Sparse canonical methods for biological data integration: application to a cross-platform study

Chemometric analysis illuminates the relationship among browning, polyphenol degradation, Maillard reaction and flavor variation of 5 jujube fruits during air-impingement jet drying

This study was designed to reveal the relationship among browning, polyphenol degradation, Maillard reaction (MR) and flavor variation in jujube fruit (JF) during air-impingement jet drying (AIJD). Five kinds of JFs were dried by AIJD at 60 °C and vacuum freeze drying. Colorimeter and chemometric analysis found that AIJD induced color changes of JF pulp and peel. AIJD also reduced the total polyphenols content and total flavonoids levels in JF. The Fe3+ reducing capacity and 2,2'-Azinobis-(3-ethylbenzothiazoline-6-sulphonate) cationic radical scavenging capacity of JF were reduced by 31.6% and 8.2%, respectively. Seven polyphenols were identified in JF, and epicatechin was found related to change of JF pulp color by sparse partial least square (sPLS). sPLS revealed that 3-deoxy glucosone, N-ε-carboxymethyl-l-lysine and 5-hydroxymethylfurfural associated with JF color. sPLS found that MR generated 3-methyl-butanoic acid and cyclobutanone during AIJD of JF. Chemometrics is an effective tool to disclose mechanism of color changes in food.

Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that typically manifests late patient presentation and poor outcomes. Furthermore, PDAC recurrence is a common challenge. Distinct patterns of PDAC recurrence have been associated with differential activation of immune pathway-related genes and specific inflammatory responses in their tumour microenvironment. However, the molecular associations between and within cellular components that underpin PDAC recurrence require further development, especially from a multi-omics integration perspective. In this study, we identified stable molecular associations across multiple PDAC recurrences and utilised integrative analytics to identify stable and novel associations via simultaneous feature selection. Spatial transcriptome and proteome datasets were used to perform univariate analysis, Spearman partial correlation analysis, and univariate analyses by Machine Learning methods, including regularised canonical correlation analysis and sparse partial least squares. Furthermore, networks were constructed for reported and new stable associations. Our findings revealed gene and protein associations across multiple PDAC recurrence groups, which can provide a better understanding of the multi-layer disease mechanisms that contribute to PDAC recurrence. These findings may help to provide novel association targets for clinical studies for constructing precision medicine and personalised surveillance tools for patients with PDAC recurrence.

Diet-omics in the Study of Urban and Rural Crohn disease Evolution (SOURCE) cohort

Crohn disease (CD) burden has increased with globalization/urbanization, and the rapid rise is attributed to environmental changes rather than genetic drift. The Study Of Urban and Rural CD Evolution (SOURCE, n = 380) has considered diet-omics domains simultaneously to detect complex interactions and identify potential beneficial and pathogenic factors linked with rural-urban transition and CD. We characterize exposures, diet, ileal transcriptomics, metabolomics, and microbiome in newly diagnosed CD patients and controls in rural and urban China and Israel. We show that time spent by rural residents in urban environments is linked with changes in gut microbial composition and metabolomics, which mirror those seen in CD. Ileal transcriptomics highlights personal metabolic and immune gene expression modules, that are directly linked to potential protective dietary exposures (coffee, manganese, vitamin D), fecal metabolites, and the microbiome. Bacteria-associated metabolites are primarily linked with host immune modules, whereas diet-linked metabolites are associated with host epithelial metabolic functions.

Integrative Analysis of Differentially Expressed Genes in Time-Course Multi-Omics Data with MINT-DE

Background

Time-course multi-omics experiments have been highly informative for obtaining a comprehensive understanding of the dynamic relationships between molecules in a biological process, especially if the different profiles are obtained from the same samples. A fundamental step in analyzing time-course multi-omics data involves selecting a short list of genes or gene regions ("sites") that warrant further study. Two important criteria for site selection are the magnitude of change and the temporal dynamic consistency. However, existing methods only consider one of these criteria, while neglecting the other.

Results

In our study, we propose a framework called MINT-DE (Multi-omics INtegration of Time-course for Diffierential Expression analysis) to address this limitation. MINT-DE is capable of selecting sites based on summarized measures of both aforementioned aspects. We calculate evidence measures assessing the extent of differential expression for each assay and for the dynamical similarity across assays. Then based on the summary of the evidence assessment measures, sites are ranked. To evaluate the performance of MINT-DE, we apply it to analyze a time-course multi-omics dataset of Drosophila development. We compare the selection obtained from MINT-DE with those obtained from other existing methods. The analysis reveal that MINT-DE is able to identify differentially expressed time-course pairs with the highest correlations. Their corresponding genes are significantly enriched for known biological functions, as measured by gene-gene interaction networks and the Gene Ontology enrichment.

Conclusions

These findings suggest the effectiveness of MINT-DE in selecting sites that are both differentially expressed within at least one assay and temporally related across assays. This highlights the potential of MINT-DE to identify biologically important sites for downstream analysis and provide a complementarity of sites that is neglected by existing methods.

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.

Machine learning approaches in microbiome research: challenges and best practices

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

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.

Identification of the specific molecular and functional signatures of pre-beta-HDL: relevance to cardiovascular disease

While low concentrations of high-density lipoprotein-cholesterol (HDL-C) are widely accepted as an independent cardiovascular risk factor, HDL-C-rising therapies largely failed, suggesting the importance of both HDL functions and individual subspecies. Indeed HDL particles are highly heterogeneous, with small, dense pre-beta-HDLs being considered highly biologically active but remaining poorly studied, largely reflecting difficulties for their purification. We developed an original experimental approach allowing the isolation of sufficient amounts of human pre-beta-HDLs and revealing the specificity of their proteomic and lipidomic profiles and biological activities. Pre-beta-HDLs were enriched in highly poly-unsaturated species of phosphatidic acid and phosphatidylserine, and in an unexpectedly high number of proteins implicated in the inflammatory response, including serum paraoxonase/arylesterase-1, vitronectin and clusterin, as well as in complement regulation and immunity, including haptoglobin-related protein, complement proteins and those of the immunoglobulin class. Interestingly, amongst proteins associated with lipid metabolism, phospholipid transfer protein, cholesteryl ester transfer protein and lecithin:cholesterol acyltransferase were strongly enriched in, or restricted to, pre-beta-HDL. Furthermore, pre-beta-HDL potently mediated cellular cholesterol efflux and displayed strong anti-inflammatory activities. A correlational network analysis between lipidome, proteome and biological activities highlighted 15 individual lipid and protein components of pre-beta-HDL relevant to cardiovascular disease, which may constitute novel diagnostic targets in a pathological context of altered lipoprotein metabolism.

Decreased metabolic diversity in common beans associated with domestication revealed by untargeted metabolomics, information theory, and molecular networking

The process of crop domestication leads to a dramatic reduction in the gene expression associated with metabolic diversity. Genes involved in specialized metabolism appear to be particularly affected. Although there is ample evidence of these effects at the genetic level, a reduction in diversity at the metabolite level has been taken for granted despite having never been adequately accessed and quantified. Here we leveraged the high coverage of ultra high performance liquid chromatography-high-resolution mass spectrometry based metabolomics to investigate the metabolic diversity in the common bean (Phaseolus vulgaris). Information theory highlights a shift towards lower metabolic diversity and specialization when comparing wild and domesticated bean accessions. Moreover, molecular networking approaches facilitated a broader metabolite annotation than achieved to date, and its integration with gene expression data uncovers a metabolic shift from specialized metabolism towards central metabolism upon domestication of this crop.

Bacterial clade-specific analysis identifies distinct epithelial responses in inflammatory bowel disease

Abnormal immune responses to the resident gut microbiome can drive inflammatory bowel disease (IBD). Here, we combine high-resolution, culture-based shotgun metagenomic sequencing and analysis with matched host transcriptomics across three intestinal sites (terminal ileum, cecum, rectum) from pediatric IBD (PIBD) patients (n = 58) and matched controls (n = 42) to investigate this relationship. Combining our site-specific approach with bacterial culturing, we establish a cohort-specific bacterial culture collection, comprising 6,620 isolates (170 distinct species, 32 putative novel), cultured from 286 mucosal biopsies. Phylogeny-based, clade-specific metagenomic analysis identifies key, functionally distinct Enterococcus clades associated with either IBD or health. Strain-specific in vitro validation demonstrates differences in cell cytotoxicity and inflammatory signaling in intestinal epithelial cells, consistent with the colonic mucosa-specific response measured in patients with IBD. This demonstrates the importance of strain-specific phenotypes and consideration of anatomical sites in exploring the dysregulated host-bacterial interactions in IBD.

Canonical correlation analysis in high dimensions with structured regularization

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.

Quantification of antibiotic resistance genes (ARGs) in clouds at a mountain site (puy de Dôme, central France)

Antibiotic resistance in bacteria is becoming a major sanitary concern worldwide. The extensive use of large quantities of antibiotics to sustain human activity has led to the rapid acquisition and maintenance of antibiotic resistant genes (ARGs) in bacteria and to their spread into the environment. Eventually, these can be disseminated over long distances by atmospheric transport. Here, we assessed the presence of ARGs in clouds as an indicator of long-distance travel potential of antibiotic resistance in the atmosphere. We hypothesized that a variety of ARGs can reach the altitude of clouds mainly located within the free troposphere. Once incorporated in the atmosphere, they are efficiently transported and their respective concentrations should differ depending on the sources and the geographical origin of the air masses. We deployed high-flow rate impingers and collected twelve clouds between September 2019 and October 2021 at the meteorological station of the puy de Dôme summit (1465 m a.s.l., France). Total airborne bacteria concentration was assessed by flow cytometry, and ARGs subtypes of the main families of antibiotic resistance (quinolone, sulfonamide, tetracycline; glycopeptide, aminoglycoside, β-lactamase, macrolide) including one mobile genetic element (transposase) were quantified by qPCR. Our results indicate the presence of 29 different ARGs' subtypes at concentrations ranging from 1.01 × 10³ to 1.61 × 10⁴ copies m^-3 of air. Clear distinctions could be observed between clouds in air masses transported over marine areas (Atlantic Ocean) and clouds influenced by continental surfaces. Specifically, quinolones (mostly qepA) resistance genes were prevalent in marine clouds (54 % of the total ARGs on average), whereas higher contributions of sulfonamide, tetracycline; glycopeptide, β-lactamase and macrolide were found in continental clouds. This study constitutes the first evidence for the presence of microbial ARGs in clouds at concentrations comparable to other natural environments. This highlights the atmosphere as routes for the dissemination of ARGs at large scale.

Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study

BACKGROUND

Identification of new physical activity (PA) and sedentary behaviour (SB) features relevant for health at older age is important to diversify PA targets in guidelines, as older adults rarely adhere to current recommendations focusing on total duration. We aimed to identify accelerometer-derived dimensions of movement behaviours that predict mortality risk in older populations.

METHODS

We used data on 21 accelerometer-derived features of daily movement behaviours in 3991 participants of the UK-based Whitehall II accelerometer sub-study (25.8% women, 60-83 years, follow-up: 2012-2013 to 2021, mean = 8.3 years). A machine-learning procedure was used to identify core PA and SB features predicting mortality risk and derive a composite score. We estimated the added predictive value of the score compared to traditional sociodemographic, behavioural, and health-related risk factors. External validation in the Switzerland-based CoLaus study (N = 1329, 56.7% women, 60-86 years, follow-up: 2014-2017 to 2021, mean = 3.8 years) was conducted.

FINDINGS

In total, 11 features related to overall activity level, intensity distribution, bouts duration, frequency, and total duration of PA and SB, were identified as predictors of mortality in older adults and included in a composite score. Both in the derivation and validation cohorts, the score was associated with mortality (hazard ratio = 1.10 (95% confidence interval = 1.05-1.15) and 1.18 (1.10-1.26), respectively) and improved the predictive value of a model including traditional risk factors (increase in C-index = 0.007 (0.002-0.014) and 0.029 (0.002-0.055), respectively).

INTERPRETATION

The identified accelerometer-derived PA and SB features, beyond the currently recommended total duration, might be useful for screening of older adults at higher mortality risk and for diversifying PA and SB targets in older populations whose adherence to current guidelines is low.

FUNDING

National Institute on Aging; UK Medical Research Council; British Heart Foundation; Wellcome Trust; French National Research Agency; GlaxoSmithKline; Lausanne Faculty of Biology and Medicine; Swiss National Science Foundation.

Multi-omics gut microbiome signatures in obese women: role of diet and uncontrolled eating behavior

BACKGROUND

Obesity and related co-morbidities represent a major health challenge nowadays, with a rapidly increasing incidence worldwide. The gut microbiome has recently emerged as a key modifier of human health that can affect the development and progression of obesity, largely due to its involvement in the regulation of food intake and metabolism. However, there are still few studies that have in-depth explored the functionality of the human gut microbiome in obesity and even fewer that have examined its relationship to eating behaviors.

METHODS

In an attempt to advance our knowledge of the gut-microbiome-brain axis in the obese phenotype, we thoroughly characterized the gut microbiome signatures of obesity in a well-phenotyped Italian female cohort from the NeuroFAST and MyNewGut EU FP7 projects. Fecal samples were collected from 63 overweight/obese and 37 normal-weight women and analyzed via a multi-omics approach combining 16S rRNA amplicon sequencing, metagenomics, metatranscriptomics, and lipidomics. Associations with anthropometric, clinical, biochemical, and nutritional data were then sought, with particular attention to cognitive and behavioral domains of eating.

RESULTS

We identified four compositional clusters of the gut microbiome in our cohort that, although not distinctly associated with weight status, correlated differently with eating habits and behaviors. These clusters also differed in functional features, i.e., transcriptional activity and fecal metabolites. In particular, obese women with uncontrolled eating behavior were mostly characterized by low-diversity microbial steady states, with few and poorly interconnected species (e.g., Ruminococcus torques and Bifidobacterium spp.), which exhibited low transcriptional activity, especially of genes involved in secondary bile acid biosynthesis and neuroendocrine signaling (i.e., production of neurotransmitters, indoles and ligands for cannabinoid receptors). Consistently, high amounts of primary bile acids as well as sterols were found in their feces.

CONCLUSIONS

By finding peculiar gut microbiome profiles associated with eating patterns, we laid the foundation for elucidating gut-brain axis communication in the obese phenotype. Subject to confirmation of the hypotheses herein generated, our work could help guide the design of microbiome-based precision interventions, aimed at rewiring microbial networks to support a healthy diet-microbiome-gut-brain axis, thus counteracting obesity and related complications.

Olive- and Coconut-Oil-Enriched Diets Decreased Secondary Bile Acids and Regulated Metabolic and Transcriptomic Markers of Brain Injury in the Frontal Cortexes of NAFLD Pigs

The objective of this study was to investigate the effect of dietary fatty acid (FA) saturation and carbon chain length on brain bile acid (BA) metabolism and neuronal number in a pig model of pediatric NAFLD. Thirty 20-day-old Iberian pigs, pair-housed in pens, were randomly assigned to receive one of three hypercaloric diets for 10 weeks: (1) lard-enriched (LAR; n = 5 pens), (2) olive-oil-enriched (OLI, n = 5), and (3) coconut-oil-enriched (COC; n = 5). Pig behavior and activity were analyzed throughout the study. All animals were euthanized on week 10 and frontal cortex (FC) samples were collected for immunohistochemistry, metabolomic, and transcriptomic analyses. Data were analyzed by multivariate and univariate statistics. No differences were observed in relative brain weight, neuronal number, or cognitive functioning between diets. Pig activity and FC levels of neuroprotective secondary BAs and betaine decreased in the COC and OLI groups compared with LAR, and paralleled the severity of NAFLD. In addition, OLI-fed pigs showed downregulation of genes involved in neurotransmission, synaptic transmission, and nervous tissue development. Similarly, COC-fed pigs showed upregulation of neurogenesis and myelin repair genes, which caused the accumulation of medium-chain acylcarnitines in brain tissue. In conclusion, our results indicate that secondary BA levels in the FCs of NAFLD pigs are affected by dietary FA composition and are associated with metabolic and transcriptomic markers of brain injury. Dietary interventions that aim to replace saturated FAs by medium-chain or monounsaturated FAs in high-fat hypercaloric diets may have a negative effect on brain health in NAFLD patients.

Dietary fat composition shapes bile acid metabolism and severity of liver injury in a pig model of pediatric NAFLD

The objective of this study was to investigate the effect of dietary fatty acid (FA) composition on bile acid (BA) metabolism in a pig model of NAFLD, by using a multiomics approach combined with histology and serum biochemistry. Thirty 20-day-old Iberian pigs pair-housed in pens were randomly assigned to receive 1 of 3 hypercaloric diets for 10 wk: 1) lard-enriched (LAR; n = 5 pens), 2) olive oil-enriched (OLI; n = 5), and 3) coconut oil-enriched (COC; n = 5). Animals were euthanized on week 10 after blood sampling, and liver, colon, and distal ileum (DI) were collected for histology, metabolomics, and transcriptomics. Data were analyzed by multivariate and univariate statistics. Compared with OLI and LAR, COC increased primary and secondary BAs in liver, plasma, and colon. In addition, both COC and OLI reduced circulating fibroblast growth factor 19, increased hepatic necrosis, composite lesion score, and liver enzymes in serum, and upregulated genes involved in hepatocyte proliferation and DNA repair. The severity of liver disease in COC and OLI pigs was associated with increased levels of phosphatidylcholines, medium-chain triacylglycerides, trimethylamine-N-oxide, and long-chain acylcarnitines in the liver, and the expression of profibrotic markers in DI, but not with changes in the composition or size of BA pool. In conclusion, our results indicate a role of dietary FAs in the regulation of BA metabolism and progression of NAFLD. Interventions that aim to modify the composition of dietary FAs, rather than to regulate BA metabolism or signaling, may be more effective in the treatment of NAFLD.NEW & NOTEWORTHY Bile acid homeostasis and signaling is disrupted in NAFLD and may play a central role in the development of the disease. However, there are no studies addressing the impact of diet on bile acid metabolism in patients with NAFLD. In juvenile Iberian pigs, we show that fatty acid composition in high-fat high-fructose diets affects BA levels in liver, plasma, and colon but these changes were not associated with the severity of the disease.

Impact of microcin J25 on the porcine microbiome in a continuous culture model

The increased prevalence of Salmonella spp. resistance in swine spurs the search for alternatives to antibiotics. Microcin J25 (MccJ25), a bacteriocin produced by Escherichia coli, is a potent inhibitor of several pathogenic bacteria including Salmonella enterica. In this study, we aimed to evaluate in vitro the impact of MccJ25 on the composition and the metabolic activity of the swine colonic microbiota. The PolyFermS in vitro continuous fermentation model was used here with modified Macfarlane medium to simulate the porcine proximal colon. During 35 days of fermentation, a first-stage reactor containing immobilized swine fecal microbiota fed two second-stage control and test reactors operated in parallel and used to test the effects of MccJ25 on the composition and the metabolic activity of the microbiota. Reuterin, a broad-spectrum antimicrobial compound produced by Limosilactobacillus reuteri, a lactic acid bacterium naturally present in the gastro-intestinal tract of human and animals, and the antibiotic rifampicin were tested for comparison. Sequencing of 16S rRNA was performed using the Illumina MiSeq technology to evaluate microbial diversity, and liquid chromatography coupled to mass spectrometry (LC-MS) followed by multivariate analysis was used to assess the bacteriocin/antibiotic degradation products and to monitor changes in the swine colonic microbiota metabolome. The results show that MccJ25 or reuterin treatments only induce subtle changes of both the microbial diversity and the metabolome of the swine colon microbiota, while rifampicin induces significant modification in amino acid levels. Although these findings need being validated in vivo, this study affords a first proof of concept for considering MccJ25 as a possible alternative to antibiotics for veterinary and farming applications, taking into account its pathogen-selective and potent inhibitory activity.

Interactions among the mycobiome, bacteriome, inflammation, and diet in people living with HIV

While the intestinal microbiome seems a major driver of persistent immune defects in people with HIV (PWH), little is known about its fungal component, the mycobiome. We assessed the inter-kingdom mycobiome-bacteriome interactions, the impact of diet, and the association with the innate and adaptive immunity in PWH on antiretroviral therapy. We included 24 PWH individuals and 12 healthy controls. We sequenced the Internal Transcribed Spacer 2 amplicons, determined amplicon sequence variants, measured biomarkers of the innate and adaptive immunity in blood and relations with diet. Compared to healthy controls, PWH subjects exhibited a distinct and richer mycobiome and an enrichment for Debaryomyces hansenii, Candida albicans, and Candida parapsilosis. In PWH, Candida and Pichia species were strongly correlated with several bacterial genera, including Faecalibacterium genus. Regarding the links between the mycobiome and systemic immunology, we found a positive correlation between Candida species and the levels of proinflammatory cytokines (sTNF-R2 and IL-17), interleukin 22 (a cytokine implicated in the regulation of mucosal immunity), and CD8+ T cell counts. This suggests an important role of the yeasts in systemic innate and adaptive immune responses. Finally, we identified inter-kingdom interactions implicated in fiber degradation, short-chain fatty acid production, and lipid metabolism, and an effect of vegetable and fiber intake on the mycobiome. Therefore, despite the great differences in abundance and diversity between the bacterial and fungal communities of the gut, we defined the changes associated with HIV, determined several different inter-kingdom associations, and found links between the mycobiome, nutrient metabolism, and systemic immunity.

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

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

Elevated gut microbiome abundance of Christensenellaceae, Porphyromonadaceae and Rikenellaceae is associated with reduced visceral adipose tissue and healthier metabolic profile in Italian elderly

Aging is accompanied by physiological changes affecting body composition and functionality, including accumulation of fat mass at the expense of muscle mass, with effects upon morbidity and quality of life. The gut microbiome has recently emerged as a key environmental modifier of human health that can modulate healthy aging and possibly longevity. However, its associations with adiposity in old age are still poorly understood. Here we profiled the gut microbiota in a well-characterized cohort of 201 Italian elderly subjects from the NU-AGE study, by 16S rRNA amplicon sequencing. We then tested for association with body composition from dual-energy X-ray absorptiometry (DXA), with a focus on visceral and subcutaneous adipose tissue. Dietary patterns, serum metabolome and other health-related parameters were also assessed. This study identified distinct compositional structures of the elderly gut microbiota associated with DXA parameters, diet, metabolic profiles and cardio-metabolic risk factors.

PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer's disease.

Synbiotics Easing Renal Failure by Improving Gut Microbiology II (SYNERGY II): A Feasibility Randomized Controlled Trial

Synbiotics have emerged as a therapeutic strategy for modulating the gut microbiome and targeting novel cardiovascular risk factors, including uremic toxins indoxyl sulfate (IS) and p-cresyl sulfate (PCS). This study aims to evaluate the feasibility of a trial of long-term synbiotic supplementation in adults with stage 3-4 chronic kidney disease (CKD). Adult participants with CKD and estimated glomerular filtration rate (eGFR) of 15-60 mL/min/1.73 m²) were recruited between April 2017 and August 2018 to a feasibility, double-blind, placebo-controlled, randomized trial of synbiotic therapy or matched identical placebo for 12 months. The primary outcomes were recruitment and retention rates as well as acceptability of the intervention. Secondary outcomes were treatment adherence and dietary intake. Exploratory outcomes were evaluation of the cardiovascular structure and function, serum IS and PCS, stool microbiota profile, kidney function, blood pressure, and lipid profile. Of 166 potentially eligible patients, 68 (41%) were recruited into the trial (synbiotic n = 35, placebo n = 33). Synbiotic and placebo groups had acceptable and comparable 12-month retention rates (80% versus 85%, respectively, p = 0.60). Synbiotic supplementation altered the stool microbiome with an enrichment of Bifidobacterium and Blautia spp., resulting in a 3.14 mL/min/1.73 m² (95% confidence interval (CI), -6.23 to -0.06 mL/min/1.73 m², p < 0.01) reduction in eGFR and a 20.8 µmol/L (95% CI, 2.97 to 38.5 µmol/L, p < 0.01) increase in serum creatinine concentration. No between-group differences were observed in any of the other secondary or exploratory outcomes. Long-term synbiotic supplementation was feasible and acceptable to patients with CKD, and it modified the gastrointestinal microbiome. However, the reduction in kidney function with synbiotics warrants further investigation.

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

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

Isolating the white matter circuitry of the dorsal language stream: Connectome-Symptom Mapping in stroke induced aphasia

The application of ℓ1-regularized machine learning models to high-dimensional connectomes offers a promising methodology to assess clinical-anatomical correlations in humans. Here, we integrate the connectome-based lesion-symptom mapping framework with sparse partial least squares regression (sPLS-R) to isolate elements of the connectome associated with speech repetition deficits. By mapping over 2,500 connections of the structural connectome in a cohort of 71 stroke-induced cases of aphasia presenting with varying left-hemisphere lesions and repetition impairment, sPLS-R was trained on 50 subjects to algorithmically identify connectomic features on the basis of their predictive value. The highest ranking features were subsequently used to generate a parsimonious predictive model for speech repetition whose predictions were evaluated on a held-out set of 21 subjects. A set of 10 short- and long-range parieto-temporal connections were identified, collectively delineating the broader circuitry of the dorsal white matter network of the language system. The strongest contributing feature was a short-range connection in the supramarginal gyrus, approximating the cortical localization of area Spt, with parallel long-range pathways interconnecting posterior nodes in supramarginal and superior temporal cortex with anterior nodes in both ventral and-notably-in dorsal premotor cortex, respectively. The collective disruption of these pathways indexed repetition performance in the held-out set of participants, suggesting that these impairments might be characterized as a parietotemporal disconnection syndrome impacting cortical area Spt and its associated white matter circuits of the frontal lobe as opposed to being purely a disconnection of the arcuate fasciculus.

Gut microbiota mediates the effects of inulin on enhancing sulfomucin production and mucosal barrier function in a pig model

Dietary fibers (DFs) have many beneficial effects on intestinal health by ameliorating intestinal inflammation and modulating the microbial community composition, thereby affecting the barrier function. This study aims to characterize the gut microbiota of pigs fed with DFs, revealing a link between the intestinal microbiota and mucin chemotypes. Pigs (six per group) were randomly allotted to consume one of the following diets: control (CON) or a diet supplemented with 5% microcrystalline cellulose (MCC) or inulin (INU) for 72 days. We found that INU but not MCC enhanced the colonic barrier function by promoting the expression of ZO-1, Occludin and MUC2 and reducing the colonic crypt depth. INU increased sulfomucin production and mRNA levels of sulfotransferases Gal3ST1 and Gal3ST2. Goblet cells in the ileum were found to contain predominantly sialomucins while colonic goblet cells were dominated by sulfomucins with sialomucins absent. DF consumption increased the concentrations of short-chain fatty acids (SCFAs) of the ileum and colon compared to the CON diet. Moreover, the results of 16S rRNA gene sequencing analysis revealed that DFs significantly altered the composition of ileal and colonic mucosal microbiota. Network analysis indicated that INU-induced changes in bacterial genera and SCFAs, such as Akkermansia and butyrate, were significantly related with sulfomucins and the mucosal barrier function-gene in pigs. Collectively, these findings suggest that the intestinal mucosal microbiota and SCFAs induced by INU play a crucial role in modulating the chemotypes of mucin and the barrier function.

Characterization of Temperature Gradients According to Height in a Baroque Church by Means of Wireless Sensors

The baroque church of Saint Thomas and Saint Philip Neri (Valencia, Spain), which was built between 1727 and 1736, contains valuable paintings by renowned Spanish artists. Due to the considerable height of the central nave, the church can experience vertical temperature gradients. In order to investigate this issue, temperatures were recorded between August 2017 and February 2018 from a wireless monitoring system composed of 21 sensor nodes, which were located at different heights in the church from 2 to 13 m from the floor level. For characterizing the temperature at high, medium and low altitude heights, a novel methodology is proposed based on sparse Partial Least Squares regression (sPLS), Linear Discriminant Analysis (LDA), and the Holt-Winters method, among others, which were applied to a time series of temperature. This approach is helpful to discriminate temperature profiles according to sensor height. Once the vertical thermal gradients for each month were characterized, it was found that temperature reached the maximum correlation with sensor height in the period between August 10th and September 9th. Furthermore, the most important features from the time series that explain this correlation are the mean temperature and the mean of moving range. In the period mentioned, the vertical thermal gradient was estimated to be about 0.043 ∘C/m, which implies a difference of 0.47 ∘C on average between sensor nodes at 2 m from the floor with respect to the upper ones located at 13 m from the floor level. The gradient was estimated as the slope from a linear regression model using height and hourly mean temperature as the predictor and response, respectively. This gradient is consistent with similar reported studies. The fact that such gradient was only found in one month suggests that the mechanisms of dust deposition on walls involved in vertical thermal gradients are not important in this case regarding the preventive conservation of artworks. Furthermore, the methodology proposed here was useful to discriminate the time series at high, medium and low altitude levels. This approach can be useful when a set of sensors is installed for microclimate monitoring in churches, cathedrals, and other historical buildings, at different levels and positions.

Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach

Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain's biological network. In some recently proposed multimodal SCCA methods, due to the limitations of penalty items, the pre-processed data needs to be further filtered to make the dimensions uniform, which may destroy the potential association of data in the same modal. In this research, in order to combine data between different modalities and to ensure that the chain relationship or graph network relationship within the same modality will not be destroyed, the original generalized fused lasso penalty was replaced with the fused pairwise group lasso (FGL) and the graph-guided pairwise group lasso (GGL) based on the method of joint sparse canonical correlation analysis (JSCCA). We used prior knowledge to construct a supervised bivariate learning model and use linear regression to select quantitative traits (QTs) of images that are strongly correlated with the Mini-mental State Examination (MMSE) scores. Compared with FGL-SCCA, the model we constructed obtained a higher gene-ROI correlation coefficient and identified more significant biomarkers, providing a theoretical basis for further understanding the complex pathology of neurodegenerative diseases.

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

Cancer is one of the most heterogeneous diseases, characterized by diverse morphological, phenotypic, and genomic profiles between tumors and their subtypes. Identifying cancer subtypes can help patients receive precise treatments. With the development of high-throughput technologies, genomics, epigenomics, and transcriptomics data have been generated for large cancer patient cohorts. It is believed that the more omics data we use, the more accurate identification of cancer subtypes. To examine this assumption, we first constructed three classes of benchmarking datasets to conduct a comprehensive evaluation and comparison of ten representative multi-omics data integration methods for cancer subtyping by considering their accuracy, robustness, and computational efficiency. Then, we investigated the influence of different omics data and their various combinations on the effectiveness of cancer subtyping. Our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. We hope that our work may help researchers choose a proper method and an effective data combination when identifying cancer subtypes using data integration methods.

Bacterial Composition and Metabolomics of Dental Plaque From Adolescents

Supragingival dental plaque samples were collected from 40 Swedish adolescents, including 20 with caries lesions (CAR) and 20 caries-free (CF). Fresh plaque samples were subjected to an ex vivo acid tolerance (AT) test where the proportion of bacteria resistant to an acid shock was evaluated through confocal microscopy and live/dead staining, and the metabolites produced were quantified by ¹H Nuclear Magnetic Resonance (¹H NMR). In addition, DNA was extracted and the 16S rRNA gene was sequenced by Illumina sequencing, in order to characterize bacterial composition in the same samples. There were no significant differences in AT scores between CAR and CF individuals. However, 7 out of the 10 individuals with highest AT scores belonged to the CAR group. Regarding bacterial composition, Abiotrophia, Prevotella and Veillonella were found at significantly higher levels in CAR individuals (p=0.0085, 0.026 and 0.04 respectively) and Rothia and Corynebacterium at significantly higher levels in CF individuals (p=0.026 and 0.003). The caries pathogen Streptococcus mutans was found at low frequencies and was absent in 60% of CAR individuals. Random-forest predictive models indicate that at least 4 bacterial species or 9 genera are needed to distinguish CAR from CF adolescents. The metabolomic profile obtained by NMR showed a significant clustering of organic acids with specific bacteria in CAR and/or high AT individuals, being Scardovia wiggsiae the species with strongest associations. A significant clustering of ethanol and isopropanol with health-associated bacteria such as Rothia or Corynebacterium was also found. Accordingly, several relationships involving these compounds like the Ethanol : Lactate or Succinate : Lactate ratios were significantly associated to acid tolerance and could be of predictive value for caries risk. We therefore propose that future caries risk studies would benefit from considering not only the use of multiple organisms as potential microbial biomarkers, but also their functional adaptation and metabolic output.

Occurrence and dynamics of potentially pathogenic vibrios in the wet-dry tropics of northern Australia

Bacteria from the Vibrio genus are a ubiquitous component of coastal and estuarine ecosystems with several pathogenic Vibrio species displaying preferences for warm tropical waters. We studied the spatial and temporal abundance of three key human potential pathogens V. parahaemolyticus, V. cholerae and V. vulnificus in northern tropical Australia, over the wet and dry seasons, to identify environmental parameters influencing their abundance. Quantitative PCR (qPCR) analysis revealed that V. parahaemolyticus occurred more frequently and in higher abundance than V. cholerae and V. vulnificus across all locations examined. All three species were more abundant during the wet season, with V. parahaemolyticus abundance correlated to temperature and conductivity, whereas nutrient concentrations and turbidity best explained V. vulnificus abundance. In addition to these targeted qPCR analyses, we assessed the composition and dynamics of the entire Vibrio community using hsp60 amplicon sequencing. Using this approach, 42 Vibrio species were identified, including a number of other pathogenic species such as V. alginolyticus, V. mimicus and V. fluvialis. The Vibrio community was more diverse in the wet season, with temperature and dissolved oxygen as the key factors governing community composition. Seasonal differences were primarily driven by a greater abundance of V. parahaemolyticus and V. vulnificus during the wet season, while spatial differences were driven by different abundances of V. harveyi, V. campbellii, V. cholerae and V. navarrensis. When we related the abundance of Vibrio to other bacterial taxa, defined using 16S rRNA gene amplicon sequencing, V. parahaemolyticus was negatively correlated to several taxa, including members of the Rickettsiales and Saccharimonadales, while V. vulnificus was negatively correlated to Rhobacteriaceae and Cyanobiaceae. In contrast, V. alginolyticus, V. harveyi and V. mediterranei were all positively correlated to Cyanobacteria. These observations highlight the dynamic nature of Vibrio communities and expands current understanding of the processes governing the occurrence of potentially pathogenic Vibrio spp. in tropical coastal ecosystems.

Multivariate Time Series Analysis of Temperatures in the Archaeological Museum of L'Almoina (Valencia, Spain)

An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019–October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums.

Statistical Integration of 'Omics Data Increases Biological Knowledge Extracted from Metabolomics Data: Application to Intestinal Exposure to the Mycotoxin Deoxynivalenol

The effects of low doses of toxicants are often subtle and information extracted from metabolomic data alone may not always be sufficient. As end products of enzymatic reactions, metabolites represent the final phenotypic expression of an organism and can also reflect gene expression changes caused by this exposure. Therefore, the integration of metabolomic and transcriptomic data could improve the extracted biological knowledge on these toxicants induced disruptions. In the present study, we applied statistical integration tools to metabolomic and transcriptomic data obtained from jejunal explants of pigs exposed to the food contaminant, deoxynivalenol (DON). Canonical correlation analysis (CCA) and self-organizing map (SOM) were compared for the identification of correlated transcriptomic and metabolomic features, and O2-PLS was used to model the relationship between exposure and selected features. The integration of both 'omics data increased the number of discriminant metabolites discovered (39) by about 10 times compared to the analysis of the metabolomic dataset alone (3). Besides the disturbance of energy metabolism previously reported, assessing correlations between both functional levels revealed several other types of damage linked to the intestinal exposure to DON, including the alteration of protein synthesis, oxidative stress, and inflammasome activation. This confirms the added value of integration to enrich the biological knowledge extracted from metabolomics.

Subgingival Microbiome and Specialized Pro-Resolving Lipid Mediator Pathway Profiles Are Correlated in Periodontal Inflammation

Failure of resolution pathways in periodontitis is reflected in levels of specialized pro-resolving lipid mediators (SPMs) and SPM pathway markers but their relationship with the subgingival microbiome is unclear. This study aimed to analyze and integrate lipid mediator level, SPM receptor gene expression and subgingival microbiome data in subjects with periodontitis vs. healthy controls. The study included 13 periodontally healthy and 15 periodontitis subjects that were evaluated prior to or after non-surgical periodontal therapy. Samples of gingival tissue and subgingival plaque were collected prior to and 8 weeks after non-surgical treatment; only once in the healthy group. Metabololipidomic analysis was performed to measure levels of SPMs and other relevant lipid mediators in gingiva. qRT-PCR assessed relative gene expression (2-ΔΔCT) of known SPM receptors. 16S rRNA sequencing evaluated the relative abundance of bacterial species in subgingival plaque. Correlations between lipid mediator levels, receptor gene expression and bacterial abundance were analyzed using the Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) and Sparse Partial Least Squares (SPLS) methods. Profiles of lipid mediators, receptor genes and the subgingival microbiome were distinct in the three groups. The strongest correlation existed between lipid mediator profile and subgingival microbiome profile. Multiple lipid mediators and bacterial species were highly correlated (correlation coefficient ≥0.6) in different periodontal conditions. Comparing individual correlated lipid mediators and bacterial species in periodontitis before treatment to healthy controls revealed that one bacterial species, Corynebacterium durum, and five lipid mediators, 5(S)6(R)-DiHETE, 15(S)-HEPE, 7-HDHA, 13-HDHA and 14-HDHA, were identified in both conditions. Comparing individual correlated lipid mediators and bacterial species in periodontitis before treatment to after treatment revealed that one bacterial species, Anaeroglobus geminatus, and four lipid mediators, 5(S)12(S)-DiHETE, RvD1, Maresin 1 and LTB4, were identified in both conditions. Four Selenomonas species were highly correlated with RvD1, RvE3, 5(S)12(S)-DiHETE and proinflammatory mediators in the periodontitis after treatment group. Profiles of lipid mediators, receptor gene and subgingival microbiome are associated with periodontal inflammation and correlated with each other, suggesting inflammation mediated by lipid mediators influences microbial composition in periodontitis. The role of correlated individual lipid mediators and bacterial species in periodontal inflammation have to be further studied.

Leukemic evolution of polycythemia vera and essential thrombocythemia: genomic profiles predict time to transformation

Among myeloproliferative neoplasms, polycythemia vera (PV) and essential thrombocythemia (ET) are the 2 entities associated with the most chronic disease course. Leukemic evolution occurs rarely but has a grim prognosis. The interval between diagnosis and leukemic evolution is highly variable, from a few years to >20 years. We performed a molecular evaluation of 49 leukemic transformations of PV and ET by targeted next-generation sequencing. Using a hierarchical classification, we identified 3 molecular groups associated with a distinct time to leukemic transformation. Short-term transformations were mostly characterized by a complex molecular landscape and mutations in IDH1/2, RUNX1, and U2AF1 genes, whereas long-term transformations were associated with mutations in TP53, NRAS, and BCORL1 genes. Studying paired samples from chronic phase and transformation, we detected some mutations already present during the chronic phase, either with a significant allele burden (short-term transformation) or with a very low allele burden (especially TP53 mutations). However, other mutations were not detected even 1 year before leukemic transformation. Our results suggest that the leukemic transformation of PV and ET may be driven by distinct time-dependent molecular mechanisms.

Multi-compartment metabolomics and metagenomics reveal major hepatic and intestinal disturbances in cancer cachectic mice

BACKGROUND

Cancer cachexia is a multifactorial syndrome characterized by multiple metabolic dysfunctions. Besides the muscle, other organs such as the liver and the gut microbiota may also contribute to this syndrome. Indeed, the gut microbiota, an important regulator of the host metabolism, is altered in the C26 preclinical model of cancer cachexia. Interventions targeting the gut microbiota have shown benefits, but mechanisms underlying the host-microbiota crosstalk in this context are still poorly understood.

METHODS

To explore this crosstalk, we combined proton nuclear magnetic resonance (¹ H-NMR) metabolomics in multiple compartments with 16S rDNA sequencing. These analyses were complemented by molecular and biochemical analyses, as well as hepatic transcriptomics.

RESULTS

¹ H-NMR revealed major changes between control (CT) and cachectic (C26) mice in the four analysed compartments (i.e. caecal content, portal vein, liver, and vena cava). More specifically, glucose metabolism pathways in the C26 model were altered with a reduction in glycolysis and gluconeogenesis and an activation of the hexosamine pathway, arguing against the existence of a Cori cycle in this model. In parallel, amino acid uptake by the liver, with an up to four-fold accumulation of nine amino acids (q-value <0.05), was mainly used for acute phase response proteins synthesis rather than to fuel the tricarboxylic acid cycle and gluconeogenesis. We also identified a 35% reduction in hepatic carnitine levels (q-value <0.05) and a lower activation of the phosphatidylcholine pathway as potential contributors to the hepatic steatosis present in this model. Our work also reveals a reduction of different beneficial intestinal bacterial activities in cancer cachexia. We found decreased levels of two short-chain fatty acids, acetate and butyrate (72% and 88% reduction in C26 caecal content; q-value <0.001), and a reduction in aromatic amino acid metabolites, which may contribute to the altered intestinal homeostasis in these mice. A member of the Ruminococcaceae family (ASV 2) was identified as the main bacterium responsible for the drop in butyrate. Finally, we report a two-fold intestinal transit acceleration (P-value <0.001) as a key factor shaping the gut microbiota composition and activity in cancer cachexia, which together lead to a faecal loss of proteins and amino acids.

CONCLUSIONS

Our work highlights new metabolic pathways potentially involved in cancer cachexia and further supports the interest of exploring the gut microbiota composition and activity, as well as intestinal transit, in cancer patients with and without cachexia.

An Information Theoretical Multilayer Network Approach to Breast Cancer Transcriptional Regulation

Breast cancer is a complex, highly heterogeneous disease at multiple levels ranging from its genetic origins and molecular processes to clinical manifestations. This heterogeneity has given rise to the so-called intrinsic or molecular breast cancer subtypes. Aside from classification, these subtypes have set a basis for differential prognosis and treatment. Multiple regulatory mechanisms-involving a variety of biomolecular entities-suffer from alterations leading to the diseased phenotypes. Information theoretical approaches have been found to be useful in the description of these complex regulatory programs. In this work, we identified the interactions occurring between three main mechanisms of regulation of the gene expression program: transcription factor regulation, regulation via noncoding RNA, and epigenetic regulation through DNA methylation. Using data from The Cancer Genome Atlas, we inferred probabilistic multilayer networks, identifying key regulatory circuits able to (partially) explain the alterations that lead from a healthy phenotype to different manifestations of breast cancer, as captured by its molecular subtype classification. We also found some general trends in the topology of the multi-omic regulatory networks: Tumor subtype networks present longer shortest paths than their normal tissue counterpart; epigenomic regulation has frequently focused on genes enriched for certain biological processes; CpG methylation and miRNA interactions are often part of a regulatory core of conserved interactions. The use of probabilistic measures to infer information regarding theoretical-derived multilayer networks based on multi-omic high-throughput data is hence presented as a useful methodological approach to capture some of the molecular heterogeneity behind regulatory phenomena in breast cancer, and potentially other diseases.

Biogeography of the Relationship between the Child Gut Microbiome and Innate Immune System

Both the gut microbiome and innate immunity are known to differ across biogeographically diverse human populations. The gut microbiome has been shown to directly influence systemic immunity in animal models.

The gut microbiome is a well-recognized modulator of host immunity, and its compositions differ between geographically separated human populations. Systemic innate immune responses to microbial derivatives also differ between geographically distinct human populations. However, the potential role of the microbiome in mediating geographically varied immune responses is unexplored. We here applied 16S amplicon sequencing to profile the stool microbiome and, in parallel, measured whole-blood innate immune cytokine responses to several pattern recognition receptor (PRR) agonists among 2-year-old children across biogeographically diverse settings. Microbiomes differed mainly between high- and low-resource environments and were not strongly associated with other demographic factors. We found strong correlations between responses to Toll-like receptor 2 (TLR2) and relative abundances of Bacteroides and Prevotella populations, shared among Canadian and Ecuadorean children. Additional correlations between responses to TLR2 and bacterial populations were specific to individual geographic cohorts. As a proof of concept, we gavaged germfree mice with human donor stools and found murine splenocyte responses to TLR stimulation were consistent with responses of the corresponding human donor populations. This study identified differences in immune responses correlating to gut microbiomes across biogeographically diverse settings and evaluated biological plausibility using a mouse model. This insight paves the way to guide optimization of population-specific interventions aimed to improve child health outcomes.

Targeting Mitochondrial Complex I Overcomes Chemoresistance in High OXPHOS Pancreatic Cancer

Mitochondrial respiration (oxidative phosphorylation, OXPHOS) is an emerging target in currently refractory cancers such as pancreatic ductal adenocarcinoma (PDAC). However, the variability of energetic metabolic adaptations between PDAC patients has not been assessed in functional investigations. In this work, we demonstrate that OXPHOS rates are highly heterogeneous between patient tumors, and that high OXPHOS tumors are enriched in mitochondrial respiratory complex I at protein and mRNA levels. Therefore, we treated PDAC cells with phenformin (complex I inhibitor) in combination with standard chemotherapy (gemcitabine), showing that this treatment is synergistic specifically in high OXPHOS cells. Furthermore, phenformin cooperates with gemcitabine in high OXPHOS tumors in two orthotopic mouse models (xenografts and syngeneic allografts). In conclusion, this work proposes a strategy to identify PDAC patients likely to respond to the targeting of mitochondrial energetic metabolism in combination with chemotherapy, and that phenformin should be clinically tested in appropriate PDAC patient subpopulations.

Untangling the fibre ball: Proteomic characterization of South American camelid hair fibres by untargeted multivariate analysis and molecular networking

The proteomic analysis of hairs, yarns or textiles has emerged as a powerful method to determine species of origin, mainly used in archaeozoological research and fraud control. Differentiation between the South American camelid (SAC) species (the wild guanaco and vicuña and their respective domesticates the llama and alpaca) is particularly challenging due to poor database information and significant hybridization between species. In this study, we analysed 41 modern and 4 archaeological samples from the four SACs species. Despite strong similarities with Old World Camelidae, we identified 7 peptides specific to SACs assigned to keratin K86 and the keratin-associated proteins KAP13-1 and KAP11-1. Untargeted multivariate analysis of the LC-MS data permitted to distinguish SAC species and propose discriminant features. MS/MS-based molecular networking combined with database-assisted de novo sequencing permitted to identify 5 new taxonomic peptides assigned to K33a, K81 and/or K83 keratins and KAP19-1. These peptides differentiate the two wild species, guanaco and vicuña. These results show the value of combining database search and untargeted metabolomic approaches for paleoproteomics, and reveal for the first time the potential of molecular networks to highlight deamidation related to diagenesis and cluster highly similar peptides related to interchain homologies or intra- or inter-specific polymorphism. SIGNIFICANCE: This study used an innovative approach combining multivariate analysis of LC-MS data together with molecular networking and database-assisted de novo sequencing to identify taxonomic peptides in palaeoproteomics. It constitutes the first attempt to differentiate between hair fibres from the four South American camelids (SACs) based on proteomic analysis of modern and archaeological samples. It provides different proteomic signatures for each of the four SAC species and proposes new SAC taxonomic peptides of interest in archaeozoology and fraud control. SACs have been extensively exploited since human colonization of South America but have not been studied to the extent of their economic, cultural and heritage importance. Applied to the analysis of ancient Andean textiles, our results should permit a better understanding of cultural and pastoral practices in South America. The wild SACs are endangered by poaching and black-market sale of their fibre. For the first time, our results provide discriminant features for the determination of species of origin of contraband fibre.

Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study

Motivation

Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets.

Results

Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets.

Availability and implementation

https://github.com/theorod93/sCCA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multi-gene metabolic engineering of tomato plants results in increased fruit yield up to 23%

The capacity to assimilate carbon and nitrogen, to transport the resultant sugars and amino acids to sink tissues, and to convert the incoming sugars and amino acids into storage compounds in the sink tissues, are key determinants of crop yield. Given that all of these processes have the potential to co-limit growth, multiple genetic interventions in source and sink tissues, plus transport processes may be necessary to reach the full yield potential of a crop. We used biolistic combinatorial co-transformation (up to 20 transgenes) for increasing C and N flows with the purpose of increasing tomato fruit yield. We observed an increased fruit yield of up to 23%. To better explore the reconfiguration of metabolic networks in these transformants, we generated a dataset encompassing physiological parameters, gene expression and metabolite profiling on plants grown under glasshouse or polytunnel conditions. A Sparse Partial Least Squares regression model was able to explain the combination of genes that contributed to increased fruit yield. This combinatorial study of multiple transgenes targeting primary metabolism thus offers opportunities to probe the genetic basis of metabolic and phenotypic variation, providing insight into the difficulties in choosing the correct combination of targets for engineering increased fruit yield.

Gut microbiota of obese and diabetic Thai subjects and interplay with dietary habits and blood profiles

Obesity and type 2 diabetes mellitus (T2DM) have become major public health issues globally. Recent research indicates that intestinal microbiota play roles in metabolic disorders. Though there are numerous studies focusing on gut microbiota of health and obesity states, those are primarily focused on Western countries. Comparatively, only a few investigations exist on gut microbiota of people from Asian countries. In this study, the fecal microbiota of 30 adult volunteers living in Chiang Rai Province, Thailand were examined using next-generation sequencing (NGS) in association with blood profiles and dietary habits. Subjects were categorized by body mass index (BMI) and health status as follows; lean (L) = 8, overweight (OV) = 8, obese (OB) = 7 and diagnosed T2DM = 7. Members of T2DM group showed differences in dietary consumption and fasting glucose level compared to BMI groups. A low level of high-density cholesterol (HDL) was observed in the OB group. Principal coordinate analysis (PCoA) revealed that microbial communities of T2DM subjects were clearly distinct from those of OB. An analogous pattern was additionally illustrated by multiple factor analysis (MFA) based on dietary habits, blood profiles, and fecal gut microbiota in BMI and T2DM groups. In all four groups, Bacteroidetes and Firmicutes were the predominant phyla. Abundance of Faecalibacterium prausnitzii, a butyrate-producing bacterium, was significantly higher in OB than that in other groups. This study is the first to examine the gut microbiota of adult Thais in association with dietary intake and blood profiles and will provide the platform for future investigations.

OmicsON - Integration of omics data with molecular networks and statistical procedures

A huge amount of atomized biological data collected in various databases and the need for a description of their relation by theoretical methods causes the development of data integration methods. The omics data analysis by integration of biological knowledge with mathematical procedures implemented in the OmicsON R library is presented in the paper. OmicsON is a tool for the integration of two sets of data: transcriptomics and metabolomics. In the workflow of the library, the functional grouping and statistical analysis are applied. Subgroups among the transcriptomic and metabolomics sets are created based on the biological knowledge stored in Reactome and String databases. It gives the possibility to analyze such sets of data by multivariate statistical procedures like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). The integration of metabolomic and transcriptomic data based on the methodology contained in OmicsON helps to easily obtain information on the connection of data from two different sets. This information can significantly help in assessing the relationship between gene expression and metabolite concentrations, which in turn facilitates the biological interpretation of the analyzed process.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Consistency and overfitting of multi-omics methods on experimental data

Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species

Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent spaces from a source single-cell RNA-sequencing (scRNA-seq) dataset, and projectR, which evaluates these latent spaces in independent target datasets via transfer learning. Application of developing mouse retina to scRNA-Seq reveals intrinsic relationships across biological contexts and assays while avoiding batch effects and other technical features. We compare the dimensions learned in this source dataset to adult mouse retina, a time-course of human retinal development, select scRNA-seq datasets from developing brain, chromatin accessibility data, and a murine-cell type atlas to identify shared biological features. These tools lay the groundwork for exploratory analysis of scRNA-seq data via latent space representations, enabling a shift in how we compare and identify cells beyond reliance on marker genes or ensemble molecular identity.