Draft Genome Sequence of Chromatium okenii Isolated from the Stratified Alpine Lake Cadagno

Abstract

Blooms of purple sulfur bacteria (PSB) are important drivers of the global sulfur cycling oxidizing reduced sulfur in intertidal flats and stagnant water bodies. Since the discovery of PSB Chromatium okenii in 1838, it has been found that this species is characteristic of for stratified, sulfidic environments worldwide and its autotrophic metabolism has been studied in depth since. We describe here the first high-quality draft genome of a large-celled, phototrophic, γ-proteobacteria of the genus Chromatium isolated from the stratified alpine Lake Cadagno, C. okenii strain LaCa. Long read technology was used to assemble the 3.78 Mb genome that encodes 3,016 protein-coding genes and 67 RNA genes. Our findings are discussed from an ecological perspective related to Lake Cadagno. Moreover, findings of previous studies on the phototrophic and the proposed chemoautotrophic metabolism of C. okenii were confirmed on a genomic level. We additionally compared the C. okenii genome with other genomes of sequenced, phototrophic sulfur bacteria from the same environment. We found that biological functions involved in chemotaxis, movement and S-layer-proteins were enriched in strain LaCa. We describe these features as possible adaptions of strain LaCa to rapidly changing environmental conditions within the chemocline and the protection against phage infection during blooms. The high quality draft genome of C. okenii strain LaCa thereby provides a basis for future functional research on bioconvection and phage infection dynamics of blooming PSB.

Purple sulfur bacteria fix N2 via molybdenum-nitrogenase in a low molybdenum Proterozoic ocean analogue

“…The genetic potential for N 2 fixation is present in several environmentally relevant PSB e.g. [27][28][29] but genetic potential alone cannot be unambiguously translated to activity in the environment 30 .…”

Persistence of bioconvection‐induced mixed layers in a stratified lake

“…Recently, Berg et al (2019) reported on C. okenii 's ability to carry out aerobic sulfide respiration, independent of light, in the presence of nano‐scale oxygen concentrations. Luedin et al (2019) provided genetic evidence for dark chemolithotrophic oxidation of sulfur compounds by C. okenii . These metabolic pathways along with the presence of intracellular sulfur globules and energy storage molecules such as polyhydroxybutyrate (PHB; Luedin et al 2019) or glycogen (Berg et al 2019) in C. okenii , indicate the existence of a nighttime energy source crucial for bacterial motility.…”

“…Luedin et al (2019) provided genetic evidence for dark chemolithotrophic oxidation of sulfur compounds by C. okenii . These metabolic pathways along with the presence of intracellular sulfur globules and energy storage molecules such as polyhydroxybutyrate (PHB; Luedin et al 2019) or glycogen (Berg et al 2019) in C. okenii , indicate the existence of a nighttime energy source crucial for bacterial motility. Chemotaxis, negative for oxygen and positive towards H 2 S, is well described in C. okenii (Luedin et al 2019).…”

“…These metabolic pathways along with the presence of intracellular sulfur globules and energy storage molecules such as polyhydroxybutyrate (PHB; Luedin et al 2019) or glycogen (Berg et al 2019) in C. okenii , indicate the existence of a nighttime energy source crucial for bacterial motility. Chemotaxis, negative for oxygen and positive towards H 2 S, is well described in C. okenii (Luedin et al 2019). During nighttime, the weakening of convection and diffusivity‐driven smoothing of the ML may re‐establish H 2 S and oxygen gradients enabling bacteria chemotactic behavior.…”

Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing

“…The widespread adoption of electronic health records (EHRs) creates an opportunity to use machine learning methods on routinely collected data for more accurate and personalized risk modeling. In recent years, there has been growing interest in the use of deep learning approaches to cater for the high-dimensional longitudinal data in the ICU, with numerous studies outperforming traditional risk scores at predicting mortality, 6 , 7 specific organ dysfunctions or syndromes, 8–10 and life-support interventions. 11 One shortcoming is that the majority of models are examples of single-task (ST) learning—trained on a specific adverse event.…”