(PSYCHIATRIC TIMES) - I experienced an interesting confluence of events the other day. My 11-year-old son has been finding out about the great power of online information. Although we limit his access to assistance with homework, he is already a digital whiz kid who knows where to find a great deal of information for writing tomes like Norse history reports. In my day, it would have taken an entire afternoon of digging through texts at a university library to obtain items he found in a few seconds.
The confluence came about because of what I was doing while sitting next to him. While he was Headshotbusy downloading information about Vikings, I was reading an update on a story that I have been following for a few years: the attempt to create a simple, objective blood test that could properly identify mood disorders. That would be a truly handy gadget for mental health professionals to have in their diagnostic tool kits! Going through the literature, which relies heavily on gene expression data, it hit me how profoundly the judicious use of online databases has contributed to the scientific rigor of the research. The Internet was not only seminal to my son’s work but also to this blood test research.
In this column, I will discuss new progress on this Internet-boosted line of inquiry. I will begin with a few basics about differential gene expression and microarrays and will then move on to something that researchers are calling “convergent functional genomics.” As you shall see, the clever use of online databases both confirmed and extended the work done at the bench. As a result, it may very well be possible in the next few years to have a clinic-ready blood test that is capable of diagnosing unipolar and bipolar depression. There may even be a diagnostic test for schizophrenia.
In order to understand this promising research, we first need to review a few facts about differential gene expression, microarrays, and their use in the laboratory. As you may remember, only about 2% of the genome encodes for messenger RNA (mRNA)—sequences usually referred to as class II genes (the rest of the genes encode either ribosomal RNA, called class I genes, or transfer RNA, called class III genes).
You can subdivide class II genes into 2 categories based on the transcriptional activity. Some class II genes are turned on all the time; we often refer to them as “housekeeping” sequences. Some class II genes are expressed quite cell-specifically (a neuron has a very different job description from, say, a gut fibroblast, after all), and they are either completely silent or are called on infrequently, depending on the needs of the cell.
Researchers can capture these “need-specific” class II mRNAs quite easily because of the binding properties of their nucleotides. Consider this example: Suppose you are interested in finding out which neural genes, if any, become activated in the presence of a test medication. You take 2 groups of cells; 1 group will not be exposed to the drug (serving as the unstimulated control), while the other will be exposed to the drug for a set period.
How do you get the medication-specific genes? You simply isolate both sets of mRNA, convert them to helical DNA, and then mix them together. The genes that are commonly expressed in both populations (like those housekeeping genes) will find each other and, with some coaxing, bind together. This makes them double-stranded. The genes that are unique to the medication stimulation have no “partners” and will not bind to anything. This makes them single-stranded.
Since it is easy in the laboratory to separate double-stranded from single-stranded snippets of DNA, we can quickly isolate our “medication-specific” gene population. (This technique can also be used in the opposite direction for some medications, or “turn off” genes.) Simply looking for unpaired populations in the controls can give the researcher valuable information about active and suppressive events related to medication exposure.
Today, populations of nucleotides can be embedded in something we call a “microarray,” which is essentially a plastic tray to which DNA samples have been previously and irreversibly bound. Any DNA can be attached to the microarray, including any (or all) products from the 40,000-plus genes that make up the human genome. Once embedded, you simply wash the plastic with the nucleotide sample that you are testing and see what does and does not bind to the nucleotides on the dish. This hybridization principle was used extensively in the data I am about to describe.
The blood test experiment was an attempt to measure whole genome expression differences in populations with mood disorders (and schizophrenia) using only their blood as the sample substrate. If any unique gene sequences were discovered, would these sequences predict mood disorders in unknown populations? The researchers had their biological work cut out for them. It is quite an experimental leap to ask about events going on in the brain by interrogating only the blood. As you shall see, using a database that looked at human brain–specific gene expression (on the Internet) turned out to be critical for this work.
Le-Niculescu and colleagues1 enrolled 3 cohorts in this study: 2 for depression and 1 for psychotic disor-ders. Twenty-nine patients in the first cohort had been given a diagnosis of bipolar I disorder. The second group, a replicant cohort, consisted of 19 patients with bipolar I disorder. The third group comprised the psychoses-related cohort and included 30 persons with schizoaffective disorders, substanceinduced psychoses, and schizophrenia.
The first task for the researchers was to isolate the genetic substrates of the patients in various phases of their mood disorder. Blood samples were collected when the patients were in a high-mood state (a visual analog scale score of 60 or higher) and in a low-mood state (visual analog scale score of 40 or lower). The various populations of mRNAs were isolated from theseFigure 1 blood samples, and the hybridization work involving microarrays began.
Unique gene expression profiles were eventually obtained in both low- and high-mood states and were then divided into forward and reverse mRNA subpopulations.
The forward population represented the manic state. Those mRNA populations were classified as absent in the low (ie, the gene was not expressed in the low-mood state) and present in the high (meaning that the gene was expressed only in the high-mood state). The isolated sequences were considered to be candidate biomarkers for the manic phase of the disorder.
The reverse populations were also isolated. They were absent-in-the-high mood but present-in-the-low mood representatives. These sequences were considered to be candidate biomarkers for the depressive phase of the disorder.
The first category was cross-validation using animal models. This vetting procedure used a pharmacogenomic mouse model for bipolar disorder. Both low-specific and high-specific populations were characterized, and gene sequences were isolated using similar microarray procedures previously deployed in the human work just discussed. In these tests, the source of the mRNAs included not only mouse blood but also brain tissue. The mouse sequences isolated in this fashion were compared with the human sequences previously described.
Next, the strongest gene candidates in each category then underwent an extensive series of tests and cross-checking (Figure). These validation exercises can be divided into 3 categories.
The second category was cross-validation using human postmortem brain sample databases. This vetting procedure involved peering into the Internet and specifically assessing a URL that was carrying data from “GeneCards”—an Online Mendelian Inheritance of Man database (http://www.nslij-genetics.org/search_omim.html
). This database contains published reports of changes in the expression of specific genes in postmortem brain tissues that were obtained from patients with bipolar disorder. The idea was to compare the sequences that were isolated from living patient blood samples with sequences that were isolated from the brain samples in deceased patients.
This was a key step because the cross-checking not only involved human-to-human comparisons but it was also the first attempt to establish blood-to-brain connections with the data. As was hinted at previously, the body spends a ridiculous amount of time and resources trying to wall these systems off from each other. Any blood test that is designed to assay something in the brain by looking for something in the blood would need the concordance between the tissues down pat. It is also tricky to determine the phase of illness at which the person died: was it at the low end or at the high end? The researchers assumed that the deaths occurred when the patients were experiencing the low symptoms. Most amazingly, perhaps, the researchers found a number of sequences that converged well with the genes they previously obtained from the blood sample.
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