<i>Salmonella</i>succinate utilisation is inhibited by multiple regulatory systems

AbstractSuccinate is a potent immune signalling molecule that is present in the mammalian gut and within macrophages. Both of these niches are colonised by the pathogenic bacteriumSalmonella entericaserovar Typhimurium during infection. Succinate is a C4-dicarboyxlate that can serve as a source of carbon for bacteria. When succinate is provided as the sole carbon source forin vitrocultivation,Salmonellaand other enteric bacteria exhibit a slow growth rate and a long lag phase. This growth inhibition phenomenon was known to involve the sigma factor RpoS, but the genetic basis of the repression of bacterial succinate utilisation was poorly understood. Here, we used an experimental evolution approach to isolate fast-growing mutants during growth ofS. Typhimurium on succinate containing minimal medium.Our approach reveals novel RpoS-independent systems that inhibit succinate utilisation. The CspC RNA binding protein restricts succinate utilisation, an inhibition that is antagonised by high levels of the small regulatory RNA (sRNA) OxyS. We discovered that the Fe-S cluster regulatory protein IscR inhibits succinate utilisation by repressing the C4-dicarboyxlate transporter DctA.The RNA chaperone Hfq, the exoribonuclease PNPase and their cognate sRNAs function together to repress succinate utilisationviaRpoS induction. Furthermore, the ribose operon repressor RbsR is required for the complete RpoS-driven repression of succinate utilisation, suggesting a novel mechanism of RpoS regulation.Our discoveries shed light on redundant regulatory systems that tightly regulate the utilisation of succinate. We propose that the control of central carbon metabolism by multiple regulatory systems inSalmonellagoverns the infection niche-specific utilisation of succinate.</jats:p

Cellular Differentiation of Human Monocytes Is Regulated by Time-Dependent Interleukin-4 Signaling and the Transcriptional Regulator NCOR2.

Human in vitro generated monocyte-derived dendritic cells (moDCs) and macrophages are used clinically, e.g., to induce immunity against cancer. However, their physiological counterparts, ontogeny, transcriptional regulation, and heterogeneity remains largely unknown, hampering their clinical use. High-dimensional techniques were used to elucidate transcriptional, phenotypic, and functional differences between human in vivo and in vitro generated mononuclear phagocytes to facilitate their full potential in the clinic. We demonstrate that monocytes differentiated by macrophage colony-stimulating factor (M-CSF) or granulocyte macrophage colony-stimulating factor (GM-CSF) resembled in vivo inflammatory macrophages, while moDCs resembled in vivo inflammatory DCs. Moreover, differentiated monocytes presented with profound transcriptomic, phenotypic, and functional differences. Monocytes integrated GM-CSF and IL-4 stimulation combinatorically and temporally, resulting in a mode- and time-dependent differentiation relying on NCOR2. Finally, moDCs are phenotypically heterogeneous and therefore necessitate the use of high-dimensional phenotyping to open new possibilities for better clinical tailoring of these cellular therapies

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Exposure of Salmonella enterica serovar typhimurium to three humectants used in the food industry induces different osmoadaptation systems

Common salt (NaCl) is frequently used by the food industry to add flavor and to act as a humectant in order to reduce the water content of a food product. The improved health awareness of consumers is leading to a demand for food products with reduced salt content; thus, manufacturers require alternative water activity-reducing agents which elicit the same general effects as NaCl. Two examples include KCl and glycerol. These agents lower the water activity of a food matrix and also contribute to limit the growth of the microbiota, including foodborne pathogens. Little is currently known about how foodborne pathogens respond to these water activity-lowering agents. Here we examined the response of Salmonella enterica serovar Typhimurium 4/74 to NaCl, KCl, and glycerol at three time points, using a constant water activity level, compared with the response of a control inoculum. All conditions induced the upregulation of gluconate metabolic genes after 6 h of exposure. Bacteria exposed to NaCl and KCl demonstrated the upregulation of the osmoprotective transporter mechanisms encoded by the proP, proU, and osmU (STM1491 to STM1494) genes. Glycerol exposure elicited the downregulation of these osmoadaptive mechanisms but stimulated an increase in lipopolysaccharide and membrane protein-associated genes after 1 h. The most extensive changes in gene expression occurred following exposure to KCl. Because many of these genes were of unknown function, further characterization may identify KCl-specific adaptive processes that are not stimulated by NaCl. This study shows that the response of S. Typhimurium to different humectants does not simply reflect reduced water activity and likely involves systems that are linked to specific humectants

Succinate utilisation by Salmonella is inhibited by multiple regulatory systems.

Succinate is a potent immune signalling molecule that is present in the mammalian gut and within macrophages. Both of these infection niches are colonised by the pathogenic bacterium Salmonella enterica serovar Typhimurium during infection. Succinate is a C4-dicarboyxlate that can serve as a source of carbon for bacteria. When succinate is provided as the sole carbon source for in vitro cultivation, Salmonella and other enteric bacteria exhibit a slow growth rate and a long lag phase. This growth inhibition phenomenon was known to involve the sigma factor RpoS, but the genetic basis of the repression of bacterial succinate utilisation was poorly understood. Here, we use an experimental evolution approach to isolate fast-growing mutants during growth of S. Typhimurium on succinate containing minimal medium. Our approach reveals novel RpoS-independent systems that inhibit succinate utilisation. The CspC RNA binding protein restricts succinate utilisation, an inhibition that is antagonised by high levels of the small regulatory RNA (sRNA) OxyS. We discovered that the Fe-S cluster regulatory protein IscR inhibits succinate utilisation by repressing the C4-dicarboyxlate transporter DctA. Furthermore, the ribose operon repressor RbsR is required for the complete RpoS-driven repression of succinate utilisation, suggesting a novel mechanism of RpoS regulation. Our discoveries shed light on the redundant regulatory systems that tightly regulate the utilisation of succinate. We speculate that the control of central carbon metabolism by multiple regulatory systems in Salmonella governs the infection niche-specific utilisation of succinate

ProP Is Required for the Survival of Desiccated <i>Salmonella enterica</i> Serovar Typimurium Cells on a Stainless Steel Surface

Consumers trust commercial food production to be safe, and it is important to strive to improve food safety at every level. Several outbreaks of food-borne disease have been caused by Salmonella strains associated with dried food. Currently we do not know the mechanisms used by Salmonella enterica serovar Typhimurium to survive in desiccated environments. The aim of this study was to discover the responses of S. Typhimurium ST4/74 at the transcriptional level to desiccation on a stainless steel surface and to subsequent rehydration. Bacterial cells were dried onto the same steel surfaces used during the production of dry foods, and RNA was recovered for transcriptomic analysis. Subsequently, dried cells were rehydrated and were again used for transcriptomic analysis. A total of 266 genes were differentially expressed under desiccation stress compared with a static broth culture. The osmoprotectant transporters proP, proU, and osmU (STM1491 to STM1494) were highly upregulated by drying. Deletion of any one of these transport systems resulted in a reduction in the long-term viability of S. Typhimurium on a stainless steel food contact surface. The proP gene was critical for survival; proP deletion mutants could not survive desiccation for long periods and were undetectable after 4 weeks. Following rehydration, 138 genes were differentially expressed, with upregulation observed for genes such as proP, proU, and the phosphate transport genes (pstACS). In time, this knowledge should prove valuable for understanding the underlying mechanisms involved in pathogen survival and should lead to improved methods for control to ensure the safety of intermediate- and low-moisture foods

Human variation in population-wide gene expression data predicts gene perturbation phenotype.

Funder: German Research FoundationFunder: Dutch Research Council (NWO)Funder: BMBF BonnFunder: Europäische KommissionPopulation-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort

Aberrant chromatin landscape following loss of the H3.3 chaperone Daxx in haematopoietic precursors leads to Pu.1-mediated neutrophilia and inflammation

Defective silencing of retrotransposable elements has been linked to inflammageing, cancer and autoimmune diseases. However, the underlying mechanisms are only partially understood. Here we implicate the histone H3.3 chaperone Daxx, a retrotransposable element repressor inactivated in myeloid leukaemia and other neoplasms, in protection from inflammatory disease. Loss of Daxx alters the chromatin landscape, H3.3 distribution and histone marks of haematopoietic progenitors, leading to engagement of a Pu.1-dependent transcriptional programme for myelopoiesis at the expense of B-cell differentiation. This causes neutrophilia and inflammation, predisposing mice to develop an autoinflammatory skin disease. While these molecular and phenotypic perturbations are in part reverted in animals lacking both Pu.1 and Daxx, haematopoietic progenitors in these mice show unique chromatin and transcriptome alterations, suggesting an interaction between these two pathways. Overall, our findings implicate retrotransposable element silencing in haematopoiesis and suggest a cross-talk between the H3.3 loading machinery and the pioneer transcription factor Pu.1