scholarly journals Analysis of the fecal microbiota of fast- and slow-growing rainbow trout (Oncorhynchus mykiss)

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Pratima Chapagain ◽  
Brock Arivett ◽  
Beth M. Cleveland ◽  
Donald M. Walker ◽  
Mohamed Salem
Keyword(s):  
Growth Rate ◽  
Gut Microbiota ◽  
Dna Extraction ◽  
Significant Variation ◽  
Extraction Methods ◽  
Fish Growth ◽  
Indicator Taxa ◽  
Fast Growing ◽  
Dna Extraction Methods ◽  
Slow Growing

Abstract Background Diverse microbial communities colonizing the intestine of fish contribute to their growth, digestion, nutrition, and immune function. We hypothesized that fecal samples representing the gut microbiota of rainbow trout could be associated with differential growth rates observed in fish breeding programs. If true, harnessing the functionality of this microbiota can improve the profitability of aquaculture. The first objective of this study was to test this hypothesis if gut microbiota is associated with fish growth rate (body weight). Four full-sibling families were stocked in the same tank and fed an identical diet. Two fast-growing and two slow-growing fish were selected from each family for 16S rRNA microbiota profiling. Microbiota diversity varies with different DNA extraction methods. The second objective of this study was to compare the effects of five commonly used DNA extraction methods on the microbiota profiling and to determine the most appropriate extraction method for this study. These methods were Promega-Maxwell, Phenol-chloroform, MO-BIO, Qiagen-Blood/Tissue, and Qiagen-Stool. Methods were compared according to DNA integrity, cost, feasibility and inter-sample variation based on non-metric multidimensional scaling ordination (nMDS) clusters. Results Differences in DNA extraction methods resulted in significant variation in the identification of bacteria that compose the gut microbiota. Promega-Maxwell had the lowest inter-sample variation and was therefore used for the subsequent analyses. Beta diversity of the bacterial communities showed significant variation between breeding families but not between the fast- and slow-growing fish. However, an indicator analysis determined that cellulose, amylose degrading and amino acid fermenting bacteria (Clostridium, Leptotrichia, and Peptostreptococcus) are indicator taxa of the fast-growing fish. In contrary, pathogenic bacteria (Corynebacterium and Paeniclostridium) were identified as indicator taxa for the slow-growing fish. Conclusion DNA extraction methodology should be carefully considered for accurate profiling of the gut microbiota. Although the microbiota was not significantly different between the fast- and slow-growing fish groups, some bacterial taxa with functional implications were indicative of fish growth rate. Further studies are warranted to explore how bacteria are transmitted and potential usage of the indicator bacteria of fast-growing fish for development of probiotics that may improve fish health and growth.

scholarly journals Gut microbiome analysis of fast- and slow-growing Rainbow Trout (Oncorhynchus mykiss)

2019 ◽  
Author(s):  
Pratima Chapagain ◽  
Brock Arivett ◽  
Beth M Cleveland ◽  
Donald M. Walker ◽  
Mohamed Salem
Keyword(s):  
Rainbow Trout ◽  
Dna Extraction ◽  
Gut Microbiome ◽  
Significant Variation ◽  
Pathogenic Bacteria ◽  
Extraction Methods ◽  
Fish Growth ◽  
Indicator Taxa ◽  
Fast Growing ◽  
Slow Growing

Abstract Background Diverse microbial communities colonizing the intestine of fish contribute to their growth, digestion, nutrition, and immune function. We hypothesized that the gut microbiome of rainbow trout could be associated with differential growth rates observed in fish breeding programs. If true, harnessing the functionality of this microbiome can improve profitability of aquaculture. To test this hypothesis, four full-sibling families were stocked in the same tank and fed an identical diet. Two fast-growing and two slow-growing fish were selected from each family. Five different extraction methods were used to obtain DNA from feces for 16S rRNA microbiome profiling. These methods were Promega-Maxwell, phenol-chloroform, MO-BIO, Qiagen-Blood, Qiagen-Stool. Methods were compared according to DNA integrity, cost, feasibility and inter-sample variation based on non-metric multidimensional scaling ordination (nMDS) clusters. Results Differences in DNA extraction methods result in significant variation in identification of bacteria that compose the gut microbiome. Promega-Maxwell had the lowest inter-sample variation and was therefore used for the subsequent analyses. The gut microbiome was different from that of the environment (feed and water). However, feed and gut shared a large portion of their microbiome suggesting significant contribution of the feed in shaping the gut microbiota. Beta diversity of the bacterial communities showed significant variation between breeding families but not between the fast- and slow-growing fish. An indicator analysis determined that cellulose, amylose degrading and amino acid fermenting bacteria (Clostridium, Leptotrichia and Peptostreptococcus) as indicator taxa of the fast-growing fish. In contrary, pathogenic bacteria (Corynebacterium and Paeniclostridium) were identified as slow-growing indicator taxa. Conclusion DNA extraction methodology should be taken into account for accurate profiling of the gut microbiome. Although the microbiome was not significantly different between the fast- and slow-growing fish groups, some bacterial taxa with functional implications were indicative of fish growth rate. Further studies are warranted to explore how bacteria are transmitted and potential usage of the indicator bacteria of fast-growing fish for development of probiotics that may improve fish health and growth.

Behavior and recruitment success in fish larvae: variation with growth rate and the batch effect

2005 ◽  
Vol 62 (6) ◽  
pp. 1337-1349 ◽  
Author(s):  
Lee A Fuiman ◽  
James H Cowan, Jr. ◽  
Michael E Smith ◽  
Jonathan P O'Neal
Keyword(s):  
Growth Rate ◽  
Fish Larvae ◽  
Fish Growth ◽  
Predatory Fish ◽  
Behavioral Performance ◽  
Treatment Groups ◽  
Fast Growing ◽  
Predation Mortality ◽  
Survival Skills ◽  
Slow Growing

Predation-mortality risk for red drum (Sciaenops ocellatus) larvae does not appear to be related to their growth rate, but important differences in behavioral performance occur between batches of larvae. This conclusion is based upon field-enclosure and laboratory experiments that assessed the degree to which predation-mortality rates and behavioral survival skills vary with growth rate. In field enclosures, populations composed of 15 fast-growing larvae and 15 slow-growing larvae of a comparable size were exposed to a predatory fish. Growth rate did not affect predation rate. In the laboratory we measured 11 survival skills on 100 larvae of a common size from 10 batches of eggs. For each batch, behavioral performance of fast-growing larvae was compared with that of slow-growing larvae. Growth rate did not affect performance in 10 of the 11 survival skills, but behavioral performance varied among treatment groups (growth rate × batch), with higher performance in most survival skills for some treatment groups and consistently poorer performance for other groups. This coordinated pattern of behavioral performance forecasts differential survival among batches. The variation among batches may be related to timing of spawning within the reproductive season of this serially spawning species.

OPTIMIZATION OF DNA EXTRACTION METHODS FROM BIOLOGICAL MATERIALS OF HONEY BEES IN A DIFFERENT METAMOTPHOSIS PHASE

2016 ◽  
Vol 52 ◽  
pp. 171-176
Author(s):  
M. Palkina ◽  
O. Metlitska
Keyword(s):  
Ion Exchange ◽  
Dna Extraction ◽  
Honey Bees ◽  
Biological Material ◽  
Exchange Resin ◽  
Ion Exchange Resin ◽  
Extraction Methods ◽  
Chelex 100 ◽  
Drone Brood ◽  
Dna Extraction Methods

The aim of the research – adaptation, optimization and using of existing DNA extraction methods from bees’ biological material with the reagent «Chelex-100" under complex economic conditions of native laboratories, which will optimize labour costs and improve the economic performance of DNA extraction protocol. Materials and methods. In order to conduct the research the samples of honey bees’ biological material: queen pupae exuviae, larvae of drone brood, some adult bees’ bodies (head and thorax) were selected. Bowl and drone brood were obtained from the experimental bee hives of Institute of Apiculture nd. a. P. I. Prokopovich of NAAS. DNA extraction from biosamples of Apis mellifera ssp. was carried out using «Chelex-100®» ion exchange resin in different concentrations and combinations. Before setting tests for determination of quantitative and quality indexes, dilution of DNA samples of the probed object was conducted in ratio 1:40. The degree of contamination with protein and polysaccharide fractions (OD 260/230), quantitative content of DNA (OD 260/280) in the extracted tests were conducted using spectrophotometer of «Biospec – nano» at the terms of sample volume in 2 µl and length of optical way in 0,7 mm [7]. Verification of DNA samples from biological material of bees, isolated by «Chelex-100®», was conducted after cold keeping during 24 hours at 20°C using PСR with primaries to the fragment of gene of quantitative trait locus (QTL) Sting-2 of next structure [8]:  3' – CTC GAC GAG ACG ACC AAC TTG – 5’; 3' – AAC CAG AGT ATC GCG AGT GTT AC – 5’ Program of amplification: 94 °C – 5 minutes – 1 cycle; 94 °C – 1 minute, 57°C – 1 minute, 72 °C – 2 minutes – 30 cycles; elongation after 72°C during 2 minutes – 1 cycle. The division of obtained amplicons was conducted by gel electrophoresis at a low current – 7 µÀ, in 1,5 % agarose gel (Sigma ®) in TAE buffer [7]. The results. At the time of optimization of DNA isolation methods, according to existing methods of foreign experts, it was found optimal volume of ion exchange resin solution was in the proposed concentration: instead of 60 µl of solution used 120 µl of «Chelex-100®», time of incubation was also amended from 30 minutes to 180 minutes [9]. The use of the author's combination of method «Chelex-100®» with lysis enzymes, proteinase K and detergents (1M dithiothreitol), as time of incubation was also amended, which was reduced to 180 minutes instead of the proposed 12 hours [10]. Changes in quality characteristics of obtained DNA in samples after reduction in incubation time were not found. Conclusions. The most economical method of DNA isolation from bees’ biological material is 20% solution of «Chelex-100» ion exchange resin with the duration of the incubation period of 180 minutes. It should also be noted that the best results can be obtained from exuviae, selected immediately after the queen’s exit from bowl, that reduces the likelihood of DNA molecules destruction under the influence of nucleases activation, but not later than 12 hours from release using the technology of isolated obtain of queens.

scholarly journals Impact of Collection Volume and DNA Extraction Method on the Detection of Biomarkers and HPV DNA in First-Void Urine

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1989
Author(s):  
Laura Téblick ◽  
Severien Van Keer ◽  
Annemie De Smet ◽  
Pierre Van Damme ◽  
Michelle Laeremans ◽  
...  
Keyword(s):  
Dna Extraction ◽  
Internal Control ◽  
Extraction Methods ◽  
Urine Collection ◽  
Hpv Dna ◽  
Non Invasive ◽  
Herpesvirus 1 ◽  
Dna Extraction Methods ◽  
Detection Of Biomarkers ◽  
High Risk Hpv

The potential of first-void (FV) urine as a non-invasive liquid biopsy for detection of human papillomavirus (HPV) DNA and other biomarkers has been increasingly recognized over the past decade. In this study, we investigated whether the volume of this initial urine stream has an impact on the analytical performance of biomarkers. In parallel, we evaluated different DNA extraction protocols and introduced an internal control in the urine preservative. Twenty-five women, diagnosed with high-risk HPV, provided three home-collected FV urine samples using three FV urine collection devices (Colli-Pee) with collector tubes that differ in volume (4, 10, 20 mL). Each collector tube was prefilled with Urine Conservation Medium spiked with phocine herpesvirus 1 (PhHV-1) DNA as internal control. Five different DNA extraction protocols were compared, followed by PCR for GAPDH and PhHV-1 (qPCR), HPV DNA, and HBB (HPV-Risk Assay), and ACTB (methylation-specific qPCR). Results showed limited effects of collection volume on human and HPV DNA endpoints. In contrast, significant variations in yield for human endpoints were observed for different DNA extraction methods (p < 0.05). Additionally, the potential of PhHV-1 as internal control to monitor FV urine collection, storage, and processing was demonstrated.

scholarly journals A Rapid Bacteriophage DNA Extraction Method

2018 ◽  
Vol 1 (3) ◽  
pp. 27 ◽  
Author(s):  
Džiuginta Jakočiūnė ◽  
Arshnee Moodley
Keyword(s):  
Dna Extraction ◽  
Illumina Miseq ◽  
Extraction Methods ◽  
Resistant Bacteria ◽  
Simple Method ◽  
Antibiotic Resistant ◽  
Phage Dna ◽  
Specialized Equipment ◽  
Miseq Platform ◽  
Dna Extraction Methods

Bacteriophages (phages) are intensely investigated as non-antibiotic alternatives to circumvent antibiotic resistance development as well as last resort therapeutic options against antibiotic resistant bacteria. As part of gaining a better understanding of phages and to determine if phages harbor putative virulence factors, whole genome sequencing is used, for which good quality phage DNA is needed. Traditional phage DNA extraction methods are tedious and time consuming, requiring specialized equipment e.g., an ultra-centrifuge. Here, we describe a quick and simple method (under four hours) to extract DNA from double stranded DNA (dsDNA) phages at titers above 1.0 × 1010 plaque-forming units (PFU)/mL. This DNA was suitable for library preparation using the Nextera XT kit and sequencing on the Illumina MiSeq platform.

131-P: Protein contamination in manual DNA extraction methods

2007 ◽  
Vol 68 (1) ◽  
pp. S80
Author(s):  
Victoriano J. Leon ◽  
Alberto J. Leon ◽  
Juan Luis Garcia
Keyword(s):  
Dna Extraction ◽  
Extraction Methods ◽  
P Protein ◽  
Protein Contamination ◽  
Dna Extraction Methods
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