Ariel L. Rivas

Longitudinal evaluation of bovine mammary gland health status by somatic cell counting, flow cytometry, and cytology

Bovine mastitis phases induced by Staphylococcus aureus were assessed in 6 lactating cows before challenge and at 1, 4–8, and 9–14 days postinoculation (dpi). Milk lymphocytes, macrophages, and polymorphonuclear cells (PMN) were counted by conventional (manual) cytology, identified by CD3+ and CD11b+ immunofluorescence and counted by flow cytometry (based on leukocyte forward and side light scatter values). Somatic cell counts (SCC) and recovery of bacteria were recorded at the same times. Preinoculation samples showed a lymphocyte-dominated composition. At 1 dpi, the percentage of PMN increased and that of lymphocytes decreased. At 4–8 dpi, PMN were predominant, but the percentage of mononuclear cells increased above that at 1 dpi and further increased by 9–14 dpi (when lymphocytes approached prechallenge values). Based on leukocyte percentages, 3 indices were created from the data: 1) the PMN/lymphocyte percentage ratio (PMN/L), 2) the PMN/macrophage percentage ratio (PMN/M), and 3) the phagocyte (PMN and macrophage)/lymphocyte percentage ratio (Phago/L). Significant correlations were found between cytologic and flow cytometric data in all of these indicators (all with P ≤ 0.01). These indices identified nonmastitic, early inflammatory (1–8 dpi), and late inflammatory (9–14 dpi) animals. In contrast, SCC and bacteriology did not. Although sensitivity of the SCC was similar to that of Phago/L, the specificity of SCC was almost half that of the Phago/L index. Based on flow cytometry indicators, an algorithm for presumptive diagnosis of bovine mastitis was developed. Flow cytometry provides results as valid as those obtained by conventional (manual) cytology, shows greater ability to identify mastitic cases than does SCC, and may identify 3 mammary gland health-related conditions.

Identification of risk factors associated with highly pathogenic avian influenza H5N1 virus infection in poultry farms, in Nigeria during the epidemic of 2006-2007.

We conducted a matched case-control study to evaluate risk factors for infection with highly pathogenic avian influenza (HPAI) H5N1 virus in poultry farms during the epidemic of 2006-2007 in Nigeria. Epidemiologic data were collected through the use of a questionnaire from 32 case farms and 83 control farms. The frequency of investigated exposure factors was compared between case and control farms by using conditional logistic regression analysis. In the multivariable analysis, the variables for (i) receiving visitors on farm premises (odds ratio [OR]=8.32; 95% confidence interval [CI]=1.87, 36.97; P<0.01), (ii) purchased live poultry/products (OR=11.91; 95% CI=3.11-45.59; P<0.01), and (iii) farm workers live outside the premises (OR=8.98; 95% CI=1.97, 40.77; P<0.01) were identified as risk factors for HPAI in poultry farms. Improving farm hygiene and biosecurity should help reduce the risk for influenza (H5N1) infection in poultry farms in Nigeria.

Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions

Background Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. Methods To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. Results In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. Conclusions More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

Lessons from Nigeria : the role of roads in the geo-temporal progression of avian influenza (H5N1) virus

The daily progression of the 2006 (January-June) Nigerian avian influenza (AI H5N1) epidemic was assessed in relation to both spatial variables and the generation interval of the invading virus. Proximity to the highway network appeared to promote epidemic dispersal: from the first AI generation interval onwards > 20% of all cases were located at < 5 km from the nearest major road. Fifty-seven per cent of all cases were located 31 km from three highway intersections. Findings suggest that the spatial features of emerging infections could be key in their control. When the spatial location of a transmission factor is well known, such as that of the highway network, and a substantial percentage of cases (e.g. > 20%) are near that factor, early interventions focusing on transmission factors, such as road blocks that prevent poultry trade, may be more efficacious than interventions applied only to the susceptible population.

Effects of intra-mammary bacterial infection with coagulase negative staphylococci and stage of lactation on shedding of epithelial cells and infiltration of leukocytes into milk: Comparison among cows, goats and sheep

The effects of mammary gland bacterial infection and stage of lactation on leukocyte infiltration into the mammary gland were compared among cows, goats and sheep. Animals were at two stages of lactation: mid or late. In mid-lactation animals, bacterial-free glands and coagulase negative Staphylococcus (CNS)-infected glands were compared. In late lactation only uninfected glands were studied. Of mid-lactation bacteria-free animals, goats had the highest number of leukocytes and % polymorphonuclears (PMNs), whereas sheep had the lowest and leukocytes number in cows were intermediate between sheep and goats. Based on %PMN, two cell clusters were found in sheep, which overlapped with the parallel cell clusters of cows and goats, but with a slightly higher number of leukocytes in each cell cluster. At late lactation, goats had higher values for %PMN and leukocyte numbers in comparison to cows, which had a similar cellular profile to sheep. The cellular immune response to CNS infection was similar for the three animal species, although the number of cells was different, while the basal cell level at mid-lactation and especially at the end of lactation was species specific.

Connecting network properties of rapidly disseminating epizoonotics.

Background To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. Methods Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) ‘connectivity’, a model that integrated bio-physical concepts (the agent’s transmission cycle, road topology) into indicators designed to measure networks (‘nodes’ or infected sites with short- and long-range links), and 2) ‘contacts’, which focused on infected individuals but did not assess connectivity. Results The connectivity model showed five network properties: 1) spatial aggregation of cases (disease clusters), 2) links among similar ‘nodes’ (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a “20∶80″ pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. Conclusions Geo-temporal constructs of Network Theory’s nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.

Systems biology and ratio-based, real-time disease surveillance.

Most infectious disease surveillance methods are not well fit for early detection. To address such limitation, here we evaluated a ratio- and Systems Biology-based method that does not require prior knowledge on the identity of an infective agent. Using a reference group of birds experimentally infected with West Nile virus (WNV) and a problem group of unknown health status (except that they were WNV-negative and displayed inflammation), both groups were followed over 22 days and tested with a system that analyses blood leucocyte ratios. To test the ability of the method to discriminate small data sets, both the reference group (n = 5) and the problem group (n = 4) were small. The questions of interest were as follows: (i) whether individuals presenting inflammation (disease-positive or D+) can be distinguished from non-inflamed (disease-negative or D-) birds, (ii) whether two or more D+ stages can be detected and (iii) whether sample size influences detection. Within the problem group, the ratio-based method distinguished the following: (i) three (one D- and two D+) data classes; (ii) two (early and late) inflammatory stages; (iii) fast versus regular or slow responders; and (iv) individuals that recovered from those that remained inflamed. Because ratios differed in larger magnitudes (up to 48 times larger) than percentages, it is suggested that data patterns are likely to be recognized when disease surveillance methods are designed to measure inflammation and utilize ratios.

Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks.

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

CD2/CD21 index: A new marker to evaluate udder health in dairy cows

Lymphocytes play a significant role in the immunological processes of the bovine mammary gland and were found to be the dominant cell population in the milk of healthy udder quarters. The objective of this study was to investigate the quantitative relationship between CD2(+) T and CD21(+) B lymphocytes using flow cytometry. In a first study, quarter foremilk samples from apparently healthy udder quarters [somatic cell counts (SCC) ≤100,000 cells/mL; n=65] were analyzed and compared with diseased quarters (SCC >100,000 cells/mL; n=15). Percentages of CD2(+) T cells were significantly higher in milk samples with SCC ≤100,000 cells/mL than in those with SCC >100,000 cells/mL, whereas percentages of CD21(+) B cells developed in the opposite direction. As a result of this opposing trend, a new variable, the CD2/CD21 index-representing the percentages of CD2(+) cells per CD21(+) cells-was defined. Although diseased quarters with SCC >100,000 cells/mL and the detection of major pathogens revealed generally CD2/CD21 indices <10, values >10 were observed in apparently healthy quarters. Hence, a CD2/CD21 index cutoff value of 10 may be suitable to aid differentiation between unsuspicious and microbiologically suspicious or diseased udder quarters. To test whether CD2/CD21 indices <10 were primarily related to pathogens, quarters with SCC ≤100,000 cells/mL and >100,000 cells/mL with different bacteriological status (culture negative, or minor or major pathogens) were selectively examined in a second biphasic study. In the first trial, 63 udder quarters were analyzed and 55 of these quarters were able to be sampled again in the second trial carried out 14 d later. In both trials, results of the first study were confirmed. Indeed, CD2/CD21 indices <10 were also found in quarters showing SCC ≤100,000 cells/mL and containing minor or major pathogens at the time of the current or previous bacteriological analysis. The results of our examinations indicated a clear relationship between the CD2/CD21 index and the bacteriological status of the mammary gland. In combination with SCC, it offers a new marker for quick differentiation of unsuspicious and microbiologically suspicious or diseased udder quarters.

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

Evolution has conserved “economic” systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions—including the use of arrows that connect pairs of consecutive observations—non-reductionist (spatial–temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.