Cyanobacterial Diversity and a New Acaryochloris-Like Symbiont from Bahamian Sea-Squirts

Symbiotic interactions between ascidians (sea-squirts) and microbes are poorly understood. Here we characterized the cyanobacteria in the tissues of 8 distinct didemnid taxa from shallow-water marine habitats in the Bahamas Islands by sequencing a fragment of the cyanobacterial 16S rRNA gene and the entire 16S–23S rRNA internal transcribed spacer region (ITS) and by examining symbiont morphology with transmission electron (TEM) and confocal microscopy (CM). As described previously for other species, Trididemnum spp. mostly contained symbionts associated with the Prochloron-Synechocystis group. However, sequence analysis of the symbionts in Lissoclinum revealed two unique clades. The first contained a novel cyanobacterial clade, while the second clade was closely associated with Acaryochloris marina. CM revealed the presence of chlorophyll d (chl d) and phycobiliproteins (PBPs) within these symbiont cells, as is characteristic of Acaryochloris species. The presence of symbionts was also observed by TEM inside the tunic of both the adult and larvae of L. fragile, indicating vertical transmission to progeny. Based on molecular phylogenetic and microscopic analyses, Candidatus Acaryochloris bahamiensis nov. sp. is proposed for this symbiotic cyanobacterium. Our results support the hypothesis that photosymbiont communities in ascidians are structured by host phylogeny, but in some cases, also by sampling location

Pulmonary function impairment of asymptomatic and persistently symptomatic patients 4 months after COVID-19 according to disease severity.

Objective: Evaluation of pulmonary function impairment after COVID-19 in persistently symptomatic and asymptomatic patients of all disease severities and characterisation of risk factors. Methods: Patients with confirmed SARS-CoV-2 infection underwent prospective follow-up with pulmonary function testing and blood gas analysis during steady-state cycle exercise 4 months after acute illness. Pulmonary function impairment (PFI) was defined as reduction below 80% predicted of DLCOcSB, TLC, FVC, or FEV1. Clinical data were analyzed to identify risk factors for impaired pulmonary function. Results: 76 patients were included, hereof 35 outpatients with mild disease and 41 patients hospitalized due to COVID-19. Sixteen patients had critical disease requiring mechanical ventilation, 25 patients had moderate–severe disease. After 4 months, 44 patients reported persisting respiratory symptoms. Significant PFI was prevalent in 40 patients (52.6%) occurring among all disease severities. The most common cause for PFI was reduced DLCOcSB (n = 39, 51.3%), followed by reduced TLC and FVC. The severity of PFI was significantly associated with mechanical ventilation (p < 0.001). Further risk factors for DLCO impairment were COPD (p < 0.001), SARS-CoV-2 antibody-Titer (p = 0.014) and in hospitalized patients CT score. A decrease of paO2 > 3 mmHg during cycle exercise occurred in 1/5 of patients after mild disease course. Conclusion: We characterized pulmonary function impairment in asymptomatic and persistently symptomatic patients of different severity groups of COVID-19 and identified further risk factors associated with persistently decreased pulmonary function. Remarkably, gas exchange abnormalities were revealed upon cycle exercise in some patients with mild disease courses and no preexisting pulmonary condition

Down under the tunic: bacterial biodiversity hotspots and widespread ammonia-oxidizing archaea in coral reef ascidians

14 páginas, 3 tablas, 3 figuras.Ascidians are ecologically important components of marine ecosystems yet the ascidian microbiota remains largely unexplored beyond a few model species. We used 16S rRNA gene tag pyrosequencing to provide a comprehensive characterization of microbial symbionts in the tunic of 42 Great Barrier Reef ascidian samples representing 25 species. Results revealed high bacterial biodiversity (3 217 unique operational taxonomic units (OTU0.03) from 19 described and 14 candidate phyla) and the widespread occurrence of ammonia-oxidizing Thaumarchaeota in coral reef ascidians (24 of 25 host species). The ascidian microbiota was clearly differentiated from seawater microbial communities and included symbiont lineages shared with other invertebrate hosts as well as unique, ascidian-specific phylotypes. Several rare seawater microbes were markedly enriched (200–700 fold) in the ascidian tunic, suggesting that the rare biosphere of seawater may act as a conduit for horizontal symbiont transfer. However, most OTUs (71%) were rare and specific to single hosts and a significant correlation between host relatedness and symbiont community similarity was detected, indicating a high degree of host-specificity and potential role of vertical transmission in structuring these communities. We hypothesize that the complex ascidian microbiota revealed herein is maintained by the dynamic microenvironments within the ascidian tunic, offering optimal conditions for different metabolic pathways such as ample chemical substrate (ammonia-rich host waste) and physical habitat (high oxygen, low irradiance) for nitrification. Thus, ascidian hosts provide unique and fertile niches for diverse microorganisms and may represent an important and previously unrecognized habitat for nitrite/nitrate regeneration in coral reef ecosystems.This research was funded by the Marie Curie International Reintegration Grant FP7-PEOPLE-2010-RG 277038 (within the 7th European Community Framework Program), the Spanish Government projects CTM2010-17755 and CTM2010-22218 and the Catalan Government grant 2009 SGR-484 for Consolidated Research Groups. NSW was funded through an Australian Research Council Future Fellowship (FT1200100480).Peer reviewe

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. © 2021, The Author(s)