Bacteria from diverse habitats colonize and compete in the mouse gut.

To study how microbes establish themselves in a mammalian gut environment, we colonized germ-free mice with microbial communities from human, zebrafish, and termite guts, human skin and tongue, soil, and estuarine microbial mats. Bacteria from these foreign environments colonized and persisted in the mouse gut; their capacity to metabolize dietary and host carbohydrates and bile acids correlated with colonization success. Cohousing mice harboring these xenomicrobiota or a mouse cecal microbiota, along with germ-free "bystanders," revealed the success of particular bacterial taxa in invading guts with established communities and empty gut habitats. Unanticipated patterns of ecological succession were observed; for example, a soil-derived bacterium dominated even in the presence of bacteria from other gut communities (zebrafish and termite), and human-derived bacteria colonized germ-free bystander mice before mouse-derived organisms. This approach can be generalized to address a variety of mechanistic questions about succession, including succession in the context of microbiota-directed therapeutics.

IL-1 is a critical regulator of group 2 innate lymphoid cell function and plasticity.

Group 2 innate lymphoid cells (ILC2 cells) are important for type 2 immune responses and are activated by the epithelial cytokines interleukin 33 (IL-33), IL-25 and thymic stromal lymphopoietin (TSLP). Here we demonstrated that IL-1ß was a critical activator of ILC2 cells, inducing proliferation and cytokine production and regulating the expression of epithelial cytokine receptors. IL-1ß also governed ILC2 plasticity by inducing low expression of the transcription factor T-bet and the cytokine receptor chain IL-12Rß2, which enabled the conversion of these cells into an ILC1 phenotype in response to IL-12. This transition was marked by an atypical chromatin landscape characterized by the simultaneous transcriptional accessibility of the locus encoding interferon-γ (IFN-γ) and the loci encoding IL-5 and IL-13. Finally, IL-1ß potentiated ILC2 activation and plasticity in vivo, and IL-12 acted as the switch that determined an ILC2-versus-ILC1 response. Thus, we have identified a previously unknown role for IL-1ß in facilitating ILC2 maturation and plasticity.

Multi-tiered analyses of honey bees that resist or succumb to parasitic mites and viruses.

BACKGROUND: Varroa destructor mites, and the numerous viruses they vector to their honey bee hosts, are among the most serious threats to honey bee populations, causing mortality and morbidity to both the individual honey bee and colony, the negative effects of which convey to the pollination services provided by honey bees worldwide. Here we use a combination of targeted assays and deep RNA sequencing to determine host and microbial changes in resistant and susceptible honey bee lineages. We focus on three study sets. The first involves field sampling of sympatric western bees, some derived from resistant stock and some from stock susceptible to mites. The second experiment contrasts three colonies more deeply, two from susceptible stock from the southeastern U.S. and one from mite-resistant bee stock from Eastern Texas. Finally, to decouple the effects of mites from those of the viruses they vector, we experimentally expose honey bees to DWV in the laboratory, measuring viral growth and host responses. RESULTS: We find strong differences between resistant and susceptible bees in terms of both viral loads and bee gene expression. Interestingly, lineages of bees with naturally low levels of the mite-vectored Deformed wing virus, also carried lower levels of viruses not vectored by mites. By mapping gene expression results against current ontologies and other studies, we describe the impacts of mite parasitism, as well as viruses on bee health against two genetic backgrounds. We identify numerous genes and processes seen in other studies of stress and disease in honey bee colonies, alongside novel genes and new patterns of expression. CONCLUSIONS: We provide evidence that honey bees surviving in the face of parasitic mites do so through their abilities to resist the presence of devastating viruses vectored by these mites. In all cases, the most divergence between stocks was seen when bees were exposed to live mites or viruses, suggesting that gene activation, rather than constitutive expression, is key for these interactions. By revealing responses to viral infection and mite parasitism in different lineages, our data identify candidate proteins for the evolution of mite tolerance and virus resistance.

A pathogenic UFSP2 variant in an autosomal recessive form of pediatric neurodevelopmental anomalies and epilepsy.

PURPOSE: Neurodevelopmental disabilities are common and genetically heterogeneous. We identified a homozygous variant in the gene encoding UFM1-specific peptidase 2 (UFSP2), which participates in the UFMylation pathway of protein modification. UFSP2 variants are implicated in autosomal dominant skeletal dysplasias, but not neurodevelopmental disorders. Homozygosity for the variant occurred in eight children from four South Asian families with neurodevelopmental delay and epilepsy. We describe the clinical consequences of this variant and its effect on UFMylation. METHODS: Exome sequencing was used to detect potentially pathogenic variants and identify shared regions of homozygosity. Immunoblotting assessed protein expression and post-translational modifications in patient-derived fibroblasts. RESULTS: The variant (c.344T>A; p.V115E) is rare and alters a conserved residue in UFSP2. Immunoblotting in patient-derived fibroblasts revealed reduced UFSP2 abundance and increased abundance of UFMylated targets, indicating the variant may impair de-UFMylation rather than UFMylation. Reconstituting patient-derived fibroblasts with wild-type UFSP2 reduced UFMylation marks. Analysis of UFSP2's structure indicated that variants observed in skeletal disorders localize to the catalytic domain, whereas V115 resides in an N-terminal domain possibly involved in substrate binding. CONCLUSION: Different UFSP2 variants cause markedly different diseases, with homozygosity for V115E causing a severe syndrome of neurodevelopmental disability and epilepsy.

Mitochondrial dysfunction during loss of prohibitin 1 triggers Paneth cell defects and ileitis.

OBJECTIVE: Although perturbations in mitochondrial function and structure have been described in the intestinal epithelium of Crohn's disease and ulcerative colitis patients, the role of epithelial mitochondrial stress in the pathophysiology of inflammatory bowel diseases (IBD) is not well elucidated. Prohibitin 1 (PHB1), a major component protein of the inner mitochondrial membrane crucial for optimal respiratory chain assembly and function, is decreased during IBD. DESIGN: Male and female mice with inducible intestinal epithelial cell deletion of Phb1 (Phb1iΔIEC ) or Paneth cell-specific deletion of Phb1 (Phb1ΔPC ) and Phb1fl/fl control mice were housed up to 20 weeks to characterise the impact of PHB1 deletion on intestinal homeostasis. To suppress mitochondrial reactive oxygen species, a mitochondrial-targeted antioxidant, Mito-Tempo, was administered. To examine epithelial cell-intrinsic responses, intestinal enteroids were generated from crypts of Phb1iΔIEC or Phb1ΔPC mice. RESULTS: Phb1iΔIEC mice exhibited spontaneous ileal inflammation that was preceded by mitochondrial dysfunction in all IECs and early abnormalities in Paneth cells. Mito-Tempo ameliorated mitochondrial dysfunction, Paneth cell abnormalities and ileitis in Phb1iΔIEC ileum. Deletion of Phb1 specifically in Paneth cells (Phb1ΔPC ) was sufficient to cause ileitis. Intestinal enteroids generated from crypts of Phb1iΔIEC or Phb1ΔPC mice exhibited decreased viability and Paneth cell defects that were improved by Mito-Tempo. CONCLUSION: Our results identify Paneth cells as highly susceptible to mitochondrial dysfunction and central to the pathogenesis of ileitis, with translational implications for the subset of Crohn's disease patients exhibiting Paneth cell defects.

GEAMP, a novel gastroesophageal junction carcinoma cell line derived from a malignant pleural effusion.

Gastroesophageal junction (GEJ) cancer remains a clinically significant disease in Western countries due to its increasing incidence, which mirrors that of esophageal cancer, and poor prognosis. To develop novel and effective approaches for prevention, early detection, and treatment of patients with GEJ cancer, a better understanding of the mechanisms driving pathogenesis and malignant progression of this disease is required. These efforts have been limited by the small number of available cell lines and appropriate preclinical animal models for in vitro and in vivo studies. We have established and characterized a novel GEJ cancer cell line, GEAMP, derived from the malignant pleural effusion of a previously treated GEJ cancer patient. Comprehensive genetic analyses confirmed a clonal relationship between GEAMP cells and the primary tumor. Targeted next-generation sequencing identified 56 nonsynonymous alterations in 51 genes including TP53 and APC, which are commonly altered in GEJ cancer. In addition, multiple copy-number alterations were found including EGFR and K-RAS gene amplifications and loss of CDKN2A and CDKN2B. Histological examination of subcutaneous flank xenografts in nude and NOD-SCID mice showed a carcinoma with mixed squamous and glandular differentiation, suggesting GEAMP cells contain a subpopulation with multipotent potential. Finally, pharmacologic inhibition of the EGFR signaling pathway led to downregulation of key downstream kinases and inhibition of cell proliferation in vitro. Thus, GEAMP represents a valuable addition to the limited number of bona fide GEJ cancer cell lines.

Analysis of archived residual newborn screening blood spots after whole genome amplification.

BACKGROUND: Deidentified newborn screening bloodspot samples (NBS) represent a valuable potential resource for genomic research if impediments to whole exome sequencing of NBS deoxyribonucleic acid (DNA), including the small amount of genomic DNA in NBS material, can be overcome. For instance, genomic analysis of NBS could be used to define allele frequencies of disease-associated variants in local populations, or to conduct prospective or retrospective studies relating genomic variation to disease emergence in pediatric populations over time. In this study, we compared the recovery of variant calls from exome sequences of amplified NBS genomic DNA to variant calls from exome sequencing of non-amplified NBS DNA from the same individuals. RESULTS: Using a standard alignment-based Genome Analysis Toolkit (GATK), we find 62,000-76,000 additional variants in amplified samples. After application of a unique kmer enumeration and variant detection method (RUFUS), only 38,000-47,000 additional variants are observed in amplified gDNA. This result suggests that roughly half of the amplification-introduced variants identified using GATK may be the result of mapping errors and read misalignment. CONCLUSIONS: Our results show that it is possible to obtain informative, high-quality data from exome analysis of whole genome amplified NBS with the important caveat that different data generation and analysis methods can affect variant detection accuracy, and the concordance of variant calls in whole-genome amplified and non-amplified exomes.

A core gut microbiome in obese and lean twins.

The human distal gut harbours a vast ensemble of microbes (the microbiota) that provide important metabolic capabilities, including the ability to extract energy from otherwise indigestible dietary polysaccharides. Studies of a few unrelated, healthy adults have revealed substantial diversity in their gut communities, as measured by sequencing 16S rRNA genes, yet how this diversity relates to function and to the rest of the genes in the collective genomes of the microbiota (the gut microbiome) remains obscure. Studies of lean and obese mice suggest that the gut microbiota affects energy balance by influencing the efficiency of calorie harvest from the diet, and how this harvested energy is used and stored. Here we characterize the faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity, and their mothers, to address how host genotype, environmental exposure and host adiposity influence the gut microbiome. Analysis of 154 individuals yielded 9,920 near full-length and 1,937,461 partial bacterial 16S rRNA sequences, plus 2.14 gigabases from their microbiomes. The results reveal that the human gut microbiome is shared among family members, but that each person's gut microbial community varies in the specific bacterial lineages present, with a comparable degree of co-variation between adult monozygotic and dizygotic twin pairs. However, there was a wide array of shared microbial genes among sampled individuals, comprising an extensive, identifiable 'core microbiome' at the gene, rather than at the organismal lineage, level. Obesity is associated with phylum-level changes in the microbiota, reduced bacterial diversity and altered representation of bacterial genes and metabolic pathways. These results demonstrate that a diversity of organismal assemblages can nonetheless yield a core microbiome at a functional level, and that deviations from this core are associated with different physiological states (obese compared with lean).

BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity.

BACKGROUND: Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a "gold standard" of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user's tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity. RESULTS: We assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC's superior variant-calling accuracy, both for somatic mutation detection and germline variant detection. CONCLUSIONS: BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.

Obligate biotrophy features unraveled by the genomic analysis of rust fungi.

Rust fungi are some of the most devastating pathogens of crop plants. They are obligate biotrophs, which extract nutrients only from living plant tissues and cannot grow apart from their hosts. Their lifestyle has slowed the dissection of molecular mechanisms underlying host invasion and avoidance or suppression of plant innate immunity. We sequenced the 101-Mb genome of Melampsora larici-populina, the causal agent of poplar leaf rust, and the 89-Mb genome of Puccinia graminis f. sp. tritici, the causal agent of wheat and barley stem rust. We then compared the 16,399 predicted proteins of M. larici-populina with the 17,773 predicted proteins of P. graminis f. sp tritici. Genomic features related to their obligate biotrophic lifestyle include expanded lineage-specific gene families, a large repertoire of effector-like small secreted proteins, impaired nitrogen and sulfur assimilation pathways, and expanded families of amino acid and oligopeptide membrane transporters. The dramatic up-regulation of transcripts coding for small secreted proteins, secreted hydrolytic enzymes, and transporters in planta suggests that they play a role in host infection and nutrient acquisition. Some of these genomic hallmarks are mirrored in the genomes of other microbial eukaryotes that have independently evolved to infect plants, indicating convergent adaptation to a biotrophic existence inside plant cells.

Frequent somatic mutations of the transcription factor ATBF1 in human prostate cancer.

Cancer often results from the accumulation of multiple genetic alterations. Although most malignancies are sporadic, only a small number of genes have been shown to undergo frequent mutations in sporadic cancers. The long arm of chromosome 16 is frequently deleted in human cancers, but the target gene for this deletion has not been identified. Here we report that ATBF1, which encodes a transcription factor that negatively regulates AFP and MYB but transactivates CDKN1A, is a good candidate for the 16q22 tumor-suppressor gene. We narrowed the region of deletion at 16q22 to 861 kb containing ATBF1. ATBF1 mRNA was abundant in normal prostates but more scarce in approximately half of prostate cancers tested. In 24 of 66 (36%) cancers examined, we identified 22 unique somatic mutations, many of which impair ATBF1 function. Furthermore, ATBF1 inhibited cell proliferation. Hence, loss of ATBF1 is one mechanism that defines the absence of growth control in prostate cancer.

Inter- and intra-specific pan-genomes of Borrelia burgdorferi sensu lato: genome stability and adaptive radiation.

BACKGROUND: Lyme disease is caused by spirochete bacteria from the Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) species complex. To reconstruct the evolution of B. burgdorferi s.l. and identify the genomic basis of its human virulence, we compared the genomes of 23 B. burgdorferi s.l. isolates from Europe and the United States, including B. burgdorferi sensu stricto (B. burgdorferi s.s., 14 isolates), B. afzelii (2), B. garinii (2), B. "bavariensis" (1), B. spielmanii (1), B. valaisiana (1), B. bissettii (1), and B. "finlandensis" (1). RESULTS: Robust B. burgdorferi s.s. and B. burgdorferi s.l. phylogenies were obtained using genome-wide single-nucleotide polymorphisms, despite recombination. Phylogeny-based pan-genome analysis showed that the rate of gene acquisition was higher between species than within species, suggesting adaptive speciation. Strong positive natural selection drives the sequence evolution of lipoproteins, including chromosomally-encoded genes 0102 and 0404, cp26-encoded ospC and b08, and lp54-encoded dbpA, a07, a22, a33, a53, a65. Computer simulations predicted rapid adaptive radiation of genomic groups as population size increases. CONCLUSIONS: Intra- and inter-specific pan-genome sizes of B. burgdorferi s.l. expand linearly with phylogenetic diversity. Yet gene-acquisition rates in B. burgdorferi s.l. are among the lowest in bacterial pathogens, resulting in high genome stability and few lineage-specific genes. Genome adaptation of B. burgdorferi s.l. is driven predominantly by copy-number and sequence variations of lipoprotein genes. New genomic groups are likely to emerge if the current trend of B. burgdorferi s.l. population expansion continues.

Functional metagenomics to mine the human gut microbiome for dietary fiber catabolic enzymes.

The human gut microbiome is a complex ecosystem composed mainly of uncultured bacteria. It plays an essential role in the catabolism of dietary fibers, the part of plant material in our diet that is not metabolized in the upper digestive tract, because the human genome does not encode adequate carbohydrate active enzymes (CAZymes). We describe a multi-step functionally based approach to guide the in-depth pyrosequencing of specific regions of the human gut metagenome encoding the CAZymes involved in dietary fiber breakdown. High-throughput functional screens were first applied to a library covering 5.4 × 10(9) bp of metagenomic DNA, allowing the isolation of 310 clones showing beta-glucanase, hemicellulase, galactanase, amylase, or pectinase activities. Based on the results of refined secondary screens, sequencing efforts were reduced to 0.84 Mb of nonredundant metagenomic DNA, corresponding to 26 clones that were particularly efficient for the degradation of raw plant polysaccharides. Seventy-three CAZymes from 35 different families were discovered. This corresponds to a fivefold target-gene enrichment compared to random sequencing of the human gut metagenome. Thirty-three of these CAZy encoding genes are highly homologous to prevalent genes found in the gut microbiome of at least 20 individuals for whose metagenomic data are available. Moreover, 18 multigenic clusters encoding complementary enzyme activities for plant cell wall degradation were also identified. Gene taxonomic assignment is consistent with horizontal gene transfer events in dominant gut species and provides new insights into the human gut functional trophic chain.

Metabolic reconstruction for metagenomic data and its application to the human microbiome.

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.

NLRP12 downregulates the Wnt/ß-catenin pathway via interaction with STK38 to suppress colorectal cancer.

Colorectal cancer (CRC) at advanced stages is rarely curable, underscoring the importance of exploring the mechanism of CRC progression and invasion. NOD-like receptor family member NLRP12 was shown to suppress colorectal tumorigenesis, but the precise mechanism was unknown. Here, we demonstrate that invasive adenocarcinoma development in Nlrp12-deficient mice is associated with elevated expression of genes involved in proliferation, matrix degradation, and epithelial-mesenchymal transition. Signaling pathway analysis revealed higher activation of the Wnt/ß-catenin pathway, but not NF-κB and MAPK pathways, in the Nlrp12-deficient tumors. Using Nlrp12-conditional knockout mice, we revealed that NLRP12 downregulates ß-catenin activation in intestinal epithelial cells, thereby suppressing colorectal tumorigenesis. Consistent with this, Nlrp12-deficient intestinal organoids and CRC cells showed increased proliferation, accompanied by higher activation of ß-catenin in vitro. With proteomic studies, we identified STK38 as an interacting partner of NLRP12 involved in the inhibition of phosphorylation of GSK3ß, leading to the degradation of ß-catenin. Consistently, the expression of NLRP12 was significantly reduced, while p-GSK3ß and ß-catenin were upregulated in mouse and human colorectal tumor tissues. In summary, NLRP12 is a potent negative regulator of the Wnt/ß-catenin pathway, and the NLRP12/STK38/GSK3ß signaling axis could be a promising therapeutic target for CRC.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

MOTIVATION: Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. RESULTS: By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. AVAILABILITY: The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. CONTACT: zliu@umm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing a model human gut microbiota composed of members of its two dominant bacterial phyla.

The adult human distal gut microbial community is typically dominated by 2 bacterial phyla (divisions), the Firmicutes and the Bacteroidetes. Little is known about the factors that govern the interactions between their members. Here, we examine the niches of representatives of both phyla in vivo. Finished genome sequences were generated from Eubacterium rectale and E. eligens, which belong to Clostridium Cluster XIVa, one of the most common gut Firmicute clades. Comparison of these and 25 other gut Firmicutes and Bacteroidetes indicated that the Firmicutes possess smaller genomes and a disproportionately smaller number of glycan-degrading enzymes. Germ-free mice were then colonized with E. rectale and/or a prominent human gut Bacteroidetes, Bacteroides thetaiotaomicron, followed by whole-genome transcriptional profiling, high-resolution proteomic analysis, and biochemical assays of microbial-microbial and microbial-host interactions. B. thetaiotaomicron adapts to E. rectale by up-regulating expression of a variety of polysaccharide utilization loci encoding numerous glycoside hydrolases, and by signaling the host to produce mucosal glycans that it, but not E. rectale, can access. E. rectale adapts to B. thetaiotaomicron by decreasing production of its glycan-degrading enzymes, increasing expression of selected amino acid and sugar transporters, and facilitating glycolysis by reducing levels of NADH, in part via generation of butyrate from acetate, which in turn is used by the gut epithelium. This simplified model of the human gut microbiota illustrates niche specialization and functional redundancy within members of its major bacterial phyla, and the importance of host glycans as a nutrient foundation that ensures ecosystem stability.

SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories.

Bioinformatics analysis is a key element in the development of in-house next-generation sequencing assays for tumor genetic profiling that can include both tumor DNA and RNA with comparisons to matched-normal DNA in select cases. Bioinformatics analysis encompasses a computationally heavy component that requires a high-performance computing component and an assay-dependent quality assessment, aggregation, and data cleaning component. Although there are free, open-source solutions and fee-for-use commercial services for the computationally heavy component, these solutions and services can lack the options commonly utilized in increasingly complex genomic assays. Additionally, the cost to purchase commercial solutions or implement and maintain open-source solutions can be out of reach for many small clinical laboratories. Here, we present Software for Clinical Health in Oncology for Omics Laboratories (SCHOOL), a collection of genomics analysis workflows that (i) can be easily installed on any platform; (ii) run on the cloud with a user-friendly interface; and (iii) include the detection of single nucleotide variants, insertions/deletions, copy number variants (CNVs), and translocations from RNA and DNA sequencing. These workflows contain elements for customization based on target panel and assay design, including somatic mutational analysis with a matched-normal, microsatellite stability analysis, and CNV analysis with a single nucleotide polymorphism backbone. All of the features of SCHOOL have been designed to run on any computer system, where software dependencies have been containerized. SCHOOL has been built into apps with workflows that can be run on a cloud platform such as DNANexus using their point-and-click graphical interface, which could be automated for high-throughput laboratories.

The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics.

The Carbohydrate-Active Enzyme (CAZy) database is a knowledge-based resource specialized in the enzymes that build and breakdown complex carbohydrates and glycoconjugates. As of September 2008, the database describes the present knowledge on 113 glycoside hydrolase, 91 glycosyltransferase, 19 polysaccharide lyase, 15 carbohydrate esterase and 52 carbohydrate-binding module families. These families are created based on experimentally characterized proteins and are populated by sequences from public databases with significant similarity. Protein biochemical information is continuously curated based on the available literature and structural information. Over 6400 proteins have assigned EC numbers and 700 proteins have a PDB structure. The classification (i) reflects the structural features of these enzymes better than their sole substrate specificity, (ii) helps to reveal the evolutionary relationships between these enzymes and (iii) provides a convenient framework to understand mechanistic properties. This resource has been available for over 10 years to the scientific community, contributing to information dissemination and providing a transversal nomenclature to glycobiologists. More recently, this resource has been used to improve the quality of functional predictions of a number genome projects by providing expert annotation. The CAZy resource resides at URL: http://www.cazy.org/.