Viruses detected in cells explanted into tissue culture from naturally occurring malignant lymphoma in rhesus monkeys

During a 4-year period 43 cases of spontaneous lymphoma occurred in macaques at the California Primate Research Center. In an attempt to determine if there might be a common viral etiology to the outbreak, lymphoma tissues from 10 rhesus monkeys were explanted into tissue culture and examined for the presence of virus. Electron microscopic studies of the cultured lymphoma cells revealed viruses morphologically characteristics of adenovirus, reovirus, foamy virus, and herpesvirus. Cell-free filtrates from tissue cultures possessing the latter three viruses produced cytopathology in virus-free indicator cells. Virus particles typical of the explant culture were demonstrable in the infected indicator cells. Type-C RNA tumor virus particles were not observed electron microscopically in any of the lymphoma tissues or lymphoma cell cultures examined.     

Analysis of cyanobacteria bloom in the Waihai part of Dianchi Lake,China

Blue-green algae (BGA) bloom is a typical phenomenon in eutrophied lakes. However, up to now, no environmental mechanism has been commonly accepted. Systematic and complete data sets of BGA blooms and environmental factors without any missing data are rare, which seriously affected previous studies. In this study, a bootstrapping based multiple imputation algorithm (EMB) was first applied to reconstruct a complete data set from the available data set with missing data, hence forming a basis for quantitatively relating BGA bloom to contributing factors. Then, the probability of BGA bloom outbreak was simulated using a binomial (or binary) logistic regression model, which is an effective tool for recognizing key contributing factors. The results suggest that 1) the outbreak frequency or probability of BGA bloom tends to first increase and then decrease with a turning point between June and September each year; 2) air temperature, relative humidity, and precipitation were significant positive factors correlated with outbreak frequency, whereas wind speed and the number of sunshine hours were negative factors; 3) water temperature had a strong positive effect on the probability of BGA bloom outbreak, whereas other water quality factors, such as concentrations of organics and nutrients, were not so significant. However, water quality factors, such as NO₃–N, SD, pH, NH₄–N, COD and DO, still need to be concerned, which had a potential to aggravate the outbreak of BGA bloom in Dianchi Lake, if they were out of control.     

Chromosomal Dynamism in Progeny of Outbreak-Related Sorbitol-Fermenting Enterohemorrhagic Escherichia coli O157:NM

Sorbitol-fermenting (SF) enterohemorrhagic Escherichia coli (EHEC) O157:NM (nonmotile) is a unique clone that causes outbreaks of hemorrhagic colitis and hemolytic-uremic syndrome. In well-defined clusters of cases, we have observed significant variability in pulsed-field gel electrophoresis (PFGE) patterns which could indicate coinfection by different strains. An analysis of randomly selected progeny colonies of an outbreak strain after subcultivation demonstrated that they displayed either the cognate PFGE outbreak pattern or one of four additional patterns and were <89% similar. These profound alterations were associated with changes in the genomic position of one of two Shiga toxin 2-encoding genes (stx₂) in the outbreak strain or with the loss of this gene. The two stx₂ alleles in the outbreak strain were identical but were flanked with phage-related sequences with only 77% sequence identity. Neither of these phages produced plaques, but one lysogenized E. coli K-12 and integrated in yecE in the lysogens and the wild-type strain. The presence of two stx₂ genes which correlated with increased production of Stx2 in vitro but not with the clinical outcome of infection was also found in 14 (21%) of 67 SF EHEC O157:NM isolates from sporadic cases of human disease. The variability of PFGE patterns for the progeny of a single colony must be considered when interpreting PFGE patterns in SF EHEC O157-associated outbreaks.     

Differentiation between Primary Cerebral Lymphoma and Glioblastoma Using the Apparent Diffusion Coefficient: Comparison of Three Different ROI Methods

Objective

Apparent diffusion coefficients (ADC) can help differentiate between central nervous system (CNS) lymphoma and Glioblastoma (GBM). However, overlap between ADCs for GBM and lymphoma have been reported because of various region of interest (ROI) methods. Our aim is to explore ROI method to provide the most reproducible results for differentiation.

Materials and Methods

We studied 25 CNS lymphomas and 62 GBMs with three ROI methods: (1) ROI₁, whole tumor volume; (2) ROI₂, multiple ROIs; and (3) ROI₃, a single ROI. Interobserver variability of two readers for each method was analyzed by intraclass correlation(ICC). ADCs were compared between GBM and lymphoma, using two-sample t-test. The discriminative ability was determined by ROC analysis.

Results

ADCs from ROI₁ showed most reproducible results (ICC >0.9). For ROI₁, ADC_mean for lymphoma showed significantly lower values than GBM (p = 0.03). The optimal cut-off value was 0.98×10⁻³ mm²/s with 85% sensitivity and 90% specificity. For ROI₂, ADC_min for lymphoma was significantly lower than GBM (p = 0.02). The cut-off value was 0.69×10⁻³ mm²/s with 87% sensitivity and 88% specificity.

Conclusion

ADC values were significantly dependent on ROI method. ADCs from the whole tumor volume had the most reproducible results. ADC_mean from the whole tumor volume may aid in differentiating between lymphoma and GBM. However, multi-modal imaging approaches are recommended than ADC alone for differentiation.     

Interspecies transmission of macaque simian T-cell leukemia/lymphoma virus type 1 in baboons resulted in an outbreak of malignant lymphoma.

An outbreak of malignant lymphoma has been observed in one of the baboon (Papio hamadryas) stocks of Sukhumi Primate Center. More than 300 cases in this "high-lymphoma stock" have been registered since 1967. Human T-cell lymphotropic virus type 1 (HTLV-1)-related virus was implicated as the etiologic agent of Sukhumi baboon lymphoma. The origin of this virus remained unclear. Two possibilities were originally considered: the origin could be baboon simian T-cell leukemia/lymphoma virus type 1 (STLV-1) or HTLV-1 (before the outbreak started, some Sukhumi baboons were inoculated with human leukemic material). The third possibility entered recently: interspecies transmission of rhesus macaque STLV-1 to baboons. It was prompted by the finding of very close similarity between STLV-1 991-1cc (the strain isolated from a non-Sukhumi baboon inoculated with material from a Sukhumi lymphomatous baboon) and rhesus STLV-1. To test this hypothesis, we investigated 37 Sukhumi STLV-1 isolates from baboons of high-lymphoma stock by PCR discriminating rhesus type and baboon type STLV-1 isolates. All of them were proved to be rhesus type STLV-1. In contrast, all six STLV-1 isolates from baboons belonging to other stocks or populations were of baboon type. The PCR results were fully confirmed by DNA sequence data. The partial env gene gene sequences of all four STLV-1 isolates from Sukhumi lymphomatous baboons were 97 to 100% similar to the sequence of known rhesus STLV-1 and only 85% homologous with the sequence of conventional baboon STLV-1. Thus, interspecies transmission of STLV-1 from rhesus macaques (or closely related species) to baboons occurred at Sukhumi Primate Center. Most probably this event initiated the outbreak of lymphoma in Sukhumi baboons.     

Sub-Typing of Extended-Spectrum-β-Lactamase-Producing Isolates from a Nosocomial Outbreak: Application of a 10-Loci Generic Escherichia coli Multi-Locus Variable Number Tandem Repeat Analysis

Extended-spectrum β-lactamase producing Escherichia coli (ESBL-E. coli) were isolated from infants hospitalized in a neonatal, post-surgery ward during a four-month-long nosocomial outbreak and six-month follow-up period. A multi-locus variable number tandem repeat analysis (MLVA), using 10 loci (GECM-10), for ‘generic’ (i.e., non-STEC) E. coli was applied for sub-species-level (i.e., sub-typing) delineation and characterization of the bacterial isolates. Ten distinct GECM-10 types were detected among 50 isolates, correlating with the types defined by pulsed-field gel electrophoresis (PFGE), which is recognized to be the ‘gold-standard’ method for clinical epidemiological analyses. Multi-locus sequence typing (MLST), multiplex PCR genotyping of bla _CTX-M, bla _TEM, bla _OXA and bla _SHV genes and antibiotic resistance profiling, as well as a PCR assay specific for detecting isolates of the pandemic O25b-ST131 strain, further characterized the outbreak isolates. Two clusters of isolates with distinct GECM-10 types (G06-04 and G07-02), corresponding to two major PFGE types and the MLST-based sequence types (STs) 131 and 1444, respectively, were confirmed to be responsible for the outbreak. The application of GECM-10 sub-typing provided reliable, rapid and cost-effective epidemiological characterizations of the ESBL-producing isolates from a nosocomial outbreak that correlated with and may be used to replace the laborious PFGE protocol for analyzing generic E. coli.     

Ultra-Deep Pyrosequencing of Partial Surface Protein Genes from Infectious Salmon Anaemia Virus (ISAV) Suggest Novel Mechanisms Involved in Transition to Virulence

Uncultivable HPR0 strains of infectious salmon anaemia viruses (ISAVs) infecting gills are non-virulent putative precursors of virulent ISAVs (vISAVs) causing systemic disease in farmed Atlantic salmon (Salmo salar). The transition to virulence involves two molecular events, a deletion in the highly polymorphic region (HPR) of the hemagglutinin-esterase (HE) gene and a Q₂₆₆→L₂₆₆ substitution or insertion next to the putative cleavage site (R₂₆₇) in the fusion protein (F). We have performed ultra-deep pyrosequencing (UDPS) of these gene regions from healthy fish positive for HPR0 virus carrying full-length HPR sampled in a screening program, and a vISAV strain from an ISA outbreak at the same farming site three weeks later, and compared the mutant spectra. As the UDPS data shows the presence of both HE genotypes at both sampling times, and the outbreak strain was unlikely to be directly related to the HPR0 strain, this is the first report of a double infection with HPR0s and vISAVs. For F amplicon reads, mutation frequencies generating L₂₆₆ codons in screening samples and Q₂₆₆ codons in outbreak samples were not higher than at any random site. We suggest quasispecies heterogeneity as well as RNA structural properties are linked to transition to virulence. More specifically, a mechanism where selected single point mutations in the full-length HPR alter the RNA structure facilitating single- or sequential deletions in this region is proposed. The data provides stronger support for the deletion hypothesis, as opposed to recombination, as the responsible mechanism for generating the sequence deletions in HE.     

Inhibition of lymphoma cell proliferation by supernatant from fibrosarcoma cultures: Preliminary evidence that the inhibitory material is prostaglandin E

Supernatants obtained from mouse fibrosarcoma cultures 48 hr after the addition of fresh medium contained dialyzable material which inhibited the proliferation of syngeneic lymphoma cells , as measured by ³H-thymidine incorporation. Three lines of evidence indicate that the supernatant inhibitory material is probably prostaglandin (PG) E. First, the supernatant and dialysate of the supernatant contained a substance with the same characteristics as PGE₁ or PGE₂ as detected by thin layer chromatography. Second, PGE₂-treatment of lymphoma cells mimicked the inhibition of proliferation observed with supernatant inhibitory substance. Third, indomethacin treatment of fibrosarcoma cultures reduced the amount of supernatant inhibitory substance present.     

Isolation of plasma membrane fragments and vesicles from ascites fluid of lymphoma-bearing mice and their possible role in the escape mechanism of tumors from host immune rejection

Summary Plasma membranes, generated in vivo by actively growing YAC lymphoma cells, were isolated from cell-free ascites fluid of lymphoma-bearing mice. Partial purification of the ascites fluid (AF) by means of ultracentrifugation resulted in the identification of two main fractions: (a) membrane fragments (AFM _s ) and (b) membrane vesicles (AFM _p ). Electron microscopy studies, polyacrylamide gel electrophoresis, marker enzymes, and binding capacity of radioactive lectins, have indicated that these membranes are released from the cell surface of YAC lymphoma cells, presumably by a shedding-off mechanism.In vitro studies have demonstrated that the isolated membranes can specifically inhibit the association of normal macrophages and YAC lymphoma cells. In vivo studies have shown that these membranes can immunize against YAC tumors if injected intramuscularly or subcutaneously into adult mice. The results indicate that the ascites fluid membranes bear tumor-specific antigenic determinants.Our results suggest that in vivo shedding of plasma membrane fragments or of membrane vesicles by actively growing YAC lymphoma cells may induce a self-protection of ascites tumors from host immune rejection.Abbreviations YAC= Moloney-virus-induced lymphoma cells grown in A-strain mice - AF= ascites fluid of YAC lymphoma-bearing mice - AFM_s and AFM_p= membrane fragments and vesicles isolated from AF - PBS= phosphate-buffered saline - Con A= Concanavalin A     

Introducing the Outbreak Threshold in Epidemiology

When a pathogen is rare in a host population, there is a chance that it will die out because of stochastic effects instead of causing a major epidemic. Yet no criteria exist to determine when the pathogen increases to a risky level, from which it has a large chance of dying out, to when a major outbreak is almost certain. We introduce such an outbreak threshold (T₀), and find that for large and homogeneous host populations, in which the pathogen has a reproductive ratio R₀, on the order of 1/Log(R₀) infected individuals are needed to prevent stochastic fade-out during the early stages of an epidemic. We also show how this threshold scales with higher heterogeneity and R₀ in the host population. These results have implications for controlling emerging and re-emerging pathogens.With the constant risk of pathogens emerging [1], such as Severe Acute Respiratory Syndrome (SARS) or avian influenza virus in humans, foot-and-mouth disease virus in cattle in the United Kingdom [2], or various plant pathogens [3], it is imperative to understand how novel strains gain their initial foothold at the onset of an epidemic. Despite this importance, it has seldom been addressed how many infected individuals are needed to declare that an outbreak is occurring: that is, when the pathogen can go extinct due to stochastic effects, to when it infects a high enough number of hosts such that the outbreak size increases in a deterministic manner (Figure 1A). Generally, the presence of a single infected individual is not sufficient to be classified as an outbreak, so how many infected individuals need to be present to cause this deterministic increase? Understanding at what point this change arises is key in preventing and controlling nascent outbreaks as they are detected, as well as determining the best course of action for prevention or treatment.Open in a separate windowFigure 1The outbreak threshold in homogeneous and heterogeneous populations.(A) A schematic of pathogen emergence. This graph shows the early stages of several strains of an epidemic, where R₀ = 1.25. The black line denotes the outbreak threshold (T ₀ = 1/Log(R₀) = 4.48). Blue thin lines show cases in which the pathogen goes extinct and does not exceed the threshold; the red thick line shows an epidemic that exceeds the threshold and persists for a long period of time. Simulations were based on the Gillespie algorithm [22]. (B) Outbreak threshold in a homogeneous (black thick line) or in a heterogeneous population, for increasing R₀. The threshold was calculated following the method described by Lloyd-Smith et al. [11] and is shown for different values of k, the dispersion parameter of the offspring distribution, as obtained from data on previous epidemics [11]. If the threshold lies below one, this means that around only one infected individual is needed to give a high outbreak probability.The classic prediction for pathogen outbreak is that the pathogen''s reproductive ratio (R₀), the number of secondary infections caused by an infected host in a susceptible population, has to exceed one [4], [5]. This criterion only strictly holds in deterministic (infinite population) models; in finite populations, there is still a chance that the infection will go extinct by chance rather than sustain itself [4]–[6]. Existing studies usually consider random drift affecting outbreaks in the context of estimating how large a host population needs to be to sustain an epidemic (the “Critical Community Size” [4], [7], [8]), calculating the outbreak probability in general [9]–[12], or ascertaining whether a sustained increase in cases over an area has occurred [13]. Here we discuss the fundamental question of how many infected individuals are needed to almost guarantee that a pathogen will cause an outbreak, as opposed to the population size needed to maintain an epidemic once it has appeared (Critical Community Size; see also Box 1). We find that only a small number of infected individuals are often needed to ensure that an epidemic will spread.

Box 1. Glossary of Key Terms

• The Basic Reproductive Ratio (R₀) is the number of secondary infections caused by a single infected individual, in a susceptible population. It is classically used to measure the rate of pathogen spread. In infinite-population models, a pathogen can emerge if R₀>1. In a finite population, the pathogen can emerge from a single infection with probability 1-1/R₀ if R₀>1, otherwise extinction is certain.
• The Critical Community Size (CCS) is defined as the total population size (of susceptible and infected individuals, or others) needed to sustain an outbreak once it has appeared. This idea was classically applied to determining what towns were most likely to maintain measles epidemics [7], so that there would always be some infected individuals present, unless intervention measures were taken.
• The Outbreak Threshold (T₀) has a similar definition to the CSS, but is instead for use at the onset of an outbreak, rather than once it has appeared. It measures how many infected individuals (not the total population size) are needed to ensure that an outbreak is very unlikely to go extinct by drift. Note that the outbreak can still go extinct in the long term, even if T₀ is exceeded, if there are not enough susceptible individuals present to carry the infection afterwards.

We introduce the concept of the outbreak threshold (denoted T₀), which we define as the number of infected individuals needed for the disease to spread in an approximately deterministic manner. T₀ can be given by simple equations. Indeed, if the host population is homogeneous (that is, where there is no individual variability in reproductive rates) and large enough so that depletion of the pool of susceptible hosts is negligible, then the probability of pathogen extinction if I infected hosts are present is (1/R₀)^I ([6], details in Material S.1 in Text S1). By solving this equation in the limit of extinction probability going to zero, we find that on the order of 1/Log(R₀) infected hosts are needed for an outbreak to be likely (black thick curve in Figure 1B), a result that reflects similar theory from population genetics [14]–[16]. Note that this result only holds in a finite population, as an outbreak in a fully susceptible infinite population is certain if R₀>1 ([4], see also Material S.1 in Text S1).This basic result can be modified to consider more realistic or precise cases, and T₀ can be scaled up if an exact outbreak risk is desired. For example, for the pathogen extinction probability to be less than 1%, there needs to be at least 5/Log(R₀) infected individuals. More generally, the pathogen extinction probability is lower than a given threshold c if there are at least −Log(c)/Log(R₀) infected individuals. Furthermore, if only a proportion p<1 of all infected individuals are detected, then the outbreak threshold order is p/Log(R₀). Also, if there exists a time-lag τ between an infection occurring and its report, then the order of T₀ is e^−τ(β-δ)/Log(R₀), where β is the infection transmission rate and 1/δ the mean duration of the infectious period (Material S.1 in Text S1). Finally, we can estimate how long it would take, on average, for the threshold to be reached and find that, if the depletion in susceptible hosts is negligible, this duration is on the order of 1/(β-δ) (Material S.1 in Text S1).So far we have only considered homogenous outbreaks, where on average each individual has the same pathogen transmission rate. In reality, there will be a large variance among individual transmission rates, especially if “super-spreaders” are present [17]. This population heterogeneity can either be deterministic, due to differences in immune history among hosts or differences in host behavior, or stochastic, due to sudden environmental or social changes. Spatial structure can also act as a form of heterogeneity, if each region or infected individual is subject to different transmission rates, or degree of contact with other individuals [18]. In such heterogeneous host populations, the number of secondary cases an infected individual engenders is jointly captured by R₀ and a dispersion parameter k (see Box 2). This dispersion parameter controls the degree of variation in individual transmission rates, while fixing the average R₀. The consequence of this model is that the majority of infected hosts tend to cause few secondary infections, while the minority behave as super-spreaders, causing many secondary infections. Host population heterogeneity (obtained with lower values of k) increases the probability that an outbreak will go extinct, as the pathogen can only really spread via one of the dwindling super-spreading individuals. In this heterogeneous case, we can find accurate values of T₀ numerically. As shown in Figure 1B, if R₀ is close to 1, host heterogeneity (k) does not really matter (T₀ tends to be high). However, if the pathogen has a high R₀ and thus spreads well, then host heterogeneity strongly affects T₀. Additionally, we find that the heterogeneous threshold simply scales as a function of k and R₀² (see Box 2). As an example, if k = 0.16, as estimated for SARS infections [11], the number of infected individuals needed to guarantee an outbreak increases 4-fold compared to a homogeneous population (Material S.3 in Text S1).

Box 2. Heterogeneous Outbreak Threshold

In a heterogeneous host population (see the main text for the bases of this heterogeneity), it has been shown that the number of secondary infections generated per infected individual can be well described by a negative binomial distribution with mean R₀ and dispersion parameter k [11]. The dispersion parameter determines the level of variation in the number of secondary infections: if k = 1, we have a homogeneous outbreak, but heterogeneity increases as k drops below 1; that is, it enlarges the proportion of infected individuals that are either “super-spreaders” or “dead-ends” (those that do not transmit the pathogen). Lloyd-Smith et al. [11] showed how to estimate R₀ and k from previous epidemics through applying a maximum-likelihood model to individual transmission data.Although in this case it is not possible to find a strict analytical form for the outbreak threshold, progress can be made if we measure the ratio of the heterogeneous and homogeneous thresholds. This function yields values that are independent of a strict cutoff probability (Material S.3 in Text S1). By investigating this ratio, we first found that for a fixed R₀, a function of order 1/k fitted the numerical solutions very well. By measuring these curves for different R₀ values, we further found that a function of order 1/R₀ ² provided a good fit to the coefficients. By fitting a function of order 1/kR₀ ² to the numerical data using least-squares regression in Mathematica 8.0 [19], we obtained the following adjusted form for the outbreak threshold T₀ in a heterogeneous population:(1)As in the homogeneous case, T₀ only provides us with an order of magnitude and it can be multiplied by −Log(c) to find the number of infected hosts required for there to be a probability of outbreak equal to 1-c. A sensitivity analysis shows that Equation 1 tends to be more strongly affected by changes in R₀ than in k (Material S.3 in Text S1).The outbreak threshold T₀ of an epidemic, which we define as the number of infected hosts above which there is very likely to be a major outbreak, can be estimated using simple formulae. Currently, to declare that an outbreak has occurred, studies choose an arbitrary low or high threshold depending, for instance, on whether they are monitoring disease outbreaks or modeling probabilities of emergence. We show that the outbreak threshold can be defined without resorting to an arbitrary cutoff. Of course, the generality of this definition has a cost, which is that the corresponding value of T₀ is only an order of magnitude. Modifications are needed to set a specific cutoff value or to capture host heterogeneity in transmission or incomplete sampling.These results are valid if there are enough susceptible individuals present to maintain an epidemic in the initial stages, as assumed in most studies on emergence [6], [11]–[13], otherwise the pathogen may die out before the outbreak threshold is reached (Box 3 and Material S.2 in Text S1). Yet the key message generally holds that while the number of infections lies below the threshold, there is a strong chance that the pathogen will vanish without causing a major outbreak. From a biological viewpoint, unless R₀ is close to one, these thresholds tend to be small (on the order of 5 to 20 individuals). This contrasts with estimates of the Critical Community Size, which tend to lie in the hundreds of thousands of susceptible individuals [3], [7], [8]. Therefore, while only a small infected population is needed to trigger a full-scale epidemic, a much larger pool of individuals are required to maintain an epidemic, once it appears, and prevent it from fading out. This makes sense, since there tends to be more susceptible hosts early on in the outbreak than late on.

Box 3. Effect of Limiting Host Population Size

The basic result for the homogeneous population, T₀∼1/Log(R₀), assumes that during the time to pathogen outbreak, there are always enough susceptible individuals available to transmit to, so R₀ remains approximately constant during emergence. This assumption can be violated if R₀ is close to 1, or if the population size is small. More precisely, if the maximum outbreak size in a Susceptible-Infected-Recovered (SIR) epidemic, which is given byis less than 1/Log(R₀), then the threshold cannot be reached. Since this maximum is dependent on the population size, outbreaks in smaller populations are less likely to reach the outbreak threshold. For example, if N = 10,000 then R₀ needs to exceed 1.06 for 1/Log(R₀) to be reached; this increases to 1.34 if N decreases to 100. Further details are in Material S.2 in Text S1.Estimates of R₀ and k from previous outbreaks can be used to infer the approximate size of this threshold, to determine whether a handful or hundreds of infected individuals are needed for an outbreak to establish itself. Box 4 outlines two case studies (smallpox in England and SARS in Singapore), estimates of T₀ for these, and how knowledge of the threshold could have aided their control. These examples highlight how the simplicity and rigorousness of the definition of T₀ opens a wide range of applications, as it can be readily applied to specific situations in order to determine the most adequate policies to prevent pathogen outbreaks.

Box 4. Two Case Studies: Smallpox in England and SARS in Singapore

A smallpox outbreak (Variola minor) was initiated in Birmingham, United Kingdom in 1966 due to laboratory release. We calculate a threshold such that the chance of extinction is less than 0.1%, which means that T₀ is equal to 7 times Equation 1. With an estimated R₀ of 1.6 and dispersion parameter k = 0.65 [11], T₀ is approximately equal to 9 infections. The transmission chain for this outbreak is now well-known [20]. Due to prior eradication of smallpox in the United Kingdom, the pathogen was not recognised until around the 45^th case was detected, by which point a full-scale epidemic was underway. A second laboratory outbreak arose in 1978, but the initial case (as well as a single secondary case) was quickly isolated, preventing a larger spread of the pathogen. Given the fairly low T₀ for the previous epidemic, early containment was probably essential in preventing a larger outbreak.The SARS outbreak in Singapore in 2003 is an example of an outbreak with known super-spreaders [21], with an estimated initial R₀ of 1.63 and a low k of 0.16 [11]. T₀ is estimated to be around 27 infections. The first cases were observed in late February, with patients being admitted for pneumonia. Strict control measures were invoked from March 22^nd onwards, including home quarantining of those exposed to SARS patients and closing down of a market where a SARS outbreak was observed. By this date, 57 cases were detected, although it is unclear how many of those cases were still ongoing on the date. This point is important, as it is the infected population size that determines T₀.Overall, very early measures were necessary to successfully prevent a smallpox outbreak due to its rapid spread. In theory, it should have been “easier” to contain the SARS outbreak, as its threshold is three times greater than that for smallpox due to higher host heterogeneity (k). However, the first reported infected individual was a super-spreader, who infected at least 21 others. This reflects that in heterogeneous outbreaks, although the emergence probability is lower, the disease spread is faster (compared to homogeneous infections) once it does appear [11]. Quick containment of the outbreak was difficult to achieve since SARS was not immediately recognised, as well as the fact that the incubation period is around 5 days, by which point it had easily caused more secondary cases. However, in subsequent outbreaks super-spreaders might not be infected early on, allowing more time to contain the spread.For newly-arising outbreaks, T₀ can be applied in several ways. If the epidemic initially spreads slowly, then R₀ and T₀ can be measured directly. Alternatively, estimates of T₀ can be calculated from previous outbreaks, as outlined above. In both cases, knowing what infected population size is needed to guarantee emergence can help to assess how critical a situation is. More generally, due to the difficulty in detecting real-world outbreaks that go extinct very quickly, experimental methods might be useful in determining to what extent different levels of T₀ capture the likelihood of full epidemic emergence.     

Differential induction of apoptosis in x-irradiated L5178Y sublines bearing p53 mutation

We examined apoptosis and expression of p53, E2F-1, bax, bclx_L and bcl2 proteins in two L5178Y (LY) murine lymphoma sublines, LY-R and LY-S, which differ in radiosensitivity and double-strand break (DSB) repair. Both sublines are heterozygous for a p53 mutation in codon 170 that precludes the transactivation function. Accordingly, there is no G1/S arrest after irradiation.We found that there is no change in expression of E2F-1, bax, bclx_L or bcl2 proteins in both LY sublines after x-irradiation. LY-R cells do not constitutively express bcl2, whereas both sublines show high bax content. Radiation induces delayed apoptosis to a greater extent in LY-S than in LY-R cells. The apoptosis can be seen 24 h after irradiation (2 Gy) of LY-S cells, with a maximum at 48 h. LY-R cells need 5 Gy and 72 h post-irradiation incubation to show marked apoptosis (identified by the TUNEL method). The reported observations support the assumption that differential radiosensitivity of LY sublines is associated with the induction of apoptosis that is not related to transactivation by p53 and is primarily related to differential DNA repair ability. Received: 19 August 1999 / Accepted in revised form: 30 November 1999     

A recycling anti-transferrin receptor-1 monoclonal antibody as an efficient therapy for erythroleukemia through target up-regulation and antibody-dependent cytotoxic effector functions

Targeting transferrin receptor 1 (TfR1) with monoclonal antibodies is a promising therapeutic strategy in cancer as tumor cells often overexpress TfR1 and show increased iron needs. We have re-engineered six anti-human TfR1 single-chain variable fragment (scFv) antibodies into fully human scFv₂-Fcγ1 and IgG1 antibodies. We selected the more promising candidate (H7), based on its ability to inhibit TfR1-mediated iron-loaded transferrin internalization in Raji cells (B-cell lymphoma). The H7 antibody displayed nanomolar affinity for its target in both formats (scFv₂-Fcγ1 and IgG1), but cross-reacted with mouse TfR1 only in the scFv₂-Fc format. H7 reduced the intracellular labile iron pool and, contrary to what has been observed with previously described anti-TfR1 antibodies, upregulated TfR1 level in Raji cells. H7 scFv₂-Fc format elimination half-life was similar in FcRn knock-out and wild type mice, suggesting that TfR1 recycling contributes to prevent H7 elimination in vivo. In vitro, H7 inhibited the growth of erythroleukemia and B-cell lymphoma cell lines (IC₅₀ 0.1 µg/mL) and induced their apoptosis. Moreover, the Im9 B-cell lymphoma cell line, which is resistant to apoptosis induced by rituximab (anti-CD20 antibody), was sensitive to H7. In vivo, tumor regression was observed in nude mice bearing ERY-1 erythroleukemia cell xenografts treated with H7 through a mechanism that involved iron deprivation and antibody-dependent cytotoxic effector functions. Therefore, targeting TfR1 using the fully human anti-TfR1 H7 is a promising tool for the treatment of leukemia and lymphoma.     

Soluble and Membrane-Bound TGF-β-Mediated Regulation of Intratumoral T Cell Differentiation and Function in B-Cell Non-Hodgkin Lymphoma

While the effect of TGF-β on malignant B cells in non-Hodgkin lymphoma (NHL) has been previously evaluated, studies to specifically define the role of TGF-β in tumor immunity in B-cell NHL are limited. We found that soluble TGF-β, secreted by both lymphoma cells and intratumoral T cells, is present in the serum of patients with B-cell NHL. Soluble TGF-β promoted regulatory T (T_reg) cells by enhancing expression of Foxp3 in CD4⁺ T cells and suppressed effector helper T (T_H) cells by inhibiting expression of IFN-γ and IL-17. Blockade of the IL-2 signaling pathway diminished the effect of soluble TGF-β on T cell differentiation. Furthermore, we found that membrane-bound TGF-β is expressed specifically on the surface of malignant B cells in B-cell NHL. TGF-β was able to bind to the surface of lymphoma B cells through an interaction with heparan sulfate (HS) but not through the TGF-β receptor. We showed that pretreatment of lymphoma B cells with TGF-β significantly inhibits the proliferation and cytokine production of intratumoral T cells. Taken together, these results suggest that tumor-associated soluble and membrane-bound TGF-β are involved in the regulation of intratumoral T cell differentiation and function in B-cell NHL.     

Thy28 partially prevents apoptosis induction following engagement of membrane immunoglobulin in WEHI-231 B lymphoma cells

Thy28 protein is conserved among plants, bacteria, and mammalian cells. Nuclear Thy28 protein is substantially expressed in testis, liver, and immune cells such as lymphocytes. Lymphocyte apoptosis plays a crucial role in homeostasis and formation of a diverse lymphocyte repertoire. In this study, we examined whether Thy28 affects induction of apoptosis in WEHI-231 B lymphoma cells following engagement of membrane immunoglobulin (mIg). Once they were established, the Thy28-overexpressing WEHI-231 cells showed similar expression levels of IgM and class I major histocompatibility complex (MHC) molecule compared with controls. The Thy28-overexpressing cells were considerably resistant to loss of mitochondrial membrane potential (ΔΨm), caspase-3 activation, and increase in annexin-positive cells upon mIg engagement. These changes were concomitant with an increase in G1 phase associated with upregulation of p27^Kip1. The anti-IgM-induced sustained activation of c-Jun N-terminal kinase (JNK), which was associated with late-phase hydrogen peroxide (H₂O₂) production, was partially reduced in the Thy28-expressing cells relative to controls. Taken together, the data suggest that in WEHI-231 B lymphoma cells, Thy28 regulates mIg-mediated apoptotic events through the JNK-H₂O₂ activation pathway, concomitant with an accumulation of cells in G1 phase associated with upregulation of p27^Kip1 in WEHI-231 B lymphoma cells.     

IP3R2 levels dictate the apoptotic sensitivity of diffuse large B-cell lymphoma cells to an IP3R-derived peptide targeting the BH4 domain of Bcl-2

Disrupting inositol 1,4,5-trisphosphate (IP₃) receptor (IP₃R)/B-cell lymphoma 2 (Bcl-2) complexes using a cell-permeable peptide (stabilized TAT-fused IP₃R-derived peptide (TAT-IDP^S)) that selectively targets the BH4 domain of Bcl-2 but not that of B-cell lymphoma 2-extra large (Bcl-Xl) potentiated pro-apoptotic Ca²⁺ signaling in chronic lymphocytic leukemia cells. However, the molecular mechanisms rendering cancer cells but not normal cells particularly sensitive to disrupting IP₃R/Bcl-2 complexes are poorly understood. Therefore, we studied the effect of TAT-IDP^S in a more heterogeneous Bcl-2-dependent cancer model using a set of ‘primed to death'' diffuse large B-cell lymphoma (DL-BCL) cell lines containing elevated Bcl-2 levels. We discovered a large heterogeneity in the apoptotic responses of these cells to TAT-IDP^S with SU-DHL-4 being most sensitive and OCI-LY-1 being most resistant. This sensitivity strongly correlated with the ability of TAT-IDP^S to promote IP₃R-mediated Ca²⁺ release. Although total IP₃R-expression levels were very similar among SU-DHL-4 and OCI-LY-1, we discovered that the IP₃R2-protein level was the highest for SU-DHL-4 and the lowest for OCI-LY-1. Strikingly, TAT-IDP^S-induced Ca²⁺ rise and apoptosis in the different DL-BCL cell lines strongly correlated with their IP₃R2-protein level, but not with IP₃R1-, IP₃R3- or total IP₃R-expression levels. Inhibiting or knocking down IP₃R2 activity in SU-DHL-4-reduced TAT-IDP^S-induced apoptosis, which is compatible with its ability to dissociate Bcl-2 from IP₃R2 and to promote IP₃-induced pro-apoptotic Ca²⁺ signaling. Thus, certain chronically activated B-cell lymphoma cells are addicted to high Bcl-2 levels for their survival not only to neutralize pro-apoptotic Bcl-2-family members but also to suppress IP₃R hyperactivity. In particular, cancer cells expressing high levels of IP₃R2 are addicted to IP₃R/Bcl-2 complex formation and disruption of these complexes using peptide tools results in pro-apoptotic Ca²⁺ signaling and cell death.     

A mathematical model of avian influenza with half-saturated incidence

The widespread impact of avian influenza viruses not only poses risks to birds, but also to humans. The viruses spread from birds to humans and from human to human In addition, mutation in the primary strain will increase the infectiousness of avian influenza. We developed a mathematical model of avian influenza for both bird and human populations. The effect of half-saturated incidence on transmission dynamics of the disease is investigated. The half-saturation constants determine the levels at which birds and humans contract avian influenza. To prevent the spread of avian influenza, the associated half-saturation constants must be increased, especially the half-saturation constant H _m for humans with mutant strain. The quantity H _m plays an essential role in determining the basic reproduction number of this model. Furthermore, by decreasing the rate β _m at which human-to-human mutant influenza is contracted, an outbreak can be controlled more effectively. To combat the outbreak, we propose both pharmaceutical (vaccination) and non-pharmaceutical (personal protection and isolation) control methods to reduce the transmission of avian influenza. Vaccination and personal protection will decrease β _m, while isolation will increase H _m. Numerical simulations demonstrate that all proposed control strategies will lead to disease eradication; however, if we only employ vaccination, it will require slightly longer to eradicate the disease than only applying non-pharmaceutical or a combination of pharmaceutical and non-pharmaceutical control methods. In conclusion, it is important to adopt a combination of control methods to fight an avian influenza outbreak.     

Correlations between Functional Imaging Markers Derived from PET/CT and Diffusion-Weighted MRI in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma

Objectives

To investigate the correlations between functional imaging markers derived from positron emission tomography/computed tomography (PET/CT) and diffusion-weighted magnetic resonance imaging (DWI) in diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). Further to compare the usefulness of these tumor markers in differentiating diagnosis of the two common types of Non-Hodgkin''s lymphoma (NHL).

Materials and Methods

Thirty-four consecutive pre-therapy adult patients with proven NHL (23 DLBCL and 11 FL) underwent PET/CT and MRI examinations and laboratory tests. The maximum standardized uptake value (SUV_max), metabolic tumor volume (MTV), and metabolic tumor burden (MTB) were determined from the PET/CT images. DWI was performed in addition to conventional MRI sequences using two b values (0 and 800 s/mm²). The minimum and mean apparent diffusion coefficient (ADC_min and ADC_mean) were measured on the parametric ADC maps.

Results

The SUV_max correlated inversely with the ADC_min (r = −0.35, p<0.05). The ADC_min, ADC_mean, serum thymidine kinase (TK), Beta 2-microglobulin (B2m), lactate dehydrogenase (LD), and C-reactive protein (CRP) correlated with both whole-body MTV and whole-body MTB (p<0.05 or 0.01). The SUV_max, TK, LD, and CRP were significantly higher in the DLBCL group than in the FL group. Receiver operating characteristic curve analysis showed that they were reasonable predictors in differentiating DLBCL from FL.

Conclusions

The functional imaging markers determined from PET/CT and DWI are associated, and the SUV_max is superior to the ADC_min in differentiating DLBCL from FL. All the measured serum markers are associated with functional imaging markers. Serum LD, TK, and CRP are useful in differentiating DLBCL from FL.     

Public Health Investigation of Two Outbreaks of Shiga Toxin-Producing Escherichia coli O157 Associated with Consumption of Watercress

An increase in the number of cases of Shiga toxin-producing Escherichia coli (STEC) O157 phage type 2 (PT2) in England in September 2013 was epidemiologically linked to watercress consumption. Whole-genome sequencing (WGS) identified a phylogenetically related cluster of 22 cases (outbreak 1). The isolates comprising this cluster were not closely related to any other United Kingdom strain in the Public Health England WGS database, suggesting a possible imported source. A second outbreak of STEC O157 PT2 (outbreak 2) was identified epidemiologically following the detection of outbreak 1. Isolates associated with outbreak 2 were phylogenetically distinct from those in outbreak 1. Epidemiologically unrelated isolates on the same branch as the outbreak 2 cluster included those from human cases in England with domestically acquired infection and United Kingdom domestic cattle. Environmental sampling using PCR resulted in the isolation of STEC O157 PT2 from irrigation water at one implicated watercress farm, and WGS showed this isolate belonged to the same phylogenetic cluster as outbreak 2 isolates. Cattle were in close proximity to the watercress bed and were potentially the source of the second outbreak. Transfer of STEC from the field to the watercress bed may have occurred through wildlife entering the watercress farm or via runoff water. During this complex outbreak investigation, epidemiological studies, comprehensive testing of environmental samples, and the use of novel molecular methods proved invaluable in demonstrating that two simultaneous outbreaks of STEC O157 PT2 were both linked to the consumption of watercress but were associated with different sources of contamination.     

Distinct Patterns of HIV-1 Evolution within Metastatic Tissues in Patients with Non-Hodgkins Lymphoma

Despite highly active antiretroviral therapy (HAART), AIDS related lymphoma (ARL) occurs at a significantly higher rate in patients infected with the Human Immunodeficiency Virus (HIV) than in the general population. HIV-infected macrophages are a known viral reservoir and have been shown to have lymphomagenic potential in SCID mice; therefore, there is an interest in determining if a viral component to lymphomagenesis also exists. We sequenced HIV-1 envelope gp120 clones obtained post mortem from several tumor and non-tumor tissues of two patients who died with AIDS-related Non-Hodgkin''s lymphoma (ARL-NH). Similar results were found in both patients: 1) high-resolution phylogenetic analysis showed a significant degree of compartmentalization between lymphoma and non-lymphoma viral sub-populations while viral sub-populations from lymph nodes appeared to be intermixed within sequences from tumor and non-tumor tissues, 2) a 100-fold increase in the effective HIV population size in tumor versus non-tumor tissues was associated with the emergence of lymphadenopathy and aggressive metastatic ARL, and 3) HIV gene flow among lymph nodes, normal and metastatic tissues was non-random. The different population dynamics between the viruses found in tumors versus the non-tumor associated viruses suggest that there is a significant relationship between HIV evolution and lymphoma pathogenesis. Moreover, the study indicates that HIV could be used as an effective marker to study the origin and dissemination of lymphomas in vivo.     

A Multicentre Hospital Outbreak in Sweden Caused by Introduction of a vanB2 Transposon into a Stably Maintained pRUM-Plasmid in an Enterococcus faecium ST192 Clone

The clonal dissemination of VanB-type vancomycin-resistant Enterococcus faecium (VREfm) strains in three Swedish hospitals between 2007 and 2011 prompted further analysis to reveal the possible origin and molecular characteristics of the outbreak strain. A representative subset of VREfm isolates (n = 18) and vancomycin-susceptible E. faecium (VSEfm, n = 2) reflecting the spread in time and location was approached by an array of methods including: selective whole genome sequencing (WGS; n = 3), multi locus sequence typing (MLST), antimicrobial susceptibility testing, virulence gene profiling, identification of mobile genetic elements conferring glycopeptide resistance and their ability to support glycopeptide resistance transfer. In addition, a single VREfm strain with an unrelated PFGE pattern collected prior to the outbreak was examined by WGS. MLST revealed a predominance of ST192, belonging to a hospital adapted high-risk lineage harbouring several known virulence determinants (n≥10). The VREfm outbreak strain was resistant to ampicillin, gentamicin, ciprofloxacin and vancomycin, and susceptible to teicoplanin. Consistently, a vanB2-subtype as part of Tn1549/Tn5382 with a unique genetic signature was identified in the VREfm outbreak strains. Moreover, Southern blot hybridisation analyses of PFGE separated S1 nuclease-restricted total DNAs and filter mating experiments showed that vanB2-Tn1549/Tn5382 was located in a 70-kb sized rep _17/pRUM plasmid readily transferable between E. faecium. This plasmid contained an axe-txe toxin-antitoxin module associated with stable maintenance. The two clonally related VSEfm harboured a 40 kb rep _17/pRUM plasmid absent of the 30 kb vanB2-Tn1549/Tn5382 gene complex. Otherwise, these two isolates were similar to the VREfm outbreak strain in virulence- and resistance profile. In conclusion, our observations support that the origin of the multicentre outbreak was caused by an introduction of vanB2-Tn1549/Tn5382 into a rep _17/pRUM plasmid harboured in a pre-existing high-risk E. faecium ST192 clone. The subsequent dissemination of VREfm to other centres was primarily caused by clonal spread rather than plasmid transfer to pre-existing high-risk clones.