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Over the last decades, several features of obesity have been identified at behavioral, physiological, endocrine and genomic levels, and they have revealed the complexity of the disease; obesity results from a combination of genetic predisposition, endocrine disorders, and dysregulation of both food intake and energy expenditure. This complexity makes the development of new therapeutic regimens challenging and bariatric surgery is still the treatment of choice for many obese patients. Given the need for noninvasive therapeutic intervention strategies, we sought to systematically study the biological manifestations of obesity in peripheral organs. We analyzed publicly available datasets of genes, genomic determinants, and levels of obesity-related hormones in the blood, using a combination of methodologies, including graph theory and dynamical modeling, that allow for the integration of different types of datasets. The analysis revealed tissue- and organ-specific metabolic impairments and potential new drug targets. All the data are organized into a tissue/organ-based subcellular-function atlas for human obesity. The data show that the complexity of the obesity arises due to the multiplicity of subcellular processes in different peripheral organs.

Support from human genetics increases the probability of success in drug development. However, few examples exist of successful genomically-driven drug repositioning. Given that a Mendelian form of severe enterocolitis is due to up-regulation of the interleukin-18 (IL18) signaling pathway, and pharmacologic inhibition of IL18 has been shown to reverse this enterocolitis, we undertook a Mendelian randomization study to test the causal effect of elevated IL18 levels on inflammatory bowel disease susceptibility (IBD) in 12,882 cases and 21,770 controls. Mendelian randomization is an established method to assess the role of biomarkers in disease etiology in a manner that minimizes confounding and prevents reverse causation. Using three SNPs that explained almost 7% of the variance in IL18 level, we found that each genetically predicted standard deviation increase in IL18 was associated with an increase in IBD susceptibility (odds ratio = 1.22, 95% CI = 1.11–1.34, P-value = 6 × 10−5). This association was further validated in 25,042 IBD cases and 34,915 controls (odds ratio = 1.13, 95% CI = 1.05–1.20). Recently, an anti-IL18 monoclonal antibody, which decreased free IL18 levels, was found to be safe, yet ineffective in a phase II trial for type 2 diabetes. Taken together, these genomic findings implicated IBD as an alternative indication for anti-IL18 therapy, which should be tested in randomized controlled trials.

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

Discovering, developing and validating new disease treatments is a challenging and time-consuming endeavor. Successful drug discovery hinges on selecting the best drug targets with relevance to human disease and evidence that modulating them will be beneficial for patients. Open data initiatives are increasingly placing such knowledge into the public domain. Areas covered: In this review, the authors discuss emerging resources such as Open Targets which integrate key information to prioritize target-disease connections. Researchers can use it, along with other resources, to select potential new therapeutic targets to initiate drug discovery projects. They also discuss public resources such as DrugBank and ChEMBL that offer potential tools to interrogate these targets. Expert opinion: In our opinion, publically available resources are democratizing and connecting information, enabling disease experts to access and prioritize targets of interest in ways that were not possible a few years ago. Moreover, there are several modalities in addition to small molecule perturbation to modulate a target's activity. Drug discovery scientists can now utilize these new resources to simultaneously evaluate a much larger number of targets than previously possible.

Psoriasis is a chronic inflammatory skin disease with complex pathological features and unmet pharmacotherapy needs. Here, we present a framework for developing new therapeutic intervention strategies for psoriasis by utilizing publicly available clinical transcriptomics data sets. By exploring the underlying molecular mechanisms of psoriasis, the effects of subsequent perturbation of these mechanisms by drugs and an integrative analysis, we propose a psoriasis disease signature, identify potential drug repurposing opportunities and present novel target selection methodologies. We anticipate that the outlined methodology or similar approaches will further support biomarker discovery and the development of new drugs for psoriasis.

It is commonly assumed that drug targets are expressed in tissues relevant to their indicated diseases, even under normal conditions. While multiple anecdotal cases support this hypothesis, a comprehensive study has not been performed to verify it. We conducted a systematic analysis to assess gene and protein expression for all targets of marketed and phase III drugs across a diverse collection of normal human tissues. For 87% of gene-disease pairs, the target is expressed in a disease-affected tissue under healthy conditions. This result validates the importance of confirming expression of a novel drug target in an appropriate tissue for each disease indication and strengthens previous findings showing that targets of efficacious drugs should be expressed in relevant tissues under normal conditions. Further characterization of the remaining 13% of gene-disease pairs revealed that most genes are expressed in a different tissue linked to another disease. Our analysis demonstrates the value of extensive tissue specific expression resources.both in terms of tissue and cell diversity as well as techniques used to measure gene expression.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

The gastrointestinal (GI) tract can have significant impact on the regulation of the whole‐body metabolism and may contribute to the development of obesity and diabetes. To systemically elucidate the role of the GI tract in obesity, we performed a transcriptomic analysis in different parts of the GI tract of two obese mouse models: ob/ob and high‐fat diet (HFD) fed mice. Compared to their lean controls, significant changes in the gene expression were observed in both obese mouse groups in the stomach (ob/ob: 959; HFD: 542). In addition, these changes were quantitatively much higher than in the intestine. Despite the difference in genetic background, the two mouse models shared 296 similar gene expression changes in the stomach. Among those genes, some had known associations to obesity, diabetes, and insulin resistance. In addition, the gene expression profiles strongly suggested an increased gastric acid secretion in both obese mouse models, probably through an activation of the gastrin pathway. In conclusion, our data reveal a previously unknown dominant connection between the stomach and obesity in murine models extensively used in research.

Drug discovery remains a difficult business with a very high level of attrition. Many steps in this long process use data generated from various species. One key challenge is to successfully translate the pre-clinical findings of target validation and safety studies in animal models to diverse human beings in the clinic. Advanced computational evolutionary analysis techniques combined with the increasing availability of sequence information enable the application of systematic evolutionary approaches to targets and pathways of interest to drug discovery. These analyses have the potential to increase our understanding of experimental differences observed between species.

Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

The year 2000 stands as a landmark in modern biology: the first draft of the human genome sequence has been completed. For the pharmaceutical industry, this achievement provides tremendous opportunities because the genomic sequence exposes all human drug targets for therapeutic intervention. The challenge for the pharmaceutical companies is to exploit this definitive resource for the identification of potential molecular targets, rapid characterization of their function and validation of their involvement in disease pathology. Bioinformatics approaches provide increasingly crucial tools to systematically support this exploratory target drug discovery activity.

The 1990s and early years of this century have seen a series of large-scale mergers and acquisitions in the Pharmaceutical and Biotech arena. These activities each required integration at multiple levels. One of the most important activities is the integration of the R&D pipelines of the participants. We outline the combined portfolio and bioinformatic strategy that was used, and detail the lessons learned for the longer term, from the GlaxoWellcome-SmithKline-Beecham merger in 2000. To date, this has been the largest merger of two equally sized Pharma R&D organisations.

About 40% (350 kb) of the human MHC class II region has been sequenced and a coordinated effort to sequence the entire MHC is underway. In addition to the coding information (22 genes/pseudogenes), the non-coding sequences reveal novel information on the organisation and evolution of the MHC as demonstrated here by the example of a 200 kb contig that has been analysed for local and global features. In conjunction with cross-species comparisons, our results present new evidence on the structure of isochores, the evolutionary dynamics of repeat-mediated recombination and its effect on certain MHC encoded genes, and a higher than average degree of natural polymorphism that has implications for sequencing the human genome. We also report the finding of a class I-related pseudogene (HLA-ZI) in the middle of the class II region, which provides the first direct evidence for DNA exchange between these two related regions in man.

Improving drug attrition remains a challenge in pharmaceutical discovery and development. A major cause of early attrition is the demonstration of safety signals which can negate any therapeutic index previously established. Safety attrition needs to be put in context of clinical translation (i.e. human relevance) and is negatively impacted by differences between animal models and human. In order to minimize such an impact, an earlier assessment of pharmacological target homology across animal model species will enhance understanding of the context of animal safety signals and aid species selection during later regulatory toxicology studies. Here we sequenced the genomes of the Sus scrofa Gottingen minipig and the Canis familiaris beagle, two widely used animal species in regulatory safety studies. Comparative analyses of these new genomes with other key model organisms, namely mouse, rat, cynomolgus macaque, rhesus macaque, two related breeds (S. scrofa Duroc and C. familiaris boxer) and human reveal considerable variation in gene content. Key genes in toxicology and metabolism studies, such as the UGT2 family, CYP2D6, and SLCO1A2, displayed unique duplication patterns. Comparisons of 317 known human drug targets revealed surprising variation such as species-specific positive selection, duplication and higher occurrences of pseudogenized targets in beagle (41 genes) relative to minipig (19 genes). These data will facilitate the more effective use of animals in biomedical research.

Tumors with high mutation rate such as those of melanoma and non-small cell lung cancers (NSCLC) have shown greatest response to the emerging immune therapies. These tumors harbor a large number of somatic mutations, some of which could be translated into neoantigens, potentially evoking host immune response. To what degree self antigens, many of which are cancer testis (CT) antigens, contribute to enhanced immune response in these tumors has never been systematically studied. Here we show that cancers with higher mutation rate frequently express a larger number of CT antigens than cancers with fewer mutations. In NSCLC, tumors with more than 300 non-synonymous mutations (75th percentile, n = 158) express more than 3 times as many CT antigens as those tumors with fewer than 100 mutations (25th percentile, n = 162), with a P-value of 1.6e-13. In head and neck cancers, tumors with more than 140 non-synonymous mutations (75th percentile, n = 79) express nearly twice as many CT antigens as those tumors with fewer than 60 mutations (25th percentile, n = 80), with a P-value of 0.004. Additionally, in NSCLC, tumors with high mutation rate are associated with higher expression of CD8B and granzyme B, markers representing cytotoxic lymphocytes. Tumors with more expressed CT antigens are also associated with higher expression of these markers. These data suggest that CT antigens, along with neoantigens, may contribute to a stronger host immune response in these hypermutative tumors. Citation Format: Junping Jing, Patrick Mayes, Yan Degenhardt, James R. Brown, Philippe Sanseau. Higher mutation rate is associated with more frequent expression of cancer/testis antigens in human tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 525.

Free DNA was prepared from routinely harvested and fixed cells for high resolution FISH mapping using either a sodium hydroxide/ethanol mixture or 70% formamide. Hybridization signals from cosmid probes appeared as extended lines. The average length of signals on DNA prepared with sodium hydroxide was significantly greater than with formamide. A set of overlapping cosmids from the HLA class II region was used to determine how precisely the actual overlap or gap between probes can be calculated from the measured overlap or gap between their signals. Lengths of the probe signals and their known kilobase lengths were used as an internal ruler. The mean values calculated from the measured length from 30 or more signals for each probe pair showed remarkable conformity with the known kilobase lengths of their overlaps and gaps. Immediately adjacent probes could also be ordered on the released DNA. These simple procedures dramatically increase the speed with which relationships between probes can be determined during contig construction.

Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.

Exon amplification is an increasingly popular approach to the identification of transcribed sequences and will complement other strategies to isolate genes. We have used this system to amplify candidate exons from 32 cosmids, including 8 cosmids which span a well characterized 185-kb region of the human major histocompatibility class II region on Chromosome (Chr) 6. We have examined the efficiency, specificity, and reproducibility of the system in isolating exons from genes known to be present on particular cosmids and have determined the nature and frequency of artefact amplifications in routine cosmid screening. We were able to clone at least one exon from 88% (7/8) of all known genes tested (including exons which are differentially spliced) and obtained artefacts from 19% (6/32) of the cosmids tested. Such artefacts generally arise from the amplification of noncoding sequences flanked by regions with high homology to acceptor and donor splice junctions. We show that the exon amplification procedure can be used successfully with a wide variety of cosmids which have different numbers of genes and gene structures and describe several approaches to the characterization of novel exons cloned in this study.

The proliferation of genomic platform data, ranging from silencing RNAs through mRNA and microRNA expression to proteomics, is providing new insights into the interplay between human and pathogen genes during infection: the so-called ‘host–pathogen interactome’. Exploiting the interactome for novel human drug targets could provide new therapeutic avenues towards the treatment of infectious disease, which could ameliorate the growing clinical challenge of drug-resistant infections. Using the hepatitis C virus interactome as an example, here we suggest a computational biology framework for identifying and prioritizing potential human host targets against infectious diseases.

A novel testis-expressed Zn finger gene (ZNF76) was identified by screening cDNA libraries with cosmids derived from 6p21. ZNF76 is a member of the GLI-Krüppel family of DNA binding proteins. It is conserved in mouse where transcription in testis is initiated at Day 20 after birth. The mouse tcp-11 gene is located in the distal inversion of the t-complex and is developmentally regulated in the same manner as ZNF76. The human homolog of tcp-11 was isolated to allow a precise chromosomal localization. By using a combination of somatic cell hybrids, radiation hybrids, metaphase and interphase fluorescent in situ hybridization, and pulsed-field gel electrophoresis, we mapped the two genes to the 6p21.2 to 6p21.3 region and linked them to each other within 300 kb of DNA, approximately 2 Mb centromeric to the major histocompatibility complex.

Background Related species, such as humans and chimpanzees, often experience the same disease with varying degrees of pathology, as seen in the cases of Alzheimer's disease, or differing symptomatology as in AIDS. Furthermore, certain diseases such as schizophrenia, epithelial cancers and autoimmune disorders are far more frequent in humans than in other species for reasons not associated with lifestyle. Genes that have undergone positive selection during species evolution are indicative of functional adaptations that drive species differences. Thus we investigate whether biomedical disease differences between species can be attributed to positively selected genes. Results We identified genes that putatively underwent positive selection during the evolution of humans and four mammals which are often used to model human diseases (mouse, rat, chimpanzee and dog). We show that genes predicted to have been subject to positive selection pressure during human evolution are implicated in diseases such as epithelial cancers, schizophrenia, autoimmune diseases and Alzheimer's disease, all of which differ in prevalence and symptomatology between humans and their mammalian relatives. In agreement with previous studies, the chimpanzee lineage was found to have more genes under positive selection than any of the other lineages. In addition, we found new evidence to support the hypothesis that genes that have undergone positive selection tend to interact with each other. This is the first such evidence to be detected widely among mammalian genes and may be important in identifying molecular pathways causative of species differences. Conclusion Our dataset of genes predicted to have been subject to positive selection in five species serves as an informative resource that can be consulted prior to selecting appropriate animal models during drug target validation. We conclude that studying the evolution of functional and biomedical disease differences between species is an important way to gain insight into their molecular causes and may provide a method to predict when animal models do not mirror human biology.

Evidence is emerging that micro RNA (miRNA) is an important and potentially polymorphic regulatory layer for silencing gene expression in vivo. Knowledge of miRNA targeting may help to elucidate the function of many human genes in common diseases, providing a powerful target validation technology. Accurate in silico prediction of miRNA targets in mRNA is a critical capability, allowing effective evaluation of the impact of variation on the creation, strengthening, weakening and destruction of miRNA binding sites. Application of such analyses identifies thousands of single-nucleotide polymorphisms, which may potentially impact miRNA regulation of mRNA. The authors believe this information may offer a real opportunity to study miRNA function at a number of levels. First, sequence-focused analysis will help to define the functional boundaries of miRNA target binding. Second, one may be able to identify miRNA target variants in mRNA with a direct role in human disease, which may be valuable therapeutic targets.

Agarwal and colleagues present an analysis of the overlap in the drug targets that are being pursued across the industry, which indicates that more than half of the novel drug targets in current pipelines are being pursued by only one company.

We describe two methods for releasing chromatin from routinely harvested and fixed cells. Using fluorescence in situ hybridization with combinations of probes from the HLA class II region, we show that good signals can be obtained on free chromatin fibers enabling determination of relationships between closely adjacent or overlapping probes.

The availability of the human genome sequence is a 'once in a lifetime' opportunity for scientists to uncover all possible human drug-targets. As the sequence is very large, the best way to identify new genes rapidly is by computational (in silico) methods. There are now many examples in which pharmaceutical companies have identified genes of interest initially by in silico analysis. High-throughput data-generation techniques, such as microarray analysis, are key to the generation of human genome data. Bioinformatics techniques are therefore certain to play an increasingly important role in drug discovery.

Remarkable progress has been made recently in identifying a new gene family related to the capsaicin (vanilloid) receptor, VR1. Using a combination of in silico analysis of expressed sequence tag (EST) databases and conventional molecular cloning, we have isolated a novel vanilloid-like receptor, which we call VRL-2, from human kidney. The translated gene shares 46% and 43% identity with VR1 and VRL-1, respectively, and maps to chromosome 12q23–24.1, a locus associated with bipolar affective disorder. VRL-2 mRNA was most strongly expressed in the trachea, kidney, and salivary gland. An affinity-purified antibody against a peptide incorporating the COOH terminal of the receptor localized VRL-2 immunolabel in the distal tubules of the kidney, the epithelial linings of both trachea and lung airways, serous cells of submucosal glands, and mononuclear cells. Unlike VR1 and VRL-1, VRL-2 was not detected in cell bodies of dorsal root ganglia (DRG) or sensory nerve fibers. However, VRL-2 was found on sympathetic and parasympathetic nerve fibers, such as those innervating the arrector pili smooth muscle in skin, sweat glands, intestine, and blood vessels. At least four vanilloid receptor-like genes exist, the newest member, VRL-2 is found in airway and kidney epithelia and in the autonomic nervous system.

Expressed sequence tags (ESTs) are short single-pass DNA sequences obtained from either end of cDNA clones. These ESTs are derived from a vast number of cDNA libraries obtained from different species. Human ESTs are the bulk of the data and have been widely used to identify new members of gene families, as markers on the human chromosomes, to discover polymorphism sites and to compare expression patterns in different tissues or pathologies states. Information strategies have been devised to query EST databases. Since most of the analysis is performed with a computer, the term "in silico" strategy has been coined. In this chapter we will review the current status of EST databases, the pros and cons of EST-type data and describe possible strategies to retrieve meaningful information.

SUMMARY: A Java interface to radiation hybrid (RH) mapping software is described which enables users to build and interactively refine RH maps over the web. AVAILABILITY: The Java applets described here are available on the internet at http://www.oxmol.com/biolib/webmap/.

International audience; This report describes the identification of a cDNA encoding STK13, a third human protein kinase related to the Drosophila Aurora and the budding yeast Ipl1 kinases. After screening of a human placental cDNA library with a Xenopus laevis cDNA encoding the pEg2 protein kinase and 5' RACE on testis mRNA, a full-length cDNA was isolated. The chromosomal localization of STK13 on 19q13.3-ter between the markers D19S210 and D19S218 was established by a combination of somatic cell and radiation hybrid panel PCR screening. The localization of STK13 on human chromosome 19 was confirmed by fluorescence in situ hybridization (FISH) using a genomic clone containing STK13 as a probe.

Motivation: Expressed Sequence Tags (ESTs) are short single-pass DNA sequences obtained from either ends of cDNA clones. To exploit these sequences efficiently, a dynamic Web-tool has been developed which uses these data to perform fast virtual cloning of cDNAs. Results: Starting with a query sequence, the user is able to identify related ESTs and extend the sequence of interest step by step, possibly to a full-length transcript. Graphical views of the clustering are used to monitor the progress of a particular 'cloning' project. Potential open reading frames are detected by positional base preference, and hyperlinks to other Worldwide Web sites allow the user to retrieve information relevant to each EST in a cluster (e.g. sequence traces, clone size, plate position). Apart from cDNA cloning, this tool also provides a mechanism for collating gene families and polymorphism sites. Availability: This tool is available at the following URL: http://www3.ebi.ac.uk/~gill/ESTblast

The genome of the pufferfish (Fugu rubripes) (400 Mb) is approximately 7.5 times smaller than the human genome, but it has a similar gene repertoire to that of man. If regions of the two genomes exhibited conservation of gene order (i.e., were syntenic), it should be possible to reduce dramatically the effort required for identification of candidate genes in human disease loci by sequencing syntenic regions of the compact Fugu genome. We have demonstrated that three genes (dihydrolipoamide succinyltransferase, S31iii125, and S20i15), which are linked to FOS in the familial Alzheimer disease focus (AD3) on human chromosome 14, have homologues in the Fugu genome adjacent to Fugu cFOS. The relative gene order of cFOS, S31iii125, and S20i15 was the same in both genomes, but in Fugu these three genes lay within a 12.4-kb region, compared to >600 kb in the human AD3 locus. These results demonstrate the conservation of synteny between the genomes of Fugu and man and highlight the utility of this approach for sequence-based identification of genes in human disease loci.

The human Major Histocompatibility Complex (MHC) is located on the short arm of chromosome 6 (6p21.3) and spans about 4 Mb. According to different gene families the MHC is subdivided into a class I, class II and class III region and many of its gene products are associated with the immune system and the susceptibility to various diseases. To date, we have sequenced about 40% (400 kb) of the class II region between HLA-DP and HLA-DQ and a coordinated effort to sequence the entire MHC is well underway. Analysis of the sequence revealed several novel genes and provides new insights into the molecular organisation and evolution of the MHC. All our data are publicly available via the MHC database (MHCDB) which allows rapid access, retrieval and display in the context of other MHC associated data. MHCDB is online available at (http:(/)/www.hgmp.mrc.ac.uk/) and, together with all our sequences also via anonymous ftp (ftp.icnet.uk/icrf-public).

The class II region of the human MHC contains all of the known class II genes: as well as antigen processing components and only one gene not obviously associated with the immune system, RING3. As an approach to understanding linkage disequilibrium and recombination in relation to polymorphism of the region we are cloning and sequencing the class II region. To date, the sequence of the DP-DQ region has almost been completed (see Report by S. Beck). Several sets of genes implicated in the immune system, especially in antigen processing and presentation, are clustered together in the MHC: class I (HLA-A, B, C etc) class II (DR, DQ, DP, DN, DO, DM) LMP2 and 7, TAP1 and 2, TNF, C2, C4, Bf, Hsp70. This situation has provoked speculation that the MHC behaves as a gene cluster in which allelic products of polymorphic genes are maintained on a haplotype so as to co-ordinate T cell repertoire development and deployment. The high levels of linkage disequilibrium across the region are consistent with this idea. Functions of the genes in the MHC are being investigated as a step towards gaining insight into antigen processing and presentation as well as understanding MHC-disease associations. We are concentrating on the functions of the class II-related genes, DM and DN/DO as well as the TAP/LMP cluster.

cDNA clones similar to rabbit muscle phosphatase inhibitor-2 (IPP-2) were isolated from human libraries. On Northern blots two transcripts of approximately 2kbp and approximately 4kbp were detected in all tissues tested. Analysis of cDNA sequences showed that the longer transcripts were similar to the shorter clones but contained extended 3' ends. The human nucleotide sequence was highly homologous (94% identity) to the rabbit IPP-2 sequence and encoded a peptide of 205 amino acids. IPP-2 sequences were highly conserved throughout vertebrates. Southern hybridization results were consistent with the existence of a family of related IPP-2 sequences in the human genome. Most of these are likely to be pseudogenes, since all of the cDNA clones examined could have originated from a single gene. By in situ hybridization IPP-2 sequences were mapped to several different human chromosomes. We sequenced one gene located in the major histocompatibility complex (MHC) on Chromosome (Chr) 6 that contained the entire coding region of IPP-2.

We present here a relational mapping strategy to establish integrated physical and transcriptional maps of large DNA regions. In this study, human chromosome 21 is used as a model because a physical map spanning 40 megabases on the 21q arm has already been achieved. The first step of this global analysis is to extract most of the genic content encoded by this chromosome. For this purpose, exon-amplification and cDNA selection methods are applied in conjunction to derive expressed sequence libraries from chromosome 21-specific cosmids. These resources will constitute one of the tools necessary for the relational analysis, allowing cross-screenings between genomic and coding sequence libraries. Eventually, the new genes will represent a resource to hunt for potential candidate genes implicated in the determinism of severe diseases associated to chromosome 21.