Search

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.

Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba.

BACKGROUND:Vector-borne pathogens are a significant public health concern worldwide. Infections with these pathogens, some of which are emerging, are likely under-recognized due to the lack of widely-available laboratory tests. There is an urgent need for further advancement in diagnostic modalities to detect new and known vector-borne pathogens. We evaluated the utility of metagenomic shotgun sequencing (MGS) as a pathogen agnostic approach for detecting vector-borne pathogens from human blood samples. METHODS:Residual whole blood samples from patients with known infection with Babesia microti, Borrelia hermsii, Plasmodium falciparum, Mansonella perstans, Anaplasma phagocytophilum or Ehrlichia chaffeensis were studied. Samples underwent DNA extraction, removal of human DNA, whole genome amplification, and paired-end library preparation, followed by sequencing on Illumina HiSeq 2500. Bioinformatic analysis was performed using the Livermore Metagenomics Analysis Toolkit (LMAT), Metagenomic Phylogenetic Analysis (MetaPhlAn2), Genomic Origin Through Taxonomic CHAllenge (GOTTCHA) and Kraken 2. RESULTS:Eight samples were included in the study (2 samples each for P. falciparum and A. phagocytophilum). An average of 27.5 million read pairs was generated per sample (range, 18.3-38.8 million) prior to removal of human reads. At least one of the analytic tools was able to detect four of six organisms at the genus level, and the organism present in five of eight specimens at the species level. Mansonella and Ehrlichia species were not detected by any of the tools; however, mitochondrial cytochrome c oxidase subunit I amino acid sequence analysis suggested the presence of M. perstans genetic material. CONCLUSIONS:MGS is a promising tool with the potential to evolve as a non-hypothesis driven diagnostic test to detect vector-borne pathogens, including protozoa and helminths.

The gut microbiome likely plays a role in the etiology of multiple health conditions, especially those affecting the gastrointestinal tract. Little consensus exists as to the best, standard methods to collect fecal samples for future microbiome analysis. We evaluated three distinct populations (N = 132 participants) using 16S rRNA gene amplicon sequencing data to investigate the reproducibility, stability, and accuracy of microbial profiles in fecal samples collected and stored via fecal occult blood test (FOBT) or Flinders Technology Associates (FTA) cards, fecal immunochemical tests (FIT) tubes, 70% and 95% ethanol, RNAlater, or with no solution. For each collection method, based on relative abundance of select phyla and genera, two alpha diversity metrics, and four beta diversity metrics, we calculated intraclass correlation coefficients (ICCs) to estimate reproducibility and stability, and Spearman correlation coefficients (SCCs) to estimate accuracy of the fecal microbial profile. Comparing duplicate samples, reproducibility ICCs for all collection methods were excellent (ICCs ≥75%). After 4-7 days at ambient temperature, ICCs for microbial profile stability were excellent (≥75%) for most collection methods, except those collected via no-solution and 70% ethanol. SCCs comparing each collection method to immediately-frozen no-solution samples ranged from fair to excellent for most methods; however, accuracy of genus-level relative abundances differed by collection method. Our findings, taken together with previous studies and feasibility considerations, indicated that FOBT/FTA cards, FIT tubes, 95% ethanol, and RNAlater are excellent choices for fecal sample collection methods in future microbiome studies. Furthermore, establishing standard collection methods across studies is highly desirable.

Staphylococcus aureus is a leading cause of bacteremia in hospitalized patients. Whether or not S. aureus bacteremia (SAB) is associated with clonality, implicating potential nosocomial transmission, has not, however, been investigated. Herein, we examined the epidemiology of SAB using whole genome sequencing (WGS). 152 SAB isolates collected over the course of 2015 at a single large Minnesota medical center were studied. Staphylococcus protein A (spa) typing was performed by PCR/Sanger sequencing; multilocus sequence typing (MLST) and core genome MLST (cgMLST) were determined by WGS. Forty-eight isolates (32%) were methicillin-resistant S. aureus (MRSA). The isolates encompassed 66 spa types, clustered into 11 spa clonal complexes (CCs) and 10 singleton types. 88% of 48 MRSA isolates belonged to spa CC-002 or -008. Methicillin-susceptible S. aureus (MSSA) isolates were more genotypically diverse, with 61% distributed across four spa CCs (CC-002, CC-012, CC-008 and CC-084). By MLST, there was 31 sequence types (STs), including 18 divided into 6 CCs and 13 singleton STs. Amongst MSSA isolates, the common MLST clones were CC5 (23%), CC30 (19%), CC8 (15%) and CC15 (11%). Common MRSA clones were CC5 (67%) and CC8 (25%); there were no MRSA isolates in CC45 or CC30. By cgMLST analysis, there were 9 allelic differences between two isolates, with the remaining 150 isolates differing from each other by over 40 alleles. The two isolates were retroactively epidemiologically linked by medical record review. Overall, cgMLST analysis resulted in higher resolution epidemiological typing than did multilocus sequence or spa typing.

The inflammatory tumoral-immune response alters the physiology of the tumor microenvironment, which may attenuate genomic instability. In addition to inducing inflammatory immune responses, several pathogenic bacteria produce genotoxins. However the extent of microbial contribution to the tumor microenvironment biology remains unknown. We utilized The Cancer Genome Atlas, (TCGA) breast cancer data to perform a novel experiment utilizing unmapped and mapped RNA sequencing read evidence to minimize laboratory costs and effort. Our objective was to characterize the microbiota and associate the microbiota with the tumor expression profiles, for 668 breast tumor tissues and 72 non-cancerous adjacent tissues. The prominent presence of Proteobacteria was increased in the tumor tissues and conversely Actinobacteria abundance increase in non-cancerous adjacent tissues. Further, geneset enrichment suggests Listeria spp to be associated with the expression profiles of genes involved with epithelial to mesenchymal transitions. Moreover, evidence suggests H. influenza may reside in the surrounding stromal material and was significantly associated with the proliferative pathways: G2M checkpoint, E2F transcription factors, and mitotic spindle assembly. In summary, further unraveling this complicated interplay should enable us to better diagnose and treat breast cancer patients.

Planctomycetes are distinguished from other Bacteria by compartmentalization of cells via internal membranes, interpretation of which has been subject to recent debate regarding potential relations to Gram-negative cell structure. In our interpretation of the available data, the planctomycete Gemmata obscuriglobus contains a nuclear body compartment, and thus possesses a type of cell organization with parallels to the eukaryote nucleus. Here we show that pore-like structures occur in internal membranes of G.obscuriglobus and that they have elements structurally similar to eukaryote nuclear pores, including a basket, ring-spoke structure, and eight-fold rotational symmetry. Bioinformatic analysis of proteomic data reveals that some of the G. obscuriglobus proteins associated with pore-containing membranes possess structural domains found in eukaryote nuclear pore complexes. Moreover, immunogold labelling demonstrates localization of one such protein, containing a β-propeller domain, specifically to the G. obscuriglobus pore-like structures. Finding bacterial pores within internal cell membranes and with structural similarities to eukaryote nuclear pore complexes raises the dual possibilities of either hitherto undetected homology or stunning evolutionary convergence.

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

To assess the vaginal microbiome throughout full-term uncomplicated pregnancy.Vaginal swabs were obtained from twelve pregnant women at 8-week intervals throughout their uncomplicated pregnancies. Patients with symptoms of vaginal infection or with recent antibiotic use were excluded. Swabs were obtained from the posterior fornix and cervix at 8-12, 17-21, 27-31, and 36-38 weeks of gestation. The microbial community was profiled using hypervariable tag sequencing of the V3-V5 region of the 16S rRNA gene, producing approximately 8 million reads on the Illumina MiSeq.Samples were dominated by a single genus, Lactobacillus, and exhibited low species diversity. For a majority of the patients (n = 8), the vaginal microbiome was dominated by Lactobacillus crispatus throughout pregnancy. Two patients showed Lactobacillus iners dominance during the course of pregnancy, and two showed a shift between the first and second trimester from L. crispatus to L. iners dominance. In all of the samples only these two species were identified, and were found at an abundance of higher than 1% in this study. Comparative analyses also showed that the vaginal microbiome during pregnancy is characterized by a marked dominance of Lactobacillus species in both Caucasian and African-American subjects. In addition, our Caucasian subject population clustered by trimester and progressed towards a common attractor while African-American women clustered by subject instead and did not progress towards a common attractor.Our analyses indicate normal pregnancy is characterized by a microbiome that has low diversity and high stability. While Lactobacillus species strongly dominate the vaginal environment during pregnancy across the two studied ethnicities, observed differences between the longitudinal dynamics of the analyzed populations may contribute to divergent risk for pregnancy complications. This helps establish a baseline for investigating the role of the microbiome in complications of pregnancy such as preterm labor and preterm delivery.

16S rDNA hypervariable tag sequencing has become the de facto method for accessing microbial diversity. Illumina paired-end sequencing, which produces two separate reads for each DNA fragment, has become the platform of choice for this application. However, when the two reads do not overlap, existing computational pipelines analyze data from read separately and underutilize the information contained in the paired-end reads.We created a workflow known as Illinois Mayo Taxon Organization from RNA Dataset Operations (IM-TORNADO) for processing non-overlapping reads while retaining maximal information content. Using synthetic mock datasets, we show that the use of both reads produced answers with greater correlation to those from full length 16S rDNA when looking at taxonomy, phylogeny, and beta-diversity.IM-TORNADO is freely available at http://sourceforge.net/projects/imtornado and produces BIOM format output for cross compatibility with other pipelines such as QIIME, mothur, and phyloseq.

BACKGROUND: The replication of DNA in Archaea and eukaryotes requires several ancillary complexes, including proliferating cell nuclear antigen (PCNA), replication factor C (RFC), and the minichromosome maintenance (MCM) complex. Bacterial DNA replication utilizes comparable proteins, but these are distantly related phylogenetically to their archaeal and eukaryotic counterparts at best. METHODOLOGY/PRINCIPAL FINDINGS: While the structures of each of the complexes do not differ significantly between the archaeal and eukaryotic versions thereof, the evolutionary dynamic in the two cases does. The number of subunits in each complex is constant across all taxa. However, they vary subtly with regard to composition. In some taxa the subunits are all identical in sequence, while in others some are homologous rather than identical. In the case of eukaryotes, there is no phylogenetic variation in the makeup of each complex-all appear to derive from a common eukaryotic ancestor. This is not the case in Archaea, where the relationship between the subunits within each complex varies taxon-to-taxon. We have performed a detailed phylogenetic analysis of these relationships in order to better understand the gene duplications and divergences that gave rise to the homologous subunits in Archaea. CONCLUSION/SIGNIFICANCE: This domain level difference in evolution suggests that different forces have driven the evolution of DNA replication proteins in each of these two domains. In addition, the phylogenies of all three gene families support the distinctiveness of the proposed archaeal phylum Thaumarchaeota.

Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large (10(5)-10(6)) 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods.