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

NCX1 represents an ionic Na+ sensing mechanism in macrophages

Inflammation and infection can trigger local tissue Na(+)accumulation. This Na+-rich environment boosts proinflammatory activation of monocyte/macrophage-like cells (M phi s) and their antimicrobial activity. Enhanced Na+-driven M phi function requires the osmoprotective transcription factor nuclear factor of activated T cells 5 (NFAT5), which augments nitric oxide (NO) production and contributes to increased autophagy. However, the mechanism of Na(+)sensing in M phi s remained unclear. High extracellular Na(+)levels (high salt [HS]) trigger a substantial Na(+)influx and Ca(2+)loss. Here, we show that the Na+/Ca(2+)exchanger 1 (NCX1, also known as solute carrier family 8 member A1 [SLC8A1]) plays a critical role in HS-triggered Na(+)influx, concomitant Ca(2+)efflux, and subsequent augmented NFAT5 accumulation. Moreover, interfering with NCX1 activity impairs HS-boosted inflammatory signaling, infection-triggered autolysosome formation, and subsequent antibacterial activity. Taken together, this demonstrates that NCX1 is able to sense Na(+)and is required for amplifying inflammatory and antimicrobial M phi responses upon HS exposure. Manipulating NCX1 offers a new strategy to regulate M phi function

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

Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics

Acute myeloid leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. We were interested in whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different studies, we present a large-scale study of machine learning-based prediction of AML in which we address key questions relating to the combination of machine learning and transcriptomics and their practical use. We find data-driven, high-dimensional approaches—in which multivariate signatures are learned directly from genome-wide data with no prior knowledge—to be accurate and robust. Importantly, these approaches are highly scalable with low marginal cost, essentially matching human expert annotation in a near-automated workflow. Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach wherein risk prediction, differential diagnosis, and subclassification of AML are achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning

Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state.

Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic

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

BCG Vaccination in Humans Elicits Trained Immunity via the Hematopoietic Progenitor Compartment.

Induction of trained immunity by Bacille-Calmette-Guérin (BCG) vaccination mediates beneficial heterologous effects, but the mechanisms underlying its persistence and magnitude remain elusive. In this study, we show that BCG vaccination in healthy human volunteers induces a persistent transcriptional program connected to myeloid cell development and function within the hematopoietic stem and progenitor cell (HSPC) compartment in the bone marrow. We identify hepatic nuclear factor (HNF) family members 1a and b as crucial regulators of this transcriptional shift. These findings are corroborated by higher granulocyte numbers in BCG-vaccinated infants, HNF1 SNP variants that correlate with trained immunity, and elevated serum concentrations of the HNF1 target alpha-1 antitrypsin. Additionally, transcriptomic HSPC remodeling was epigenetically conveyed to peripheral CD14+ monocytes, displaying an activated transcriptional signature three months after BCG vaccination. Taken together, transcriptomic, epigenomic, and functional reprogramming of HSPCs and peripheral monocytes is a hallmark of BCG-induced trained immunity in humans

Western Diet Triggers NLRP3-Dependent Innate Immune Reprogramming

Long-term epigenetic reprogramming of innate immune cells in response to microbes, also termed "trained immunity,'' causes prolonged altered cellular functionality to protect from secondary infections. Here, we investigated whether sterile triggers of inflammation induce trained immunity and thereby influence innate immune responses. Western diet (WD) feeding of Ldlr(-/-) mice induced systemic inflammation, which was undetectable in serum soon after mice were shifted back to a chow diet (CD). In contrast, myeloid cell responses toward innate stimuli remained broadly augmented. WD-induced transcriptomic and epigenomic reprogramming of myeloid progenitor cells led to increased proliferation and enhanced innate immune responses. Quantitative trait locus (QTL) analysis in human monocytes trained with oxidized low-density lipoprotein (oxLDL) and stimulated with lipopolysaccharide (LPS) suggested inflammasome-mediated trained immunity. Consistently, Nlrp3(-/-)/Ldlr(-/-) mice lacked WD-induced systemic inflammation, myeloidprogenitor proliferation, and re-programming. Hence, NLRP3 mediates trained immunity following WD and could thereby mediate the potentially deleterious effects of trained immunity in inflammatory diseases