Expression analysis of the genes identified in GWAS of the postmortem brain tissues from patients with schizophrenia.

Many gene expression studies have examined postmortem brain tissues of
patients with schizophrenia. However, only a few expression studies of
the genes identified in genome-wide association study (GWAS) have been
published to date. We measured the expression levels of the genes
identified in GWAS (ZNF804A, OPCML, RPGRIP1L, NRGN, and TCF4) of the
postmortem brain tissues of patients with schizophrenia and controls
from two separate sample sets (i.e., the Australian Tissue Resource
Center and Stanley Medical Research Institute). We also determined
whether the single-nucleotide polymorphisms (SNPs) identified in the
GWAS were related to the gene expression changes in the prefrontal
cortex. No difference was observed between the patients with
schizophrenia and controls from the Australian Tissue Resource Center
samples in the mRNA levels of ZNF804A, OPCML, RPGRIP1L, NRGN, or TCF4.
The lack of mRNA change for these five transcripts was also found in the
brain samples from the Stanley Medical Research Institute. In addition,
no relationship between the schizophrenia-associated SNPs identified in
the GWAS and the corresponding gene expression was observed in either
sample set. Our results suggest that major changes in the transcript
levels of the five candidate genes identified in the GWAS may not occur
in adult patients with schizophrenia. The lack of linkage between the
risk gene polymorphisms and the expression levels of their major
transcripts suggests that the control of pan mRNA levels may not be a
prominent mechanism by which the genes identified in the GWAS contribute
to the pathophysiology of schizophrenia. Further studies are needed to
examine how the genes identified in the GWAS contribute to the
pathophysiology of schizophrenia.

Gene expression profiles in febrile children with defined viral and bacterial infection

Viral infections are common causes of fever without an apparent source in young children. Despite absence of bacterial infection, many febrile children are treated with antibiotics. Virus and bacteria interact with different pattern recognition receptors in circulating blood leukocytes, triggering specific host transcriptional programs mediating immune response. Therefore, unique transcriptional signatures may be defined that discriminate viral from bacterial causes of fever without an apparent source. Gene expression microarray analyses were conducted on blood samples from 30 febrile children positive for adenovirus, human herpesvirus 6, or enterovirus infection or with acute bacterial infection and 22 afebrile controls. Blood leukocyte transcriptional profiles clearly distinguished virus-positive febrile children from both virus-negative afebrile controls and afebrile children with the same viruses present in the febrile children. Virus-specific gene expression profiles could be defined. The IFN signaling pathway was uniquely activated in febrile children with viral infection, whereas the integrin signaling pathway was uniquely activated in children with bacterial infection. Transcriptional profiles classified febrile children with viral or bacterial infection with better accuracy than white blood cell count in the blood. Similarly accurate classification was shown with data from an independent study using different microarray platforms. Our results support the paradigm of using host response to define the etiology of childhood infections. This approach could be an important supplement to highly sensitive tests that detect the presence of a possible pathogen but do not address its pathogenic role in the patient being evaluated.

Observing live gene expression in the body

 "Most of our physiological functions fluctuate throughout the day. They are coordinated by a central clock in the brain and by local oscillators, present in virtually every cell. Many molecular gearwheels of this internal clock have been described by Ueli Schibler, professor at the Faculty of Science of the University of Geneva (UNIGE), Switzerland. To study how the central clock synchronizes subordinate oscillators, the researcher's group used a variety of genetic and technological tools developed in collaboration with a team of UNIGE physicians. In this way, the scientists were able to directly observe the bioluminescence emitted by 'clock genes' in mice for several months. This biotechnology is applicable to numerous sectors of biomedical research, which attracted the attention of the editors from the journal Genes & Development."

Identifying All Factors Modulating Gene Expression Is Actually Possible!

MNT:  "The screening technique developed by the researchers, called Synthetic Tandem Repeat PROMoter (STAR-PROM), is a pioneering technology: "The 850 or so elements constituting this library, constructed and screened in a year and a half, should allow us to identify the majority of factors modulating gene expression in a particular context," says Alan Gerber. Whether in the context of drug treatment, the exploration of a specific signalling pathway, the identification of new regulators, with any stimulus, the applications of this technique are countless. "

Allen Brain Atlas - The human brain whole genome transcriptome

An "all genes, all structures" gene expression survey in multiple adult control brains.

• > 62,000 gene probes per profile
• ~ 500 samples per hemisphere across cerebrum, cerebellum and brainstem
• Data mapped with histology into unified 3-D anatomic framework based on MRI

GENEVESTIGATOR - shaping biological discovery

Genevestigator is a high performance search engine for gene expression. Our focus is on the deep integration of high quality, well annotated data with high-performance computing. This allows users to run unique types of queries across thousands of datasets simultaneously.

GENEVESTIGATOR - shaping biological discovery

Genevestigator is a high performance search engine for gene expression. Our focus is on the deep integration of high quality, well annotated data with high-performance computing. This allows users to run unique types of queries across thousands of datasets simultaneously.