A BLUEPRINT of immune cell development: To determine the epigenetic mechanisms that direct blood cells to develop into the many components of our immune system, the BLUEPRINT consortium examined the regulation of DNA and RNA transcription to dissect the molecular traits that govern blood cell differentiation. By inducing immune responses, Saeed et al. document the epigenetic changes in the genome that underlie immune cell differentiation. Cheng et al. demonstrate that trained monocytes are highly dependent on the breakdown of sugars in the presence of oxygen, which allows cells to produce the energy needed to mount an immune response. Chen et al. examine RNA transcripts and find that specific cell lineages use RNA transcripts of different length and composition (isoforms) to form proteins. Together, the studies reveal how epigenetic effects can drive the development of blood cells involved in the immune system. Science , this issue 10.1126/science.1251086 , 10.1126/science.1250684 , 10.1126/science.1251033

Proper management of COVID-19 mandates better understanding of disease pathogenesis. The sudden clinical deterioration 7-8 days after initial symptom onset suggests that severe respiratory failure (SRF) in COVID-19 is driven by a unique pattern of immune dysfunction. We studied immune responses of 54 COVID-19 patients, 28 of whom had SRF. All patients with SRF displayed either macrophage activation syndrome (MAS) or very low human leukocyte antigen D related (HLA-DR) expression accompanied by profound depletion of CD4 lymphocytes, CD19 lymphocytes, and natural killer (NK) cells. Tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) production by circulating monocytes was sustained, a pattern distinct from bacterial sepsis or influenza. SARS-CoV-2 patient plasma inhibited HLA-DR expression, and this was partially restored by the IL-6 blocker Tocilizumab; off-label Tocilizumab treatment of patients was accompanied by increase in circulating lymphocytes. Thus, the unique pattern of immune dysregulation in severe COVID-19 is characterized by IL-6-mediated low HLA-DR expression and lymphopenia, associated with sustained cytokine production and hyper-inflammation.

A BLUEPRINT of immune cell development: To determine the epigenetic mechanisms that direct blood cells to develop into the many components of our immune system, the BLUEPRINT consortium examined the regulation of DNA and RNA transcription to dissect the molecular traits that govern blood cell differentiation. By inducing immune responses, Saeed et al. document the epigenetic changes in the genome that underlie immune cell differentiation. Cheng et al. demonstrate that trained monocytes are highly dependent on the breakdown of sugars in the presence of oxygen, which allows cells to produce the energy needed to mount an immune response. Chen et al. examine RNA transcripts and find that specific cell lineages use RNA transcripts of different length and composition (isoforms) to form proteins. Together, the studies reveal how epigenetic effects can drive the development of blood cells involved in the immune system. Science , this issue 10.1126/science.1251086 , 10.1126/science.1250684 , 10.1126/science.1251033

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine^1,2. 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 legislation^4,5. 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.

Netea and colleagues provide a general guide to the cellular and humoral contributors to inflammation as well as the pathways that characterize inflammation in specific organs and tissues. Biologists, physicians and immunologists have contributed to the understanding of the cellular participants and biological pathways involved in inflammation. Here, we provide a general guide to the cellular and humoral contributors to inflammation as well as to the pathways that characterize inflammation in specific organs and tissues.