Identification of three new DRB3* (DRB3*0106, DRB3*0107 and DRB3*02022) alleles

Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans

Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.

HLA DNA sequence variation among human populations: molecular signatures of demographic and selective events

Molecular differences between HLA alleles vary up to 57 nucleotides within the peptide binding coding region of human Major Histocompatibility Complex (MHC) genes, but it is still unclear whether this variation results from a stochastic process or from selective constraints related to functional differences among HLA molecules. Although HLA alleles are generally treated as equidistant molecular units in population genetic studies, DNA sequence diversity among populations is also crucial to interpret the observed HLA polymorphism. In this study, we used a large dataset of 2,062 DNA sequences defined for the different HLA alleles to analyze nucleotide diversity of seven HLA genes in 23,500 individuals of about 200 populations spread worldwide. We first analyzed the HLA molecular structure and diversity of these populations in relation to geographic variation and we further investigated possible departures from selective neutrality through Tajima's tests and mismatch distributions. All results were compared to those obtained by classical approaches applied to HLA allele frequencies.Our study shows that the global patterns of HLA nucleotide diversity among populations are significantly correlated to geography, although in some specific cases the molecular information reveals unexpected genetic relationships. At all loci except HLA-DPB1, populations have accumulated a high proportion of very divergent alleles, suggesting an advantage of heterozygotes expressing molecularly distant HLA molecules (asymmetric overdominant selection model). However, both different intensities of selection and unequal levels of gene conversion may explain the heterogeneous mismatch distributions observed among the loci. Also, distinctive patterns of sequence divergence observed at the HLA-DPB1 locus suggest current neutrality but old selective pressures on this gene. We conclude that HLA DNA sequences advantageously complement HLA allele frequencies as a source of data used to explore the genetic history of human populations, and that their analysis allows a more thorough investigation of human MHC molecular evolution.

Identification of three novel alleles: DRB3*0110, DRB1*1140, and DRB1*140102

Three novel human leukocyte antigen class II alleles (DRB3*0110, DRB1*1140, and DRB1*140102) are described here. The three novel alleles were initially detected as previously unidentified SSO hybridization patterns using CANTYPE((R)) reverse hybridization assay. Sequences were determined by cloning/sequencing. DRB3*0110 allele is identical to DRB3*010101, except for a single nucleotide substitution (CGC-->AGC) changing codon 39 from Arg to Ser. This polymorphism has not, until now, been identified in DRB allele. Thus, this is an unusual mutation as the codon 39 is a fairly conserved region. The new DRB1*1140 is identical to DRB1*1116, except for a single nucleotide substitution at codon 67 from ATC (encoding for isoleucine) to TTC (encoding for phenylalanine). This polymorphism is commonly found in DRB1*11 alleles. Compared with DRB1*140101, DRB1*140102 contains a single silent nucleotide substitution (TAT-->TAC, both encoding for tyrosine) at codon 78. This polymorphism is commonly found in DRB1*14 alleles. The three new DRB alleles may have been generated by a point mutation event. The DRB3*0110 and DRB1*140102 were identified in Caucasoid individuals. The ethnic origin of the subject carrying the DRB1*1140 allele is Egyptian. The DRB1*140102 was detected in two unrelated individuals; the DRB3*0110 and DRB1*1140 were only identified once, in a total population of 80,000.