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IntroductionHemibiotrophic Phytophthora are a group of agriculturally and ecologically important pathogenic oomycetes causing severe decline in plant growth and fitness. The lifestyle of these pathogens consists of an initial biotrophic phase followed by a switch to a necrotrophic phase in the latter stages of infection. Between these two phases is the biotrophic to necrotrophic switch (BNS) phase, the timing and controls of which are not well understood particularly in Phytophthora spp. where host resistance has a purely quantitative genetic basis.MethodsTo investigate this we sequenced and annotated the genome of Phytophthora medicaginis, causal agent of root rot and substantial yield losses to Fabaceae hosts. We analyzed the transcriptome of P. medicaginis across three phases of colonization of a susceptible chickpea host (Cicer arietinum) and performed co-regulatory analysis to identify putative small secreted protein (SSP) effectors that influence timing of the BNS in a quantitative pathosystem.ResultsThe genome of P. medicaginis is ~78 Mb, comparable to P. fragariae and P. rubi which also cause root rot. Despite this, it encodes the second smallest number of RxLR (arginine-any amino acid-leucine-arginine) containing proteins of currently sequenced Phytophthora species. Only quantitative resistance is known in chickpea to P. medicaginis, however, we found that many RxLR, Crinkler (CRN), and Nep1-like protein (NLP) proteins and carbohydrate active enzymes (CAZymes) were regulated during infection. Characterization of one of these, Phytmed_10271, which encodes an RxLR effector demonstrates that it plays a role in the timing of the BNS phase and root cell death.DiscussionThese findings provide an important framework and resource for understanding the role of pathogenicity factors in purely quantitative Phytophthora pathosystems and their implications to the timing of the BNS phase.

Abstract Diabetic foot infections (DFIs) are a major cause of hospitalization and can lead to lower extremity amputation. In this pilot study, we used a multiomics approach to explore the host–microbe complex within DFIs. We observed minimal differences in the overall microbial composition between PEDIS infection severities, however Staphylococcus aureus and Streptococcus genera were abundant and highly active in most mild to moderate DFIs. Further, we identified the significant enrichment of several virulence factors associated with infection pathogenicity belonging to both Staphylococcus aureus and Streptococcus. In severe DFIs, patients demonstrated a greater microbial diversity and differential gene expression demonstrated the enrichment of multispecies virulence genes suggestive of a complex polymicrobial infection. The host response in patients with severe DFIs was also significantly different as compared to mild to moderate DFIs. This was attributed to the enrichment of host genes associated with inflammation, acute phase response, cell stress and broad immune-related responses, while those associated with wound healing and myogenesis were significantly depleted.

Introduction: Rice bran arabinoxylan compound (RBAC) is a nutraceutical for enhancing a depleted immune system during and after cancer treatment. This pilot feasibility trial aims to evaluate the effects of RBAC on cancer patients' quality of life during active treatment, compared to placebo, using a validated questionnaire. Other outcome measures include changes in inflammatory and nutritional status, cytokine profile, and gut microbiota. Methods/Design: The study will recruit 50 participants from a regional cancer center in Australia. Patients aged 18–70, diagnosed with solid organ cancers stage II and above, and currently undergoing active systemic therapies, are eligible. Random allocation of participants into two groups is stratified based on metastatic status and treatment type. The dosage is either 3 g/day of RBAC or placebo in identical packaging. The participants, study coordinator, and treating oncologists are blinded to the interventions. Data collections are at baseline and at four follow-up sessions, which are six weeks apart (24 weeks). Statistical analysis will involve a protected p-value with multiple dependent values and analyzed by ANOVA with repeated measures on the occasion of testing and with both a full Bonferroni or Sidak corrections applied to protect against Type I errors. Any observed significance warrants further analysis with pairwise comparisons. Analysis of covariance will also be performed to assess any influence of the demographic data, cancer diagnosis, as well as changes in physical activity, dietary habits, and complementary medicine usage. Comparisons of gut microbiota will be based on the analysis of the fecal microbiome using 16S ribosomal ribonucleic acid amplicon sequencing. The proposed research timeline is from October 2018 to May 2022. Trial registration: ANZCTR. Reg No: ACTRN12619000562178p.

Abstract Background Scheffersomyces stipitis is an important yeast species in the field of biorenewables due to its desired capacity for xylose utilization. It has been recognized that redox balance plays a critical role in S. stipitis due to the different cofactor preferences in xylose assimilation pathway. However, there has not been any systems level understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. Genome-scale metabolic network models (GEMs) offer the opportunity to gain such systems level understanding; however, currently the two published GEMs for S. stipitis cannot be used for this purpose, as neither of them is able to capture the strain’s fermentative metabolism reasonably well due to their poor prediction of xylitol production, a key by-product under oxygen limited conditions. Results A system identification-based (SID-based) framework that we previously developed for GEM validation is expanded and applied to refine a published GEM for S. stipitis, iBB814. After the modified GEM, named iDH814, was validated using literature data, it is used to obtain genome-scale understanding on how redox cofactor shifts when cells respond to reduced oxygen supply. The SID-based framework for GEM analysis was applied to examine how the environmental perturbation (i.e., reduced oxygen supply) propagates through the metabolic network, and key reactions that contribute to the shifts of redox and metabolic state were identified. Finally, the findings obtained through GEM analysis were validated using transcriptomic data. Conclusions iDH814, the modified model, was shown to offer significantly improved performance in terms of matching available experimental results and better capturing available knowledge on the organism. More importantly, our analysis based on iDH814 provides the first genome-scale understanding on how redox balance in S. stipitis was shifted as a result of reduced oxygen supply. The systems level analysis identified the key contributors to the overall metabolic state shift, which were validated using transcriptomic data. The analysis confirmed that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the overall shift in metabolic states.

Seagrasses are globally distributed marine plants that represent an extremely valuable component of coastal ecosystems. Like terrestrial plants, seagrass productivity and health are likely to be strongly governed by the structure and function of the seagrass microbiome, which will be distributed across a number of discrete microenvironments within the plant, including the phyllosphere, the endosphere and the rhizosphere, all different in physical and chemical conditions. Here we examined patterns in the composition of the microbiome of the seagrass Zostera muelleri, within six plant-associated microenvironments sampled across four different coastal locations in New South Wales, Australia. Amplicon sequencing approaches were used to characterize the diversity and composition of bacterial, microalgal, and fungal microbiomes and ultimately identify “core microbiome” members that were conserved across sampling microenvironments. Discrete populations of bacteria, microalgae and fungi were observed within specific seagrass microenvironments, including the leaves and roots and rhizomes, with “core” taxa found to persist within these microenvironments across geographically disparate sampling sites. Bacterial, microalgal and fungal community profiles were most strongly governed by intrinsic features of the different seagrass microenvironments, whereby microscale differences in community composition were greater than the differences observed between sampling regions. However, our results showed differing strengths of microbial preferences at the plant scale, since this microenvironmental variability was more pronounced for bacteria than it was for microalgae and fungi, suggesting more specific interactions between the bacterial consortia and the seagrass host, and potentially implying a highly specialized coupling between seagrass and bacterial metabolism and ecology. Due to their persistence within a given seagrass microenvironment, across geographically discrete sampling locations, we propose that the identified “core” microbiome members likely play key roles in seagrass physiology as well as the ecology and biogeochemistry of seagrass habitats.

The marine sediment-water interface is an important location for microbially controlled nutrient and gas exchange processes. While microbial distributions on the sediment side of the interface are well established in many locations, the distributions of microbes on the water side of the interface are less well known. Here, we measured that distribution for marine virio- and bacterioplankton with a new two-dimensional technique. Our results revealed higher heterogeneity in sediment-water interface biomass distributions than previously reported with a greater than 45- and 2500-fold change cm(-1) found within bacterial and viral subpopulations compared to previous maxima of 1.5- and 1.4-fold cm(-1) in bacteria and viruses in the same environments. The 45-fold and 2500-fold changes were due to patches of elevated and patches of reduced viral and bacterial abundance. The bacterial and viral hotspots were found over single and multiple sample points and the two groups often coincided whilst the coldspots only occurred over single sample points and the bacterial and viral abundances showed no correlation. The total mean abundances of viruses strongly correlated with bacteria (r = 0.90, p

Metagenomic analysis was used to examine the taxonomic diversity and metabolic potential of an Australian sea lion (Neophoca cinerea) gut microbiome. Bacteria comprised 98% of classifiable sequences and of these matches to Firmicutes (80%) were dominant, with Proteobacteria and Actinobacteria representing 8% and 2% of matches respectively. The relative proportion of Firmicutes (80%) to Bacteriodetes (2%) is similar to that in previous studies of obese humans and obese mice, suggesting the gut microbiome may confer a predisposition towards the excess body fat that is needed for thermoregulation within the cold oceanic habitats foraged by Australian sea lions. Core metabolic functions, including carbohydrate utilisation (14%), protein metabolism (9%) and DNA metabolism (7%) dominated the metagenome, but in comparison to human and fish gut microbiomes there was a significantly higher proportion of genes involved in phosphorus metabolism (2.4%) and iron scavenging mechanisms (1%). When sea lions defecate at sea, the relatively high nutrient metabolism potential of bacteria in their faeces may accelerate the dissolution of nutrients from faecal particles, enhancing their persistence in the euphotic zone where they are available to stimulate marine production.

We describe here a useful metabolic engineering tool, multiple-gene-promoter shuffling (MGPS), to optimize expression levels for multiple genes. This method approaches an optimized gene overexpression level by fusing promoters of various strengths to genes of interest for a particular pathway. Selection of these promoters is based on the expression levels of the native genes under the same physiological conditions intended for the application. MGPS was implemented in a yeast xylose fermentation mixture by shuffling the promoters for GND2 and HXK2 with the genes for transaldolase (TAL1), transketolase (TKL1), and pyruvate kinase (PYK1) in the Saccharomyces cerevisiae strain FPL-YSX3. This host strain has integrated xylose-metabolizing genes, including xylose reductase, xylitol dehydrogenase, and xylulose kinase. The optimal expression levels for TAL1, TKL1, and PYK1 were identified by analysis of volumetric ethanol production by transformed cells. We found the optimal combination for ethanol production to be GND2.TAL1-HXK2-TKL1-HXK2-PYK1. The MGPS method could easily be adapted for other eukaryotic and prokaryotic organisms to optimize expression of genes for industrial fermentation. [ABSTRACT FROM AUTHOR]

Abstract  Xylose was fermented using Pichia stipitis CBS 6054 at different initial cell concentrations. A high initial cell concentration increased the rate of xylose utilization, ethanol formation, and the ethanol yield. The highest ethanol concentration of 41.0 g/L and a yield of 0.38 g/g was obtained using an initial cell concentration of 6.5 g/L. Even though more xylitol was produced when the initial cell concentrations were high, cell density had no effect on the final ethanol yield. A two-parameter mathematical model was used to predict the cell population dynamics at the different initial cell concentrations. The model parameters, a and b correlate with the initial cell concentrations used with an R 2 of 0.99. [ABSTRACT FROM AUTHOR]