Genomic diversity and ecological distribution of marine Pseudoalteromonas phages

Pseudoalteromonas,with a ubiquitous distribution,is one of the most abundant marine bacterial genera.It is especially abundant in the deep sea and polar seas,where it has been found to have a broad metabolic capacity and unique co-existence strategies with other organisms.However,only a few Pseudoalteromonas phages have so far been isolated and investigated and their genomic diversity and distribution patterns are still unclear.Here,the genomes,taxonomic features and distribution patterns of Pseudoalteromonas phages are systematically analyzed,based on the microbial and viral genomes and metagenome datasets.A total of 143 complete or nearly complete Pseudoalteromonas-associated phage genomes(PSAPGs)were identifed,including 34 Pseudoalteromonas phage isolates,24 proviruses,and 85 Pseudoalteromonas-associated uncultured viral genomes(UViGs);these were assigned to 47 viral clusters at the genus level.Many integrated proviruses(n=24)and flamentous phages were detected(n=32),suggesting the prevalence of viral lysogenic life cycle in Pseudoalteromonas.PSAPGs encoded 66 types of 249 potential auxiliary metabolic genes(AMGs)relating to peptidases and nucleotide metabolism.They may also participate in marine biogeochemical cycles through the manipulation of the metabolism of their hosts,especially in the phosphorus and sulfur cycles.Siphoviral and flamentous PSAPGs were the predominant viral lineages found in polar areas,while some myoviral and siphoviral PSAPGs encoding transposase were more abundant in the deep sea.This study has expanded our understanding of the taxonomy,phylogenetic and ecological scope of marine Pseudoalteromonas phages and deepens our knowledge of viral impacts on Pseudoalteromonas.It will provide a baseline for the study of interactions between phages and Pseudoalteromonas in the ocean.

The effect of iron on the preservation of organic carbon in marine sediments and its implications for carbon sequestration

Marine sediments are the most significant reservoir of organic carbon(OC)in Earth′s surface system.Iron,a crucial component of the marine biogeochemical cycle,has a considerable impact on marine ecology and carbon cycling.Understanding the effect of iron on the preservation of OC in marine sediments is essential for comprehending biogeochemical processes of carbon and climate change.This review summarizes the methods for characterizing the content and structure of iron-bound OC and explores the influencing mechanism of iron on OC preservation in marine sediments from two aspects:the selective preservation of OC by reactive iron minerals(iron oxides and iron sulfides)and iron redox processes.The selective preservation of sedimentary OC is influenced by different types of reactive iron minerals,OC reactivity,and functional groups.The iron redox process has dual effects on the preservation and degradation of OC.By considering sedimentary records of iron-bound OC across diverse marine environments,the role of iron in long-term preservation of OC and its significance for carbon sequestration are illustrated.Future research should focus on identifying effective methods for extracting reactive iron,the effect of diverse functional groups and marine sedimentary environments on the selective preservation of OC,and the mediation of microorganisms.Such work will help elucidate the influencing mechanisms of iron on the long-term burial and preservation of OC and explore its potential application in marine carbon sequestration to maximize its role in achieving carbon neutrality.

Correction: Genomic diversity and ecological distribution of marine Pseudoalteromonas phages

In this article the graphics relating to Figs.2 and 3 captions had been interchanged;the fgure(s)should have appeared as shown below.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing.

Carbon pools and fluxes in the China Seas and adjacent oceans

The China Seas include the South China Sea, East China Sea, Yellow Sea, and Bohai Sea. Located off the Northwestern Pacific margin, covering 4700000 km^2 from tropical to northern temperate zones, and including a variety of continental margins/basins and depths, the China Seas provide typical cases for carbon budget studies. The South China Sea being a deep basin and part of the Western Pacific Warm Pool is characterized by oceanic features; the East China Sea with a wide continental shelf, enormous terrestrial discharges and open margins to the West Pacific, is featured by strong cross-shelf materials transport; the Yellow Sea is featured by the confluence of cold and warm waters; and the Bohai Sea is a shallow semiclosed gulf with strong impacts of human activities. Three large rivers, the Yangtze River, Yellow River, and Pearl River, flow into the East China Sea, the Bohai Sea, and the South China Sea, respectively. The Kuroshio Current at the outer margin of the Chinese continental shelf is one of the two major western boundary currents of the world oceans and its strength and position directly affect the regional climate of China. These characteristics make the China Seas a typical case of marginal seas to study carbon storage and fluxes. This paper systematically analyzes the literature data on the carbon pools and fluxes of the Bohai Sea,Yellow Sea, East China Sea, and South China Sea, including different interfaces(land-sea, sea-air, sediment-water, and marginal sea-open ocean) and different ecosystems(mangroves, wetland, seagrass beds, macroalgae mariculture, coral reefs, euphotic zones, and water column). Among the four seas, the Bohai Sea and South China Sea are acting as CO_2 sources, releasing about0.22 and 13.86–33.60 Tg C yr^(-1) into the atmosphere, respectively, whereas the Yellow Sea and East China Sea are acting as carbon sinks, absorbing about 1.15 and 6.92–23.30 Tg C yr^(-1) of atmospheric CO_2, respectively. Overall, if only the CO_2 exchange at the sea-air interface is considered, the Chinese marginal seas appear to be a source of atmospheric CO_2, with a net release of 6.01–9.33 Tg C yr^(-1), mainly from the inputs of rivers and adjacent oceans. The riverine dissolved inorganic carbon (DIC) input into the Bohai Sea and Yellow Sea, East China Sea, and South China Sea are 5.04, 14.60, and 40.14 Tg C yr^(-1),respectively. The DIC input from adjacent oceans is as high as 144.81 Tg C yr^(-1), significantly exceeding the carbon released from the seas to the atmosphere. In terms of output, the depositional fluxes of organic carbon in the Bohai Sea, Yellow Sea, East China Sea, and South China Sea are 2.00, 3.60, 7.40, and 5.92 Tg C yr^(-1), respectively. The fluxes of organic carbon from the East China Sea and South China Sea to the adjacent oceans are 15.25–36.70 and 43.93 Tg C yr^(-1), respectively. The annual carbon storage of mangroves, wetlands, and seagrass in Chinese coastal waters is 0.36–1.75 Tg C yr^(-1), with a dissolved organic carbon(DOC) output from seagrass beds of up to 0.59 Tg C yr^(-1). Removable organic carbon flux by Chinese macroalgae mariculture account for 0.68 Tg C yr^(-1) and the associated POC depositional and DOC releasing fluxes are 0.14 and 0.82 Tg C yr^(-1), respectively. Thus, in total, the annual output of organic carbon, which is mainly DOC, in the China Seas is 81.72–104.56 Tg C yr^(-1). The DOC efflux from the East China Sea to the adjacent oceans is 15.00–35.00 Tg C yr^(-1). The DOC efflux from the South China Sea is 31.39 Tg C yr^(-1). Although the marginal China Seas seem to be a source of atmospheric CO_2 based on the CO_2 flux at the sea-air interface, the combined effects of the riverine input in the area, oceanic input, depositional export,and microbial carbon pump(DOC conversion and output) indicate that the China Seas represent an important carbon storage area.

Minireview: The role of viruses in marine photosynthetic biofilms

Microphytobenthos and sea ice algae comprise globally significant photosynthetic biofilms.While their microalgal and bacterial constituents are well characterized,there is very little information on their viral communities or on the virus-bacteria and virus-algae interactions within them.While high levels of interaction might be expected because of the high density of cells,infection rates,particularly of microalgae,have been found to be low.It remains unclear whether this is a result of environment characteristics,developed resistance or because of the small number of studies.

An improved GNSS ambiguity best integer equivariant estimation method with Laplacian distribution for urban low-cost RTK positioning

The integer least squares(ILS)estimation is commonly used for carrier phase ambiguity resolution(AR).More recently,the best integer equivariant(BIE)estimation has also attracted an attention for complex application scenarios,which exhibits higher reliability by a weighted fusion of integer candidates.However,traditional BIE estimation with Gaussian distribution(GBIE)faces challenges in fully utilizing the advantages of BIE for urban low-cost positioning,mainly due to the presence of outliers and unmodeled errors.To this end,an improved BIE estimation method with Laplacian distribution(LBIE)is proposed,and several key issues are discussed,including the weight function of LBIE,determination of the candidates included based on the OIA test,and derivation of the variance of LBIE solutions for reliability evaluation.The results show that the proposed LBIE method has the positioning accuracy similar to the BIE using multivariate t-distribution(TBIE),and significantly outperforms the ILS-PAR and GBIE methods.In an urban expressway test with a Huawei Mate40 smartphone,the LBIE method has positioning errors of less than 0.5 m in three directions and obtains over 50%improvements compared to the ILS-PAR and GBIE methods.In an urban canyon test with a low-cost receiver STA8100 produced by STMicroelectronics,the positioning accuracy of LBIE in three directions is 0.112 m,0.107 m,and 0.252 m,respectively,with improvements of 17.6%,27.2%,and 26.1%compared to GBIE,and 23.3%,28.2%,and 30.6%compared to ILS-PAR.Moreover,its computational time increases by 30–40%compared to ILS-PAR and is approximately half of that using TBIE.