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Posts Tagged ‘upregulation’


Transcription Modulation

Author and Curator: Larry H. Bernstein, MD, FCAP 

 

This portion of the transcription series deals with transcription factors and the effects of their binding on metabolism. This also has implications for pharmaceutical target identification.

The Functional Consequences of Variation in Transcription Factor Binding
DA. Cusanovich, B Pavlovic, JK. Pritchard*, Y Gilad*
1 Department of Human Genetics, 2 Howard Hughes Medical Institute, University of Chicago, Chicago, IL 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA.
PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then

  • examined which genes were impacted by the reduced transcription factor level.

Our results implicate some attributes

  • that might influence what binding is functional, but they also suggest that
  • a simple model of functional vs. non-functional binding may not suffice.

Many studies have focused on characterizing the genomic locations of TF binding, but

  • it is unclear whether TF binding at any specific locus has
  • functional consequences with respect to gene expression output.

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone. However, the regulatory code of the human genome is much more complicated than

  • the triplet code of protein coding sequences, and is highly context-specific,
  • depending on cell-type and other factors.

Moreover, regulatory regions are not necessarily organized into

  • discrete, easily identifiable regions of the genome and
  • may exert their influence on genes over large genomic distances

Genomic studies addressing questions of the regulatory logic of the human genome have largely taken one of two approaches.

  1. collecting transcription factor binding maps using techniques such as ChIPseq
    and DNase-seq
  2. mapping various quantitative trait loci (QTL), such as gene expression levels
    (eQTLs) [7], DNA methylation (meQTLs) [8] and chromatin accessibility (dsQTLs)

Cumulatively, binding map studies and QTL map studies have

  • led to many insights into the principles and mechanisms of gene regulation.

However, there are questions that neither mapping approach on its own is well equipped to address. One outstanding issue is

  • the fraction of factor binding in the genome that is ‘‘functional’’,
    which we define here to mean that
  • disturbing the protein-DNA interaction leads to a measurable
  • downstream effect on gene regulation.

Transcription factor knockdown could be used to address this problem, whereby

  • the RNA interference pathway is employed to greatly reduce
  • the expression level of a specific target gene by using small interfering RNAs (siRNAs).

The response to the knockdown can then be measured by collecting RNA after the knockdown and

  • measuring global changes in gene expression patterns
  • after specifically attenuating the expression level of a given factor.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

The expression data from all samples were normalized together using

  • quantile  normalization followed by batch correction using the RUV-2 method.

We then performed several quality control analyses to confirm

  1. that the quality of the data was high,
  2. that there were no outlier samples, and
  3. that the normalization methods reduced the influence of confounders

In order to identify genes that were expressed at a significantly different level

  • in the knockdown samples compared to the negative controls,
  • we used likelihood-ratio tests within the framework of a fixed effect linear model.

Following normalization and quality control of the arrays,

  • we identified genes that were differentially expressed between
  • the three knockdown replicates of each factor and the six controls.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

Reassuringly, we did not observe a significant correlation between

  • the knockdown efficiency of a given factor and
  • the number of genes classified as differentially expressed foci.

Because we knocked down 59 different factors in this experiment

  • we were able to assess general patterns associated with the perturbation of transcription factors
  • beyond merely the number of affected target genes.

Globally, despite the range in the number of genes we identified as

  • differentially expressed in each knockdown,
  • the effect sizes of the differences in expression were relatively modest and
  • consistent in magnitude across all knockdowns.

The median effect size following the knockdown experiment for genes classified as

  • differentially expressed at an FDR of 5% in any knockdown was
  • a 9.2% difference in expression level between the controls and the knockdown (Figure 2),
  • while the median effect size for any individual knockdown experiment ranged between 8.1% and 11.0%.
    (this was true whether we estimated the knockdown effect based on qPCR (P = 0.10; Figure 1D) or microarray (P = 0.99; not shown) data.

Nor did we observe a correlation between

  • variance in qPCR-estimated knockdown efficiency (between replicates) and
  • the number of genes differentially expressed (P = 0.94; Figure 1E).

We noticed that the large variation in the number of differentially expressed genes

  • extended even to knockdowns of factors from the same gene family.

Figure 1. Differential expression analysis.
(a) Examples of differential expression analysis results for the genes HCST and IRF4. The top two panels are ‘MA plots’ of the mean Log2(expression level) between the knockdown arrays and the controls for each gene (x-axis) to the Log2(Fold-Change) between the knockdowns and controls (y-axis). Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The gene targeted by the siRNA is highlighted in red. The bottom two panels are ‘volcano plots’ of the Log2(Fold-Change) between the knockdowns and controls (x-axis) to the P-value for differential expression (y-axis). The dashed line marks the 5% FDR threshold. Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The red dot marks the gene targeted by the siRNA.
(b) Barplot of number of differentially expressed genes in each knockdown experiment.
(c) Comparison of the knockdown level measured by qPCR (RNA sample collected 48 hours posttransfection) and the knockdown level measured by microarray.
(d) Comparison of the level of knockdown of the transcription factor at 48 hrs (evaluated by qPCR; x-axis) and the number of genes differentially expressed in the knockdown experiment (y-axis).
(e) Comparison of the variance in knockdown efficiency between replicates for each transcription factor (evaluated by qPCR; x-axis) and the number of differentially expressed genes in the knockdown experiment (y-axis).

Differential expression analysis

Differential expression analysis

http://dx.doi.org:/10.1371/journal.pgen.1004226.g001

Figure 2. Effect sizes for differentially expressed genes.
Boxplots of absolute Log2(fold-change) between knockdown arrays and control arrays for all genes identified as differentially expressed in each experiment. Outliers are not plotted. The gray bar indicates the interquartile range across all genes differentially expressed in all knockdowns. Boxplots are ordered by the number of genes differentially expressed in each experiment. Outliers were not plotted.

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

http://dx.doi.org:/10.1371/journal.pgen.1004226.g002

Knocking down SREBF2 (1,286 genes differentially expressed), a key regulator of cholesterol homeostasis,

  • results in changes in the expression of genes that are
  • significantly enriched for cholesterol and sterol biosynthesis annotations.

While not all factors exhibited striking enrichments for relevant functional categories and pathways,

  • the overall picture is that perturbations of many of the factors
  • primarily affected pathways consistent with their known biology.

In order to assess functional TF binding, we next incorporated

  • binding maps together with the knockdown expression data.

We combined binding data based on DNase-seq footprints in 70 HapMap LCLs, reported by Degner et al. (Table S5)

  • and from ChIP-seq experiments in LCL GM12878, published by ENCODE.

We were thus able to obtain genome wide binding maps for a total of 131 factors that were either

  • directly targeted by an siRNA in our experiment (29 factors) or were
  • differentially expressed in one of the knockdown experiments.

We classified a gene as a bound target of a particular factor when

  • binding of that factor was inferred within 10kb of the transcription start site (TSS) of the target gene.

Using this approach, we found that the 131 TFs were bound

  • in proximity to a median of 1,922 genes per factor (range 11 to 7,053 target genes).

We considered binding of a factor to be functional if the target gene

  • was differentially expressed after perturbing the expression level the bound transcription factor.

We then asked about the concordance between

  • the transcription factor binding data and the knockdown expression data.
  •  the extent to which differences in gene expression levels following the knockdowns
  • might be predicted by binding of the transcription factors
  • within the putative regulatory regions of the responsive genes. and also
  • what proportion of putative target (bound) genes of a given TF were
  • differentially expressed following the knockdown of the factor.

Focusing only on the binding sites classified using the DNase-seq data
(which were assigned to a specific instance of the binding motif, unlike the ChIP data),

  • we examined sequence features that might distinguish functional binding.

In particular, whether binding at conserved sites was more likely to be functional  and

  • whether binding sites that better matched the known PWM for the factor were more likely to be functional.

We did not observe a significant shift in the conservation of functional binding sites (Wilcoxon rank sum P = 0.34),

  • but we did observe that binding around differentially expressed genes occurred at sites
  • that were significantly better matches to the canonical binding motif.

Figure 3. Intersecting binding data and expression data for each knockdown.
(a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1).
(b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors.
(c) Boxplot summarizing the distribution of the fraction of bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g003

Considering bound targets determined from either the ChIP-seq or DNase-seq data, we observed that

  • differentially expressed genes were associated with both
  • a higher number of binding events for the relevant factors within 10 kb of the TSS (P,10216; Figure 4A)
  • as well as with a larger number of different binding factors
    (considering the siRNA-targeted factor and any TFs that were DE in the knockdown; P,10216; Figure 4B).

Figure 4. Degree of binding correlated with function. Boxplots comparing
(a) the number of sites bound, and
(b) the number of differentially expressed transcription factors binding events near functionally or non-functionally bound genes. We considered binding for siRNA-targeted factor and any factor differentially expressed in the knockdown.
(c) Focusing only on genes differentially expressed in common between each pairwise set of knockdowns we tested for enrichments of functional binding (y-axis). Pairwise comparisons between knockdown experiments were binned by the fraction of differentially expressed transcription factors in common between the two experiments. For these boxplots, outliers were not plotted.

Degree of binding correlated with function

Degree of binding correlated with function

http://dx.doi.org:/10.1371/journal.pgen.1004226.g004

We examined the distribution of binding about the TSS. Most factor binding was concentrated

  • near the TSS whether or not the genes were classified as differentially expressed (Figure 5A).
  • the distance from the TSS to the binding sites was significantly longer for differentially expressed genes (P,10216; Fig. 5B).

Figure 5. Distribution of functional binding about the TSS.
(a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming the distances and outliers were not plotted.

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

http://dx.doi.doi:/10.1371/journal.pgen.1004226.g005

We investigated the distribution of factor binding across various chromatin states, as defined by Ernst et al. This dataset lists

  • regions of the genome that have been assigned to different activity states
  • based on ChIP-seq data for various histone modifications and CTCF binding.

For each knockdown, we separated binding events

  • by the genomic state in which they occurred and then
  • tested whether binding in that state was enriched around differentially expressed genes.

After correcting for multiple testing of genes that were differentially expressed.

  • 19 knockdowns showed significant enrichment for binding in ‘‘strong enhancers’’
  • four knockdowns had significant enrichments for ‘‘weak enhancers’’,
  • eight knockdowns showed significant depletion of binding in ‘‘active promoters’’ ,
  • six knockdowns had significant depletions for ‘‘transcription elongation’’,

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Figure 6. Magnitude and direction of differential expression after knockdown.
(a) Density plot of all Log2(fold-changes) between the knockdown arrays and controls for genes that are differentially expressed at 5% FDR in one of the knockdown experiments as well as bound by the targeted transcription factor.
(b) Plot of the fraction of differentially expressed putative direct targets that were up-regulated in each of the knockdown experiments.

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g006

We found no correlation between the number of paralogs and the fraction of bound targets that were differentially expressed. We also did not observe a significant correlation when we considered whether

  • the percent identity of the closest paralog might be predicative of
  • the fraction of bound genes that were differentially expressed following the knockdown (Figure S8).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

In addition to considering

  • the distinguishing characteristics of functional binding, we also examined
  • the direction of effect that perturbing a transcription factor had on the expression level of its direct targets.

We specifically addressed whether

  • knocking down a particular factor tended to drive expression of its putatively direct (namely, bound) targets up or down,
  • which can be used to infer that the factor represses or activates the target, respectively.

Transcription factors have traditionally been thought of primarily as activators, and previous work from our group is consistent with that notion. Surprisingly, the most straightforward inference from the present study is that

  • many of the factors function as repressors at least as often as they function as activators.
  1. EZH2 had a negative regulatory relationship with the largest fraction of direct targets (68.8%),
    consistent with – the known role of EZH2 as the active member of the Polycomb group complex PC2
  2. while JUND seemed to have a positive regulatory relationship with the largest fraction of direct targets (66.7%),
    and with – the biochemical characterization of the AP-1 complex (of which JUND is a component) as a transactivator.

More generally, however, our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

To more explicitly address the effect that our proximity-based definition of target genes might have on our analyses, we reanalyzed

  • the overlap between factor binding and differential expression following the knockdowns
  • using an independent, empirically determined set of target genes.

Thurman et al. used correlations in DNase hypersensitivity between

  • intergenic hypersensitive sites and promoter hypersensitive sites across diverse tissues
  • to assign intergenic regulatory regions to specific genes,
  • independently of proximity to a particular promoter.

We performed this alternative analysis in which we

  • assigned binding events to genes based on the classification of Thurman et al.

We then considered the overlap between binding and differential expression in this new data set. The results were largely

  • consistent with our proximity-based observations.

A median of 9.5% of genes that were bound by a factor were

  • also differentially expressed following the knockdown of that factor
    (compared to 11.1% when the assignment of binding sites to genes is based on proximity).

From the opposite perspective, a median of 28.0% of differentially expressed genes were bound by that factor
(compared to 32.3% for the proximity based definition). The results of this analysis are summarized in Table S7.

Our results should not be considered a comprehensive census of regulatory events in the human genome. Instead, we adopted a gene-centric approach,

  • focusing only on binding events near the genes for which we could measure expression
  • to learn some of the principles of functional transcription factor binding.

In light of our observations a reassessment of our estimates of binding may be warranted. In particular, because functional binding is skewed away from promoters (our system is apparently not well-suited to observe functional promoter binding, perhaps because of protection by large protein complexes),

  • a more conservative estimate of the fraction of binding that is indeed functional would not consider data within the promoter.

Importantly, excluding the putative promoter region from our analysis (i.e. only considering a window .1 kb from the TSS and ,10 kb from the TSS)

  • does not change our conclusions.

Considering this smaller window,

  • a median of 67.0% of expressed genes are still classified as bound by
  1. either the knocked down transcription factor or
  2. a downstream factors that is differentially expressed in each experiment,

yet a median of only 8.1% of the bound genes are

  • also differentially expressed after the knockdowns.

Much of what distinguishes functional binding (as we define it) has yet to be explained. We are unable to explain much of the differential expression observed in our experiments by the presence of least one relevant binding event. This may not be altogether surprising, as

  • we are only considering binding in a limited window around the transcription start site.

To address these issues, more factors should be perturbed to further evaluate the robustness of our results and to add insight. Together, such studies will help us develop a more sophisticated understanding of functional transcription factor binding in particular, the gene regulatory logic more generally.

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

E Shmelkov, Z Tang, I Aifantis, A Statnikov*
Biology Direct 2011; 6(15).  http://www.biology-direct.com/content/6/1/15

Recently the biological pathways have become a common and probably the most popular form of representing biochemical information for hypothesis generation and validation. These maps store wide knowledge of complex molecular interactions and regulations occurring in the living organism in a simple and obvious way, often using intuitive graphical notation. Two major types of biological pathways could be distinguished.

  1. Metabolic pathways incorporate complex networks of protein-based interactions and modifications, while
  2. signal transduction and transcriptional regulatory pathways are usually considered to provide information on mechanisms of transcription

While there are a lot of data collected on human metabolic processes,

  • the content of signal transduction and transcriptional regulatory pathways varies greatly in quality and completeness.

An indicative comparison of MYC transcriptional targets reported in ten different pathway databases reveals that these databases differ greatly from each other (Figure 1). Given that MYC is involved

  • in the transcriptional regulation of approximately 15% of all genes,

one cannot argue that the majority of pathway databases that contain

  • less than thirty putative transcriptional targets of MYC are even close to complete.

More importantly, to date there have been no prior genome-wide evaluation studies (that are based on genome-wide binding and gene expression assays) assessing pathway databases

Background: While pathway databases are becoming increasingly important in most types of biological and translational research, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors:

  1. MYC,
  2. NOTCH1,
  3. BCL6,
  4. TP53,
  5. AR,
  6. STAT1,
  7. RELA.

The employed benchmarking methodology first involves integrating

  • genome-wide binding with functional gene expression data
  • to derive direct targets of transcription factors.

Then the lists of experimentally obtained direct targets

  • are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

Results: The results of this study show that for the majority of pathway databases,

  • the overlap between experimentally obtained target genes and
  • targets reported in transcriptional regulatory pathway databases is
  • surprisingly small and often is not statistically significant.

The only exception is MetaCore pathway database which

  • yields statistically significant intersection with experimental results in 84% cases.

The lists of experimentally derived direct targets obtained in this study can be used

  • to reveal new biological insight in transcriptional regulation,  and we
  • suggest novel putative therapeutic targets in cancer.

Conclusions: Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by

  • solid scientific evidence and rigorous empirical evaluation.

In the current study we perform

(1) an evaluation of ten commonly used pathway databases,

  • assessing the transcriptional regulatory pathways, considered in the current study as
  • the interactions of the type ‘transcription factor-transcriptional targets’.

This involves integration of human genome wide functional microarray or RNA-seq gene expression data with

  • protein-DNA binding data from ChIP-chip, ChIP-seq, or ChIP-PET platforms
  • to find direct transcriptional targets of the seven well known transcription factors:
  • MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The choice of transcription factors is based on their important role in oncogenesis and availability of binding and expression data in the public domain.

(2) the lists of experimentally derived direct targets are used to assess the quality and completeness of 84 transcriptional regulatory pathways from four publicly available (BioCarta, KEGG, WikiPathways and Cell Signaling Technology) and six commercial (MetaCore, Ingenuity Pathway Analysis, BKL TRANSPATH, BKL TRANSFAC, Pathway Studio and GeneSpring Pathways) pathway databases.

(3) We measure the overlap between pathways and experimentally obtained target genes and assess statistical significance of this overlap, and we demonstrate that experimentally derived lists of direct transcriptional targets

  • can be used to reveal new biological insight on transcriptional regulation.

We show this by analyzing common direct transcriptional targets of

  • MYC, NOTCH1 and RELA
  • that act in interconnected molecular pathways.

Detection of such genes is important as it could reveal novel targets of cancer therapy.

Figure 1 Number of genes in common between MYC transcriptional targets derived from ten different pathway databases. Cells are colored according to their values from white (low values) to red (high values). (not shown)

statistical methodology for comparison

statistical methodology for comparison

Figure 2 Illustration of statistical methodology for comparison between a gold-standard and a pathway database

Since we are seeking to compare gene sets from different studies/databases, it is essential to transform genes to standard identifiers. That is why we transformed all
gene sets to the HUGO Gene Nomenclature Committee approved gene symbols and names. In order to assess statistical significance of the overlap between the resulting gene sets, we used the hypergeometric test at 5% a-level with false discovery rate correction for multiple comparisons by the method of Benjamini and Yekutieli. The alternative hypothesis of this test is that two sets of genes (set A from pathway
database and set B from experiments) have greater number of genes in common than two randomly selected gene sets with the same number of genes as in sets A and B. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 700 in the experimentally derived list (gold-standard), and their intersection is 16 genes (Figure 2a). If we select on random from a total of
20,000 genes two sets with 300 and 700 genes each, their overlap would be greater or equal to 16 genes in 6.34% times. Thus, this overlap will not be statistically significant at 5% a-level (p = 0.0634). On the other hand, consider that for the pathway database #2, there are 30 direct targets of that transcription factor, and their intersection with the 700-gene gold-standard is only 6 genes. Even though the size of this intersection is rather small, it is unlikely to randomly select 30 genes (out of 20,000) with an overlap greater or equal to 6 genes with a 700-gene gold-standard (p = 0.0005, see Figure 2a). This overlap is statistically significant at 5% a-level.

We also calculate an enrichment fold change ratio (EFC) for every intersection between a gold-standard and a pathway database. For a given pair of a gold-standard and a pathway database, EFC is equal to the observed number of genes in their intersection, divided by the expected size of intersection under the null hypothesis (plus machine epsilon, to avoid division by zero). Notice however that larger values of EFC may correspond to databases that are highly incomplete and contain only a few relations. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 50 in the experimentally derived list (gold-standard), and their intersection is 30 genes (Figure 2b). If we select on random from a total of 20,000 genes two sets with 300 and 50 genes each, their expected overlap under the null hypothesis will be equal to 0.75. Thus, the EFC ratio will be equal to 40 (= 30/0.75). On the other hand, consider that for the pathway database #2, there are 2 direct
targets of that transcription factor, and their intersection with the 50-gene gold-standard is only 1 gene. Even though the expected overlap under the null hypothesis will be equal to 0.005 and EFC equal to 200 (5 times bigger than for the database #1), the size of this intersection with the gold-standard is 30 times less than for database #1 (Figure 2b).

Figure 3 Comparison between different pathway databases and experimentally derived gold-standards for all considered transcription factors. Value in a given cell is a number of overlapping genes between a gold-standard and a pathway-derived gene set. Cells
are colored according to their values from white (low values) to red (high values). Underlined values in red represent statistically significant intersections. (not shown)

Figure 4 Summary of the pathway databases assessment. Green cells represent statistically significant intersections between experimentally derived gold-standards and transcriptional regulatory pathways. White cells denote results that are not statistically significant. Numbers are the enrichment fold change ratios (EFC) calculated for each intersection. (not shown)

At the core of this study was creation of gold-standards of transcriptional regulation in humans that can be compared with target genes reported in transcriptional regulatory pathways. We focused on seven well known transcription factors and obtained gold-standards

  • by integrating genome-wide transcription factor-DNA binding data (from ChIP-chip, ChIP-seq, or ChIP-PET platforms)
  • with functional gene expression microarray and RNA-seq data.

The latter data allows to survey changes in the transcriptomes on a genome-wide scale

  • after the inhibition or over-expression of the transcription factor in question.

However, change in the expression of a particular gene could be caused either by the direct effect of the removal or introduction of a given transcription factor, as well as by an indirect effect, through the change in expression level of some other gene(s). It is essential

  • to integrate data from these two sources to
  • obtain an accurate list of gene targets that are directly regulated by a transcription factor.

It is worth noting that tested pathway databases typically do not give distinction between cell-lines, experimental conditions, and other details relevant to experimental systems in which data were obtained. These databases in a sense propose a ‘universal’ list of transcriptional targets. However, it is known that

  • transcriptional regulation in a cell is dynamic and works differently for different systems and stimuli.

This accentuates the major limitation of pathway databases and emphasizes

  • importance of deriving a specific list of transcriptional targets for the current experimental system.

In this study we followed the latter approach by developing gold-standards for specific cell characterized biological systems and experimental conditions.

The approach used here  for building gold-standards of direct mechanistic knowledge has several limitations. (see article).  Nevertheless, our results suggest that multiple transcription factors can co-operate and control both physiological differentiation and malignant transformation, as demonstrated utilizing combinatorial gene-profiling for

  • NOTCH1, MYC and RELA targets.

These studies might lead us to multi-pathway gene expression “signatures”

  • essential for the prediction of genes that could be targeted in cancer treatments.

In agreement with this hypothesis, several of the genes identified in our analysis have been suggested to be putative therapeutic targets in leukemia, with either preclinical or clinical trials underway (CDK4, CDK6, GSK3b, MYC, LCK, NFkB2, BCL2L1, NOTCH1).

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

I Izeddin†, V Récamier†‡, L Bosanac, II Cissé, L Boudarene, et al.
1Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Inserm, and CNRS UMR; 2Laboratoire Kastler Brossel, CNRS UMR, Departement de Physique et Institut de Biologie
de l’Ecole Normale Supérieure (IBENS), Paris, Fr; 3Transcription Imaging Consortium, Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, US; + more.
Biophysics and structural biology | Cell biology eLife 2014;3:e02230. http://dx.doi.org:/10.7554/eLife.02230

Transcription factors are

  • proteins that control the expression of genes in the nucleus, and
  • they do this by binding to other proteins or DNA.

First, however, these regulatory proteins need to overcome the challenge of

  • finding their targets in the nucleus, which is crowded with other proteins and DNA.

Much research to date has focused on measuring how fast proteins can diffuse and spread out throughout the nucleus. However these measurements only make sense if these proteins have access to the same space within the nucleus.

Now, Izeddin, Récamier et al. have developed a new technique to track

  • single protein molecules in the nucleus of mammalian cells.

A transcription factor called c-Myc and another protein called P-TEFb

  • were tracked and while they diffused at similar rates,
  • they ‘explored’ the space inside the nucleus in very different ways.

Izeddin, Récamier et al. found that c-Myc explores the nucleus in a so-called ‘non-compact’ manner: this means that it

  • can move almost everywhere inside the nucleus, and has an equal chance
  • of reaching any target regardless of its position in this space.

P-TEFb, on the other hand, searches

  • the nucleus in a ‘compact’ way.

This means that it is constrained to follow a specific path

  • through the nucleus and is therefore guided to its potential targets.

Izeddin, Récamier et al. explain that

  • the different ‘search strategies’ used by these two proteins
  • influence how long it takes them to find their targets and
  • how far they can travel in a given time.

These findings, together with information about

  • where and when different proteins interact in the nucleus,

will be essential to understand how the organization of the genome within the nucleus

  • can control the expression of genes.

The next challenge will now be to

  • uncover what determines a
  • protein’s search strategy in the nucleus, as well as
  • the potential ways that this strategy might be regulated.

Mueller et al., 2010; Normanno et al., 2012). These transient interactions are essential to ensure a fine regulation of binding site occupancy—by competition or by altering the TF concentration—but must also be persistent enough to enable the assembly of multicomponent complexes (Dundr, 2002; Darzacq and Singer, 2008; Gorski et al., 2008; Cisse et al., 2013).
In parallel to the experimental evidence of the fast diffusive motion of nuclear factors, our understanding of the intranuclear space has evolved from a homogeneous environment to an organelle where spatial arrangement among genes and regulatory sequences play an important role in transcriptional control (Heard and Bickmore, 2007). The nucleus of eukaryotes displays a hierarchy of organized structures (Gibcus and Dekker, 2013) and is often referred to as a
crowded environment.
How crowding influences transport properties of macromolecules and organelles in the cell is a fundamental question in quantitative molecular biology. While a restriction of the available space for diffusion can slow down transport processes, it can also channel molecules towards their targets increasing their chance to meet interacting partners. A widespread observation in quantitative cell biology is that the diffusion of molecules is anomalous, often attributed to crowding in the nucleoplasm, cytoplasm, or in the membranes of the cell (Höfling and Franosch, 2013). An open debate remains on how to determine whether diffusion is anomalous or normal (Malchus and Weiss, 2009; Saxton, 2012), and the mechanisms behind anomalous diffusion (Saxton, 2007). The answer to these questions bears important consequences for the understanding of the biochemical reactions of the cell.
The problem of diffusing molecules in non-homogenous media has been investigated in different fields. Following the seminal work of de Gennes (1982a), (1982b) in polymer physics, the study of diffusivity of particles and their reactivity has been generalized to random or disordered media (Kopelman, 1986; Lindenberg et al., 1991). These works have set a framework to interpret the mobility of macromolecular complexes in the cell, and recently in terms of kinetics of biochemical reactions (Condamin et al., 2007). Experimental evidence has also been found, showing the influence
of the glass-like properties of the bacterial cytoplasm in the molecular dynamics of intracellular processes (Parry et al., 2014). These studies demonstrate that the geometry of the medium in which diffusion takes place has important repercussions for the search kinetics of molecules. The notion of compact and non-compact exploration was introduced by de Gennes (1982a) in the context of dense polymers and describes two fundamental types of diffusive behavior. While a non-compact explorer leaves a significant number of available sites unvisited, a compact explorer performs a redundant
exploration of the space. In chemistry, the influence of compactness is well established to describe dimensional effects on reaction rates (Kopelman, 1986).
In this study, we aim to elucidate the existence of different types of mobility of TFs in the eukaryotic nucleus, as well as the principles governing nuclear exploration of factors relevant to transcriptional control. To this end, we used single-molecule (SM) imaging to address the relationship between the nuclear geometry and the search dynamics of two nuclear factors having distinct functional roles: the proto-oncogene c-Myc and the positive transcription elongation factor (P-TEFb). c-Myc is a basic helix-loop-helix DNA-binding transcription factor that binds to E-Boxes; 18,000 E-boxes are found in the genome, and c-Myc affects the transcription of numerous genes (Gallant and Steiger, 2009).
Recently, c-Myc has been demonstrated to be a general transcriptional activator upregulating transcription of nearly all genes (Lin et al., 2012; Nie et al., 2012). P-TEFb is an essential actor in the transcription regulation driven by RNA Polymerase II. P-TEFb is a cyclin-dependent kinase, comprising a CDK9 and a Cyclin T subunit. It phosphorylates the elongation control factors SPT5 and NELF to allow productive elongation of class II gene transcription (Wada et al., 1998). The carboxy-terminal domain (CTD) of the catalytic subunit RPB1 of polymerase II is also a major target of P-TEFb (Zhou et al., 2012). c-Myc and P-TEFb are therefore two good examples of transcriptional regulators binding to numerous sites in the nucleus; the latter binds to the transcription machinery itself and the former directly to DNA.

Single particle tracking (SPT) constitutes a powerful method to probe the mobility of molecules in living cells (Lord et al., 2010). In the nucleus, SPT has been first employed to investigate the dynamics of mRNAs (Fusco et al., 2003; Shav-Tal et al., 2004) or for rheological measurements of the nucleoplasm using inert probes (Bancaud et al., 2009). Recently, the tracking of single nuclear factors has been facilitated by the advent of efficient in situ tagging methods such as Halo
tags (Mazza et al., 2012). An alternative approach takes advantage of photoconvertible tags (Lippincott-Schwartz and Patterson, 2009) and photoactivated localization microscopy (PALM) (Betzig et al., 2006; Hess et al., 2006). Single particle tracking PALM (sptPALM) was first used to achieve high-density diffusion maps of membrane proteins (Manley et al., 2008). However, spt-PALM experiments have typically been limited to proteins with slow mobility (Manley et al., 2008) or those that undergo restricted motions (Frost et al., 2010; English et al., 2011).

Recently, by inclusion of light-sheet illumination, it has been used to determine the binding characteristics of TFs to DNA (Gebhardt et al., 2013). In this study, we developed a new sptPALM procedure adapted for the recording of individual proteins rapidly diffusing in the nucleus of mammalian cells. We used the photoconvertible fluorophore Dendra2 (Gurskaya et al., 2006) and took advantage of tilted illumination (Tokunaga et al., 2008). A careful control of the photoconversion rate minimized the background signal due to out-of-focus activated molecules, and we could thus follow the motion of individual proteins freely diffusing within the nuclear volume. With this sptPALM technique, we recorded large data sets (on the order of 104 single translocations in a single imaging session), which were essential for a proper statistical analysis of the search dynamics.
We applied our technique to several nuclear proteins and found that diffusing factors do not sense a unique nucleoplasmic architecture: c-Myc and P-TEFb adopt different nuclear space-exploration strategies, which drastically change the way they reach their specific targets. The differences observed between the two factors were not due to their diffusive kinetic parameters but to the geometry of their exploration path. c-Myc and our control protein, ‘free’ Dendra2, showed free diffusion in a three-dimensional nuclear space. In contrast, P-TEFb explored the nuclear volume by sampling a space of reduced dimensionality, displaying characteristics of exploration constrained in fractal structures.
The role of the space-sampling mode in the search strategy has long been discussed from a theoretical point of view (de Gennes, 1982a; Kopelman, 1986; Lindenberg et al., 1991). Our experimental results support the notion that it could indeed be a key parameter for diffusion-limited chemical reactions in the closed environment of the nucleus (Bénichou et al., 2010). We discuss the implications of our observations in terms of gene expression control, and its relation to the spatial organization of genes within the nucleus.

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Directions for Genomics in Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP

 

J. Craig Venter

J. Craig Venter (Photo credit: Wikipedia)

Otto Heinrich Warburg

Otto Heinrich Warburg (Photo credit: Wikipedia)

Purpose

This discussion will identify the huge expansion of genomic technology in the search for  biopharmacotherapeutic targets that continue to be explored involving different levels and interacting signaling pathways.   There are several methods of analyzing gene expression that will be discussed. Great primary emphasis required investigation of combinations of mutations expressed in different cancer types.  James Watson has proposed a major hypothesis that expresses the need to focus on “central” “driver mutations” that correspond with the regulation of gene expression, cell proliferation, and cell metabolism eith a critical rejection of antioxiant benefits.  What hasn’t been know is why drug resistance develops and whether the cellular migration and aerobic glycolysis can be redirected after cell metastasis occurs.  I attempt to bring out the complexities of current efforts.

.Introduction

  • This discussion is a continuation of a previous discussion on the role of genomics if discovery of therapeutic targets for cancer, each somewhat different, but all related to:
  • The reversal of carcinoma by targeting a key driver of multiple signaling pathways that activate cell proliferation
  • Pinpointing a stage in a multistage process at which tumor progression links to changes in morphology from basal cells to invasive carcinoma with changes in polarity and loss of glandular architecture
  • Reversal of the carcinoma through using a small molecule that either is covalently bound to a nanoparticle delivery system that blocks or reverses tumor development
  • Synthesis of a small molecule that interacts with the translation of the genome either by substitution of a key driver molecule or by blocking at the mRNA stage of translation
  • Blocking more than one signaling pathway that are links to carcinogenesis and cellular proliferation and invasion

Difficulty of the problem

A problem expressed by James Watson is that the investigations that are ongoing

  • are following a pathway that is not driven by attacking the “primary” driver of carcinogenesis.

He uses the Myc gene as an example, as noted in the previous discussion. The problem may be more complicated than he envisions.

  • The most consistent problem in chemotherapy, irrespective of the design and the target has been cancer remission for a short time followed by recurrence, and then
  • switching to another drug, or combination chemotherapy.

It is common to “clean” the field at the time of resection using radiotherapy before chemotherapy.

  • But the goal is understood to be “palliation”, not cure.

This raises a serious issue in the hypothesis posed by Watson. The issue is

  • whether there is a core locus of genetic regulation that is common to carcinogenesis irrespective of tissue metabolic expression.
  • This is supported by the observation that tissue specific express is lost in cancer cells by de-differentiation.

Historical Perspective

AEROBIC GLYCOLYSIS

In 1967 Otto Warburg published his view in a paper “The prime cause and prevention of cancer”.
There are primary and secondary causes of all diseases

  • plague – primary: plague bacillus
  • plague – secondary: filth, rats, and fleas

cancer, above all diseases,

  • has countless seconday causes
  • primary – replacement of respiration of oxygen in normal body tissue by fermentation of glucose with conversion from obligate aerobic to anaerobic, as in bacterial cells

The cornerstone to understanding cancer is in study of the energetics of life

This thinking came out of decades of work in the Dahlem Institute Kaiser Wilhelm pre WWII and Max Planck Institute after WWII, supported by the Rockefeller Foundation.

  • The oxygen- and hydrogen-transferring enzymes were discovered and isolated.
  • The methods were elegant for that time, using a manometer that improved on the method used by Haldane, that did not allow the leakage of O2 or CO2.
  • The interest was initiated by the increased growth of Sea Urchin eggs after fertization, which turned out to be not comparable to the rapid growth of cancer cells.
  • Warburg used both normal and cancer tissue and measured the utilization of O2. He found
    • that the normal tissue did not accumulate lactic acid.
    • Cancer tissue generated lactic acid
    • the rate of O2 consumption the same as normal tissue, but
    • the rate of lactate formation far exceeded any tissue, except the retina.
    • This was a discovery studied by “Pasteur” 60 years earlier (facultative aerobes), which he called the Pasteur effect.
    • Hematopoietic cells of bone marrow develop aerobic glycolysis when exposed to a low oxygen condition.

He then followed on an observation by Otto Meyerhoff (Embden-Myerhoff cycle) that in muscle

  • the consumption of one molecule of oxygen generates two molecules of lactate, but in aerobic glycolysis, the relationship disappears.
  • He expressed the effectiveness of respiration by the ‘Meyerhoff quotient’.
  • He found that cancer cells didn’t have a quotient of ‘2’

The role of the allosteric enzyme phosphofructokinase (PFK) not then known, would tie together the glycolytic and gluconeogenic pathways.
He used a heavy metal ion chelator ethylcarbylamine to

  • sever the link and turn on aerobic glycolysis.

The explanation for this was provided years later by the work fleshed out by Lynen, Bucher, Lowry, Racker, and Sols.

  • The rate-limiting enzyme, PFK is regulated by the concentrations of ATP, ADP, and inorganic phosphate. The ethylcarbylamide was an ‘uncoupler’ of oxidative phosphorylation.

Warburg understood that when normal cells switched to aerobic glycolysis

  • it is a re-orientation of normal cell expression.
  • this provides the basis for the inference that neoplastic cells become more like each other than their cell of origin.
  • embryonic cells can be transformed into cancer cells under hypoxic conditions
  • re-exposure to higher oxygen did not cause reversion back to normal cells.

Warburg publically expressed the rejected view in 1954 (at age 83) that restriction of chemical wastes, food additives, and air pollution would substantially reduce cancer rates.

His emphasis on the impairment of respiration was inadequate.

  • the prevailing view today is loss of controlled growth of normal cells in cancer cells.

Otto Warburg: Cell Physiologist, Biochemist, and Eccentric. Hans Krebs, in collaboration with Roswitha Schmid. Clarendon Press, Oxford. 1991.ISBN 0-19-858171-8.

The Human Genome Project

The Human Genome Project, driven by Francis Collins at NIH, and by Craig Venter at the Institute for Genome Research (TIGR) had parallel projects to map the human chromosome, completed in 2003. It originally aimed to map the nucleotides contained in a human haploid reference genome (more than three billion). TIGR was the first complete genomic sequencing of a free living organism, Haemophilus influenzae, in 1995. This used a shotgun sequencing technique pioneered earlier, but which had never been used for a whole bacterium.
Venter broke away from the HGP and started Celera in 1998 because of resistance to the shotgun sequency method, and his team completed the genome sequence in three years – seven years’ less time than the HGP timetable (using the gene of Dr. Venter). TIGR eventually sequenced and analyzed more than 50 microbial genomes. Its bioinformatics group developed

  • pioneering software algorithms that were used to analyze these genomes,
  • including the automatic gene finder GLIMMER and
  • the sequence alignment program MUMmer.

In 2002, Venter created and personally funded the J. Craig Venter Institute (JCVI) Joint Technology Center (JTC), which specialized in high throughput sequencing.  The JTC, in the top ranks of scientific institutions worldwide, sequenced nearly 100 million base pairs of DNA per day for its affiliated institutions (JCVI) .

He received his his Ph.D. degree in physiology and pharmacology from the University of California, San Diego in 1975 under biochemist Nathan O. Kaplan. A full professor at the State University of New York at Buffalo, he joined the National Institutes of Health in 1984. There he learned of a technique for rapidly identifying all of the mRNAs present in a cell and began to use it to identify human brain genes. The short cDNA sequence fragments discovered by this method are called expressed sequence tags (ESTs), a name coined by Anthony Kerlavage at TIGR.
Venter believed that shotgun sequencing was the fastest and most effective way to get useful human genome data. There was a belief that shotgun sequencing was less accurate than the clone-by-clone method chosen by the HGP, but the technique became widely accepted by the scientific community and is still the de facto standard used today.

References

Shreeve, James (2004). The Genome War: How Craig Venter Tried to Capture the Code of Life and Save the World. Knopf. ISBN 0375406298.
Sulston, John (2002). The Common Thread: A Story of Science, Politics, Ethics and the Human Genome. Joseph Henry Press. ISBN 0309084091.
“The Human Genome Project Race”. Center for Biomolecular Science & Engineering, UC Santa Cruz. Retrieved 20 March 2012.
Venter, J. Craig (2007). A Life Decoded: My Genome: My Life. Viking Adult. ISBN 0670063584.

Use of a Fluorophor Probe

An article has been discussed by Dr.  Tilda Barilya on use of a sensitive fluorescent probe in the near IR spectrum at > 700 nm to identify malignant ovarian cells in-vivo in abdominal exploration by tagging an overexpressed FR-α (folate-FITA)
The author makes the point that:

  • In ovarian cancer, the FR-α appears to constitute a good target because it is overexpressed in 90–95% of malignant tumors, especially serous carcinomas.
  • Targeting ligand, folate, is attractive as it is nontoxic, inexpensive and relatively easily conjugated to a fluorescent dye to create a tumor-specific fluorescent contrast agent.
  • The report is identified as “ the first in-human proof-of-principle of the use of intraoperative tumor-specific fluorescence imaging in staging and debulking surgery for ovarian cancer using the systemically administered targeted fluorescent agent folate-FITC.”

While this does invoke possibilities for prognosis, the decision to perform the surgery, whether laparoscopic or open, is late in the discovery process. However, it does suggest the possibility that the discovery and the treatment might be combined if the biomarker itself had the fluorescence to identify the overexpression, but it also is combined with a tag to block the overexpession. This hypothetical possibility is now expressed below.
https://pharmaceuticalintelligence.com/2013/01/19/ovarian-cancer-and-fluorescence-guided-surgery-a-report/

Gene Editing

Dr. Aviva Lev-Ari reports that a new technique developed at MIT Broad Institute and the Rockefeller University can edit DNA in precise locations taken from Science News titled Editing Genome With High Precision: New Method to Insert Multiple Genes in Specific Locations, Delete Defective Genes”.

Using this system, scientists can alter

  • several genome sites simultaneously and
  • can achieve much greater control over where new genes are inserted

According to Feng Zhang, this is an improvement beyond splicing the gene in specific locations and insertion of complexes difficult to assemble known as transcription activator-like effector nucleases (TALENs).

  • The researchers create DNA-editing complexes
  • using naturally occurring bacterial protein-RNA systems
  • that recognize and snip viral DNA, including
  • a nuclease called Cas9 bound to short RNA sequences.
  • they target specific locations in the genome, and
  • when they encounter a match, Cas9 cuts the DNA.

This approach can be used either to

  • disrupt the function of a gene or
  • to replace it with a new one.
  • To replace the gene, a DNA template for the new gene has to be copied into the genome after the DNA is cut. The method is also very precise —
  • if there is a single base-pair difference between the RNA targeting sequence and the genome sequence, Cas9 is not activated.

In its first iteration, it appears comparable in efficiency to what zinc finger nucleases and TALENs have to offer.
The research team has deposited the necessary genetic components with a nonprofit called Addgene, and they have also created a website with tips and tools for using this new technique.
The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Anne Trafton.
Le Cong, F. Ann Ran, David Cox, Shuailiang Lin, Robert Barretto, Naomi Habib, Patrick D. Hsu, Xuebing Wu, Wenyan Jiang, Luciano Marraffini, and Feng Zhang. Multiplex Genome Engineering Using CRISPR/Cas Systems. Science, 3 January 2013 DOI: 10.1126/science.1231143. http://Science.com. Editing genome with high precision: New method to insert multiple genes in specific locations, delete defective genes. ScienceDaily. Retrieved January 20, 2013, from http://www.sciencedaily.com­ /releases/2013/01/130103143205.htm?goback=%2Egde_4346921_member_205356312.

Dr. Lev-Ari also reports on a study of early detection of breast cancer in “Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment“, by Dr. Rotem Karni and PhD student Vered Ben Hur at the Institute for Medical Research Israel-Canada of the Hebrew University. https://pharmaceuticalintelligence.com/2013/01/17/mechanism-involved-in-breast-cancer-cell-growth-function-in-early-detection-treatment/
These researchers have discovered a new mechanism by which breast cancer cells switch on their aggressive cancerous behavior. The discovery provides a valuable marker for the early diagnosis and follow-up treatment of malignant growths.
The method they use is

  • RNA splicing and insertion.
  • The information needed for the production of a mature protein is encoded in segments called exons .
  • In the splicing process, the non-coding segments of the RNA (introns) are spliced from the pre-mRNA and
  • the exons are joined together.

Alternative splicing is when a specific ”scene” (or exon) is either inserted or deleted from the movie (mRNA), thus changing its meaning.

  • Over 90 percent of the genes in our genome undergo alternative splicing of one or more of their exons, and
  • the resulting changes in the proteins encoded by these different mRNAs are required for normal function.
  • the normal process of alternative splicing is altered in cancer, and
  • ”bad” protein forms are generated that aid cancer cell proliferation and survival.

The researchers reported in online Cell Reports that breast cancer cells

  • change the alternative splicing of an important enzyme, called S6K1, which is
  • a protein involved in the transmission of information into the cell.
  • when this happens, breast cancer cells start to produce shorter versions of this enzyme and
  • these shorter versions transmit signals ordering the cells to grow, proliferate, survive and invade other tissues (otherwise proliferation is suppressed)

The application to biotherapeutics would be to ”reverse” the alternative splicing of S6K1 in cancer cells back to the normal situation as a novel anti-cancer therapy.

Additional Developments:

A*STAR Scientists Pinpoint Genetic Changes that Spell Cancer: Fruit flies light the way for scientists to uncover genetic changes.

With a new approach, researchers may rapidly distinguish the range of

  • genetic changes that are causally linked to cancer (i.e. “driver” mutations)
  • versus those with limited impact on cancer progression.

This study published in the prestigious journal Genes & Development could pave the way to design more targeted treatment against different cancer types, based on the specific cancer-linked mutations present in the patient, an advance in the development of personalized medicine.

Signaling pathways involved in tumour formation are conserved from fruit flies to humans. In fact, about 75 percent of known human disease genes have a recognizable match in the genome of fruit flies.
Leveraging on their genetic similarities, Dr Hector Herranz, a post-doctorate from the Dr. Stephen Cohen’s team developed an innovative strategy to genetically screen the whole fly genome for “cooperating” cancer genes.

  • These genes appear to have little or no impact on cancer.
  • However, they cooperate with other cancer genes, so that
  • the combination causes aggressive cancer, which
  • neither would cause alone.

In this study, the team was specifically looking for genes that

  • could cooperate with EGFR “driver” mutation,
  • a genetic change commonly associated with breast and lung cancers in humans.
  • SOCS5 (reported in this paper) is one of the several new “cooperating” cancer genes to be identified.

Already, there are indications that levels of SOCS5 expression are

  • reduced in breast cancer, and
  • patients with low levels of SOCS5 have poor prognosis.”

The IMCB team is preparing to explore the use of SOCS5 as a biomarker in diagnosis for cancer.
http://genes&development.com

Probing What Fuels Cancer

‘Altered cellular metabolism is a hallmark of cancer,’ says Dr Patrick Pollard, in the Nuffield Department of Clinical Medicine at Oxford. Most cancer cells get the energy they need predominantly through a high rate of glycolysis, allowing cancer cells deal with the low oxygen levels that tend to be present in a tumour.

But whether dysfunctional metabolism causes cancer, as Warburg believed, or is something that happens afterwards is a different question. In the meantime, gene studies rapidly progressed and indicated that genetic changes occur in cancer.

DNA mutations spring up all the time in the body’s cells, but

  • most are quickly repaired.
  • Alternatively the cell might shut down or be killed off (apoptosis) before any damage is caused. However, the repair machinery is not perfect.
  • If changes occur that bypass parts of the repair machinery or sabotage it,
  • the cell can escape the body’s normal controls on growth and
  • DNA changes can begin to accumulate as the cell becomes cancerous.

Patrick believes certain changes in cells can’t always be accounted for by ‘genetics.’
He is now collaborating with Professor Tomoyoshi Soga’s large lab at Keio University in Japan, which has been at the forefront of developing the technology for metabolomics research over the past couple of decades.

The Japanese lab’s ability to

  • screen samples for thousands of compounds and metabolites at once, and
  • the access to tumour material and cell and animal models of disease
  • enables them to probe the metabolic changes that occur in cancer.

There is reason to believe that

  • dysfunctional cell metabolism is important in cancer.
  • genes with metabolic functions are associated with some cancers
  • changes in the function of a metabolic enzyme have been implicated in the development of gliomas.

These results have led to the idea that some metabolic compounds, or metabolites, when they accumulate in cells, can cause changes to metabolic processes and set cells off on a path towards cancer.

Patrick Pollard and colleagues have now published a perspective article in the journal Frontiers in Molecular and Cellular Oncology that proposes fumarate as such an ‘oncometabolite’. Fumarate is a standard compound involved in cellular metabolism.

The researchers summarize evidence that shows how

  • accumulation of fumarate when an enzyme goes wrong affects various biological pathways in the cell.
  • It shifts the balance of metabolic processes and disrupts the cell in ways that could favour development of cancer.

Patrick and colleagues write in their latest article that the shift in focus of cancer research to include cancer cell metabolism ‘has highlighted how woefully ignorant we are about the complexities and interrelationships of cellular metabolic pathways’.

http://FrontiersMolecularCellularOncology.com

Extensive Promoter-Centered Chromatin Interactions Provide a Topological Basis for Transcription Regulation
(Li G, Ruan X, Auerbach RK, Sandhu KS, et al.) Cell 2012; 148(1-2): 84-98. http://cell.com

Using genome-wide Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET),
mapped long-range chromatin interactions associated with RNA polymerase II in human cells
uncovered widespread promoter-centered intragenic, extragenic, and intergenic interactions.

  • These interactions further aggregated into higher-order clusters
  • proximal and distal genes were engaged through promoter-promoter interactions.
  • most genes with promoter-promoter interactions were active and transcribed cooperatively
  • some interacting promoters could influence each other implying combinatorial complexity of transcriptional controls.

Comparative analyses of different cell lines showed that

  • cell-specific chromatin interactions could provide structural frameworks for cell-specific transcription,
  • and suggested significant enrichment of enhancer-promoter interactions for cell-specific functions.
  • genetically-identified disease-associated noncoding elements were spatially engaged with corresponding genes through long-range interactions.

Overall, our study provides insights into transcription regulation by

  • three-dimensional chromatin interactions for both housekeeping and
  • cell-specific genes in human cells.

New Nucleoporin: Regulator of Transcriptional Repression and Beyond.

(NJ Sarma and K Willis) Nucleus 2012; 3(6): 1–8; http://Nucleus.com © 2012 Landes Bioscience

Transcriptional regulation is a complex process that requires the integrated action of many multi-protein complexes.
The way in which a living cell coordinates the action of these complexes in time and space is still poorly understood.

  • nuclear pores, well known for their role in 3′ processing and export of transcripts, also participate in the control of transcriptional initiation.
  • nuclear pores interface with the well-described machinery that regulates initiation.

This work led to the discovery that

  • specific nucleoporins are required for binding of the repressor protein Mig1 to its site in target promoters.
  • Nuclear pores are involved in repressing, as well as activating, transcription.

Here we discuss in detail the main models explaining our result and consider what each implies about the roles that nuclear pores play in the regulation of gene expression.

Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes.

(M Burton, M Thomassen, Q Tan, and TA Kruse.) Cancer Informatics 2012:11 193–217 doi: 10.4137/CIN.S10375

The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that

  • genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location.

In this study we compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance

  • within the same dataset,
  • feature set validation perfor­mance, and
  • validation performance of entire classifiers in strictly independent datasets

were assessed by

  • 10 times repeated 10-fold cross validation,
  • leave-one-out cross validation, and
  • one-fold validation, respectively.

To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome

  • in strictly independent data sets, both
  • between different and
  • within similar microarray platforms, while
  • classifiers had a poorer performance when validated in strictly independent datasets.

The study showed that MG- and SG-feature sets perform equally well in classifying indepen­dent data. Furthermore, SG-classifiers significantly outperformed MG-classifier

  • when validation is conducted between datasets using similar platforms, while
  • no significant performance difference was found when validation was performed between different platforms.

Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers.

M Beta, A Venkatesan, M Vasudevan, U Vetrivel, et al. Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers.

http://Bioinformatics and Biology Insights 2013:7 21–34. doi: 10.4137/BBI.S10501

This study was undertaken

  • to identify the differentially expressed miRNAs in the serum of children with RB in comparison with the normal age matched serum,
  • to analyze its concurrence with the existing RB tumor miRNA profile,
  • to identify its novel gene targets specific to RB, and
  • to study the expression of a few of the identified oncogenic miRNAs in the advanced stage primary RB patient’s serum sample.

MiRNA profiling performed on 14 pooled serum from chil­dren with advanced RB and 14 normal age matched serum samples

  • 21 miRNAs found to be upregulated (fold change > 2.0, P < 0.05) and
  • 24 downregulated (fold change > 2.0, P < 0.05).

Intersection of 59 significantly deregulated miRNAs identified from RB tumor profiles with that of miRNAs detected in serum profile revealed that

  • 33 miRNAs had followed a similar deregulation pattern in RB serum.

Later we validated a few of the miRNAs (miRNA 17-92) identified by microarray in the RB patient serum samples (n = 20) by using qRT-PCR.

Expression of the oncogenic miRNAs, miR-17, miR-18a, and miR-20a by qRT-PCR was significant in the serum samples

  • exploring the potential of serum miRNAs identification as noninvasive diagnosis.

Moreover, from miRNA gene target prediction, key regulatory genes of

  • cell proliferation,
  • apoptosis, and
  • positive and negative regulatory networks

involved in RB progression were identified in the gene expression profile of RB tumors.
Therefore, these identified miRNAs and their corresponding target genes could give insights on

  • potential biomarkers and key events involved in the RB pathway.

Computational Design of Targeted Inhibitors of Polo-Like Kinase 1 ( lk1).

(KS Jani and DS Dalafave) Bioinformatics and Biology Insights 2012:6 23–31.doi: 10.4137/BBI.S8971.

Computational design of small molecule putative inhibitors of Polo-like kinase 1 (Plk1) is presented. Plk1, which regulates the cell cycle, is often over expressed in cancers.

  • Down regulation of Plk1 has been shown to inhibit tumor progression.
  • Most kinase inhibitors interact with the ATP binding site on Plk1, which is highly conserved.
  • This makes the development of Plk1-specific inhibitors challenging, since different kinases have similar ATP sites.

However, Plk1 also contains a unique region called the polo-box domain (PBD), which is absent from other kinases.

  • the PBD site was used as a target for designed Plk1 putative inhibitors.
  • Common structural features of several experimentally known Plk1 ligands were first identified.
  • The findings were used to design small molecules that specifically bonded Plk1.
  • Drug likeness and possible toxicities of the molecules were investigated.
  • Molecules with no implied toxicities and optimal drug likeness values were used for docking studies.
  • Several molecules were identified that made stable complexes only with Plk1 and LYN kinases, but not with other kinases.
  • One molecule was found to bind exclusively the PBD site of Plk1.

Possible utilization of the designed molecules in drugs against cancers with over expressed Plk1 is discussed.

Conclusions

The previous discussions reviewed the status of an evolving personalized medicine multicentered and worldwide enterprise.  It is also clear from these reports that the search for targeted drugs matched to a cancer profile or signature has identified several approaches that show great promise.

  • We know considerably  more about metabolic pathways and linked changes in transcription that occur in neoplastic development.
  • There are several methods used to do highly accurate  insertions in gene sequences that are linked to specific metabolic changes, and
  • some may have significant implications for therapeutics, if
    • the link is a change that is associated with a driver mutation
    • the link can be identified by a fluorescent or other probe
    • the link is tied to a mRNA or peptide product that is a biomarker measured in the circulation
  • We have probes to genetic links to the control of many and interacting signaling pathways.
  • We know more about transcription through mRNA.
  • We are closer to the possibility that metabolic substrates, like ‘fumarate’ (a key intermediate in the TCA cycle), may provide a means to reverse regulate the neoplastic cells.
  • We may also find metabolic channels that drive the cells from proliferation to apoptosis or normal activity.

Summary

This discussion identified the huge expansion of genomic technology in the investigation of biopharmacotherapeutic targets that have been identified involving different levels and interacting signaling pathways.   There are several methods of analyzing gene expression, and a primary emphasis is given to combinations of mutations expressed in different cancer types.  There is a major hypothesis that expresses the need to focus on “central” “driver mutations” that correspond with the regulation of gene expression, cell proliferation, and cell metabolism.  What hasn’t been know is why drug resistance develops and whether the cellular migration and aerobic glycolysis can be redirected after cell metastasis occurs.

.

A slight mutation in the matched nucleotides c...

A slight mutation in the matched nucleotides can lead to chromosomal aberrations and unintentional genetic rearrangement. (Photo credit: Wikipedia)

Deutsch: Regulation der Phosphofructokinase

Deutsch: Regulation der Phosphofructokinase (Photo credit: Wikipedia)

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Nanotechnology: Detecting and Treating metastatic cancer in the lymph node, T. Barliya
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