The composition of bacterial communities in Lake Baikal in different hydrological periods and at different depths (down to 1515 m) has been analyzed using pyrosequencing of the 16S rRNA gene V3 variable region. Most of the resulting 34 562 reads of the Bacteria domain have clustered into 1693 operational taxonomic units (OTUs) classified with the phyla Proteobacteria, Actinobacteria, Chloroflexi, Bacteroidetes, Firmicutes, Acidobacteria and Cyanobacteria. It has been found that their composition at the family level and relative contributions to bacterial communities distributed over the water column vary depending on hydrological period. The number of OTUs and the parameters of taxonomic richness (ACE, Chao1 indices) and diversity (Shannon and inverse Simpson index) reach the highest values in water layers. The composition of bacterial communities in these layers remains relatively constant, whereas that in surface layers differs between hydrological seasons. The dynamics of physicochemical conditions over the water column and their relative constancy in deep layers are decisive factors in shaping the pattern of bacterial communities in Lake Baikal.
The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51 strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days.
Background: The escalating demographic shift towards an aging population and the widespread occurrence of immunological diseases have contributed to an elevation in the frequency of community-acquired infections. Notably, among these infections, community-acquired bloodstream infections (CABSI) stand out due to their significant lethality. Detailed medical history inquiries, assessment of underlying immune status, detection of the source of infection, and initial precise identification and treatment of the infectious agents can improve the prognosis of CABSI.
State Key Laboratory of Biocontrol, Guangdong Provincial Key Laboratory for Aquatic Economic Animals and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
This study examined the effects of on the growth performance, innate immunity, and gut microbiota of under different water temperature conditions. Feeding regimens included a 20% fishmeal diet (control), a low-fish meal (LFM) diet with 10% fishmeal and an LFM diet supplemented with 0.03% .
Introduction: The Sanxingdui site (Sichuan, China) is the typical representative of the ancient Shu culture, which lasts from the late Neolithic to early Western Zhou. The sacrificial pits are located in the core region of Sanxingdui site, and numerous artifacts are unearthed including ivory, seashells, bronzes, pottery, jade, stone, gold, bone, and horn products. The function of the pits and buried artifacts has always been the focus, but the microbiome around artifacts attracts less attention.
The bacterial microbiome of the ant has been well characterized across body regions and maturation levels. However, potential effects of entomopathogens on the gut microbiome, and the fungal communities therein, are yet to be assessed. Additionally, the mycobiome remains often overlooked despite playing a vital role in gut ecology with potential implications for health and infection outcomes.
