cultivars under control and osmotic stress conditions. The root growth of the allopolyploid ABB cultivar was 42% higher under control and 61% higher under osmotic stress. By integrating transcriptomics and proteomics, we studied the gene expression of all 3 cultivars, resulting in 2,749 identified root proteins. 383 gene loci displayed genotype specific differential expression whereof 252 showed at least one Single Amino Acid Polymorphism (SAAP). In the ABB cultivar, allele expressions supposedly follow a 1/3 and 2/3 pattern for respectively the A and the B allele. Using transcriptome read alignment to assess the homeoallelic contribution we found that 63% of the allele specific genes deviated from this expectation. 32 gene loci even did not express the A allele. The identified ABB allele- specific proteins correlate well with the observed growth phenotype as they are enriched in energy related functions such as ATP metabolic processes, nicotinamide nucleotide metabolic processes, and glycolysis.

Somatic cell count-based selection reduces susceptibility to energy shortage during early lactation in a sheep model

During the transition from late gestation to early lactation ruminants experience a negative energy balance (NEB), which is considered to increase susceptibility to mammary infections. Our previous study in 2 divergent lines of sheep selected for high and low somatic cell score (SCS) suggested an association between the response to NEB and genetic susceptibility to mastitis. Forty-eight early-lactation primiparous dairy ewes from the 2 SCS genetic lines were allocated to 2 homogeneous subgroups-an NEB group, which was energy restricted and received 60% of the energy requirements for 15 d, and a control-fed group-to obtain 4 balanced groups of 12 ewes: high-SCS positive energy balance, low-SCS positive energy balance, high-SCS NEB, and low-SCS NEB. High-SCS ewes showed greater weight loss and increased plasmatic concentrations of β-hydroxybutyrate and nonesterified fatty acids than low-SCS ewes when confronted with an induced NEB. The aim of this study was to further characterize this interaction by combining transcriptomic and phenotypic data with a generalized partial least squares discriminant analysis using mixOmics package framework. A preliminary analysis using 3 blocks of phenotypes (fatty acids, weight and production, blood metabolites) revealed a high correlation between fat-to-protein ratio, β-hydroxybutyrate, and nonesterified fatty acids concentrations with milk long-chain fatty acid yields. These phenotypes allowed good discrimination of the energy-restricted high-SCS ewes and confirmed a high level of adipose tissue mobilization in this group. A second analysis, which included RNA-seq data, revealed high correlations between the long-chain fatty acid yields in milk and PDK4, CPT1A, SLC25A20, KLF10, and KLF11 expression, highlighting the relationship between mobilization of body reserves and enhanced fatty acids utilization for energy production in blood cells. Finally, analysis of milk composition measured in 1,025 ewes from the 2 genetic lines over 10 yr confirmed significant higher fat-to-protein ratio in high-SCS ewes in early lactation. Altogether, our results strongly confirmed a genetic link between susceptibility to mastitis and metabolic adaptation to energy shortage. Improving genetic resistance to mastitis using SCS should be accompanied by a favorable effect on the response to metabolic stress, especially in highly stressful early lactation. Moreover, this study suggests that the fat-to-protein ratio could be used as a low-cost tool for monitoring energy balance and ketosis during this critical phase of lactation.

Overexpression of SbMyb60 in Sorghum bicolor impacts both primary and secondary metabolism

Few transcription factors have been identified in C₄ grasses that either positively or negatively regulate monolignol biosynthesis. Previously, the overexpression of SbMyb60 in sorghum (Sorghum bicolor) has been shown to induce monolignol biosynthesis, which leads to elevated lignin deposition and altered cell wall composition. To determine how SbMyb60 overexpression impacts other metabolic pathways, RNA-Seq and metabolite profiling were performed on stalks and leaves. 35S::SbMyb60 was associated with the transcriptional activation of genes involved in aromatic amino acid, S-adenosyl methionine (SAM) and folate biosynthetic pathways. The high coexpression values between SbMyb60 and genes assigned to these pathways indicate that SbMyb60 may directly induce their expression. In addition, 35S::SbMyb60 altered the expression of genes involved in nitrogen (N) assimilation and carbon (C) metabolism, which may redirect C and N towards monolignol biosynthesis. Genes linked to UDP-sugar biosynthesis and cellulose synthesis were also induced, which is consistent with the observed increase in cellulose deposition in the internodes of 35S::SbMyb60 plants. However, SbMyb60 showed low coexpression values with these genes and is not likely to be a direct regulator of cell wall polysaccharide biosynthesis. These findings indicate that SbMyb60 can activate pathways beyond monolignol biosynthesis, including those that synthesize the substrates and cofactors required for lignin biosynthesis.

1HNMR-Based metabolomic profiling method to develop plasma biomarkers for sensitivity to chronic heat stress in growing pigs

The negative impact of heat stress (HS) on the production performances in pig faming is of particular concern. Novel diagnostic methods are needed to predict the robustness of pigs to HS. Our study aimed to assess the reliability of blood metabolome to predict the sensitivity to chronic HS of 10 F1 (Large White × Creole) sire families (SF) reared in temperate (TEMP) and in tropical (TROP) regions (n = 56±5 offsprings/region/SF). Live body weight (BW) and rectal temperature (RT) were recorded at 23 weeks of age. Average daily feed intake (AFDI) and average daily gain were calculated from weeks 11 to 23 of age, together with feed conversion ratio. Plasma blood metabolome profiles were obtained by Nuclear Magnetic Resonance spectroscopy (^1HNMR) from blood samples collected at week 23 in TEMP. The sensitivity to hot climatic conditions of each SF was estimated by computing a composite index of sensitivity (I_sens) derived from a linear combination of t statistics applied to familial BW, ADFI and RT in TEMP and TROP climates. A model of prediction of sensitivity was established with sparse Partial Least Square Discriminant Analysis (sPLS-DA) between the two most robust SF (n = 102) and the two most sensitive ones (n = 121) using individual metabolomic profiles measured in TEMP. The sPLS-DA selected 29 buckets that enabled 78% of prediction accuracy by cross-validation. On the basis of this training, we predicted the proportion of sensitive pigs within the 6 remaining families (n = 337). This proportion was defined as the predicted membership of families to the sensitive category. The positive correlation between this proportion and I_sens (r = 0.97, P < 0.01) suggests that plasma metabolome can be used to predict the sensitivity of pigs to hot climate.

mixOmics: An R package for 'omics feature selection and multiple data integration

The advent of high throughput technologies has led to a wealth of publicly available 'omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a 'molecular signature') to explain or predict biological conditions, but mainly for a single type of 'omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous 'omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple 'omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of 'omics data available from the package.

Recombinant Incretin-Secreting Microbe Improves Metabolic Dysfunction in High-Fat Diet Fed Rodents

The gut hormone glucagon-like peptide (GLP)-1 and its analogues represent a new generation of anti-diabetic drugs, which have also demonstrated propensity to modulate host lipid metabolism. Despite this, drugs of this nature are currently limited to intramuscular administration routes due to intestinal degradation. The aim of this study was to design a recombinant microbial delivery vector for a GLP-1 analogue and assess the efficacy of the therapeutic in improving host glucose, lipid and cholesterol metabolism in diet induced obese rodents. Diet-induced obese animals received either Lactobacillus paracasei NFBC 338 transformed to express a long-acting analogue of GLP-1 or the isogenic control microbe which solely harbored the pNZ44 plasmid. Short-term GLP-1 microbe intervention in rats reduced serum low-density lipoprotein cholesterol, triglycerides and triglyceride-rich lipoprotein cholesterol substantially. Conversely, extended GLP-1 microbe intervention improved glucose-dependent insulin secretion, glucose metabolism and cholesterol metabolism, compared to the high-fat control group. Interestingly, the microbe significantly attenuated the adiposity associated with the model and altered the serum lipidome, independently of GLP-1 secretion. These data indicate that recombinant incretin-secreting microbes may offer a novel and safe means of managing cholesterol metabolism and diet induced dyslipidaemia, as well as insulin sensitivity in metabolic dysfunction.

Subphenotypes of inflammatory bowel disease are characterized by specific serum protein profiles

OBJECTIVE

Genetic and immunological data indicate that inflammatory bowel disease (IBD) are characterized by specific inflammatory protein profiles. However, the serum proteome of IBD is still to be defined. We aimed to characterize the inflammatory serum protein profiles of Crohn's disease (CD) and ulcerative colitis (UC), using the novel proximity extension assay.

METHODS

A panel of 91 inflammatory proteins were quantified in a discovery cohort of CD (n = 54), UC patients (n = 54), and healthy controls (HCs; n = 54). We performed univariate analyses by t-test, with false discovery rate correction. A sparse partial least-squares (sPLS) approach was used to identify additional discriminative proteins. The results were validated in a replication cohort.

RESULTS

By univariate analysis, 17 proteins were identified with significantly different abundances in CD and HCs, and 12 when comparing UC and HCs. Additionally, 64 and 45 discriminant candidate proteins, respectively, were identified with the multivariate approach. Correspondingly, significant cross-validation error rates of 0.12 and 0.19 were observed in the discovery cohort. Only FGF-19 was identified from univariate comparisons of CD and UC, but 37 additional discriminant candidates were identified using the multivariate approach. The observed cross-validation error rate for CD vs. UC remained significant when restricting the analyses to patients in clinical remission. Using univariate comparisons, 16 of 17 CD-associated proteins and 8 of 12 UC-associated proteins were validated in the replication cohort. The area under the curve for CD and UC was 0.96 and 0.92, respectively, when the sPLS model from the discovery cohort was applied to the replication cohort.

CONCLUSIONS

By using the novel PEA method and a panel of inflammatory proteins, we identified proteins with significantly different quantities in CD patients and UC patients compared to HCs. Our data highlight the potential of the serum IBD proteome as a source for identification of future diagnostic biomarkers.

Surgically Induced Changes in Gut Microbiome and Hedonic Eating as Related to Weight Loss: Preliminary Findings in Obese Women Undergoing Bariatric Surgery

OBJECTIVE

Weight loss surgery results in significant changes in the anatomy, function, and intraluminal environment of the gastrointestinal tract affecting the gut microbiome. Although bariatric surgery results in sustained weight loss, decreased appetite, and hedonic eating, it is unknown whether the surgery-induced alterations in gut microbiota play a role in the observed changes in hedonic eating. We explored the following hypotheses: (1) laparoscopic sleeve gastrectomy (LSG) results in changes in gut microbial composition; (2) alterations in gut microbiota are related to weight loss; (3) alterations in gut microbiome are associated with changes in appetite and hedonic eating.

METHODS

Eight obese women underwent LSG. Their body mass index, body fat mass, food intake, hunger, hedonic eating scores, and stool samples were obtained at baseline and 1-month postsurgery. 16S ribosomal RNA gene sequencing was performed on stool samples. DESeq2 changes in microbial abundance. Multilevel-sparse partial least squares discriminant analysis was applied to genus-level abundance for discriminative microbial signatures.

RESULTS

LSG resulted in significant reductions in body mass index, food intake, and hedonic eating. A microbial signature composed of five bacterial genera discriminated between pre- and postsurgery status. Several bacterial genera were significantly associated with weight loss (Bilophila, q = 3E-05; Faecalibacterium q = 4E-05), lower appetite (Enterococcus, q = 3E-05), and reduced hedonic eating (Akkermansia, q = .037) after surgery.

CONCLUSIONS

In this preliminary analysis, changes in gut microbial abundance discriminated between pre- and postoperative status. Alterations in gut microbiome were significantly associated with weight loss and with reduced hedonic eating after surgery; however, a larger sample is needed to confirm these findings.

Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women

OBJECTIVE

Brain-gut-microbiota interactions may play an important role in human health and behavior. Although rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive, and social behaviors, there is little translational human evidence to date. In this study, we identify brain and behavioral characteristics of healthy women clustered by gut microbiota profiles.

METHODS

Forty women supplied fecal samples for 16S rRNA profiling. Microbial clusters were identified using Partitioning Around Medoids. Functional magnetic resonance imaging was acquired. Microbiota-based group differences were analyzed in response to affective images. Structural and diffusion tensor imaging provided gray matter metrics (volume, cortical thickness, mean curvature, surface area) as well as fiber density between regions. A sparse Partial Least Square-Discrimination Analysis was applied to discriminate microbiota clusters using white and gray matter metrics.

RESULTS

Two bacterial genus-based clusters were identified, one with greater Bacteroides abundance (n = 33) and one with greater Prevotella abundance (n = 7). The Prevotella group showed less hippocampal activity viewing negative valences images. White and gray matter imaging discriminated the two clusters, with accuracy of 66.7% and 87.2%, respectively. The Prevotella cluster was associated with differences in emotional, attentional, and sensory processing regions. For gray matter, the Bacteroides cluster showed greater prominence in the cerebellum, frontal regions, and the hippocampus.

CONCLUSIONS

These results support the concept of brain-gut-microbiota interactions in healthy humans. Further examination of the interaction between gut microbes, brain, and affect in humans is needed to inform preclinical reports that microbial modulation may affect mood and behavior.

Cell wall modifications of two Arabidopsis thaliana ecotypes, Col and Sha, in response to sub-optimal growth conditions: An integrative study

With the global temperature change, plant adaptations are predicted, but little is known about the molecular mechanisms underlying them. Arabidopsis thaliana is a model plant adapted to various environmental conditions, in particular able to develop along an altitudinal gradient. Two ecotypes, Columbia (Col) growing at low altitude, and Shahdara (Sha) growing at 3400m, have been studied at optimal and sub-optimal growth temperature (22°C vs 15°C). Macro- and micro-phenotyping, cell wall monosaccharides analyses, cell wall proteomics, and transcriptomics have been performed in order to accomplish an integrative analysis. The analysis has been focused on cell walls (CWs) which are assumed to play roles in response to environmental changes. At 15°C, both ecotypes presented characteristic morphological traits of low temperature growth acclimation such as reduced rosette diameter, increased number of leaves, modifications of their CW composition and cuticle reinforcement. Altogether, the integrative analysis has allowed identifying several candidate genes/proteins possibly involved in the cell wall modifications observed during the temperature acclimation response.

Urinary Metabolomics for Noninvasive Detection of Antibody-Mediated Rejection in Children After Kidney Transplantation

BACKGROUND

Biomarkers are needed that identify patients with antibody-mediated rejection (AMR). The goal of this study was to evaluate the utility of urinary metabolomics for early noninvasive detection of AMR in pediatric kidney transplant recipients.

METHODS

Urine samples (n = 396) from a prospective, observational cohort of 59 renal transplant patients with surveillance or indication biopsies were assayed for 133 unique metabolites by quantitative mass spectrometry. Samples were classified according to Banff criteria for AMR and partial least squares discriminant analysis was used to identify associated changes in metabolite patterns by creating a composite index based on all 133 metabolites.

RESULTS

Urine samples of patients with (n = 40) and without AMR (n = 278) were analyzed and a classifier for AMR was identified (area under receiver operating characteristic curve = 0.84; 95% confidence interval, 0.77-0.91; P = 0.006). Application of the classifier to "indeterminate" samples (samples that partially fulfilled Banff criteria for AMR; n = 65) yielded an AMR score of 0.19 ± 0.15, intermediate between scores for AMR and No AMR (0.28 ± 0.14 and 0.10 ± 0.13 respectively, P ≤ 0.001). The AMR score was associated with the presence of donor-specific antibodies, biopsy indication, Banff ct, t, ah and cg scores, and retained accuracy when applied to subclinical cases (creatinine, <25% increase from baseline) or had minimal or no transplant glomerulopathy (Banff cg0-1). Exploratory classifiers that segregated samples based on concurrent T cell-mediated rejection (TCMR) identified overlapping metabolite signatures between AMR and TCMR, suggesting similar pathophysiology of tissue injury.

CONCLUSIONS

These preliminary findings identify a urine metabolic classifier for AMR. Independent validation is needed to verify its utility for accurate, noninvasive AMR detection.

Integration of Biochemical, Biophysical and Transcriptomics Data for Investigating the Structural and Nanomechanical Properties of the Yeast Cell Wall

The yeast cell is surrounded by a cell wall conferring protection and resistance to environmental conditions that can be harmful. Identify the molecular cues (genes) which shape the biochemical composition and the nanomechanical properties of the cell wall and the links between these two parameters represent a major issue in the understanding of the biogenesis and the molecular assembly of this essential cellular structure, which may have consequences in diverse biotechnological applications. We addressed this question in two ways. Firstly, we compared the biochemical and biophysical properties using atomic force microscopy (AFM) methods of 4 industrial strains with the laboratory sequenced strain BY4743 and used transcriptome data of these strains to infer biological hypothesis about differences of these properties between strains. This comparative approach showed a 4–6-fold higher hydrophobicity of industrial strains that was correlated to higher expression of genes encoding adhesin and adhesin-like proteins and not to their higher mannans content. The second approach was to employ a multivariate statistical analysis to identify highly correlated variables among biochemical, biophysical and genes expression data. Accordingly, we found a tight association between hydrophobicity and adhesion events that positively correlated with a set of 22 genes in which the main enriched GO function was the sterol metabolic process. We also identified a strong association of β-1,3-glucans with contour length that corresponds to the extension of mannans chains upon pulling the mannosyl units with the lectin-coated AFM tips. This association was positively correlated with a group of 27 genes in which the seripauperin multigene family was highly documented and negatively connected with a set of 23 genes whose main GO biological process was sulfur assimilation/cysteine biosynthetic process. On the other hand, the elasticity modulus was found weakly associated with levels of β-1,6-glucans, and this biophysical variable was positively correlated with a set of genes implicated in microtubules polymerization, tubulin folding and mitotic organization.

Integrated Analysis of Biopsies from Inflammatory Bowel Disease Patients Identifies SAA1 as a Link Between Mucosal Microbes with TH17 and TH22 Cells

BACKGROUND

Inflammatory bowel diseases (IBD) are believed to be driven by dysregulated interactions between the host and the gut microbiota. Our goal is to characterize and infer relationships between mucosal T cells, the host tissue environment, and microbial communities in patients with IBD who will serve as basis for mechanistic studies on human IBD.

METHODS

We characterized mucosal CD4 T cells using flow cytometry, along with matching mucosal global gene expression and microbial communities data from 35 pinch biopsy samples from patients with IBD. We analyzed these data sets using an integrated framework to identify predictors of inflammatory states and then reproduced some of the putative relationships formed among these predictors by analyzing data from the pediatric RISK cohort.

RESULTS

We identified 26 predictors from our combined data set that were effective in distinguishing between regions of the intestine undergoing active inflammation and regions that were normal. Network analysis on these 26 predictors revealed SAA1 as the most connected node linking the abundance of the genus Bacteroides with the production of IL17 and IL22 by CD4 T cells. These SAA1-linked microbial and transcriptome interactions were further reproduced with data from the pediatric IBD RISK cohort.

CONCLUSIONS

This study identifies expression of SAA1 as an important link between mucosal T cells, microbial communities, and their tissue environment in patients with IBD. A combination of T cell effector function data, gene expression and microbial profiling can distinguish between intestinal inflammatory states in IBD regardless of disease types.

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L₀-norm penalty is applied. This L₀-norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model.

Unperturbed Cytotoxic Lymphocyte Phenotype and Function in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Patients

Myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) is a debilitating disorder linked to diverse intracellular infections as well as physiological stress. Cytotoxic lymphocytes combat intracellular infections. Their function is attenuated by stress. Despite numerous studies, the role of cytotoxic lymphocytes in ME/CFS remains unclear. Prompted by advances in the understanding of defects in lymphocyte cytotoxicity, the discovery of adaptive natural killer (NK) cell subsets associated with certain viral infections, and compelling links between stress, adrenaline, and cytotoxic lymphocyte function, we reassessed the role of cytotoxic lymphocytes in ME/CFS. Forty-eight patients from two independent cohorts fulfilling the Canada 2003 criteria for ME/CFS were evaluated with respect to cytotoxic lymphocyte phenotype and function. Results were compared to values from matched healthy controls. Reproducible differences between patients and controls were not found in cytotoxic lymphocyte numbers, cytotoxic granule content, activation status, exocytotic capacity, target cell killing, or cytokine production. One patient expressed low levels of perforin, explained by homozygosity for the PRF1 p.A91V variant. However, overall, this variant was present in a heterozygous state at the expected population frequency among ME/CFS patients. No single patient displayed any pathological patterns of cellular responses. Increased expansions of adaptive NK cells or deviant cytotoxic lymphocyte adrenaline-mediated inhibition were not observed. In addition, supervised dimensionality reduction analyses of the full, multidimensional datasets did not reveal any reproducible patient/control discriminators. In summary, employing sensitive assays and analyses for quantification of cytotoxic lymphocyte differentiation and function, cytotoxicity lymphocyte aberrances were not found among ME/CFS patients. These assessments of cytotoxic lymphocytes therefore do not provide useful biomarkers for the diagnosis of ME/CFS.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Furanoterpene Diversity and Variability in the Marine Sponge Spongia officinalis, from Untargeted LC-MS/MS Metabolomic Profiling to Furanolactam Derivatives

The Mediterranean marine sponge Spongia officinalis has been reported as a rich source of secondary metabolites and also as a bioindicator of water quality given its capacity to concentrate trace metals. In this study, we evaluated the chemical diversity within 30 S. officinalis samples collected over three years at two sites differentially impacted by anthropogenic pollutants located near Marseille (South of France). Untargeted liquid chromatography—mass spectrometry (LC–MS) metabolomic profiling (C18 LC, ESI-Q-TOF MS) combined with XCMS Online data processing and multivariate statistical analysis revealed 297 peaks assigned to at least 86 compounds. The spatio-temporal metabolite variability was mainly attributed to variations in relative content of furanoterpene derivatives. This family was further characterized through LC–MS/MS analyses in positive and negative ion modes combined with molecular networking, together with a comprehensive NMR study of isolated representatives such as demethylfurospongin-4 and furospongin-1. The MS/MS and NMR spectroscopic data led to the identification of a new furanosesterterpene, furofficin (2), as well as two derivatives with a glycinyl lactam moiety, spongialactam A (12a) and B (12b). This study illustrates the potential of untargeted LC–MS metabolomics and molecular networking to discover new natural compounds even in an extensively studied organism such as S. officinalis. It also highlights the effect of anthropogenic pollution on the chemical profiles within the sponge.

Integrating transcriptomics and metabolomics for the analysis of the aroma profiles of Saccharomyces cerevisiae strains from diverse origins

Background

During must fermentation thousands of volatile aroma compounds are formed, with higher alcohols, acetate esters and ethyl esters being the main aromatic compounds contributing to floral and fruity aromas. The action of yeast, in particular Saccharomyces cerevisiae, on the must components will build the architecture of the wine flavour and its fermentation bouquet. The objective of the present work was to better understand the molecular and metabolic bases of aroma production during a fermentation process. For such, comparative transcriptomic and metabolic analysis was performed at two time points (5 and 50 g/L of CO₂ released) in fermentations conducted by four yeast strains from different origins and/or technological applications (cachaça, sake, wine, and laboratory), and multivariate factorial analyses were used to rationally identify new targets for improving aroma production.

Results

Results showed that strains from cachaça, sake and wine produced higher amounts of acetate esters, ethyl esters, acids and higher alcohols, in comparison with the laboratory strain. At fermentation time T1 (5 g/L CO₂ released), comparative transcriptomics of the three S. cerevisiae strains from different fermentative environments in comparison with the laboratory yeast S288c, showed an increased expression of genes related with tetracyclic and pentacyclic triterpenes metabolism, involved in sterol synthesis. Sake strain also showed upregulation of genes ADH7 and AAD6, involved in the formation of higher alcohols in the Ehrlich pathway. For fermentation time point T2 (50 g/L CO₂ released), again sake strain, but also VL1 strain, showed an increased expression of genes involved in formation of higher alcohols in the Ehrlich pathway, namely ADH7, ADH6 and AAD6, which is in accordance with the higher levels of methionol, isobutanol, isoamyl alcohol and phenylethanol observed.

Conclusions

Our approach revealed successful to integrate data from several technologies (HPLC, GC-MS, microarrays) and using different data analysis methods (PCA, MFA). The results obtained increased our knowledge on the production of wine aroma and flavour, identifying new gene in association to the formation of flavour active compounds, mainly in the production of fatty acids, and ethyl and acetate esters.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3816-1) contains supplementary material, which is available to authorized users.

Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield

A combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize (Zea mays) lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines. During the same period, there was a significant correlation between the genetic distance of the lines and the activities of enzymes involved in carbon metabolism, notably glycolysis. Although large differences were observed in terms of leaf metabolic fluxes, these variations were not tightly linked to the genome structure of the lines. Both correlation studies and metabolic network analyses allowed the description of a maize ideotype with a high grain yield potential. Such an ideotype is characterized by low accumulation of soluble amino acids and carbohydrates in the leaves and high activity of enzymes involved in the C4 photosynthetic pathway and in the biosynthesis of amino acids derived from glutamate. Chlorogenates appear to be important markers that can be used to select for maize lines that produce larger kernels.

Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms

Background

Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods.

Results

To limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures.

Conclusions

MINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/and http://cran.r-project.org/web/packages/mixOmics/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1553-8) contains supplementary material, which is available to authorized users.

Integrative miRNA-Gene Expression Analysis Enables Refinement of Associated Biology and Prediction of Response to Cetuximab in Head and Neck Squamous Cell Cancer

This paper documents the process by which we, through gene and miRNA expression profiling of the same samples of head and neck squamous cell carcinomas (HNSCC) and an integrative miRNA-mRNA expression analysis, were able to identify candidate biomarkers of progression-free survival (PFS) in patients treated with cetuximab-based approaches. Through sparse partial least square–discriminant analysis (sPLS-DA) and supervised analysis, 36 miRNAs were identified in two components that clearly separated long- and short-PFS patients. Gene set enrichment analysis identified a significant correlation between the miRNA first-component and EGFR signaling, keratinocyte differentiation, and p53. Another significant correlation was identified between the second component and RAS, NOTCH, immune/inflammatory response, epithelial–mesenchymal transition (EMT), and angiogenesis pathways. Regularized canonical correlation analysis of sPLS-DA miRNA and gene data combined with the MAGIA2 web-tool highlighted 16 miRNAs and 84 genes that were interconnected in a total of 245 interactions. After feature selection by a smoothed t-statistic support vector machine, we identified three miRNAs and five genes in the miRNA-gene network whose expression result was the most relevant in predicting PFS (Area Under the Curve, AUC = 0.992). Overall, using a well-defined clinical setting and up-to-date bioinformatics tools, we are able to give the proof of principle that an integrative miRNA-mRNA expression could greatly contribute to the refinement of the biology behind a predictive model.

Characterisation of Early-Life Fecal Microbiota in Susceptible and Healthy Pigs to Post-Weaning Diarrhoea

Early-life microbial exposure is of particular importance to growth, immune system development and long-lasting health. Hence, early microbiota composition is a promising predictive biomarker for health and disease but still remains poorly characterized in regards to susceptibility to diarrhoea. In the present study, we aimed to assess if gut bacterial community diversity and composition during the suckling period were associated with differences in susceptibility of pigs to post-weaning diarrhoea. Twenty piglets from 5 sows (4 piglets / litter) were weaned in poor housing conditions to challenge their susceptibility to post-weaning diarrhoea. Two weeks after weaning, 13 pigs exhibited liquid faeces during 2 or 3 days and were defined as diarrhoeic (D) pigs. The other 7 pigs did not have diarrhea during the whole post-weaning experimental periodand were defined as healthy (H) pigs. Using a molecular characterisation of fecal microbiota with CE-SSCP fingerprint, Next Generation Sequencing and qPCR, we show that D and H pigs were mainly discriminated as early as postnatal day (PND) 7, i.e. 4 weeks before post-weaning diarrhoea occurence. At PND 7 H pigs displayed a lower evenness and a higher abundance of Prevotellaceae, Lachnospiraceae, Ruminocacaceae and Lactobacillaceae compared to D pigs. The sPLS regression method indicates that these bacterial families were strongly correlated to a higher Bacteroidetes abundance observed in PND 30 H pigs one week before diarrhoea. These results emphasize the potential of early microbiota diversity and composition as being an indicator of susceptibility to post-weaning diarrhoea. Furthermore, they support the health promoting strategies of pig herds through gut microbiota engineering.

Defining the purity of exosomes required for diagnostic profiling of small RNA suitable for biomarker discovery

Small non-coding RNAs (ncRNA), including microRNAs (miRNA), enclosed in exosomes are being utilised for biomarker discovery in disease. Two common exosome isolation methods involve differential ultracentrifugation or differential ultracentrifugation coupled with Optiprep gradient fractionation. Generally, the incorporation of an Optiprep gradient provides better separation and increased purity of exosomes. The question of whether increased purity of exosomes is required for small ncRNA profiling, particularly in diagnostic and biomarker purposes, has not been addressed and highly debated. Utilizing an established neuronal cell system, we used next-generation sequencing to comprehensively profile ncRNA in cells and exosomes isolated by these 2 isolation methods. By comparing ncRNA content in exosomes from these two methods, we found that exosomes from both isolation methods were enriched with miRNAs and contained a diverse range of rRNA, small nuclear RNA, small nucleolar RNA and piwi-interacting RNA as compared with their cellular counterparts. Additionally, tRNA fragments (30-55 nucleotides in length) were identified in exosomes and may act as potential modulators for repressing protein translation. Overall, the outcome of this study confirms that ultracentrifugation-based method as a feasible approach to identify ncRNA biomarkers in exosomes.

Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. The predictive modeling of such microbiota count data for the classification of human infection from parasitic worms, such as helminths, can help in the detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse, containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously, using different methods of feature reduction. To our knowledge, integrative methods, such as transfer learning, have not yet been explored in the microbiome domain as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster, grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis by using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling by using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under the receiver operating characteristic (ROC) Curve (AUC) and Balanced Accuracy (Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.

Tomato Juice Consumption Modifies the Urinary Peptide Profile in Sprague-Dawley Rats with Induced Hepatic Steatosis

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in Western countries, with a high prevalence, and has been shown to increase the risk of type 2 diabetes, cardiovascular disease (CVD), etc. Tomato products contain several natural antioxidants, including lycopene—which has displayed a preventive effect on the development of steatosis and CVD. Accordingly, the aim of the present work was to evaluate the effect of tomato juice consumption on the urinary peptide profile in rats with NAFLD induced by an atherogenic diet and to identify potential peptide biomarkers for diagnosis. Urine samples, collected weekly for four weeks, were analyzed by capillary electrophoresis (CE) coupled to a mass spectrometer (MS). A partial least squares-discriminant analysis (PLS-DA) was carried out to explore the association between differential peptides and treatments. Among the 888 peptides initially identified, a total of 55 were obtained as potential biomarkers. Rats with steatosis after tomato juice intake showed a profile intermediate between that of healthy rats and that of rats with induced hepatic steatosis. Accordingly, tomato products could be considered as a dietary strategy for the impairment of NAFLD, although further research should be carried out to develop a specific biomarkers panel for NAFLD.

Differentially expressed genes selection via Laplacian regularized low-rank representation method

With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data. In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods. The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection.

Deconstructing the pig sex metabolome: Targeted metabolomics in heavy pigs revealed sexual dimorphisms in plasma biomarkers and metabolic pathways

Metabolomics has opened new possibilities to investigate metabolic differences among animals. In this study, we applied a targeted metabolomic approach to deconstruct the pig sex metabolome as defined by castrated males and entire gilts. Plasma from 545 performance-tested Italian Large White pigs (172 castrated males and 373 females) sampled at about 160 kg live weight were analyzed for 186 metabolites using the Biocrates AbsoluteIDQ p180 Kit. After filtering, 132 metabolites (20 AA, 11 biogenic amines, 1 hexose, 13 acylcarnitines, 11 sphingomyelins, 67 phosphatidylcholines, and 9 lysophosphatidylcholines) were retained for further analyses. The multivariate approach of the sparse partial least squares discriminant analysis was applied, together with a specifically designed statistical pipeline, that included a permutation test and a 10 cross-fold validation procedure that produced stability and effect size statistics for each metabolite. Using this approach, we identified 85 biomarkers (with metabolites from all analyzed chemical families) that contributed to the differences between the 2 groups of pigs ( < 0.05 at the stability statistic test). All acylcarnitines and almost all biogenic amines were higher in castrated males than in gilts. Metabolites involved in tryptophan catabolism had the largest differences (i.e., delta = 20% for serotonin) between castrated males (higher) and gilts (lower). The level of several AA (Ala, Arg, Gly, His, Lys, Ser, Thr, and Trp) was higher in gilts (delta was from approximately 1.0 to approximately 4.8%) whereas products of AA catabolism (taurine, 2-aminoadipic acid, and methionine sulfoxide) were higher in castrated males (delta was approximately 5.0-6.0%), suggesting a metabolic shift in castrated males toward energy storage and lipid production. Similar general patterns were observed for most sphingomyelins, phosphatidylcholines, and lysophosphatidylcholines. Metabolomic pathway analysis and pathway enrichment identified several differences between the 2 sexes. This metabolomic overview opened new clues on the biochemical mechanisms underlying sexual dimorphism that, on one hand, might explain differences in terms of economic traits between castrated male pigs and entire gilts and, on the other hand, could strengthen the pig as a model to define metabolic mechanisms related to fat deposition.

MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

Age-Related 1H NMR Characterization of Cerebrospinal Fluid in Newborn and Young Healthy Piglets

When it comes to neuroscience, pigs represent an important animal model due to their resemblance with humans’ brains for several patterns including anatomy and developmental stages. Cerebrospinal fluid (CSF) is a relatively easy-to-collect specimen that can provide important information about neurological health and function, proving its importance as both a diagnostic and biomedical monitoring tool. Consequently, it would be of high scientific interest and value to obtain more standard physiological information regarding its composition and dynamics for both swine pathology and the refinement of experimental protocols. Recently, proton nuclear magnetic resonance (¹H NMR) spectroscopy has been applied in order to analyze the metabolomic profile of this biological fluid, and results showed the technique to be highly reproducible and reliable. The aim of the present study was to investigate in both qualitative and quantitative manner the composition of Cerebrospinal Fluid harvested form healthy newborn (5 days old-P5) and young (30-P30 and 50-P50 days old) piglets using ¹H NMR Spectroscopy, and to analyze any possible difference in metabolites concentration between age groups, related to age and Blood-Brain-Barrier maturation. On each of the analyzed samples, 30 molecules could be observed above their limit of quantification, accounting for 95–98% of the total area of the spectra. The concentrations of adenine, tyrosine, leucine, valine, 3-hydroxyvalerate, 3-methyl-2-oxovalerate were found to decrease between P05 and P50, while the concentrations of glutamine, creatinine, methanol, trimethylamine and myo-inositol were found to increase. The P05-P30 comparison was also significant for glutamine, creatinine, adenine, tyrosine, leucine, valine, 3-hydroxyisovalerate, 3-methyl-2-oxovalerate, while for the P30-P50 comparison we found significant differences for glutamine, myo-inositol, leucine and trimethylamine. None of these molecules showed at P30 concentrations outside the P05 –P50 range.