Dennis Kostka

A high-resolution map of human evolutionary constraint using 29 mammals

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease.

Regulatory networks define phenotypic classes of human stem cell lines

Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells—typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation—to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine has highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (which we call the ‘stem cell matrix’) that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method to categorize a collection of ∼150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein–protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks.

Gibbon genome and the fast karyotype evolution of small apes.

Gibbons are small arboreal apes that display an accelerated rate of evolutionary chromosomal rearrangement and occupy a key node in the primate phylogeny between Old World monkeys and great apes. Here we present the assembly and analysis of a northern white-cheeked gibbon (Nomascus leucogenys) genome. We describe the propensity for a gibbon-specific retrotransposon (LAVA) to insert into chromosome segregation genes and alter transcription by providing a premature termination site, suggesting a possible molecular mechanism for the genome plasticity of the gibbon lineage. We further show that the gibbon genera (Nomascus, Hylobates, Hoolock and Symphalangus) experienced a near-instantaneous radiation ∼5 million years ago, coincident with major geographical changes in southeast Asia that caused cycles of habitat compression and expansion. Finally, we identify signatures of positive selection in genes important for forelimb development (TBX5) and connective tissues (COL1A1) that may have been involved in the adaptation of gibbons to their arboreal habitat.

Integrating diverse datasets improves developmental enhancer prediction.

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.

Expression of Late Cell Cycle Genes and an Increased Proliferative Capacity Characterize Very Early Relapse of Childhood Acute Lymphoblastic Leukemia

Purpose: In childhood acute lymphoblastic leukemia (ALL), ∼25% of patients suffer from relapse. In recurrent disease, despite intensified therapy, overall cure rates of 40% remain unsatisfactory and survival rates are particularly poor in certain subgroups. The probability of long-term survival after relapse is predicted from well-established prognostic factors (i.e., time and site of relapse, immunophenotype, and minimal residual disease). However, the underlying biological determinants of these prognostic factors remain poorly understood. Experimental Design: Aiming at identifying molecular pathways associated with these clinically well-defined prognostic factors, we did gene expression profiling on 60 prospectively collected samples of first relapse patients enrolled on the relapse trial ALL-REZ BFM 2002 of the Berlin-Frankfurt-Münster study group. Results: We show here that patients with very early relapse of ALL are characterized by a distinctive gene expression pattern. We identified a set of 83 genes differentially expressed in very early relapsed ALL compared with late relapsed disease. The vast majority of genes were up-regulated and many were late cell cycle genes with a function in mitosis. In addition, samples from patients with very early relapse showed a significant increase in the percentage of S and G2-M phase cells and this correlated well with the expression level of cell cycle genes. Conclusions: Very early relapse of ALL is characterized by an increased proliferative capacity of leukemic blasts and up-regulated mitotic genes. The latter suggests that novel drugs, targeting late cell cycle proteins, might be beneficial for these patients that typically face a dismal prognosis.

A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available.

The Role of GC-Biased Gene Conversion in Shaping the Fastest Evolving Regions of the Human Genome

GC-biased gene conversion (gBGC) is a recombination-associated evolutionary process that accelerates the fixation of guanine or cytosine alleles, regardless of their effects on fitness. gBGC can increase the overall rate of substitutions, a hallmark of positive selection. Many fast-evolving genes and noncoding sequences in the human genome have GC-biased substitution patterns, suggesting that gBGC—in contrast to adaptive processes—may have driven the human changes in these sequences. To investigate this hypothesis, we developed a substitution model for DNA sequence evolution that quantifies the nonlinear interacting effects of selection and gBGC on substitution rates and patterns. Based on this model, we used a series of lineage-specific likelihood ratio tests to evaluate sequence alignments for evidence of changes in mode of selection, action of gBGC, or both. With a false positive rate of less than 5% for individual tests, we found that the majority (76%) of previously identified human accelerated regions are best explained without gBGC, whereas a substantial minority (19%) are best explained by the action of gBGC alone. Further, more than half (55%) have substitution rates that significantly exceed local estimates of the neutral rate, suggesting that these regions may have been shaped by positive selection rather than by relaxation of constraint. By distinguishing the effects of gBGC, relaxation of constraint, and positive selection we provide an integrated analysis of the evolutionary forces that shaped the fastest evolving regions of the human genome, which facilitates the design of targeted functional studies of adaptation in humans.

The Importance of Being Cis: Evolution of Orthologous Fish and Mammalian Enhancer Activity

Conserved noncoding elements (CNEs) in vertebrate genomes often act as developmental enhancers, but a critical issue is how well orthologous CNE sequences retain the same activity in their respective species, a characteristic important for generalization of model organism studies. To quantify how well CNE enhancer activity has been preserved, we compared the anatomy-specific activities of 41 zebra fish CNEs in zebra fish embryos with the activities of orthologous human CNEs in mouse embryos. We found that 13/41 (∼30%) of the orthologous CNE pairs exhibit conserved positive activity in zebra fish and mouse. Conserved positive activity is only weakly associated with either sequence conservation or the absence of bases undergoing accelerated evolution. A stronger effect is that disparate activity is associated with transcription factor binding site divergence. To distinguish the contributions of cis- versus trans-regulatory changes, we analyzed 13 CNEs in a three-way experimental comparison: human CNE tested in zebra fish, human CNE tested in mouse, and orthologous zebra fish CNE tested in zebra fish. Both cis- and trans-changes affect a significant fraction of CNEs, although human and zebra fish sequences exhibit disparate activity in zebra fish (indicating cis regulatory changes) twice as often as human sequences show disparate activity when tested in mouse and zebra fish (indicating trans regulatory changes). In all four cases where the zebra fish and human CNE display a similar expression pattern in zebra fish, the human CNE also displays a similar expression pattern in mouse. This suggests that the endogenous enhancer activity of ∼30% of human CNEs can be determined from experiments in zebra fish alone, and to identify these CNEs, both the zebra fish and the human sequences should be tested.

MicroRNA-17~92 Is Required for Nephrogenesis and Renal Function

Deletion of all microRNAs (miRNAs) in nephron progenitors leads to premature loss of these cells, but the roles of specific miRNAs in progenitors have not been identified. Deletions in the MIR17HG cluster (miR-17~92 in mice), detected in a subset of patients with Feingold syndrome, represent the first miRNA mutations to be associated with a developmental defect in humans. Although MIR17HG is expressed in the developing kidney, and patients with Feingold syndrome caused by MYCN mutations have renal anomalies, it remains unclear to what extent MIR17HG contributes to renal development and function. To define the role of miR-17~92, we generated mice with a conditional deletion of miR-17~92 in nephron progenitors and their derivatives. The nephron progenitor population was preserved in these mice; however, this deletion impaired progenitor cell proliferation and reduced the number of developing nephrons. Postnatally, mutant mice developed signs of renal disease, including albuminuria by 6 weeks and focal podocyte foot process effacement and glomerulosclerosis at 3 months. Taken together, these data support a role for this miRNA cluster in renal development, specifically in the regulation of nephron development, with subsequent consequences for renal function in adult mice.

Microarray based diagnosis profits from better documentation of gene expression signatures.

Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study-external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures.