An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. © 2023, The Author(s)

Sarcoma and the 100,000 Genomes Project: our experience and changes to practice

The largest whole genome sequencing (WGS) endeavour involving cancer and rare diseases was initiated in the UK in 2015 and ran for 5 years. Despite its rarity, sarcoma ranked third overall among the number of patients' samples sent for sequencing. Herein, we recount the lessons learned by a specialist sarcoma centre that recruited close to 1000 patients to the project, so that we and others may learn from our experience. WGS data was generated from 597 patients, but samples from the remaining approximately 400 patients were not sequenced. This was largely accounted for by unsuitability due to extensive necrosis, secondary to neoadjuvant radiotherapy or chemotherapy, or being placed in formalin. The number of informative genomes produced was reduced further by a PCR amplification step. We showed that this loss of genomic data could be mitigated by sequencing whole genomes from needle core biopsies. Storage of resection specimens at 4 °C for up to 96 h overcame the challenge of freezing tissue out of hours including weekends. Removing access to formalin increased compliance to these storage arrangements. With over 70 different sarcoma subtypes described, WGS was a useful tool for refining diagnoses and identifying novel alterations. Genomes from 350 of the cohort of 597 patients were analysed in this study. Overall, diagnoses were modified for 3% of patients following review of the WGS findings. Continued refinement of the variant-calling bioinformatic pipelines is required as not all alterations were identified when validated against histology and standard of care diagnostic tests. Further research is necessary to evaluate the impact of germline mutations in patients with sarcoma, and sarcomas with evidence of hypermutation. Despite 50% of the WGS exhibiting domain 1 alterations, the number of patients with sarcoma who were eligible for clinical trials remains small, highlighting the need to revaluate clinical trial design

An action for the exact string black hole

A local action is constructed describing the exact string black hole discovered by Dijkgraaf, Verlinde and Verlinde in 1992. It turns out to be a special 2D Maxwell-dilaton gravity theory, linear in curvature and field strength. Two constants of motion exist: mass M>1, determined by the level k, and U(1)-charge Q>0, determined by the value of the dilaton at the origin. ADM mass, Hawking temperature T_H \propto \sqrt{1-1/M} and Bekenstein-Hawking entropy are derived and studied in detail. Winding/momentum mode duality implies the existence of a similar action, arising from a branch ambiguity, which describes the exact string naked singularity. In the strong coupling limit the solution dual to AdS_2 is found to be the 5D Schwarzschild black hole. Some applications to black hole thermodynamics and 2D string theory are discussed and generalizations - supersymmetric extension, coupling to matter and critical collapse, quantization - are pointed out.Comment: 41 pages, 2 eps figures, dedicated to Wolfgang Kummer on occasion of his Emeritierung; v2: added ref; v3: extended discussion in sections 3.2, 3.3 and at the end of 5.3 by adding 2 pages of clarifying text; updated refs; corrected typo

Prediction of Co-Receptor Usage of HIV-1 from Genotype

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937±0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine

Machine learning on normalized protein sequences

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

Uterine Epithelial Cell Regulation of DC-SIGN Expression Inhibits Transmitted/Founder HIV-1 Trans Infection by Immature Dendritic Cells

Sexual transmission accounts for the majority of HIV-1 infections. In over 75% of cases, infection is initiated by a single variant (transmitted/founder virus). However, the determinants of virus selection during transmission are unknown. Host cell-cell interactions in the mucosa may be critical in regulating susceptibility to infection. We hypothesized in this study that specific immune modulators secreted by uterine epithelial cells modulate susceptibility of dendritic cells (DC) to infection with HIV-1.Here we report that uterine epithelial cell secretions (i.e. conditioned medium, CM) decreased DC-SIGN expression on immature dendritic cells via a transforming growth factor beta (TGF-β) mechanism. Further, CM inhibited dendritic cell-mediated trans infection of HIV-1 expressing envelope proteins of prototypic reference. Similarly, CM inhibited trans infection of HIV-1 constructs expressing envelopes of transmitted/founder viruses, variants that are selected during sexual transmission. In contrast, whereas recombinant TGF- β1 inhibited trans infection of prototypic reference HIV-1 by dendritic cells, TGF-β1 had a minimal effect on trans infection of transmitted/founder variants irrespective of the reporter system used to measure trans infection.Our results provide the first direct evidence for uterine epithelial cell regulation of dendritic cell transmission of infection with reference and transmitted/founder HIV-1 variants. These findings have immediate implications for designing strategies to prevent sexual transmission of HIV-1

Tracking CNS and systemic sources of oxidative stress during the course of chronic neuroinflammation

The functional dynamics and cellular sources of oxidative stress are central to understanding MS pathogenesis but remain elusive, due to the lack of appropriate detection methods. Here we employ NAD(P)H fluorescence lifetime imaging to detect functional NADPH oxidases (NOX enzymes) in vivo to identify inflammatory monocytes, activated microglia, and astrocytes expressing NOX1 as major cellular sources of oxidative stress in the central nervous system of mice affected by experimental autoimmune encephalomyelitis (EAE). This directly affects neuronal function in vivo, indicated by sustained elevated neuronal calcium. The systemic involvement of oxidative stress is mirrored by overactivation of NOX enzymes in peripheral CD11b(+) cells in later phases of both MS and EAE. This effect is antagonized by systemic intake of the NOX inhibitor and anti-oxidant epigallocatechin-3-gallate. Together, this persistent hyper-activation of oxidative enzymes suggests an "oxidative stress memory" both in the periphery and CNS compartments, in chronic neuroinflammation

From Poverty to Disaster and Back: a Review of the Literature

Poor people are disproportionally affected by natural hazards and disasters. This paper provides a review of the multiple factors that explain why this is the case. It explores the role of exposure (often, but not always, poor people are more likely to be affected by hazards), vulnerability (when they are affected, poor people tend to lose a larger fraction of their wealth), and socio-economic resilience (poor people have a lower ability to cope with and recover from disaster impacts). Finally, the paper highlights the vicious circle between poverty and disaster losses: poverty is a major driver of people’s vulnerability to natural disasters, which in turn increase poverty in a measurable and significant way. The main policy implication is that poverty reduction can be considered as disaster risk management, and disaster risk management can be considered as poverty reduction