28.1: Principles of Gene Regulation - Biology

28.1: Principles of Gene Regulation - Biology

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28.1: Principles of Gene Regulation

28.1: Principles of Gene Regulation - Biology

For a cell to function properly, necessary proteins must be synthesized at the proper time. All cells control or regulate the synthesis of proteins from information encoded in their DNA. The process of turning on a gene to produce RNA and protein is called gene expression. Whether in a simple unicellular organism or a complex multi-cellular organism, each cell controls when and how its genes are expressed. For this to occur, there must be a mechanism to control when a gene is expressed to make RNA and protein, how much of the protein is made, and when it is time to stop making that protein because it is no longer needed.

The regulation of gene expression conserves energy and space. It would require a significant amount of energy for an organism to express every gene at all times, so it is more energy efficient to turn on the genes only when they are required. In addition, only expressing a subset of genes in each cell saves space because DNA must be unwound from its tightly coiled structure to transcribe and translate the DNA. Cells would have to be enormous if every protein were expressed in every cell all the time.

The control of gene expression is extremely complex. Malfunctions in this process are detrimental to the cell and can lead to the development of many diseases, including cancer.

Learning Objectives

  • Discuss why every cell does not express all of its genes
  • Compare prokaryotic and eukaryotic gene regulation

Introduction to Gene Regulation

These brief articles are a supplement to the readings from your textbook on Gene Regulation. You do not have to finish these articles today, but they will help you understand gene regulation at a deeper level. They also make great references for your next milestone paper.

  • Do we express all of our genes at the same time? Why?
  • Do we need all of our genes expressed all the time? Why?
  • Why do we have so many genes?

These are just a few of the questions you need to start asking yourself. Humans have hundreds of thousands of genes. Many are needed all the time (constitutive), but others are only needed when the cell get’s certain signals. So how do we control the expression of all this genetic knowledge?

During mitosis, for example, did you see the production of DNA polymerase and the replication complex during the start of G1, or did you only see it after you passed the first restriction point? Do we keep DNA polymerase around just in case we are going to do some nuclear division? or do we unlock its expression only when needed?

Consider: The first restriction point determines if you are going to prep for division. When you have enough cyclin-dependent kinase available, you pass the restriction point. CDK signals the cell to get ready for division. How does this signal work? It changes gene expression (i.e., we activate regulated genes).

Think about the human body and homeostasis. Think about hormones. Are you always producing everything, or do you need to trigger some events? Could that trigger then be a regulated gene?

Remember that you need at minimum the equivalent of 4ATP per amino acid incorporated into a protein. Add to this 1 ATP equivalent for each nucleotide during transcription. You should quickly realize that gene expression is energy expensive.
Your goal today is to start reading about gene regulation, and more specifically, come to an understanding of the necessity of gene regulation.

Daily Challenge

Why do we need gene regulation? Today, reflect on the need and use of gene regulation. Why would an organism need to have some genes that it could turn on or off? Why would you need to control gene expression? Can the environment affect gene regulation? Can gene regulation affect evolution?

Regulation of Gene Expression in Prokaryotes (With Diagram)

All the activities of an organism are controlled by genes. Most of the genes of an organism express themselves by producing proteins. The genes which produce proteins are called structural genes or cistrons. Every cell of an organism posses all the genes. But all of them are not functional all the time. If all the genes function all the time, enzymatic chaos will prevail and there will not be much cell differentiation.

The products of many genes are needed only occasionally by the cell. Therefore, those proteins are synthesized only when the substrate on which they act is present or when they are needed by the cell. In highly differentiated cells of eukaryotes only a few genes are functional and all other genes are permanently shut off. Even a lowly E. coli bacterium expresses only some of its genes at any given time out of the total of about three thousand genes.

Various mechanisms exist in the cell, which control and regulate the expression of genes. The regulatory system turns the genes “on” when needed and turns “off’ when not needed. This proves that gene activity can be regulated.

There are various stages at which the expression of a gene can be regulated but most common is the initiation of transcription. It is here that bulk of the gene regulation takes place. Other levels of gene regulation are transcriptional elongation, mRNA processing during translation and post translation stage.

Gene Regulation in Prokaryotes:

In bacteria the expression of genes is controlled by extracellular signals often present in the medium in which bacteria are grown. These signals are carried to the genes by regulatory proteins. Regulatory proteins are of two types. They are positive regulators called activators and negative regulators called repressors. These activators and repressors are DNA binding proteins.

Negative Regulators or Repressors:

The repressor or inhibitor protein binds to the target site (operator) on DNA. These block the RNA polymerase enzyme from binding to the promoter, thus preventing the transcription. The repressor binds to the site where it overlaps the polymerase enzyme. Thus, activity of the genes is turned off. It is called negative control mechanism.

An anti-repressor or anti-inhibitor called inducer is needed to inactivate the repressor and thereby activating the genes. Thus, the genes are switched on. This is demonstrated by lactose operon.

Positive Regulators or Activators:

To activate the transcription by the promoter, the activator helps polymerase enzyme to bind to the promoter.

Genes under positive control mechanism are expressed only when an activator or stimulator or active regulator is present.


In bacteria cistrons or structural genes, producing enzymes of a metabolic pathway are organised in a cluster whose functions are related. Polycistronic genes of prokaryotes along with their regulatory genes constitute a system called operon. Operon is a unit of expression and regulation.

1. Lactose Operon or Lac Operon:

This is a negative control mechanism. In 1961 Francois Jacob and Jacques Monod proposed operon model for the regulation of gene expression in E. coli. The synthesis of enzyme (3-galactosidase has been studied in detail. This enzyme causes the breakdown of lactose into glucose and galactose.

In the absence of lactose, β-galactosidase is present in negligible amounts. As soon as lactose is added from outside, the production of β- galactosidase increases thousand times. As soon as the lactose in consumed, the production of the enzyme again drops. The enzymes whose production can be increased by the presence of the substrate on which it acts are called inducible enzymes.

Addition of lactose to the culture medium of E. coli induces the formation of three enzymes (5-galactosidase, permease and transacetylase, which degrade lactose into glucose and galactose. The genes, which code for these enzymes lie in a cluster and are called cistrons or structural genes. They are transcribed simultaneously into a single mRNA chain, which has codons for all the three enzymes. The mRNA transcribed from many genes is called polycistronic. The functioning of structural genes to produce mRNA is controlled by regulatory genes.

There are three structural genes Z, Y and A, which code for enzymes p-galactosidase, lac permease and transacetylase respectively. Regulatory genes consist of Regulator I, Promoter P and a control gene called operator gene O. Regulator I gene produces a protein called repressor or inhibitor. The repressor is active and binds to the operator gene O and switches it “off” and the transcription is stopped.

This happens because RNA polymerase enzyme which binds to the promoter is unable to do so because binding site of RNA polymerase and the binding site of repressor on operator overlap each other. Hence in negative control mechanism, the active genes are turned “off” by the repressor protein.

When the inducer (lactose) in supplied from outside, the inducers binds to the repressor. The lactose on entering the bacteria changes into allolactase. Allolactose changes the shape of the repressor (conformational changes) which renders it inactive and unable to bind to the operator. The operator becomes free and is “turned on” and thus transcription starts.

In this way, the presence of the inducer permits the transcription of Lac operon, which is no longer blocked by the repressor protein. The synthesis of enzymes in response to the presence of specific substrate (lactose) is called induction. It is also called de-repression.

The inducible system operates in a catabolic pathway. In the absence of lactose, E. coli cells have an average of only three molecules of P-galactosidase enzyme per cell. Within 2-3 minutes of induction of lactose, 3000 molecules of P-galactosidase are produced in each cell.

It is also a negative control system but forms a biosynthetic pathway. It is known as repressible system. It works on the principle that when the amino acid tryptophan is present, there is no need to activate the tryptophan operon.

Repressor protein is activated by the co-repressor (tryptophan-the end product) and it binds the operator to switch it “off’. Tryptophan is synthesized in five steps, each step requiring a particular enzyme. The genes for encoding these enzymes lie adjacent to one another, called trp E, trp D, trp C, trp B and trp A.

Tryptophan operon codes for five enzymes that are required for the synthesis of amino acid tryptophan. In repressible system, the regulatory gene produces a repressor protein, which is normally inactive and unable to bind to operator on DNA. The repressor upon joining the co-repressor (which is the end product tryptophan in this case) undergoes conformational changes that activate it and enable it to bind to the operator. This prevents the binding of RNA polymerase enzyme to the promoter. This is opposite to the situation of lac operon in which the repressor is active on its own and loses the affinity for the operator when bound to the inducer.

Here the availability of tryptophan which is the end product regulates the expression of this operon and represses the synthesis of tryptophan. In this way the synthesis of enzymes of a metabolic pathway is stopped by the end product of the metabolic chain. This mechanism enables the bacteria to synthesize enzymes only when they are required. This is known as feed back repression.

In feed back inhibition the end product of a metabolic pathway acts as an allosteric inhibitor of the first enzyme of the metabolic chain.

Induction and repression save valuable energy by preventing the synthesis of unnecessary enzymes.

Positive Control of Transcription:

The system of regulation in lactose and tryptophan operon is essentially a negative control in the sense that the operon is normally “on” but is kept “off’ by the regulator protein. In other words the structural genes are not allowed to express unless required.

Catabolic Repression:

Lac operon also shows positive control by catabolic repression. This is an additional control system, which binds the repressor-operator. In E. coli, in the presence of both glucose and lactose, the glucose in first fully utilized and then lactose is taken up for production of energy.

Glucose is richest and more efficient source of energy. Glucose has an inhibitory effect on the expression of lac operon. The mechanism of positive control enables E. coli to adapt more efficiently to the changing environment of its natural habitat, which is the human intestine.

In the presence of glucose, synthesis of β-galactosidase enzyme becomes suppressed. The inhibitory effect of glucose is due to the marked drop in the level of a nucleotide called cyclic AMP (c-AMP), which inhibits the transcription of mRNA.

Lactose operon transcription requires not only cyclic AMP but also another protein called catabolic activator protein (CAP). The cAMP and CAP form a complex called cAMP-CRP complex, which is necessary for the functioning of lactose operon.

A catabolic breakdown product of glucose, called glucose catabolite, prevents the activation of lac operon by lactose. This effect is called catabolic repression. When glucose concentration increases, the cAMP concentration decreases and vice versa. High concentration of cAMP is necessary for the activation of lac operon.

Normally in the presence of glucose, the lactose operon remains inactive.

Glucose catabolite prevents the formation cAMP-CRP complex.

In this way cAMP-CRP system is positive control because expression of lac operon requires the presence of an activating signal which is this case in cAMP-CRP complex.

There are some promoters on DNA at which RNA polymerase cannot initiate transcription without the presence of some additional protein factors such as cAMP-CRP complex. These factors are positive regulators because their presence is necessary to switch on the cistrons. These are called activators or stimulators.


In this section, we will introduce some definitions of information theory including entropy, MI and CMI, as well as the algorithm of PCA-CMI for inferring GRNs.

2.1 Information theory

MI from information theory has been used to construct GRNs from gene expression data ( Altay and Emmert-Streib, 2010). In particular, MI is generally used as a powerful criterion for measuring the dependence between two variables (genes) X and Y. For gene expression data, variable X is a vector, in which the elements denote its expression values in different conditions (samples).

When variables (genes) X and Y are independent, we get I(X,Y)=0. Similarly, if the variables X and Y are conditional independence given Z, we have I(X, Y|Z)=0.

2.2 PCA

After we obtain MI and CMI through formulation ( 9) and ( 10), the PCA is used to remove the edges with (conditional) independent correlation from the graph. The inference of GRNs will be performed by deleting the edges with independent correlation recursively, i.e. from low to high order independent correlation until there is no edge that can be deleted.

We describe the process of PCA-CMI in detail as follows. First, generate a complete graph according to the number of genes. Second, for adjacent gene pair i and j, compute MI (zero-order CMI) I(i,j). If the gene pair i and j has low or zero MI, it represents independent correlation, then we delete the edge between genes i and j. Third, for adjacent gene pair i and j, select the adjacent gene k of them and compute first-order CMI I(i,j|k). If the gene pair i and j has low or zero CMI which represent their independent correlation, delete the edge between them. The next step is to compute higher order CMI until there are no more adjacent edges.

The following gives the algorithm to infer a GRN.

PC-Algorithm based on CMI (PCA-CMI)

Step-0: initialization. Input the gene expression data A and set the parameter θ for deciding the independence. Generate the complete network G for all genes (i.e. the clique graph of all genes). Set L=−1.

Step-1: L=L+1 For a non-zero edge G(i,j) ≠ 0, select adjacent genes connected with both genes i and j. Compute the number T of the adjacent genes (not including genes i and j).

Figure 1 shows a diagram of PCA-based CMI for a five-gene network. Microarray data is the expression data of genes X, Y, Z, W and V. The first step is to generate the complete network with these five genes. Then the independence between gene pairs is decided by the MI between them. If the MI is smaller than a given threshold θ, the edge between the two genes is deleted for the independence. Here, the MI I(X, Z) and I(W, V) are approximately equal to zero on assumption, so the edges E(X, Z) and E(W, V) are deleted and the zero-order network is reconstructed. Then, the first-order CMIs between genes with common adjacent edges in the zero-order network are computed, and the CMI I(X, W|Y) and I(X, V|Y) are assumed to equal to zero, so the edges E(X, W) and E(X, V) are deleted and the first-order network is obtained. Based on the first-order network, the second-order CMIs between genes can be computed and the CMI I(Y, Z|W, V) is assumed approximately equal to zero, so the edge E(Y, Z) is deleted and the second-order network is inferred. There is no third-order CMI, so the algorithm terminates and the second-order network is the inferred GRN (or final GRN).

Diagram of method PCA-CMI. In the figure, I(·, ·) is the MI and I(·, ·|·) is the CMI. They are calculated from gene expression data by a concise formula of computation. The MI and CMI equal to zero or lower than given threshold represent independence between variables (genes). The left graph is the true network of the microarray dataset with gene expression profiles under different conditions (samples). The graph in pink box with dashes is the diagram of PCA-CMI, which detects the true network step by step according to the (conditional) independency of gene pairs.

Diagram of method PCA-CMI. In the figure, I(·, ·) is the MI and I(·, ·|·) is the CMI. They are calculated from gene expression data by a concise formula of computation. The MI and CMI equal to zero or lower than given threshold represent independence between variables (genes). The left graph is the true network of the microarray dataset with gene expression profiles under different conditions (samples). The graph in pink box with dashes is the diagram of PCA-CMI, which detects the true network step by step according to the (conditional) independency of gene pairs.

Lehninger Principles of Biochemistry

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On all accounts, the many modifications between the 4th and 5th edition bring the book into line with the latest research. The scope of this work also warrants its purchase, since one can be almost certain just from the Table of Contents that Lehninger has the important areas covered. Though some students will probably be taken aback at the shear wealth of information, they nevertheless will have a book that proves quite handy later in their careers, when they need to find the details of the biochemical principles that underlie specific problems. Lehninger is also recommended for those who are already working in medicinal chemistry or related areas, since in addition to the elementary background material contained, current research topics such as signal transduction are addressed in exacting detail in the book. In many subject areas, Lehninger even bridges the gap to modern drug discovery, since a number of drugs and their functions are described in information boxes scattered throughout the text.

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1 The Foundations of Biochemistry
1.1 Cellular Foundations
1.2 Chemical Foundations
1.3 Physical Foundations
1.4 Genetic Foundations
1.5 Evolutionary Foundations
Box 1𔂿 Molecular Weight, Molecular Mass, and Their Correct Units
Box 1𔃀 Louis Pasteur and Optical Activity: In Vino, Veritas
Box 1𔃁 Entropy: The Advantages of Being Disorganized
• Now introduces the concepts of proteomes and proteomics
• Updated section on how a new species evolves
• Increased emphasis on the interdependence of life forms in global cycles of energy

2 Water
2.1 Weak Interactions in Aqueous Systems
2.2 Ionization of Water, Weak Acids, and Weak Bases
2.3 Buffering against pH Changes in Biological Systems
2.4 Water as a Reactant
2.5 The Fitness of the Aqueous Environment for Living Organisms
Box 2𔂿 Medicine: On Being One’s Own Rabbit (Don’t Try This at Home!)
• Expanded discussion of blood pH buffering by the bicarbonate system,including a new box describing Haldane’s use of himself as a guinea pig in experiments aimed at changing the acidity of blood
• New section on ketoacidosis in diabetes

3 Amino Acids, Peptides, and Proteins
3.1 Amino Acids
3.2 Peptides and Proteins
3.3 Working with Proteins
3.4 The Structure of Proteins: Primary Structure
Box 3𔂿 Methods: Absorption of Light by Molecules: The Lambert-Beer Law
Box 3𔃀 Methods: Investigating Proteins with Mass Spectrometry
Box 3𔃁 Medicine: Consensus Sequences and Sequence Logos
• Significant revision to bioinformatics
• More thorough explanation of consensus sequences, including an illustration of common ways to depict consensus sequences

4 The Three-Dimensional Structure of Proteins
4.1 Overview of Protein Structure
4.2 Protein Secondary Structure
4.3 Protein Tertiary and Quaternary Structures
4.4 Protein Denaturation and Folding
Box 4𔂿 Methods: Knowing the Right Hand from the Left
Box 4𔃀 Permanent Waving Is Biochemical Engineering
Box 4𔃁 Medicine: Why Sailors, Explorers, and College Students Should Eat Their Fresh Fruits and Vegetables
Box 4𔃂 The Protein Data Bank
Box 4𔃃 Methods: Methods for Determining the Three-Dimensional Structure of a Protein
Box 4𔃄 Medicine: Death by Misfolding: The Prion Diseases
• New section, Defects in protein folding may be the molecular basis for a wide range of human genetic disorders, discusses a variety of amyloid diseases
• New section on circular dichroism

5 Protein Function
5.1 Reversible Binding of a Protein to a Ligand: Oxygen-Binding Proteins
5.2 Complementary Interactions between Proteins and Ligands: The Immune System and Immunoglobulins
5.3 Protein Interactions Modulated by Chemical Energy: Actin, Myosin, and Molecular Motors
Box 5𔂿 Medicine: Carbon Monoxide: A Stealthy Killer

6 Enzymes
6.1 An Introduction to Enzymes
6.2 How Enzymes Work
6.3 Enzyme Kinetics as an Approach to Understanding Mechanism
6.4 Examples of Enzymatic Reactions
6.5 Regulatory Enzymes
Box 6𔂿 Transformations of the Michaelis-Menten Equation: The Double-Reciprocal Plot
Box 6𔃀 Kinetic Tests for Determining Inhibition Mechanisms
Box 6𔃁 Evidence for Enzyme–Transition State Complementarity
• More explanatory text added to the mechanisms for the enolase and lysozyme reactions
• New section on pharmaceuticals developed from an understanding of enzyme mechanism, using penicillin and HIV protease inhibitors as examples

7 Carbohydrates and Glycobiology
7.1 Monosaccharides and Disaccharides
7.2 Polysaccharides
7.3 Glycoconjugates: Proteoglycans, Glycoproteins, and Glycolipids
7.4 Carbohydrates as Informational Molecules: The Sugar Code
7.5 Working with Carbohydrates
Box 7𔂿 Medicine: Blood Glucose Measurements in the Diagnosis and Treatment of Diabetes
• New medical box, introduces hemoglobin glycation and AGEs and their role in the pathology of advanced diabetes
• New section on sugar analogs as drugs that target viral neuraminidase
• Introduction to the new field of glycomics, including methods for determining oligosaccharide structure using MALDI-MS

8 Nucleotides and Nucleic Acids
8.1 Some Basics
8.2 Nucleic Acid Structure
8.3 Nucleic Acid Chemistry
8.4 Other Functions of Nucleotides

9 DNA-Based Information Technologies
9.1 DNA Cloning: The Basics
9.2 From Genes to Genomes
9.3 From Genomes to Proteomes
9.4 Genome Alterations and New Products of Biotechnology
Box 9𔂿 Medicine: A Potent Weapon in Forensic Medicine
Box 9𔃀 Medicine: The Human Genome and Human Gene Therapy
• New material on the green fluorescent protein
• Thorough updating of genomics section

10 Lipids
10.1 Storage Lipids
10.2 Structural Lipids in Membranes
10.3 Lipids as Signals, Cofactors, and Pigments
10.4 Working with Lipids
Box 10𔂿 Sperm Whales: Fatheads of the Deep
Box 10𔃀 Medicine: Abnormal Accumulations of Membrane Lipids: Some Inherited Human Diseases
• New medical section on the role of polyunsaturated fatty acids and trans fatty acids in cardiovascular disease
• New section on lipidomics
• New descriptions of volatile lipids used as signals by plants, and pigments of bird feathers derived from colored lipids in plant foods

11 Biological Membranes and Transport
11.1 The Composition and Architecture of Membranes
11.2 Membrane Dynamics
11.3 Solute Transport across Membranes
Box 11𔂿 Methods: Atomic Force Microscopy to Visualize Membrane Proteins
Box 11𔃀 Medicine: Defective Glucose and Water Transport in Two Forms of Diabetes
Box 11𔃁 Medicine: A Defective Ion Channel in Cystic Fibrosis
• Expanded section on bilayer dynamics covers flippases, floppases, scramblases, and bilayer asymmetry
• Expanded and updated section on lipid rafts and caveolae includes new material on membrane curvature and the proteins that influence it, and introduces amphitropic proteins and annular lipids
• New information on the structural basis for voltage gating in a K+ channel

12 Biosignaling
12.1 General Features of Signal Transduction
12.2 G Protein–Coupled Receptors and Second Messengers
12.3 Receptor Tyrosine Kinases
12.4 Receptor Guanylyl Cyclases, cGMP, and Protein Kinase G
12.5 Multivalent Scaffold Proteins and Membrane Rafts
12.6 Gated Ion Channels
12.7 Integrins: Bidirectional Cell Adhesion Receptors
12.8 Regulation of Transcription by Steroid Hormones
12.9 Signaling in Microorganisms and Plants
12.10 Sensory Transduction in Vision, Olfaction, and Gustation
12.11 Regulation of the Cell Cycle by Protein Kinases
12.12 Oncogenes, Tumor Suppressor Genes, and Programmed Cell Death
Box 12𔂿 Methods: Scatchard Analysis Quantifies the Receptor-Ligand Interaction
Box 12𔃀 Medicine: G Proteins: Binary Switches in Health and Disease
Box 12𔃁 Methods: FRET: Biochemistry Visualized in a Living Cell
Box 12𔃂 Medicine: Color Blindness: John Dalton’s Experiment from the Grave
Box 12𔃃 Medicine: Development of Protein Kinase Inhibitors for Cancer Treatment
• New Medical section on G protein coupled receptors (GCPRs) discusses the range of diseases for which drugs target GPCRs
• New box on G proteins, proteins that regulate their GTPase activity, and the medical consequences of defective G protein function
• Expanded and integrated treatment of local signaling circuits, including AKAPs and signaling complexes that include protein kinase A, adenylyl cyclase, and phosphodiesterase, and localized puffs and waves of Ca2+
• New medical box on the use of protein kinase inhibitors in cancertherapy

13 Bioenergetics and Biochemical Reaction Types
13.1 Bioenergetics and Thermodynamics
13.2 Chemical Logic and Common Biochemical Reactions
13.3 Phosphoryl Group Transfers and ATP
13.4 Biological Oxidation-Reduction Reactions
Box 13𔂿 Firefly Flashes: Glowing Reports of ATP
• New section, Chemical logic and common biochemical reactions, discusses common biochemical reaction types

14 Glycolysis, Gluconeogenesis, and the Pentose Phosphate Pathway
14.1 Glycolysis
14.2 Feeder Pathways for Glycolysis
14.3 Fates of Pyruvate under Anaerobic Conditions: Fermentation
14.4 Gluconeogenesis
14.5 Pentose Phosphate Pathway of Glucose Oxidation
Box 14𔂿 Medicine: High Rate of Glycolysis in Tumors Suggests Targets for Chemotherapy and Facilitates Diagnosis
Box 14𔃀 Athletes, Alligators, and Coelacanths: Glycolysis at Limiting Concentrations of Oxygen
Box 14𔃁 Ethanol Fermentations: Brewing Beer and Producing Biofuels
Box 14𔃂 Medicine: Why Pythagoras Wouldn’t Eat Falafel: Glucose 6-Phosphate Dehydrogenase Deficiency
• New medical box on glucose uptake deficiency in type 1 diabetes
• New medical box on how the high rate of glycolysis in cancerous tissue aids cancer diagnosis and treatment

15 Principles of Metabolic Regulation
15.1 Regulation of Metabolic Pathways
15.2 Analysis of Metabolic Control
15.3 Coordinated Regulation of Glycolysis and Gluconeogenesis
15.4 The Metabolism of Glycogen in Animals
15.5 Coordinated Regulation of Glycogen Synthesis and Breakdown
Box 15𔂿 Methods: Metabolic Control Analysis: Quantitative Aspects
Box 15𔃀 Isozymes: Different Proteins That Catalyze the Same Reaction
Box 15𔃁 Medicine: Genetic Mutations That Lead to Rare Forms of Diabetes
Box 15𔃂 Carl and Gerty Cori: Pioneers in Glycogen Metabolism and Disease
• New section on emerging role of ribulose 5-phosphate as central regulator of glycolysis and gluconeogenesis
• Expanded discussion of phosphoprotein phosphatases in metabolic regulation
• Expanded coverage of the role of transcriptional regulators in metabolic regulation
• New medical box on mutations that lead to rare forms of diabetes regulation (MODY)

16 The Citric Acid Cycle
16.1 Production of Acetyl-CoA (Activated Acetate)
16.2 Reactions of the Citric Acid Cycle
16.3 Regulation of the Citric Acid Cycle
16.4 The Glyoxylate Cycle
Box 16𔂿 Moonlighting Enzymes: Proteins with More Than One Job
Box 16𔃀 Synthases and Synthetases Ligases and Lyases Kinases,Phosphatases, and Phosphorylases: Yes, the Names Are Confusing!
Box 16𔃁 Citrate: A Symmetric Molecule That Reacts Asymmetrically
Box 16𔃂 Citrate Synthase, Soda Pop, and the World Food Supply
• New box on effect of diabetes on the citric acid cycle and ketone body formation
• Expanded discussion of substrate channeling
• New section on mutations in citric acid cycle that lead to cancer
• New box on moonlighting enzymes

17 Fatty Acid Catabolism
17.1 Digestion, Mobilization, and Transport of Fats
17.2 Oxidation of Fatty Acids
17.3 Ketone Bodies
Box 17𔂿 Fat Bears Carry Out b Oxidation in Their Sleep
Box 17𔃀 Coenzyme B12: A Radical Solution to a Perplexing Problem
• New section on the role of transcription factors (PPARs) in regulation of lipid catabolism

18 Amino Acid Oxidation and the Production of Urea
18.1 Metabolic Fates of Amino Groups
18.2 Nitrogen Excretion and the Urea Cycle
18.3 Pathways of Amino Acid Degradation
Box 18𔂿 Medicine: Assays for Tissue Damage
Box 18𔃀 Medicine: Scientific Sleuths Solve a Murder Mystery
• New section on pernicious anemia and associated problems in strict vegetarians.

19 Oxidative Phosphorylation and Photophosphorylation
Oxidative Phosphorylation

19.1 Electron-Transfer Reactions in Mitochondria
19.2 ATP Synthesis
19.3 Regulation of Oxidative Phosphorylation
19.4 Mitochondria in Thermogenesis, Steroid Synthesis, and Apoptosis
19.5 Mitochondrial Genes: Their Origin and the Effects of Mutations

Photosynthesis: Harvesting Light Energy
19.6 General Features of Photophosphorylation
19.7 Light Absorption
19.8 The Central Photochemical Event: Light-Driven Electron Flow
19.9 ATP Synthesis by Photophosphorylation
19.10 The Evolution of Oxygenic Photosynthesis
Box 19𔂿 Hot, Stinking Plants and Alternative Respiratory Pathways
• Updated discussion of the structure of the electron transfer complexes of mitochondria and chloroplasts, and of the Fo complex
• Updated description of the water-splitting complex’s structure in chloroplasts
• Expanded description of mitochondrial diseases and mitochondrial role in diabetes

20 Carbohydrate Biosynthesis in Plants and Bacteria
20.1 Photosynthetic Carbohydrate Synthesis
20.2 Photorespiration and the C4 and CAM Pathways
20.3 Biosynthesis of Starch and Sucrose
20.4 Synthesis of Cell Wall Polysaccharides: Plant Cellulose and Bacterial Peptidoglycan
20.5 Integration of Carbohydrate Metabolism in the Plant Cell

21 Lipid Biosynthesis
21.1 Biosynthesis of Fatty Acids and Eicosanoids
21.2 Biosynthesis of Triacylglycerols
21.3 Biosynthesis of Membrane Phospholipids
21.4 Biosynthesis of Cholesterol, Steroids, and Isoprenoids
Box 21𔂿 Mixed-Function Oxidases, Oxygenases, and Cytochrome P-450
• Revised and updated section on fatty acid synthase includes new structural information on FAS I
• Updated information on cyclooxygenase inhibitors (pain relievers Vioxx, Celebrex, Bextra)
• New information on HMG-CoA reductase and new medical box on statins

22 Biosynthesis of Amino Acids, Nucleotides, and Related Molecules
22.1 Overview of Nitrogen Metabolism
22.2 Biosynthesis of Amino Acids
22.3 Molecules Derived from Amino Acids
22.4 Biosynthesis and Degradation of Nucleotides
Box 22𔂿 Unusual lifestyles of the obscure but abundant
Box 22𔃀 Medicine: On Kings and Vampires
Box 22𔃁 Medicine: Curing African Sleeping Sickness with a Biochemical Trojan Horse
• Updated coverage of nitrogen cycle section includes a new box on anammox bacteria
• New information on therapy for acute lymphoblastic leukemia
• New information on folic acid deficiency

23 Hormonal Regulation and Integration of Mammalian Metabolism
23.1 Hormones: Diverse Structures for Diverse Functions
23.2 Tissue-Specific Metabolism: The Division of Labor
23.3 Hormonal Regulation of Fuel Metabolism
23.4 Obesity and the Regulation of Body Mass
23.5 Obesity, the Metabolic Syndrome, and Type 2 Diabetes
Box 23𔂿 Medicine: How Is a Hormone Discovered? The Arduous Path to Purified Insulin
• Expanded coverage and updating of the biochemical connections between obesity, metabolic syndrome, and type 2 diabetes
• Updated discussion of the integration of fuel metabolism in fed and starved states in diabetes

24 Genes and Chromosomes
24.1 Chromosomal Elements
24.2 DNA Supercoiling
24.3 The Structure of Chromosomes
Box 24𔂿 Medicine: Curing Disease by Inhibiting Topoisomerases
Box 24𔃀 Medicine: Epigenetics, Nucleosome Structure, and Histone Variants
• New material on histone modification, histone variants, and nucleosome deposition
• New medical box on the use of topoisomerase inhibitors in the treatment of bacterial infections and cancer, includes material on ciprofloxacin (the antibiotic effective for anthrax)
• New box on the role of histone modification and nucleosome deposition in the transmission of epigenetic information in heredity

25 DNA Metabolism
25.1 DNA Replication
25.2 DNA Repair
25.3 DNA Recombination
Box 25𔂿 Medicine: DNA Repair and Cancer
• New information on the initiation of replication and the dynamics at the replication fork, introducing AAA+ ATPases and their functions in replication and other aspects of DNA metabolism

26 RNA Metabolism
26.1 DNA-Dependent Synthesis of RNA
26.2 RNA Processing
26.3 RNA-Dependent Synthesis of RNA and DNA
Box 26𔂿 Methods: RNA Polymerase Leaves Its Footprint on a Promoter
Box 26𔃀 Fighting AIDS with Inhibitors of HIV Reverse Transcriptase
Box 26𔃁 Methods: The SELEX Method for Generating RNA Polymers with New Functions
Box 26𔃂 An Expanding RNA Universe Filled with TUF RNAs
• New section on the expanding roles of RNA in cells

27 Protein Metabolism
27.1 The Genetic Code
27.2 Protein Synthesis
27.3 Protein Targeting and Degradation
Box 27𔂿 Exceptions That Prove the Rule: Natural Variations in the Genetic Code
Box 27𔃀 From an RNA World to a Protein World
Box 27𔃁 Natural and Unnatural Expansion of the Genetic Code
Box 27𔃂 Induced Variation in the Genetic Code: Nonsense Suppression
• Expanded section on protein synthesis coupled to the advances in ribosome structure
• New information on the roles of RNA in protein biosynthesis

28 Regulation of Gene Expression
28.1 Principles of Gene Regulation
28.2 Regulation of Gene Expression in Bacteria
28.3 Regulation of Gene Expression in Eukaryotes
Box 28𔂿 Of Fins, Wings, Beaks, and Things
• New information about roles of RNA in gene regulation
• New box on the connections between evolution and development

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Gene regulation

Gene regulation is a label for the cellular processes that control the rate and manner of gene expression. A complex set of interactions between genes, RNA molecules, proteins (including transcription factors) and other components of the expression system determine when and where specific genes are activated and the amount of protein or RNA product produced.

Some genes are expressed continuously, as they produce proteins involved in basic metabolic functions some genes are expressed as part of the process of cell differentiation and some genes are expressed as a result of cell differentiation.

Mechanisms of gene regulation include:

  • Regulating the rate of transcription. This is the most economical method of regulation.
  • Regulating the processing of RNA molecules, including alternative splicing to produce more than one protein product from a single gene.
  • Regulating the stability of mRNA molecules.
  • Regulating the rate of translation.

Transcription factors are proteins that play a role in regulating the transcription of genes by binding to specific regulatory nucleotide sequences.

NSF-Simons Center for Quantitative Biology logo

Interdisciplinary collaboration uncovers common mechanism regulating gene expression during development

A team of quantitative biology researchers from Northwestern Engineering and the Weinberg College of Arts and Sciences has uncovered new insights into the impact of stochasticity in gene expression, offering new evolutionary clues into organismal design principles in the face of physical constraints.

In cells, genes are expressed through transcription, a process where genetic information encoded in DNA is copied into messenger RNA (mRNA). The mRNA is then translated to make protein molecules, the workhorses of cells. This entire process is subject to bursts of natural stochasticity — or randomness — which can impact the outcome of biological processes that proteins carry out.

The researchers’ new experimental and theoretical analyses studied a collection of genes in Drosophila, a family of fruit flies, and found that gene expression is regulated by the frequency of these transcriptional bursts.

“It has been known for almost two decades that protein levels can demonstrate large levels of stochasticity owing to their small numbers, but this has never been empirically demonstrated in multicellular organisms during the course of their development,” said Madhav Mani, assistant professor of engineering sciences and applied mathematics at the McCormick School of Engineering. “This work for the first time identifies the role of randomness in altering the outcome of a developmental process.”

A paper outlining the work, titled “The Wg and Dpp Morphogens Regulate Gene Expression by Modulating the Frequency of Transcriptional Bursts,” was published June 22 in the journal eLife. Mani is a co-corresponding author on the study along with Richard Carthew, professor of molecular biosciences in the Weinberg College of Arts and Sciences. Both are members of Northwestern’s NSF-Simons Center for Quantitative Biology, which brings together mathematical scientists and developmental biologists to investigate the biology of animal development.

This study builds upon a recent paper in which the researchers studied the role of stochastic gene expression on sensory pattern formation in Drosophila. By analyzing experimental perturbations of Drosophila’s senseless gene against mathematical models, the team determined the sources of the gene’s stochasticity, and found that the randomness appears to be leveraged in order to accurately determine sensory neuron fates.

The researchers applied that understanding to this latest study using a technique called single molecule fluorescence in situ hybridization (smFISH) to measure nascent and mature mRNA in genes downstream of two key patterning factors, Wg and Dpp, responsible for the organ development of fruit fly wings. In comparing the measurements to their data models, the researchers found that, while each gene’s pattern of expression is unique, the mechanism by which expression is regulated — which the team named “burst frequency modulation” — is the same.

“Our results show that proteins’ levels of randomness are impacted by the physical structure of the genome surrounding the gene of interest by modulating the features of the ‘software’ that control the levels of gene expression,” Mani said. “We developed an experimental approach to study a large collection of genes in order to discern overall trends as to how the stochastic software of gene regulation is itself regulated.”

The observed patterns of gene regulation, Mani said, works like a stochastic light switch.

“Let’s say you are quickly flipping a light switch on and off, but you want more brightness out of your bulb. You could either get a brighter bulb that produced more photons per unit time, or you could leave the switch ‘on’ more than ‘off,’” Mani said. “What we found is that organisms control the amount of gene expression by regulating how often the gene is permitted to switch on, rather than making more mRNAs when it is on.”

Carthew, director of the Center for Quantitative Biology, added that this mode of gene expression regulation was observed for multiple genes, which hints at the possibility of a broader biological principle where quantitative control of gene expression leverages the random nature of the process.

“From these studies, we are learning rules for how genes can be made more or less noisy,” Carthew said. “Sometimes cells want to harness the genetic noise — the level of variation in gene expression — to make randomized decisions. Other times cells want to suppress the noise because it makes cells too variable for the good of the organism. Intrinsic features of a gene can imbue them with more or less noise.”

While engineers are excited by the ability to control and manipulate biological systems, Mani said, more fundamental knowledge needs to be discovered.

“We only know the tip of the iceberg,” Mani said. “We are far from a time when basic science is considered complete and all that is left is engineering and design. The natural world is still hiding its deepest mysteries.”

Written by Alex Gerage, originally published on Northwestern Engineering News.

Watch the video: Gene Regulation and the Order of the Operon (October 2022).