Maternal binge alcohol consumption leads to distinctive acute perturbations in embryonic cardiac gene expression profiles.

BACKGROUND: Excessive alcohol consumption during pregnancy is associated with high risk of congenital heart defects, but it is unclear how alcohol specifically affects heart development during the acute aftermath of a maternal binge drinking episode. We hypothesize that administration of a single maternal binge dose of alcohol to pregnant mice at embryonic day 9.5 (E9.5) causes perturbations in the expression patterns of specific genes in the developing heart in the acute period (1-3 days) following the binge episode. To test this hypothesis and identify strong candidate ethanol-sensitive target genes of interest, we adapted a mouse binge alcohol model that is associated with a high incidence of congenital heart defects as described below. METHODS/RESULTS: Pregnant mice were administered a single dose of alcohol (2.5 g/kg in saline) or control (saline alone) via oral gavage. To evaluate the impact of maternal binge alcohol on cardiac gene expression profiles, we isolated embryonic hearts from both groups (n = 5/group) at 24, 48, and 72 h post-gavage for transcriptomic analyses. RNA was extracted and evaluated using quantitative RNA-sequencing (RNA-Seq) methods. To identify a cohort of binge-altered cardiac genes, we set the threshold for change at >2.0-fold difference with adjusted p < 0.05 versus control.  RNA-Seq analysis of cardiac gene expression revealed that of the 17 genes that were altered within the first 48 h post-binge, with the largest category consisting of transcription factors (Alx1, Alx4, HoxB7, HoxD8, and Runx2), followed by signaling molecules (Adamts18, Dkk2, Rtl1, and Wnt7a). Furthermore, multiple comparative and pathway analyses suggested that several of the candidate genes identified through differential RNA-Seq analysis may interact through certain common pathways. To investigate this further, we performed gene-specific qPCR analyses for three representative candidate targets: Runx2, Wnt7a, and Mlxipl. Notably, only Wnt7a showed significantly (p < 0.05) decreased expression in response to maternal binge alcohol in the qPCR assays. CONCLUSIONS: These findings identify Wnt7a and a short list of potential other candidate genes and pathways for further study, which could provide mechanistic insights into how maternal binge alcohol consumption produces congenital cardiac malformations.

Bacterial community structure alterations within the colorectal cancer gut microbiome.

BACKGROUND: Colorectal cancer is a leading cause of cancer-related deaths worldwide. The human gut microbiome has become an active area of research for understanding the initiation, progression, and treatment of colorectal cancer. Despite multiple studies having found significant alterations in the carriage of specific bacteria within the gut microbiome of colorectal cancer patients, no single bacterium has been unequivocally connected to all cases. Whether alterations in species carriages are the cause or outcome of cancer formation is still unclear, but what is clear is that focus should be placed on understanding changes to the bacterial community structure within the cancer-associated gut microbiome. RESULTS: By applying a novel set of analyses on 252 previously published whole-genome shotgun sequenced fecal samples from healthy and late-stage colorectal cancer subjects, we identify taxonomic, functional, and structural changes within the cancer-associated human gut microbiome. Bacterial association networks constructed from these data exhibited widespread differences in the underlying bacterial community structure between healthy and colorectal cancer associated gut microbiomes. Within the cancer-associated ecosystem, bacterial species were found to form associations with other species that are taxonomically and functionally dissimilar to themselves, as well as form modules functionally geared towards potential changes in the tumor-associated ecosystem. Bacterial community profiling of these samples revealed a significant increase in species diversity within the cancer-associated gut microbiome, and an elevated relative abundance of species classified as originating from the oral microbiome including, but not limited to, Fusobacterium nucleatum, Peptostreptococcus stomatis, Gemella morbillorum, and Parvimonas micra. Differential abundance analyses of community functional capabilities revealed an elevation in functions linked to virulence factors and peptide degradation, and a reduction in functions involved in amino-acid biosynthesis within the colorectal cancer gut microbiome. CONCLUSIONS: We utilize whole-genome shotgun sequenced fecal samples provided from a large cohort of late-stage colorectal cancer and healthy subjects to identify a number of potentially important taxonomic, functional, and structural alterations occurring within the colorectal cancer associated gut microbiome. Our analyses indicate that the cancer-associated ecosystem influences bacterial partner selection in the native microbiota, and we highlight specific oral bacteria and their associations as potentially relevant towards aiding tumor progression.

Learning a mixture of microbial networks using minorization-maximization.

MOTIVATION: The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network. RESULTS: We present a mixture model framework to address the scenario when the sample-taxa matrix is associated with K microbial networks. This count matrix is modeled using a mixture of K Multivariate Poisson Log-Normal distributions and parameters are estimated using a maximum likelihood framework. Our parameter estimation algorithm is based on the minorization-maximization principle combined with gradient ascent and block updates. Synthetic datasets were generated to assess the performance of our approach on absolute count data, compositional data and normalized data. We also addressed the recovery of sparse networks based on an l1-penalty model. AVAILABILITY AND IMPLEMENTATION: MixMPLN is implemented in R and is freely available at https://github.com/sahatava/MixMPLN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GRASP2: fast and memory-efficient gene-centric assembly and homolog search for metagenomic sequencing data.

BACKGROUND: A crucial task in metagenomic analysis is to annotate the function and taxonomy of the sequencing reads generated from a microbiome sample. In general, the reads can either be assembled into contigs and searched against reference databases, or individually searched without assembly. The first approach may suffer from fragmentary and incomplete assembly, while the second is hampered by the reduced functional signal contained in the short reads. To tackle these issues, we have previously developed GRASP (Guided Reference-based Assembly of Short Peptides), which accepts a reference protein sequence as input and aims to assemble its homologs from a database containing fragmentary protein sequences. In addition to a gene-centric assembly tool, GRASP also serves as a homolog search tool when using the assembled protein sequences as templates to recruit reads. GRASP has significantly improved recall rate (60-80% vs. 30-40%) compared to other homolog search tools such as BLAST. However, GRASP is both time- and space-consuming. Subsequently, we developed GRASPx, which is 30X faster than GRASP. Here, we present a completely redesigned algorithm, GRASP2, for this computational problem. RESULTS: GRASP2 utilizes Burrows-Wheeler Transformation (BWT) and FM-index to perform assembly graph generation, and reduces the search space by employing a fast ungapped alignment strategy as a filter. GRASP2 also explicitly generates candidate paths prior to alignment, which effectively uncouples the iterative access of the assembly graph and alignment matrix. This strategy makes the execution of the program more efficient under current computer architecture, and contributes to GRASP2's speedup. GRASP2 is 8-fold faster than GRASPx (and 250-fold faster than GRASP) and uses 8-fold less memory while maintaining the original high recall rate of GRASP. GRASP2 reaches ~ 80% recall rate compared to that of ~ 40% generated by BLAST, both at a high precision level (> 95%). With such a high performance, GRASP2 is only ~3X slower than BLASTP. CONCLUSION: GRASP2 is a high-performance gene-centric and homolog search tool with significant speedup compared to its predecessors, which makes GRASP2 a useful tool for metagenomics data analysis, GRASP2 is implemented in C++ and is freely available from http://www.sourceforge.net/projects/grasp2 .

Co-occurrence of Anaerobes in Human Chronic Wounds.

Chronic wounds are wounds that have failed to heal after 3 months of appropriate wound care. Previous reports have identified a diverse collection of bacteria in chronic wounds, and it has been postulated that bacterial profile may contribute to delayed healing. The purpose of this study was to perform a microbiome assessment of the Wound Healing and Etiology (WE-HEAL) Study cohort, including underlying comorbidities less commonly studied in the context of chronic wounds, such as autoimmune diseases, and investigate possible relationships of the wound microbiota with clinical healing trends. We examined chronic wound specimens from 60 patients collected through the WE-HEAL Study using 16S ribosomal RNA gene sequencing. A group of co-occurring obligate anaerobes was identified from taxonomic analysis guided by Dirichlet multinomial mixtures (DMM) modeling. The group includes members of the Gram-positive anaerobic cocci (GPAC) of the Clostridia class (i.e., Anaerococcus, Finegoldia, and Peptoniphilus) and additional strict anaerobes (i.e., Porphyromonas and Prevotella). We showed that the co-occurring group of obligate anaerobes not only co-exists with commonly identified wound species (such as Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas, Corynebacterium, and Streptococcus), but importantly, they could also predominate the wound microbiota. Furthermore, examination of clinical comorbidities of the WE-HEAL specimens showed that specific obligate and facultative anaerobes were significantly reduced in wounds presented with autoimmune disease. With respect to future healing trends, no association with the wound microbiome community or the abundance of individual wound species could be established. In conclusion, we identified a co-occurring obligate anaerobic community type that predominated some human chronic wounds and underrepresentation of anaerobes in wounds associated with autoimmune diseases. Possible elucidation of host environments or key factors that influence anaerobe colonization warrants further investigation in a larger cohort.

Papio spp. Colon microbiome and its link to obesity in pregnancy.

INTRODUCTION: Gut microbial communities are critical players in the pathogenesis of obesity. Pregnancy is associated with increased bacterial load and changes in gut bacterial diversity. Sparse data exist regarding composition of gut microbial communities in obesity combined with pregnancy. MATERIAL AND METHODS: Banked tissues were collected under sterile conditions during necropsy, from three non-obese (nOb) and four obese (Ob) near-term pregnant baboons. Sequences were assigned taxonomy using the Ribosomal Database Project classifier. Microbiome abundance and its difference between distinct groups were assessed by a nonparametric test. RESULTS: Three families predominated in both the nOb and Ob colonic microbiome: Prevotellaceae (25.98% and 32.71% respectively), Ruminococcaceae (12.96% and 7.48%), and Lachnospiraceae (8.78% and 11.74%). Seven families of the colon microbiome displayed differences between Ob and nOb groups. CONCLUSION: Changes in gut microbiome in pregnant obese animals open the venue for dietary manipulation in pregnancy.

Library preparation methodology can influence genomic and functional predictions in human microbiome research.

Observations from human microbiome studies are often conflicting or inconclusive. Many factors likely contribute to these issues including small cohort sizes, sample collection, and handling and processing differences. The field of microbiome research is moving from 16S rDNA gene sequencing to a more comprehensive genomic and functional representation through whole-genome sequencing (WGS) of complete communities. Here we performed quantitative and qualitative analyses comparing WGS metagenomic data from human stool specimens using the Illumina Nextera XT and Illumina TruSeq DNA PCR-free kits, and the KAPA Biosystems Hyper Prep PCR and PCR-free systems. Significant differences in taxonomy are observed among the four different next-generation sequencing library preparations using a DNA mock community and a cell control of known concentration. We also revealed biases in error profiles, duplication rates, and loss of reads representing organisms that have a high %G+C content that can significantly impact results. As with all methods, the use of benchmarking controls has revealed critical differences among methods that impact sequencing results and later would impact study interpretation. We recommend that the community adopt PCR-free-based approaches to reduce PCR bias that affects calculations of abundance and to improve assemblies for accurate taxonomic assignment. Furthermore, the inclusion of a known-input cell spike-in control provides accurate quantitation of organisms in clinical samples.

Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle.

The candidate phylum TM7 is globally distributed and often associated with human inflammatory mucosal diseases. Despite its prevalence, the TM7 phylum remains recalcitrant to cultivation, making it one of the most enigmatic phyla known. In this study, we cultivated a TM7 phylotype (TM7x) from the human oral cavity. This extremely small coccus (200-300 nm) has a distinctive lifestyle not previously observed in human-associated microbes. It is an obligate epibiont of an Actinomyces odontolyticus strain (XH001) yet also has a parasitic phase, thereby killing its host. This first completed genome (705 kb) for a human-associated TM7 phylotype revealed a complete lack of amino acid biosynthetic capacity. Comparative genomics analyses with uncultivated environmental TM7 assemblies show remarkable conserved gene synteny and only minimal gene loss/gain that may have occurred as TM7x adapted to conditions within the human host. Transcriptomic and metabolomic profiles provided the first indications, to our knowledge, that there is signaling interaction between TM7x and XH001. Furthermore, the induction of TNF-α production in macrophages by XH001 was repressed in the presence of TM7x, suggesting its potential immune suppression ability. Overall, our data provide intriguing insights into the uncultivability, pathogenicity, and unique lifestyle of this previously uncharacterized oral TM7 phylotype.

Utilization of defined microbial communities enables effective evaluation of meta-genomic assemblies.

BACKGROUND: Metagenomics is the study of the microbial genomes isolated from communities found on our bodies or in our environment. By correctly determining the relation between human health and the human associated microbial communities, novel mechanisms of health and disease can be found, thus enabling the development of novel diagnostics and therapeutics. Due to the diversity of the microbial communities, strategies developed for aligning human genomes cannot be utilized, and genomes of the microbial species in the community must be assembled de novo. However, in order to obtain the best metagenomic assemblies, it is important to choose the proper assembler. Due to the rapidly evolving nature of metagenomics, new assemblers are constantly created, and the field has not yet agreed on a standardized process. Furthermore, the truth sets used to compare these methods are either too simple (computationally derived diverse communities) or complex (microbial communities of unknown composition), yielding results that are hard to interpret. In this analysis, we interrogate the strengths and weaknesses of five popular assemblers through the use of defined biological samples of known genomic composition and abundance. We assessed the performance of each assembler on their ability to reassemble genomes, call taxonomic abundances, and recreate open reading frames (ORFs). RESULTS: We tested five metagenomic assemblers: Omega, metaSPAdes, IDBA-UD, metaVelvet and MEGAHIT on known and synthetic metagenomic data sets. MetaSPAdes excelled in diverse sets, IDBA-UD performed well all around, metaVelvet had high accuracy in high abundance organisms, and MEGAHIT was able to accurately differentiate similar organisms within a community. At the ORF level, metaSPAdes and MEGAHIT had the least number of missing ORFs within diverse and similar communities respectively. CONCLUSIONS: Depending on the metagenomics question asked, the correct assembler for the task at hand will differ. It is important to choose the appropriate assembler, and thus clearly define the biological problem of an experiment, as different assemblers will give different answers to the same question.

Meta-omic analyses of Baltic Sea cyanobacteria: diversity, community structure and salt acclimation.

Cyanobacteria are important phytoplankton in the Baltic Sea, an estuarine-like environment with pronounced north to south gradients in salinity and nutrient concentrations. Here, we present a metagenomic and -transcriptomic survey, with subsequent analyses targeting the genetic identity, phylogenetic diversity, and spatial distribution of Baltic Sea cyanobacteria. The cyanobacterial community constituted close to 12% of the microbial population sampled during a pre-bloom period (June-July 2009). The community was dominated by unicellular picocyanobacteria, specifically a few highly abundant taxa (Synechococcus and Cyanobium) with a long tail of low abundance representatives, and local peaks of bloom-forming heterocystous taxa. Cyanobacteria in the Baltic Sea differed genetically from those in adjacent limnic and marine waters as well as from cultivated and sequenced picocyanobacterial strains. Diversity peaked at brackish salinities 3.5-16 psu, with low N:P ratios. A shift in community composition from brackish to marine strains was accompanied by a change in the repertoire and expression of genes involved in salt acclimation. Overall, the pre-bloom cyanobacterial population was more genetically diverse, widespread and abundant than previously documented, with unicellular picocyanobacteria being the most abundant clade along the entire Baltic Sea salinity gradient.

Metagenome and Metatranscriptome Analyses Using Protein Family Profiles.

Analyses of metagenome data (MG) and metatranscriptome data (MT) are often challenged by a paucity of complete reference genome sequences and the uneven/low sequencing depth of the constituent organisms in the microbial community, which respectively limit the power of reference-based alignment and de novo sequence assembly. These limitations make accurate protein family classification and abundance estimation challenging, which in turn hamper downstream analyses such as abundance profiling of metabolic pathways, identification of differentially encoded/expressed genes, and de novo reconstruction of complete gene and protein sequences from the protein family of interest. The profile hidden Markov model (HMM) framework enables the construction of very useful probabilistic models for protein families that allow for accurate modeling of position specific matches, insertions, and deletions. We present a novel homology detection algorithm that integrates banded Viterbi algorithm for profile HMM parsing with an iterative simultaneous alignment and assembly computational framework. The algorithm searches a given profile HMM of a protein family against a database of fragmentary MG/MT sequencing data and simultaneously assembles complete or near-complete gene and protein sequences of the protein family. The resulting program, HMM-GRASPx, demonstrates superior performance in aligning and assembling homologs when benchmarked on both simulated marine MG and real human saliva MG datasets. On real supragingival plaque and stool MG datasets that were generated from healthy individuals, HMM-GRASPx accurately estimates the abundances of the antimicrobial resistance (AMR) gene families and enables accurate characterization of the resistome profiles of these microbial communities. For real human oral microbiome MT datasets, using the HMM-GRASPx estimated transcript abundances significantly improves detection of differentially expressed (DE) genes. Finally, HMM-GRASPx was used to reconstruct comprehensive sets of complete or near-complete protein and nucleotide sequences for the query protein families. HMM-GRASPx is freely available online from http://sourceforge.net/projects/hmm-graspx.

Effect of Macondo Prospect 252 Oil on Microbiota Associated with Pelagic Sargassum in the Northern Gulf of Mexico.

The environmental impact of major oil spills on marine microorganisms has yet to be thoroughly investigated using molecular biology techniques. The Deepwater Horizon (DWH) drilling rig explosion of 2010 affected an approximately 176,000 km2 surface area of the Gulf of Mexico (GOM) when an estimated 210 million gallons of oil from the Macondo Prospect spilled into the environment. Pelagic Sargassum, a complex of two surface drifting species (Sargassum natans and Sargassum fluitans) of marine brown macroalgae and a critically important habitat in the GOM ecosystem, was suffused by Macondo Prospect 252 oil released during the DWH event. Using 16S rRNA PCR and Roche 454 pyrosequencing, the effect of the oil on the bacterial population associated with pelagic Sargassum and contiguous waters was examined by comparing sequence data generated from samples collected from oiled and non-oiled locations in the northern GOM. Sequence data showed similar microbial composition in Sargassum regardless of exposure to oil primarily dominated by five phyla; Proteobacteria, Bacteroidetes, Actinobacteria, Verrucomicrobia, and unclassified bacteria. The microbial composition in water samples was significantly less diverse than for Sargassum and consisted primarily of Proteobacteria, Firmicutes, and Bacteroidetes. Due to the evenly distributed abundance of microbial species on oiled and non-oiled pelagic Sargassum, study findings indicate that DWH spilled oil had minimal effect on the composition and diversity of the microbial community associated with Sargassum and contiguous waters. However, higher abundances of Sulfitobacter and one species of Psychrobacter were found in oiled water samples when compared to non-oiled water samples indicating some effect of DHW oil in the microbial composition of seawater. Though there are a number of marine studies using molecular biology approaches, this is the first molecular examination of the impact of the DWH oil spill on bacterial communities associated with pelagic Sargassum and contiguous waters from the GOM.

GRASP: guided reference-based assembly of short peptides.

Protein sequences predicted from metagenomic datasets are annotated by identifying their homologs via sequence comparisons with reference or curated proteins. However, a majority of metagenomic protein sequences are partial-length, arising as a result of identifying genes on sequencing reads or on assembled nucleotide contigs, which themselves are often very fragmented. The fragmented nature of metagenomic protein predictions adversely impacts homology detection and, therefore, the quality of the overall annotation of the dataset. Here we present a novel algorithm called GRASP that accurately identifies the homologs of a given reference protein sequence from a database consisting of partial-length metagenomic proteins. Our homology detection strategy is guided by the reference sequence, and involves the simultaneous search and assembly of overlapping database sequences. GRASP was compared to three commonly used protein sequence search programs (BLASTP, PSI-BLAST and FASTM). Our evaluations using several simulated and real datasets show that GRASP has a significantly higher sensitivity than these programs while maintaining a very high specificity. GRASP can be a very useful program for detecting and quantifying taxonomic and protein family abundances in metagenomic datasets. GRASP is implemented in GNU C++, and is freely available at http://sourceforge.net/projects/grasp-release.

Differences in the Nasopharyngeal Microbiome During Acute Respiratory Tract Infection With Human Rhinovirus and Respiratory Syncytial Virus in Infancy.

Respiratory viruses alter the nasopharyngeal microbiome and may be associated with a distinct microbial signature. To test this hypothesis, we compared the nasopharyngeal microbiome of 135 previously healthy infants with acute respiratory infection due to human rhinovirus (HRV; n = 52) or respiratory syncytial virus (RSV; n = 83). The nasopharyngeal microbiome was assessed by sequencing the V4 region of the 16S ribosomal RNA. Respiratory viruses were identified by quantitative reverse-transcription polymerase chain reaction. We found significant differences in the overall taxonomic composition and abundance of certain bacterial genera between infants infected with HRV and those infected with RSV. Our results suggest that respiratory tract viral infections are associated with different nasopharyngeal microbial profiles.

GRASPx: efficient homolog-search of short peptide metagenome database through simultaneous alignment and assembly.

BACKGROUND: Metagenomics is a cultivation-independent approach that enables the study of the genomic composition of microbes present in an environment. Metagenomic samples are routinely sequenced using next-generation sequencing technologies that generate short nucleotide reads. Proteins identified from these reads are mostly of partial length. On the other hand, de novo assembly of a large metagenomic dataset is computationally demanding and the assembled contigs are often fragmented, resulting in the identification of protein sequences that are also of partial length and incomplete. Annotation of an incomplete protein sequence often proceeds by identifying its homologs in a database of reference sequences. Identifying the homologs of incomplete sequences is a challenge and can result in substandard annotation of proteins from metagenomic datasets. To address this problem, we recently developed a homology detection algorithm named GRASP (Guided Reference-based Assembly of Short Peptides) that identifies the homologs of a given reference protein sequence in a database of short peptide metagenomic sequences. GRASP was developed to implement a simultaneous alignment and assembly algorithm for annotation of short peptides identified on metagenomic reads. The program achieves significantly improved recall rate at the cost of computational efficiency. In this article, we adopted three techniques to speed up the original version of GRASP, including the pre-construction of extension links, local assembly of individual seeds, and the implementation of query-level parallelism. RESULTS: The resulting new program, GRASPx, achieves >30X speedup compared to its predecessor GRASP. At the same time, we show that the performance of GRASPx is consistent with that of GRASP, and that both of them significantly outperform other popular homology-search tools including the BLAST and FASTA suites. GRASPx was also applied to a human saliva metagenome dataset and shows superior performance for both recall and precision rates. CONCLUSIONS: In this article we present GRASPx, a fast and accurate homology-search program implementing a simultaneous alignment and assembly framework. GRASPx can be used for more comprehensive and accurate annotation of short peptides. GRASPx is freely available at http://graspx.sourceforge.net/ .

Gastrointestinal microbial populations can distinguish pediatric and adolescent Acute Lymphoblastic Leukemia (ALL) at the time of disease diagnosis.

BACKGROUND: An estimated 15,000 children and adolescents under the age of 19 years are diagnosed with leukemia, lymphoma and other tumors in the USA every year. All children and adolescent acute leukemia patients will undergo chemotherapy as part of their treatment regimen. Fortunately, survival rates for most pediatric cancers have improved at a remarkable pace over the past three decades, and the overall survival rate is greater than 90 % today. However, significant differences in survival rate have been found in different age groups (94 % in 1-9.99 years, 82 % in ≥10 years and 76 % in ≥15 years). ALL accounts for about three out of four cases of childhood leukemia. Intensive chemotherapy treatment coupled with prophylactic or therapeutic antibiotic use could potentially have a long-term effect on the resident gastrointestinal (GI) microbiome. The composition of GI microbiome and its changes upon chemotherapy in pediatric and adolescent leukemia patients is poorly understood. In this study, using 16S rRNA marker gene sequences we profile the GI microbial communities of pediatric and adolescent acute leukemia patients before and after chemotherapy treatment and compare with the microbiota of their healthy siblings. RESULTS: Our study cohort consisted of 51 participants, made up of matched pediatric and adolescent patients with ALL and a healthy sibling. We elucidated and compared the GI microbiota profiles of patients and their healthy sibling controls via analysis of 16S rRNA gene sequencing data. We assessed the GI microbiota composition in pediatric and adolescent patients with ALL during the course of chemotherapy by comparing stool samples taken before chemotherapy with stool samples collected at varying time points during the chemotherapeutic treatment. The microbiota profiles of both patients and control sibling groups are dominated by members of Bacteroides, Prevotella, and Faecalibacterium. At the genus level, both groups share many taxa in common, but the microbiota diversity of the patient group is significantly lower than that of the control group. It was possible to distinguish between the patient and control groups based on their microbiota profiles. The top taxa include Anaerostipes, Coprococcus, Roseburia, and Ruminococcus2 with relatively higher abundance in the control group. The observed microbiota changes are likely the result of several factors including a direct influence of therapeutic compounds on the gut flora and an indirect effect of chemotherapy on the immune system, which, in turn, affects the microbiota. CONCLUSIONS: This study provides significant information on GI microbiota populations in immunocompromised children and opens up the potential for developing novel diagnostics based on stool tests and therapies to improve the dysbiotic condition of the microbiota at the time of diagnosis and in the earliest stages of chemotherapy.

Distribution and expression of microbial rhodopsins in the Baltic Sea and adjacent waters.

Rhodopsins are light-driven ion-pumping membrane proteins found in many organisms and are proposed to be of global importance for oceanic microbial energy generation. Several studies have focused on marine environments, with less exploration of rhodopsins in brackish waters. We investigated microbial rhodopsins in the Baltic Sea using size-fractionated metagenomic and metatranscriptomic datasets collected along a salinity gradient spanning from ∼0 to 35 PSU. The normalised genomic abundance of rhodopsins in Bacteria, as well as rhodopsin gene expression, was highest in the smallest size fraction (0.1-0.8 µm), relative to the medium (0.8-3.0 µm) and large (>3.0 µm) size fractions. The abundance of rhodopsins in the two smaller size fractions displayed a positive correlation with salinity. Proteobacteria and Bacteroidetes rhodopsins were the most abundant while Actinobacteria rhodopsins, or actinorhodopsins, were common at lower salinities. Phylogenetic analysis indicated that rhodopsins have adapted independently to the marine-brackish transition on multiple occasions, giving rise to green light-adapted variants from ancestral blue light-adapted ones. A notable diversity of viral-like rhodopsins was also detected in the dataset and potentially linked with eukaryotic phytoplankton blooms. Finally, a new clade of likely proton-pumping rhodopsin with non-canonical amino acids in the spectral tuning and proton accepting site was identified.

SFA-SPA: a suffix array based short peptide assembler for metagenomic data.

UNLABELLED: The determination of protein sequences from a metagenomic dataset enables the study of metabolism and functional roles of the organisms that are present in the sampled microbial community. We had previously introduced algorithm and software for the accurate reconstruction of protein sequences from short peptides identified on nucleotide reads in a metagenomic dataset. Here, we present significant computational improvements to the short peptide assembly algorithm that make it practical to reconstruct proteins from large metagenomic datasets containing several hundred million reads, while maintaining accuracy. The improved computational efficiency is achieved using a suffix array data structure that allows for fast querying during the assembly process, and a significant redesign of assembly steps that enables multi-threaded execution. AVAILABILITY AND IMPLEMENTATION: The program is available under the GPLv3 license from sourceforge.net/projects/spa-assembler.

Genomic and functional adaptation in surface ocean planktonic prokaryotes.

The understanding of marine microbial ecology and metabolism has been hampered by the paucity of sequenced reference genomes. To this end, we report the sequencing of 137 diverse marine isolates collected from around the world. We analysed these sequences, along with previously published marine prokaryotic genomes, in the context of marine metagenomic data, to gain insights into the ecology of the surface ocean prokaryotic picoplankton (0.1-3.0 µm size range). The results suggest that the sequenced genomes define two microbial groups: one composed of only a few taxa that are nearly always abundant in picoplanktonic communities, and the other consisting of many microbial taxa that are rarely abundant. The genomic content of the second group suggests that these microbes are capable of slow growth and survival in energy-limited environments, and rapid growth in energy-rich environments. By contrast, the abundant and cosmopolitan picoplanktonic prokaryotes for which there is genomic representation have smaller genomes, are probably capable of only slow growth and seem to be relatively unable to sense or rapidly acclimate to energy-rich conditions. Their genomic features also lead us to propose that one method used to avoid predation by viruses and/or bacterivores is by means of slow growth and the maintenance of low biomass.

Candidate phylum TM6 genome recovered from a hospital sink biofilm provides genomic insights into this uncultivated phylum.

The "dark matter of life" describes microbes and even entire divisions of bacterial phyla that have evaded cultivation and have yet to be sequenced. We present a genome from the globally distributed but elusive candidate phylum TM6 and uncover its metabolic potential. TM6 was detected in a biofilm from a sink drain within a hospital restroom by analyzing cells using a highly automated single-cell genomics platform. We developed an approach for increasing throughput and effectively improving the likelihood of sampling rare events based on forming small random pools of single-flow-sorted cells, amplifying their DNA by multiple displacement amplification and sequencing all cells in the pool, creating a "mini-metagenome." A recently developed single-cell assembler, SPAdes, in combination with contig binning methods, allowed the reconstruction of genomes from these mini-metagenomes. A total of 1.07 Mb was recovered in seven contigs for this member of TM6 (JCVI TM6SC1), estimated to represent 90% of its genome. High nucleotide identity between a total of three TM6 genome drafts generated from pools that were independently captured, amplified, and assembled provided strong confirmation of a correct genomic sequence. TM6 is likely a Gram-negative organism and possibly a symbiont of an unknown host (nonfree living) in part based on its small genome, low-GC content, and lack of biosynthesis pathways for most amino acids and vitamins. Phylogenomic analysis of conserved single-copy genes confirms that TM6SC1 is a deeply branching phylum.