Robert G. Halgren

The transcriptome of human oocytes

The identification of genes and deduced pathways from the mature human oocyte can help us better understand oogenesis, folliculogenesis, fertilization, and embryonic development. Human metaphase II oocytes were used within minutes after removal from the ovary, and its transcriptome was compared with a reference sample consisting of a mixture of total RNA from 10 different normal human tissues not including the ovary. RNA amplification was performed by using a unique protocol. Affymetrix Human Genome U133 Plus 2.0 GeneChip arrays were used for hybridizations. Compared with reference samples, there were 5,331 transcripts significantly up-regulated and 7,074 transcripts significantly down-regulated in the oocyte. Of the oocyte up-regulated probe sets, 1,430 have unknown function. A core group of 66 transcripts was identified by intersecting significantly up-regulated genes of the human oocyte with those from the mouse oocyte and from human and mouse embryonic stem cells. GeneChip array results were validated using RT-PCR in a selected set of oocyte-specific genes. Within the up-regulated probe sets, the top overrepresented categories were related to RNA and protein metabolism, followed by DNA metabolism and chromatin modification. This report provides a comprehensive expression baseline of genes expressed in in vivo matured human oocytes. Further understanding of the biological role of these genes may expand our knowledge on meiotic cell cycle, fertilization, chromatin remodeling, lineage commitment, pluripotency, tissue regeneration, and morphogenesis.

Comparative Genomics of Two Closely Related Unicellular Thermo-Acidophilic Red Algae, Galdieria sulphuraria and Cyanidioschyzon merolae, Reveals the Molecular Basis of the Metabolic Flexibility of Galdieria sulphuraria and Significant Differences in Carbohydrate Metabolism of Both Algae

Unicellular algae serve as models for the study and discovery of metabolic pathways, for the functional dissection of cell biological processes such as organellar division and cell motility, and for the identification of novel genes and gene functions. The recent completion of several algal genome sequences and expressed sequence tag collections and the establishment of nuclear and organellar transformation methods has opened the way for functional genomics approaches using algal model systems. The thermo-acidophilic unicellular red alga Galdieria sulphuraria represents a particularly interesting species for a genomics approach owing to its extraordinary metabolic versatility such as heterotrophic and mixotrophic growth on more than 50 different carbon sources and its adaptation to hot acidic environments. However, the ab initio prediction of genes required for unknown metabolic pathways from genome sequences is not trivial. A compelling strategy for gene identification is the comparison of similarly sized genomes of related organisms with different physiologies. Using this approach, candidate genes were identified that are critical to the metabolic versatility of Galdieria. Expressed sequence tags and high-throughput genomic sequence reads covering >70% of the G. sulphuraria genome were compared to the genome of the unicellular, obligate photoautotrophic red alga Cyanidioschyzon merolae. More than 30% of the Galdieria sequences did not relate to any of the Cyanidioschyzon genes. A closer inspection of these sequences revealed a large number of membrane transporters and enzymes of carbohydrate metabolism that are unique to Galdieria. Based on these data, it is proposed that genes involved in the uptake of reduced carbon compounds and enzymes involved in their metabolism are crucial to the metabolic flexibility of G. sulphuraria.

Effects of gestational and lactational exposure to Aroclor 1242 on sperm quality and in vitro fertility in early adult and middle-aged mice.

The objective of this study was to examine the effects of gestational and lactational exposure to Aroclor 1242 (0, 10, 25, 50, and 100 mg/kg-bw) on male fertility. Doses were administered to C57BL6 female mice orally every two days from two weeks before mating, during mating, and through gestation until postnatal day 21. Male B6D2F1 offspring were examined for anogenital distance, organ development, epididymal sperm count, sperm motility, and in vitro fertility at 16 and 45 weeks of age. Stomach samples of pups nursing from PCB-treated mothers in the 50 mg/kg dose group were analyzed for PCBs and chlorobiphenylols by high resolution gas chromatography coupled with low resolution mass spectrometry. It was estimated that the nursing pups were exposed to 0.2, 0.6, 1.2, and 2.4 mg/kg/day total PCBs in the 10, 25, 50, and 100 mg/kg dose groups, respectively. This exposure level approaches the maximum FDA recommended levels for PCBs in food and breast milk. The composition of the PCBs in the stomach samples was different from the parent mixture, as there was a higher proportion of heavily chlorinated congeners, as well as chlorobiphenylols. Anogenital distance at weaning, and liver, thymus, and testes weight at 16 and 45 weeks of age were not affected by PCB exposure. Epididymal sperm velocity and linearity were significantly increased in the 25 mg/kg dose group at 16 weeks of age. Sperm count was increased by 36% in this dose group (P = 0.06). By 45 weeks of age, average sperm count in this dose group was similar to that of controls. With the exception of the 50 mg/kg dose group at 16 weeks of age, sperm fertilizing ability in vitro was significantly decreased in all PCB-exposed groups at 16 and 45 weeks of age. These results suggest that fertility in the adult mouse is susceptible to developmental exposure to Aroclor 1242 and is independent of testis weight or epididymal sperm count.

GP3: GenePix post-processing program for automated analysis of raw microarray data

UNLABELLED Here we describe an automated and customizable program to correct, filter and normalize raw microarray data captured using GenePix, a commonly used microarray image analysis application. Files can be processed individually or in batch mode for increased throughput. User defined inputs specify the stringency of data filtering and the method and conditions of normalization. The output includes gene summaries for replicate spots and descriptive statistics for each experiment. The source code (Perl) can also be adapted to handle raw data output from other image analysis applications. AVAILABILITY http://bch.msu.edu/~zacharet/microarray/GP3.html

In Silico Approaches to Mechanistic and Predictive Toxicology: An Introduction to Bioinformatics for Toxicologists

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.