GABA-modulating bacteria of the human gut microbiota.

The gut microbiota affects many important host functions, including the immune response and the nervous system1. However, while substantial progress has been made in growing diverse microorganisms of the microbiota2, 23-65% of species residing in the human gut remain uncultured3,4, which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence in artificial media of key growth factors that are provided by neighbouring bacteria in situ5,6. In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bioassay-driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter GABA (γ-aminobutyric acid). GABA was the only tested nutrient that supported the growth of KLE1738, and a genome analysis supported a GABA-dependent metabolism mechanism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome-based metabolic modelling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. A transcriptome analysis of human stool samples from healthy individuals showed that GABA-producing pathways are actively expressed by Bacteroides, Parabacteroides and Escherichia species. By coupling 16S ribosmal RNA sequencing with functional magentic resonance imaging in patients with major depressive disorder, a disease associated with an altered GABA-mediated response, we found that the relative abundance levels of faecal Bacteroides are negatively correlated with brain signatures associated with depression

Predicting stress response and improved protein overproduction in Bacillus subtilis

Abstract Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria

Maximal interferon induction by influenza lacking NS1 is infrequent owing to requirements for replication and export.

Influenza A virus exhibits high rates of replicative failure due to a variety of genetic defects. Most influenza virions cannot, when acting as individual particles, complete the entire viral life cycle. Nevertheless influenza is incredibly successful in the suppression of innate immune detection and the production of interferons, remaining undetected in >99% of cells in tissue-culture models of infection. Notably, the same variation that leads to replication failure can, by chance, inactivate the major innate immune antagonist in influenza A virus, NS1. What explains the observed rarity of interferon production in spite of the frequent loss of this, critical, antagonist? By studying how genetic and phenotypic variation in a viral population lacking NS1 correlates with interferon production, we have built a model of the "worst-case" failure from an improved understanding of the steps at which NS1 acts in the viral life cycle to prevent the triggering of an innate immune response. In doing so, we find that NS1 prevents the detection of de novo innate immune ligands, defective viral genomes, and viral export from the nucleus, although only generation of de novo ligands appears absolutely required for enhanced detection of virus in the absence of NS1. Due to this, the highest frequency of interferon production we observe (97% of infected cells) requires a high level of replication in the presence of defective viral genomes with NS1 bearing an inactivating mutation that does not impact its partner encoded on the same segment, NEP. This is incredibly unlikely to occur given the standard variation found within a viral population, and would generally require direct, artificial, intervention to achieve at an appreciable rate. Thus from our study, we procure at least a partial explanation for the seeming contradiction between high rates of replicative failure and the rarity of the interferon response to influenza infection

Analysis of A/Hamburg/4/2009 infecting NHBE cells from donor 2405 Kelly <i>et al</i>.

Thresholding after SoupX to identify confident-positive populations. (A) Low and high UMI cells were excluded as poor-quality or doublets (dotted lines). (B) Distribution of unique genes with >0 counts in thresholded human cells. Cells with less than 2000 unique, non-zero, genes were excluded from this analysis as poor-quality (dotted line). (C) Density distribution of the log fraction of transcripts derived from influenza in all cells with at least one influenza-derived UMI after SoupX correction. Threshold was set to be consistent with that set in S5 Fig. (D) Density distribution of the log fraction of transcripts derived from type I interferons in all cells with at least one type I interferon-derived UMI after SoupX correction. Threshold was set to include the highest modes, but exclude lower mode consistent with only one or two reads derived from a type I interferon (dotted line). (E) Density distribution of the log fraction of transcripts derived from type III interferons in all cells with at least one type III interferon-derived UMI after SoupX correction. Threshold was set to include the highest modes, but exclude lower mode consistent with only one or two reads derived from a type III interferon (dotted line). (F) Summary of cells after thresholding. (G) Distribution of influenza transcript frequencies (as a percentage of all transcripts recovered from a cell) in influenza-positive cells. (H) Fraction of influenza reads derived from each influenza segment in influenza-positive cells. (I) The number of average emulsions associated with any given deletion at the indicated read support is shown. At lower read support, deletions are more broadly distributed, suggesting contamination or template-switching. At higher support, they are less broadly distributed, suggesting bone-fide deletions. Cutoff chosen for this study indicated by the dotted line. (J) Summary of deletion counts after thresholding in I. (TIF)</p

All deletion junctions and number of supporting reads from bulk sequencing.

All deletion junctions and number of supporting reads from bulk sequencing.</p

Sequence of construct used to generate NS1-expressing MDCK-SIAT1 cells.

Sequence of construct used to generate NS1-expressing MDCK-SIAT1 cells.</p

Deletions associate weakly with interferon induction in NS1-impaired single-cell data.

(A) Number of unique cell/deletion combinations observed for each segment across each single-cell dataset. Thresholds for deletion identification presented in S3, S5 and S7 Figs. (B) Frequencies of deletion detection across influenza-infected cells in each single-cell experiment. (C) Fraction of cells with, and without, detectible deletions identified as interferon-positive, considering only infections with a greater fraction of influenza transcripts than the median value observed in infections in which deletions were confidently identified. While all populations trended in the same direction, only one population was found to have significant co-occurrence of deletions and interferon production, Fisher’s exact test (p<0.05). The lack of significance in 2405 is likely in part due to the small number of sampled events.</p

Curated deletion junctions and number of supporting reads from single-cell sequencing for wild-type A/Hamburg/4/2009 donor 2405.

Curated deletion junctions and number of supporting reads from single-cell sequencing for wild-type A/Hamburg/4/2009 donor 2405.</p

interferon-positive cells tend to express more NS1.

Analysis of NS1stop data from S18 Fig. NS1 and HA staining in double-positive cells were normalized by quantiles, ranking cells by percentile out of one-hundred percent. The NS1 rank in IFNL1+ cells was then divided by the HA rank, if we see less NS1 staining than expected from HA staining the log-transformed value should be less than 0 (dotted line), if more, greater. There is significantly more NS1 staining in IFNL+ cells than would be expected from HA staining, one sample two-tailed t test, p (TIF)</p

Full flow data for Fig 4.

Data showing full flow data and measurements of IFNL1 reporter, and M2 staining. Cells infected the indicated, particle-corrected MOI, and measurements made 13h post-infection. Individual replicates shown. Interferon-positive events colored in orange. Data subsetted to 5000 events to show equivalent numbers between conditions. (TIF)</p