New paper in Cell

Cell. 2018 Dec 10. pii: S0092-8674(18)31518-6. doi: 10.1016/j.cell.2018.11.026. [Epub ahead of print]

Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity.

Rodriguez J¹, Ren G², Day CR¹, Zhao K², Chow CC³, Larson DR⁴.

Author information

Abstract

Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic “bursts” of RNA synthesis. To understand the relationship between these two phenomena and the dynamic regulation of genes in response to upstream signals, we describe the use of live-cell RNA imaging coupled with Hi-C measurements and dissect the endogenous regulation of the estrogen-responsive TFF1 gene. Although TFF1 is highly induced, we observe short active periods and variable inactive periods ranging from minutes to days. The heterogeneity in inactive times gives rise to the widely observed “noise” in human gene expression and explains the distribution of protein levels in human tissue. We derive a mathematical model of regulation that relates transcription, chromosome structure, and the cell’s ability to sense changes in estrogen and predicts that hypervariability is largely dynamic and does not reflect a stable biological state.

KEYWORDS:

RNA; chromosome; estrogen; fluorescence; heterogeneity; imaging; live-cell; single-molecule; steroid; transcription

PMID: 30554876

 

DOI: 10.1016/j.cell.2018.11.026

Two new papers

Pradhan MA1, Blackford JA Jr1, Devaiah BN2, Thompson PS2, Chow CC3, Singer DS2, Simons SS Jr4.  Kinetically Defined Mechanisms and Positions of Action of Two New Modulators of Glucocorticoid Receptor-regulated Gene Induction.  J Biol Chem. 2016 Jan 1;291(1):342-54. doi: 10.1074/jbc.M115.683722. Epub 2015 Oct 26.

Abstract: Most of the steps in, and many of the factors contributing to, glucocorticoid receptor (GR)-regulated gene induction are currently unknown. A competition assay, based on a validated chemical kinetic model of steroid hormone action, is now used to identify two new factors (BRD4 and negative elongation factor (NELF)-E) and to define their sites and mechanisms of action. BRD4 is a kinase involved in numerous initial steps of gene induction. Consistent with its complicated biochemistry, BRD4 is shown to alter both the maximal activity (Amax) and the steroid concentration required for half-maximal induction (EC50) of GR-mediated gene expression by acting at a minimum of three different kinetically defined steps. The action at two of these steps is dependent on BRD4 concentration, whereas the third step requires the association of BRD4 with P-TEFb. BRD4 is also found to bind to NELF-E, a component of the NELF complex. Unexpectedly, NELF-E modifies GR induction in a manner that is independent of the NELF complex. Several of the kinetically defined steps of BRD4 in this study are proposed to be related to its known biochemical actions. However, novel actions of BRD4 and of NELF-E in GR-controlled gene induction have been uncovered. The model-based competition assay is also unique in being able to order, for the first time, the sites of action of the various reaction components: GR < Cdk9 < BRD4 ≤ induced gene < NELF-E. This ability to order factor actions will assist efforts to reduce the side effects of steroid treatments.

Li Y, Chow CC, Courville AB, Sumner AE, Periwal V. Modeling glucose and free fatty acid kinetics in glucose and meal tolerance test. Theor Biol Med Model. 2016 Mar 2;13:8. doi: 10.1186/s12976-016-0036-3.

Abstract:
BACKGROUND:
Quantitative evaluation of insulin regulation on plasma glucose and free fatty acid (FFA) in response to external glucose challenge is clinically important to assess the development of insulin resistance (World J Diabetes 1:36-47, 2010). Mathematical minimal models (MMs) based on insulin modified frequently-sampled intravenous glucose tolerance tests (IM-FSIGT) are widely applied to ascertain an insulin sensitivity index (IEEE Rev Biomed Eng 2:54-96, 2009). Furthermore, it is important to investigate insulin regulation on glucose and FFA in postprandial state as a normal physiological condition. A simple way to calculate the appearance rate (Ra) of glucose and FFA would be especially helpful to evaluate glucose and FFA kinetics for clinical applications.
METHODS:
A new MM is developed to simulate the insulin modulation of plasma glucose and FFA, combining IM-FSIGT with a mixed meal tolerance test (MT). A novel simple functional form for the appearance rate (Ra) of glucose or FFA in the MT is developed. Model results are compared with two other models for data obtained from 28 non-diabetic women (13 African American, 15 white).
RESULTS:
The new functional form for Ra of glucose is an acceptable empirical approximation to the experimental Ra for a subset of individuals. When both glucose and FFA are included in FSIGT and MT, the new model is preferred using the Bayes Information Criterion (BIC).
CONCLUSIONS:
Model simulations show that the new MM allows consistent application to both IM-FSIGT and MT data, balancing model complexity and data fitting. While the appearance of glucose in the circulation has an important effect on FFA kinetics in MT, the rate of appearance of FFA can be neglected for the time-period modeled.

What is wrong with obesity research

This paper in Nature Communications 14-3-3ζ Coordinates Adipogenesis of Visceral Fat has garnered some attention in the popular press. It is also a perfect example of what is wrong with the way modern obesity research is conducted and reported. This paper finds a protein that regulates adipogenesis or fat cell production. I haven’t gone into details of the results but let’s just assume that it is correct. The problem is that the authors and the press then make the statement that this provides a possible drug target for obesity. Why is this a problem? Well consider the analogy with a car. The gas tank represents the adipocytes, – it is the store of energy. Now, you find a “gene” that shrinks the gas tank and then publish in Nature Automobiles and the press release states that that you have found a potential treatment for car obesity. If it is really true that the car (mouse) still takes in the same amount of petrol (food) as before, then where did this excess energy go? The laws of thermodynamics must still hold. The only possibilities are that your gas mileage went down (energy expenditure increased) or the energy is being stored in some other auxiliary gas tank (liver?). A confounding problem is that rodents have very high metabolic rates compared to humans. They must eat a significant fraction of their body weight each day just to stay alive. Deprive a mouse or rat of food for a few days and it will expire. The amount of energy going into fat storage per day is a small amount by comparison. It is difficult to measure food intake precisely enough to resolve whether or not two rats are eating the same thing and most molecular biology labs are not equipped to make these precise measurements nor understand that they are necessary. One rat needs to only eat more by a small amount to gain more weight. If two cars (mice) grow at different weights then the only two possible explanations is that they have different energy expenditures or they are eating different amounts. Targeting the gas tank (adipocytes) simply does not make sense as a treatment of obesity. It might be interesting from the point of view of understanding development or even cancer but not weight gain. I have argued in the past that if you find that you have too much gas in the car then the most logical thing to do is to put less gas in the car, not to drive faster so you burn up the gas. If you are really interested in understanding obesity, you should try to understand appetite and satiety because that has the highest leverage for affecting body weight.

New paper on steroid-regulated gene expression

I am extremely pleased that the third leg of our theory on steroid-regulated gene expression is finally published.

Theory of partial agonist activity of steroid hormones
Abstract: The different amounts of residual partial agonist activity (PAA) of antisteroids under assorted conditions have long been useful in clinical applications but remain largely unexplained. Not only does a given antagonist often afford unequal induction for multiple genes in the same cell but also the activity of the same antisteroid with the same gene changes with variations in concentration of numerous cofactors. Using glucocorticoid receptors as a model system, we have recently succeeded in constructing from first principles a theory that accurately describes how cofactors can modulate the ability of agonist steroids to regulate both gene induction and gene repression. We now extend this framework to the actions of antisteroids in gene induction. The theory shows why changes in PAA cannot be explained simply by differences in ligand affinity for receptor and requires action at a second step or site in the overall sequence of reactions. The theory also provides a method for locating the position of this second site, relative to a concentration limited step (CLS), which is a previously identified step in glucocorticoid-regulated transactivation that always occurs at the same position in the overall sequence of events of gene induction. Finally, the theory predicts that classes of antagonist ligands may be grouped on the basis of their maximal PAA with excess added cofactor and that the members of each class differ by how they act at the same step in the overall gene induction process. Thus, this theory now makes it possible to predict how different cofactors modulate antisteroid PAA, which should be invaluable in developing more selective antagonists.

Steroids are crucial hormones in the body, which are involved in development and homeostasis. They regulate gene expression by first binding to nuclear receptors that freely float in the cytosol. The receptor-steroid complex is activated somehow and transported to the nucleus, where it binds to a hormone response element and initiates transcription. Steroids can either induce or repress genes in a dose dependent way and the dose-response function is generally a linear-fractional function. In our work, we modeled the whole sequence of events as a complex-building biochemical reaction sequence and showed that a linear-fractional dose response could only arise under some specific but biophysically plausible conditions. See here, here, and here for more background.

Given the importance of steroids and hormones, several important drugs target these receptors. They include tamoxifen and raloxifene, and RU486. These drugs are partial agonists in that bind to nuclear receptors and either, block, reduce, or even increase gene expression. However, it was not really known how partial agonists or antagonists work. In this paper, we show that they work by altering the affinity of some reaction downstream of receptor-ligand binding and thus they can do this in a gene specific way. We show that the activity of a given partial agonist can be reversed by some other downstream transcription factor provided it act after this reaction. The theory also explains why receptor-ligand binding affinity has no affect on the partial agonist activity. The theory makes specific predictions on the mechanisms of partial agonists based on how the maximal activity and the EC50 of the dose response change as you add various transcription factors.

The big problem with these drugs is that nuclear receptors act all over the body and thus the possibility of side effects is high. I think our theory could be used as a guide for developing new drugs or combinations of drugs that can target specific genes and reduce side effects.

Talk at Michigan

I gave a talk in the Department of Computational Medicine and Bioinformatics at the University of Michigan yesterday. The video of the talk is here.  The talk is similar to the one I gave at the SMB annual meeting this summer.  My slides can be found here.

New paper on gene repression

CC Chow, KK Finn, GB Storchan, X Lu, X Sheng, SS Simons Jr., Kinetically-Defined Component Actions in Gene Repression. PLoS Comp Bio. 11:e1004122, (2015)

Abstract

Gene repression by transcription factors, and glucocorticoid receptors (GR) in particular, is a critical, but poorly understood, physiological response. Among the many unresolved questions is the difference between GR regulated induction and repression, and whether transcription cofactor action is the same in both. Because activity classifications based on changes in gene product level are mechanistically uninformative, we present a theory for gene repression in which the mechanisms of factor action are defined kinetically and are consistent for both gene repression and induction. The theory is generally applicable and amenable to predictions if the dose-response curve for gene repression is non-cooperative with a unit Hill coefficient, which is observed for GR-regulated repression of AP1LUC reporter induction by phorbol myristate acetate. The theory predicts the mechanism of GR and cofactors, and where they act with respect to each other, based on how each cofactor alters the plots of various kinetic parameters vs. cofactor. We show that the kinetically-defined mechanism of action of each of four factors (reporter gene, p160 coactivator TIF2, and two pharmaceuticals [NU6027 and phenanthroline]) is the same in GR-regulated repression and induction. What differs is the position of GR action. This insight should simplify clinical efforts to differentially modulate factor actions in gene induction vs. gene repression.

Author Summary

While the initial steps in steroid-regulated gene induction and repression are known to be identical, the same cannot be said of cofactors that modulate steroid-regulated gene activity. We describe the conditions under which a theoretical model for gene repression reveals the kinetically-defined mechanism and relative position of cofactor action. This theory has been validated by experimental results with glucocorticoid receptors. The mode and position of action of four factors is qualitatively identical in gene repression to that previously found in gene induction. What changes is the position of GR action. Therefore, we predict that the same kinetically-defined mechanism usually will be utilized by cofactors in both induction and repression pathways. This insight and simplification should facilitate clinical efforts to maximize desired outcomes in gene induction or repression.

I am so happy that this paper is finally published.  It was a two-year ordeal from the time I had the idea of what to do until it finally came out. This is the second leg of the three-legged stool for a theory of steroid-regulated gene expression. The first was developing the theory for gene induction (e.g. see here) that started over ten years ago when Stoney and I first talked about trying to understand his data and really took off when Karen Ong turned her summer internship into a two-year baccalaureate fellowship. She’s now finishing up the PhD part of her MD-PhD at the Courant Institute at NYU.

In the first leg, we showed that if the dose-response curve for steroid-regulated gene induction (i.e. gene product as a function of ligand concentration), had the form  , (which has been variously called noncooperative, Michaelis-Menten function, Hill function with Hill coefficient equal to 1, hyperbolic, first order Hill dose response curve, to give a few), then the dose-response could be written down in closed form.  The theory considers gene induction to be a sequence of complex forming reactions for , and the dose-response is given by as a function of , which in general is a very high order polynomial which is not Michaelis-Menten. However,  when some biophysically plausible conditions on the parameters are met, the polynomial can be represented by the group of lower triangular matrices and can be solved exactly.  We can then use the formulae to make predictions for the mechanisms of various transcription factors.

However, steroids also repress genes and interestingly enough the repression curve is also noncooperative and is given by the linear fractional function . The question then was how does this work. I was puzzled for a while on how to solve this but then thought that if we believe that the transcription machinery after initiation is mostly conserved then the induction theory we had previously derived should still be in place. What is different is that in repression instead of steroids initiating the cascade, there was some other agonist and steroid repressed this. In our induction theory, we included the effects of activators and inhibitors from enzyme kinetics, which we called accelerators and decelerators to avoid confusing with previously used terms. Because of the group property of the reactions, basically any function you are interested in has linear-fractional form. I thus postulated that steroids, after binding to a nuclear steroid receptor, acts like a decelerator. I then had to work out all the possible cases for where the decelerator could act and the large number of them made the calculations rather tedious. As a result, I made lots of mistakes initially and the theory just wouldn’t fit the data. I finally had a breakthrough in the fall of 2013 when I was in Taiwan for a workshop and everything started to come together. It then took another six months to work out the details and write the paper, which was then followed by several back and forth’s with the referees, a major rewriting and a final acceptance a few months ago. In the process of working on this paper, I discovered a lot of properties about the induction system that I didn’t realize. I still didn’t believe it was finished until I saw it posted on the PLoS Comp Bio website this week.

I’m currently putting on the finishing touches for revisions on the third leg of the stool now. We have even reunited the band and convinced Karen to take some time away from her thesis to help finish it. This paper is about how partial agonists or antagonists like tamoxifen work, which could have implications for drug development and avoiding side effects. Steroids are not the only ligand that can activate a steroid-regulated gene. The steroid cream that you use for rashes consists of a highly potent steroid agonist. There are also molecules that block or impede the action of steroids by binding to steroid receptors and these are called partial agonists, antagonists or antisteroids. However, steroid receptors are widely expressed and that is why when you take them they can have severe side effects. Hence, it would be nice to be able to control where they act and by how much. This third leg paper is the theory behind how to do this.

New paper on steroid-regulated gene expression

Recent paper in Molecular Endocrinology 7:1194-206. doi: 10.1210/me.2014-1069:

Research Resource: Modulators of glucocorticoid receptor activity identified by a new high-throughput screening assay

John A. Blackford, Jr., Kyle R. Brimacombe, Edward J. Dougherty , Madhumita Pradhan, Min Shen, Zhuyin Li, Douglas S. Auld, Carson C. Chow, Christopher P. Austin, and S. Stoney Simons, Jr.

Abstract: Glucocorticoid steroids affect almost every tissue-type and thus are widely used to treat a variety of human pathologies. However, the severity of numerous side-effects limits the frequency and duration of glucocorticoid treatments. Of the numerous approaches to control off-target responses to glucocorticoids, small molecules and pharmaceuticals offer several advantages. Here we describe a new, extended high throughput screen in intact cells to identify small molecule modulators of dexamethasone-induced glucocorticoid receptor (GR) transcriptional activity. The novelty of this assay is that it monitors changes in both GR maximal activity (Amax) and EC50, or the position of the dexamethasone dose-response curve. Upon screening 1280 chemicals, ten with the greatest change in the absolute value of Amax or EC50 were selected for further examination. Qualitatively identical behaviors for 60 –90% of the chemicals were observed in a completely different system, suggesting that other systems will be similarly affected by these chemicals. Additional analysis of the ten chemicals in a recently described competition assay determined their kinetically-defined mechanism and site of action. Some chemicals had similar mechanisms of action despite divergent effects on the level of GR-induced product. These combined assays offer a straightforward method of identifying numerous new pharmaceuticals that can alter GR transactivation in ways that could be clinically useful.

Talk at UMBC

I’m giving a talk at University of Maryland, Baltimore County today at noon. The talk will be similar to the one I gave at SMB this past summer.  Slides can be found here.

New paper in eLife

Kinetic competition during the transcription cycle results in stochastic RNA processing

Antoine Coulon, Matthew L Ferguson, Valeria de Turris, Murali Palangat, Carson C Chow, Daniel R Larson

Abstract

Synthesis of mRNA in eukaryotes involves the coordinated action of many enzymatic processes, including initiation, elongation, splicing, and cleavage. Kinetic competition between these processes has been proposed to determine RNA fate, yet such coupling has never been observed in vivo on single transcripts. In this study, we use dual-color single-molecule RNA imaging in living human cells to construct a complete kinetic profile of transcription and splicing of the β-globin gene. We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing. Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint. The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription. A single missense point mutation (S34F) in the essential splicing factor U2AF1 which occurs in human cancers perturbs this kinetic balance and defers splicing to occur entirely post-release.

DOI: http://dx.doi.org/10.7554/eLife.03939.001

JSMB/SMB talk

I just gave an invited plenary talk at the joint annual meeting of the Japanese Society of Mathematical Biology and Society of Mathematical Biology in Osaka Japan.  I talked about my work on steroid-regulated gene transcription.  The slides are here.  Previous posts on the topic, including background summaries, can be found here.

Slides for talk on gene expression

Here are the slides for my talk today at the NIH Systems Biology Forum on gene expression.  Background for the talk can be found here.

Two New Papers

I have two new papers in the Journal of Biological Chemistry:

Z Zhang, Y Sun, YW Cho, CC Chow, SS Simons. PA1 Protein, a New Competitive Decelerator Acting at More than One Step to Impede Glucocorticoid Receptor-mediated Transactivation. J Biol Chem:42-58 (2012). [PDF]

JA Blackford, C Guo, R Zhu, EJ Dourgherty, CC Chow and SS Simons. Identification of Location and Kinetically Defined Mechanism of Cofactors and Reporter Genes in the Cascade of Steroid-regulated Transactivation. J Biol Chem:40982-95 (2012). [PDF]

Both are applications of our theory for steroid-mediated gene induction.  The theory is applicable for any biochemical system where the dose response curve strictly follows a Michaelis-Menten curve.  A summary of the theory can be found here and here.  Slides for talks on the topic can be found here.  In Zhang et al, we use the theory to predict a mechanism for a protein called PA1.  In Blackford et al, we show that DNA is the effective rate limiting step for gene transcription in steady state, which we dub concentration limiting step, since there are really no rates at steady state.

Lyon Talk

I just got back from a trip to Lyon, France.  I served on the thesis committee of newly minted doctor Bertrand Care, had some hearty Bouchon Lyonnais food,  and gave a talk on steroid-mediated gene expression.  The slides can be found here.

Talk on gene induction

I gave a talk to the math department at the University of Delaware today.  My slides are here.  Papers and summaries related to the talk can be found here, here and here.

New paper on steroid-mediated gene induction

A follow-up  to our PNAS paper on a new theory of steroid-mediated gene induction is now available on PLoS One here.  The title and abstract is below.  In the first paper, we proposed a general mathematical framework to compute how much protein will be produced from a steroid-mediated gene.  It had been noted in the past that the dose response curve of product given steroid amount follows a Michaelis-Menten curve or first order Hill function (e.g. Product = Amax [S]/(EC50+[S], where [S] is the added steroid concentration)..  In our previous work, we exploited this fact and showed that a complete closed form expression for the dose response curve could be written down for an arbitrary number of linked reactions.  The formula also indicates how added cofactors could increase or decrease the Amax or EC50.  What we do in this paper is to show how this expression can be used to predict the mechanism and order in the sequence of reactions a given cofactor will act by analyzing how two cofactors affect the Amax and EC50.

Deducing the Temporal Order of Cofactor Function in Ligand-Regulated Gene Transcription: Theory and Experimental Verification

Edward J. Dougherty, Chunhua Guo, S. Stoney Simons Jr, Carson C. Chow

Abstract: Cofactors are intimately involved in steroid-regulated gene expression. Two critical questions are (1) the steps at which cofactors exert their biological activities and (2) the nature of that activity. Here we show that a new mathematical theory of steroid hormone action can be used to deduce the kinetic properties and reaction sequence position for the functioning of any two cofactors relative to a concentration limiting step (CLS) and to each other. The predictions of the theory, which can be applied using graphical methods similar to those of enzyme kinetics, are validated by obtaining internally consistent data for pair-wise analyses of three cofactors (TIF2, sSMRT, and NCoR) in U2OS cells. The analysis of TIF2 and sSMRT actions on GR-induction of an endogenous gene gave results identical to those with an exogenous reporter. Thus new tools to determine previously unobtainable information about the nature and position of cofactor action in any process displaying first-order Hill plot kinetics are now available.

Review paper on steroid-mediated gene expression

Mol Cell Endocrinol. 2011 Jun 1. [Epub ahead of print]

The road less traveled: New views of steroid receptor action 
from the path of dose-response curves. 
Simons SS Jr, Chow CC.

Steroid Hormones Section, NIDDK/CEB, NIDDK, National Institutes of Health,
Bethesda, MD, United States.

Conventional studies of steroid hormone action proceed via quantitation of the
maximal activity for gene induction at saturating concentrations of agonist
steroid (i.e., A(max)). Less frequently analyzed parameters of receptor-mediated
gene expression are EC(50) and PAA. The EC(50) is the concentration of steroid
required for half-maximal agonist activity and is readily determined from the
dose-response curve. The PAA is the partial agonist activity of an antagonist
steroid, expressed as percent of A(max) under the same conditions. Recent results
demonstrate that new and otherwise inaccessible mechanistic information is
obtained when the EC(50) and/or PAA are examined in addition to the A(max).
Specifically, A(max), EC(50), and PAA can be independently regulated, which
suggests that novel pathways and factors may preferentially modify the EC(50)
and/or PAA with little effect on A(max). Other approaches indicate that the
activity of receptor-bound factors can be altered without changing the binding of
factors to receptor. Finally, a new theoretical model of steroid hormone action
not only permits a mechanistically based definition of factor activity but also
allows the positioning of when a factor acts, as opposed to binds, relative to a
kinetically defined step. These advances illustrate some of the benefits of
expanding the mechanistic studies of steroid hormone action to routinely include
EC(50) and PAA.

PMID: 21664235  [PubMed - as supplied by publisher]

New paper on gene induction

Karen M. Ong, John A. Blackford, Jr., Benjamin L. Kagan, S. Stoney Simons, Jr., and Carson C. Chow. A theoretical framework for gene induction and experimental comparisons PNAS 200911095; published ahead of print March 29, 2010, doi:10.1073/pnas.0911095107

This is an open access article so it can be downloaded directly from the PNAS website here.

This is a paper where group theory appears unexpectedly.  The project grew out of a chance conversation between Stoney Simons and myself in 2004.  I had arrived recently at the NIH and was invited to give a presentation at the NIDDK retreat.  I spoke about how mathematics could be applied to obesity research and I’ll talk about the culmination of that effort in my invited plenary talk at the joint SIAM Life Sciences and Annual meeting this summer.  Stoney gave a summary of his research on steroid-mediated gene induction.  He showed some amazing data of the dose response curve of the amount of gene product induced for a given amount of steroid.  In these experiments,  different concentrations of steroid are added to cells  in a dish and then after waiting awhile, the total amount of gene product in the form of luciferase is measured.  Between steroid and gene product is a lot of messy and complicated biology starting with  steroid binding to a steroid receptor, which translocates to the nucleus while interacting with many molecules on the way. The steroid-receptor complex then binds  to DNA, which triggers a transcription cascade involving many other factors binding to DNA, which gives rise to mRNA, which is then translated into luciferase and then measured as photons.  Amazingly, the dose response curve after all of this was fit almost perfectly ( > 95%) by a Michaelis-Menton or first order Hill function

where  P is the amount of gene product, [S] is the concentration of steroid, Amax is the maximum possible amount of product and EC50 is the concentration of steroid giving half the maximum of product. Stoney also showed that Amax and EC50 could be moved in various directions by the addition of various cofactors.  I remember thinking to myself during his talk that there must be a nice mathematical explanation for this.  After my talk, Stoney came up to me  and asked me if I thought I could model his data.  We’ve been in sync like this ever since.