Temporal profiling of cytokine-induced genes in pancreatic ß-cells by meta-analysis and network inference.

Type 1 Diabetes (T1D) is an autoimmune disease where local release of cytokines such as IL-1ß and IFN-γ contributes to ß-cell apoptosis. To identify relevant genes regulating this process we performed a meta-analysis of 8 datasets of ß-cell gene expression after exposure to IL-1ß and IFN-γ. Two of these datasets are novel and contain time-series expressions in human islet cells and rat INS-1E cells. Genes were ranked according to their differential expression within and after 24 h from exposure, and characterized by function and prior knowledge in the literature. A regulatory network was then inferred from the human time expression datasets, using a time-series extension of a network inference method. The two most differentially expressed genes previously unknown in T1D literature (RIPK2 and ELF3) were found to modulate cytokine-induced apoptosis. The inferred regulatory network is thus supported by the experimental validation, providing a proof-of-concept for the proposed statistical inference approach.

Challenges and recommendations for the translation of biomarkers of aging.

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

Dense genome-wide SNP linkage scan in 301 hereditary prostate cancer families identifies multiple regions with suggestive evidence for linkage.

The search for susceptibility loci in hereditary prostate cancer (HPC) has proven challenging due to genetic and disease heterogeneity. Multiple risk loci have been identified to date, however few loci have been replicated across independent linkage studies. In addition, most previous analyses have been hampered by the relatively poor information content provided by microsatellite scans. To overcome these issues, we have performed linkage analyses on members of 301 HPC families genotyped using the Illumina SNP linkage panel IVb. The information content for this panel, averaged over all pedigrees and all chromosomes, was 86% (range 83-87% over chromosomes). Analyses were also stratified on families according to disease aggressiveness, age at diagnosis and number of affected individuals to achieve more genetically homogeneous subsets. Suggestive evidence for linkage was identified at 7q21 (HLOD = 1.87), 8q22 (KCLOD = 1.88) and 15q13-q14 (HLOD = 1.99) in 289 Caucasian families, and nominal evidence for linkage was identified at 2q24 (LOD = 1.73) in 12 African American families. Analysis of more aggressive prostate cancer phenotypes provided evidence for linkage to 11q25 (KCLOD = 2.02), 15q26 (HLOD = 1.99) and 17p12 (HLOD = 2.13). Subset analyses according to age at diagnosis and number of affected individuals also identified several regions with suggestive evidence for linkage, including a KCLOD of 2.82 at 15q13-q14 in 128 Caucasian families with younger ages at diagnosis. The results presented here provide further evidence for a prostate cancer susceptibility locus on chromosome 15q and demonstrate the power of utilizing high information content SNP scans in combination with homogenous collections of large prostate cancer pedigrees.

Alternating Hemiplegia of Childhood: Retrospective Genetic Study and Genotype-Phenotype Correlations in 187 Subjects from the US AHCF Registry.

Mutations in ATP1A3 cause Alternating Hemiplegia of Childhood (AHC) by disrupting function of the neuronal Na+/K+ ATPase. Published studies to date indicate 2 recurrent mutations, D801N and E815K, and a more severe phenotype in the E815K cohort. We performed mutation analysis and retrospective genotype-phenotype correlations in all eligible patients with AHC enrolled in the US AHC Foundation registry from 1997-2012. Clinical data were abstracted from standardized caregivers' questionnaires and medical records and confirmed by expert clinicians. We identified ATP1A3 mutations by Sanger and whole genome sequencing, and compared phenotypes within and between 4 groups of subjects, those with D801N, E815K, other ATP1A3 or no ATP1A3 mutations. We identified heterozygous ATP1A3 mutations in 154 of 187 (82%) AHC patients. Of 34 unique mutations, 31 (91%) are missense, and 16 (47%) had not been previously reported. Concordant with prior studies, more than 2/3 of all mutations are clusteredin exons 17 and 18. Of 143 simplex occurrences, 58 had D801N (40%), 38 had E815K(26%) and 11 had G947R (8%) mutations [corrected].Patients with an E815K mutation demonstrate an earlier age of onset, more severe motor impairment and a higher prevalence of status epilepticus. This study further expands the number and spectrum of ATP1A3 mutations associated with AHC and confirms a more deleterious effect of the E815K mutation on selected neurologic outcomes. However, the complexity of the disorder and the extensive phenotypic variability among subgroups merits caution and emphasizes the need for further studies.

Diseases as network perturbations.

The tremendous amount of the data obtained from the study of complex biological systems changes our view on the pathogenesis of human diseases. Instead of looking at individual components of biological processes, we focus our attention more on the interaction and dynamics of biological systems. A network representation and analysis of the physiology and pathophysiology of biological systems is an effective way to study their complex behavior. Specific perturbations can trigger cascades of failures, which lead to the malfunctioning of cellular networks and as a result to the development of specific diseases. In this review we discuss recent developments in the field of disease network analysis and highlight some of the topics and views that we think are important for understanding network-based disease mechanisms.

Report on EU-USA workshop: how systems biology can advance cancer research (27 October 2008).

The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer-related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5-20 years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine).Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ-line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer-relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression.A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer-relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data.Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high-quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects.Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas.A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.

A personal journey of discovery: developing technology and changing biology.

This autobiographical article describes my experiences in developing chemically based, biological technologies for deciphering biological information: DNA, RNA, proteins, interactions, and networks. The instruments developed include protein and DNA sequencers and synthesizers, as well as ink-jet technology for synthesizing DNA chips. Diverse new strategies for doing biology also arose from novel applications of these instruments. The functioning of these instruments can be integrated to generate powerful new approaches to cloning and characterizing genes from a small amount of protein sequence or to using gene sequences to synthesize peptide fragments so as to characterize various properties of the proteins. I also discuss the five paradigm changes in which I have participated: the development and integration of biological instrumentation; the human genome project; cross-disciplinary biology; systems biology; and predictive, personalized, preventive, and participatory (P4) medicine. Finally, I discuss the origins, the philosophy, some accomplishments, and the future trajectories of the Institute for Systems Biology.

Identification and characterization of novel SNPs in CHEK2 in Ashkenazi Jewish men with prostate cancer.

Checkpoint kinase 2 (CHEK2) is a protein involved in arresting cell cycle in response to DNA damage. To investigate whether it plays an important role in the development of prostate cancer (PRCA) in the Ashkenazi Jewish (AJ) population, we sequenced CHEK2 in 75 AJ individuals with prostate, breast, or no cancer (n=25 each). We identified seven coding SNPs (five are novel) that changed the amino-acid sequence, resulting in R3W, E394F, Y424H, S428F, D438Y, P509S, and P509L. We determined the frequency of each variant in 76 AJ families collected by members of the International Consortium for Prostate Cancer Genetics (ICPCG) where >or=2 men were affected by PRCA. Only one variant, Y424H in exon 11, was identified in more than two families. Exon 11 was then screened in nine additional AJ ICPCG families (a total of 85 families). The Y424H variant occurred in nine affected cases from four different families; however, it did not completely segregate with the disease. We performed bioinformatics analysis, which showed that Y424H is a non-conservative missense substitution that falls at a position that is invariant in vertebrate CHEK2 orthologs. Both SIFT and Align-GVGD predict that Y424H is a loss of function mutation. However, the frequency of Y424H was not significantly different between unselected AJ cases from Montreal/Memorial Sloan Kettering Cancer Centre (MSKCC) and AJ controls from Israel/MSKCC (OR 1.18, 95%CI: 0.34-4.61, p=.99). Moreover, functional assays using Saccharomyces cerevisiae revealed that the Y424H substitution did not alter function of CHEK2 protein. Although we cannot rule out a subtle influence of the CHEK2 variants on PRCA risk, these results suggest that germline CHEK2 mutations have a minor role in, if any, PRCA susceptibility in AJ men.

Genomic scan of 12 hereditary prostate cancer families having an occurrence of pancreas cancer.

BACKGROUND: Prostate cancer is a genetically heterogeneous disease. Using the occurrence of other cancers in hereditary prostate cancer (HPC) families is a promising strategy for developing genetically homogeneous data sets that can enhance the ability to identify susceptibility loci using linkage analysis. METHODS: Twelve HPC families with the co-occurrence of adenocarcinoma of the pancreas were selected from the Prostate Cancer Genetic Research Study (PROGRESS). Non-parametric linkage analysis for a prostate/pancreas cancer susceptibility phenotype was performed using 441 genome-wide microsatellite markers. RESULTS: No statistically significant linkage signal was detected in this analysis. The strongest linkage signals, as measured by Kong and Cox LOD score (KC LOD), were observed on chromosomes 2q37.2-q37.3 (KC LOD = 1.01; P = 0.02) and 16q23.2 (KC LOC = 1.05; P = 0.01). CONCLUSIONS: Despite the lack of statistically significant findings, four chromosomal regions, three of which (2q, 16q, 17q) were previously noted as harboring potential susceptibility loci, showed suggestive linkage results in this scan.

Genome-wide linkage scan of prostate cancer Gleason score and confirmation of chromosome 19q.

Despite evidence that prostate cancer has a genetic etiology, it has been extremely difficult to confirm genetic linkage results across studies, emphasizing the large extent of genetic heterogeneity associated with this disease. Because prostate cancer is common--approximately one in six men will be diagnosed with prostate cancer in their life--genetic linkage studies are likely plagued by phenocopies (i.e., men with prostate cancer due to environmental or lifestyle factors), weakly penetrant alleles, or a combination of both, making it difficult to replicate linkage findings. One way to account for heterogeneous causes is to use clinical information that is related to the aggressiveness of disease as an endpoint for linkage analyses. Gleason grade is a measure of prostate tumor differentiation, with higher grades associated with more aggressive disease. This semi-quantitative score has been used as a quantitative trait for linkage analysis in several prior studies. Our aim was to determine if prior linkage reports of Gleason grade to specific loci could be replicated, and to ascertain if new regions of linkage could be found. Gleason scores were available for 391 affected sib pairs from 183 hereditary prostate cancer pedigrees as part of the PROGRESS study. Analyzing Gleason score as a quantitative trait, and using microsatellite markers, suggestive evidence for linkage (P-value

Prostate cancer and genetic susceptibility: a genome scan incorporating disease aggressiveness.

BACKGROUND: Prostate cancer is a heterogeneous disease, both genetically and phenotypically. Linkage studies attempting to map genes for hereditary prostate cancer (HPC) have proved challenging, and one potential problem contributing to this challenge is the variability in disease phenotypes. METHODS: We collected clinical data on 784 affected men with prostate cancer from 248 HPC families for whom a genomic screen was performed. Disease characteristics (i.e., Gleason score, stage, prostate-specific antigen (PSA)) were used to classify affected men into categories of clinically insignificant, moderate, or aggressive prostate cancer. To potentially enrich for a genetic etiology, we restricted linkage analyses to only men with aggressive disease, although we used genotype information from all family members; linkage analyses used both dominant and recessive models. In addition, subset analyses considered age at diagnosis, number of affected men per family and other stratifications to try to increase genetic homogeneity. RESULTS: Several regions of interest (heterogeneity LOD score, HLOD>1.0) were identified in families (n=123) with >or=2 affecteds with aggressive prostate cancer. "Suggestive" linkage was observed at chromosome 22q11.1 (Dominant model HLOD=2.18) and the result was stronger (Dominant HLOD=2.75) in families with evidence of male-to-male transmission. A second region at 22q12.3-q13.1 was also highlighted (Recessive model HLOD=1.90) among men with aggressive disease, as was a region on chromosome 18. CONCLUSIONS: These analyses suggest that using clinically defined phenotypes may be a useful approach for simplifying the locus heterogeneity problems that confound the search for prostate cancer susceptibility genes.

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry.

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information.

Oligonucleotide arrays for high-throughput SNPs detection in the MHC class I genes: HLA-B as a model system.

A simple and efficient oligonucleotide array was developed to identify single nucleotide polymorphisms (SNPs) encoded within the highly polymorphic human major histocompatibility complex (MHC) using HLA-B as a model system. A total of 137 probes were designed to represent all known polymorphisms encoded in exons 2 and 3. PCR products were amplified from human genomic DNA and allowed to hybridize with the oligonucleotide array. Hybridization was detected by fluorescence scanning, and HLA-B alleles were assigned by quantitative analysis of the hybridization results. Variables known to influence the specificity of hybridization, such as oligonucleotide probe size, spacer length, surface density, hybridization conditions, and array uniformity and stability were studied. The efficiency and specificity of identifying HLA-B SNPs using the oligonucleotide arrays was evaluated by blinded analysis of 100 samples from unrelated individuals representing all HLA-B phenotypes. The oligonucleotide array method described in this paper provides unambiguous detection of complex heterozygous SNP combinations. This methodological approach may be applied to other highly polymorphic gene systems.

POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer.

DNA arrays are valuable tools in molecular biology laboratories. Their rapid acceptance was aided by the release of plans for a pin-spotting microarrayer by researchers at Stanford. Inkjet microarraying is a flexible, complementary technique that allows the synthesis of arrays of any oligonucleotide sequences de novo. We describe here an open-source inkjet arrayer capable of rapidly producing sets of unique 9,800-feature arrays.

Oligogenic segregation analysis of hereditary prostate cancer pedigrees: evidence for multiple loci affecting age at onset.

Previous studies have suggested strong evidence for a hereditary component to prostate cancer (PC) susceptibility. Here, we analyze 3,796 individuals in 263 PC families recruited as part of the ongoing Prostate Cancer Genetic Research Study (PROGRESS). We use Markov chain Monte Carlo (MCMC) oligogenic segregation analysis to estimate the number of quantitative trait loci (QTLs) and their contribution to the variance in age at onset of hereditary PC (HPC). We estimate 2 covariate effects: diagnosis of PC before and after prostate-specific antigen (PSA) test availability, and presence/absence of at least 1 blood relative with primary neuroepithelial brain cancer (BC). We find evidence that 2 to 3 QTLs contribute to the variance in age at onset of HPC. The 2 QTLs with the largest contribution to the total variance are both effectively dominant loci. We find that the covariate for diagnosis before and after PSA test availability is important. Our findings for the number of QTLs contributing to HPC and the variance contribution of these QTLs will be instructive in mapping and identifying these genes.