Statistical tools for transgene copy number estimation based on real-time PCR

Background As compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination. Results Three experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination. Conclusion These statistical methods allow the real-time PCR-based transgene copy number estimation to be more reliable and precise with a proper statistical estimation. Proper confidence intervals are necessary for unambiguous prediction of trangene copy number. The four different statistical methods are compared for their advantages and disadvantages. Moreover, the statistical methods can also be applied for other real-time PCR-based quantification assays including transfection efficiency analysis and pathogen quantification

Statistical tools for transgene copy number estimation based on real-time PCR

Background As compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination. Results Three experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination. Conclusion These statistical methods allow the real-time PCR-based transgene copy number estimation to be more reliable and precise with a proper statistical estimation. Proper confidence intervals are necessary for unambiguous prediction of trangene copy number. The four different statistical methods are compared for their advantages and disadvantages. Moreover, the statistical methods can also be applied for other real-time PCR-based quantification assays including transfection efficiency analysis and pathogen quantification

Incomplete homogenization of 18 S ribosomal DNA coding regions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>As a result of concerted evolution, coding regions of ribosomal DNA sequences are highly conserved within species and variation is generally thought to be limited to a few nucleotides. However, rDNA sequence variation has not been systematically examined in plant genomes, including that of the model plant <it>Arabidopsis thaliana </it>whose genome was the first to be sequenced.</p> <p>Findings</p> <p>Both genomic and transcribed 18 S sequences were sampled and revealed that most deviation from the consensus sequence was limited to single nucleotide substitutions except for a variant with a 270 bp deletion from position 456 to 725 in <it>Arabidopsis </it>numbering. The deletion maps to the functionally important and highly conserved 530 loop or helix18 in the structure of <it>E. coli </it>16 S. The expression of the deletion variant is tightly controlled during developmental growth stages. Transcripts were not detectable in young seedlings but could be amplified from RNA extracts of mature leaves, stems, flowers and roots of <it>Arabidopsis thaliana </it>ecotype Columbia. We also show polymorphism for the deletion variant among four <it>Arabidopsis </it>ecotypes examined.</p> <p>Conclusion</p> <p>Despite a strong purifying selection that might be expected against functionally impaired rDNAs, the newly identified variant is maintained in the <it>Arabidopsis </it>genome. The expression of the variant and the polymorphism displayed by <it>Arabidopsis </it>ecotypes suggest a transition state in concerted evolution.</p

RNA-seq analysis of the effect of kanamycin and the ABC transporter AtWBC19 on Arabidopsis thaliana seedlings reveals changes in metal content.

Plants are exposed to antibiotics produced by soil microorganisms, but little is known about their responses at the transcriptional level. Likewise, few endogenous mechanisms of antibiotic resistance have been reported. The Arabidopsis thaliana ATP Binding Cassette (ABC) transporter AtWBC19 (ABCG19) is known to confer kanamycin resistance, but the exact mechanism of resistance is not well understood. Here we examined the transcriptomes of control seedlings and wbc19 mutant seedlings using RNA-seq analysis. Exposure to kanamycin indicated changes in the organization of the photosynthetic apparatus, metabolic fluxes and metal uptake. Elemental analysis showed a 60% and 80% reduction of iron uptake in control and wbc19 mutant seedlings respectively, upon exposure to kanamycin. The drop in iron content was accompanied by the upregulation of the gene encoding for FERRIC REDUCTION OXIDASE 6 (FRO6) in mutant seedlings but not by the differential expression of other transport genes known to be induced by iron deficiency. In addition, wbc19 mutants displayed a distinct expression profile in the absence of kanamycin. Most notably the expression of several zinc ion binding proteins, including ZINC TRANSPORTER 1 PRECURSOR (ZIP1) was increased, suggesting abnormal zinc uptake. Elemental analysis confirmed a 50% decrease of zinc content in wbc19 mutants. Thus, the antibiotic resistance gene WBC19 appears to also have a role in zinc uptake

Visionary Projects

Presented on April 9, 2019 at 10:00 a.m. in the Technology Square Research Building (TSRB) Banquet Hall, Georgia Institute of Technology.South Big Data Innovation Hub 2019 All Hands Meeting - Panel 2: Visionary ProjectsMentewab Ayalew, PhD. Spoke Co-PI, Integrating Biological Big Data Research into Student Training and Education, is Vice Chair and Associate Professor in Biology at Spelman College. The project is a collaborative effort among the University of Tennessee Chattanooga, Tuskegee University, Spelman College, and West Virginia University to integrate biological big data into student training and education. Leveraging the team’s expertise in computer science and biological sciences, the project will offer training workshops on using network models to integrate heterogeneous genomic big data and heterogeneous ecological big data to address life sciences questions. The team will also engage faculty and students in developing a protocol to automate field data collection, will prototype methods to enhance plant digitization, leveraging the collection of digitized plant images and meta-information at the Southeast Regional Network of Expertise and Collections, as well as the ecological datasets in collaboration with the Encyclopedia of Life.Gari Clifford, PhD. Spoke PI, Large Scale Medical Informatics for Patient Care Coordination and Engagement, is Chair, Associate Professor, Biomedical Informatics at Emory University and Associate Professor, Biomedical Engineering at Georgia Tech. This project brings together six universities to design and construct a patient-focused and personalized health system that addresses the fractured nature of healthcare information and the lack of engagement of individuals in their own healthcare. By taking advantage of the enormous amount of information generated by real-time, mobile and wearable devices and the availability of rich social media data on patient behavior, the team will create a comprehensive picture of patient health and a tool to help manage patients’ engagement with their healthcare providers. As its first pilot, the researchers will focus on African Americans and Hispanics/Latinos diagnosed with cardiovascular disease.Ashok K. Goel, PhD. Spoke PI, Using Big Data for Environmental Sustainability: Big Data + AI Technology = Accessible, Usable, Useful Knowledge, is a Professor of Computer Science and Cognitive Science in the School of Interactive Computing at Georgia Tech and the Director of the School’s Ph.D. Program in Human-Centered Computing. This project brings together scientists to translate big data into meaningful knowledge that supports research and education in environmental sustainability. The project will use AI methods and tools for modeling and simulation in conjunction with the Encyclopedia of Life (EOL), the world’s largest database of biological species, and other biodiversity data sources.Santiago Grijalva, PhD. Spoke Co-PI, Smart Grids Big Data, is the Georgia Power Distinguished Professor of Electrical and Computer Engineering and Director of the Georgia Tech Advanced Computational Electricity Systems (ACES) Laboratory. Dr. Grijalva is a pioneer on decentralized power system control and energy internet, and a leading researcher on smart power systems and renewable energy integration. The introduction of new technologies for monitoring electrical power grids has led to an abundance of data that can be used to improve power generation and transmission and to enhance customer service. However, this data is still vastly underutilized. This project aims to increase our understanding of the merged data collected from physical systems in order to better understand how energy flows through grids, how to prevent emergencies such as blackouts and brownouts, and how to improve asset management and increase energy efficiency.Frank Muller-Karger, PhD. Spoke PI, Enhanced 3-D Mapping for Habitat, Biodiversity, and Flood Hazard Assessments of Coastal and Wetland Areas of the Southern US, is a biological oceanographer (Professor) at the College of Marine Science, University of South Florida. The vision of this project is that communities occupying low-lying coastal areas of the southern US will be protected and develop in a sustainable manner through planning based on knowledge, conservation, and wise use of sensitive lands. Researchers from the University of South Florida’s College of Marine Science and the School of Geosciences, Texas A&M University – Corpus Christi, and Google Earth Engine are collaborating with the South Big Data Hub through this project to develop more accurate, ultra-high resolution topographic, land cover, and urban environment geospatial products. The project examines in detail areas that were directly impacted by Hurricanes Harvey and Irma in 2017, and identifies flood-prone areas across the region.John Verdi, JD. Spoke Representative, Collaborative to Protect Privacy and Use Data Responsibly, is Vice President of Policy at the Future of Privacy Forum (FPF). FPF is a Washington, DC-based nonprofit organization that serves as a catalyst for privacy leadership and scholarship, advancing principled data practices in support of emerging technologies. FPF is supported by the chief privacy officers of more than 110 leading companies, several foundations, and an advisory board comprised of leading academics and advocates. John supervises FPF’s policy portfolio, which advances FPF’s agenda on a broad range of issues, including: Artificial Intelligence & Machine Learning; Algorithmic Decision-Making; Ethics; Connected Cars; Smart Communities; Student Privacy; Health; the Internet of Things; Wearable Technologies; De-Identification; and Drones. John previously served as Director of Privacy Initiatives at the National Telecommunications and Information Administration, where he crafted policy recommendations for the US Department of Commerce and President Obama regarding technology, trust, and innovation.Runtime: 80:35 minute

Transcripts encoding for metal binding proteins differentially expressed upon seedling germination on kanamycin containing media.

<p>The terms metal, iron, zinc, copper, manganese, magnesium, cobalt, molybdenum etc were searched in the GO terms associated with genes differentially expressed when comparing transcripts from control seedlings germinating on MS media to transcripts from control seedlings germinating on MS media with 50 mg/l kanamycin (minimum 2 fold difference in expression, P<0.001). * denotes genes also identified as encoding for oxidoreductases in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109310#pone-0109310-t001" target="_blank"><b>Table 1</b></a><b>.</b></p><p>Transcripts encoding for metal binding proteins differentially expressed upon seedling germination on kanamycin containing media.</p

Metal content of seedlings grown in the presence or absence of kanamycin (50mg/l).

<p>Iron, zinc, manganese and copper contents from control and <i>wbc19</i> mutant seedlings were measured by ICP-MS five days after germination and shown in parts per million (ppm). Error bars represent±standard deviation from 3 biological replicates. **, *** indicate significant difference with the control on media with no kanamycin at P<0.01 and 0.001 respectively (<i>t</i>- test).</p

Transcripts encoding for metal binding proteins differentially expressed when comparing control and <i>wbc19</i> mutant seedlings geminating on MS media with 50 m/l kanamycin.

<p>Transcripts encoding for metal binding proteins differentially expressed when comparing control and <i>wbc19</i> mutant seedlings geminating on MS media with 50 m/l kanamycin.</p

Transcripts encoding for proteins involved in photosynthesis and oxidoreduction differentially expressed when comparing control and <i>wbc19</i> mutant seedlings geminating on MS media with 50 mg/l kanamycin.

<p>The terms photosynthesis, oxidoreduction and electron carrier were searched in the GO IDs associated with genes differentially expressed when comparing transcripts from control and <i>wbc19</i> mutant seedlings germinating on MS media with 50 mg/l of kanamycin (minimum 2 fold difference in expression; P<0.001).</p><p>Transcripts encoding for proteins involved in photosynthesis and oxidoreduction differentially expressed when comparing control and <i>wbc19</i> mutant seedlings geminating on MS media with 50 mg/l kanamycin.</p