Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology.Proteomics technologies have evolved to produce larg...     

The origin of cellular life

This essay presents a scenario of the origin of life that is based on analysis of biological architecture and mechanical design at the microstructural level. My thesis is that the same architectural and energetic constraints that shape cells today also guided the evolution of the first cells and that the molecular scaffolds that support solid-phase biochemistry in modern cells represent living microfossils of past life forms. This concept emerged from the discovery that cells mechanically stabilize themselves using tensegrity architecture and that these same building rules guide hierarchical self-assembly at all size scales (Sci. Amer 278:48-57;1998). When combined with other fundamental design principles (e.g., energy minimization, topological constraints, structural hierarchies, autocatalytic sets, solid-state biochemistry), tensegrity provides a physical basis to explain how atomic and molecular elements progressively self-assembled to create hierarchical structures with increasingly complex functions, including living cells that can self-reproduce.     

Parkinson's disease in 1984: an update.

This update reviews several important topics in the field of Parkinson''s disease, including etiologic studies, the types and mechanisms of drug complications and their treatment, when and how to begin treatment, the association of dementia with Parkinson''s disease, and the development of the newer research tools. The recent discovery of a highly selective neurotoxin (MPTP) that causes parkinsonism in humans and other primates and the use of positron emission tomography in living patients should improve our understanding of the cause of cell death in Parkinson''s disease and assist in the development of more definitive treatment for this common, disabling neurologic condition.     

Genome-Wide Association Study of Maternal and Inherited Loci for Conotruncal Heart Defects

Conotruncal and related heart defects (CTDs) are a group of serious and relatively common birth defects. Although both maternal and inherited genotypes are thought to play a role in the etiology of CTDs, few specific genetic risk factors have been identified. To determine whether common variants acting through the genotype of the mother (e.g. via an in utero effect) or the case are associated with CTDs, we conducted a genome-wide association study of 750 CTD case-parent triads, with follow-up analyses in 358 independent triads. Log-linear analyses were used to assess the association of CTDs with the genotypes of both the mother and case. No association achieved genomewide significance in either the discovery or combined (discovery+follow-up) samples. However, three loci with p-values suggestive of association (p<10⁻⁵) in the discovery sample had p-values <0.05 in the follow-up sample and p-values in the combined data that were lower than in the discovery sample. These included suggestive association with an inherited intergenic variant at 20p12.3 (rs6140038, combined p = 1.0×10⁻⁵) and an inherited intronic variant in KCNJ4 at 22q13.1 (rs2267386, combined p = 9.8×10⁻⁶), as well as with a maternal variant in SLC22A24 at 11q12.3 (rs11231379, combined p = 4.2×10⁻⁶). These observations suggest novel candidate loci for CTDs, including loci that appear to be associated with the risk of CTDs via the maternal genotype, but further studies are needed to confirm these associations.     

A whole-cell computational model predicts phenotype from genotype

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.     

Targeting SLC transporters: small molecules as modulators and therapeutic opportunities

Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery.     

Late life: a new frontier for physiology

Late life is a distinct phase of life that occurs after the aging period and is now known to be general among aging organisms. While aging is characterized by a deterioration in survivorship and fertility, late life is characterized by the cessation of such age-related deterioration. Thus, late life presents a new and interesting area of research not only for evolutionary biology but also for physiology. In this article, we present the theoretical and experimental background to late life, as developed by evolutionary biologists and demographers. We discuss the discovery of late life and the two main theories developed to explain this phase of life: lifelong demographic heterogeneity theory and evolutionary theory based on the force of natural selection. Finally, we suggest topics for future physiological research on late life.     

Linkage disequilibrium: ancient history drives the new genetics

This brief review provides a summary of the biological causes of genetic association between tightly linked markers--termed linkage disequilibrium--and unlinked markers--termed population structure. We also review the utility of linkage disequilibrium data in gene mapping in isolated populations, in the estimation of recombination rates and in studying the history of particular alleles, including the detection of natural selection. We discuss current understanding of the extent and patterns of linkage disequilibrium in the genome, and its promise for genetic association studies in complex disease. Finally, we highlight the importance of using appropriate statistical procedures, such as the false discovery rate, to maximize the chances of success in large scale association studies.     

Hot crenarchaeal viruses reveal deep evolutionary connections

The discovery of archaeal viruses provides insights into the fundamental biochemistry and evolution of the Archaea. Recent studies have identified a wide diversity of archaeal viruses within the hot springs of Yellowstone National Park and other high-temperature environments worldwide. These viruses are often morphologically unique and code for genes with little similarity to other known genes in the biosphere, a characteristic that has complicated efforts to trace their evolutionary history. Comparative genomics combined with structural analysis indicate that spindle-shaped virus lineages might be unique to the Archaea, whereas other icosahedral viruses might share a common lineage with viruses of Bacteria and Eukarya. These studies provide insights into the evolutionary history of viruses in all three domains of life.     

Candidate-gene association study of mothers with pre-eclampsia, and their infants, analyzing 775 SNPs in 190 genes

Pre-eclampsia (PE) affects 5-7% of pregnancies in the US, and is a leading cause of maternal death and perinatal morbidity and mortality worldwide. To identify genes with a role in PE, we conducted a large-scale association study evaluating 775 SNPs in 190 candidate genes selected for a potential role in obstetrical complications. SNP discovery was performed by DNA sequencing, and genotyping was carried out in a high-throughput facility using the MassARRAY(TM) System. Women with PE (n = 394) and their offspring (n = 324) were compared with control women (n = 602) and their offspring (n = 631) from the same hospital-based population. Haplotypes were estimated for each gene using the EM algorithm, and empirical p values were obtained for a logistic regression-based score test, adjusted for significant covariates. An interaction model between maternal and offspring genotypes was also evaluated. The most significant findings for association with PE were COL1A1 (p = 0.0011) and IL1A (p = 0.0014) for the maternal genotype, and PLAUR (p = 0.0008) for the offspring genotype. Common candidate genes for PE, including MTHFR and NOS3, were not significantly associated with PE. For the interaction model, SNPs within IGF1 (p = 0.0035) and IL4R (p = 0.0036) gave the most significant results. This study is one of the most comprehensive genetic association studies of PE to date, including an evaluation of offspring genotypes that have rarely been considered in previous studies. Although we did not identify statistically significant evidence of association for any of the candidate loci evaluated here after adjusting for multiple testing using the false discovery rate, additional compelling evidence exists, including multiple SNPs with nominally significant p values in COL1A1 and the IL1A region, and previous reports of association for IL1A, to support continued interest in these genes as candidates for PE. Identification of the genetic regulators of PE may have broader implications, since women with PE are at increased risk of death from cardiovascular diseases later in life.     

Genome-wide association study in a Lebanese cohort confirms PHACTR1 as a major determinant of coronary artery stenosis

The manifestation of coronary artery disease (CAD) follows a well-choreographed series of events that includes damage of arterial endothelial cells and deposition of lipids in the sub-endothelial layers. Genome-wide association studies (GWAS) of multiple populations with distinctive genetic and lifestyle backgrounds are a crucial step in understanding global CAD pathophysiology. In this study, we report a GWAS on the genetic basis of arterial stenosis as measured by cardiac catheterization in a Lebanese population. The locus of the phosphatase and actin regulator 1 gene (PHACTR1) showed association with coronary stenosis in a discovery experiment with genome wide data in 1,949 individuals (rs9349379, OR?=?1.37, p?=?1.57×10(-5)). The association was replicated in an additional 2,547 individuals (OR?=?1.31, p?=?8.85×10(-6)), leading to genome-wide significant association in a combined analysis (OR?=?1.34, p?=?8.02×10(-10)). Results from this GWAS support a central role of PHACTR1 in CAD susceptibility irrespective of lifestyle and ethnic divergences. This association provides a plausible component for understanding molecular mechanisms involved in the formation of stenosis in cardiac vessels and a potential drug target against CAD.     

Helping Science and Drug Development to Succeed through Pharma-Academia Partnerships: Yale Healthcare Conference 2013

The theme of the 2013 Yale Healthcare Conference was “Partnerships in Healthcare: Cultivating Collaborative Solutions.” The April conference brought together leaders across several sectors of health care, including academic research, pharmaceuticals, information technology, policy, and life sciences investing. In particular, the breakout session titled “Taking R&D Back to School: The Rise of Pharma-Academia Alliances” centered on the partnerships between academic institutions and pharmaceutical companies. Attendees of the session included members of the pharmaceutical industry, academic researchers, and physicians, as well as graduate and professional students. The discussion was led by Dr. Thomas Lynch of Yale University. Several topics emerged from the discussion, including resources for scientific discovery and the management of competing interests in collaborations between academia and the pharmaceutical industry.     

Integrative subtype discovery in glioblastoma using iCluster

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.     

Neonatal DNA methylation patterns associate with gestational age

Risk for adverse neonatal outcome increases with declining gestational age (GA), and changes in DNA methylation may contribute to the relationship between GA and adverse health outcomes in offspring. To test this hypothesis, we evaluated the association between GA and more than 27,000 CpG sites in neonatal DNA extracted from umbilical cord blood from two prospectively-characterized cohorts: (1) a discovery cohort consisting of 259 neonates from women with a history of neuropsychiatric disorders and (2) a replication cohort consisting of 194 neonates of uncomplicated mothers. GA was determined by obstetrician report and maternal last menstrual period. The associations between proportion of DNA methylated and GA were evaluated by fitting a separate linear mixed effects model for each CpG site, adjusting for relevant covariates including neonatal sex, race, parity, birth weight percentile and chip effects. CpG sites in 39 genes were associated with GA (false discovery rate < 0.05) in the discovery cohort. The same CpG sites in 25 of these genes replicated in the replication cohort, with each association replicating in the same direction. Notably, these CpG sites were located in genes previously implicated in labor and delivery (e.g., AVP, OXT, CRHBP and ESR1) or that may influence the risk for adverse health outcomes later in life (e.g., DUOX2, TMEM176A and CASP8). All associations were independent of method of delivery or induction of labor. These results suggest neonatal DNA methylation varies with GA even in term deliveries. The potential contribution of these changes to clinically significant postnatal outcomes warrants further investigation.     

Increased 90-kDa ribosomal S6 kinase (Rsk) activity is protective against mutant huntingtin toxicity

Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.     

细胞微系统技术及其应用

细胞微系统技术研究是目前细胞生物学、微系统科学及药物筛选等学科交叉领域的一个研究热点,其综合利用了微系统平台技术,将细胞的培养、观测和分析在微系统平台上完成,丰富了细胞研究方法,为细胞研究提供了一个全新的研究平台。现对目前细胞微系统研究中几种典型的方法,如立体微结构模型、软光刻、微流体、芯片毛细管电泳、微电极等进行综述,并阐述其在细胞生物学、生命科学等领域相关研究中的应用。     

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.     

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.     

History of the discovery of the life cycle of Toxoplasma gondii

It has been 100 years since the discovery of Toxoplasma gondii in 1908. Its full life cycle was not discovered until 1970 when it was found that it is a coccidian parasite of cats with all non-feline warm blooded animals (including humans) as intermediate hosts. The discovery of the environmentally resistant stage of the parasite, the oocyst, made it possible to explain its worldwide prevalence. In the present paper, events associated with the discovery of its life cycle are recalled.