1. Lecture 5 Pathogen and Host Properties and Microbial Quantification ENVR 133 Mark D. Sobsey Spring, 2006

2. Part I: Pathogen and Host Properties and Their Interactions

3. Pathogen Characteristics or Properties Favoring Environmental Transmission Multiple sources and high endemicity in humans, animals and environment High concentrations released into or present in environmental media (water, food, air) High carriage rate in human and animal hosts Asymptomatic carriage in non-human hosts Proliferate in water and other media Adapt to and persist in different media or hosts – mutation and gene expression Seasonality and climatic effects Natural and anthropogenic sources

4. Pathogen Characteristics or Properties Favoring Environmental Transmission Ability to Persist or Proliferate in Environment and Survive or Penetrate Treatment Processes Stable environmental forms; mechanisms to survive/multiply – spores, cysts, oocysts, stable outer viral layer (protein coat), capsule, etc. – Colonization, biofilm formation, resting stages, protective stages, parasitism – Spatial distribution – Aggregation, particle association, etc. Resistance to environmental stressors and antagonists: – Biodegradation, heat, cold (freezing), drying, dessication, UV light, ionizing radiation, pH extremes, etc. – Resist proteases, amylases, lipases and nucleases Posses DNA repair mechanisms and other injury repair processes

5. Pathogen Characteristics or Properties Favoring Environmental Transmission Genetic properties favoring survival and pathogenicity Double-stranded DNA or RNA DNA repair Ability for genetic exchange, mutation and selection – recombination – plasmid exchange, transposition, conjugation, etc. – point mutation – reassortment – gene expression control Virulence properties: expression, acquisition, exchange Antibiotic resistance

6. Pathogen Characteristics or Properties Favoring Environmental Transmission Ability to cause colonization, infection and illness Low infectious dose Infects by multiple routes –ingestion (GI) –inhalation (respiratory) –cutaneous (skin) –eye –etc. Does not kill off its hosts –“agreeable” host-parasite relationship

7. Virulence Properties of Pathogenic Bacteria Favoring Environmental Transmission Virulence properties: structures or chemical constituents that contribute to pathophysiology: – Outer cell membrane of Gram negative bacteria: endotoxin (fever producer) – Exotoxins – Pili: for attachment and effacement to cells and tissues – Invasins: to facilitate cell invasion – Effacement factors – Cell binding epitopes and receptors Spores: – High resistance to physical and chemical agents – very persistent in the environment Others: – plasmids, lysogenic bacteriophages, VBNC state, etc.

8. Role of Selection of New Microbial Strains in Susceptibility to Infection and Illness Antigenic changes in microbes overcome immunity, increasing risks of re-infection or illness – Antigenically different strains of microbes appear and are selected for over time and space – Constant selection of new strains (by antigenic shift and drift) – Partly driven by “herd” immunity and genetic recombination, reassortment, bacterial conjugation, bacteriophage infection and point mutations Antigenic Shift: – Major change in virus genetic composition by gene substitution or replacement (e.g., reassortment) Antigenic Drift: – Minor changes in virus genetic composition, often by mutation involving specific codons in existing genes (point mutations) A single point mutation can greatly alter microbial virulence

9. Microbe Levels in Environmental Media Vary Over Time - Occurrence of Giardia Cysts in Water: Cumulative Frequency Distribution

10. Other Factors Influencing Pathogen Occurrence and Proliferation Identification of water, food or other media/vehicles of exposure Units of exposure (individual cells, virions, etc.) Routes of exposure and transmission potential Size of exposed population Demographics of exposed population Spatial and temporal nature of exposure (single or multiple; intervals) Behavior of exposed population Treatment, processing, and recontamination

11. Host Factors in Pathogen Transmission Age Immune status Concurrent illness or infirmity Genetic background Pregnancy Nutritional status Demographics of the exposed population (density, etc.) Social and behavioral traits

12. Infection and Illness Factors in Pathogen Occurrence and Transmission Duration of illness Severity of illness Infectivity Morbidity, mortality, sequelae of illness Extent or amount of secondary spread Quality of life Chronicity or recurrence

13. Characteristics or Properties of Pathogen Interactions with Hosts Disease characteristics and spectrum Persistence in hosts: –Chronicity –Persistence –Recrudescence –Sequelae and other post-infection health effects cancer, heart disease, arthritis, neurological effects Secondary spread

14. Other Infectious Disease Considerations Health Outcomes of Microbial Infection Identification and diagnosis of disease caused by the microbe – Disease (symptom complex and signs; case definition) – Acute and chronic disease outcomes – Mortality – Diagnostic tests and other detection tools Sensitive populations and effects on them Surveillance (syndromic, hospital-based, etc.), disease databases, and epidemiological data

15. Health Outcomes of Microbial Infection Acute Outcomes –Diarrhea, vomiting, rash, fever, etc. Chronic Outcomes –Paralysis, hemorrhagic uremia, reactive arthritis, encephalitis, heart disease, etc. Hospitalizations Deaths

16. Methods to Diagnose Infectious Disease Symptoms (subjective: headache, pain) and Signs (objective: fever, rash, diarrhea) Case definition and syndromic surveillance Clinical diagnosis: lab tests – Detect causative organism in clinical specimens – Detect other specific factors associated with infection Immune response – Detect and assay antibodies – Detect and assay other specific immune responses

17. Morbidity Ratios for Salmonella (Non-typhi)

18. Acute and Chronic Outcomes Associated with Microbial Infections

19. Outcomes of Infection Process to be Quantified Hospitalization InfectionAsymptomatic Infection Mortality Disease Advanced Illness, Chronic Infections and Sequelae Acute Symptomatic Illness: Severity and Debilitation Exposure Sensitive Populations

20. Sensitive Populations – Increased Infectious Disease Risks Infants and young children Elderly Immunocompromized –Persons with AIDs –Cancer patients –Transplant patients Pregnant Malnourished

21. Mortality Ratios for Enteric Pathogens in Nursing Homes Versus General Population

22. Impact of Waterborne Outbreaks of Cryptosporidiosis on AIDS Patients

23. Mortality Ratios Among Specific Immunocompromized Patient Groups with Adenovirus Infection

24. Part II - Microbial Quantification Determining microbial concentrations and loads in various specimens and samples Critical information for: –measuring exposures from environmental media –for quantify human health risks of exposures; human infectivity and dose-response –risk assessment and risk management Based on fundamental statistical principles of measuring concentrations of discrete objects Requires consideration of sources of non- homogeniety, variability and uncertainty

25. Quantifying Microbes Essential for environmental monitoring and surveillance Quality standards and guidelines Performance standards; –Prevention and control processes; reductions Risk analysis (assessment and management) –Dose-response and setting acceptable risk levels Ecology and natural history Analysis of vehicles, vectors, reservoirs, etc.

26. Distributions of Microorganisms in Hosts and Environmental Media Mean or Median Range Concentration

28. No. Organisms Cumulative Frequency Distribution of Number of Organisms in Unit Volume of Sample

29. Probability Density Function

32. Estimating Microbial Concentrations from Quantal Data: Relationship Between Dilution and Percentage of Positive Sample Volumes Dose or Dilution (log scale) % Pos. 100 0 50 Dose-response relationships: Often based on few data points Often sigmoidal Difficult to estimate mid-point Difficult to extrapolate to low dose or dilution

33. Probits and Their Application to Estimating 50% Infectious Dose Assume population response is "normally distributed", i.e., a Gaussian distribution Useful for “straightening” plots and facilitate extrapolation. Cumulative dose-responses as % are often sigmoidal and not linear. The most accurate data are near the mid-point, which is the average or mean or 50% point. How does one extrapolate to the extremes where there are no experimental data? To avoid positive and negative values (and be consistent with the expression of cumulative frequency distributions and dose- response data) the mean value of a normal probability function is assigned a probit value of 5 – consistent with the 50% response point in a cumulative frequency distribution or a dose-response relationship

34. Relationship Between Dilution and Percentage of Positive Sample Volumes Dose or Dilution (log scale) Probits 1 5 9 Log-Normal Distribution % Responses expressed as probits: Can linearize the dose-response Facilitates extrapolation

35. Probits and Their Use for Log-Normally Distributed Data Express ordinate (Y-axis) in multiples of the standard deviation, i.e., "normal equivalent deviates.” Hence, probits are unit values assigned to standard deviations  1 standard deviation =  1 probit unit  2 standard deviations =  2 probits units …etc.

36. Dose-Response Relationship Based on % Response and Probits as a Function of Dose on a Log Scale

37. Estimating Microbial Concentrations from Quantal Data: the Poisson Distribution and the Most Probable Number (MPN) Poisson distribution describes “low probability” or rare events – such as, the probability that an inoculated culture of broth will or will not contain 1 or more bacteria that will grow. If large numbers of replicate tubes are inoculated with large numbers of closely-spaced dilutions of a sample containing microbes, a sigmoidal dose-response curve for % positive tubes per dilution is likely to be generated.

38. Estimating Microbial Concentrations from Quantal Data: Relationship Between Dilution, % of Positive Sample Volumes and The MPN Unit of Concentration Dose or Dilution % Pos. 100 0 50 With increasing sample dilution, fewer and fewer culture tubes are positive For the sample dilution that contains on average 1 microbe per volume inoculated into a culture tube, what %age of culture tubes would be positive? According to the Poisson distribution, 63% would be positive So, the “unit” of the Poisson distribution estimated by the Most Probable Number is the inoculum volume that contains on average 1 organism and gives 63% positive cultures 63 1 organism

39. Random (Poisson) Distributions of Organisms Organisms are randomly distributed P(x = N) = [(  V) N /N!] e(-  V) Where: N = number of organisms u = mean density of organisms “true concentration” V = volume of sample P = probability

40. Poisson Distribution Example If the “true” mean number of microbes in a sample is 1 per ml, what is the probability that a given 1 ml sample will contain 0, 1, 2,…..n microbes per ml? P(x = N) = [(  V) N /N!] e(-  V) So, P(0) = [(1x1) 0 /0!] e-1x1 = (1/1) e(-1) = 1 e-1 = 0.37 P(1) = [(1x1) 1 /1!] e-1x1 = (1/1) e-1 = 1 e(-1) = 0.37 P(2) = [(1x1) 2 /2!] e-1x1 = (1/2) e-1 = 0.5 e(-1) = 0.185 For the same sample, what is the probability that a given 1 ml sample will be positive (contain 1 or more microbes per ml?) P(  1) = 1 - P(0) = 1 - 0.37 = 0.63 –(see previous figure showing dose-response curve)

41. Random (Poisson) Distributions of Organisms The mean (  ) is equal to the variance (s 2) :  = s 2 As an approximation:  = s 2 and as an approximation the standard error, s, equals the square root of (  /n): s = (  /n) 1/2 where n = number of samples If n = 1, this becomes the standard deviation S.D. =  x 1/2 as an approximation, the 95% confidence interval is  2(S.D.)

42. Standard Error (of the Mean) of Poisson Counts According to Poisson distribution, the standard error of the mean (SE) for n number of assays is given by: (x/n) 1/2 The 95% confidence limit of the mean count for n number of assays in then approximated by: 95% CL = x  2 (x/n) 1/2 If the number of replicate assays, n, is small, then the 95% confidence limits should be adjusted by substituting the appropriate t-value for n-1 degress of freedom in place of the number “2” in the equation above

43. Standard Deviation of Poisson Counts Standard deviation (s): s = x 1/2 Example: if x = 64 (e.g., colony counts on a plate) s = 64 1/2 =  8 95% confidence limit =  2s, therefore: 2s = 2(  8) =  16 and the upper and lower 95% confidence limits are 64  16 or 80 and 48, respectively

44. Application of Poisson Distribution to Enumerative (Count) Data When total counts of microbes are small, <30, it is useful to estimate central tendencies and dispersions based on properties of the Poisson distribution For the simplest example, a single count or count total, as an approximation, the standard deviation equals the square root of the microbe count: count = 4SD =  2 count = 9 SD =  3 count = 16SD =  4 count = 25SD =  5

45. Estimating Microbial Concentration and Its Variability (Dispersion) from Enumerative Data: Data Analyses Based on Normal Distribution Compute mean, standard deviation, 95% confidence intervals and other confidence intervals using statistical methods based on normal distribution. Mean: For N counts of numbers of organisms per unit volume (say, per ml) of x 1, x 2, x 3 ….x n, the mean concentration, , is:  =  (x n )/N

46. Estimating Microbial Concentration and Its Variability (Dispersion) from Enumerative Data Normal Probability Curve: Counts range according to a symmetrical bell-shaped curve Height of the curve is a measure of frequency Middle of the curve is the mean,  or  Width of the curve is a measure of variability or dispersion Standard deviation,s or , is a measure of variability or dispersion  1s = 68% of area under the curve  2s = 95% of area under the curve

47. Estimating Microbial Concentration and Its Variability (Dispersion) from Enumerative Data The estimation of viarability or dispersion of microbe counts based on the suumprion of a normal distribution works best when the counts are high, that is >20 or 30. A measure of variation among N observations of concentration, x 1, x 2, x 3 ….x N, can be based on the deviations from the sample mean, , or the values of x N -  The set of N deviations can be combined into a single index called the variance, s 2, the square root of which, s, is the standard deviation.

48. Estimating Microbial Concentration and Its Variability (Dispersion) from Enumerative Data Based on the Normal Distribution The sum of the squares of the individual deviations from the sample mean is designated  (x N -  ) 2 and The sample variance is defined as: s 2 =  (x N -  ) 2 N - 1 The sample standard deviation is: s =  (x N -  ) 2 N - 1

49. Enumerative Measures of Microbial Concentration Examples: Bacteria: colony counts or colony forming units (CFU) –on agar medium plates –on membrane filters on media Viruses: plaque forming units (PFU), pock-forming units or focus-forming units (FFU) Parasites: numbers of ova, cysts, oocysts or spores