Integrated Analysis of Seaweed Components during Seasonal Fluctuation by Data Mining Across Heterogeneous Chemical Measurements with Network Visualization

Biological information is intricately intertwined with several factors. Therefore, comprehensive analytical methods such as integrated data analysis, combining several data measurements, are required. In this study, we describe a method of data preprocessing that can perform comprehensively integrated analysis based on a variety of multimeasurement of organic and inorganic chemical data from <i>Sargassum fusiforme</i> and explore the concealed biological information by statistical analyses with integrated data. Chemical components including polar and semipolar metabolites, minerals, major elemental and isotopic ratio, and thermal decompositional data were measured as environmentally responsive biological data in the seasonal variation. The obtained spectral data of complex chemical components were preprocessed to isolate pure peaks by removing noise and separating overlapping signals using the multivariate curve resolution alternating least-squares method before integrated analyses. By the input of these preprocessed multimeasurement chemical data, principal component analysis and self-organizing maps of integrated data showed changes in the chemical compositions during the mature stage and identified trends in seasonal variation. Correlation network analysis revealed multiple relationships between organic and inorganic components. Moreover, in terms of the relationship between metal group and metabolites, the results of structural equation modeling suggest that the structure of alginic acid changes during the growth of <i>S. fusiforme</i>, which affects its metal binding ability. This integrated analytical approach using a variety of chemical data can be developed for practical applications to obtain new biochemical knowledge including genetic and environmental information

Pretreatment and Integrated Analysis of Spectral Data Reveal Seaweed Similarities Based on Chemical Diversity

Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (<i>Plectropomus leopardus</i>) - Fig 1

<p>(A) Phylum level microbial taxonomic composition of gut contents. (B) Class level microbial taxonomic composition of gut contents. (C) Biodiversity of microbiota at each sampling point. Black squares designate the average and the bars indicate the standard error means. White circles denote costal seawater samples, white down-pointing triangles indicate inlet seawater samples, white diamonds signify rearing tank seawater samples, and white up-pointing triangles indicate outlet seawater samples. *Note that inlet seawater was subjected to UV sanitization; therefore, the number of microbiota was significantly low. The PCR cycles of inlet seawater was twice as many as other seawater samples. The replicate number of inlet seawater sequencing was one to two. NF, non-feeding; F, feeding; and ZT, Zeitgeber time.</p

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (<i>Plectropomus leopardus</i>) - Fig 3

<p>(A) Discrimination analysis by PLS-DA. Left, results described by the first and second component. Right, loading plot indicating the contributor expressed by reliability and intensity (B) SOM of data neural network analysis. The microbiota results are presented in blue and pink. Metabolome and transcriptome results are presented as red and greed gradation, respectively. The representative metabolites, isoleucine(Ile) and leucine (Leu) were chosen based on our previous data [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197256#pone.0197256.ref024" target="_blank">24</a>]. The representative genes, thyroid hormone receptor a (<i>trα</i>) and adenosine monophosphate deaminase 3a (<i>ampd3a</i>), were selected by OPLS-DA from our previous studies [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197256#pone.0197256.ref024" target="_blank">24</a>]. The class numbers of the samples are: 1, ZT2 of fasting Day1 (NF1-ZT2); 2, NF1-ZT6; 3, NF1-ZT10; 4, NF1-ZT14; 5, ZT2 of fasting Day2 (NF2-ZT2); 6, NF2-ZT6; 7, NF2-ZT10; 8, NF2-ZT14; 9, NF2-ZT18; 10, NF2-ZT22; 11, ZT2 of feeding Day1 (F1-ZT2); 12, F1-ZT6; 13, F1-ZT10; 14, F1-ZT14; 15, ZT2 of feeding Day2 (F2-ZT2); 16, F2-ZT6; 17, F2-ZT10; 18, F2-ZT14; 19, F2-ZT18; and 20, F2-ZT22.</p

Overview of this study and proposed model for monitoring aquaculture environment and symbiotic metabolism upon feeding, as well as timing, via comprehensive analyses of microbiome, host metabolism, and host transcriptome.

<p>Overview of this study and proposed model for monitoring aquaculture environment and symbiotic metabolism upon feeding, as well as timing, via comprehensive analyses of microbiome, host metabolism, and host transcriptome.</p

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (<i>Plectropomus leopardus</i>) - Fig 2

<p>(A) Heatmap of microbiota abundance. Red, high abundance; blue, low abundance. The hierarchical cluster analysis tree is shown on the right. NF, non-feeding; F, feeding; and ZT, zeitgeber time. (B) Microbiota dynamics. White circles indicate the sampling points. (C) Network analysis of <i>Firmicutes</i>. Red denotes a positive correlation and blue designates a negative correlation. Thickness of the bar indicates the intensity of correlation; the thicker the bar the stronger the correlation.</p

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (<i>Plectropomus leopardus</i>) - Fig 4

<p>(A) Functional structure of intestinal microbiota exhibited by COG categories. (B) The dynamics pattern of the putative functional genes. Eight metabolism categories in COG classification; Y-axis, Z-normalization score. (C) Hierarchal clustering analysis of the presumptive functional genes. Highly expressed genes are in red and lowly expressed genes are in blue. The expression levels of genes in C1 were high in fasting and those in C2 were high in feeding. (D) Variation in the number of genes exhibiting dynamics similar to the microbiota. <i>Firmicutes</i>, yellow bars; <i>Fusobacteria</i>, pink bars; <i>Proteobacteria</i>, blue bars. Y-axis indicates the Z-normalization score. Capital letters indicate COG ID. The information storage and processing category (green bar) includes [J], Translation, ribosomal structure and biogenesis; [A], RNA processing and modification; [K], Transcription; [L], Replication, recombination and repair; and [B], Chromatin structure and dynamics. The cellular processes and signaling category (purple bar) includes [D], Cell cycle control, cell division, chromosome partitioning; [Y], Nuclear structure; [V], Defense mechanisms; [T], Signal transduction mechanisms; [M], Cell wall/membrane/envelope biogenesis; [N], Cell motility; [Z], Cytoskeleton; [W], Extracellular structures; [U], Intracellular trafficking, secretion, and vesicular transport; and [O], Posttranslational modification, protein turnover, chaperones. The metabolism category (orange bar) includes [C], Energy production and conversion; [G], Carbohydrate transport and metabolism; [E], Amino acid transport and metabolism; [F], Nucleotide transport and metabolism; [H], Coenzyme transport and metabolism; [I], Lipid transport and metabolism; [P], Inorganic ion transport and metabolism; and [Q], Secondary metabolites biosynthesis, transport and catabolism. The poorly characterized category (black bar) includes [R], General function prediction only; and [S], Function unknown. The light blue bar indicates [X], Mobilome: prophages, transposons.</p

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (<i>Plectropomus leopardus</i>)

<div><p>Aquaculture is currently a major source of fish and has the potential to become a major source of protein in the future. These demands require efficient aquaculture. The intestinal microbiota plays an integral role that benefits the host, providing nutrition and modulating the immune system. Although our understanding of microbiota in fish gut has increased, comprehensive studies examining fish microbiota and host metabolism remain limited. Here, we investigated the microbiota and host metabolism in the coral leopard grouper, which is traded in Asian markets as a superior fish and has begun to be produced via aquaculture. We initially examined the structural changes of the gut microbiota using next-generation sequencing and found that the composition of microbiota changed between fasting and feeding conditions. The dominant phyla were <i>Proteobacteria</i> in fasting and <i>Firmicutes</i> in feeding; interchanging the dominant bacteria required 12 hours. Moreover, microbiota diversity was higher under feeding conditions than under fasting conditions. Multivariate analysis revealed that <i>Proteobacteria</i> are the key bacteria in fasting and <i>Firmicutes</i> and <i>Fusobacteria</i> are the key bacteria in feeding. Subsequently, we estimated microbiota functional capacity. Microbiota functional structure was relatively stable throughout the experiment; however, individual function activity changed according to feeding conditions. Taken together, these findings indicate that the gut microbiota could be a key factor to understanding fish feeding conditions and play a role in interactions with host metabolism. In addition, the composition of microbiota in ambient seawater directly affects the fish; therefore, it is important to monitor the microbiota in rearing tanks and seawater circulating systems.</p></div

Histological characteristics of fibrosis in biopsy specimens in the 21 patients with HCM.

<p>Interstitial fibrosis; increased interstitial collagen without evidence of myocyte loss. Myocardial scarring; increased interstitial collagen with evidence of myocyte loss <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101465#pone.0101465-Seidman1" target="_blank">[9]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101465#pone.0101465-John1" target="_blank">[22]</a>. The presence of islands of surviving cardiomyocyte among fibrotic tissues was considers as evidence of cardiomyocyte loss <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101465#pone.0101465-Basso1" target="_blank">[23]</a>. HCM as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101465#pone-0101465-t001" target="_blank">Table 1</a>.</p

Diagnostic values of LGE for detecting microscopic myocardial scarring or interstitial fibrosis in biopsied specimens in the HCM patients (N = 21).

<p>NPV  =  negative predictive value; PPV  =  positive predictive value. HCM as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101465#pone-0101465-t001" target="_blank">Table 1</a>.</p