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Bacteroides, the prominent bacteria in the human gut, play a crucial role in degrading complex polysaccharides. Their abundance is influenced by phages belonging to theCrassviralesorder. Despite identifying over 600Crassviralesgenomes computationally, only few have been successfully isolated. Continued efforts in isolation of moreCrassviralesgenomes can provide insights into phage-host-evolution and infection mechanisms. We focused on wastewater samples, as potential sources of phages infecting variousBacteroideshosts. Sequencing, assembly, and characterization of isolated phages revealed 14 complete genomes belonging to three novelCrassviralesspecies infectingBacteroides cellulosilyticusWH2. These species,Kehishuvirussp. ‘tikkala’ strain Bc01,Kolpuevirussp. ‘frurule’ strain Bc03, and ‘Rudgehvirus jaberico’ strain Bc11, spanned two families, and three genera, displaying a broad range of virion productions. Upon testing all successfully culturedCrassviralesspecies and their respective bacterial hosts, we discovered that they do not exhibit co-evolutionary patterns with their bacterial hosts. Furthermore, we observed variations in gene similarity, with greater shared similarity observed within genera. However, despite belonging to different genera, the three novel species shared a unique structural gene that encodes the tail spike protein. When investigating the relationship between this gene and host interaction, we discovered evidence of purifying selection, indicating its functional importance. Moreover, our analysis demonstrated that this tail spike protein binds to the TonB-dependent receptors present on the bacterial host surface. Combining these observations, our findings provide insights into phage-host interactions and present threeCrassviralesspecies as an ideal system for controlled infectivity experiments on one of the most dominant members of the human enteric virome.

 

ABSTRACT Many commensal bacteria antagonize each other or their host by producing syringe-like secretion systems called contractile injection systems (CIS). Members of the Bacteroidales family have been shown to produce only one type of CIS—a contact-dependent type 6 secretion system that mediates bacterium-bacterium interactions. Here, we show that a second distinct cluster of genes from Bacteroidales bacteria from the human microbiome may encode yet-uncharacterized injection systems that we term Bacteroidales injection systems (BIS). We found that BIS genes are present in the gut microbiomes of 99% of individuals from the United States and Europe and that BIS genes are more prevalent in the gut microbiomes of healthy individuals than in those individuals suffering from inflammatory bowel disease. Gene clusters similar to that of the BIS mediate interactions between bacteria and diverse eukaryotes, like amoeba, insects, and tubeworms. Our findings highlight the ubiquity of the BIS gene cluster in the human gut and emphasize the relevance of the gut microbiome to the human host. These results warrant investigations into the structure and function of the BIS and how they might mediate interactions between Bacteroidales bacteria and the human host or microbiome. IMPORTANCE To engage with host cells, diverse pathogenic bacteria produce syringe-like structures called contractile injection systems (CIS). CIS are evolutionarily related to the contractile tails of bacteriophages and are specialized to puncture membranes, often delivering effectors to target cells. Although CIS are key for pathogens to cause disease, paradoxically, similar injection systems have been identified within healthy human microbiome bacteria. Here, we show that gene clusters encoding a predicted CIS, which we term Bacteroidales injection systems (BIS), are present in the microbiomes of nearly all adult humans tested from Western countries. BIS genes are enriched within human gut microbiomes and are expressed both in vitro and in vivo . Further, a greater abundance of BIS genes is present within healthy gut microbiomes than in those humans with with inflammatory bowel disease (IBD). Our discovery provides a potentially distinct means by which our microbiome interacts with the human host or its microbiome. 

ABSTRACT Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. Environmental factors and microbe-microbe interactions act simultaneously on the microbial community structure, making the microbiome dynamics challenging to predict. The coral microbiome is essential to the health of coral reefs and sensitive to environmental changes. Here, we develop a dynamic model to determine the microbial community structure associated with the surface mucus layer (SML) of corals using temperature as an extrinsic factor and microbial network as an intrinsic factor. The model was validated by comparing the predicted relative abundances of microbial taxa to the relative abundances of microbial taxa from the sample data. The SML microbiome from Pseudodiploria strigosa was collected across reef zones in Bermuda, where inner and outer reefs are exposed to distinct thermal profiles. A shotgun metagenomics approach was used to describe the taxonomic composition and the microbial network of the coral SML microbiome. By simulating the annual temperature fluctuations at each reef zone, the model output is statistically identical to the observed data. The model was further applied to six scenarios that combined different profiles of temperature and microbial network to investigate the influence of each of these two factors on the model accuracy. The SML microbiome was best predicted by model scenarios with the temperature profile that was closest to the local thermal environment, regardless of the microbial network profile. Our model shows that the SML microbiome of P. strigosa in Bermuda is primarily structured by seasonal fluctuations in temperature at a reef scale, while the microbial network is a secondary driver. IMPORTANCE Coral microbiome dysbiosis (i.e., shifts in the microbial community structure or complete loss of microbial symbionts) caused by environmental changes is a key player in the decline of coral health worldwide. Multiple factors in the water column and the surrounding biological community influence the dynamics of the coral microbiome. However, by including only temperature as an external factor, our model proved to be successful in describing the microbial community associated with the surface mucus layer (SML) of the coral P. strigosa . The dynamic model developed and validated in this study is a potential tool to predict the coral microbiome under different temperature conditions. 

Intestinal stem cells are non-quiescent, dividing epithelial cells that rapidly differentiate into progenitor cells of the absorptive and secretory cell lineages. The kinetics of this process is rapid such that the epithelium is replaced weekly. To determine how the transcriptome and proteome keep pace with rapid differentiation, we developed a new cell sorting method to purify mouse colon epithelial cells. Here we show that alternative mRNA splicing and polyadenylation dominate changes in the transcriptome as stem cells differentiate into progenitors. In contrast, as progenitors differentiate into mature cell types, changes in mRNA levels dominate the transcriptome. RNA processing targets regulators of cell cycle, RNA, cell adhesion, SUMOylation, and Wnt and Notch signaling. Additionally, global proteome profiling detected >2,800 proteins and revealed RNA:protein patterns of abundance and correlation. Paired together, these data highlight new potentials for autocrine and feedback regulation and provide new insights into cell state transitions in the crypt.

 

Bacterial growth curves are essential representations for characterizing bacteria metabolism within a variety of media compositions. Using high-throughput, spectrophotometers capable of processing tens of 96-well plates, quantitative phenotypic information can be easily integrated into the current data structures that describe a bacterial organism. The PMAnalyzer pipeline performs a growth curve analysis to parameterize the unique features occurring within microtiter wells containing specific growth media sources. We have expanded the pipeline capabilities and provide a user-friendly, online implementation of this automated pipeline. PMAnalyzer version 2.0 provides fast automatic growth curve parameter analysis, growth identification and high resolution figures of sample-replicate growth curves and several statistical analyses.

PMAnalyzer v2.0 can be found at https://edwards.sdsu.edu/pmanalyzer/. Source code for the pipeline can be found on GitHub at https://github.com/dacuevas/PMAnalyzer. Source code for the online implementation can be found on GitHub at https://github.com/dacuevas/PMAnalyzerWeb.

Supplementary data are available at Bioinformatics online.