Natural History

without treatment

New methods for finding disease-susceptibility genes: impact and potential

Improved techniques for defining disease-gene location and evaluating the biological candidacy of regional transcripts will hasten disease-gene discovery

Hard-Wired Control of Bacterial Processes by Chromosomal Gene Location

Bacterial processes, such as stress responses and cell differentiation, are controlled at many different levels. While some factors, such as transcriptional regulation, are well appreciated, the importance of chromosomal gene location is often underestimated or even completely neglected. A combination of environmental parameters and the chromosomal location of a gene determine how many copies of its DNA are present at a given time during the cell cycle. Here, we review bacterial processes that rely, completely or partially, on the chromosomal location of involved genes and their fluctuating copy numbers. Special attention will be given to the several different ways in which these copy-number fluctuations can be used for bacterial cell fate determination or coordination of interdependent processes in a bacterial cell

Functional Bias and Spatial Organization of Genes in Mutational Hot and Cold Regions in the Human Genome

The neutral mutation rate is known to vary widely along human chromosomes, leading to mutational hot and cold regions. We provide evidence that categories of functionally-related genes reside preferentially in mutationally hot or cold regions, the size of which we have measured. Genes in hot regions are biased toward extra-cellular communication (surface receptors, cell adhesion, immune response, etc.) while those in cold regions are biased toward essential cellular processes (gene regulation, RNA processing, protein modification, etc.). From a selective perspective, this organization of genes could minimize the mutational load on genes that need to be conserved and allow fast evolution for genes that must frequently adapt. We also analyze the effect of gene duplication and chromosomal recombination, which contribute significantly to these biases for certain categories of hot genes. Overall, our results show that genes are located non-randomly with respect to hot and cold regions, offering the possibility that selection acts at the level of gene location in the human genome.Comment: 17 pages, 6 figures, 2 tables. accepted to PLOS Biology, Feb. 2004 issu

Apolipoprotein M

Apolipoprotein M (apoM) is a 26-kDa protein that is mainly associated with high-density lipoprotein (HDL) in human plasma, with a small proportion present in triglyceride-rich lipoproteins (TGRLP) and low-density lipoproteins (LDL). Human apoM gene is located in p21.31 on chromosome 6 (chromosome 17, in mouse). Human apoM cDNA (734 base pairs) encodes 188-amino acid residue-long protein. It belongs to lipocalin protein superfamily. Human tissue expression array study indicates that apoM is only expressed in liver and in kidney and small amounts are found in fetal liver and kidney. In situ apoM mRNA hybridization demonstrates that apoM is exclusively expressed in the hepatocytes and in the tubule epithelial cells in kidney. Expression of apoM could be regulated by platelet activating factor (PAF), transforming growth factors (TGF), insulin-like growth factor (IGF) and leptin in vivo and/or in vitro. It has been demonstrated that apoM expression is dramatically decreased in apoA-I deficient mouse. Hepatocyte nuclear factor-1α (HNF-1α) is an activator of apoM gene promoter. Deficiency of HNF-1α mouse shows lack of apoM expression. Mutations in HNF-1α (MODY3) have reduced serum apoM levels. Expression of apoM is significantly decreased in leptin deficient (ob/ob) mouse or leptin receptor deficient (db/db) mouse. ApoM concentration in plasma is positively correlated to leptin level in obese subjects. These may suggest that apoM is related to the initiation and progression of MODY3 and/or obesity

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications, Springer. The article contains 23 pages, 4 figures, 8 tables and 51 reference

A Novel Bioinformatic Approach to Understanding Addiction

Finding the genetic markers that influence complex, multigenic substance addiction phenotypes has been an area of significant medical study. Understanding complex disease traits like addiction has been hampered by the lack of functional insights into novel variants to the human genome. We hypothesized that gene location plays a role in functional genomic neighborhoods. To test whether there is a relationship between opiate, dopamine, and GABA disease and population allele frequencies, we used genes obtained from addiction literature curated by the National Center for Biotechnology Information (NCBI). These addiction and metabolism focused search terms generated opiate, dopamine, and GABA addiction results (N=587 genes). These genes were then projected onto the genome to identify cluster regions of genetic importance for substance addiction. Clusters were defined as regions of the genome with more than six genes within a 1.5Mb linear genomic window. We identified seven hotspots located on chromosomes 4, 6 (2 clusters), 10, 11, and 19. Human polymorphism data was surveyed from the 1148 individuals comprising the 11 sample populations of the HapMap Project dataset. Our analyses demonstrate that when human populations are assessed, ten candidate addiction alleles were identified. Finally assessments of public genome wide association studies show long range linkages to canonical addiction genes. This study delineates a novel method to identify novel candidate addiction variants using a systems biology approach that relies on an interdisciplinary set of data, including genomic, pathway data, and population variation. Important connections to sociological and environmental data are discussed to contextualize addiction data