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Lymphotoxin-α (LTα), located in the Major Histocompatibility Complex (MHC) class III region on chromosome 6, encodes a cytotoxic protein that mediates a variety of antiviral responses among other biological functions. Furthermore, several genotypes at this gene have been implicated in the onset of a number of complex diseases, including myocardial infarction, autoimmunity, and various types of cancer. However, little is known about levels of nucleotide variation and linkage disequilibrium (LD) in and near LTα, which could also influence phenotypic variance. To address this gap in knowledge, we examined sequence variation across ~ 10 kilobases (kbs), encompassing LTα and the upstream region, in 2039 individuals from the 1000 Genomes Project originating from 21 global populations.Here, we observed striking patterns of diversity, including an excess of intermediate-frequency alleles, the maintenance of multiple common haplotypes and a deep coalescence time for variation (dating 1.0 million years ago), in global populations. While these results are generally consistent with a model of balancing selection, we also uncovered a signature of positive selection in the form of long-range LD on chromosomes with derived alleles primarily in Eurasian populations. To reconcile these findings, which appear to support different models of selection, we argue that selective sweeps (particularly, soft sweeps) of multiple derived alleles in and/or near LTα occurred in non-Africans after their ancestors left Africa. Furthermore, these targets of selection were predicted to alter transcription factor binding site affinity and protein stability, suggesting they play a role in gene function. Additionally, our data also showed that a subset of these functional adaptive variants are present in archaic hominin genomes.Overall, this study identified candidate functional alleles in a biologically-relevant genomic region, and offers new insights into the evolutionary origins of these loci in modern human populations.

Bacterial leaf streak (BLS), caused by the pathogen Xanthomonas campestris pv. Oryzicola, is a major rice disease in tropical and subtropical regions of Asia. Rice proteins responsive to BLS are still not well characterized. We took a proteomics approach to identify the proteins that are up-regulated in rice leaves after infection. Approximately 1,500 protein spots were detected on each 2-D gel after silver-staining; those with increased protein levels were selected for MALDI-TOF-MS analysis. We identified 32 up-regulated proteins that might be involved in disease resistance signal transduction, pathogenesis, and regulation of cell metabolism. By using publicly available microarray data, we determined the mRNA transcripts of 23 proteins expressed in the leaves. Seven genes were analyzed by northern blots, which demonstrated that transcript levels were increased after bacterial infection. Our findings help elucidate the molecular mechanisms underlying BLS and provide a solid foundation for further research on the functions of relevant genes.

Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In this study, a new machine learning approach has been developed for predicting sumoylation sites from protein sequence information. Random forests (RFs) and support vector machines (SVMs) were trained with the data collected from the literature. Domain-specific knowledge in terms of relevant biological features was used for input vector encoding. It was shown that RF classifier performance was affected by the sequence context of sumoylation sites, and 20 residues with the core motif ΨKXE in the middle appeared to provide enough context information for sumoylation site prediction. The RF classifiers were also found to outperform SVM models for predicting protein sumoylation sites from sequence features. The results suggest that the machine learning approach gives rise to more accurate prediction of protein sumoylation sites than the other existing methods. The accurate classifiers have been used to develop a new web server, called seeSUMO (http://bioinfo.ggc.org/seesumo/), for sequence-based prediction of protein sumoylation sites.

We report here the cloning and tissue distribution of the human B3GALT7 gene, a member of the β1,3-Glycosyltransferase family, structurally related to the β1,3-Galactosyltransferase family and β1,3-N-acetylglucosaminyltransferase family, isolated from a human lung cDNA library. B3GALT7 is mapped to chromosome 19q13.2 by browsing the UCSC genomic database. It contains an ORF with length of 1191bp, encoding a protein with a signal peptide sequence and galactosyl-T domain, and its molecular weight and isoelectric point is predicted to be 43.3 kDa and 8.67 respectively. The molecular weight of the protein when expressed in E. coli corresponded to that expected. Northern blotting showed that B3GALT7 was highly expressed in lung, throat and ileum, whereas the expression level was low in tongue, breast, uteri, testis. In addition, it was also demonstrated that B3GALT7 is differentially transcribed in human tumor cell lines. Published in 2003.

Background Protein destabilization is a common mechanism by which amino acid substitutions cause human diseases. Although several machine learning methods have been reported for predicting protein stability changes upon amino acid substitutions, the previous studies did not utilize relevant sequence features representing biological knowledge for classifier construction. Results In this study, a new machine learning method has been developed for sequence feature-based prediction of protein stability changes upon amino acid substitutions. Support vector machines were trained with data from experimental studies on the free energy change of protein stability upon mutations. To construct accurate classifiers, twenty sequence features were examined for input vector encoding. It was shown that classifier performance varied significantly by using different sequence features. The most accurate classifier in this study was constructed using a combination of six sequence features. This classifier achieved an overall accuracy of 84.59% with 70.29% sensitivity and 90.98% specificity. Conclusions Relevant sequence features can be used to accurately predict protein stability changes upon amino acid substitutions. Predictive results at this level of accuracy may provide useful information to distinguish between deleterious and tolerant alterations in disease candidate genes. To make the classifier accessible to the genetics research community, we have developed a new web server, called MuStab (http://bioinfo.ggc.org/mustab/).