Mariana Buongermino MB. Pereira, Mikael M. Wallroth, Viktor V. Jonsson, Erik E. Kristiansson.
2018 Apr; (19):274 1
In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated.
Federica F. Del Chierico, Francesca F. Abbatini, Alessandra A. Russo, Andrea A. Quagliariello, Sofia S. Reddel, Danila D. Capoccia, Romina R. Caccamo, Stefano S. Ginanni Corradini, Valerio V. Nobili, Francesco F. De Peppo, Bruno B. Dallapiccola, Frida F. Leonetti, Gianfranco G. Silecchia, Lorenza L. Putignani.
Frontiers in microbiology
2018 ; (9):1210
Obesity levels, especially in children, have dramatically increased over the last few decades. Recently, several studies highlighted the involvement of gut microbiota in the pathophysiology of obesity. We investigated the composition of gut microbiota in obese adolescents and adults compared to age-matched normal weight (NW) volunteers in order to assemble age- and obesity-related microbiota profiles. The composition of gut microbiota was analyzed by 16S rRNA-based metagenomics. Ecological representations of microbial communities were computed, and univariate, multivariate, and correlation analyses performed on bacterial profiles. The prediction of metagenome functional content from 16S rRNA gene surveys was carried out. Ecological analyses revealed a dissimilarity among the subgroups, and resultant microbiota profiles differed between obese adolescents and adults. Using statistical analyses, we assigned, as microbial markers, and to the microbiota of obese adolescents, and , Rikenellaceae, , Barnesiellaceae, and to the microbiota of NW adolescents. The predicted metabolic profiles resulted different in adolescent groups. Particularly, biosynthesis of primary bile acid and steroid acids, metabolism of fructose, mannose, galactose, butanoate, and pentose phosphate and glycolysis/gluconeogenesis were for the majority associated to obese, while biosynthesis and metabolism of glycan, biosynthesis of secondary bile acid, metabolism of steroid hormone and lipoic acid were associated to NW adolescents. Our study revealed unique features of gut microbiota in terms of ecological patterns, microbial composition and metabolism in obese patients. The assignment of novel obesity bacterial markers may open avenues for the development of patient-tailored treatments dependent on age-related microbiota profiles.
Alejandra A. Ochoa, Jerica M JM. Berge.
Journal of immigrant and minority health
2017 04; (19):430-447 2
Latinos are the largest and fastest-growing ethnically diverse group in the United States. Latino children are also among the most overweight and obese ethnic groups of children in the United States. Research over the last decade has identified the home environment as a key influence on the diet and physical activity of children. To summarize cross-sectional and longitudinal research that has identified factors within the home environment of Latino families that are associated with childhood obesity and to provide recommendations for future research and intervention development with Latino families. A decade review from 2005 to 2015 was conducted. Studies identifying factors within the home environments of Latino families that were associated with childhood obesity were examined. Five main factors were identified across the literature as home environment factors that are associated with childhood obesity in Latino children. These factors included: parental influences (e.g., parent feeding practices, modeling), screen time, physical activity/sedentary behavior, socioeconomic status/food security and sleep duration. The current review identified several home environment factors that may contribute to the disparities in childhood obesity for Latino children. Results from this review such as, focusing on decreasing controlling parent feeding practices, and increasing parent modeling of healthy behaviors and child sleep duration, can be used in developing culturally-specific interventions for Latino children.
Ya-Ping YP. Hou, Qing-Qing QQ. He, Hai-Mei HM. Ouyang, Hai-Shan HS. Peng, Qun Q. Wang, Jie J. Li, Xiao-Fei XF. Lv, Yi-Nan YN. Zheng, Shao-Chuan SC. Li, Hai-Liang HL. Liu, Ai-Hua AH. Yin.
BioMed research international
2017 ; (2017):7585989
To investigate the gut microbiota differences of obese children compared with the control healthy cohort to result in further understanding of the mechanism of obesity development.
Craig M CM. Hales, Margaret D MD. Carroll, Cheryl D CD. Fryar, Cynthia L CL. Ogden.
NCHS data brief
2017 10; ():1-8 288
Obesity is associated with serious health risks. Monitoring obesity
prevalence is relevant for public health programs that focus on reducing
or preventing obesity. Between 2003–2004 and 2013–2014, there were no
significant changes in childhood obesity prevalence, but adults showed an
increasing trend. This report provides the most recent national estimates
from 2015–2016 on obesity prevalence by sex, age, and race and Hispanic
origin, and overall estimates from 1999–2000 through 2015–2016.
Luz Elvia LE. Vera-Becerra, Martha L ML. Lopez, Lucia L LL. Kaiser.
2016 Feb; (97):87-93
The purpose of this study was to examine relative validity of a food frequency questionnaire (FFQ) to measure food acculturation in young Mexican-origin children. In 2006, Spanish-speaking staff interviewed mothers in a community-based sample of households from Ventura, California (US) (n = 95) and Guanajuato, Mexico (MX) (n = 200). Data included two 24-h dietary recalls (24-DR); a 30-item FFQ; and anthropometry of the children. To measure construct, convergent, and discriminant validity, data analyses included factor analysis, Spearman correlations, t-test, respectively. Factor analysis revealed two constructs: 1) a US food pattern including hamburgers, pizza, hot dogs, fried chicken, juice, cereal, pastries, lower fat milk, quesadillas, and American cheese and 2) a MX food pattern including tortillas, fried beans, rice/noodles, whole milk, and pan dulce (sweet bread). Out of 22 food items that could be compared across the FFQ and mean 24-DRs, 17 were significantly, though weakly, correlated (highest r = 0.62, for whole milk). The mean US food pattern score was significantly higher, and the MX food pattern score, lower in US children than in MX children (p < 0.0001). After adjusting for child's age and gender; mother's education; and household size, the US food pattern score was positively related to body mass index (BMI) z-scores (beta coefficient: +0.29, p = - 0.004), whereas the MX food pattern score was negatively related to BMI z-scores (beta coefficient: -0.28, p = 0.002). This tool may be useful to evaluate nutrition education interventions to prevent childhood obesity on both sides of the border.
Dinghua D. Li, Chi-Man CM. Liu, Ruibang R. Luo, Kunihiko K. Sadakane, Tak-Wah TW. Lam.
Bioinformatics (Oxford, England)
2015 May; (31):1674-6 10
MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252 Gbps in 44.1 and 99.6 h on a single computing node with and without a graphics processing unit, respectively. MEGAHIT assembles the data as a whole, i.e. no pre-processing like partitioning and normalization was needed. When compared with previous methods on assembling the soil data, MEGAHIT generated a three-time larger assembly, with longer contig N50 and average contig length; furthermore, 55.8% of the reads were aligned to the assembly, giving a fourfold improvement.
Anthony M AM. Bolger, Marc M. Lohse, Bjoern B. Usadel.
Bioinformatics (Oxford, England)
2014 Aug; (30):2114-20 15
Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data.
Derrick E DE. Wood, Steven L SL. Salzberg.
2014 Mar; (15):R46 3
Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.
Alexander A. Dobin, Carrie A CA. Davis, Felix F. Schlesinger, Jorg J. Drenkow, Chris C. Zaleski, Sonali S. Jha, Philippe P. Batut, Mark M. Chaisson, Thomas R TR. Gingeras.
Bioinformatics (Oxford, England)
2013 Jan; (29):15-21 1
Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.
Doug D. Hyatt, Philip F PF. LoCascio, Loren J LJ. Hauser, Edward C EC. Uberbacher.
Bioinformatics (Oxford, England)
2012 Sep; (28):2223-30 17
Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data.
R L RL. Tatusov, E V EV. Koonin, D J DJ. Lipman.
Science (New York, N.Y.)
1997 Oct; (278):631-7 5338
In order to extract the maximum amount of information from the rapidly accumulating genome sequences, all conserved genes need to be classified according to their homologous relationships. Comparison of proteins encoded in seven complete genomes from five major phylogenetic lineages and elucidation of consistent patterns of sequence similarities allowed the delineation of 720 clusters of orthologous groups (COGs). Each COG consists of individual orthologous proteins or orthologous sets of paralogs from at least three lineages. Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. This relation automatically yields a number of functional predictions for poorly characterized genomes. The COGs comprise a framework for functional and evolutionary genome analysis.
Cindy D CD. Davis.
; (51):167-174 4
The human body is host to a vast number of microbes, including bacterial, fungal and protozoal microoganisms, which together constitute our microbiota. Evidence is emerging that the intestinal microbiome is intrinsically linked with overall health, including obesity risk. Obesity and obesity-related metabolic disorders are characterized by specific alterations in the composition and function of the human gut microbiome. Mechanistic studies have indicated that the gastrointestinal microbiota can influence both sides of the energy balance equation; namely, as a factor influencing energy utilization from the diet and as a factor that influences host genes that regulate energy expenditure and storage. Moreover, its composition is not fixed and can be influenced by several dietary components. This fact raises the attractive possibility that manipulating the gut microbiota could facilitate weight loss or prevent obesity in humans. Emerging as possible strategies for obesity prevention and/or treatment are targeting the microbiota, in order to restore or modulate its composition through the consumption of live bacteria (probiotics), nondigestible or limited digestible food constituents such as oligosaccharides (prebiotics), or both (synbiotics), or even fecal transplants.