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Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner [email protected]

Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner [email protected]

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Page 1: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Human Disease(Part 1 of 2)

Monday, November 10, 2003

Introduction to BioinformaticsJohns Hopkins School of Medicine

ME:440.714J. Pevsner

[email protected]

Page 2: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Many of the images in this powerpoint presentationare from Bioinformatics and Functional Genomicsby Jonathan Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by John Wiley & Sons, Inc.

These images and materials may not be usedwithout permission from the publisher. We welcomeinstructors to use these powerpoints for educationalpurposes, but please acknowledge the source.

The book has a homepage at http://www.bioinfbook.orgIncluding hyperlinks to the book chapters.

Copyright notice

Page 3: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Today: human disease (last lecture)

Wednesday Nov 20: final exam, in class

Find-a-gene project due Wednesday

Schedule

Page 4: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Barton Childs and David Valle “Genetics, Biologyand Disease” Annu Rev. Genomics Hum. Genet.2000, 01:1-19.

Nature and Science human genome issues have articles from Valle, McKusick & colleagues.

References

Jimenez-Sanchez G, Childs B, Valle D. Human disease genes.Nature 2001 Feb 15;409(6822):853-5.

Peltonen L, McKusick VA. Genomics and medicine. Dissecting human disease in the postgenomic era. Science 2001 Feb 16;291(5507):1224-9.

Page 5: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Life is a relationship between molecules, not a property of any one molecule. So is therefore disease, which endangers life. While there are molecular diseases, there are no diseased molecules. At the level of the molecules we find only variations in structure and physicochemical properties. Likewise, at that level we rarely detect any criterion by virtue of which to place a given molecule “higher” or “lower” on the evolutionary scale. Human hemoglobin, although different to some extent from that of the horse (Braunitzer and Matsuda, 1961), appears in no way more highly organized. Molecular disease and evolution are realities belonging to superior levels of biological integration. There they are found to be closely linked, with no sharp borderline between them. The mechanism of molecular disease represents one element of the mechanism of evolution. Even subjectively the two phenomena of disease and evolution may at times lead to identical experiences. The appearance of the concept of good and evil, interpreted by man as his painful expulsion from Paradise, was probably a molecular disease that turned out to be evolution. Subjectively, to evolve must most often have amounted to suffering from a disease. And these diseases were of course molecular.

Emile Zuckerkandl and Linus Pauling (1962)

Page 647

Page 6: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Genetic variation is responsible for the adaptive changes that underlie evolution.

Some changes improve the fitness of a species.Other changes are maladaptive.

For the individual in a species, these maladaptivechanges represent disease.

Molecular perspective: mutation and variation

Medical perspective: pathological condition

Human disease: a consequence of variation

Page 647

Page 7: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

-- many regions of the genome may be affected

-- there are many mechanisms of mutation

-- genes and gene products interact with their molecular environments

-- an individual interacts with the environment in ways that may promote disease

Why is there such a diversity of diseases?

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Page 8: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Overview of human disease (classification)

Molecular levelDNA, RNA, protein

Systems levelOrganellar, systems disease databases

Organismal levelClinical phenotypesAnimal modelsDisease organizations

Outline

Page 9: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

The field of bioinformatics involves the use of computer algorithms and databases to study genes, genomes,and proteins.

• DNA databases offer the reference sequences with which to compare normal sequences and those associated with disease• Physical and genetic maps are used in gene-finding studies• Protein structure studies allow study of effects of mutation• Many functional genomics approaches applied to genes• Insight into human disease genes is provided through the study of orthologs

Bioinformatics perspectives on disease

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Page 10: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Medicine: diagnosis, treatment, prognosis, prevention of disease

Genetics: understanding the origin and expression of individual human uniqueness

Genomics: identifying and characterizing genes and their arrangement in chromosomes

Bioinformatics: the use of computer algorithms and computer databases to study genes, genomes, and proteins.

Perspectives on disease

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Page 11: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Overview of human disease (classification)

Molecular levelDNA (OMIM, SNP, LSDB), RNA, protein

Systems levelOrganellar, systems disease databases

Organismal levelClinical phenotypesAnimal modelsDisease organizations

Outline

Page 12: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Sir Archibald Garrod (1857-1936) made important contributions to our understanding of disease.

In a 1902 paper (see page 650 for URL of on-line version),he described the rare inherited disorder alkaptonuria.He argued that variations in metabolic processes betweenindividuals might include disease-causing changes.Such traits are inherited according to Mendel’s laws.In his book Inborn Errors of Metabolism (1909), Garrod discusses how the disease phenotype reflectsthe chemical individuality of the each person.

Archibald Garrod’s view of disease

Page 650

Page 13: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

In a second book, Inborn Factors in Disease (1931), Garrod discusses how chemical individuality predisposesus to various diseases (whether the disease is inherited,caused by infection, or by an environmental agent). Every disease process is affected by both internaland external forces.

Archibald Garrod’s view of disease

Page 651

Page 14: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

We can consider five main categories of human disease.

Categories of disease

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Page 15: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Single gene disorders rare

Complex disorders common

Chromosomal disorders very common

Infectious disease most common

Environmental disease common

Categories of disease

Page 652

Page 16: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Single gene disorders rareautosomal dominantautosomal recessiveX-linked recessive

Complex disorders commoncongenital anomaliesCNScardiovascular

Chromosomal disorders very common

Infectious disease most common

Environmental disease common

Categories of disease

Page 652

Page 17: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Single gene disorders rare multigenicautosomal dominant pathophysiologyautosomal recessiveX-linked recessive

Complex disorders common multigeniccongenital anomaliesCNScardiovascular

Chromosomal disorders common multigenic

Infectious disease common multigenic

Environmental disease common multigenic

Categories of disease

Page 18: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Example:

Lead poisoning is an environmental disease. It is common (about 9% of US children have high blood levels).

But two children exposed to the same dose of leadmay have entirely different phenotypes.

This susceptibility has a genetic basis.

Conclusion: genes affect susceptibility to environmentalinsults, and infectious disease. Even single-gene disordersinvolve many genes in their phenotypic expression.

Categories of disease

Page 653

Page 19: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

There are several approaches to disease classification that are relevant to our bioinformatics perspective of disease.(Also, the field of bioinformatics [health sciences informatics] is specifically involved in disease classification.)

Death rankings

Disability-adjusted life years (DALYs)

Classification systems:-- International Statistical Classification of Diseases and Related Health Problems (ICD-9 and ICD-10)-- National Library of Medicine (MeSH terms)

Classification of disease

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Page 20: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Leading causes of death (U.S., 1999) number of % total

Rank Cause deaths deaths 1 heart disease 725,192 30.3 2 malignant neoplasm 549,192 23.0 3 cerebrovascular disease 167,366 7.0 4 chronic lower respiratory 124,181 5.2 5 accidents 97,860 4.1 6 diabetes mellitus 68,399 2.9 7 influenza, pneumonia 63,730 2.7 8 Alzheimer’s disease 44,536 1.9 9 nephritis & related 35,525 1.510 septicemia 30,680 1.3

all other 2,391,399 20.2Source: National Vital Statistics Reports 49(11):1-87, 2001.

Classification of disease

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Page 21: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Fig. 18.3Page 654

The global burden of disease

See p. 654 for URL

Page 22: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Fig. 18.4Page 654See p. 654 for URL

Page 23: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

The International Statistical Classification of Diseases andRelated Health Problems (ICD) is the main disease classification system used in health care. Examples ofcategories are:1. Infectious and parastic disease2. Neoplasms3. Endocrine, nutritional, and metabolic diseases…4. Diseases of the blood and blood-forming organs5. Mental disorders6. Diseases of the nervous system and sense organs7. Diseases of the circulatory system8. Diseases of the respiratory system9. Diseases of the digestive system

See http://www.who.int/whosis/icd10/

Classification of disease

Page 655

Page 24: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Fig. 18.5Page 656

The National Library of Medicine (NLM) includesMedical Subject Heading (MeSH) terms for disease

Page 25: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Fig. 18.5Page 656

The National Library of Medicine (NLM) includesMedical Subject Heading (MeSH) terms for disease

Page 26: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Fig. 18.5Page 656

The National Library of Medicine (NLM) includesMedical Subject Heading (MeSH) terms for disease

Page 27: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Previously, a large distinction was made betweenmonogenic (single gene) and polygenic (complex) disorders. They are now seen to be more on a continuum.

We may define a single-gene disorder as a disorder that is caused primarily by mutation(s) in a single gene.However, as we will see below, all monogenic disordersinvolve many genes.

Monogenic (single gene) disorders

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Page 28: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Autosomal dominantBRCA1, BRCA2 1:1000Huntington chorea 1:2,500Tuberous sclerosis 1:15,000

Autosomal recessiveAlbinism 1:10,000Sickle cell anemia 1:655 (U.S. Afr.Am)Cystic fibrosis 1:2,500 (Europeans)Phenylketonuria 1:12,000

X-linkedHemophilia A 1:10,000 (males)Rett Syndrome 1:10,000 (females)Fragile X Syndrome 1:1,250 (males)

Monogenic (single gene) disorders

Table 18.4Page 656

Page 29: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Sickle cell anemia is an example of a single gene disorder.

It is caused by mutations in beta globin (HBB). We sawthat the E6V mutation is very common (Chapter 9).This mutation causes hemoglobin molecules (22) to aggregate, giving red blood cells a sickled appearance.

This single gene disorder is unusually prevalent becausethe heterozygous state confers protection to thoseexposed to the malaria parasite.

You can read Linus Pauling’s 1949 article describing theabnormal electrophoretic mobility of HBB on-line athttp://profiles.nlm.nih.gov/MM/B/B/R/L/

Monogenic (single gene) disorders

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Page 30: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

A monogenic disorder: Rett Syndrome

Rett syndrome (RTT) is another example of a single genedisorder. We will discuss the following aspects:

Clinical presentationNeurobiologyGene defect: MECP2, a transcriptional repressor (Xq28)OMIM entryLocus-specific database entrySingle nucleotide polymorphisms (SNPs)

Box 18.2Page 658

Page 31: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Rett Syndrome: Clinical Presentation

Normal pre- and perinatal development

Neurocognitive regression Deceleration of head and brain growth Loss of speech + social skills (autistic) Loss of purposeful hand movements Truncal ataxia Repetitive hand movements Seizures

Box 18.2Page 658

Page 32: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Rett Syndrome: Neurobiology

• Decreased Total Brain Volume

• Reduced Cortical Thickness

• Nigrostriatal Pathology

• Basal Forebrain Cholinergic System

• Glutamatergic Abnormalities

• Disruption of Neuronal Markers in olfactory epithelium

Box 18.2Page 658

Page 33: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Rett Syndrome: Genetics

• Affects only females (~1/15,000)

• X-linked male-lethal?

• “Genetic Lethality”

• >99% of cases are sporadic

• Twins: MZ - 7/8 DZ - 2/13

• Mother - daughter pair

• X exclusion mapping: Xq28

• Linkage analysis: Xq28

Box 18.2Page 658

Page 34: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Mutations in MECP2 cause Rett Syndrome

Rett Syndrome is Caused by Mutations in X-linked MECP2,Encoding Methyl-CpG-Binding Protein

R.E. Amir et al. (Nature Genetics October 1, 1999)

Transcriptionalrepression domain

Methyl CpGbinding domain

Box 18.2Page 658

Page 35: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

HistoneDeacetyl

mSin3a

MeCP2

Overview of MeCP2 Function

Active Transcription

TranscriptionalRepression

methylation

Box 18.2Page 658

Page 36: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Disease principles highlighted by RTT

-- sex ratio (almost exclusively females) is likely caused by a high mutation rate in fathers. (An alternativeexplanation would be male lethality in utero.)

XY XX

XY XXBox 18.2Page 658

Page 37: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Disease principles highlighted by RTT

-- phenotype in males (severe neonatal encephalopathy, often fatal) does not resemble that of females

-- females may be spared a more severe phenotype because of random X chromosome inactivation. In all females, each cell chooses to express either the maternal or paternal X chromosome, early in life. Thus RTT females are a mosaic of cells expressing normal and mutated copies of MECP2.

-- X-inactivation patterns in females are normally about 50-50. However they may be skewed 99-1, allowing a female to be a carrier. Several females have given birth to affected daughters.

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Page 38: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

Disease principles highlighted by RTT

-- an identical mutation in MECP2 in two females may result in extremely different phenotypes. There are two main explanations:

[1] Additional, modifier genes may affect the disease process. This is seen for sickle cell anemia and for many other single gene disorders.

[2] Many epigenetic factors may influence the clinical phenotype. In RTT, the methylation status of genomic DNA could be important. Skewed X-inactivation can cause even identical twins to exhibit different phenotypes.

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Page 39: Human Disease (Part 1 of 2) Monday, November 10, 2003 Introduction to Bioinformatics Johns Hopkins School of Medicine ME:440.714 J. Pevsner pevsner@jhmi.edu

This lecture continues in part 2 with a discussion of OMIM…

http://pevsnerlab.kennedykrieger.org/ppts/lecture_bioinf_ch18_part2.ppt