extfieldsofstudy: Medicine Biology

text:
Non-genetic cancer cell plasticity and therapy-induced stemness in tumour relapse: ‘What does not kill me strengthens me'

Therapy resistance and tumour relapse after drug therapy are commonly explained by Darwinian selection of pre-existing drug-resistant, often stem-like cancer cells resulting from random mutations. However, the ubiquitous non-genetic heterogeneity and plasticity of tumour cell phenotype raises the question: are mutations really necessary and sufficient to promote cell phenotype changes during tumour progression? Cancer therapy inevitably spares some cancer cells, even in the absence of resistant mutants. Accumulating observations suggest that the non-killed, residual tumour cells actively acquire a new phenotype simply by exploiting their developmental potential. These surviving cells are stressed by the cytotoxic treatment, and owing to phenotype plasticity, exhibit a variety of responses. Some are pushed into nearby, latent attractor states of the gene regulatory network which resemble evolutionary ancient or early developmental gene expression programs that confer stemness and resilience. By entering such stem-like, stress-response states, the surviving cells strengthen their capacity to cope with future noxious agents. Considering non-genetic cell state dynamics and the relative ease with which surviving but stressed cells can be tipped into latent attractors provides a foundation for exploring new therapeutic approaches that seek not only to kill cancer cells but also to avoid promoting resistance and relapse that are inherently linked to the attempts to kill them.

The failure of generic chemotherapy as well as targeted therapy, embodied by drug resistance and tumour relapse, demands a broader understanding of the tumour's response to therapeutic perturbation, which is more complex than the binary outcome -'responding' (e.g., tumour shrinks) vs 'non-responding'. A more encompassing approach has to take into account the complexity of the regulatory networks that govern cell phenotype changes because the very same cellular machinery that affords a single metazoan genome the ability to produce the multitude of distinct, stable phenotypic cell states also accounts for the broad behavioural repertoire of cancer cells.
The current explanation for the development of therapy resistance and tumour recurrence invokes genomic alterations and selection of mutant cells carrying a relevant mutation acquired by chance before therapy (Nowell, 1976;Yachida et al, 2010;Sottoriva et al, 2013). Such somatic Darwinian selection leads to a shift in cell population composition but does not involve individual cells actively changing their phenotype: the mutant cells are passively selected by the therapy and overtake the cell population. Whole-genome sequencing of tumours with reconstruction of progression history (Yachida et al, 2010;Sottoriva et al, 2013) as well as the finding that 'driver mutations' affecting oncogenes within the same pathway are mutually exclusive (Sherr, 1996;DeNicola et al, 2011;Nakasone et al, 2012;Rosenzweig, 2012) offer convincing molecular footprints of Darwinian somatic evolution. The molecular nature of genetic mutations, such as point mutations that alter the target domain of protein kinases where selective inhibitor drugs bind (Vogelstein and Kinzler, 1993;DeNicola et al, 2011;Hanahan and Weinberg, 2011;Nakasone et al, 2012;Rosenzweig, 2012), also provide evidence of Darwinian selection. Accordingly, phenotype innovation during tumour progression, such as development of drug resistance or acquisition of any hallmark of cancer, is now, by default, explained by a genetic mutation (Vogelstein and Kinzler, 1993;Hanahan and Weinberg, 2011). However, a more recent study (Sottoriva et al, 2015) revealed the possibility of neutral evolution, which complicates the interpretation of genomic mutations found in the tumour tissue. In this process, which is well familiar to evolution biologists, most mutations are not selected for but instead accumulate as they are carried along and may by chance become sufficiently predominant to be detectable owing to genetic drift in relatively small cell populations.
In any case, it has long been suspected that driver mutations may only be part of the equation. The scheme of Darwinian evolution relies on the tacit assumption of a rigid one-to-one mapping from genotype to phenotype, such that a phenotype innovation can only result directly from a genomic mutation, which are random in nature but can be selected for. This line of thought ignores the diversity of cell phenotypes that can be generated by a single metazoan genome. In fact, a long line of evidence points to mechanisms that defy the scheme of genetic mutation/selection as the driver of tumour progression. The recent spate of cancer genome sequencing studies has reopened our eyes to the flexibility in the genotype-phenotype mapping, pushing us to think beyond the paradigm of mutation/selection. Such flexibility re-emerges under the term 'phenotype plasticity' and entails a departure from the classical mutations/selection scheme as an agent of tumour progression. The case of ependymoma without mutation (Mack et al, 2014;Shlush et al, 2014) or the reappearance of early clones, which if progression were to follow the Darwinian paradigm of selection would have been outcompeted by fitter clones (Nowak, 2006;Hoek and Goding, 2010;Roesch et al, 2010;Sharma et al, 2010;Chaffer and Weinberg, 2011;Shlush et al, 2014;Sun et al, 2014), underscore the need to embrace the nonuniqueness in the genotype-phenotype mapping and limited efficacy of selection.
In this review, we consider non-genetic cell phenotype plasticity as a central process in therapy resistance. We take into account the ability of cells to produce discretely distinct phenotypes, switch between them without genomic alterations and inherit the new phenotype non-genetically across cell generations (Brock et al, 2009). Here, we explain the underlying biology of cell state dynamics and the formal framework of cancer attractors that link cell phenotype dynamics to first principles of dynamical systems theory and offer a conceptual aid for comprehending mutationindependent tumour progression. As illustration, we present examples of therapy-induced drug resistance and discuss implications for new therapies.

ATTRACTORS AND NON-GENETIC SWITCHING ON THE EPIGENETIC LANDSCAPE
In the past years, cell fate-switching between a more stem-like, therapy-resistant state and a more differentiated, drug-sensitive state has been observed in a number of tumour cell populations (Hoek and Goding, 2010;Sharma et al, 2010;Chaffer and Weinberg, 2011;DeNicola et al, 2011;Nakasone et al, 2012;Sun et al, 2014). The reversibility of such switching and the clonality of the populations in which both states coexist indicate that this cell phenotype change is not caused by mutations.
Because of their longevity in the tissue, normal stem cells are endowed with increased xenobiotic resistance, in part, due to the expression of efflux pumps (Zhou et al, 2001;Challen and Little, 2006;Kenyon and Gerson, 2007;DeNicola et al, 2011;Nakasone et al, 2012;Rosenzweig, 2012), more efficient DNA repair (Kenyon and Gerson, 2007) and they also orchestrate tissue changes during wound healing. Thus, the stem-like state is deeply linked to resilience and stress response -a relationship that appears to hold for their neoplastic counterpart, the cancer stem cells (CSC) (Dean et al, 2005;Donnenberg and Donnenberg, 2005;Medema, 2013). A fundamental biological fact is then that the axis of cell phenotype with respect to drug tolerance is aligned with that of cellular stemness.
Here, we will rely on the example of a two-state dichotomy that can often be observed by measuring expression status of a single protein marker X (e.g., X High and X Low ). This may appear simplistic, but serves well for the conceptualisation of cell state dynamics ( Figure 1). Phenotype switching between the cancer stem-cell state, here X High (where X can be one of the stem-cell markers, such as CD117, MDR1, CD44, ALDH1; Hanahan and Weinberg, 2011;Medema, 2013), and the more differentiated, here X Low , state epitomises non-genetic plasticity of cancer cells. The underlying principles are the same as those that govern cell phenotype changes in development and tissue homeostasis: the acquisition of a stable and distinct cell phenotype governed by a coordinated gene expression pattern that can be passed along to daughter cells upon division. Although trivial, one often forgets that multiple distinct phenotypic states can be produced by the very same genome, which is the foundation for non-genetic switching between functional states during tumour progression.
The existence of multiple, almost discretely distinct, stable states, each characterised by its own stable gene expression profiles that is produced by the very same genome, can be explained by the concept of multistability. Herein, the stable states are the so-called attractor states -the potential wells in an energy landscape ( Figure 1 and Box 1).
With this formal conceptual framework, non-genetic switching between phenotypic states can be represented as switching between distinct attractor states -a conceptualisation well familiar to physical scientists. The key properties that distinguish such nongenetic state switching from a mutation-driven phenotype alteration are that the former (i) occurs much more frequently S(X Low ) S(X High ) Cell state, S X Low X High Stemness marker, X

U(S)
Cell number Multistability Figure 1. State transition and non-genetic plasticity. The cellular transition from the state with X High (red) to X Low (blue) and vice versa can be thought of as a state transition between the two subattractors on the epigenetic landscape (see Box 1). The reversibility of such switching and the clonality of the populations in which both states coexist indicate that such cell phenotype changes are not caused by mutations. As in multistable systems, the transition is noise driven but modulated by external conditions, including the presence of the drug. Note that by monitoring one dimension of the gene expression state space, for example, X ¼ MDR1 expression, we are able to observe cell transition only as a projection (horizontal axis) and do not know what happens in orthogonal (non-observable) dimensions.
than mutations, (ii) is in principle reversible and (iii) is linked to regulatory dynamics of the GRN, and thus has features of nonrandomness. The last property is a central distinctive feature of non-genetic phenotype transitions: unlike undirected (blinded) mutations of DNA, non-genetic switching between attractor states is likely to turn on existing (evolved) cellular programs and to be influenced by external cues. This framework could explain why tumour cells can so readily acquire complex developmental programs, such as a stem-like state or any of the 'hallmarks of cancer' (Hoek and Goding, 2010;Hanahan and Weinberg, 2011). Their gradual Darwinian evolution de novo is not required because these states have equivalents in normal development, homeostasis and regeneration. For such mutation-less cell phenotype switching in tumour progression, we can distinguish between two scenarios: (1) spontaneous (random) phenotype switching between a stem-like, resistant and a more differentiated (less aggressive) state and (2) externally induced (directed) switching to the stem-like state. The switching between the more mature and the stem-like states has been intensely studied in melanoma (Sharma et al, 2010;Hoek and Goding, 2010), where the bidirectional switching, that is, the reversibility of differentiation of CSC into multiple cell types, long thought to be an one-way process, supports the emerging notion that CSCs do not represent a static mutant clone but rather a functional state that can be induced in some cells under some conditions (Gupta et al, 2009a;Dirks, 2010;Gerlinger et al, 2012).

SPONTANEOUS STATE SWITCHING
The first scenario, spontaneous switching between attractors, is evident in cell population measurements of a marker X in (clonal) tumour cell cultures by flow cytometry. Such measurements can reveal the coexistence of the two states, occupied by two fractions of a clonal population, through the appearance of a bimodal distribution of the flow cytometry histogram (two 'peaks') ( Figure 1). The presence of distinct peaks suggests (but does not prove) multistability. Such non-genetic heterogeneity must be distinguished from genetic heterogeneity caused by genomic instability (Gerlinger et al, 2012;Pisco et al, 2013).
Using fluorescent-activated cell sorting to isolate cells from one peak followed by reculturing often -but not always -shows that one sub-population (e.g., X High ) can repopulate the other (e.g., X Low ) (Chang et al, 2006;Sharma et al, 2010;Pisco et al, 2013), demonstrating spontaneous, non-genetic transitions across a sufficiently low 'energy' barrier. Therefore, individual cells switch in a stochastic manner from one (attractor) state (X High ) to the other (X Low ), revealing characteristic rates for transitions in either direction. The driving force and source of randomness for such state switching is gene expression noise ( ¼ fluctuations of protein levels due to the random nature of molecular reactions within small volumes in the cell) (Kaern et al, 2005;Raj and van Oudenaarden, 2008;Zhou et al, 2014). Transitions between the X High and X Low state, although bidirectional, are often asymmetric -reflecting the relative propensity of a given cancer cell type to differentiate into a more mature state, or of the latter to dedifferentiate back into the stem-like state. 'Dedifferentiation' results in the continuous production of CSCs by the more differentiated cells and has been observed in various cancers posing a challenge to therapy aimed at targeting CSC (Brock et al, 2009;Gupta et al, 2009b).
Cell population dynamics with spontaneous conversions between resistant and sensitive states can be subjected to Darwinian selection in the same manner as phenotype variability caused by mutations ( Figure 2A): the stochastic but non-genetic (mutation-independent) transitions between attractor states act in the same way as mutations in Darwinian evolution in producing a phenotype variability as substrate for selection -albeit with much higher frequency and repeatedly generating a particular complex phenotype (as it is a latently 'preprogrammed' attractor). Thus, in the presence of cytotoxic drugs the cells in the more stem-like, more drug-tolerant state in a population will have a proliferation advantage and can be selected for if they endure over several cell generations in that stemlike state (Brock et al, 2009). Such 'non-genetic selection' can provide temporary resistance and allow the sub-population of these cells to expand. As these non-genetically fitter variants are present in the multimodal population due to a dynamic equilibrium, they exist by default and at much higher frequency than genetic mutants (accounting for 1% or more in a clonal population). Thus, they can jump start classical (mutation-based) somatic evolution because they guarantee that there is always a small population of cells that independently of mutations can survive the treatment. As these cells Box 1. Multistability, attractors and the quasi-potential landscape The capacity of a system to produce multiple stable steady states without changing its underlying components and wiring diagram is technically referred to as multi-stability (Huang, 2013). However, it requires nonlinear interactions within the system. The genome-wide gene regulatory network (GRN) represents a nonlinear dynamical system and has the same 'wiring diagram' of regulatory interactions in each cell. The GRN coordinates gene expression across the genome, thereby producing the characteristic gene expression profiles that define distinct cell phenotypes. Most prosaically epitomised by the 'cell types', the distinct, physiological cell states have gene activity profiles which have self-stabilising capacity because of the constraints on gene expression emanating from the gene-gene regulatory interactions. Such stables states of gene expression across the GRN are mathematically defined as attractor states. An attractor (state) can be represented by a 'potential well' or a 'valley' in an epigenetic landscape ). This landscape was first metaphorically proposed by Waddington (1956) to model development and differentiation as a descent of cells into the low valleys, which hence automatically assume stable, discretely distinct phenotypes. The epigenetic landscape is actually a mathematical representation of all the theoretical states of a GRN (i.e., the gene activation profile) along with their 'relative stabilities' (quasi-potential), thus capturing the entire theoretical behavioural repertoire of a GRN (Zhou et al, 2012). Normal cell types (stem cells and differentiated cells) are represented by their respective attractors (low-energy valleys), which guarantee stability of their characteristic genome-wide gene expression patterns (Huang, 2011). The attractors, or stable cell phenotypes, are separated by 'energy barriers' (the hills). Note that here 'energy' does not represent free energy in the sense of thermodynamics, but the quasipotential that stands for, in a mathematically exact manner, the 'effort' needed to change the gene expression state against the regulatory constraints imposed by the GRN. In an extension of the classical model of cancer cells as abnormal cell types and cell types as attractors (Kauffman, 1971), one can view cancer cells as cells in 'abnormal attractors'. Instead of descending to a normal attractor state, cells become cancerous if they enter an aberrant path and are trapped in an abnormal attractor present in the landscape but normally not occupied by cells: much as a side valley, branching off at higher altitude of the epigenetic landscape into a dead-end valley. This topographic view would naturally explain the invariantly immature phenotype of cancer cells. The very existence of unused attractors is the mathematical consequence of the high-dimensional dynamics of the GRN. These pathological attractors are developmentally not accessible owing to evolutionary fine-tuning of the landscape topography such that it buffers developmental trajectories and canalises them to the normal attractors (Waddington, 1956). Unused attractors can however be accidentally accessed if mutations lower the energy barrier that have evolved to seclude these pathological states ). expand, they can accumulate genetic mutations that are then selected for. This scheme of preselection due to non-genetic variability followed by later genetic fixation is in essence Waddington's genetic assimilation (Waddington, 1942;Zhou et al, 2001;Kim et al, 2002). An interesting twist that may further accelerate this process of somatic evolution is that since many anticancer drugs are genotoxic, mutation rate is further increased among these initial stem-like survivors.

THERAPY-INDUCED STATE SWITCHING: THERAPY AS DOUBLED-EDGED SWORD
In the second scenario, an external stimulation that affects signalling pathways can change the gene expression state, thus triggering a state transition between attractor states in a multistable system. In normal tissue development, attractor transitions correspond to cell differentiation and are induced by tightly regulated developmental signals that trigger transitions to lower attractor states. Of particular interest for tumour progression is the stress inflicted onto the (non-killed) cancer cells by treatment and the ensuing state transition. Given the commonalities between stress-response and stemness programmes (Zhou et al, 2001;Kim et al, 2002;Blanpain et al, 2011) and the inherent immaturity of cancer cells explained above, it is possible that cell stress imparted by cytotoxic agents actively induces a state transition specifically into a stem-like state, which actually is a stress-response state.
Such cell response is not unexpected if we consider the theoretical epigenetic landscape ( Figure 2B). In this framework, there are many possible (unused) cell states in the neighbourhood of a cancer attractor, which due to the tight relationship between ontogenesis and phylogenesis likely represent evolutionary ancient, stem-like immature states. Therefore, entry into these unoccupied attractors does not require very specific signals but can be triggered by many unrelated, nonspecific cell perturbations. As normal stem cells are specialised to respond to tissue damage by providing a protective response (Blanpain et al, 2011;Huang, 2013), these nearby abnormal immature states occupied by cancer states may express similar properties. Thus, a cytotoxic, non-lethal cell stress applied to an already pathologically immature cell in a cancer attractor has a high probability to push the cell further into more immature ('dedifferentiated') regions of the epigenetic landscape (Huang, 2013).
A therapy-induced, directed somatic evolution towards an advantageous phenotype would correspond to a Lamarckian scheme: a new, better 'adapted', inheritable state is induced by an environmental input (Mayr, 1972). In view of the conceptual framework of the epigenetic landscape, this event would not be uncommon, but in contrary almost a default reaction of cancer cells to massive, near-lethal perturbations. In fact, the list of cases in which chemotherapy appears to induce either a stem-like or/and drug-resistant state is quite old and growing (Bates et al, 1989;Chin et al, 1990;Ciocca et al, 1992;Chaudhary and Roninson, 1993;Abolhoda et al, 1999;Stein, 2001;Iyer and Lehnert, 2002;Notarbartolo et al, 2002;Bortul et al, 2003;Gréen et al, 2003;Baker et al, 2005;Camphausen et al, 2005;Andarawewa et al, 2007;Xia et al, 2008;Li et al, 2009;Pajic et al, 2009;Taube et al, 2010;Lee et al, 2010;Balachandran et al, 2011;Chang et al, 2011;Ghisolfi et al, 2012;Lagadec et al, 2012;Razandi et al, 2012;Abubaker et al, 2013;Pisco et al, 2013Pisco et al, , 2014. The induction of multidrug resistance (MDR) proteins, the ABC family membrane pumps that mediate the active efflux of drugs and also are markers of stem cells ('dye exclusion'), is the best documented example of an induced adaptive trait (Bates et al, 1989;Chin et al, 1990;Chaudhary and Roninson, 1993;Abolhoda et al, 1999;Yamada et al, 2000;Stein, 2001;Notarbartolo et al, 2002;Gréen et al, 2003;Baker et al, 2005;Pajic et al, 2009;Kim et al, 2012;Stein et al, 2012;Pisco et al, 2013Pisco et al, , 2014. Wnt signalling has a central role in induced stemness and MDR upregulation in various cancer cells (Yamada et al, 2000;Lee et al, 2010;Kim et al, 2012;Stein et al, 2012;Pisco et al, 2013). In irradiated leukaemia cells, resistance to radiotherapy was acquired without 'clonal predisposition' Lagadec et al, 2012). Similarly, ionising radiation reprogrammed differentiated breast cancer cells into CSCs via Notch (Lagadec et al, 2012) and the CSC fraction increased following In the first scenario, according to the Darwinian selection, the drug-resistant phenotype results from selection of resistant clones, which were produced by genetic mutations. The genetic mutation alters the genome, causing a rewiring of the gene regulatory network (GRN), which in turn changes the epigenetic landscape, creating a new phenotype (attractor state, S(X High )). (B) In the second scenario, the drug causes an attractor transition from the sensitive (blue) to the resistant (red) state in a multistable system (landscape with two potential wells). The GRN and the landscape remain unchanged.
Treatment-induced stress response might indeed protect the cancer cell (Iliopoulos et al, 2011;Jackson et al, 2013;Halliday et al, 2014). This protective stress response was explicitly observed by exposing cancer cells to nonlethal heatshock before chemotherapy and was associated with the upregulation of heat-shock proteins and ABC transporters (Ciocca et al, 1992).

MOST STUDIES DO NOT DISTINGUISH BETWEEN DARWINIAN AND LAMARCKIAN SCHEME IN RESISTANCE DEVELOPMENT
Often in the literature terms such as 'acquisition', 'enrichment' or 'induction' of stemness and resistance (Pardoll, 2012;Heams et al, 2014;Sun et al, 2014), as well as expressions such as 'activation of alternative pathways' that circumvent the blockade by targetselective drug or 'adaptive resistance' (Abolhoda et al, 1999;Huang et al, 2012;Pardoll, 2012;Tenbaum et al, 2012;Das Thakur et al, 2013;Sun et al, 2014), are used in a hand-waving manner without specifying the class of mechanisms behind the development of therapy resistance. Three types of scenarios are possible: tumour progression can be driven by (i) selection of genetic mutants (the classical Darwinian 'adaptation'); (ii) selection of cells in nongenetic, pre-existing states occupied by chance by a sub-population of cells (see Section 'Spontaneous state switching'); (iii) a true induction of cell state transition by the therapeutic agent in individual surviving cells (see Section 'Therapy-induced state switching: therapy as doubled-edged sword'). The former two scenarios are cell population-level processes, involving selection of inherently fitter cells. Older analyses, based on the upregulation of resistance/stemness genes at the whole-cell population level (e.g., western blot) and lacking detailed kinetics, obscure the difference between population (selection) and cell-individual (induction) phenomena (Abolhoda et al, 1999;Huang et al, 2012;Tenbaum et al, 2012;Das Thakur et al, 2013;Sun et al, 2014).
The short timing for the appearance of stem-like or MDR þ cells after treatment (hours to few days) and the regularity with which any anticancer treatment triggers a complex stress-response phenotype suggests that the non-genetic process is often behind the observed change (scenario ii or iii). However, the question whether this apparent 'enrichment' of stem-like cells is due to selection of cells that occupied the stem-like state or induction (conversion of an individual cell by treatment) is usually not explicitly raised. Experimental demonstration of the latter requires longitudinal monitoring of phenotype change in individual cells (Nowell, 1976;Pisco et al, 2013). The essential difference between a Lamarckian, non-genetic, induced cell response and a classical Darwinian selection (of mutants or of attractor states) is that the induced response is mounted by surviving cells that were not a priori more resistant. This conceptual difference has therapeutic consequences for preventing resistance.

OUTLOOK: THERAPEUTIC IMPLICATIONS BEYOND KILLING
The mechanisms of resistance described here rest on the fact that non-mutant, non-resistant, treatment-naive cancer cells can survive treatment. However, why do they survive in the first place? First, tumour therapy has to balance between killing tumour cells and avoiding host toxicity, thus limiting the dose used. Second, penetration of tumour tissue by drug molecules is not uniform, creating ample opportunity for many tumour cells survive even if they are not a priori resistant. And third, even within a clonal population, the cell-intrinsic sensitivity to drug fluctuates, as do most cellular traits, resulting in a broad statistical distribution (heterogeneity) of drug sensitivity at any time point (Huang, 2009). Therefore, therapy will almost inevitably not kill all tumour cells, independently of pre-existing genetic mutants and even when considering cell-cycle phase. However, the surviving cancer cells are not innocent bystanders: having encountered a  Figure 3. Different anticancer therapy schemes can minimise the strengthening of surviving cells. If rather than treating the tumour using conventional, aggressive therapy that may stimulate the stressed, stem-like ( ¼ more aggressive) state, cancer is treated using alternative, gentler modification of cell growth we might be able to control the ratio between the stressed cells and the naive, non-stressed cancer cells. The idea of treating cancer as a chronic disease is to prevent the cells from transiting into the aggressive state in response to cytotoxic stress. This can be achieved by blocking transition into that state (right blue arrow) or by containing cells without cytotoxity, for example, by promoting differentiation (green arrow). sublethal perturbation, which is a massive blow, some of them will be pushed into nearby (unused) attractors, the dead-end sidevalleys of the epigenetic landscape, which likely, as discussed above, encode stem-like, stress-response phenotypes. This explains why non-killed but near-lethally stressed cells are not necessarily weaker but instead might become more resilient (McDunn, 2005;Huang, 2014). Hence, rather than describing tumour progression with the principle of Darwin's 'Survival of the fittest', it may be more suited to think of Nietzsche's concept that 'What does not kill me strengthens me'.
Although progression is an intrinsic trait of malignant tumours and debulking of the primary tumour is clinically necessary to alleviate disease burden and lessen the probability of metastatic dissemination, we also need to consider the progression-stimulating effect of therapy in the non-killed cells. Therefore, tumouricidal treatment is inherently a double-edged sword. The question is: how can we maximise the desired effects of therapy while minimising the strengthening of the (unavoidable) surviving cells? We can consider two types of approaches ( Figure 3): the proactive solution would be to combine cytotoxic therapy with drugs that block the Lamarckian induction of a stress response. For instance, Wnt signalling has emerged as a central pathway in stress-induced stemness and resistance in tumour and stromal cells (Corrêa et al, 2012;He et al, 2014). Inhibitors of the Wnt-b-catenin axis, currently tested as differentiating agents (Liu et al, 2013), may be used before chemo-or targeted therapy to prevent stress response in surviving cells. The second approach is more passive and seeks 'gentle containment': avoid triggering the stress response associated with aggressive attempts to kill. This can be achieved by a departure from the traditional maxim of 'maximal-tolerated dose' through a variety of scheduling (Liao et al, 2012), such as metronomic therapy (lower dose, more frequent) (Scharovsky et al, 2009) or drug holidays to allow for reversion to the sensitive state (Sun et al, 2014). Non-cytotoxic differentiation therapy (Sell, 2004) may also belong to this category.
The immense non-genetic phenotypic heterogeneity of tumour cells, which entails, by statistical necessity, incompleteness of any tumouricidal treatment, and the phenotype plasticity, which allows perturbed surviving cells to aberrantly access hidden, preexisting, pathological stem-like states, offers a new set of principles for future exploration of therapeutic options that will reach beyond the current emphasis on killing.


extfieldsofstudy: Biology Medicine

text:
Genomic comparison of closely related Giant Viruses supports an accordion-like model of evolution

Genome gigantism occurs so far in Phycodnaviridae and Mimiviridae (order Megavirales). Origin and evolution of these Giant Viruses (GVs) remain open questions. Interestingly, availability of a collection of closely related GV genomes enabling genomic comparisons offer the opportunity to better understand the different evolutionary forces acting on these genomes. Whole genome alignment for five groups of viruses belonging to the Mimiviridae and Phycodnaviridae families show that there is no trend of genome expansion or general tendency of genome contraction. Instead, GV genomes accumulated genomic mutations over the time with gene gains compensating the different losses. In addition, each lineage displays specific patterns of genome evolution. Mimiviridae (megaviruses and mimiviruses) and Chlorella Phycodnaviruses evolved mainly by duplications and losses of genes belonging to large paralogous families (including movements of diverse mobiles genetic elements), whereas Micromonas and Ostreococcus Phycodnaviruses derive most of their genetic novelties thought lateral gene transfers. Taken together, these data support an accordion-like model of evolution in which GV genomes have undergone successive steps of gene gain and gene loss, accrediting the hypothesis that genome gigantism appears early, before the diversification of the different GV lineages.

Introduction
The order Megavirales (formerly NCLDVs: Nucleo Cytoplasmic Large DNA Viruses) comprises very diverse double strand DNA viruses infecting a wide range of eukaryotic hosts, including vertebrates, protists, and unicellular alga . This group is supposed to be monophyletic on the basis of a conserved set of 25-50 genes encoding mainly structural and informational proteins (Iyer et al., 2006;. Seven different families compose the order: Poxviridae, Asfarviridae, Iridoviridae, Ascoviridae, Phycodnaviridae, Mimiviridae, and Marseilleviridae. Among them, two families display important variations in term of genome size: the Mimiviridae ranging from 371 to 1259 kb (Arslan et al., 2011;Moniruzzaman et al., 2014) and the Phycodnaviridae ranging from 160 to 2550 kb (Lee et al., 1995;Philippe et al., 2013;. Viral genome gigantism challenges the traditional definition of viruses conceived as small and simple organisms. Understanding the evolutionary forces acting on the Giant Virus (GV) genomes have profound implication for our knowledge of the viral world and the interplays between cellular organisms and viruses. Additionally, there are accumulating evidences that several GVs caused respiratory tract infections. Although the exact mechanism of their pathogenocity is currently unknown, mimiviruses are suspected to be the ethiologic agents of numerous cases of pneumonia acquired by patients in intensivecare institution but also by apparent healthy patients (Kutikhin et al., 2014). Origin of genome gigantism in these families is still a matter of an intense controversy between the advocates of the "genome degradation hypothesis" (Claverie, 2006) and those defending the "genome expansion hypothesis" (Moreira and Lopez-Garcia, 2005;Yutin et al., 2014). The "genome degradation hypothesis" postulates that Giant Viruses (GVs) derive from a cellular ancestor by progressive genome simplification linked to the adaptation to a parasitic lifestyle. Notably, presence of typical cellular hallmark genes as translational genes, supports the hypothesis that GVs derive from a cellular ancestor (Arslan et al., 2011). However, phylogenetic studies indicate that most, if not all, of these translational genes result from lateral gene transfers (LGTs) from cellular organisms (Moreira and Lopez-Garcia, 2005;Yutin et al., 2014). Indeed, many studies have pointed out the central role of lateral gene transfers during the evolution of GVs (Iyer et al., 2006;Filee et al., 2007; Moreira and Brochier-Armanet, 2008;. Finally, gene and genome duplications (Suhre, 2005; in addition to dissemination of various mobile genetic elements as introns or transposons (Filee et al., 2007;Desnues et al., 2012) have also been identified as important player for GV genome evolution. The combination of these forces support the "genome expansion hypothesis" in which GVs evolved from a relatively simple viral ancestor by progressive gene accretion and duplication. The nature of this ancestor remains speculative but recent discoveries indicate that GVs may derive from DNA transposons belonging to the Polinton/Virophage superfamily (Krupovic and Koonin, 2015). However, experimental data have shown that under laboratory conditions, members of the Mimiviridae can experience rapid genome expansion/contraction. Indeed, under peculiar selective constraints, Poxvirus genomes undergo successive steps of gene duplications and gene losses (Elde et al., 2012). Symmetrically, when mimiviruses are cultivated in bacteria-free media (the preys of their amoebal hosts), numerous genome reductions occur, mainly caused by large deletions (Boyer et al., 2011). On the basis of these data, I recently proposed a model in which GVs evolved using a complex process of "genomic accordion" instead of a general tendency toward either genome expansion or reduction (Filee, 2013). According to this hypothesis, GVs should undergo successive cycles of genome expansion and reduction in order to adapt to modified environmental conditions or new hosts. This hypothesis is readily testable by genomic comparisons of closely related GVs. Thus, I took advantage of the availability of clusters of very similar GVs genomes belonging to the Phycodnaviridae and the Mimiviridae families to analyze the pattern of gene loss/gene gain, using whole genome alignments. In this paper, I show that genomic mutations occur regularly over the time (clock-like). However, a global trend of genome evolution was not observed, each lineage having a specific mode of evolution as duplication and subsequent loss of genes belonging to large multigene families (mimiviruses and megaviruses), recursive insertion and excision of transposons and mobile endonucleases (Chlorella viruses) and acquisition of new genes via LGTs (Ostreococcus and Micromonas viruses). Thus, these observations support the hypothesis that ancestors of the present-day GVs were already GVs that have evolved through a balanced, predominantly neutral process of gene gains and gene losses.

Data Collection
GV genomes were downloaded from the NCBI Viral Genome database at http://www.ncbi.nlm.nih.gov/genome/viruses/. Minimal requirements needed are: at least three nearly complete genomes per group with sufficient genome syntenies to identify orthologous genes without ambiguities. As GVs display numerous gene duplications (Suhre, 2005), synteny is a determinant criterion to recognize the orthologous genes by positional homology. Identical or nearly identical genomes as the three Phaeocystis viruses for example were excluded. The final dataset includes 4 mimiviruses and 4 megaviruses for the Mimiviridae family and 3 Micromonas viruses, 5 Ostreococcus viruses, and 5 Chlorella viruses for the Phycodnaviridae family ( Table 1).

Whole Genome Alignment
In order to identify positional homologs and infer the ancestral state of each gene, multiple genome alignments were carried out and visualized using progressiveMauve (Darling et al., 2004). Whole genome phylogenetic trees based on gene content/substitution distances were computed. Roots of trees were chosen to minimize the evolutionary distance: i.g. the mimivirus tree was rooted with a megavirus and vice versa. Concerning the Chlorella viruses, as the Ostreococcus and the Micromonas virus genomes are too divergent to find reliable genome syntenies, the sequence of Chlorella virus PBCV-1 genome was used. Two by two, whole genome comparisons were achieved with wgVISTA (Couronne et al., 2003) and unaligned DNA segments larger than 300 bp containing at least one ORF were extracted. These sequences were searched with a TBLASTN (Altschul et al., 1990) against a NR database to identify the genomic variations (gene transfers, gene duplications, gene losses, movement of mobile genetic elements, orphan etc...). In order to identify the nature of the genomic events (a gene duplication rather than a gene loss for example), the most parsimonious scenario principle was applied using the whole genome trees as templates. When two scenario are equally parsimonious (implying the same number of mutation steps) the corresponding genes were marked as "ambiguous."

Sequence Analysis and Phylogenies
Four taxonomic markers (DNA polymerase, A2L Transcripition factor, D5 Primase-helicase, packaging ATPase) were used to build a global phylogenetic tree of the GVs used in this study. The ancestral sequence of each group was reconstructed using FastML (Ashkenazy et al., 2012) and pairwise comparisons were computed using BLASTN (Altschul et al., 1990). Sequences were aligned using MUSCLE (Edgar, 2004) with default parameters and conserved parts of the alignments usable for phylogenetic analyses were chosen using Gblocks (Castresana, 2000). Concerning phylogenies, best-fitting ML models were selected using Protest (Abascal et al., 2005) and the trees were implemented using PhyML 3.0 (Guindon et al., 2009). Branch supports were calculated using a LRT Shimodaira-Hasegawa (SH) procedure.

A Predominant Model of Neutral Evolution due to Regularly Accumulated Mutations over the Time
The pattern of gene loss/gene gain at a small evolutionary scale for five groups of closely related GVs belonging to the families Megavirales and Phycodnaviridae was analyzed. Up to 626 genomic events were detected ( Table 1) corresponding to 212 gene gains, 251 gene losses, and 163 ambiguous variants (including 74 orphans). Insertion and excision of mobile genetic elements are additional factors (44) and very few gene translocations have been evidenced (13). Gene duplication (105 events), lateral gene transfers (85) and gene losses (232) were by far the major evolutionary forces shaping the GV genomes.
For each group of GVs, ancestral sequence reconstruction of four taxonomic gene markers was computed. Percentages of identity between these ancestral sequences and the present ones were indicated in Table 1. If a molecular clock of sequence evolution is assumed, this analysis provides inside each group of viruses, a proxy of the time of divergence between each viruses and the last common ancestor of their respective groups. Thus, mimiviruses, megaviruses, and Chlorella viruses appear closely related (94 to 99% of sequence identity compared to their respective ancestral sequences) whereas Ostreococcus and Micromonas viruses are substantially more divergent (81 to 92% of sequence identity). Figure 1 plots the number of genomic events per kb for each virus, with respect to the sequence identity of the four marker genes. The regression line indicated that there is a negative correlation between the quantity of genomic variations and the sequence identity. In other words, the amount of genomic variations increased linearly with the time of divergence from the last common ancestor. There is no evident deviation from the regression line indicating slow down or increase of the rate of genomic mutation for peculiar groups or individual virus. Consequently, these data support the idea that GV genomic variations accumulate regularly over the time in an approximately clock-like manner. Adaptive genomic mutations could occur at a constant rate but a predominant neutral model of genomic evolution seems more possible.

Evidences for Group-specific Evolutionary Patterns
Analyses of the spatial patterns of gene loss and gain along the genomes were displayed in With the exception of the megavirus LBA, most of mimiviruses and megaviruses display an equilibrium between gains and losses. The megavirus LBA has significant higher number of gene losses than gene gains leading to a smaller genome compared to the other megaviruses ( Table 1). As previously observed, these results confirm that the extremities of the Mimiviridae genomes are hot spots of genome variation (Filee et al., 2007;Colson et al., 2011). In addition, most of the genomic variations in the two viral groups concern multigenic families: more than 75% of duplications concern families with at least three additional representatives in the corresponding genomes and 50% of the gene losses also impact duplicated families. Duplicated genes families represent roughly one third of the total genes content of the mimivirus (Suhre, 2005) and of the megavirus chiliensis (Arslan et al., 2011). This over-representation of duplicated families compared to their current distribution in the Mimiviridae genomes suggests that they are the primary source of variation in this viral group. Indeed, LGTs, movement of genes or mobile genetic elements as transposon or mobile endonuclease and generation of orphans represent an anecdotal fraction of the genomic variation identified in this study. The rarity of LGTs is somewhat surprising, as many studies have pointed out their importance during the evolution of the Mimiviridae (Iyer et al., 2006;Filee et al., 2007Moreira and Brochier-Armanet, 2008;. The Phycodnaviridae family displays clearly different patterns of genome evolution (Figure 3). Ostreococcus viruses demonstrate a slight tendency of genome expansion for most of their representatives whereas Chlorella viruses display very different orientations including significant genome reduction for MA-1D and NYs-1, significant genome expansion for NY2A and relative stability for the others. A similar situation is observed for the three Micromonas virus genomes ( Table 1). Another striking difference between the Phycodnaviridae and the Mimiviridae is the location of the genomic variations: there are scattered along the genomes for the Phycodnaviridae rather than being concentrated at the extremities as observed for the Mimiviridae. Interestingly, a study of laboratory variants of Chlorella viruses has evidenced the occurrence of large deletions at the left ends of the genomes (Landstein et al., 1995). However, an analysis of naturally occurring Chlorella viruses showed that insertion/deletion are more disseminated, corroborating our results (Nishida et al., 1999). Insertion and excision of mobile genetic elements represent a significant part of the Chlorella virus genomic mutations (23% of the total events). This observation confirms the importance of the GIY-YIG mobile endonuclease, associated or not with introns, and IS607 transposons during the genome evolution of the Chlorella viruses (Filee et al., 2007). Chlorella viruses also display an important number of multigene families gene losses/gene duplications. which account for 40% of the genomic events whereas they represent only 15-20% of the total gene content . Conversely, Ostreococcus virus and Micromonas viruses do not display any mobile elements movement and have a low amount of gene duplication (Figure 1, Table 1). Another difference is the importance of LGTs in Ostreococcus and Micromonas viruses.
LGT is by far the major evolutionary force acting on the genomes of these viruses representing more than one third of the total number of FIGURE 2 | Genomic map of the megaviruses and the mimiviruses describing the different genomic mutations that have occurred after the divergence with their last common ancestor genome. Symbols below the line indicated gene losses (dots for gene losses, arrows for excision of mobile genetic elements and stars for translocation) and symbols above the line indicate gene gains (squares for duplications, triangle for LGTs and arrows for insertion of mobile genetic elements). Interrogation marks represent ambiguous cases or presence of orphans. Squares and dots in red indicate genomic events of genes belonging to paralogous families. The trees represent the whole genome phylogeny, the numbers above and below the terminal branches indicate the amount of gene gain and loss for each virus. The scale bar represents the equivalent of 100 kb. mutations observed here. This observation contrasts with the rarity of LGT observed in the other GVs.
Taken together, these data suggest that there is no general trend of genome expansion or reduction in the family. Indeed, each group of viruses displays specific patterns of gene gain/gene loss. mimiviruses, megaviruses, and Chlorella viruses are mainly affected by gene loss and duplication targeting the multigene families. Multigene families appear as the main evolutionary tools in these viruses. Ostreococcus and Micromonas viruses are principally affected by LGTs and display few events of gene duplications.

Characterization of the LGTs Involved a Large Diversity of Sources and Protein Functions
Genomic comparison of closely related GVs lead to the identification of 71 individual cases of gene transfers (Supplementary Table 1). Representative sets of phylogeny of these genes with identifiable functions are given Figure 4. As previously noticed, the vast majority of the LGTs concern the Ostreococcus and Micromonas viruses. Indeed, only 4 transfers for the Chlorella Phycodnaviruses and 5 for the mimiviruses and the megaviruses were observed. Most of these transfers concern genes coding proteins of unknown functions. Among the 24 genes displaying strong sequence similarity with genes associated with identifiable functions, 9 are homologs of DNA methyl transferase/methylase genes. These enzymes are components of Restriction-Modification systems, a prokaryotic system of defense against invading DNA. These enzymes are prone to LGTs and may behave as selfish mobile genetic elements (Kobayashi, 2001). The other functions implicated in LGTs compose a heterogeneous set of diverse informational or metabolic proteins: a Cytochrome B5, a Heat Shock protein, a RNA polymerase sigma factor etc. Interpretation of the phylogenies is generally unequivocal: GV genes are most often well nested among a forest of bacterial or eukaryotic genes (MpV-201, MpVPL1_135, OtV2_29, OtV2_30, NY2A_137, OtV2_78 + OlV1_89, OtVRT-2011_115) ( Figure 4A). Thus, this situation strongly favors a LGT polarized from the cell to the virus. Sometimes, the GVs sequences appear as a sister group of the host sequences, well nested inside the eukaryotic sub-tree (OtV2_200/OlV1_214, OtV2_222/OlV1_234, OtVRT-2011_115 etc...) (Figure 4B). This pattern indicates strongly that the FIGURE 3 | Genomic map of the Chlorella and Ostreococcus Phycodnaviruses describing the different genomic mutations that have occurred after the divergence with their last common ancestor. Symbols below the line indicated gene losses (dots for gene losses, arrows for excision of mobile genetic elements) and symbols above the line indicate gene gains (squares for duplications, triangle for LGTs and arrows for insertion of mobile genetic elements). Interrogation marks represent ambiguous cases or presence of orphans. Squares and dots in red indicate genomic events of genes belonging to paralogous families. The trees represent the whole genome phylogeny, the numbers above and below the terminal branches indicate the amount of gene gain and loss for each virus. The scale bar represents the equivalent of 100 kb.
viruses have acquired the genes from their algal hosts. However, more ambiguous patterns are also encountered ( Figure 4C): in the OtVRT-2011_237 tree, the Ostreococcus viral sequence is located close to host sequence but distantly related from the other eukaryotic sequences (the tree has been rooted with Bacterial sequences). In this case, the polarization of the transfers is unclear. However, the fact that this gene is absent in all Phycodnaviruses, with the exception of OtVRT-2011, favors the hypothesis of a gene transfer from the cells to the virus. The NY2A_359/NY2A_543/AR158_487 gene phylogeny is even more puzzling: several Phycodnaviridae and Mimiviridae sequences are represented but form a deeply polyphyletic group. In this case, a possible explanation involves two independent LGTs from bacteria. Alternatively the sequence NY2A_359 has been acquired from another GVs, possibly a Mimiviridae infecting "Alga" as a Phaeocystis virus or an Aureococcus virus. Finally, in the MpV1_42 gene phylogeny, the viral sequence appears as a sister taxa with the Ostreococcus lucimarinus sequence (a potential host). However, the GV and host sequences are also closely related with diverse cyanobacterial sequences and their associated phages. This situation leads to two possible scenarios: -The "two steps scenario" implies first, acquisition of the bacterial gene by MpV1 and second, transfers of the viral genes to the host genome.
-Alternatively, it could be envisaged independent captures of the bacterial gene by the virus and the alga.
The "two steps scenario" could provide an interesting situation in which the virus has served as a "shuttle" for an inter-kingdom LGT.
Prokaryotes represent the predominant source of genes acquired by LGTs (Figure 5). Various phyla are involved including Cyanobacteria, Proteobacteria, Firmicutes, Spirochetes, Verrucomicrobia... (Supplementary Table 1). Genes derived from the hosts and from other eukaryotic organisms represent additional but minor sources. These include mainly fungi and various marine organisms (Hydra, the anemone Nematostella, the crustacean Daphnia, the ascidian Ciona, diverse alga as Chlorella or Cyanidioschyzon etc...). Finally, 4 virus-to-virus LGTs were also identified, two implicating other GVs (a Marseillevirus and a Mimivirus), the two others ones with Cyanophages. Moreover, a substantial fraction of the LGTs displays ambiguous phylogenies blurring the identification of the donors (Figure 5). Possible additional cases of virus-to-virus LGTs are also possible (OtV2_40, OtVRT-2011_55, MpV1_243, MpVPL147...). As discussed previously with the NY2A_359/NY2A_543/AR158_487 gene phylogeny, the trees display complex evolutionary histories with possible multiple and independent acquisitions of the genes by the viruses rather than LGTs between the viruses themselves.
In summary, more than 70 cases of LGTs with a clear polarization from cells to the GVs have been evidenced. These transfers concern genes encoding proteins of very diverse functions and prokaryotes are the predominant source of gene acquisitions.
LGTs from the hosts and from other eukaryotic organisms also occurred and isolated cases of gene transfers between viruses, including GV-to-GV LGTs, are also reported.

Discussion
Comparative genome analysis of closely related GVs revealed that there are no general trends of genome reduction or genome expansion. Genomic mutations occur at a clock-like, constant rate, in both GV lineages but each group displays specific patterns of genome evolution: -GVs with the largest genome as the Mimiviridae (mimiviruses and megaviruses) evolved mainly by a balanced process of duplications and losses of multigene families. Paralogous gene families appear as a genomic toolbox by providing most of the new genes but subsequent losses equilibrate the game and genome expansion is not discernible.
LGTs and movements of mobile genetic elements are additional but marginal forces of genome evolution. LGTs are rarely observed. These processes lead one of these Chlorella viruses to significant genome expansion, whereas the others display some genome reduction or genome size stability. It is noteworthy that the data support the fact that these mobile genetic elements are effectively able to transpose in the GV genomes. Their presences do not result from successive and independent LGTs from cellular sources. Abundance of these selfish genetic elements in GV genomes is a very specific feature among eukaryotic viruses (Filee et al., 2007). Indeed with the exception of the polydnaviruses that have a symbiotic, intra-cellular life style (Dupuy et al., 2011), mobile genetic elements are absent or very rare in eukaryotic viruses (Gilbert et al., 2014). Thus, the results presented here indicate that their importance during the evolutionary history of the GVs should not be underestimated.
It should be noted that the Ostreococcus and the Micromonas viruses are more distantly related from each others (80-90% identity of the four taxonomic markers with the ancestral sequence) than the Mimiviridae (97-99%) and the Chlorella viruses (94-99%). Despite their long period of divergence, the genome synteny of the Ostreococcus and the Micromonas viruses are still well conserved. By opposition, the genome colinearity has been largely disrupted between the mimiviruses and the megaviruses: only a 600 kb central segment is conserved (Arslan et al., 2011;Yoosuf et al., 2012). Gene duplication and gene losses that occur at high rate at the extremities of the Mimiviridae genomes explain the fast disruption of the genome synteny. Similar processes are acting on the Chlorella virus genomes but the repartition of the genomic mutations is not biased toward the extremities. Among the 35 partial or nearly complete Chlorella virus genomes, this process leads to a fast and complete disappearance of the synteny, even for relatively close viruses (Jeanniard et al., 2013). Thus, the remarkable level of genome co-linearity of the Micromonas and Ostreococcus viruses despite a long period of divergence from their common ancestor might reflect fundamental different evolutionary processes compared to the GVs with larger genomes. Additionally, specific constraints on the genome size, for example for the packaging of the DNA into the capsid, might also limit the extent of genome variation and expansion. Interestingly, Mimiviridae and Chlorella Viruses display a large spectra of host specificity compared to Ostreococcus and Micromonas viruses. Mimiviruses have been isolated from Amoeba, but recent data indicate that they are also found in human (Kutikhin et al., 2014), leeche (Boughalmi et al., 2013), or hydra (Grasis et al., 2014). Chlorella phycodnaviruses are able to infect a large variety of protists harboring Chlorella symbiont (Van Etten, 2003) but can also infect mammals (Yolken et al., 2014). By contrast, Ostreococcus viruses display a strong host specificity (Clerissi et al., 2012). Thus, it is tempting to suggest that the broad host range of mimiviruses and Chlorella viruses are linked to their propensity to generate paralogous gene families, allowing the viruses to adapt to new hosts or changing ecological niches. In support to this hypothesis, some of the larger multigene families encode virus-host interaction functions (ankyrin-repeat, protein kinase, glysosyl transferase, FNIP-like repeats etc...). Interestingly this hypothesis offers further developments to determine the genes and molecular mechanisms which allow these GVs to adapt to a wide range of hosts, including diverse mammals, including human in relation with their potential health care concerns.
The paucity of LGTs detected in the genome of the Mimiviridae and the Chlorella viruses examined here is intriguing as many studies pointed out the importance of gene transfers during the evolution of these groups (Iyer et al., 2006;Filee et al., 2007Moreira and Brochier-Armanet, 2008;. It was proposed that the sympatric lifestyle of these viruses with various prokaryotic species (symbionts or preys of their protist hosts) have facilitated the access to a very diverse gene pool as sources of new genes and functions (Filee et al., 2007;Raoult and Boyer, 2010). However, we identified only 4 LGTs for the Chlorella viruses and 5 for the Mimiviridae. This observation raises the question of the overestimation of the importance of LGTs during the evolution of the GVs. An important point is the determination of the polarization of the transfers and the interpretation of the phylogenies. A large fraction of phylogenies including viral and cellular genes are inconclusive: low level of resolution, complex patterns involving multiple transfers/losses, ambiguities in the polarization as previously stated (Forterre, 2010). Interestingly, acquisition of numerous GV genes by various eukaryotes has been evidenced recently (Filee, 2014;Maumus et al., 2014). Thus, some LGTs wrongly polarized from the cell to the virus correspond in turn to acquisition of GV genes by cells. However, an important fraction of the GV genes possibly acquired from cells have homologs only in prokaryotes (Filee et al., 2007). In this case, the polarization of the transfers is unequivocal as it seems unlikely that prokaryotes frequently acquired genes from eukaryote viruses. Interestingly, the major part of LGTs identified in this study concern genes from bacterial origin suggesting that the possible over-estimation of the importance of LGTs essentially concerns genes from eukaryotic (host) origins. Finally, LGTs are the most frequent genomic events observed in the Ostreococcus and Micromonas viruses, but these viruses are distantly related to each other compared to the Mimiviridae and Chlorella viruses. This suggests that LGTs play a significant role in GV evolution, but they probably arise at a lower rate than previous thought for several GV lineages in which gene duplications/losses or movements of mobile genetic elements are the prevalent evolutionary forces. Additionally, it is also possible that many LGTs identified previously in the literature have occurred early, before the diversification of the different GV families. Supplementary data with a broader viral genome sampling and comparisons of more distantly GVs are clearly needed to address the exact importance of LGTs in the evolution of this viral family.
Taken together, these interpretations are not compatible with the hypothesis indicating that GVs derive from a cellular ancestor via progressive genome degradation in each lineage (Claverie, 2006). These data also reject the "pick pocket" hypothesis in which GVs experience massive LGTs leading to genome expansion (Moreira and Lopez-Garcia, 2005). By contrast, these data support the idea of a "genomic accordion" in which GVs evolved through a balanced process of gene gains (duplication mainly but also LGTs) compensated by gene losses. Indeed, each GV lineage most likely has a specific pattern of genome evolution, probably reflecting different host-virus interactions. However, there is little doubt that the last common ancestor of the Megavirales was a relatively simple virus, encoding a limited genomic repertoire that has successively expanded over the times (Yutin et al., 2014;Krupovic and Koonin, 2015). Our results indicate that the rate of genome expansion has slow down before the diversification of the Mimiviridae and the Chlorella viruses. Thus, these data support the idea that the ancestor of these viral families was already a GV, indicating that genome gigantism has emerged early during the evolution of the Megavirales (Yutin et al., 2009).


Purification and characterization of therapeutic enzyme l-asparaginase from a tropical soil fungal isolate Fusarium culmorum ASP-87

Microbial L-asparaginase is one of the most important industrial enzymes of interest accounting for about 40% of the total worldwide enzyme sales.1 The enzyme L-asparagine amido hydrolase E.C.3.5.1.1 belongs to an amidase group that catalyzes the hydrolysis of the amino acid L-asparagine to L-aspartic acid and ammonia. This enzyme has got much significance in medical field for the treatment of leukemia especially acute lymphoblastic leukemia (ALL)2 and also widely used in baking and food industries to reduce the formation of carcinogenic acrylamides in biscuits and in deep fried potato.3,4 L-asparaginase is widely distributed in plants, animals and microorganisms. However, L-asparaginase from microbial sources has gained much attention because of its high productivity. It is extracellular and therefore secreted in to the fermentation medium. Among microbes, this enzyme is produced by bacteria, fungi and actinomycetes. Microbial strains like Escherichia coli,5 Erwinia caratovora,6 Pseudomonas aeruginosa,7 Streptomyces gulbargensis,8 Aspergillus terreus,9 Aspergillus niger,10 Penicillium brevicompactum,1 Cladosporium sp11 are the main source of L-asparaginase. Bacterial L-asparaginase has been reported to cause hypersensitivity leading to allergic reactions and anaphylaxis.12 Hence, L-asparaginase from eukaryotic microorganisms is gaining much importance as it is known to have less adverse effects.13 The main objective of the present work was to purify and characterize L-asparaginase from Fusarium culmorum ASP-87 isolated from tropical soil and to demonstrate its physio-chemical properties which are indispensible in both fundamental and applied research.

Assay of L-asparaginase activity
The activity of L-asparaginase in the culture filtrate was assayed using the method of Imada. 15 The rate of hydrolysis of L-asparagine was determined by measuring the ammonia released using Nessler's reagent. A mixture of 0.5ml of enzyme extract, 0.5ml of 0.04M L-asparagine, 0.5ml of 0.05M Tris-HCl buffer (pH 7.2) and 0.5ml of distilled water was incubated at 37°C for 30min. The reaction was stopped by the addition of 0.5ml of 1.5M trichloroacetic acid (TCA). The ammonia released in the supernatant was determined calorimetrically by adding 0.2ml of Nessler's reagent into tubes containing 0.1ml of supernatant and 3.7ml of distilled water and incubated at room temperature for 20min. The absorbance was read at 450nm. One international unit (IU) of L-asparaginase activity is defined as the amount of enzyme required to produce 1µmol of ammonia per min under the conditions of the assay.

Purification of L-asparaginase
Ammonium sulfate precipitation: A four day old culture filtrate (200ml) of F. culmorum ASP-87 grown in modified Czapek-dox broth was collected after centrifugation at 8000rpm for 10min at 4°C. All subsequent purification steps were carried out at 4°C. The crude enzyme was subjected to ammonium sulfate precipitation and the protein precipitate at 80% salt saturation was allowed to stand overnight. The precipitate was collected by centrifugation at 10,000rpm for 15min and resuspended in 0.01M Tris-HCl buffer (pH-7.2) and dialyzed overnight against the same buffer.
Ion exchange chromatography: The dialyzed ammonium sulfate fraction was applied to an anion exchange chromatography on DEAE cellulose (1.5×15cm) column pre-equilibrated with the Tris-HCl buffer (0.01M; pH 7.2). The unbound proteins were washed repeatedly for five times and then eluted with the same buffer containing NaCl (1M) by a stepwise gradient at a flow rate of 1 ml/min. Fractions containing L-asparaginase activity were pooled and lyophilized.

Gel filtration chromatography:
The lyophilized samples were loaded onto Sephadex G-100 (1.5×15cm) column pre-equilibrated with 100mM phosphate buffer pH 7.0 and eluted with Tris-HCl buffer (0.01M; pH 7.2). Fractions were collected (1ml/tube) and the absorbance of all the fractions was recorded at 280nm and active fractions were pooled and concentrated. The purified L-asparaginase was stored at 4°C and used for further characterization.

Estimation of protein:
The concentration of protein was estimated by the method of Bradford 16 using bovine serum albumin as the standard.
Determination of molecular weight of purified protein by SDS-PAGE: The molecular weight of purified L-asparaginase was determined by (SDS-PAGE) the method of Laemmli. 17 SDS-PAGE was performed using a 12% polyacrylamide gel. The proteins were stained with coomassie brilliant blue R-250. The molecular weight of purified enzyme was determined using standard molecular weight markers (Bio-Rad).

Enzyme characterization
Effect of pH on enzyme activity and stability: In order to determine the effect of pH on purified L-asparaginase, the purified enzyme was pre-incubated in 0.1M buffer in the range between pH 3 and pH 11 without the addition of substrate, the enzyme activity was determined under standard assay conditions. Buffers used were sodium phosphatecitrate (pH 3.0-6.0), tris-HCl (pH 7.0-9.0) and glycine-NaOH (pH 10.0-11.0). In order to determine the pH stability, the purified enzyme was pre-incubated in pH buffer range between pH 3 and pH 11 at room temperature for 24hrs. The residual activity was determined for every 12hrs intervals under standard assay conditions. The activity of the enzyme at zero minute of the reaction was considered as 100% and served as control.
Effect of temperature on enzyme activity and stability: In order to determine the effect of temperature on purified L-asparaginase, the enzyme activity was studied at different temperatures ranging from 20°C to 80°C at 5°C increments, the enzyme activity was determined under standard assay conditions. Thermal stability of the purified enzyme was evaluated by incubating the enzyme for 10, 20, 30, 40, 50, 60minutes at different temperatures ranging from 30°C to 80°C. The residual activity was determined under standard assay conditions. The activity of the enzyme at zero minute of the reaction was considered as 100% and served as control.

Effect of metal ions, inhibitors and surfactants on L-asparaginase activity:
In order to determine the effect of metal ions, inhibitors and surfactants like Mn 2+ , Zn 2+ , Fe 2+ , Cu 2+ , Mg 2+ , Ca 2+ , Hg 2+ , Co 2+ , Fe 3+ , EDTA, β-mercaptoethanol, tween 80 and SDS on L-asparaginase activity, the purified enzyme was preincubated with different metal solutions at 5mM concentration and inhibitors and surfactants at 1mM concentration for 30min and the residual activity of the enzyme was assessed. The activity of the enzyme without the addition of metal ions or inhibitors or surfactants was considered as 100% and served as control.

Substrate specificity
To assess the activity of L-asparaginase, different substrates such as L-asparagine, L-aspartic acid, L-glutamine and L-glutamic acid were used. The substrates were prepared in 0.05M tris-HCl buffer pH-7.5 at 10mM concentration. The enzyme was mixed with substrate and the reaction mixture was incubated for 30min at 40°C and enzyme activity was determined. Results were expressed as relative percentage.

Determination of kinetic constants
The kinetic parameters K m and V max of purified L-asparaginase were determined by the method of Lineweaver and Burk 18 with different concentrations of L-asparagine (1Mm-10mM) dissolved in 0.05M of tris-HCl buffer pH 8.0. The enzyme activity was determined by measuring the rate of hydrolysis of L-asparagine under standard assay conditions.

Purification of L-asparaginase
The enzyme L-asparaginase was purified from the culture filtrate of F. culmorum ASP-87 using ammonium sulfate precipitation, ion exchange chromatography followed by gel filtration. The purification procedure is summarized in (Table 1). The first step of purification by ammonium sulfate precipitation achieved 2.63-fold purification with 26% enzyme recovery. The second purification step was done by ion exchange chromatography using DEAE cellulose. This step showed 8.42-fold increase in enzyme activity with 6.57% enzyme recovery. The final step of purification was performed by gel filtration using Sephadex G-100 column. The fractions showing L-asparaginase activity in this step were collected and pooled. The final step of purification resulted in 14.03-fold increase in enzyme activity with overall specific activity of 16.66U/mg of protein and with a net yield of 2.6% enzyme recovery. L-asparaginase from various fungal species have been purified and characterized and reported earlier. L-asparaginase from Penicillium brevicompactum NRC 829 was purified to 151.12 fold with a specific activity of 574.24U/mg and yield of 39.90% 1 and L-asparaginase purified from Mucor hiemalis exhibited a specific activity 69U/mg with 18.46% recovery and 4.59 purification fold. 19 Interestingly, L-asparaginase from Cladosporium sp was purified to 867.7 fold with a specific activity of 83.8U/mg. 11 Although, the purification steps followed by various researchers are almost similar for different fungal species, the purification fold and yield varies. This could be due to the interference of different proteins present in the culture filtrate. from F. culmorum ASP-87 showed homogeneity and the molecular mass was estimated as 90kDa, by SDS-PAGE analysis (Figure 1). The molecular weight of L-asparaginase from F. culmorum ASP-87 was almost similar to that of Penicillium brevicompactum NRC 829 (94kDa) and Trichoderma viride (99kDa). 1,20 Whereas, L-asparaginase from Cladosporium sp has a molecular weight of 117kDa and Aspergillus niger with 48kDa. 13,21 The variability in the molecular weight of L-asparaginase in different organisms may be inferable to its genetic diversities.  Effect of pH on enzyme activity and stability: pH is a critical factor for stability and activity of purified enzyme, as it impacts on the ionic form of the enzyme active site residues. The effect of pH on the activity of purified L-asparaginase was done over a wide range of pH from 3.0 to 11.0 at 30°C. The results revealed that L-asparaginase from F. culmorum ASP-87 was active over a broad range of pH, optimum being pH 8.0 ( Figure 2). Comparable results were reported by earlier workers in Penicillium brevicompactum NRC 829 1 and Streptomyces sp. 22 But, on the contrary Lincoln and Monica reported that pH 7.0 was the optimum pH for the activity of L-asparaginase in Trichoderma viride and Mucor hiemalis. 19,20 The purified L-asparaginase retained 100% activity at pH 8.0 up to 24hrs of incubation ( Figure 3). However, L-asparaginase from Trichoderma viride retained 82% of its activity after 24hrs of incubation 20 and Mucor hiemalis maintained its stability only for 4hrs there after started declining. 19 The stability of L-asparaginase from F. culmorum ASP-87 at alkaline pH is a promising factor to be used as a therapeutic agent.

Effect of temperature on enzyme activity and stability:
Temperature is an important physical parameter which influences the enzyme activity. Effect of temperature on purified L-asparaginase was studied over a broad range of temperature from 30°C to 80°C. The optimum temperature for L-asparaginase activity was 40°C ( Figure  4). However, further increase in temperature, declined the reaction rate sharply. Similar such results were reported by earlier workers in Aspergillus nidulans 23 and Streptomyces griseoluteus. 24 But, on the contrary Lincoln, Monica, Elshafi and Abraham reported that 37°C was the optimum temperature for the activity of L-asparaginase in Trichoderma viride, Mucor hiemalis, Penicillium brevicompactum NRC 829 and Fusarium sp. 1,19,20,25 The thermal stability studies of purified L-asparaginase revealed that the enzyme was highly stable for 120min at 30°Cto 40°C ( Figure 5), but however, inactivated at temperature above 40°C while retaining 50% activity at 60°C for 1hour. L-asparaginase from Penicillium brevicompactumNRC 829 was reported to be stable up to 1hour at 37°C. 1 Considering the thermal stability of other organisms reported earlier, L-asparaginase from F. culmorum ASP-87 shows moderate thermostability, a factor of significant importance in pharmaceutical industry.

Effect of metal ions, inhibitors and surfactants on L-asparaginase activity:
The influence of various metal ions on purified L-asparaginase activity was studied. Among the different metal ions tested Mn2+ enhanced the activity of enzyme by 18% (Table 2) as reported by earlier worker in Mucor hiemalis. 19 Whereas, Cu2+ and Hg2+ inhibited the activity of enzyme by 84 and 80%, as observed by Archana in Aspergillus nidulans 23  did not have any effect on enzyme activity. 20 However, Senthil kumar & Selvam 27 reported that EDTA act as an inducer in Streptomyces radiopugnans MS1. 27 Among the surfactants tested, the non ionic surfactant tween 80 was found to enhance the activity of the enzyme by 16%, whereas, the anionic surfactant SDS completely inhibited the enzyme activity. Kumar and Monica also reported similar such finding that tween 80 at 2mM concentration induced the production of L-asparaginase in Cladosporium sp and Mucor hiemalis. 11,19 Figure 4 Effect of temperature on the activity of purified L-asparaginase from F. culmorum ASP-87.

Figure 5
Effect of temperature on stability of purified L-asparaginase from F. culmorum ASP-87.

Substrate specificity
The property of enzymes that makes them important as diagnostic tools is the specificity they exhibit towards the substrate. Among the different substrates investigated, the enzyme showed high specificity towards its natural substrate L-asparagine, very low specificity towards L-aspartic acid, while no activity towards L-glutamine and L-glutamic acid ( Figure 6). Our findings were in concordance with that of Prabhu, Sahira and Lincoln in Vibrio costicola, Acinetobacter baumannii and Trichoderma viride respectively. 20,28,29 This property of purified enzyme increases its potential to be used in therapeutics and food industries.

Determination of kinetic constants
The K m and V max of purified L-asparaginase from F. culmorum ASP-87 were determined using various concentrations of L-asparagine. The values of K m and V max were 3.1mM and 0.77μmol/ ml/min respectively (Figure 7) which is lower than the Km value of L-asparaginase purified from E. coli, Penicillium sp and Mucor hiemalis. 19,30,31 However, a slightly higher K m value of 12.5mM and lower K m value of 1.05mM were reported in Aspergillus aculeatus and Penicillium brevicompactum. 1,32 The purified L-asparaginase from Fusarium culmorum ASP-87 has stronger affinity towards its natural substrate L-asparagine, a positive property to be useful towards the treatment of tumors.

Conclusion
L-asparaginase from microbial source is of great interest owing to its significance in pharmaceutical and food industries. Therefore, in the present study, an attempt is made to purify L-asparaginase from Fusarium culmorum ASP-87 and its physical and chemical characteristics were studied. The stability of L-asparaginase at alkaline pH and temperature is an advantage for pharmaceutical and food applications. The specificity of L-asparaginase to its natural substrate L-asparagine is of greater importance for making diagnostic biosensors. However, further in depth studies are required to optimize cost effective substrate for bulk production of enzymes. Although, a strain of Fusarium culmorum IFO5902 is been reported to possess L-asparaginase activity by Nakahama way back in 1973, 33 no attempt was made so far to purify and characterize the physiochemical nature of the enzyme. This is the first report which gives the complete physical and chemical nature of the purified L-asparaginase from Fusarium culmorum, isolated and identified as one of the best producers of L-asparaginase by dye based rapid screening method among the 364 soil fungal isolates screened. 34


s2fieldsofstudy: Biology Medicine

text:
Development and validation of a Pneumocystis jirovecii real-time polymerase chain reaction assay for diagnosis of Pneumocystis pneumonia.

Pneumocystis pneumonia is caused by Pneumocystis jirovecii, an opportunistic fungal pathogen. Presently, many clinical microbiology laboratories rely on direct microscopic detection of P jirovecii. The validation, and clinical and laboratory development of a qualitative P jirovecii real-time polymerase chain reaction assay for the rapid detection of Pneumocystis pneumonia is discussed by the authors. In addition, this new technique is compared with the existing gold-standard immunofluorescence assay.

respiratory tract samples (9,10). Laboratory-developed molecular methods have recently been reported, and have a higher sensitivity for the detection of PJ in lower respiratory tract samples compared with microscopy (11)(12)(13)(14). Due to a lower cyst burden, diagnosis of PCP may be difficult using microscopic methods for immunocompromised patients without HIV infection, and in HIV-infected patients receiving antiretroviral therapy. Conventional staining and microscopic examination of bronchoalveolar lavage (BAL) fluid ranged between 70% and 92% in non-HIV infected immunocompromised patients, while the sensitivity for sputa was even lower, between 38% and 53% (14,15). Although several laboratory-developed qualitative PJ real-time polymerase chain reaction (PCR) assays had been reported for the diagnosis of PCP (16)(17)(18)(19)(20), at the time of the present study, no commercial molecular assays were available (21). The present study describes the clinical and laboratory development, and validation of a unique qualitative PJ real-time PCR assay for the rapid detection of PCP.

Clinical setting
Calgary Laboratory Services (CLS) is a large regional centralized laboratory that provides diagnostic testing for infectious diseases for a population of approximately 1.5 million in Calgary and southern Alberta. Approximately 1200 BAL fluid samples are submitted each year from immunocompromised patients for PJ detection. Immunocompromised patients in the present study included those with solid organ or hematopoietic stem cell transplants, HIV or malignancy (solid/haematological). Demographical data and brief clinical history were initially obtained for each enrolled patient through completed laboratory requisitions, which included the patient's age, sex, date of birth and reason for immune compromise including the presence of neutropenia. The CLS study microbiologist (DLC) also subsequently reviewed positive PJ real-time PCR results with the attending physician to determine whether that positive result correlated with the presence of PCP versus asymptomatic colonization.

Sample collection
A total of 132 BAL fluid samples were collected from patients in whom PCP was considered in the differential diagnosis and tested during the study. Forty BAL fluid samples collected between 2006 and 2012 were obtained (20 known PJ positive [PJ+] and 20 known PJ negative [PJ-]) from St Paul's Hospital, Vancouver, British Columbia, designated as the referral laboratory (RL). All BAL samples from the RL were initially analyzed using microscopic examination after staining, and secondarily tested using their laboratory developed PJ realtime PCR assay (see Methods). Aliquots of the BAL fluid samples were then stored frozen at −80 o C until referral. Another 92 BAL fluid samples were sequentially collected retrospectively at CLS between May 2011 and September 2012. Aliquots of the BAL fluid samples were frozen at −80 o C after initially being tested using an immunofluorescence assay (IFA). Storage of multiple sample aliquots allowed later use in discrepant analysis without an additional freeze-thaw. If multiple BAL fluid samples were stored from the same patient, each was tested individually and not pooled.

Microscopic examination
All BAL fluid samples from the RL were initially analyzed using Toluidine Blue O stain (22) and/or a modified Gomori's methenamine silver borate stain and microscopic examination. All of the BAL fluid samples collected at CLS were initially tested using a commercial direct IFA. The Genetic Systems Monofluor Pneumocystis (Meridian Bioscience Inc, USA) immunofluorescence assay was performed according to the manufacturer's instructions.

CLS PJ real-time PCR assay
The BAL fluid samples were centrifuged before DNA extraction. DNA was extracted from stored BAL samples (180 μL aliquot) using the QuickGene 810 on the DNA Tissue Mode program according to the manufacturer's protocol (FujiFilm Life Sciences, Japan). The QuickGene 810 extraction procedure typically yields 100 μL of eluent. A DNA purity check of the extract was performed by measuring the 260/280 nm ratio using a spectrophotometer (ND-100, NanoDrop Technologies Inc, USA), which consistently yielded 260 nm to 280 nm ratios of approximately 1.8. Aliquots (10 μL) of the DNA extract were stored frozen at −80 o C.
Five microlitres of this DNA extract was used for each real-time PCR assay. The PJ real-time PCR assay was based on a previously described method with a positive cut-off cycle threshold (Ct) ≤37 (23). Integrated DNA Technologies (www.idtdna.com) supplied the hybridization probe and primers specific for the mitochondrial large subunit (mtLSU) ribosomal RNA gene of PJ. The forward primer (mtLSU FWD: 5'-TGG TAA GTA GTG AAA TAC AAA TCG G-3' [start = 178, stop = 203]) and the reverse primer (mtLSU REV: 5'-ACT CCC TCG AGA TAT TCA GTG C-3' [start = 277, stop = 299]) were identical to those previously published (18). However, the sequence of the hybridization probe (mtLSU PRB: 5'-/56-FAM/ ACT AGG ATA / ZEN/TAG CTG GTT TTC TGC GA/3IABkFQ/-3') and the fluorescent dyes used to tag the 5' and 3' probe ends differed from those previously published (23). PJ real-time PCR reactions contained 20 μL SmartMix HM Mastermix (5 Smart Mix beads, one QC bead, 185 μL of Sigma H 2 0 and 5 μL of each of the primers and the probe) and 5 μL purified DNA. During each run, a PJ DNA positive control and a template negative control were included. The SmartCycler (Cepheid, USA) program followed 50 cycles of 10 s at 95° C, 10 s at 60° C and 10 s at 72° C.
A positive patient sample that was IFA and PJ real-time PCR positive (ie, with an estimated 1×10 5 copies/mL) was used to estimate the assay's limit of detection. The BAL sample was serially diluted approximately 10,000-fold that of the original material before the PJ real-time PCR became negative (ie, no amplification signal above baseline). The dilution series of samples were also repeatedly tested (ie, up to six times) using the real-time PCR assay to establish the reproducibility of the Ct values for PJ+ samples over the range of dilutions in which PJ was detected (ie, 1:2, up to 1: approximately 10,000). Clinical BAL fluid samples were also tested in duplicate on three separate days to establish the precision of the assay, including two negative and three positive samples within the low, mid and high Ct range of the assay.

RL PJ real-time PCR assay
The St Paul's Hospital laboratory-developed PJ real-time-PCR assay was adapted from one previously published (19). Briefly, 400 μL of BAL fluid in phosphate-buffered saline was extracted with 10 μL of internal amplification control according to the total nucleic acid isolation blood protocol using a Roche Magna Pure Compact instrument (Roche Molecular Diagnostics, USA) and eluted in 100 μL. Respiratory samples containing thick mucus were pretreated with proteinase K (25 μL 1 g/mL) and sodium dodecyl sulfate then lysed using zirconium beads (150 μL 2.4 mm) and silica beads (50 μL 0.1 mm) (Biospec Products, USA) before DNA extraction. Primers and fluorescence resonance evergy transfer probes were previously described, and were designed to detect a 166-base pair region of the cdc2 gene of PJ (19). PCR was performed on 5 μL of DNA extract using the LightCycler-FastStart DNA Master Hybridization Probes kit (Roche Diagnostics, Canada) in the presence of 4 mmol/L MgCl 2 using the Light Cycler 2.0 (Roche Diagnostics, Canada). Melting curve analysis was performed at the end of the cdc2 PCR amplification to confirm positives. All primers and probes were synthesized by Metabion (Germany).

Data analysis
All patient information and PJ test results were entered into an Excel spreadsheet (Microsoft Corporation, USA) in an anonymous nonnominal fashion using the patient study number. Descriptive statistics were performed using standard methods. Performance of the CLS PJ real-time PCR assay was compared with the IFA as the existing gold standard during the study period. Discrepancies between IFA and the CLS PJ real-time PCR assay were resolved by secondarily testing the discrepant BAL samples with the PJ real-time PCR used by the RL (see Methods). The modified gold standard (ie, a true positive result) was defined as a BAL sample that tested positive according to both molecular assays and/or IFA in a patient suspected of having PCP. The presence of PCP was not only defined by the PJ test, but also after clinical review of each patient by DLC with the attending physician. Performance of each test was calculated using standard statistical methods with the modified gold standard. All statistical analyses were performed using standard methods with Analyze-It software version 2.6.1 (Microsoft Corporation, USA).

Ethics
Once obtained, all clinical information was handled in an anonymous fashion in the working dataset at each site. BAL samples from the RL were provided in an anonymous fashion to ensure patient confidentiality. The study was granted ethics approval by the Conjoint Health Research Ethics Board at the University of Calgary/Alberta Health Services (Calgary, Alberta) and St Paul's Hospital/University of British Columbia (Vancouver, British Columbia) Ethics Review Boards. A total of 92 BAL samples from 87 patients were collected by CLS; both a right and left BAL fluid sample were tested for five patients. According to the clinical history provided on the laboratory requisition, 50 of 87 (57.5%) of the study samples were collected from immunocompromised patients including: nine who had HIV (18%), 23 who were solid-organ transplant recipients (46%), two who had received previous hematopoietic stem cell transplantation (4%) and 14 with malignancies (28%). The remaining 4% of the sample order requisitions did not provide descriptions of the cause of their immunosuppression.

CLS PJ real-time PCR assay validation
The PJ real-time PCR was initially used to test the 40 clinical samples (20 known PJ+ and 20 known PJ-) from the RL, with consistent results. The developed molecular assay could detect the approximately 1:10,000 diluted sample estimated to contain 10 cysts of organism per 2 μL, yielding an estimated limit of detection of at least 10 copies per PCR. Amplification of the serially diluted BAL fluid sample (ie, estimated to contain 1×10 5 cysts) generated Ct values of 20 for a 1:2 dilution, up to 37 for the 1:10,000 dilution. The CLS real-time PCR assay also provided excellent reproducibility. Both PJ-BAL fluid samples repeatedly tested negative including a Ct value of 0 on separate runs repeated on different days, and in two carryover runs performed back to back on the same day. The three PJ+ BAL fluid samples also repeatedly tested positive with a mean (± SD) Ct value of 20.85±0.13, 20.78±0.08, 21.2±0.12, 21.8±0.12 and 21.9±0.19, respectively, for the individual samples on two separate runs repeated on different days, and in two carryover runs performed back to back on the same day. When CLS PJ real-time PCR was performed on the 40 clinical samples (20 known PJ+ and 20 known PJ-samples) sent from the RL, similar results were found for all samples. Figure 1 compares range and CIs for the PJ real-time assay Ct compared with the detection of PJ cysts using IFA. The 95% upper CI for PJ+ BAL fluid samples that were detected by both the IFA and molecular assays was 26 cycles. The lower and upper 95% CIs for BAL fluid samples that were PJ-according to IFA, but PJ+ according to molecular assay, was 30 and 37 cycles, respectively. Therefore, the reportable range for the PJ real-time assay was established as follows: Ct ≤30 cycles = true positive PJ result; Ct ≥30 but <37 = low positive result that may be indicative of early infection or colonization in which clinical correlation is required to establish the presence of disease; and >37 = negative result.
The clinical performance of the PJ real-time PCR assay was compared with both the IFA and the modified gold standard. PJ cysts were detected in a total of 13 of 92 (15%) BAL fluid samples. Twelve of 13 (92.3%) of these PJ+ samples were derived from immunocompromised patients, while the clinical history for the last sample only mentioned pneumonia. Eight patients had HIV infection, three had a malignancy, and one had immunosuppression for an unspecified reason. All of the BAL fluid samples that were PJ+ according to IFA were also positive according to the molecular assay with Ct values ≤30 cycles. Another seven samples from six patients that were PJ-according to IFA, were PJ+ according to the PJ real-time PCR assay. The Ct values for these discrepant samples were all >30 but <37 (ie, 30.9, 33.9, 35.9, 33.6, 36.1, 33.2 and 34.9). Clinical review regarding these results with the attending physician enabled these results to be correlated with the presence of PCP in all cases. Two of these samples were taken from a patient (ie, right lower lobe and left lower lobe samples) with interstitial lung disease receiving high-dose steroids who was considered to have early infection. The remaining five patients had clinical PCP, and included one patient with HIV infection, one with a brain tumour receiving high-dose steroids, one hematopoietic stell cell transplantation patient with severe hypoxemia, one patient with interstitial lung disease and one patient with new-onset fever and pneumonia in whom HIV infection was suspected but not yet confirmed. One other BAL fluid sample from a patient receiving renal dialysis was PJ-according to IFA, but provided a very late Ct value (42.8) according to molecular testing. Because there was no evidence of PCP on clinical review, this result was called a true negative according to the PJ real-time PCR assay.
All of the seven BAL fluid samples that provided discrepant results between IFA (negative) and the CLS PJ real-time PCR assay (positive) were also PJ+ according to the RL real-time PCR assay. Table 1 summarizes the performance of the IFA and PJ real-time PCR assays for diagnosis of PCP. Although the IFA assay was highly specific, it was less sensitive than the molecular assay at diagnosing PCP. The IFA also produced a lower negative predictive value because it was false negative in six patients with PCP and one who was suspected of having early PJ infection.
Performance of the PJ real-time PCR assay was similar in immunocompromised patients with HIV and those who were immune suppressed for other reasons. For the IFA, the sensitivity among non-HIV infected patients was slightly higher at 70.59% with the specificity preserved at 100%. Of the nine HIV positive patients, eight had a positive IFA test, and the ninth sample was positive based on the modified gold standard and a clinical diagnosis of PCP. The IFA test resulted in higher sensitivity (88.89%) for HIV patients, with 100% specificity.

DISCUSSION
We described a laboratory-developed real-time assay for the highly reliable detection of PJ from BAL fluid. Overall, the method can be performed the same day the sample is received and has a turnaround time of 2.5 h including DNA extraction, and has an overall increase in sensitivity of 35% over our existing IFA method. The present study confirms that PCR is more sensitive than staining and microscopic examination of BAL samples to diagnose PCP (17,24-28). The PJ realtime PCR developed and validated during the present study was highly sensitive and specific at diagnosing PCP in immunocompromised patients, particularly those without HIV infection. Real-time PCR detection of PJ is more rapid than conventional PCR, and reduces the potential for carry-over contamination run-to-run because the reaction is performed in a closed system. Real-time PCR also offers a high degree of objectivity that cannot be obtained by staining and microscopic examination of a BAL sample. In our laboratory, even highly experienced technologists would have difficulty calling the IFA positive when few cysts were present. This was also reflected in the performance of the IFA compared with the PJ real-time assay in our study. The sensitivity of IFA in our study was highest among HIV infected individuals (88.89%), which was similar to the molecular assay, although IFA was falsely negative in one patient with PCP. The sensitivity of IFA was lower when analyzing BAL samples from non-HIV immunocompromised patients (70.6%), and considerably less than the molecular assay when all patient samples were considered (65%).
The higher sensitivity of molecular detection of PJ in BAL fluid samples is consistent with the results of other studies (17,(24)(25)(26)(27)(28). A systematic review that evaluated PJ PCR assays testing BAL fluid samples revealed an overall sensitivity of 98.3% and specificity of 91% for all of the studies included in their analysis (27), and highlighted that one can expect false-positive PCR tests to occur in patients with no clinical evidence of PCP. All positive PCR results should, therefore, by correlated with the patient's clinical symptoms and radiological evidence of PCP; however, a negative PCR result essentially allows the diagnosis to be ruled out if controls perform to specification and PCR inhibition has not occurred (27,29). A more recent meta-analysis of reported molecular assays that included real-time PCR for PJ detection from BAL fluid samples produced a similar overall sensitivity of 98% but a higher specificity of 93% (29). Our study also confirmed the high inter-laboratory agreement among real-time PCR assays previously used for the detection of PJ (30).
Although there were no false-positive PJ real-time PCR tests in our study, previously reported validations of other laboratory developed qualitative molecular assays for PJ detection have found false-positive tests due to the higher sensitivity of molecular methods (23,31,32). Therefore, a diagnostic cut-off should be established, such as what occurred in our study, for the reportable range of the real-time PCR assay, that includes true positive, a 'grey-zone' for low-level positives, which may represent early infection or asymptomatic colonization and negative results. Clinical correlation of the results of our PJ real-time PCR assay with the presence of PCP validated that the established cut-offs for a true positive (ie, Ct ≤30) and low positive (ie, Ct >30 but <37) accurately diagnosed the presence of PCP. All of the patients with low positive PJ real-time PCR results who were negative according to IFA were confirmed to have definite PCP or suspected early infection (one patient) on clinical review. Confirmation that these seven discrepant BAL results were true positives was also documented by testing them using a second Pneumocystis gene target, the cdc2 gene, by the RL. The second positive real-time PCR result, using another independent molecular method, further supported the presence of this fungal organism in the patient's sample.
Although the use of qualitative real-time PCR for PJ detection would improve our ability to rapidly and accurately diagnose PCP in our jurisdiction and our molecular assay demonstrated complete agreement with the RL's assay, standardized diagnostic performance across laboratories awaits the development of a quantitative PJ real-time PCR assay (33). A commercial quantitative PJ real-time PCR assay used in combination with serum (1,3)-β-D-glucan may enable improved differentiation of patients with PJ colonization and those with early or suppressed PCP infection (34,35), and also enhance therapeutic monitoring of PCP resolution while receiving treatment.

ACkNOWLEDGEMENTS: This study was supported by a Calgary
Laboratory Services Research Grant that was awarded through a competitive peer-review process. The work was completed as part of an Internal Medicine residency project (A Ambasta). The work was presented as a poster presentation at the IDWeek Meeting, October 8 to 12, 2014, Philadelphia, Pennsylvania.

DISCLOSURES:
The authors have no conflicts of interest to declare.