Autophagic dysfunction and gut microbiota dysbiosis cause chronic immune activation in a Drosophila model of Gaucher disease.

Mutations in the GBA1 gene cause the lysosomal storage disorder Gaucher disease (GD) and are the greatest known genetic risk factors for Parkinson's disease (PD). Communication between the gut and brain and immune dysregulation are increasingly being implicated in neurodegenerative disorders such as PD. Here, we show that flies lacking the Gba1b gene, the main fly orthologue of GBA1, display widespread NF-kB signalling activation, including gut inflammation, and brain glial activation. We also demonstrate intestinal autophagic defects, gut dysfunction, and microbiome dysbiosis. Remarkably, modulating the microbiome of Gba1b knockout flies, by raising them under germ-free conditions, partially ameliorates lifespan, locomotor and immune phenotypes. Moreover, we show that modulation of the immune deficiency (IMD) pathway is detrimental to the survival of Gba1 deficient flies. We also reveal that direct stimulation of autophagy by rapamycin treatment achieves similar benefits to germ-free conditions independent of gut bacterial load. Consistent with this, we show that pharmacologically blocking autophagosomal-lysosomal fusion, mimicking the autophagy defects of Gba1 depleted cells, is sufficient to stimulate intestinal immune activation. Overall, our data elucidate a mechanism whereby an altered microbiome, coupled with defects in autophagy, drive chronic activation of NF-kB signaling in a Gba1 loss-of-function model. It also highlights that elimination of the microbiota or stimulation of autophagy to remove immune mediators, rather than prolonged immunosuppression, may represent effective therapeutic avenues for GBA1-associated disorders.

MIC distribution analysis identifies differences in AMR between population sub-groups.

Background: Phenotypic data, such as the minimum inhibitory concentrations (MICs) of bacterial isolates from clinical samples, are widely available through routine surveillance. MIC distributions inform antibiotic dosing in clinical care by determining cutoffs to define isolates as susceptible or resistant. However, differences in MIC distributions between patient sub-populations could indicate strain variation and hence differences in transmission, infection, or selection. Methods: The Vivli AMR register contains a wealth of MIC and metadata for a vast range of bacteria-antibiotic combinations. Using a generalisable methodology followed by multivariate regression, we explored MIC distribution variations across 4 bacteria, covering 7,135,070 samples, by key population sub-groups such as age, sex and infection type, and over time. Results: We found clear differences between MIC distributions across various patient sub-groups for a subset of bacteria-antibiotic pairings. For example, within Staphylococcus aureus, MIC distributions by age group and infection site displayed clear trends, especially for levofloxacin with higher resistance levels in older age groups (odds of 2.17 in those aged 85+ compared to 19-64), which appeared more often in men. This trend could reflect greater use of fluoroquinolones in adults than children but also reveals an increasing MIC level with age, suggesting either transmission differences or accumulation of resistance effects. We also observed high variations by WHO region, and over time, with the latter likely linked to changes in surveillance. Conclusions: We found that MIC distributions can be used to identify differences in AMR levels between population sub-groups. Our methodology could be used more widely to unveil hidden transmission sources and effects of antibiotic use in different patient sub-groups, highlighting opportunities to improve stewardship programmes and interventions, particularly at local scales.

Resistance of bacteria to antibiotics is a global problem and causes millions of deaths every year. How resistant an organism is to an antibiotic can be measured very easily and cheaply and can potentially provide a lot of information about bacterial evolution and how to use the right antibiotics to treat infections. We took multiple large, global collections of these measurements and combined them together. We then took this large dataset, and looked at whether any differences in the degree of resistance could be seen when you separated the bacteria by the background of the patient they came from. In other words, we looked at whether certain groups of patients had more or less resistant bacteria. For some very important bacterial species, we found that age played a strong role, with some bacteria from older people having more resistance against some antibiotics. Also, generally men had infections with bacteria with more resistance. The type of infection was also important, as was the region of the world that the patient was from, with South-East Asia generally having more risk of higher resistance. These results highlight that we can use this data to discover more subtle differences in the bacteria causing infections that different patients suffer from. This could help us to change how we use antibiotics, so that we can maximise their effectiveness for longer. Whilst these results were very interesting, the main thing that we hope to highlight is that this method could be used effectively in local hospitals, where resistance data is collected routinely and often, to try and help doctors to understand AMR in their settings, intervene to prevent spread and better prescribe antibiotics day-to-day.

Growth-Dependent Predation and Generalized Transduction of Antimicrobial Resistance by Bacteriophage.

Bacteriophage (phage) are both predators and evolutionary drivers for bacteria, notably contributing to the spread of antimicrobial resistance (AMR) genes by generalized transduction. Our current understanding of this complex relationship is limited. We used an interdisciplinary approach to quantify how these interacting dynamics can lead to the evolution of multidrug-resistant bacteria. We cocultured two strains of methicillin-resistant Staphylococcus aureus, each harboring a different antibiotic resistance gene, with generalized transducing phage. After a growth phase of 8 h, bacteria and phage surprisingly coexisted at a stable equilibrium in our culture, the level of which was dependent on the starting concentration of phage. We detected double-resistant bacteria as early as 7 h, indicating that transduction of AMR genes had occurred. We developed multiple mathematical models of the bacteria and phage relationship and found that phage-bacteria dynamics were best captured by a model in which phage burst size decreases as the bacteria population reaches stationary phase and where phage predation is frequency-dependent. We estimated that one in every 108 new phage generated was a transducing phage carrying an AMR gene and that double-resistant bacteria were always predominantly generated by transduction rather than by growth. Our results suggest a shift in how we understand and model phage-bacteria dynamics. Although rates of generalized transduction could be interpreted as too rare to be significant, they are sufficient in our system to consistently lead to the evolution of multidrug-resistant bacteria. Currently, the potential of phage to contribute to the growing burden of AMR is likely underestimated. IMPORTANCE Bacteriophage (phage), viruses that can infect and kill bacteria, are being investigated through phage therapy as a potential solution to the threat of antimicrobial resistance (AMR). In reality, however, phage are also natural drivers of bacterial evolution by transduction when they accidentally carry nonphage DNA between bacteria. Using laboratory work and mathematical models, we show that transduction leads to evolution of multidrug-resistant bacteria in less than 8 h and that phage production decreases when bacterial growth decreases, allowing bacteria and phage to coexist at stable equilibria. The joint dynamics of phage predation and transduction lead to complex interactions with bacteria, which must be clarified to prevent phage from contributing to the spread of AMR.

Age-Related Dynamics of Circulating Innate Lymphoid Cells in an African Population.

Innate lymphoid cell (ILC) lineages mirror those of CD4+ T helper cell subsets, producing type 1, 2 and 3 cytokines respectively. Studies in adult human populations have shown contributions of non-cytotoxic ILC to immune regulation or pathogenesis in a wide range of diseases and have prompted investigations of potential functional redundancy between ILC and T helper cell compartments in neonates and children. To investigate the potential for ILC to contribute to immune responses across the human lifespan, we examined the numbers and frequencies of peripheral blood ILC subsets in a cohort of Gambians aged between 5 and 73 years of age. ILC2 were the most abundant peripheral blood ILC subset in this Gambian cohort, while ILC1 were the rarest at all ages. Moreover, the frequency of ILC1s (as a proportion of all lymphocytes) was remarkably stable over the life course whereas ILC3 cell frequencies and absolute numbers declined steadily across the life course and ILC2 frequencies and absolute numbers declined from childhood until the age of approx. 30 years of age. Age-related reductions in ILC2 cell numbers appeared to be partially offset by increasing numbers of total and GATA3+ central memory (CD45RA-CCR7+) CD4+ T cells, although there was also a gradual decline in numbers of total and GATA3+ effector memory (CD45RA-CCR7-) CD4+ T cells. Despite reduced overall abundance of ILC2 cells, we observed a coincident increase in the proportion of CD117+ ILC2, indicating potential for age-related adaptation of these cells in childhood and early adulthood. While both CD117+ and CD117- ILC2 cells produced IL-13, these responses occurred predominantly within CD117- cells. Furthermore, comparison of ILC frequencies between aged-matched Gambian and UK young adults (25-29 years) revealed an overall higher proportion of ILC1 and ILC2, but not ILC3 in Gambians. Thus, these data indicate ongoing age-related changes in ILC2 cells throughout life, which retain the capacity to differentiate into potent type 2 cytokine producing cells, consistent with an ongoing role in immune modulation.