Thursday, May 12, 2011

Accessing the Degradation of RNA Using RT-qPCR by Comparing Amplifications of Short to Long Fragments

By Ann LaJuan Bohannon-Stewart
Department of Biological Sciences, Tennessee State University

Class: Biology 5110 Research in Biology
Professor: Dr. B. Washington
Date Due: 4-29-11
Semester: Spring 2011

RNA quality has a profound influence on the validity and reliability of quantitative PCR results. Therefore, the verification of RNA integrity prior to applications like RT-qPCR and microarrays is indispensable. RNA degradation is one of the major quality factors that influence RT-qPCR microarray assay results. It is often assessed with electrophoresis. However, this method is tedious, requires extreme care, additional equipment and expensive reagent. To circumvent these obstacles, we are developing an RT-qPCR based method which can be performed by any lab equipped with qPCR instrument without the need of additional instrumentation and reagent. This RT-qPCR approach is based on the assumption that RNA breaks at random and the point of breakage follows Poisson distribution. A longer RNA molecule is more likely to break than shorter ones. Therefore, more short templates are available for PCR amplification than long ones in a somewhat degraded RNA sample. To test this approach, intact and degraded total RNA samples were prepared an RNeasy kit, amplified with two sets of primers targeting GAPDH transcripts. One set of the primers was used to amplify a 400-bp fragment and the other 64-bp fragment within the 400-bp region. Each RNA sample was amplified simultaneously with both primer sets in separate tubes. Results indicate that this approach can distinguish among RNA samples at various levels of degradation.

Degradation of RNA in diagnostic specimens can cause false-negative or positive test results and potential misdiagnosis when lab tests that rely on the detection and quality of specific RNA sequences. RNA quality and degradation influence the analysis results of gene expression profiles in PCR, microarray, and other lab tests.1 Traditionally, agarose gel electrophoresis is used to test RNA degradation in the lab.5 But this method is time consuming and difficult to perform. Specific instruments such as Experion (BioRad)4 or Bioanalyzer (Agilent)6 have been developed to assess RNA degradation more precisely.5 However, these types of instruments requires more expenses.4 Some laboratories, especially small beginning laboratories, may not be equipped with this type of instrument. In addition, some of the quality issue may not be detected with Experion10 or Bioanalyzer,9 such as the existence of certain enzyme inhibitors.4,6
We are developing RT-qPCR based methods to assess RNA quality. Because RT-PCR based methods employ the same principles and supplies of regular RT-qPCR experiments,5 it has added advantage of close mimicking the reaction conditions, with a minimal additional cost. Here, we present a preliminary experiment to assess RNA degradation. This experiment serves as a proof of concept of a method that employs two sets of primers, respectively targeting a short and a long segment. Theoretically, longer RNA molecules are more susceptible to degradation than shorter ones.1,2,3 Thus, in a somewhat degraded RNA sample, there would be more available shorter templates than the longer ones. In RT-PCR results, this will be reflected by the number of threshold cycles.
In this report, we describe a preliminary study using GAPDH as a template. RNA samples that were degraded to various degrees were generated, assessed using Experion instrument, and then tested with RT-qPCR method. Results suggest that this method may be feasible. It could be a useful technique to labs that work with limited resources.

Materials & Method
To generate RNA, 30mg samples that were degraded at various extents, a frozen liver tissue sample was broken into five parts, left at room temperature for 0, 1, 2, 4, 24 h, and then used for RNA extraction. RNA was also extracted from other liver samples. All RNA extractions were done using Qiagen Mini Tissue kit.
Two pairs of GAPDH primers were designed on Primer Express 2.0 (Applied bBiosystems) to amplify a shorter fragment of 64-bp, and a longer fragment of 400-bp, respectively (Table 1). The 64-bp primer fragment was inside the larger 400-bp fragment. Primers were purchased through Invitrogen.
RNA concentrations were measured using a Nanodrop spectrophotometer, and tested with Experion, using Bio-Rad kit following the protocol recommended by the manufacturer. An intact RNA sample was diluted to 200, 100, 50, 25, 6.25, 1.56, 0.39, 0.97 ng/ul and used to generate standard curve. Each individual RNA sample was diluted to 25ng/ml.
Using a One-Step RT-qPCR Kit Qiagen kit, RT-qPCR tests were performed with iCycler (Bio-Rad). Standards and all samples using both GAPDH primers were PCR tested in a 96 well plates (4 wells per sample, 2 wells per standards).
Table 1: Primers used in RT-qPCR analysis
The primer sequences for 64-bp shorter fragment:
Name: cGAPDH-728F
Name: cGAPDH-792R
The primer sequences for 400-bp longer fragment:
Name: cGAPDH-646F
Name: cGAPDH-1046R

The Experion test 1 shows RNA liver samples that had very little degradation with high RQI numbers (Fig 1). The Experion test 2 results shows RNA that had high amounts of degradation with low RQI numbers. Test 2 demonstrated the longer a tissue sample was left thawed at room temperature before extraction, the more degraded the RNA was after extraction.

Figure 1. Experion test comparison of High RQI vs Low RQI numbers
Experion Test 1 Lanes 1-12 were taken from several different chickens at 0 hours thawed. Experion Test 2 Lanes was from a single chicken liver. 1-4 thawed for 0 hours, Lanes 5-6 thawed for 1 hour, Lanes 7-10 thawed for 4 hours, Lanes 11-12 thawed at 24 hours

Several diagnostic RTq-PCR tests were performed on the RNA samples. The samples that had RQI numbers below 6.3 on Experion did not show any product for the long 400 bp fragment because some samples were too degraded. Therefore, we could not compare some of the products of short fragments vs. the long fragments on all samples.
There were three samples in the RT-qPCR that did show product. These samples had RQI numbers of 9.9 (sample 1), 8.9 (sample 2) and 6.3 (sample 3). Mean Ct values were subtracted to get the differences (Fig. 2) in template availability levels from long to short fragments and compared to their RQI.

Figure 2. Difference in threshold cycles between long and short amplified Fragments

RQI numbers from Experion test shows the amount of degradation of the RNA (X axis) compared to the mean Ct difference (Y axis) between base pair products of long (400-bp) and short (64-bp) RTq-PCR.

Discussion and Conclusion:
When threshold cycle of the short fragment amplification is compared with that of the long fragment amplification from the same sample, the more degraded the RNA sample is, the greater the difference is between the Ct means. The longer and the shorter base pair amplification products works, but if there is too much degradation long bp fragments may not show product levels in the RTq-PCR results. This demonstrates the amplification method is a possible method for testing RNA degradation in laboratory, but it can be difficult to get readable results, and although this method is less costly, Experion (BioRad) or Bioanalyzer (Agilent) seem to assess RNA degradation more precisely. (Fig 3) This gives you RNA Area, Concentration, Ratio (28S/18S), and RQI.4

Figure 3 Reading Experion Results
Computer generated Experion results of RNA Area, concentration, Ratio (28S/18S), and RQI on samples: single chicken liver. 1-4 thawed for 0 hours, Lanes 5-6 thawed for 1 hour, Lanes 7-10 thawed for 4 hours, Lanes 11-12 thawed at 24 hours

Figure 4 Comparing RNA Ladders and StdSens Chip
Comparison of RNA separation on automated systems. Upper panel (A) Experion RNA ladder separated on a RNA StdSens Chip(C) Lower panel (B) is a competitors electrophoresis system RNA ladder separated.

These figures should give some information why the extra expense would be worth it, if the Experion method can be afforded for RNA testing. Experion RNA ladder (Fig. 4A) separated on graph is displayed when using Experion software compared to a Electrophoresis system (Fig. 4B) shows Experion RNA ladder provides more uniform peak heights (fluorescence intensities), resulting in improved quantitation, but as you can see the Experion StdSens chip (Fig. 4C) only lets you test 12 samples at a time. Still Experion is a easier and more accurate way of testing RNA for degradation.4,10
The testing of threshold cycle of short fragment amplification vs long fragment amplification could possibly be improved if the long bp fragment was shorter. Also improvements could be more feasible if the RNA degradation is not to the lower extreme of what was presented in this study. This study should be repeated with different lengths of bp primer amplification.
Additional note:
If you would like to see videos I have personally made of how to perform the Experion procedure, click on the links below and watch the videos I have made.
Experion Computerized Electrophorisis Gel
Experion example for Genomics Class
Dr. Washington….This can give more insight on how to read Experion results that you can give to your students to teach them how to do the test or I’ll be glad to show them the test too if any of your students want to learn it.


1.Simone Fleige and Michael W. Pfaffl (April-June 2006, Pages 126-139) Molecular Aspects of Medicine, Volume 27,RNA integrity and the effect on the real-time qRT-PCR performance Molecular Aspects of Medicine, Volume 27, Issues 2-3, April-June 2006, Pages 126-139

2.Zuker, M., and Stiegler, P. (1981). Optimal computer folding of large RNA sequences using thermodynamics and auxiliary Genetic and biochemical dissection of Res. 9, 133–148.

3. Wang, H.W. and Noland C, Structural basis of microRNA length variety Nucleic Acids Res January 2011: 257-268.Structural insights into RNA processing by the human RISC-loading complex. Nat. Struct. Mol. Biol. 2009;16:1148-1153.

4. Bio-Rad Experion Analysis Booklet and Software. Pg 19-28 Website:

5. Bruns, David E., Ashwood E. R., Burtis, Carl A. Fundamentals of molecular diagnostics 2007 Sections I, II & III, chapters 1-10

6. Diaz E, Barisone GA. DNA microarrays: sample quality control, array hybridization and scanning. J Vis Exp. 2011 Mar 15;(49). pii: 2546. doi: 10.3791/2546

7. Qiagen molecular diagnostics Sample and Assay Tech

8. Invitrogen

9. AB Applied Biosystems

10. GQ Experion HighSens and RNA StdSens Analysis Kits

Wednesday, January 05, 2011

Science Vs Christian Faith

by Ann LaJuan Stewart
(Biological Sciences PhD Student)
I once read an article that fascinated me. It stated that in the future there will be a universal religion that will be lead by science, & it will combine all of Earths largest religions, such as Hindu, Buddhist, Christian, Jew, & Muslim, and this religion will be Anti-Christ. How many of you believe that is possible?
My name is Ann LaJuan Bohannon-Stewart. I’m a PhD Biological Sciences student at Tennessee State University, but I grew up as the daughter of Evangelical Pentecostal Minster named Jethery E. Bohannon, who I love and adore very much. When I was child I was taught the Bible deeply. I learned every story in the Bible and I believed. Now as I’ve gotten older, I’ve been taught or shown other things that question my faith in the Bible and the way l learned it as a child. I am only writing this article to help others who are like me in the Evangelical world and are trying to understand the contradictory teachings as I am.
For the most part, I see science as a study that goes along with the Bible or supports Biblical teachings, but there are times when it conflicts in literal translations of the Bible, and I want to teach both of these areas when I graduate with my PhD in Biological Sciences. I also want it understood what I believe about the Bible. I believe the Bible is a book of faith and history, and it was written in a time when man didn’t have the technology that we have today. It wasn’t meant to be a book of science. I believe it is in often times symbolic and although it is highly supported by a lot of scientific findings we study today, it is a book that was meant to give people hope, inspiration, faith, and the belief of right and wrong or good and evil and every word of it is true. There is nothing in science that contradicts the real meaning and real purpose of the Bible, at least not in my opinion.
The parts of the Bible that are challenged the most by science, is usually in the literal interpretation of Genesis. The scientific theories of the Big Bang and Evolution are the two theories that challenge the book of Genesis the most, and understand that I believe that God is so great and so vast that we can’t really explain what God is in our human words, or what he has done in creation. We may not even be able to comprehend what God is in our simple human minds, but in order to get closer to God we have to try to understand him. Because most religions believe in a higher power, which is something that governs this world, we all should seek the truth.
I have been reading statistics on which religious faiths believe in evolution and which ones do not, and I have found that over 80% of Hindus, and Buddhists believe in evolution. This is the highest believing group of evolutionist on the planet, and many Jews are in this group of evolution believers as well. The lowest group is Christians. There are less Christians on the planet that believe in Evolution than any other religion on the planet, and let me tell you what I believe is the reason why. Christians see Evolution as a religion itself and believe that is being used by atheists to disprove the Bible. I’m not here to dispute that. I’m here to tell you the different theories of our creation. What you believe personally about creation is totally up to you, but I am going to give the evidence that supports evolution and the evidence that defies it as well as showing you the evidence of where the Bible is supported by science.
There are probably hundreds of different creation stories among the world's religions that account for the diversity of life forms on Earth. All of the stories are different. A literal reading of the book of Genesis illustrates creation as being done in 7 days and 7 nights by God, when it is interpreted in English. Many people believe this literal interpretation and most believe that this happened less than 10,000 years ago, and that God created all of the species of life along with the world itself, and that the planet earth and our universe is very young. Naturalistic evolution believers have a belief that the universe originated about 14 billion years ago; the earth coalesced about 4.5 billion years ago; life subsequently began, probably as bacteria deep in rocks, and has been evolving ever since. The process of evolution has been driven by purely natural forces, without input from a God or multiple deities. Than there is the belief of Theistic evolution which believe in naturalistic evolution, except that God used evolution as a tool to guide the development of the different species from the simplest to most complex forms that man is today.
Also a debate exists within conservative Christianity over the length of each "day" in Genesis. This debate is whether the word "day" means an interval of 24 hours, or an era of indeterminate duration. Some theologians believe that the Hebrew word "yom" has multiple meanings in various locations in Genesis. A number of other theories of time intervals have been formulated by Christian creation scientists in an attempt to harmonize Genesis and the fossil record, and the carbon dating of earth’s rocks, which is part of scientific evidence of the earth being over 4 billion years old.
For the last few decades Christian Creationist have been using the words microevolution and macroevolution and claiming that microevolution is proven and macroevolution is still an unproven theory. Biologists or scientists usually don’t see a difference in the words microevolution and macroevolution. Most scientists believe that the same results of diversity in species happen when the gene pool of a species is changed.
Now let me define these terms microevolution and macroevolution. These terms “microevolution” and “macroevolution” are often used by creationists in their attempts to explain their belief on the evolutionary theory. Microevolution is used to refer to changes in the gene pool of a population over time which result in relatively small changes to the organisms in the population. Macroevolution, in contrast, is used to refer to changes in organisms which are significant enough that, over a period of time, the newer organisms would be considered an entirely new species. In other words, the new organisms would be unable to mate with their original ancestor species, assuming we were able to bring them together. Biologists also call this process speciation.
You can frequently hear creationists argue that they accept microevolution but not macroevolution; an easier way to understand this is to say that dogs may change to become bigger or smaller, but they never become cats. Therefore, microevolution may occur within the dog species, but macroevolution never will, but if a dog becomes so small or large that it can’t interbreed with other kinds of dogs a scientist may look at this as a form of speciation or a part of becoming another species. Understand also that wolves, dogs and coyotes all were from a common ancestor, but they are so different now in size, shapes, and chromosome numbers that some of them can’t be called the same species anymore.
For biologists, there is no relevant difference between microevolution and macroevolution. Both happen in the same way and for the same reasons, so there is no real reason to differentiate them, but many creationists seem to have the need to believe in microevolution and not macroevolution.
I personally believe that macroevolution is real, and it’s undeniably happening all over our great planet earth. Let me give some examples of macroevolution that have occurred over the last few 1000 years in our earthly planets and animals. The different dog breeds gives strong evidence of macroevolution. Man is partly responsible for the big evolutionary changes in dogs, by taking in wild wolves and domesticating them into our best friends that we call them today. Dogs haven’t just changed in physical form, their number of chromosomes have changed as well. The domestic dog (Canis lupus familiaris) is generally considered as a sub-species of the wolf (Canis lupus). Members of the dog genus (Canis) wolves, dogs both common dogs or also called dingoes, coyotes, and golden jackals cannot interbreed with members of the wider dog family or Canidae. Canidae is the biological family of carnivorous and omnivorous mammals that includes the wolves, foxes, jackals, coyotes. The Canidae, such as South American canids, foxes, African Wild dogs, bat-eared foxes, or raccoon dogs don’t interbreed with Canis or if they could, their offspring would be infertile, but members of the genus Canis species can interbreed to produce fertile offspring, with two exceptions: the side-striped jackal and black-backed jackal. Although these two could theoretically interbreed with each other to produce fertile offspring, they cannot hybridize successfully with the rest of the genus Canis.
The reason for this lies in their genetic codes. The wolf, dingo, dog, coyote, and golden jackal have been considered to be diverged fairly recently, around 3 to 4 million years ago, and all have 78 chromosomes arranged in 39 pairs. This allows them to hybridize freely, but barring size as an issue in breeding, they should be able to produce fertile offspring. The side-striped jackal and black-backed jackal both have 74 chromosomes. Other members of the Canidae are considered to have diverged 7 to 10 million years ago and are less closely related and cannot hybridize with the wolf-like canids: , the red fox has 38 chromosomes, the raccoon dog has 42 chromosomes, and the fennec fox has 64 chromosomes, and the African Wild Dog has 78 chromosomes. To me it’s easy to see that wolves, jackals, dogs, coyotes, foxes all have a past common ancestor and now they are changing into different species and man has watched them make this change. That seriously supports the theory of evolution, but there are also many animals out there that evolution can’t explain and biologist or scientists have still not been able to classify or find their past ancestor or ancestors. I believe ones of those species that scientists are still searching for a past ancestor on is us humans (Homo sapiens)
There are also other scientists who claim that their view of beginnings or evolution can be tested in a laboratory, so evolution is testable. There is a process known as Hybrid Speciation that has been documented a number of times in different plants and animals. For instance, scientists have mutated fruit flies and show speciation observed in today’s scientific labs. Other examples are new species of mosquitoes, seedless watermelons, different kinds of flowering plants and certain kinds of fish that have gone though the hybridization or species change (speciation). These things are proven to be true to many scientists because they see it happening and understand it is cause by chromosome changes. There is nothing in the Bible that states that speciation is not real, but because the Bible states the words “yields according to its own kind.” creationists often refutes macroevolution.

I want to give examples of this phase “yields according to its own kind” in the Bible
“And God created great whales, and every living creature that moveth, which the waters brought forth abundantly, after their kind, and every winged fowl after his kind: and God saw that it was good.” King James Version Genesis 1
Then God said, “Let the earth bring forth grass, the herb that yields seed, and the fruit tree that yields fruit according to its kind, whose seed is in itself, on the earth”; and it was so. And the earth brought forth grass, the herb that yields seed according to its kind, and the tree that yields fruit, whose seed is in itself according to its kind. And God saw that it was good. Genesis 1:11,12
So God created great sea creatures and every living thing that moves, with which the waters abounded, according to their kind, and every winged bird according to its kind. And God saw that it was good. Genesis 1:21
And God made the beast of the earth according to its kind, cattle according to its kind, and everything that creeps on the earth according to its kind. And God saw that it was good. Genesis 1:25

Biological science supports these scriptures by teaching, biogenesis. The phrase “according to its own kind” occurs repeatedly, stressing the reproductive integrity of each kind of animal and plant. Today we know this occurs because all of these reproductive systems are programmed by their genetic codes. Now these genetic codes may get altered, but nowhere in the Bible does it state that speciation can’t occur and make other similar forms of life. If anything, this phrase in the Bible “according to their own kind” supports the biology theory of biogenesis. Biogenesis is the theory that living things come only from other living things. In the field of biology, one of the most commonly accepted and widely used laws of science is the law of biogenesis. This law was set forth many years ago to dictate what both theory and experimental evidence showed to be true among living organisms that life comes only from preceding life, and perpetuates itself by reproducing only its own kind or type. The bottom line of this is. It doesn’t matter how hard biologists or scientists have tried to create life from non-living matter, they have never been able to do it. Honestly that is proof to me there is a God. A higher power that is able to make life from the non-living matter and we do reproduce our own kind as the Bible says we do, but nowhere does it state in the Bible that speciation or the changing of life forms can’t occur. In my opinion God made life to evolve, because life is evolving now. We humans cannot create life from non-living matter. We just don’t know how and no matter how far we humans have come in science, we are not as smart as God is or we would be able to create life from non-living matter or dirt, and we just haven’t been able to do that.
So where does life come from? Scientists have tried to recreate old Earth’s conditions when they believe life began and they did this inside of containment units full of non-living matter and they have never been able to create life from non-living matter. There had to be something that started life on this planet. I find it very hard to look at the life on this planet and not see the undeniable signs of God everywhere. Believing in God is a personal journey that all people have in their life. God will put signs in your life that show you he is real. You either chose to overlook the signs of God or you look around you and see this earth truly has to be the work of a higher power.
There are many Christians who except the belief of evolution and still believe in the Bible fully, and there are other Christians who believe if you believe in evolution you can’t possibly be a real Christian, but I believe you can be. Here are some popular theories that are usually understood by people who believe in the teachings of the Bible and science as well. The principles of theistic science usually teach how God creates the world by slow successive degrees, and by successive means, not by instantaneous creation of organisms and persons in all their detail.
Then there is something called Intelligent Design that is a fairly recently religious beginning. Intelligent design is the proposition that "certain features of the universe and of living things are best explained by an intelligent cause or creator, not an undirected process such as natural selection teaches. It is a form of creationism and a contemporary adaptation of the traditional teleological argument for the existence of God, but one which deliberately avoids specifying the nature or identity of the designer (God). That means many people who study ID believe there is a God, but just may not understand what God is. Intelligent design was started by a group of American creationists who revised their argument in the controversy of creation and evolution.
Intelligent design appears to teach that God has arbitrary power, and can create any design that he likes, and populate the world according to any chosen plan and the Lord does not create immediately, but progressively. He does not create angels immediately, but 'grows' them gradually, beginning with humans on earth. They seem to teach God is involved on earth in all things such as 'growing' processes, but always allows some crucial decisions to be made by man on their own, and sometimes it seems to teach that God does not even create humans immediately, but grows them 'out of the earth', in a way such as the evolution theory describes.

I grew up a Evangelical Christian and still believe in many of teachings but most evangelicals don’t believe in evolution. Here are some comments from some evangelicals on the different theories of creation:
Rev. Jim Harding of the Utah/Idaho Southern Baptist Convention commented: "We were created by God, we didn't just evolve by accident. It was not a process of moving from one animal form to another, but rather, as Genesis teaches, that each was made in its own order. In fact, [humans and animals] were made on different days." (source from Christianity and Modern Science)
Quote of Famous Evangelical Billy Graham-"I don't think that there's any conflict at all between science today and the Scriptures. I think that we have misinterpreted the Scriptures many times and we've tried to make the Scriptures say things they weren't meant to say, I think that we have made a mistake by thinking the Bible is a scientific book. The Bible is not a book of science. The Bible is a book of Redemption, and of course I accept the Creation story. I believe that God did create the universe. I believe that God created man, and whether it came by an evolutionary process and at a certain point He took this person or being and made him a living soul or not, does not change the fact that God did create man. ... whichever way God did it makes no difference as to what man is and man's relationship to God.”
I tend to see things closer to what Billy Graham states. I don’t know if speciation has happened in man or not. Science hasn’t shown me enough evidence to make me believe in it or not, and I haven’t seen it with my own eyes, but maybe that is the way God the creator chose to do it. I honestly can’t tell you, and I’m soon to be a biologist, who’s main study is in the current genetic field, but I honestly have a hard time believing all life on earth is from one single kind of cell, and it doesn’t matter to me what Evolution tells me, I still can’t look at creation and not believe in a God. I see signs of God everywhere and in everything, and for those who don’t believe in God or don’t see his signs in creation. I see them as blind.

Now understand that most conservative Christians especially evangelicals are particularly insistent on the literal truth or words of the creation stories in Genesis. Many of them believe if those passages were shown to be false, then the Garden of Eden story, the fall the Bible teachings and the alienation between God and man would all be in doubt. Some feel that this could negate the need for Jesus' execution and resurrection, and place to many questions in people’s minds about the truth of the Bible. Some believe that the entire conservative Christian message would collapse if evolution is accepted and if Genesis is shown to be a fable.
But on the other hand, Liberal Protestant Churches sometimes go the other way. Many followers in these churches have accepted and sometimes even promoted the theory of evolution for the last few decades. Understanding there is unresolved details about the evolution of species on earth and of the matter and energy in the rest of the universe, scientists have reached a consensus on the broad mechanisms of evolution and they accept it as possible truth and teach it in some churches as well. Many protestant scientists believe that the universe originated at a "Big Bang" some 20 billion years ago, and evolution and all life on earth is still under God the creator.
The Roman Catholics are even farther ahead of the liberal protestants. Statistics show that over 50% of Catholics are reported to be believers in evolution. Even Pope John Paul II has been said to publically embrace the theory of evolution. Also this statement posted below was taken from New Advent. “Taking into account the state of scientific research at the time as well as of the requirements of theology, the encyclical Humani Generis considered the doctrine of "evolutionism" a serious hypothesis, worthy of investigation and in-depth study equal to that of the opposing hypothesis. Pope Pius XII (The Vatican’s View)

You see I am a Biological Sciences PhD major at Tennessee State University…and I should be a scientist in a few years. I specialize in genetics and I have a need to teach Christian people what I have learned in college. You see I believe there is a God, who is the creator, and I believe in Jesus Christ, and living by the teachings of Jesus Christ, but there are things that are being taught in our schools that make people question our Christian faith because of its comparison to the Bible. Theories such as Evolution, The Big Bang Theory, and many other theories in science put questions in our children’s minds about Christianity. This is the part I want to teach. I want to teach people where science supports Bible teachings, why it sometimes seems to conflict and what they will have to face and learn when they are in school, and let them understand that science isn’t always correct. We are teaching theories in schools and colleges, but we have been teaching these theories in schools since the 1970’s so students are accepting them as facts. There are flaws in the theory of Evolution, and I can teach those flaws in way people can understand them, and I can also teach where the theory of Evolution is supported by evidence. This is one issue that will either hurt Christian faith or help it in the future of our children.
Why I think it is so important for people to read the Bible. Even though it can be difficult to understand, it is the one book of faith that teaches morals that should be lived by, and this is a book that can be trusted and not disproved by science, because many atheists and people of other religious faiths would like the public to believe that science disproves the Bible. I would like to say IT DOES NOT!!!
Even if either the theory of evolution is true or not, it doesn’t disprove the Bible, but I want you to understand that everything in science is just a theory and speciation has not been observed in man. We haven’t seen it happen in front of our eyes like we have with the case of dogs and wolves. Science is always going to find more evidence to provide a clearer understanding of what it made theories on in the past, and science has often disproved itself on many things in the past. Just a wait a few minutes and a new scientific theory may pop up to replace the old one as I promise you it is not an exact science. In other words science is constantly contradicting itself.
Now the main Bible verse that makes evolution look to be unacceptable by Christians “And the LORD God formed man [of] the dust of the ground, and breathed into his nostrils the breath of life; and man became a living soul.” King James Bible Genesis 2.7
This verse is why many Christians don’t except the theory of evolution, but understand that in the study of biology all life is believed to have come from bacteria in rocks or dust, and a person who believes in evolution and the Bible could also interpret this verse as saying “ok all life is from dust, and who knows when God decided to give man a soul? It was when Adam and Eve was in the Garden of Eden, but maybe a Christian Evolutionist could believe at one point and time God took an animal that was very ignorant and intervened in this animal that God made out of dust and gave him a soul and made the animal a human with the ability to learn more and understand more than other animals on the planet. A Christian Evolutionist could also believe that Adam and Eve became a more intelligent animal when they ate from the Tree of Knowledge of good and evil. I have my own personal theory on this one. I study genetics and I know that what we eat changes our gene expression. I believe when Eve and Adam took of the fruit that made humans smarter and much more intelligent than other animals on the planet, and with wisdom comes more responsibility. In my opinion man is the only species on the planet that is responsible to know right from wrong. Other animals are not held responsible as we are. We can lose our soul by doing acts against God’s teachings or wishes. That is why the Bible is so important. It’s the one book that tries to teach us how to live right. Whether we seek to do the right thing or not is up to us, and where our soul ends up, will be up to our actions, and this will also be completely up to God’s judgment based on our life we lived on this earth. It’s my belief that the Bible is the best guide we have for our souls to reach a better place.

About a decade ago scientists mapped the human genome. The Human Genome Project was a big accomplishment in the world of science. Now human DNA is being compared to animal DNA more than ever before. Now biologists, geneticists and other scientist are in a race to see how other mapped genomes of animals compare to humans. Colleges and schools are now changing the way they teach creation theories now in schools. We have gone from the old taxonomy system to a new system. Something scientists call the Tree of Life. This Tree of Life joins species together by a system of branching called cladogram. A cladogram is considered like branches of a tree which shows ancestral relations between organisms, to represent the evolutionary tree of life by joining closely related species. Although traditionally such cladograms were generated largely on the basis of morphological characters in the past, now DNA and RNA sequencing data and computational phylogenetics are very commonly used in the generation of cladograms.This is a new way of teaching in school biology classes and programs, that church teachers and Christian religious leaders or pastors need to know about and teach people, especially children in church. Because people need to understand that the literal words in Genesis state creation was done in 7 days and 7 nights and in schools they are going to be taught that the earth is theoretically over 4 billion years old, and human evolved from a more animal like primate. Children especially need to hear what’s going to be taught to them in school compared to what they are going to read in the book of Genesis. It can cause confusion in their minds, to where they don’t see truth in the Bible at all, and may even question if there is a God. So tell them the different theories of creation and let them know what they will learn in school about creation is a theory.
So what you believe about creation, evolution, and the interpretation of the book of Genesis is totally up to you, but don’t let anyone ever tell you science disproves the Bible, cause that is just not happening. If anything science gives more support to Bible teachings than ever before. It’s just people who don’t believe in the Christian faith don’t want you to believe either.
I want to be able to visit radio stations, churches, and colleges after I graduate and teach them what I have learned…and why evolution made me question Christian faith at one time…and what gave me my personal faith back in God our creator. There are many people who are using the theories of science against our Christian faith…I sincerely want to try to stop that.
Ann LaJuan Bohannon-Stewart PhD Student at Tennessee State University

Sunday, May 16, 2010

Graphs for: An initial map of chromosomal segmental copy number variations in the chicken

An initial map of chromosomal segmental copy number variations in the chicken

Xiaofei Wang1,*, Samuel Nahashon2, Tromondae K. Feaster1, Ann Bohannon-Stewart1 and Nathaniel Adefope2†

Address: 1 Department of Biological Sciences and 2 Department of Agricultural Sciences, Tennessee State University, 3500 John A Merritt Blvd, Nashville, TN 37221, USA

*Author of correspondence: Xiaofei Wang, Department of Biological Science, Tennessee State University, Nashville, TN 37209, Tel: (615)963-2541, Email:

†In memory of our beloved colleague Nathanial Adefope.


BACKGROUND: Chromosomal segmental copy number variation (CNV) has been recently recognized as a very important source of genetic variability. Some CNV loci involve genes or conserved regulatory elements. Compelling evidence indicates that CNVs impact genome functions. The chicken is a very important farm animal species which has also served as a model for biological and biomedical research for hundreds of years. A map of CNVs in chickens could facilitate the identification of chromosomal regions that segregate for important agricultural and disease phenotypes.

RESULTS: Ninety six CNVs were identified in three lines of chickens (Cornish Rock broiler, Leghorn and Rhode Island Red) using whole genome tiling array. These CNVs encompass 16 Mb (1.3%) of the chicken genome. Twenty six CNVs were found in two or more animals. Whereas most small sized CNVs reside in none coding sequences, larger CNV regions involve genes (for example prolactin receptor, aldose reductase and zinc finger proteins). These results suggest that chicken CNVs potentially affect agricultural or disease related traits.

CONCLUSION: An initial map of CNVs for the chicken has been described. Although chicken genome is approximately one third the size of a typical mammalian genome, the pattern of chicken CNVs is similar to that of mammals. The number of CNVs detected per individual was also similar to that found in dogs, mice, rats and macaques. A map of chicken CNVs provides new information on genetic variations for the understanding of important agricultural traits and disease.


Genomic variations within a species may involve changes as small as a single nucleotide to as large as microscopically visible chromosome segments, even whole sets of chromosomes. While microscopic genome variations were studied in cytogenetic laboratories for a long time, the readily availability of DNA sequencing technology and high throughput approach has popularized analysis on single nucleotide polymorphism (SNP) and microsatellites. It was not until recently that genome variation involving intermediate DNA segments, called segmental copy number variation (CNV), was recognized. This type of genome variation involves submicroscopic insertion, deletion, segmental duplication and complex changes of greater than 1 kb to several Mb in size [1-3]. Whole genome scanning studies for CNV have been conducted extensively in humans [4-10], chimpanzees [11, 12], dogs [13, 14], mice [15-18], rats [19], swine [20]. Although several chicken CNV loci have been studied in a case-by-case manner [21, 22], CNV in birds received little attention. To our understanding, few publications are available describing whole genome CNV studies in birds [23].

Since its domestication 8,000 years ago, the chicken has provided table eggs, meat and ritual values to human society. Over the last 100 years, the chicken has also served as a model organism for fundamental biological and biomedical studies [24]. The first examples of oncogene and viral induced tumor were demonstrated in the chicken [25]. The B-lymphocytes were first identified in chickens. Spontaneous chicken mutants, such as the dwarf [26, 27] and the retina degeneration [28], have provided rich information regarding particular gene functions. The regulation of chicken ovalbumin expression was studied extensively to elucidate the mechanism of eukaryotic transcription control [29] and steroid hormone actions [30, 31]. Because of the historical, biomedical and agricultural importance, evolutionary distance and readily availability, the chicken is the leading species among farm animals in the development of genomics tools and resources, including the chicken genome assembly [32], genetic variation map of single nucleotide polymorphism [24, 33], collections of comprehensive expressed sequence tags (EST) [34-37] and DNA microarray [38-40].

Distinct from mammalian genomes, typical avian genomes are composed of several large chromosomes and a group of microchromosomes that are indistinguishable microscopically with conventional karyotyping techniques [41, 42]. The chicken genome has 1.2 billion base pairs on 39 pairs of chromosomes, including a pair of sex chromosomes ZZ for males and ZW for females [43, 44]. Despite that the chicken genome is one third of a typical mammalian genome in DNA content, it was predicted to have a similar number of genes [37]. Thus, it would be conceivable that the chicken has reduced intergenic spaces and reduced repetitive sequence content. How the compacted genomes vary in chromosomal segmental copy number and how these variations affect important agricultural and biomedical traits are of great interest. Here we provide a snapshot of CVNs in the chicken genome.

Results and Discussion

Mapping of CNVs in chickens

NimbleGen whole genome tiling arrays containing 385,000 probes were used to analyze chicken CNVs. Four broilers (Cornish Rock, 2 males and 2 females), four Leghorns (2 males and 2 females) and two Rhode Island Reds (males) were analyzed with array comparative genome hybridization (aCGH). One additional male broiler DNA was used as a reference for all hybridizations. CNVs were identified by comparing ratio between the test and the reference and all CNV loci were visually inspected on aCGH data plots (Additional file 1, Figure. S1). We identified 96 high confidence or suggestive CNVs. When CNV signals in two or more animals overlapped on a chromosome, they were considered to be high confidence CNV. On average, seventeen CNVs were called in each bird.

These CNVs were found on chicken chromosomes (GGA) 1-8, 10-18, 20, 22-27, and Z (Table 1 and Additional file 2, Table S1). Due to poor probe coverage, data on W chromosome were removed from analysis. The 96 CNVs encompassed 16 Mb, which is about 1.34% of the entire chicken genome. Among the 96 CNV loci, forty six loci were non-coding sequences (5.1 Mb).

There were 26 high confidence CNVs that were observed at least in two birds (Table 1). Among these high confidence loci, eleven loci involve non-coding sequence only, which sum up to 525 kb. The remaining 15 loci occupied about 1.5 Mb, involving one or more coding sequences.

Variations at locus 13 (chr4:88,897,639-89,072,982) on GGA4 (Table 1) appear to be complex. When compared with the reference, three birds showed loss of 150-170 kb, while two other birds showed a gain of 112 kb and 172 kb. Locus 10 on GGA2 has three clearly different alleles, one of which was a gain of 300 kb and the other was a loss of 20 kb region and the third allele was without gain or lost. However, visual inspection of corresponding aCGH plots revealed that several other samples may also have copy gains (Additional file 3, Fig. S2 A).

In our dataset, two CNV calls were made for chromosome 25. One of the calls appears to be a gain of an entire chromosome 25 in bird #5849 (Additional file 2, Table S1), but no obvious visual abnormality of the bird was observed. The other CNV involves the first 10-kb region of the chromosome assembly.

The majority of the high confidence CNVs was shared across breeds, suggesting their relative “ancient” origin. Some high confidence CNVs were specific to individual breeds. Whether they are breed-specific requires further evaluation of a much larger sample size. It is not clear whether these putative private CNVs contribute to breed specific biology. Fewer high confidence CNVs were found in Rhode Island Reds when compared with other strains evaluated. This may be attributable to the small number of animals analyzed, small founder population and/or population diversity.

Seventy CNVs, which encompassed 14 Mb, were observed only once in our data set (Additional file 2, Table S1). Of the seventy CNVs, twenty seven CNVs involved only non-coding sequences (1.8 Mb). Twenty nine CNVs showed loss of DNA while the rest showed gains of DNA. The majority (62%) of CNVs with DNA loss were non-coding sequences. In contrast, the majority (80.5%) of CNVs with gain of DNA involve coding sequences. In addition, sizes in CNVs of DNA loss tend to be smaller (mean 43.2 kb and median 30 kb) compared with that of DNA gain (mean 313 kb and median 67 kb).

Although probes assigned to unknown chromosome locations were excluded from CNV calls, an ambiguous segment on GGA 20 (chr20_random in chicken genome draft 2.1 at UCSC genome database) is noteworthy. The entire segment was 72 kb according to the genome assembly. Several ESTs and mRNAs were mapped to this segment. Data from aCGH strongly support that this region be assigned to W chromosome [45]: All female birds showed gain of copies with high scores, when compared with the reference.

Quantitative PCR analysis and CNV validation

Real time quantitative PCR (qPCR) was performed to validate aCGH data at five loci. Two of the five loci (e. g. PCCA, THRSP) served as references of no variation in copy number, while three loci were CNV detected with aCGH. The PCCA locus encodes propionyl coenzyme A carboxylase. Analysis of chicken genome assembly indicated that a single copy of this gene exists in a haploid genome. Two copies of THRSP genes exist in chickens [46]. The qPCR results for PCCA locus showed minimal variations among 20 birds (including 11 birds examined with aCGH). We attribute these variations to random errors, including DNA dilution error. Similar qPCR results were obtained for THRSP locus in 23 birds (including 20 birds examined for PCCA), which also showed minimal variations among birds except that two birds appeared to have lost copies (data not shown). Three CNV loci (e. g. locus 13: CD8α-RHACD, locus 24: PRLR, and a suggestive locus AKR1B) were examined twice: once estimated with standard curve method (Fig 1B, C, D) in 20 birds and once with 2-ΔCt method in 23 birds (not shown). Results of the two separate qPCR assays were concordant. F-tests were performed to determine whether copy numbers detected with qPCR have the same variance between the reference locus and CNV loci. Results indicate that all three CNV loci had greater variance than the references (P > 0.05 for PRLR locus and P < 0.01 for AKR1B and RHACD8), suggesting the three loci were truly CNV.

Locus 13 involves RHACD8, which was reported to have variable copy numbers among different breeds of chickens. Our qPCR data indicated relative copy number of RHACD8 locus was highly variable among chickens. Birds with the greatest copy number had seven times as much as those with the smallest copy number.

Locus 24 involves prolactin receptor gene (PRLR). In our aCGH assay, the CNV of locus 24 was identified in 2 females. Another bird appears to be false negative, because visual inspection of aCGH plots revealed likely shifting of the log2 ratio (Additional file 3, Fig. S2 B). Subsequent qPCR analysis showed that all female birds have a single copy, and males showed 3 or 4 copies. Since PRLR is located on GGA Z, males are expected to have two copies per cell, whereas females have one copy should there be no CNV. However, χ2 tests indicated that the relative copy number ratio were 3:1 between males and females (Fig. 1C). It appeared that the male chicken # 6953 did not have three folds the copy of females. However, after DNA concentration correction, its relative copy number was the same as other males. Results of the separate qPCR assay with 2-ΔCt method agreed with this notion. The sex specific CNV at this locus can be explained by the industrial practice, as recently reported by Elferink [47]. A sex-linked late feathering allele K containing 2 copies of PRLR has been introduced to commercial flocks and used widely for sexing hatchlings. This K allele is incomplete dominant to the early feathering k+ allele containing one copy of PRLR. One of our bird suppliers crosses k+k+ males with KW females, such that progeny females are early feathering k+W containing one copy of PRLR, while the male progeny are late feathering Kk+ containing three copies of PRLR.

The CNV locus on GGA 1, involving aldo-keto reductase 1B (Additional file 1, Table S1, AKR1B1 locus, chr1:64280187-64310165), was first identified as a suggestive CNV found in only one bird with a gain of copy. A qPCR assay showed that the variation in copy number was far more frequent (Fig. 1D) than it appeared in the aCGH: All Leghorn chickens had the least copies (presumably two copies) and all Rhode Island Reds doubled that figure, while Cornish Rock broilers have variable numbers from 2 to 7 copies. Visual inspection of locus in aCGH plots did not reveal convincingly significant variations.

Complexity of chicken CNVs

In order to understand genomic organizations of DNA sequences involved in CNV, we mapped DNA sequences that are similar to the ones in CNV regions by BLAT search. This mapping revealed the organization complexity of some CNVs. For example, according to chicken genome build 2.1, locus 17 on GGA 10 showed duplication of various blocks in a 90-kb region (Fig. 2). This locus contains at least 6 copies olfactory receptor-like sequences, organized in grossly three larger repeating units in the same orientation. Blocks of several hundred to several thousand base pairs are highly conserved (>95% identity in nucleotide sequences) among these repeating units. However, the relative positions of these blocks were shuffled to different places.

The CD8α locus on GGA 4 is also organized in a complex way. Although it is known to be polymorphic among breeds [48], we did not anticipate such complex and extensive variations as revealed by qPCR. Other loci examined also show more or less complexity.

Functional implications of chicken CNVs

Among the high confidence CNV loci, at least 15 loci involve partial or entire functional genes. Many of these functional genes have paralogs in the chicken genome. For example, CNV locus 5 on GGA 1 (Chr1: 165880299-166002720) encodes a zinc finger RNA binding protein (ZFR). In the chicken, a second copy of ZFR is located on Z-chromosome (chrZ: 9,104,112-9,144,261). Locus 17 (chr10: 125113-157964) involves an olfactory receptor 6K1 (OR6K1)–like sequence.

The CNV at CHRM2 locus (locus 4) was observed in two of the Leghorn birds. CHRM2 is one of the five muscarinic acetylcholine receptor genes that play important roles in numerous physiological functions including higher cognitive processes such as memory and learning [49]. EST and other sequence data indicate that the chicken CHRM1, 4 and 5 are located on chromosome 5, while CHRM2 and 3 are mapped to chromosomes 1 and 3 respectively. Our aCGH data suggest the loss of CHRM2 copies in Leghorn chickens. Analysis of chicken whole genome assembly has not found any evidence that CHRM2 locus involves a recent duplication. Thus, deletion of CHRM2 is more likely the scenario in the Leghorn birds.

According to the May 2006 chicken (Gallus gallus) v2.1 draft assembly, the chicken AKR1B locus contains 4 consecutive copies of AKR1B organized head-to-tail. Although all copies appear to be transcribed since ESTs were found for all of them, the telomere-proximal copy appears to be more actively transcribed, as evidenced by the greater number of ESTs found for this copy. The four AKR1B copies share 80-92% amino acid residues. However, the telomere-proximal two copies are less similar from each other as well as from the two centromere-proximal copies in intron sequences. The two centromere-proximal genes contain large blocks of sequences similar to each other, including introns. In vertebrates, each species has several AKR1Bs that are expressed in most tissues [50]. The AKR 1B subfamily catalyzes the reduction of aldehydes [51]. Members of aldose reductase (AKR1B7, AKR1B10) may regulate fatty acid synthesis [52, 53].

An EST was found to be derived from CNV locus 13. The sequence have been predicted to be a CD8α- like messenger RNA--RHACD8, which was shown to be expressed in spleen [48]. We seek to determine whether the copy number variation could affect the level of the RHACD8 transcript. Spleen RNA levels of RHACD8 and CD8α were examined in five broiler and five leghorn females with RT-qPCR in two separate experiments (one with standard curve method and one with 2-ΔCt method, Fig 3). RNA levels determined with 2-ΔCt method were highly correlated with those determined with standard curve method (r = 0.88 for RHACD8 and r = 0.96 for CD8α). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and β-actin mRNAs were examined as a proof of no systematic bias. Levels of β-actin mRNA varied remarkably among these chickens (data not shown). GAPDH mRNA levels were less variable (Fig. 3B), though bird # 5994 showed much higher level of GAPDH transcript. CD8α mRNA level appeared to be correlated with the level of RHACD8 transcripts (r = 0.64, or 0.56), at a marginal statistical significance (P = 0.045 or 0.08). But there was no evidence of correlation between levels of RHACD8 transcript and DNA copy number (P > 0.05, Fig 3).

Comparative genomics of chicken CNVs

Since the platforms for CNV detection vary from species to species, much of the information cannot be compared directly across species. However, similarities can be found among studies using species-specific NimbleGen tiling arrays [13, 16, 19, 54]. The NimbleGen mammalian arrays usually have a greater median probe space (5 kb) than the chicken array (2.6 kb). Accordingly, the average CNV sizes in mammals are larger than in chickens. However, the CNV calls in each individual are comparable among mouse, dog, and rhesus macaques [54]. Because the human genome was studied more extensively, up to 18.8% of human genome have been found in CNV regions [55]. Lower CNV coverage was found in other species. Our current data showed that chicken DNA sequences residing in CNV regions account for 1.34% of the genome, similar to that found in rat [19]. However, it is likely a significant underestimation of real CNVs in chickens, since a limited number of individuals have been surveyed. Furthermore, due to the incompleteness of the chicken genome assembly, a significant portion of the genome was not surveyed. The entire W chromosome was excluded from the analysis, and all probes that were assigned to ChrUn and chromosome-random were also excluded.

Several CNV loci identified by this study overlap with some loci reported by Griffin and colleagues [23]. Out of thirteen chicken-turkey CNVs reported by Griffin et al, three loci overlapped with our high confidence CNVs and four loci overlapped with our CNVs of single observation. These data suggest that some of our single observations are true CNVs. The overlapping loci have size disagreement between Griffin’s and our study, possibly due to the use of different references. Red jungle fowl was used as the reference by Griffins et al , while a Cornish Rock broiler bird was used as the reference in our study. The discrepancy may be attributable to non-recurrent rearrangement. Apparently, larger populations need be examined to obtain a comprehensive picture of chicken CNV.

It is conceivable that lost DNA segments tend to be small in size and non-coding, while large CNV regions tend to emerge from gain of DNA and involve more functional genes, because large segment loss may be detrimental when these alleles are homozygous. Similar observations were also found in other species [2, 56]. A significant number of CNVs involves members of paralogs. This can be explained by the fact that paralogous genes may compensate for lost copies. Similarly, a significant enrichment of CNVs in segmental duplications was found in the mouse [18].

It appeared that the aCGH method tends to report larger segments being involved in the duplication/deletion than they really are. For example, the array CGH reported the involvement of 190 kb in the duplication of the K locus on Z chromosome. Detailed studies by Elferink et al [47] showed this duplication involves only 176 kb. This discrepancy is consistent with recent report that most CNVs are smaller in size than revealed by larger probe spacing [57].

It is of major interest to map the impact of CNVs in relation to disease, immunity, and agricultural traits. It has been shown that some CNVs contribute to phenotypic variations while others are amenable for genome-wide association study for their influence on genetic disease or disease susceptibility. Nevertheless, large amount of putatively functional sequences, including protein coding sequences and conserved non-coding sequences, fall within or flank CNVs. In humans, although most CNVs were detected in apparently “healthy” individuals, many CNVs may have subtle, quantitative or late-onset phenotypic implications [58]. Functional attributes of the currently known CNVs are remarkably enriched in genes involved in environmental molecular interactions, including cytochrome p450 genes, immunoglobin-like receptors, defensins [59]. CNVs may affect phenotype by altering transcriptional level of genes within or adjacent to CNVR and subsequently alters translation levels. Such transcriptional and translational changes have already been demonstrated [60, 61].


The chicken genome was examined for chromosomal segmental copy number variations with whole genome tiling arrays. Twenty six high confidence CNVs that were observed in two or more birds and seventy CNVs that were observed once were identified. The majority of the high confidence CNVs was shared across breeds (broiler, Leghorn, and Rhode Island reds). Fifteen CNV loci involve functional genes, or spliced EST coding sequences. Although CNVs that were observed once require further confirmation, some of them represent true CNVs.

The mapping of CNVs in chicken could provide new opportunity for understanding genomic variation and related phenotypic characteristics. This mapping will also contribute to association studies in the effort to map traits of economic importance.


DNA samples: Blood samples were collected from 3 strains of chickens (Cornish Rock broiler, Leghorn and Rhode Island Red) with 0.5M EDTA and stored at -20°C until DNA isolation. Leghorn and broiler bird (commercial generation) were purchased from Ideal Poultry (Texas, USA) Rhode Island Red was purchased from Murray McMurray Hatchery (Iowa, USA). DNA was isolated with DNeasy genomic DNA isolation kit or phenol chloroform extraction. All DNA samples for array hybridization were analyzed with agarose gel electrophoresis and spectrophotometry. DNA concentrations were measured with NanoDrop spectrophotometer (NanoDrop Technologies, Willmington, DE). Eleven samples (4 Leghorns, 5 broilers and 2 Rhode Island Reds) were analyzed with array CGH and twenty three samples were analyzed by qPCR. The use of animals was approved by Tennessee State University Institutional Animal Care and Use Committee (IACUC).

Hybridization: ACGH was carried out using whole genome tiling array galGal3_WG_CGH. This array platform was designed from the chicken genome build 2.1 from UCSC genome database (2006). The array contained 385,000 probes of 50-75mer. The mean probe spacing was 2557 bp and the median probe spacing was 2586 bp.

Each test DNA sample, labeled with Cy3, was co-hybridized with the reference male broiler sample (labeled with Cy5). The hybridization and initial data analysis (normalization and segmentation) were performed by NimbleGen Systems Inc (Madison, WI, USA). Segmentation analysis was performed with NimbleScan 2.4 software (segMNT algorithm). NimbleGen has provided literature package describing the technical specifics ( We applied similar criteria for CNV calls [13, 16]. Segments of five or more probes with mean log2 ratio shift from baseline greater than +/- 0.3 were flagged as candidate CNV. Probes from uncertain chromosomal loci (Chr#-random and ChrUn-random in the UCSC database) and from W chromosome were removed from the results. Raw aCGH data for this study have been deposited to GenBank GEO database under accession GSE19469 (

QPCR: PCR primers were designed using Primer Express 2.0 (Applied Biosystems) Sequences of the primers are available in Additional file 4, Table S2. All qPCR assays were conducted using SYBR GreenER qPCR kit (Invitrogen). Reaction was done in 20 µl containing 20 ng of genomic DNA (approximately 16,000 copies), 0.4 µM of each primer. Thermal cycles contained 1 cycle of pre-incubation at 50°C for 2 min and 95°C for 8 min, 35 cycles of amplification (95°C for 15 s and 60°C for 60 s). Primers were validated by melting curve analysis, amplification analysis by generating standard curve, and no-template control reactions. For standard curve analysis, one DNA sample was serial diluted to 10, 20, 40 and 80 ng/µl, and measured again with spectrophotometer. Each concentration was analyzed in quadruplicates with qPCR to determine amplification efficiency. These assays showed amplification efficiencies were between 112.4%-132.7% and correlation coefficients R were between 0.971and 0.990. For melting curve analysis, PCR product of each primer set that was used to generate CNV data showed single melting peak. Two separate qPCR assays were performed to determine relative copy numbers for each of three CNV loci (RHACD8, PRLR, and AKR1b). The first assay tested 23 birds and copy number was estimated with 2-ΔCt method after primer validation. The second assay was done essentially the same for 20 birds (included in the first assay) except that standard curve was generated concomitantly in the same plate with 4 concentrations (80, 20, 10 and 2.5 ng/μl) and copy numbers were estimated based on standard curve. Efficiencies of the second qPCR assay were 78.7%, 94.8%, 91.3% 95.7%, for PCCA, RHACD8, PRLR and AKR1B loci respectively.

Each test genomic DNA was diluted in Tris-EDTA (10 mM TRIS-HCl, 1 mM EDTA) buffer to 10 ng/µl, assessed with qPCR in quadruple reactions. QPCR was performed with iCycler (Bio-Rad), 96-well plate (Bio-Rad, cat# 2239441) and optical adhesive film (Applied Biosystems, Part # 4311971). To avoid potential uneven heating of reactions at the edge of thermal block, perimeter wells of PCR plates were routinely avoided when possible. In the first qPCR assay, copy numbers were assigned using the relative method 2-ΔCt, where ΔCt is the threshold cycle difference between the test sample and an arbitrarily selected reference sample that was used as a standard. The reference was considered to have two copies (when the standard is an autosomal locus) or one copy (when a locus is on Z chromosome of a female). In the second qPCR assay, relative copy numbers were assigned by comparing the Ct values with standard curve and the amount copies in 1 ng of reference DNA was assumed as one unit.

RT-qPCR: Spleen of adult broiler and Leghorn was removed immediately after sacrificing birds by cervical dislocation, briefly frozen in liquid nitrogen, and transferred to -80°C until RNA isolation. Total RNA was extracted with RNeasy kit (Qiagen). RNA concentration was determined with NanDrop and diluted to 25 ng/µl for transcript level analysis. An EST (GenBank accession CF255001) derived from CNV locus 13 (chr4:88,954,181-88,987,642) was used to design primers for transcript level analysis. RT-qPCR was carried out as described previously [62] with slight modifications using QuantiTect SYBR Green RT-PCR kit (Qiagen). To use iCycler (Bio-Rad) equipment with the the RT-PCR kit, fluorescein (Bio-Rad) was added to each reaction in a final concentration of 10 nM. Equipment and plastics were the same as used in QPCR. Each reaction was carried out in 20-µl volume containing 50 ng of total RNA and 0.4 µM of forward and reverse primers. Thermal cycles consist of 1 cycle of reverse transcription at 50°C for 10 min, followed by 1 cycle of incubation at 90°C for 15 min, and then 35 cycles of amplification (95°C for 15 s and 60°C for 60 s). No amplifications product was seen in no-template control reactions. Threshold cycles for no-reverse transcriptase control were at least 7 cycles greater than that for reactions with reverse transcriptase. Similar to qPCR assay on DNA, two separate RT-qPCR assays were conducted for CD8α and RHACD8: the first one with 2-ΔCt method the second one with standard curve method. RT-qPCR efficiencies for CD8α and RHACD8 primers, obtained by at least three reproducible standard curve analyses, were 96.3% ~ 97.5% and 80.1% ~ 106.2% respectively, all with correlation coefficient > 0.99. The second RT-qPCR assay was performed for GAPDH, β-actin CD8α and RHACD8, concomitantly with standard curve. Efficiencies were 102.1%, 127.2%, 85.7% and 83.9% respectively. Levels of transcripts were expressed relative to the amount in 1 ng of total RNA in the reference sample.

Abbreviations: aCGH, array comparative genome hybridization; AKR1B, aldo-keto reductase 1B; CD8α, CD8 antigen alpha chain; aCGH, array comparative genome hybridization; CHRM, cholinergic receptor muscarinic; CNV, copy number variation; CNVR, CNV region; EST, expressed sequence tag; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; Mb, million base pair; PCCA, propionyl coenzyme A carboxylase; PCR, polymerase chain reaction; PRLR, prolactin receptor; qPCR, quantitative real time PCR; RT-qPCR, quantitative real time reverse transcriptase-PCR; SNP, single nucleotide polymorphism; THRSP, thyroid hormone responsive spot 14; ZFR, zinc finger RNA binding protein

Author’s contributions: XW conceived, designed and performed experiment, analyzed data, wrote the manuscript. SN conceived, designed and performed experiment, analyzed data, wrote the manuscript. TKF performed DNA isolation, qPCR analysis and discussed manuscript. AB-S performed qPCR, RT-qPCR assay. NA managed animals and collected samples and discussed the manuscript. All authors read and approved the final manuscript, except NA, who passed away during the review.

Acknowledgement: We thank Dr. Philip Ganter for his critical reading and editing of this manuscript. We greatly appreciate Dr. Dafeng Hui’s valuable discussion on statistic test. We also thank James Tyus for assistance in sample collection. We are grateful to Dr. Michael Ivy for his contribution of reagent and discussion. We thank Mr. Gregory Coates, Miss Niesha Bonner and Mr. Johnson Anthony for assistance in DNA preparation and qPCR assay. This study was support in part by TSU faculty research award and USDA grants (2008-38814-04728 and Evans Allan funds).

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Figure Legend

Fig. 1: Quantitative PCR analysis of CNVs in 20 birds. DNA were diluted to 10 ng/μl and the conecentrations were meassured with Nanodrop spectrophotometer. Relative copy number was obtained by comparing threshold cyles of test DNA with a reference DNA that was serial diluted to 80, 20, 20 and 2.5 ng/μl. One unit of relative copies is the amount in 1 ng of reference DNA. Values represent mean ± SD of four reactions. Data were not normalized to the reference locus and copy number was not rounded. Numbers on x-axis are bird ID.

Fig. 2: Organization of CNV region at locus 17 located on GGA 10 (Chr10: 125113-157964). A block of sequence (Chr10:125051-149999) was aligned with chicken genome assembly build 2.1 using BLAT algorithm at UCSC genome database. Numbers at the top of the graph represent nucleotide positions in the chicken genome assembly. Long arrows indicate higher order repeat organization and orientation. Short green arrows indicate orientation and location of human olfactory receptor homologs aligned to GGA 10. Symbols of the same style (line color and fillings) on the same side of the blue line represent sequence blocks sharing >95% identity.

Fig. 3: Relationship between copy number and transcript levels at RHACD8 locus. (A) Plot spleen CD8α and RHACD8 mRNA levels. Spleen RNA samples were isolated from adult broilers and Leghorns. (B) Plot of GAPDH mRNA levels in spleen of the same 10 chickens. (C) Pearson correlation between CD8α and RHACD8 mRNA levels. (C) Pearson correlation between RHACD8 mRNA level and copy number of RHACD8 repeats.

Table 1: High confidence CNVs in chickens

Locus ID Chromo-some Start position* Size (bp) Gene Status# Number of observations
Broiler Leghorn RIR†
1 1 4015022 47654 LOC419112 loss loss loss 6
2 1 44980061 80399 Non-coding loss loss 7
3 1 48005486 34931 Non-coding gain 3
4 1 59885173 32693 CHRM2 loss 2
5 1 165880299 112347 ZFR, IL-3 gain gain 4
6 2 40647961 39933 Spliced ESTs gain loss 2
7 2 95665092 10297 Non-coding gain gain 4
8 2 97295434 44839 Non-coding gain gain 5
9 2 134727846 102330 MGC24975(SZD6) gain gain 3
10 2 154562776 299829 SCRIB loss gain 2
11 3 113612615 40053 MRPL19, loss loss 2
12 4 62172811 12364 Non-coding loss loss 2
13 4 88897639 175343 RHACD8 gain loss loss 5
14 4 89602897 42219 Non-coding loss 3
15 5 22120222 92556 Non-coding loss 3
16 6 12150334 84982 Non-coding loss loss 3
17 10 125113 32851 olfactory receptor 6k2 gain gain 2
18 11 2670295 14897 Non-coding gain 3
19 12 15208 77317 Non-coding loss 2
20 13 2722503 60308 EST loss loss 3
21 16 200114 12820 zipper protein loss loss 2
22 16 270019 162832 HLA class I antigen loss loss 2
23 17 567532 57890 PRF1 gain 2
24 Z 9965426 192201 PRLR loss loss 2
25 Z 71975037 142814 CDKN2A, MTAP loss loss loss 3
26 Z 73495364 29964 Non-coding gain gain 2

Note: *, the start position of the CNV was based on 2006 chicken genome assembly. #, gain or loss was assigned based on common reference DNA from one broiler bird (#6281). †, Rhode Island Red.

Description of additional files

Additional file 1, Figure S1: Examples of aCGH plot for 26 high confidence CNV

Additional file 2, Table S1: CNV loci observed once

Additional file 3,

Additional file 4, Table S2: Primers used in the study

Supplement_Fig_2: Examples of likely false negative CNV occurrence

An Experimental Analysis of Major Factors Contributing to Unexpected Real Time RT-PCR Efficiency

Student: Ann L. Bohannon-Stewart
Class: Individual Studies Spring Semester 2010
Professor: Benny Washington

Real time reverse transcription polymerase chain reaction (RT-qPCR) assay is widely used in the determination of RNA expression in various tissues of various organisms. If properly validate, this convenient method can quantify specific RNA sequence in few hours, comparing with days needed for Northern blot analysis. RT-qPCR assay is commonly performed either in two steps or one step. In many reports, one step RT-qPCR using SYBR green chemistry is the method of choice. Despite the widespread application, the extent to which RT-qPCR data are unreliable and the resulting biological validity are under-appreciated and not well acknowledged. Many gene expression studies using RT-qPCR method convert gene expression level with 2-ΔΔCt. While this conversion is convenient and appropriate when amplification efficiency is close 100%, problems arise when efficiency significantly deviates from the presumptive value. Using SYBR green one step RT-qPCR kit, sometimes RT-qPCR efficiency may appear to be greater than 400%. It is necessary to determine the cause of the appeared high efficiency and to develop approaches to overcome the problem. The major factors contributing to the efficiency variations are experimentally tested in the current study. Primers for chicken CD8α mRNA were used to determine how reagent and different parameters in RT and PCR stages affect the efficiency of RT-qPCR assay.

The efficiency of RT-qPCR may be affected by many factors, including template and primer concentrations, specific template sequences, reverse transcriptases, DNA polymerases and other components of the reaction system. In addition, sporadic factors such as inactivated enzymes, inaccuracy of pipetting and human errors also contribute to problems of amplification efficiency. In this study, we tested how some of these factors work. First we examined PCR efficiency using DNA template under RT-qPCR chemistry. We then examined the effect of primer concentrations on RT-qPCR efficiencies. Third, we tested how different primers behave under similar conditions. We have also carefully controlled experimental procedures try to minimize the human errors so that experiment become more reliable. This study has provided valuable information for future RT-qPCR assay design and quality control in gene expression studies.

Materials and Methods:
DNA template was generated by RT-PCR amplification of chicken CD8a mRNA with gene-specific primers, purified with gel electrophoresis and gel extraction kit (Qiagen). Serial dilutions of the DNA template were made for high copy number range (1.22 x 10^19, 6.08 x 10^18, 3.04 x 10^18 copies/µl) and low copy number range (1.2 x10^7, 3.0x10^6, 7.5x10^5,1.85x10^5, 4.7x10^4 copies/µl). In order to avoid template DNA loss, salmon DNA was used as a carrier for the low copy number range dilutions such that the total concentrations of nucleic acids in all dilutions are identical. qPCR was preformed with iCycler (BioRad) using Quantitect RT-PCR kit (Qiagen). The composition of the qPCR reactions are the same as RT-qPCR except the template was purified PCR product. Thermal cycle parameters were also identical to RT-qPCR so that results from DNA template can be compared with that from RNA template. All primer concentrations were set at 0.25 µM in all cDNA PCRs. For RT-qPCR analysis, two sets of primers respectively for chicken CD8a and RHACD were tested. The three levels of primer concentrations were 0.125, 0.25, and 0.5 µM. Total RNA was diluted to 80 ng/µl, 20 ng/µl, 5 ng/µl, 1.25 ng/µl, and 0.3125 ng/µl. Quadruplicated test was performed for each template concentration. Each well contained 20 µl in total including 2 µl of template. Thermal cycles were: reverse transcription at 50°C for 10 min, activation at 95°C for 15 min, 35 cycles of amplification (95°C for 15 s and 60°C for 60 s). Melting curve analysis was done for all reactions.

Three tests were conducted using CD8α DNA template. At high DNA copy number range , the efficiency of qPCR was 97%. The second and third qPCRs tests using low copy number range dilutions demonstrated efficiencies of 116% and 120% (see DNA diagram). With RNA as template, the efficiencies of RT-qPCR for CD8α primers at three concentrations 0.125, 0.25 and 0.5 µM were 97%, 95% and 97% respectively. Since these tests were performed separately, it is not possible to make strict comparisons between the efficiencies. However, these results were comparable to those with DNA template. Although the efficiencies were similar at the three primer concentration levels, higher primer concentrations showed consistently smaller Ct values for CD8a, suggest a slightly increased efficiencies.RHACD primer showed similar results. When primer concentration was 0.5 µM, the efficiency was 105%. When primer concentration was 0.25 µM, the efficiency was 99.7%. When primer concentration was 0.125 µM, the efficiency was 80%.

Higher levels of primer concentrations produce higher efficiencies. The RT-qPCR reagents must be very accurately mixed in order for the efficiency to be in an acceptable range. Sporadic high efficiencies may be avoided by eliminate pipetting errors. The results of qPCR with DNA template demonstrate that the linear relationship between the logarithmic of template concentration and threshold cycle hold at template concentration as low as 40,000 copies per reaction.

I learned about PCR’s though various sources:
Xiaofei Wang, PhD.Assistant Professor Endocrinology/Genomics Department of Biological Sciences Tennessee State University Nashville, TN37209 Tel: (615)963 2541

National Human Genome Ins