The ecology of disease interactions in CF

Transcription

1 1/44 The ecology of disease in CF University of Utah Department of Mathematics and Department of Biology September 4, 2014

2 2/44 The big questions in ecology Why are there so many species? Charley Harper

3 2/44 The big questions in ecology Why are there so many species? What explains the distribution and abundance of organisms? Charley Harper

4 2/44 The big questions in ecology Why are there so many species? What explains the distribution and abundance of organisms? Why are there specialists and generalists? Charley Harper

5 3/44 Applications of ecological thinking Conservation Biology Preserving important species Maintaining healthy ecosystem function

6 3/44 Applications of ecological thinking Conservation Biology Preserving important species Maintaining healthy ecosystem function Restoration Ecology Reintroducing preferred species Restoring ecosystem services (like water or air cleaning) Removing unwanted invasive species

7 4/44 Parallels with Goals Conserve and restore healthy function Remove unwanted species Avoid unintended consequences

8 4/44 Parallels with Goals Conserve and restore healthy function Remove unwanted species Avoid unintended consequences Scientific challenges Associational data do not reveal causality Laboratory data can be hard to extrapolate Long-term data are powerful, but noisy Controlled clinical trials are difficult

9 5/44 Metacommunity Theory Describes how communities interact across space

10 5/44 Metacommunity Theory Describes how communities interact across space Should work brilliantly for disease metacommunities if we can separate

11 5/44 Metacommunity Theory Describes how communities interact across space Should work brilliantly for disease metacommunities if we can separate Interactions... Among infections (interference or enhancement) With environment (immune system or pollution) Among individuals (transmission)

12 5/44 Metacommunity Theory Describes how communities interact across space Should work brilliantly for disease metacommunities if we can separate Interactions... Among infections (interference or enhancement) With environment (immune system or pollution) Among individuals (transmission)...from heterogeneity Fixed differences (gender and genetics) Predictable differences (age) Caused by disease (state of the immune system)

13 6/44 Outline Metacommunity theory has been hampered by incomplete characterization of and heterogeneity, and disease systems could provide a useful corrective. I ve been studying two main systems and will discuss just cystic fibrosis today.

14 6/44 Outline Metacommunity theory has been hampered by incomplete characterization of and heterogeneity, and disease systems could provide a useful corrective. I ve been studying two main systems and will discuss just cystic fibrosis today. in cystic fibrosis Virus in families

15 7/44 Acknowledgments Cystic Fibrosis Dr. Ted Liou The Intermountain Cystic Fibrosis Center The Cystic Fibrosis Foundation and the CFFPR James S. McDonnell Foundation Modeling the Dynamics of Life fund National Institutes of Health NSF RTG programs

16 8/44 Overview of CF Cystic fibrosis is an autosomal recessive defect in a chloride channel

17 8/44 Overview of CF Cystic fibrosis is an autosomal recessive defect in a chloride channel progression results from a feedback between bacterial infections and lung damage

18 9/44 History of the project An M.D. (Dr. Ted Liou) took my ing class

19 9/44 History of the project An M.D. (Dr. Ted Liou) took my ing class Developed a statistical survival of CF

20 9/44 History of the project An M.D. (Dr. Ted Liou) took my ing class Developed a statistical survival of CF Predicted which patients would benefit from transplant

21 9/44 History of the project An M.D. (Dr. Ted Liou) took my ing class Developed a statistical survival of CF Predicted which patients would benefit from transplant Merged the CF data base with the UNOS transplant database

22 9/44 History of the project An M.D. (Dr. Ted Liou) took my ing class Developed a statistical survival of CF Predicted which patients would benefit from transplant Merged the CF data base with the UNOS transplant database Got into a lot of trouble when we applied it to children

23 The figure that caused the trouble Figure 2 Estimated Hazard Factor of Lung Transplantation S aureus, n = 149 Diabetes, n = 32 Both, n = 15 Neither, n = 318 Black boxes mark patients also infected with B cepacia, n = 32 p < 0.05, hazard > 1, n = 283 p > 0.05, hazard ~ 1, n = 226 p < 0.05, hazard < 1, n = S aureus, n = 83 Diabetes, n = 12 Both, n = 5 Neither, n = 148 Black boxes mark patients also infected with B cepacia, n = 19 p < 0.05, hazard > 1, n = 145 p > 0.05, hazard ~ 1, n = 102 p < 0.05, hazard < 1, n = 1 A B Liou et. al Age at Clinic Visit (Years) 10/44

24 11/44 The web of... Genetic Defect Abnormal Secretions Sinusitis Inflammation Infertility Acute Exacerbations Pancreatic insufficiency Malnutrition Lung Function(FEV1%) Lung Transplant Bacterial Infections S aureus P aeruginosa MRSA Burkholderia Candida MAI Liver disease Liver Transplant Height Weight Sex Age Survival Diabetes courtesy of T. G. Liou

25 12/44 The bacteria and clinical measurements Key bacteria (the species): Pseudomonas auruginosa (PA): Aggressive chronic infection Staphylococcus aureus (SA): Chronic, less aggressive Burkholderia cepacia (BC): Deadly, antibiotic resistant Stenotrophomonas maltophilia (Steno): Emerging

26 12/44 The bacteria and clinical measurements Key bacteria (the species): Pseudomonas auruginosa (PA): Aggressive chronic infection Staphylococcus aureus (SA): Chronic, less aggressive Burkholderia cepacia (BC): Deadly, antibiotic resistant Stenotrophomonas maltophilia (Steno): Emerging Key clinical covariates (the environment): FEV1% (measure of lung function) Acute Pulmonary Exacerbations (APE)

27 13/44 Strategy Because transmission is limited, individual patients serve as independent replicates

28 13/44 Strategy Because transmission is limited, individual patients serve as independent replicates The Cystic Fibrosis Foundation Patient Registry has millions of records on tens of thousands of patients

29 13/44 Strategy Because transmission is limited, individual patients serve as independent replicates The Cystic Fibrosis Foundation Patient Registry has millions of records on tens of thousands of patients 1 Establish associations

30 13/44 Strategy Because transmission is limited, individual patients serve as independent replicates The Cystic Fibrosis Foundation Patient Registry has millions of records on tens of thousands of patients 1 Establish associations 2 Use temporal data to identify causal links between bacterial infections and clinical covariates

31 13/44 Strategy Because transmission is limited, individual patients serve as independent replicates The Cystic Fibrosis Foundation Patient Registry has millions of records on tens of thousands of patients 1 Establish associations 2 Use temporal data to identify causal links between bacterial infections and clinical covariates 3 Use s to investigate the that shape the course of disease

32 14/44 What associations do we see? Among four prominent infections BC PA SA Negative Positive Steno

33 14/44 What associations do we see? Among four prominent infections With age Age BC PA SA Negative Positive Steno

34 14/44 What associations do we see? Among four prominent infections With age With APE BC PA SA Negative Positive Steno APE

35 14/44 What associations do we see? Among four prominent infections With age With APE With lung function BC PA FEV1% SA Negative Positive Steno

36 14/44 What associations do we see? Among four prominent infections With age With APE With lung function BC With one-year mortality PA SA death Steno

37 15/44 Infection progression looks like succession Prevalence Staph Pseudomonas Burkholderia Steno MRSA Age

38 16/44 There are temporal trends Prevalence Staph Pseudomonas Burkholderia Steno MRSA Year

39 17/44 Survival bias is pervasive APE per year normalized FEV Age

40 18/44 Longitudinal versus cross-sectional data Associational data are based on snapshots. But if we follow individuals over time, we can try to establish causality. How do bacteria affect each other?

41 18/44 Longitudinal versus cross-sectional data Associational data are based on snapshots. But if we follow individuals over time, we can try to establish causality. How do bacteria affect each other? Do bad bacteria cause lung damage?

42 18/44 Longitudinal versus cross-sectional data Associational data are based on snapshots. But if we follow individuals over time, we can try to establish causality. How do bacteria affect each other? Do bad bacteria cause lung damage? Does lung damage enable invasion by bad bacteria?

43 19/44 Example: Staph vs Pseudomonas Probability Gain SA Without PA With PA Lose SA

44 19/44 Example: Staph vs Pseudomonas Probability Gain SA Without PA With PA Lose SA Probability Gain PA Without SA With SA Lose PA Antagonistic relationship: Each reduces the colonization and increases the loss of the other.

45 20/44 Matrix of effects BC BC SA PA APE Fev1 Death gain gain gain loss loss loss SA PA APE Fev1 Up arrows increase that measurement in the next year Red is bad, blue is good

46 Grand matrix of effects BC SA mrsa PA alc steno asp cand diabet apes logfev1 zscore dflag burkcomplex staph mrsa pseudo alcalig steno aspergillus candida diabet apes logfev1pct zscore YRM age sex 21/44

47 22/44 The web of revisited How did an innocent mathematical er end up here? Genetic Defect Abnormal Secretions Sinusitis Inflammation Infertility Acute Exacerbations Pancreatic insufficiency Malnutrition Lung Function(FEV1%) Lung Transplant Bacterial Infections S aureus P aeruginosa MRSA Burkholderia Candida MAI Liver disease Liver Transplant Height Weight Sex Age Survival Diabetes

48 23/44 Method Included Pseudomonas, Staph, Burkholderia, FEV1%, and one-year mortality Logistic regression (glm) or generalized additive s (gam) to predict mortality, and maintenance and loss of bacteria Ordinary regression or generalized additive s to predict FEV1% in following year Started with measurements from real patients and projected throughout life

49 24/44 Comparisons The essence of mathematical ing is making long-term projections from short-term mechanisms, and allows us to comare different scenarios. A complete A simplified without disease Models with and without noise in FEV1% Models with different subsets of bacteria removed

50 A word on competing risks How much longer would people live if smallpox were eliminated? If smallpox killed those doomed to die soon anyway... Lifespan without With smallpox Without smallpox Lifespan with smallpox Fraction alive With smallpox Without smallpox Age 25/44

51 26/44 A word on competing risks How much longer would people live if smallpox were eliminated? If smallpox killed the healthiest destined for long life... Lifespan without With smallpox Without smallpox Lifespan with smallpox Fraction alive With smallpox Without smallpox Age

52 Full Survivorship Staph allages Fev1% Burkholderia gam glm all data Pseudomonas Pseudo and Staph Age Age Age 27/44

53 Without disease Survivorship Staph allages Fev1% Burkholderia gam glm all data Pseudomonas Pseudo and Staph Age Age Age 28/44

54 Without noise in FEV1% Survivorship Staph allages Fev1% Burkholderia gam glm all data Pseudomonas Pseudo and Staph Age Age Age 29/44

55 Without Staph Survivorship Staph allages Fev1% Burkholderia gam glm all data Pseudomonas Pseudo and Staph Age Age Age 30/44

56 Without Pseudomonas Survivorship Staph allages Fev1% Burkholderia gam glm all data Pseudomonas Pseudo and Staph Age Age Age 31/44

57 32/44 Next steps Estimate the role of noise Add additional covariates Find way to approximate results mathematically

58 33/44 The search for order in the immune universe These s do not explain the mechanisms underlying the slow progression of infection and inflammation in CF

59 33/44 The search for order in the immune universe These s do not explain the mechanisms underlying the slow progression of infection and inflammation in CF Markers of imminent problems would be useful

60 33/44 The search for order in the immune universe These s do not explain the mechanisms underlying the slow progression of infection and inflammation in CF Markers of imminent problems would be useful HMGB-1 has emerged as candidate A highly conserved DNA-shepherding protein Also released by macrophages

61 34/44 HMGB-1 and lions listdose.com

62 35/44 The search for order in the immune universe This is little more than a summary of the statistics We want to understand the mechanisms underlying the slow progression of infection and inflammation in CF Markers of imminent problems would be useful HMGB-1 has emerged as candidate A highly conserved DNA-shepherding protein Also released by macrophages Potent cytokine associated with prolonged inflammation Potential target of treatment

63 HMGB-1 is not a marker of current status Log HMGB p=0.47 sick Patient status well 36/44

64 37/44 HMGB-1 may be a marker of future status Liou et al, 2012, PLoS ONE

65 38/44 The search for order in the genetic universe Cystic fibrosis mutations are broken into multiple classes Classes I-III are loss of function, and include the most common mutation F508del and G551D Classes IV-V retain some function and show generally better progression

66 38/44 The search for order in the genetic universe Cystic fibrosis mutations are broken into multiple classes Classes I-III are loss of function, and include the most common mutation F508del and G551D Classes IV-V retain some function and show generally better progression More specific associations might pinpoint mechanisms

67 39/44 R117H is a class IV amino acid change

68 40/44 R117H has surprising effects on microbiology Coefficient * Gain Loss * * Pseudomonas Staph Burkholderia..

69 41/44 Models meet data Putting the data together can be harder than ing

70 41/44 Models meet data Putting the data together can be harder than ing Murphy s law of large data sets

71 41/44 Models meet data Putting the data together can be harder than ing Murphy s law of large data sets Data are noisy Bacterial species are poorly-defined The lungs are not a well-mixed environment Testing is error-prone and timing is biased

72 41/44 Models meet data Putting the data together can be harder than ing Murphy s law of large data sets Data are noisy Bacterial species are poorly-defined The lungs are not a well-mixed environment Testing is error-prone and timing is biased The dream of big data: With enough data, patterns loom through the fog

73 42/44 Theorist nightmares Everything is simple Everything is too complex Data are hopeless contaminated by bias Immunologists will discover TH-65/CD4.8 + cells Our s cannot or will not be tested

74 43/44 Theory and the law of unintended consequences Urban ecology is the study of unintended consequences Build a road to drive on

75 43/44 Theory and the law of unintended consequences Urban ecology is the study of unintended consequences Build a road to drive on Changes water and nutrient flow Changes solar absorption Alters local temperature and populations

76 43/44 Theory and the law of unintended consequences Urban ecology is the study of unintended consequences Build a road to drive on Changes water and nutrient flow Changes solar absorption Alters local temperature and populations Plans are useless, but planning is essential, Eisenhower

77 43/44 Theory and the law of unintended consequences Urban ecology is the study of unintended consequences Build a road to drive on Changes water and nutrient flow Changes solar absorption Alters local temperature and populations Plans are useless, but planning is essential, Eisenhower Theories are useless, but theorizing is essential, Adler

78 44/44 Why are randomized controlled trials easier in? Patients want to contribute to knowledge

79 44/44 Why are randomized controlled trials easier in? Patients want to contribute to knowledge Well defined protections and remuneration

80 44/44 Why are randomized controlled trials easier in? Patients want to contribute to knowledge Well defined protections and remuneration Well defined process and follow-up

81 44/44 Why are randomized controlled trials easier in? Patients want to contribute to knowledge Well defined protections and remuneration Well defined process and follow-up No conflict between science and advocacy Physicians are advocates for a cause But we agree on that cause: good health

82 44/44 Why are randomized controlled trials easier in? Patients want to contribute to knowledge Well defined protections and remuneration Well defined process and follow-up No conflict between science and advocacy Physicians are advocates for a cause But we agree on that cause: good health By preparing us for unintended consequences, perhaps s will not only identify targets of intervention, but establish common ground when goals are contentious No computers were mistreated by the use of Microsoft products in creating this talk