Exploring and Accounting for Genetically Driven Effect Heterogeneity in Mendelian Randomization.

Mendelian randomization (MR) is a framework to estimate the causal effect of a modifiable health exposure, drug target or pharmaceutical intervention on a downstream outcome by using genetic variants as instrumental variables. A crucial assumption allowing estimation of the average causal effect in MR, termed homogeneity, is that the causal effect does not vary across levels of any instrument used in the analysis. In contrast, the science of pharmacogenetics seeks to actively uncover and exploit genetically driven effect heterogeneity for the purposes of precision medicine. In this study, we consider a recently proposed method for performing pharmacogenetic analysis on observational data-the Triangulation WIthin a STudy (TWIST) framework-and explore how it can be combined with traditional MR approaches to properly characterise average causal effects and genetically driven effect heterogeneity. We propose two new methods which not only estimate the genetically driven effect heterogeneity but also enable the estimation of a causal effect in the genetic group with and without the risk allele separately. Both methods utilise homogeneity-respecting and homogeneity-violating genetic variants and rely on a different set of assumptions. Using data from the ALSPAC study, we apply our new methods to estimate the causal effect of smoking before and during pregnancy on offspring birth weight in mothers whose genetics mean they find it (relatively) easier or harder to quit smoking.

Neuronal network dynamics in the posterodorsal amygdala: shaping reproductive hormone pulsatility.

Normal reproductive function and fertility rely on the rhythmic secretion of gonadotropin-releasing hormone (GnRH), which is driven by the hypothalamic GnRH pulse generator. A key regulator of the GnRH pulse generator is the posterodorsal subnucleus of the medial amygdala (MePD), a brain region that is involved in processing external environmental cues, including the effect of stress. However, the neuronal pathways enabling the dynamic, stress-triggered modulation of GnRH secretion remain largely unknown. Here, we employ in silico modelling in order to explore the impact of dynamic inputs on GnRH pulse generator activity. We introduce and analyse a mathematical model representing MePD neuronal circuits composed of GABAergic and glutamatergic neuronal populations, integrating it with our GnRH pulse generator model. Our analysis dissects the influence of excitatory and inhibitory MePD projections' outputs on the GnRH pulse generator's activity and reveals a functionally relevant MePD glutamatergic projection to the GnRH pulse generator, which we probe with in vivo optogenetics. Our study sheds light on how MePD neuronal dynamics affect the GnRH pulse generator activity and offers insights into stress-related dysregulation.

Phage-induced efflux down-regulation boosts antibiotic efficacy.

The interactions between a virus and its host vary in space and time and are affected by the presence of molecules that alter the physiology of either the host or the virus. Determining the molecular mechanisms at the basis of these interactions is paramount for predicting the fate of bacterial and phage populations and for designing rational phage-antibiotic therapies. We study the interactions between stationary phase Burkholderia thailandensis and the phage ΦBp-AMP1. Although heterogeneous genetic resistance to phage rapidly emerges in B. thailandensis, the presence of phage enhances the efficacy of three major antibiotic classes, the quinolones, the beta-lactams and the tetracyclines, but antagonizes tetrahydrofolate synthesis inhibitors. We discovered that enhanced antibiotic efficacy is facilitated by reduced antibiotic efflux in the presence of phage. This new phage-antibiotic therapy allows for eradication of stationary phase bacteria, whilst requiring reduced antibiotic concentrations, which is crucial for treating infections in sites where it is difficult to achieve high antibiotic concentrations.

Estradiol elicits distinct firing patterns in arcuate nucleus kisspeptin neurons of females through altering ion channel conductances.

Hypothalamic kisspeptin (Kiss1) neurons are vital for pubertal development and reproduction. Arcuate nucleus Kiss1 (Kiss1ARH) neurons are responsible for the pulsatile release of Gonadotropin-releasing Hormone (GnRH). In females, the behavior of Kiss1ARH neurons, expressing Kiss1, Neurokinin B (NKB), and Dynorphin (Dyn), varies throughout the ovarian cycle. Studies indicate that 17ß-estradiol (E2) reduces peptide expression but increases Vglut2 mRNA and glutamate neurotransmission in these neurons, suggesting a shift from peptidergic to glutamatergic signaling. To investigate this shift, we combined transcriptomics, electrophysiology, and mathematical modeling. Our results demonstrate that E2 treatment upregulates the mRNA expression of voltage-activated calcium channels, elevating the whole-cell calcium current and that contribute to high-frequency burst firing. Additionally, E2 treatment decreased the mRNA levels of Canonical Transient Receptor Potential (TPRC) 5 and G protein-coupled K+ (GIRK) channels. When TRPC5 channels in Kiss1ARH neurons were deleted using CRISPR, the slow excitatory postsynaptic potential (sEPSP) was eliminated. Our data enabled us to formulate a biophysically realistic mathematical model of the Kiss1ARH neuron, suggesting that E2 modifies ionic conductances in Kiss1ARH neurons, enabling the transition from high frequency synchronous firing through NKB-driven activation of TRPC5 channels to a short bursting mode facilitating glutamate release. In a low E2 milieu, synchronous firing of Kiss1ARH neurons drives pulsatile release of GnRH, while the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons.

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error.

Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.

Smoking during pregnancy and its effect on placental weight: a Mendelian randomization study.

BACKGROUND: The causal relationship between maternal smoking in pregnancy and reduced offspring birth weight is well established and is likely due to impaired placental function. However, observational studies have given conflicting results on the association between smoking and placental weight. We aimed to estimate the causal effect of newly pregnant mothers quitting smoking on their placental weight at the time of delivery. METHODS: We used one-sample Mendelian randomization, drawing data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (N = 690 to 804) and the Norwegian Mother, Father and Child Cohort Study (MoBa) (N = 4267 to 4606). The sample size depends on the smoking definition used for different analyses. The analysis was performed in pre-pregnancy smokers only, due to the specific role of the single-nucleotide polymorphism (SNP) rs1051730 (CHRNA5 - CHRNA3 - CHRNB4) in affecting smoking cessation but not initiation. RESULTS: Fixed effect meta-analysis showed a 182 g [95%CI: 29,335] higher placental weight for pre-pregnancy smoking mothers who continued smoking at the beginning of pregnancy, compared with those who stopped smoking. Using the number of cigarettes smoked per day in the first trimester as the exposure, the causal effect on placental weight was 11 g [95%CI: 1,21] per cigarette per day. Similarly, smoking at the end of pregnancy was causally associated with higher placental weight. Using the residuals of birth weight regressed on placental weight as the outcome, we showed evidence of lower offspring birth weight relative to the placental weight, both for continuing smoking at the start of pregnancy as well as continuing smoking throughout pregnancy (change in z-score birth weight adjusted for z-score placental weight: -0.8 [95%CI: -1.6,-0.1]). CONCLUSION: Our results suggest that continued smoking during pregnancy causes higher placental weights.

NK3R signalling in the posterodorsal medial amygdala is involved in stress-induced suppression of pulsatile LH secretion in female mice.

Psychosocial stress negatively impacts reproductive function by inhibiting pulsatile luteinizing hormone (LH) secretion. The posterodorsal medial amygdala (MePD) is responsible in part for processing stress and modulating the reproductive axis. Activation of the neurokinin 3 receptor (NK3R) suppresses the gonadotropin-releasing hormone (GnRH) pulse generator, under hypoestrogenic conditions, and NK3R activity in the amygdala has been documented to play a role in stress and anxiety. We investigate whether NK3R activation in the MePD is involved in mediating the inhibitory effect of psychosocial stress on LH pulsatility in ovariectomised female mice. First, we administered senktide, an NK3R agonist, into the MePD and monitored the effect on pulsatile LH secretion. We then delivered SB222200, a selective NK3R antagonist, intra-MePD in the presence of predator odour, 2,4,5-trimethylthiazole (TMT) and examined the effect on LH pulses. Senktide administration into the MePD dose-dependently suppresses pulsatile LH secretion. Moreover, NK3R signalling in the MePD mediates TMT-induced suppression of the GnRH pulse generator, which we verified using a mathematical model. The model verifies our experimental findings: (i) predator odour exposure inhibits LH pulses, (ii) activation of NK3R in the MePD inhibits LH pulses and (iii) NK3R antagonism in the MePD blocks stressor-induced inhibition of LH pulse frequency in the absence of ovarian steroids. These results demonstrate for the first time that NK3R neurons in the MePD mediate psychosocial stress-induced suppression of the GnRH pulse generator.

The prospect of artificial intelligence to personalize assisted reproductive technology.

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.

HormoneBayes: A novel Bayesian framework for the analysis of pulsatile hormone dynamics.

The hypothalamus is the central regulator of reproductive hormone secretion. Pulsatile secretion of gonadotropin releasing hormone (GnRH) is fundamental to physiological stimulation of the pituitary gland to release luteinizing hormone (LH) and follicle stimulating hormone (FSH). Furthermore, GnRH pulsatility is altered in common reproductive disorders such as polycystic ovary syndrome (PCOS) and hypothalamic amenorrhea (HA). LH is measured routinely in clinical practice using an automated chemiluminescent immunoassay method and is the gold standard surrogate marker of GnRH. LH can be measured at frequent intervals (e.g., 10 minutely) to assess GnRH/LH pulsatility. However, this is rarely done in clinical practice because it is resource intensive, and there is no open-access, graphical interface software for computational analysis of the LH data available to clinicians. Here we present hormoneBayes, a novel open-access Bayesian framework that can be easily applied to reliably analyze serial LH measurements to assess LH pulsatility. The framework utilizes parsimonious models to simulate hypothalamic signals that drive LH dynamics, together with state-of-the-art (sequential) Monte-Carlo methods to infer key parameters and latent hypothalamic dynamics. We show that this method provides estimates for key pulse parameters including inter-pulse interval, secretion and clearance rates and identifies LH pulses in line with the widely used deconvolution method. We show that these parameters can distinguish LH pulsatility in different clinical contexts including in reproductive health and disease in men and women (e.g., healthy men, healthy women before and after menopause, women with HA or PCOS). A further advantage of hormoneBayes is that our mathematical approach provides a quantified estimation of uncertainty. Our framework will complement methods enabling real-time in-vivo hormone monitoring and therefore has the potential to assist translation of personalized, data-driven, clinical care of patients presenting with conditions of reproductive hormone dysfunction.

Quantifying the variability in the assessment of reproductive hormone levels.

OBJECTIVE: To quantify how representative a single measure of reproductive hormone level is of the daily hormonal profile using data from detailed hormonal sampling in the saline placebo-treated arm conducted over several hours. DESIGN: Retrospective analysis of data from previous interventional research studies evaluating reproductive hormones. SETTING: Clinical Research Facility at a tertiary reproductive endocrinology centre at Imperial College Hospital NHS Foundation Trust. PATIENTS: Overall, 266 individuals, including healthy men and women (n = 142) and those with reproductive disorders and states (n = 124 [11 with functional hypothalamic amenorrhoea, 6 with polycystic ovary syndrome, 62 women and 32 men with hypoactive sexual desire disorder, and 13 postmenopausal women]), were included in the analysis. INTERVENTIONS: Data from 266 individuals who had undergone detailed hormonal sampling in the saline placebo-treated arms of previous research studies was used to quantify the variability in reproductive hormones because of pulsatile secretion, diurnal variation, and feeding using coefficient of variation (CV) and entropy. MAIN OUTCOME MEASURES: The ability of a single measure of reproductive hormone level to quantify the variability in reproductive hormone levels because of pulsatile secretion, diurnal variation, and nutrient intake. RESULTS: The initial morning value of reproductive hormone levels was typically higher than the mean value throughout the day (percentage decrease from initial morning measure to daily mean: luteinizing hormone level 18.4%, follicle-stimulating hormone level 9.7%, testosterone level 9.2%, and estradiol level 2.1%). Luteinizing hormone level was the most variable (CV 28%), followed by sex-steroid hormone levels (testosterone level 12% and estradiol level 13%), whereas follicle-stimulating hormone level was the least variable reproductive hormone (CV 8%). In healthy men, testosterone levels fell between 9:00 am and 5:00 pm by 14.9% (95% confidence interval 4.2, 25.5%), although morning levels correlated with (and could be predicted from) late afternoon levels in the same individual (r2 = 0.53, P<.0001). Testosterone levels were reduced more after a mixed meal (by 34.3%) than during ad libitum feeding (9.5%), after an oral glucose load (6.0%), or an intravenous glucose load (7.4%). CONCLUSION: Quantification of the variability of a single measure of reproductive hormone levels informs the reliability of reproductive hormone assessment.

Smoking during pregnancy and its effect on placental weight: A Mendelian randomization study.

Background: The causal relationship between maternal smoking in pregnancy and reduced offspring birth weight is well established and is likely due to impaired placental function. However, observational studies have given conflicting results on the association between smoking and placental weight. We aimed to estimate the causal effect of newly pregnant mothers quitting smoking on their placental weight at the time of delivery. Methods: We used one-sample Mendelian randomization, drawing data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (up to N = 805) and the Norwegian Mother, Father and Child Cohort Study (MoBa) (up to N = 4475). The analysis was performed in pre-pregnancy smokers only, due to the specific role of the genetic instrument SNP rs1051730 (CHRNA5 - CHRNA3 - CHRNB4) in affecting smoking cessation but not initiation. Results: Fixed effect meta-analysis showed a 175 g [95%CI: 16, 334] higher placental weight for pre-pregnancy smoking mothers who continued smoking at the beginning of pregnancy, compared with those who stopped smoking. Using the number of cigarettes smoked per day in the first trimester as the exposure, the causal estimate was a 12 g [95%CI: 2,22] higher placental weight per cigarette per day. Results were similar when the smoking exposures were measured at the end of pregnancy. Using the residuals of birth weight regressed on placental weight as the outcome, we showed weak evidence of lower offspring birth weight relative to the placental weight for continuing smoking. Conclusion: Our results suggest that continued smoking during pregnancy causes higher placental weights.

The potential of digital behavioural tests as a diagnostic aid for psychosis.

Timely interventions have a proven benefit for people experiencing psychotic illness. One bottleneck to accessing timely interventions is the referral process to the specialist team for early psychosis (STEP). Many general practitioners lack awareness or confidence in recognising psychotic symptoms or state. Additionally, referrals for people without apparent psychotic symptoms, although beneficial at a population level, lead to excessive workload for STEPs. There is a clear unmet need for accurate stratification of STEPs users and healthy cohorts. Here we propose a new approach to addressing this need via the application of digital behavioural tests. To demonstrate that digital behavioural tests can be used to discriminate between the STEPs users (SU; n = 32) and controls (n = 32, age and sex matched), we compared performance of five different classifiers applied to objective, quantitative and interpretable features derived from the 'mirror game' (MG) and trail making task (TMT). The MG is a movement coordination task shown to be a potential socio-motor biomarker of schizophrenia, while TMT is a neuropsychiatric test of cognitive function. All classifiers had AUC in the range of 0.84-0.92. The best of the five classifiers (linear discriminant classifier) achieved an outstanding performance, AUC = 0.92 (95%CI 0.75-1), Sensitivity = 0.75 (95%CI 0.5-1), Specificity = 1 (95%CI 0.75-1), evaluated on 25% hold-out and 1000 folds. Performance of all analysed classifiers is underpinned by the large effect sizes of the differences between the cohorts in terms of the features used for classification what ensures generalisability of the results. We also found that MG and TMT are unsuitable in isolation to successfully differentiate between SU with and without at-risk-mental-state or first episode psychosis with sufficient level of performance. Our findings show that standardised batteries of digital behavioural tests could benefit both clinical and research practice. Including digital behavioural tests into healthcare practice could allow precise phenotyping and stratification of the highly heterogenous population of people referred to STEPs resulting in quicker and more personalised diagnosis. Moreover, the high specificity of digital behavioural tests could facilitate the identification of more homogeneous clinical high-risk populations, benefiting research on prognostic instruments for psychosis. In summary, our study demonstrates that cheap off-the-shelf equipment (laptop computer and a leap motion sensor) can be used to record clinically relevant behavioural data that could be utilised in digital mental health applications.

Using immersive virtual reality to remotely examine performance differences between dominant and non-dominant hands.

Circle drawing may be a useful task to study upper-limb function in patient populations. However, previous studies rely on expensive and bulky robotics to measure performance. For clinics or hospitals with limited budgets and space, this may be unfeasible. Virtual reality (VR) provides a portable and low-cost tool with integrated motion capture. It offers potentially a more feasible medium by which to assess upper-limb motor function. Prior to use with patient populations, it is important to validate and test the capabilities of VR with healthy users. This study examined whether a VR-based circle drawing task, completed remotely using participant's own devices, could capture differences between movement kinematics of the dominant and non-dominant hands in healthy individuals. Participants (n = 47) traced the outline of a circle presented on their VR head-mounted displays with each hand, while the positions of the hand-held controllers were continuously recorded. Although there were no differences observed in the size or roundness of circles drawn with each hand, consistent with prior literature our results did show that the circles drawn with the dominant hand were completed faster than those with the non-dominant hand. This provides preliminary evidence that a VR-based circle drawing task may be a feasible method for detecting subtle differences in function in clinical populations. Supplementary Information: The online version contains supplementary material available at 10.1007/s10055-023-00794-z.

Synthesis of vancomycin fluorescent probes that retain antimicrobial activity, identify Gram-positive bacteria, and detect Gram-negative outer membrane damage.

Antimicrobial resistance is an urgent threat to human health, and new antibacterial drugs are desperately needed, as are research tools to aid in their discovery and development. Vancomycin is a glycopeptide antibiotic that is widely used for the treatment of Gram-positive infections, such as life-threatening systemic diseases caused by methicillin-resistant Staphylococcus aureus (MRSA). Here we demonstrate that modification of vancomycin by introduction of an azide substituent provides a versatile intermediate that can undergo copper-catalysed azide-alkyne cycloaddition (CuAAC) reaction with various alkynes to readily prepare vancomycin fluorescent probes. We describe the facile synthesis of three probes that retain similar antibacterial profiles to the parent vancomycin antibiotic. We demonstrate the versatility of these probes for the detection and visualisation of Gram-positive bacteria by a range of methods, including plate reader quantification, flow cytometry analysis, high-resolution microscopy imaging, and single cell microfluidics analysis. In parallel, we demonstrate their utility in measuring outer-membrane permeabilisation of Gram-negative bacteria. The probes are useful tools that may facilitate detection of infections and development of new antibiotics.

Quasipotentials for coupled escape problems and the gate-height bifurcation.

The escape statistics of a gradient dynamical system perturbed by noise can be estimated using properties of the associated potential landscape. More generally, the Freidlin and Wentzell quasipotential (QP) can be used for similar purposes, but computing this is nontrivial and it is only defined relative to some starting point. In this paper we focus on computing quasipotentials for coupled bistable units, numerically solving a Hamilton- Jacobi-Bellman type problem. We analyze noise induced transitions using the QP in cases where there is no potential for the coupled system. Gates (points on the boundary of basin of attraction that have minimal QP relative to that attractor) are used to understand the escape rates from the basin, but these gates can undergo a global change as coupling strength is changed. Such a global gate-height bifurcation is a generic qualitative transition in the escape properties of parametrized nongradient dynamical systems for small noise.

Biophysical models of early mammalian embryogenesis.

Embryo development is a critical and fascinating stage in the life cycle of many organisms. Despite decades of research, the earliest stages of mammalian embryogenesis are still poorly understood, caused by a scarcity of high-resolution spatial and temporal data, the use of only a few model organisms, and a paucity of truly multidisciplinary approaches that combine biological research with biophysical modeling and computational simulation. Here, we explain the theoretical frameworks and biophysical processes that are best suited to modeling the early mammalian embryo, review a comprehensive list of previous models, and discuss the most promising avenues for future work.

Sensitisation to recombinant Aspergillus fumigatus allergens and clinical outcomes in COPD.

BACKGROUND: Variable clinical outcomes are reported with fungal sensitisation in chronic obstructive pulmonary disease (COPD), and it remains unclear which fungi and what allergens associate with the poorest outcomes. The use of recombinant as opposed to crude allergens for such assessment is unknown. METHODS: A prospective multicentre assessment of stable COPD (n=614) was undertaken in five hospitals across three countries: Singapore, Malaysia and Hong Kong. Clinical and serological assessment was performed against a panel of 35 fungal allergens including crude and recombinant Aspergillus and non-Aspergillus allergens. Unsupervised clustering and topological data analysis (TDA) approaches were employed using the measured sensitisation responses to elucidate if sensitisation subgroups exist and their related clinical outcomes. RESULTS: Aspergillus fumigatus sensitisation was associated with increased exacerbations in COPD. Unsupervised cluster analyses revealed two "fungal sensitisation" groups. The first was characterised by Aspergillus sensitisation and increased exacerbations, poorer lung function and worse prognosis. Polysensitisation in this group conferred even poorer outcome. The second group, characterised by Cladosporium sensitisation, was more symptomatic. Significant numbers of individuals demonstrated sensitisation responses to only recombinant (as opposed to crude) A. fumigatus allergens f 1, 3, 5 and 6, and exhibited increased exacerbations, poorer lung function and an overall worse prognosis. TDA validated these findings and additionally identified a subgroup within Aspergillus-sensitised COPD of patients with frequent exacerbations. CONCLUSION: Aspergillus sensitisation is a treatable trait in COPD. Measuring sensitisation responses to recombinant Aspergillus allergens identifies an important patient subgroup with poor COPD outcomes that remains overlooked by assessment of only crude Aspergillus allergens.

Microbial Dysregulation of the Gut-Lung Axis in Bronchiectasis.

Rationale: Emerging data support the existence of a microbial "gut-lung" axis that remains unexplored in bronchiectasis. Methods: Prospective and concurrent sampling of gut (stool) and lung (sputum) was performed in a cohort of n = 57 individuals with bronchiectasis and subjected to bacteriome (16S rRNA) and mycobiome (18S Internal Transcribed Spacer) sequencing (total, 228 microbiomes). Shotgun metagenomics was performed in a subset (n = 15; 30 microbiomes). Data from gut and lung compartments were integrated by weighted similarity network fusion, clustered, and subjected to co-occurrence analysis to evaluate gut-lung networks. Murine experiments were undertaken to validate specific Pseudomonas-driven gut-lung interactions. Results: Microbial communities in stable bronchiectasis demonstrate a significant gut-lung interaction. Multibiome integration followed by unsupervised clustering reveals two patient clusters, differing by gut-lung interactions and with contrasting clinical phenotypes. A high gut-lung interaction cluster, characterized by lung Pseudomonas, gut Bacteroides, and gut Saccharomyces, is associated with increased exacerbations and greater radiological and overall bronchiectasis severity, whereas the low gut-lung interaction cluster demonstrates an overrepresentation of lung commensals, including Prevotella, Fusobacterium, and Porphyromonas with gut Candida. The lung Pseudomonas-gut Bacteroides relationship, observed in the high gut-lung interaction bronchiectasis cluster, was validated in a murine model of lung Pseudomonas aeruginosa infection. This interaction was abrogated after antibiotic (imipenem) pretreatment in mice confirming the relevance and therapeutic potential of targeting the gut microbiome to influence the gut-lung axis. Metagenomics in a subset of individuals with bronchiectasis corroborated our findings from targeted analyses. Conclusions: A dysregulated gut-lung axis, driven by lung Pseudomonas, associates with poorer clinical outcomes in bronchiectasis.