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Abstract The human gut pathogen Clostridioides difficile displays substantial inter-strain genetic variability and confronts a changeable nutrient landscape in the gut. We examined how human gut microbiota inter-species interactions influence the growth and toxin production of various C. difficile strains across different nutrient environments. Negative interactions influencing C. difficile growth are prevalent in an environment containing a single highly accessible resource and sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile toxin production displays significant community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile strains exhibit differences in interactions with Clostridium scindens and the ability to compete for proline. Further, C. difficile shows substantial differences in transcriptional profiles in co-culture with C. scindens or Clostridium hiranonis. C. difficile exhibits massive alterations in metabolism and other cellular processes in co-culture with C. hiranonis, reflecting their similar metabolic niches. C. hiranonis uniquely inhibits the growth and toxin production of diverse C. difficile strains across different nutrient environments and robustly ameliorates disease severity in mice. In sum, understanding the impact of C. difficile strain variability and nutrient environments on inter-species interactions could help improve the effectiveness of anti-C. difficile strategies.

To assess the burden of food-borne trematodiases in China from 1990 to 2021 and project the burden through 2035, data were captured from the Global Burden of Disease Study (GBD) 2021 datasets. The estimated prevalent food-borne trematodiase cases were 33.32 million (95% uncertainty interval (UI): 29.25–38.35 million) in China in 2021, contributing to 768,297.4 disability-adjusted life years (DALYs) (95% UI: 383,882.8–1,367,826.1). The number of prevalent cases and DALYs declined by 9.02% and 18.11%, and a downward decline was seen in age-standardized prevalence and DALY rates (estimated annual percentage change: −0.96% and −1.21%, respectively). A higher prevalence and DALY rates were observed among males than females, and the middle-aged group bore the highest burden, while the older population showed the most rapid increase in prevalent cases and DALY numbers. Projected DALY counts and rates remain stable through 2035 using the Bayesian age–period–cohort (BAPC) model. These findings demonstrate a decline in the burden of food-borne trematodiases in China from 1990 to 2021; however, the prevalence remained high, which contributed considerably to disability and premature death. Continued control efforts and targeted interventions are essential to further reducing the burden of food-borne trematodiases in China.

Abstract Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Abstract Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile using a synthetic human gut microbiome. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. Our results show that C. difficile is in a unique class of species that display a strong negative dependence between growth and species richness. We identify molecular mechanisms of inhibition including acidification of the environment and competition over resources. We demonstrate that Clostridium hiranonis strongly inhibits C. difficile partially via resource competition. Increasing the initial density of C. difficile can increase its abundance in the assembled community, but community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities featuring a combination of mechanisms including species richness, environment acidification, and resource competition.

CRISPRi allows for the simultaneous control of many genes, however the sgRNAs compete for binding to dCas9. Here the authors design a dCas9 concentration regulator to allow independent regulation of multiple genes.

Accurately predicting the behaviour of a genetic circuit remains difficult due to the lack of modularity. Here the authors quantify the effects of resource loading in mammalian systems and develop an endoribonuclease-based feedfoward controller to adapt gene expression to the effects of resource loading.

Background: Although sustainable control since 1950s has achieved great successes, schistosomiasis japonica remains a major public health problem in China. Since 2004, a new integrated strategy was developed aiming to control the transmission of Schistosoma japonicum through the implementation of a package of interventions. To date, no systematic review or meta-analysis assessing the effectiveness of this new integrated strategy for schistosomiasis control in China has been published. We performed a PubMed-based bibliometric assessment of publications on the new integrated strategy for schistosomiasis japonica control in China, to understand the global transmissibility and sharing of the new integrated strategy. Methods: An in-depth bibliometric analysis of all publications on the new integrated strategy for schistosomiasis japonica control in China was performed through a PubMed search using the terms “schistosomiasis” and “China,” from January 1, 2004 to August 31, 2018. All titles and abstracts were read carefully, and the publications reporting the effectiveness, experiences, lessons, or problems of the new integrated strategy were included in the bibliometric analysis. Results: Overall, 2,361 titles were screened, and 70 eligible publications were accessed for analyses, including 23 studies in English, published in 15 international journals, and 47 studies in Chinese with abstracts in English, published in 3 national journals. Chinese Journal of Schistosomiasis Control (Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi) published 60% of the research output. Research articles (48.6%) and short reports (37.1%) were the dominant manuscript types. Furthermore, 471 contributing authors from 277 affiliations across 9 countries produced these 70 publications. Conclusion: This is the first PubMed-based quantitative analysis of the research output of the new integrated strategy, and our data indicate a low global transmissibility of Chinese new integrated strategy. We therefore call for more research outputs of the new integrated strategy for schistosomiasis japonica control in China to be communicated through international platforms.

Competition for shared cellular resources often renders genetic circuits poorly predictable. Here the authors design a biomolecular quasi-integral controller that allows gene expression to adapt to variable demand in translation resources.