Philippa Kaur

Stringed instruments can be worth millions of dollars, particularly those made in northern Italy in the seventeenth and eighteenth centuries. There are also many copies and forgeries.As a forest ecologist, I use dendrochronology — or tree-ring dating — to understand how trees grow, as well as to investigate historical environmental conditions. The widths of tree rings vary according to meteorological conditions, so samples can be dated by cross-referencing against databases of ring-width series.In 2010, I was an expert witness in a legal case about a viola supposedly made in the sixteenth century. I agreed with two laboratories that had independently concluded that it could not have been made before the late eighteenth century.Dendrochronology cannot precisely date when an instrument was made, but it can identify the most recent year that the wood it was made from was part of a growing tree. Tree rings give probabilities and levels of confidence in a date according to the availability of appropriate reference series.Dendrochronological analyses can produce conflicting dates, creating confusion about the method’s reliability. The most famous example is the ‘Messiah’ violin made by Antonio Stradivari in 1716. In 1999, doubts were raised about its authenticity when, among other reasons, tree-ring analysis found that the instrument could not have been made before 1738, a year after Stradivari’s death. The finding was based on examining a photograph of the instrument, and was later retracted. The controversy highlighted the limitations of the technique for verifying the authenticity of musical instruments.I also use dendrochronology to understand the impacts of droughts, and I’m fascinated by how trees grow, how water is transported in them and whether species differences are evolutionary adaptations. More

Boreal forests contain about half the carbon (C) of terrestrial forests worldwide, and as such, they play an immense role in the global C cycle. Therefore, accurately predicting the global C balance requires understanding of C fluxes in boreal trees and how they respond to climate change. While the relationships between climate and boreal tree growth are generally non-stationary, it remains unknown whether the same is true of the relationships between climate and C fluxes. More

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Model overview and parametersOur mathematical model of the CF lung microbiome dynamics, originally developed in [20], is based on knowledge of the physiology and interactions among community members from experimental data and evidence in the literature. The model setting is a mucus-plugged tube, open to the air at the top and sealed at the bottom, mimicking a lung bronchiole. This setting is meant to pair with a previously established experimental microcosm called the WinCF system [21], which we use below for experiments. There is an important spatial component to the model, as oxygen penetration from the open top of the tube is constant and shapes the community structure. The consequences of these chemical gradients were first modelled in our initial study [20]. The community members are classified as either “pathogens”, representing classic CF pathogens, or “fermenters”, representing other anaerobic organisms commonly encountered in CF airways. These classifications are a significant simplification, but they can be considered as guilds, in that their individual members have similar inherent properties defined by their core metabolism, antibiotic resistance, and niche occupancy [20]. The definition of classic pathogens and anaerobic fermenters is also clinically relevant, as the former are those assayed in clinical labs for antibiotic resistance to inform treatment decisions, whereas anaerobic fermenters are not cultured or tested for susceptibility in most clinical labs. Classifications of each microbiome member into these guilds are available in Tables S2–S4. Fermenters reside in low oxygen areas and utilize sugars to produce acids [20] (Fig. 1). Pathogens, principally, but not exclusively, Pseudomonas aeruginosa, occupy high oxygen regions where they aerobically respire and utilize amino acids as a carbon source producing ammonium, which increases the surrounding pH [20] (Fig. 1). Pathogens can also respire anaerobically, with nitrate as an electron acceptor (Fig. 1). In addition to increasing the surrounding pH, they produce inhibitor molecules (such as phenazines and quinolones) that inhibit the growth of fermenters [20] (Fig. 1). This model is hereon referred to as the “mathematical model”.Fig. 1: Schematic of principles and interacations defining the mathematical model.All consitunents of the model are represented in illustrating basic assumptions and interactions. Fermenters (θf) metabolize (SG) as a carbon source, which produce acid (F) leading to an increase in hydrons (H+) (i.e. lowering the pH) under anaerobic conditions. This pH decrease inhibitis the growth of pathogens. Pathogens (θP) in the presence of oxygen (SO) (i.e., aerobic conditions) use amino acids (SA) as their primary carbon source. The byproduct of this metabolism is ammonium (P), which produces hydroxide (OH-) leading to an increase in pH, inhibiting fermenter growth. Under anaerobic conditions pathogens use nitrate (SN) as an electron acceptor. In addition to this pathogens produce a chemical inhibitor of fermenters (I).Full size imagePredicting and modelling outcomes of antibiotic therapyTo better conceputalize and compare our modeling and experimental results, we first theoretically predicted the outcomes of antimicrobial therapy against the two guilds using three theoretical drugs: one with fermenter coverage (denoted Tf), one with pathogen coverage (denoted Tp), and one with broad spectrum coverage (denoted Tw). This approach is hereon referred to as the “theoretical prediction”. To further enable comparison to experimental data we outline characteristics of the two guilds we expect to observe in the experiments. Firstly, the growth of anaerobic fermenters is positively correlated with an increase in gas production (bubble formation in the WinCF system) [21]. Second, an increase in P. aeruginosa positively correlates with an increase in its inhibitor molecule (e.g., Quinolone HHQ) and P. aeruginosa does not produce gas in the WinCF system [21]. Thirdly, based on Tables S1–S4 and the CF microbiome literature, fermenters are more diverse than pathogens [2, 43, 44]. These characteristics of our theoretical prediction enable direct comparison to microbiome measures of experimental results, such as alpha diversity, beta diversity, pathogen relative abundance, fermenter relative abundance and total bacterial load (TBL).With our theoretical prediction we expect the following outcomes when communities are exposed to antibiotics: (1) community resistance, (2) community death, (3) pathogen death, and (4) fermenter death (Fig. 2A–E). In both the complete absence of an antibiotic and community resistance, we expect TBL, pathogens, fermenters, HHQ, and gas production measures to increase until reaching carrying capacity (Fig. 2B). The opposite, community death (treatment with Tw) results in both microbial entities failing to grow (Fig. 2C). Tw treatment would not change alpha or beta diversity, as we would simply measure the initial inoculum due to total community death. Outcomes 1 and 2 have a degree of uncertainty due to the fact that it is difficult to assume the community would not change from the inoculum without an antibiotic present, but it is expected that Tw would have less impact on microbiome diversity than Tp or Tf (Fig. 1C). Treatment with Tp results in an anaerobic fermenter bloom, increasing alpha and beta diversity along with gas production and a decrease in HHQ production (Fig. 2D). Finally, in the case of Tf treatment, fermenter abundance and gas production would decrease while HHQ abundance would increase (Fig. 1E). Treatment with Tf will also result in a decrease in alpha diversity and an increase in beta diversity because of changes in community structure when the diverse anaerobic fermenters are killed (Fig. 2E).Fig. 2: Theoretical predictions and Model iteration 1.The initial microbiome is composed of both pathogens and fermenters and is illustrated in (A), but the proportions of these are unique to each patient. Under pressure of the various treatments (B) NT, (C) Tw, (D) Tp, and (E) Tf the predicted community response is illustrated. The response i.e., (expected change) in common microbiome measures as indicated in the legend (yellow = increase, red = decrease). The measures are the following: Alpha diversity (AD), Beta diversity (BD), gas production (GP), total bacterial load (TBL), pathogen abundance (P), fermenter abundance (F), and 2-heptyl-4quinolone abundance (HHQ). The model output treatment-to-NT log-ratio of (F) fermenter population and (G) pathogen population of patient 12 as an example with spatial variation at t = 50 h. Boxplots showing model outcomes of the (H) 16S rRNA gene copy ratio and (I) Pathogen to Fermenter log-ratio compared to the control. Each patients’ actual sputum Pathogen/Fermenter ratio was used as input to the model (n = 24). The dotted grey line denotes no change from treatment.Full size imageThe theoretical prediction was then tested with the mathematical model hereon referred to as “model iteration 1”. Importantly, our model parameters can use relative abundance data of the two guilds as input. Therefore, we used the sputum microbiome data of all 24 subjects as inputs for model interation 1 (Fig. 2F–H). The outputs were in line with our theoretical prediction and showed that the fermenter drug would reduce the fermenter load, with little effect on the pathogens, the pathogen drug vice versa, and the broad-spectrum antibiotic would kill both (Fig. 2F–H). However, model iteration 1 did produce some unexpected results. The TBL of the Tw decreased to similar levels as Tf and Tp, indicating similar levels of killing whether there was selection against a single guild or the whole community (Fig. 2H). In addition, the TBL and Pathogen/Fermenter log-ratio were variable, indicating the carrying capacity and community dynamics were predicated upon characteristics of this initial sputum inoculum (Fig. 2F–H). Our theoretical prediction (Fig. 2A–E), in tandem with model iteration 1 (Fig. 2F–H), provided a platform for comparison to the in vitro antibiotic experiments with the WinCF system described below.Experimental results of antibiotic therapy against the lung microbiomeWe examined the effects of antibiotics (n = 11) on the CF sputum microbiome cultured in a lung bronchiole microcosm (WinCF system, n = 24) using a combination of 16S rRNA gene amplicon sequencing, metabolomics, and qPCR analysis and compared to our theoretical prediction and model iteration 1. This is hereon referred to as the “antibiotic experiment”. The antibiotics were chosen to represent the main chemical classes commonly used in CF clinics and included: amoxicillin, azithromycin, aztreonam, ciprofloxacin, colistin, doxycycline, levofloxacin, meropenem, metronidazole, bactrim (a combination of sulfamethoxazole/trimethoprim), and tobramycin. Each of the 24 sputum samples were used as an incoculum in ASM treated with one of 11 different antibiotics cultured at 37 °C for 48 h (Table S1) and compared to a no-treatment control. WinCF tubes were also inoculated with this media/sputum/antibiotic mixture to quantify gas bubble production from fermentation (as described in [21]). The antibiotic concentration for each drug was variable and chosen to match the measured concentrations in the blood or sputum of pwCF in pharmacokinetic studies (Table S1). The most prominent genera across all samples after growth were Pseudomonas, Streptococcus, Veillonella, Haemophilus, Fusobacterium, Prevotella, Staphylococcus, Achromobacter, and Neisseria (Fig. S2). A principal component analysis (PCA) biplot, examining the top five factors by percent contribution, showed the primary genera driving community differentiation were Pseudomonas, Streptococcus, and Staphylococcus (Fig. S3). The effects of antibiotics and individual patients on the composition of the communities were compared via PERMANOVA (Table S7). Tested separately, both antibiotic and subject source had a highly significant effect on the community structure (p 40%), which occurred in 6.8% of samples. The microbiomes of outcome 6 were predominantly dominated by pathogens compared to the control samples (Fig. S7). We found this outcome to be especially interesting, with potential clinical relevance; we therefore performed follow up experiments to understand it further.Fig. 4: Characterizing outcomes in the antibiotic experiment.Weighted UniFrac distance compared to (A) rRNA gene copies, (B) Gas production, (C) Pathogen to fermenter log ratio, (D) Shannon index. Individual points are colored by antibiotic treatment (n = 11). Observed outcomes (Community resistance, community death, pathogen death, anaerobe death, niche replacement, and release of community level inhibition) are highlighted via large cogs on each of the panels colored by the outcome they represent. These highlighted regions are meant to aid in visualization of their presence in the overlying data. Cutoff values of for the outcomes are further described in Table S17.Full size imageOther interesting data relationships were found in these experiments (Fig. S8) though they were not defined as outcomes. For example, the changing UniFrac distance and change in alpha diversity were negatively correlated (Fig. S8a). A large increase in UniFrac distance (over 40% increase), was generally associated with takeover by a particular ASV, driving this phenomenon (Figs. S7 and S9). According to prevalence measures of theses samples the prominent genera in these instances were Pseudomonas and Streptococcus (Fig. S9). In the cases of meropenem and amoxicillin, UniFrac distances were increased while the Shannon indices were decreased, due to the killing of diverse anaerobic community, but there were fewer cases of an increase in alpha diversity and a significant microbiome change (observed in 3 samples only) indicating a kind of buffering of the microbiome by the diverse anaerobic community (Fig. S7a). The increase in TBL characterizing outcome 6 was rarely associated with an increase in alpha diversity (Table S17). Finally, similar to a phenomenon described in CF sputum [31], when the microbiome alpha diversity increases the metabolome diversity decreases, likely reflecting consumption of different metabolites by a more diverse microbiome (Fig. S7c).Model iteration 2 and experimental validation to explain increase in TBLBecause model iteration 1 did not predict the interesting outcome 6, we altered its parameters to determine if we could observe an increase in TBL in the presence of an antibiotic, hereon referred to as “model iteration 2”. In model iteration 1, parameter λ in the function g2(Z) was set to 0.1, which represents pH driven inhibition of fermenters on pathogen growth. Due to the inverse relationship of this parameter, reducing it to 0.05 increased the strength of inhibition, resulting in an increase in TBL for some subjects, akin to that observed in our experimental outcome 6 (Fig. 5A). This only occurred in Tf treatments in model iteration 2, corresponding to a bloom in pathogens after killing of anaerobes. Furthermore, this phenomenon was only present in modelled samples that initially contained much lower populations of the fermenter guild compared to pathogens and is dependent on the spatial structure driven by oxygen gradients that is an inherent property the modeled system (Figs. 1 and 5A). This finding suggests that outcome 6 in the antibiotic experiment may be driven by an antibiotic mediated release of community level inhibition driven by the effect of low pH from fermenters on pathogens and the inhibition of anaerobes by oxygen [20]. Thus, we set out to explore this phenomenon in more detail experimentally.Fig. 5: Model alteration and verification.(A) Model iteration 2 outcomes of 16S rRNA gene copy ratio of each patients’ actual sputum Pathogen/Fermenter ratio was used as input to the model (n = 24). Individual points are colored by antibiotic treatment (n = 11). The dotted grey line denotes no change from treatment. Subsequent experimental validation using two communities, P1 and P2 (n = 10), showing the (B) pH in relation to log rRNA gene copies, (C) Approximate pH, (D) Pathogen/Fermenter log ratio, (E) log rRNA gene copies, (F) Genera abundance, (G) Distribution based on genera-classification as classical pathogen or anaerobic fermenter. Asterisks denote p-value significance where ****p ≥ 0.0001, ***p ≥ 0.001, **p ≥ 0.01, *p ≥ 0.05.Full size imageA simple in vitro experiment was performed where three antibiotics, meropenem (Tw), tobramycin (Tp), and metronidazole (Tf), were added at 2.048 mg/L in ASM media inoculated with two representative communities obtained from pwCF: P1 and P2 (n = 10 replicates) (Fig. 5B–F). The three drugs were selected based on their common uses against CF infections based on pathogen and/or anaerobic coverage, but we acknowledge that their effects are not exclusive to these organisms. Community P1 did not contain P. aeruginosa via culturing on cetrimide agar, whereas the bacterium was isolated from the sputum of P2. This provided a unique opportunity to test the predictions from model iteration 2 on the outcomes of a community with or without P. aeruginosa. A lower concentration of antibiotics was chosen to avoid widespread killing of the communities. We examined the following: rRNA gene copies, approximate pH (based on RGB color values inferred from phenol red buffered media standards) and 16S rRNA gene amplicon sequencing (Fig. 5). This is hereon referred to as the validation experiment. The validation experiment reproduced outcome 6, where both the number of rRNA gene copies were higher when the antibiotic was present than in the no treatment control for both P1 and P2 (Fig. 5C). In contrast to model iteration 2, this only occurred in treatment Tw (paired t-test, p = 0.000831) (Fig. 5). Accordingly, this increase in TBL corresponded to an increase in pH of the cultures, validating the association of the anaerobe induced fermentation with an inhibition of the communities’ total carrying capacity (p = 1.69 × 10−9, Fig. 5B–E). In fact, there was a strong positive correlation between the TBL and media pH overall (Fig. 5B). Furthermore, P2 reached a higher bacterial load overall than P1 in the validation experiment, indicating that the pathogen’s presence drove the community to a higher carrying capacity (Fig. 5E). The lower growth in community P1 shows that a community of primarily anaerobic fermenters struggles without the aerobic pathogen present. Microbiome profiles of these follow up experiments validated the predictions of model iteration 2 and initial findings of outcome 6 (Fig. 5F, G). Meropenem killed the anaerobic community (primarily Streptococci) and the increase in TBL was driven by a bloom of Pseudomonas (P2 community) and Staphylococcus (P1 community) to a higher level than the communities’ inherent carrying capacity (Fig. 5F, G). This experiment was subsequently repeated (n = 5), with the same results observed (Fig. S10). It was interesting that a similar increase in TBL occurred from a community without a dominant pathogen (P1, Fig. 5G). We hypothesize that this result is due to the importance of both oxygen and pH in the governing dynamics. With very low levels of the pathogen guild, the community struggles to grow due to high oxygen penetration. When the anaerobes are inhibited by antibiotics, even low levels of an initial pathogen can begin to bloom, as they are not inhibited by oxygen or the antibiotic, and this leads to an increase in total carrying capacity.Antibiotic effects at the strain level in pwCFTo explore similar phenomena in outcomes 5 and 6 from pwCF treated with antibiotics we sequenced the metagenomes of sputum samples collected from subjects immediately prior to and during antibiotic treatment (n = 6) (Table S19). To minimize the effects of multiple therapies at once, a common occurrence in CF therapeutics, these samples were selected based on the treatment provided being the only known antibiotic prescribed to the subject at the time. Metagenomes were analyzed at the strain level and TBL was examined using qPCR. Overall, there was no significant decrease in TBL (Fig. 6A, Wilcoxon rank-sum test, p = 0.095), but alpha diversity significantly decreased (Fig. 6B, Wilcoxon rank-sum test, p = 0.045). Analysis of the rank abundance changes of the microbiome at the strain level showed that all six subjects had dynamic changes in their sputum microbiomes associated with antibiotic treatment despite little decrease in TBL (Fig. 6C). Thus, like outcome 5, and indicative of outcome 6, dynamic community changes occur in pwCF with minor changes in TBL.Fig. 6: In vivo changes across individuals.qPCR and shotgun metagenomics were performed on sputum samples from individuals (n = 6) before and after exacerbation. We examined the following: (A) rRNA gene copies (B) Shannon Index, and (C) Rank abundance. Each point on the rank abundance represents an individual strain. The color of lines on the rank abundance represents type of bacterium based on our model definitions where blue equates to Fermenters, red to Pathogens, and green to other.Full size image More

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