DNA microarrays are widely used to study changes in gene expression in tumors, but such studies a... more DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules,
Proceedings of the sixth annual international conference on Computational biology - RECOMB '02, 2002
We present a probabilistic framework that models the process by which transcriptional binding exp... more We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show that the learned model predicts expression from sequence, and that it identifies coherent co-regulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these co-regulated "modules" and the combinatorial regulation effects that govern their behavior. ¡ and transcription factor ¢ , whether ¢ regulates ¡ by binding to ¡ 's promoter sequence. These variables are hidden, and a key part of our learning algorithm is to induce their values from the data. The model then contains two components. The first is a model that predicts, based on ¡ 's promoter sequence, whether ¢ regulates ¡ (or more precisely, when ¢ is active, whether it can regulate ¡ ). The second predicts, based on the regulation events for a particular gene ¡ , its expression profile in different settings.
Clustering is commonly used for analyzing gene expression data. Despite their successes, clusteri... more Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. Second, clustering methods cannot readily incorporate additional types of information, such as clinical data or known attributes of genes. To circumvent these shortcomings, we propose the use of a single coherent probabilistic model, that encompasses much of the rich structure in the genomic expression data, while incorporating additional information such as experiment type, putative binding sites, or functional information. We show how this model can be learned from the data, allowing us to discover patterns in the data and dependencies between the gene expression patterns and additional attributes. The learned model reveals context-specific relationships, that exist only over a subset of the experiments in the dataset. We demonstrate the power of our approach on synthetic data and on two real-world gene expression data sets for yeast. For example, we demonstrate a novel functionality that falls naturally out of our framework: predicting the "cluster" of the array resulting from a gene mutation based only on the gene's expression pattern in the context of other mutations. Contact:
Background: Transforming growth factor beta 1 (TGFb1) plays a major role in many lung diseases in... more Background: Transforming growth factor beta 1 (TGFb1) plays a major role in many lung diseases including lung cancer, pulmonary hypertension, and pulmonary fibrosis. TGFb1 activates a signal transduction cascade that results in the transcriptional regulation of genes in the nucleus, primarily through the DNA-binding transcription factor SMAD3. The objective of this study is to identify genome-wide scale map of SMAD3 binding targets and the molecular pathways and networks affected by the TGFb1/SMAD3 signaling in lung epithelial cells.
Meiosis is a complex developmental process that generates haploid cells from diploid progenitors.... more Meiosis is a complex developmental process that generates haploid cells from diploid progenitors. We measured messenger RNA (mRNA) abundance and protein production through the yeast meiotic sporulation program and found strong, stage-specific expression for most genes, achieved through control of both mRNA levels and translational efficiency. Monitoring of protein production timing revealed uncharacterized recombination factors and extensive organellar remodeling. Meiotic translation is also shifted toward noncanonical sites, including short open reading frames (ORFs) on unannnotated transcripts and upstream regions of known transcripts (uORFs). Ribosome occupancy at near-cognate uORFs was associated with more efficient ORF translation; in contrast, some AUG uORFs, often exposed by regulated 5′ leader extensions, acted competitively. This work reveals pervasive translational control in meiosis and helps to illuminate the molecular basis of the broad restructuring of meiotic cells.
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Nature, Jan 19, 2014
High-throughput single-cell transcriptomics offers an unbiased approach for understanding the ext... more High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript's level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a 'core' module of antiviral genes is expressed very early by a few 'precocious' cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secreti...
Proceedings of the National Academy of Sciences, 2004
The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of ma... more The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in ''younger'' classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with ''young'' genes (e.g., mammal-specific human ones), ''old'' genes (e.g., eukaryotespecific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. gene age ͉ gene expression ͉ genome evolution ͉ intron density
Proceedings of the National Academy of Sciences, 2009
Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a cha... more Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a challenging experimental task. Current approaches, relying on sequencing of ESTs or cDNA libraries, are expensive and labor-intensive. Here, we present a general approach for ab initio discovery of the complete transcriptome of the budding yeast, based only on the unannotated genome sequence and millions of short reads from a single massively parallel sequencing run. Using novel algorithms, we automatically construct a highly accurate transcript catalog. Our approach automatically and fully defines 86% of the genes expressed under the given conditions, and discovers 160 previously undescribed transcription units of 250 bp or longer. It correctly demarcates the 5 and 3 UTR boundaries of 86 and 77% of expressed genes, respectively. The method further identifies 83% of known splice junctions in expressed genes, and discovers 25 previously uncharacterized introns, including 2 cases of condition-dependent intron retention. Our framework is applicable to poorly understood organisms, and can lead to greater understanding of the transcribed elements in an explored genome.
Kidney transplantation has been one of the major medical advances of the past 30 years. However, ... more Kidney transplantation has been one of the major medical advances of the past 30 years. However, tissue availability remains a major obstacle. This can potentially be overcome by the use of undifferentiated or partially developed kidney precursor cells derived from early embryos and fetal tissue. Here, transplantation in mice reveals the earliest gestational time point at which kidney precursor cells, of both human and pig origin, differentiate into functional nephrons and not into other, non-renal professional cell types. Moreover, successful organogenesis is achieved when using the early kidney precursors, but not later-gestation kidneys. The formed, miniature kidneys are functional as evidenced by the dilute urine they produce. In addition, decreased immunogenicity of the transplants of early human and pig kidney precursors compared with adult kidney transplants is demonstrated in vivo. Our data pinpoint a window of human and pig kidney organogenesis that may be optimal for trans...
Kidney transplantation has been one of the major medical advances of the past 30 years. However, ... more Kidney transplantation has been one of the major medical advances of the past 30 years. However, tissue availability remains a major obstacle. This can potentially be overcome by the use of undifferentiated or partially developed kidney precursor cells derived from early embryos and fetal tissue. Here, transplantation in mice reveals the earliest gestational time point at which kidney precursor cells, of both human and pig origin, differentiate into functional nephrons and not into other, non-renal professional cell types. Moreover, successful organogenesis is achieved when using the early kidney precursors, but not later-gestation kidneys. The formed, miniature kidneys are functional as evidenced by the dilute urine they produce. In addition, decreased immunogenicity of the transplants of early human and pig kidney precursors compared with adult kidney transplants is demonstrated in vivo. Our data pinpoint a window of human and pig kidney organogenesis that may be optimal for transplantation in humans.
Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an... more Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higherlevel modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.
and then merge sequences with overlapping alignment, spanning splice junctions with reads and pai... more and then merge sequences with overlapping alignment, spanning splice junctions with reads and paired-ends. Assembly-first (de novo) methods, such as ABySS 1 , SOAPdenovo 6 or Oases (E. Birney, European Bioinformatics Institute, personal communication), use the reads to assemble transcripts directly, which can be mapped subsequently to a reference genome, if available. Mapping-first approaches promise, in principle, maximum sensitivity, but depend on correct read-to-reference alignment, a task that is complicated by splicing, sequencing errors and the lack or incompleteness of many reference genomes. Conversely, assembly-first approaches do not require any read-reference alignments, important when the genomic sequence is not available, is gapped, highly fragmented or substantially altered, as in cancer cells.
Trauma survivors show marked differences in the severity and persistence of post-traumatic stress... more Trauma survivors show marked differences in the severity and persistence of post-traumatic stress disorder (PTSD) symptoms. Early symptoms subside in most, but persist as acute and chronic PTSD in a significant minority. The underlying molecular mechanisms or outcome predictors determining these differences are not known. Molecular markers for identifying any mental disorder are currently lacking. Gene expression profiling during the triggering and development of PTSD may be informative of its onset and course. We used oligonucleotide microarrays to measure peripheral blood mononuclear cell (PBMC) gene expression of trauma survivors at the emergency room and 4 months later. Gene expression signatures at both time points distinguished survivors who met DSM-IV diagnostic criteria for PTSD at 1 and 4 months, from those who met no PTSD criterion. Expression signatures at both time points correlated with the severity of each of the three PTSD symptom clusters assessed 4 months following exposure among all survivors. Results demonstrate a general reduction in PBMCs' expression of transcription activators among psychologically affected trauma survivors. Several differentiating genes were previously described as having a role in stress response. These findings provide initial evidence that peripheral gene expression signatures following trauma identify an evolving neuropsychiatric disorder and are informative of its key clinical features and outcome. Replications in larger samples, as well as studies focusing on specific markers within the signatures discovered, are warranted to confirm and extend the diagnostic utility and pathogenetic implications of our results. Molecular Psychiatry (2005) 10, 500-513.
Blood mononuclear cell gene expression signature of postpartum depression
Molecular Psychiatry, 2010
In sorrow thou shalt bring forth children (Genesis 3:16) seems as relevant today, with one of sev... more In sorrow thou shalt bring forth children (Genesis 3:16) seems as relevant today, with one of seven mothers afflicted by a depressive episode, constituting the most common medical complication after delivery. Why mothers are variably affected by mood symptoms postpartum remains unclear, and the pathogenesis and early molecular indicators of this divergent outcome have not been described. We applied a case-control design comparing differential global gene expression profiles in blood mononuclear cells sampled shortly after delivery at the time of inception of postpartum depression (PD). Nine antidepressant naive mothers showing high depressive scores and developing a persisting major depressive episode with postpartum onset were compared with 10 mothers showing low depressive scores and no depressive symptoms on prospective follow-up. A distinctive gene expression signature was observed after delivery among mothers with an emergent PD, with a significant overabundance of transcripts showing a high-fold differential expression between groups, and correlating with depressive symptom severity among all mothers. Early expression signatures correctly classified the majority of PD patients and controls. Those developing persisting PD exhibit a relative downregulation of transcription after delivery, with differential immune activation, and decreased transcriptional engagement in cell proliferation, and DNA replication and repair processes. Our data provide initial evidence indicating that blood cells sampled shortly after delivery may harbor valuable prognostic information for identifying the onset of persisting PD. Some of the informative transcripts and pathways may be implicated in the differential vulnerability that underlies depression pathogenesis.
Understanding the principles governing mammalian gene regulation has been hampered by the difficu... more Understanding the principles governing mammalian gene regulation has been hampered by the difficulty in measuring in vivo binding dynamics of large numbers of transcription factors (TF) to DNA. Here, we develop a high-throughput Chromatin Immuno-Precipitation (HT-ChIP) method to systematically map protein-DNA interactions. HT-ChIP was applied to define the dynamics of DNA binding by 25 TFs and 4 chromatin marks at 4 time-points following pathogen stimulus of dendritic cells. Analyzing over 180,000 TF-DNA interactions we find that TFs vary substantially in their temporal binding landscapes. This data suggests a model for transcription regulation whereby TF networks are hierarchically organized into cell differentiation factors, factors that bind targets prior to stimulus to prime them for induction, and factors that regulate specific gene programs. Overlaying HT-ChIP data on gene-expression dynamics shows that many TF-DNA interactions are established prior to the stimuli, predominantly at immediate-early genes, and identified specific TF ensembles that coordinately regulate geneinduction.
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree f... more A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural EM for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-Step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a sub-optimal one. To escape from such "local optima", we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data, and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.
DNA microarrays are widely used to study changes in gene expression in tumors, but such studies a... more DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules,
Proceedings of the sixth annual international conference on Computational biology - RECOMB '02, 2002
We present a probabilistic framework that models the process by which transcriptional binding exp... more We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show that the learned model predicts expression from sequence, and that it identifies coherent co-regulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these co-regulated "modules" and the combinatorial regulation effects that govern their behavior. ¡ and transcription factor ¢ , whether ¢ regulates ¡ by binding to ¡ 's promoter sequence. These variables are hidden, and a key part of our learning algorithm is to induce their values from the data. The model then contains two components. The first is a model that predicts, based on ¡ 's promoter sequence, whether ¢ regulates ¡ (or more precisely, when ¢ is active, whether it can regulate ¡ ). The second predicts, based on the regulation events for a particular gene ¡ , its expression profile in different settings.
Clustering is commonly used for analyzing gene expression data. Despite their successes, clusteri... more Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. Second, clustering methods cannot readily incorporate additional types of information, such as clinical data or known attributes of genes. To circumvent these shortcomings, we propose the use of a single coherent probabilistic model, that encompasses much of the rich structure in the genomic expression data, while incorporating additional information such as experiment type, putative binding sites, or functional information. We show how this model can be learned from the data, allowing us to discover patterns in the data and dependencies between the gene expression patterns and additional attributes. The learned model reveals context-specific relationships, that exist only over a subset of the experiments in the dataset. We demonstrate the power of our approach on synthetic data and on two real-world gene expression data sets for yeast. For example, we demonstrate a novel functionality that falls naturally out of our framework: predicting the "cluster" of the array resulting from a gene mutation based only on the gene's expression pattern in the context of other mutations. Contact:
Background: Transforming growth factor beta 1 (TGFb1) plays a major role in many lung diseases in... more Background: Transforming growth factor beta 1 (TGFb1) plays a major role in many lung diseases including lung cancer, pulmonary hypertension, and pulmonary fibrosis. TGFb1 activates a signal transduction cascade that results in the transcriptional regulation of genes in the nucleus, primarily through the DNA-binding transcription factor SMAD3. The objective of this study is to identify genome-wide scale map of SMAD3 binding targets and the molecular pathways and networks affected by the TGFb1/SMAD3 signaling in lung epithelial cells.
Meiosis is a complex developmental process that generates haploid cells from diploid progenitors.... more Meiosis is a complex developmental process that generates haploid cells from diploid progenitors. We measured messenger RNA (mRNA) abundance and protein production through the yeast meiotic sporulation program and found strong, stage-specific expression for most genes, achieved through control of both mRNA levels and translational efficiency. Monitoring of protein production timing revealed uncharacterized recombination factors and extensive organellar remodeling. Meiotic translation is also shifted toward noncanonical sites, including short open reading frames (ORFs) on unannnotated transcripts and upstream regions of known transcripts (uORFs). Ribosome occupancy at near-cognate uORFs was associated with more efficient ORF translation; in contrast, some AUG uORFs, often exposed by regulated 5′ leader extensions, acted competitively. This work reveals pervasive translational control in meiosis and helps to illuminate the molecular basis of the broad restructuring of meiotic cells.
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Nature, Jan 19, 2014
High-throughput single-cell transcriptomics offers an unbiased approach for understanding the ext... more High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript's level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a 'core' module of antiviral genes is expressed very early by a few 'precocious' cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secreti...
Proceedings of the National Academy of Sciences, 2004
The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of ma... more The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in ''younger'' classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with ''young'' genes (e.g., mammal-specific human ones), ''old'' genes (e.g., eukaryotespecific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. gene age ͉ gene expression ͉ genome evolution ͉ intron density
Proceedings of the National Academy of Sciences, 2009
Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a cha... more Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a challenging experimental task. Current approaches, relying on sequencing of ESTs or cDNA libraries, are expensive and labor-intensive. Here, we present a general approach for ab initio discovery of the complete transcriptome of the budding yeast, based only on the unannotated genome sequence and millions of short reads from a single massively parallel sequencing run. Using novel algorithms, we automatically construct a highly accurate transcript catalog. Our approach automatically and fully defines 86% of the genes expressed under the given conditions, and discovers 160 previously undescribed transcription units of 250 bp or longer. It correctly demarcates the 5 and 3 UTR boundaries of 86 and 77% of expressed genes, respectively. The method further identifies 83% of known splice junctions in expressed genes, and discovers 25 previously uncharacterized introns, including 2 cases of condition-dependent intron retention. Our framework is applicable to poorly understood organisms, and can lead to greater understanding of the transcribed elements in an explored genome.
Kidney transplantation has been one of the major medical advances of the past 30 years. However, ... more Kidney transplantation has been one of the major medical advances of the past 30 years. However, tissue availability remains a major obstacle. This can potentially be overcome by the use of undifferentiated or partially developed kidney precursor cells derived from early embryos and fetal tissue. Here, transplantation in mice reveals the earliest gestational time point at which kidney precursor cells, of both human and pig origin, differentiate into functional nephrons and not into other, non-renal professional cell types. Moreover, successful organogenesis is achieved when using the early kidney precursors, but not later-gestation kidneys. The formed, miniature kidneys are functional as evidenced by the dilute urine they produce. In addition, decreased immunogenicity of the transplants of early human and pig kidney precursors compared with adult kidney transplants is demonstrated in vivo. Our data pinpoint a window of human and pig kidney organogenesis that may be optimal for trans...
Kidney transplantation has been one of the major medical advances of the past 30 years. However, ... more Kidney transplantation has been one of the major medical advances of the past 30 years. However, tissue availability remains a major obstacle. This can potentially be overcome by the use of undifferentiated or partially developed kidney precursor cells derived from early embryos and fetal tissue. Here, transplantation in mice reveals the earliest gestational time point at which kidney precursor cells, of both human and pig origin, differentiate into functional nephrons and not into other, non-renal professional cell types. Moreover, successful organogenesis is achieved when using the early kidney precursors, but not later-gestation kidneys. The formed, miniature kidneys are functional as evidenced by the dilute urine they produce. In addition, decreased immunogenicity of the transplants of early human and pig kidney precursors compared with adult kidney transplants is demonstrated in vivo. Our data pinpoint a window of human and pig kidney organogenesis that may be optimal for transplantation in humans.
Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an... more Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higherlevel modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.
and then merge sequences with overlapping alignment, spanning splice junctions with reads and pai... more and then merge sequences with overlapping alignment, spanning splice junctions with reads and paired-ends. Assembly-first (de novo) methods, such as ABySS 1 , SOAPdenovo 6 or Oases (E. Birney, European Bioinformatics Institute, personal communication), use the reads to assemble transcripts directly, which can be mapped subsequently to a reference genome, if available. Mapping-first approaches promise, in principle, maximum sensitivity, but depend on correct read-to-reference alignment, a task that is complicated by splicing, sequencing errors and the lack or incompleteness of many reference genomes. Conversely, assembly-first approaches do not require any read-reference alignments, important when the genomic sequence is not available, is gapped, highly fragmented or substantially altered, as in cancer cells.
Trauma survivors show marked differences in the severity and persistence of post-traumatic stress... more Trauma survivors show marked differences in the severity and persistence of post-traumatic stress disorder (PTSD) symptoms. Early symptoms subside in most, but persist as acute and chronic PTSD in a significant minority. The underlying molecular mechanisms or outcome predictors determining these differences are not known. Molecular markers for identifying any mental disorder are currently lacking. Gene expression profiling during the triggering and development of PTSD may be informative of its onset and course. We used oligonucleotide microarrays to measure peripheral blood mononuclear cell (PBMC) gene expression of trauma survivors at the emergency room and 4 months later. Gene expression signatures at both time points distinguished survivors who met DSM-IV diagnostic criteria for PTSD at 1 and 4 months, from those who met no PTSD criterion. Expression signatures at both time points correlated with the severity of each of the three PTSD symptom clusters assessed 4 months following exposure among all survivors. Results demonstrate a general reduction in PBMCs' expression of transcription activators among psychologically affected trauma survivors. Several differentiating genes were previously described as having a role in stress response. These findings provide initial evidence that peripheral gene expression signatures following trauma identify an evolving neuropsychiatric disorder and are informative of its key clinical features and outcome. Replications in larger samples, as well as studies focusing on specific markers within the signatures discovered, are warranted to confirm and extend the diagnostic utility and pathogenetic implications of our results. Molecular Psychiatry (2005) 10, 500-513.
Blood mononuclear cell gene expression signature of postpartum depression
Molecular Psychiatry, 2010
In sorrow thou shalt bring forth children (Genesis 3:16) seems as relevant today, with one of sev... more In sorrow thou shalt bring forth children (Genesis 3:16) seems as relevant today, with one of seven mothers afflicted by a depressive episode, constituting the most common medical complication after delivery. Why mothers are variably affected by mood symptoms postpartum remains unclear, and the pathogenesis and early molecular indicators of this divergent outcome have not been described. We applied a case-control design comparing differential global gene expression profiles in blood mononuclear cells sampled shortly after delivery at the time of inception of postpartum depression (PD). Nine antidepressant naive mothers showing high depressive scores and developing a persisting major depressive episode with postpartum onset were compared with 10 mothers showing low depressive scores and no depressive symptoms on prospective follow-up. A distinctive gene expression signature was observed after delivery among mothers with an emergent PD, with a significant overabundance of transcripts showing a high-fold differential expression between groups, and correlating with depressive symptom severity among all mothers. Early expression signatures correctly classified the majority of PD patients and controls. Those developing persisting PD exhibit a relative downregulation of transcription after delivery, with differential immune activation, and decreased transcriptional engagement in cell proliferation, and DNA replication and repair processes. Our data provide initial evidence indicating that blood cells sampled shortly after delivery may harbor valuable prognostic information for identifying the onset of persisting PD. Some of the informative transcripts and pathways may be implicated in the differential vulnerability that underlies depression pathogenesis.
Understanding the principles governing mammalian gene regulation has been hampered by the difficu... more Understanding the principles governing mammalian gene regulation has been hampered by the difficulty in measuring in vivo binding dynamics of large numbers of transcription factors (TF) to DNA. Here, we develop a high-throughput Chromatin Immuno-Precipitation (HT-ChIP) method to systematically map protein-DNA interactions. HT-ChIP was applied to define the dynamics of DNA binding by 25 TFs and 4 chromatin marks at 4 time-points following pathogen stimulus of dendritic cells. Analyzing over 180,000 TF-DNA interactions we find that TFs vary substantially in their temporal binding landscapes. This data suggests a model for transcription regulation whereby TF networks are hierarchically organized into cell differentiation factors, factors that bind targets prior to stimulus to prime them for induction, and factors that regulate specific gene programs. Overlaying HT-ChIP data on gene-expression dynamics shows that many TF-DNA interactions are established prior to the stimuli, predominantly at immediate-early genes, and identified specific TF ensembles that coordinately regulate geneinduction.
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree f... more A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural EM for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-Step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a sub-optimal one. To escape from such "local optima", we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data, and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.
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Papers by Nir Friedman