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The 2016 International Myeloma Working Group consensus recommendations emphasize high-sensitivity methods for minimal residual disease (MRD) detection, treatment response assessment, and prognostication. Next-generation sequencing (NGS) of IGH gene rearrangements is highly specific and sensitive, but its description in routine clinical practice and performance comparison with high-sensitivity flow cytometry (hsFC) remain limited. In this large, single-institution study including 438 samples from 251 patients, the use of NGS targeting the IGH and IGK genes for clonal characterization and monitoring, with comparison to hsFC, is described. The index clone characterization success rate was 93.6% (235/251), which depended on plasma cell (PC) cellularity, reaching 98% when PC ≥10% and below 80% when PC

Approximately 15% of chronic lymphocytic leukemia/small lymphocytic lymphomas (CLL/SLL) transform into aggressive lymphomas, often diffuse large B cell lymphomas (DLBCL), known as Richter Syndrome ...

Multilocus sequence typing (MLST) is a low-resolution but rapid genotyping method for Clostridioides difficile. Whole-genome sequencing (WGS) has emerged as the new gold standard for C. difficile typing, but cost and lack of standardization still limit broad utilization. In this study, we evaluated the potential to combine the portability of MLST with the increased resolution of WGS for a cost-saving approach to routine C. difficile typing. C. difficile strains from two New York City hospitals (hospital A and hospital B) were selected. WGS single-nucleotide polymorphism (wgSNP) was performed using established methods. Sequence types (ST) were determined using PubMLST, while wgSNP analysis was performed using the Bionumerics software. An additional analysis of a subset of data (hospital A) was made comparing the Bionumerics software to the CosmosID pipeline. Cost and turnaround time to results were compared for the algorithmic approach of MLST followed by wgSNP versus direct wgSNP. Among the 202 C. difficile isolates typed, 91% (n = 185/203) clustered within the representative ST, showing a high agreement between MLST and wgSNP. While clustering was similar between the Bionumerics and CosmosID pipelines, large differences in the overall number of SNPs were noted. A two-step algorithm for routine typing results in significantly lower cost than routine use of WGS. Our results suggest that using MLST as a first step in routine typing of C. difficile followed by WGS for MLST concordant strains is a less technically demanding, cost-saving approach for performing C. difficile typing than WGS alone without loss of discriminatory power.

Summary Background Cases of Clostridiodes difficile infection (CDI) diagnosed after hospital discharge account for a substantial proportion of new infections. It is unclear whether post-discharge infections originate from hospital-based transmission. Methods This was a Retrospective cohort study at a tertiary-care cancer center (non-outbreak setting). For all laboratory-identified cases of CDI in 2015–2016, patients with post-discharge (PD) CDI within eight weeks of their hospital stay were included in the study. Isolates from PD-CDI cases and their CDI-positive unit-based contacts were first genotyped by multilocus sequence typing (MLST). Common strains were further examined by core genome sequencing (CGS) to evaluate transmission links. Results Of 173 cases examined by MLST, 50% of PD cases matched previous unit contacts. Next, 34 isolates, including 16 PD cases and their 18-unit contacts were examined by CGS. None were ≤3 single-nucleotide variants apart. Seventy percent of PD cases had in-hospital antibiotic exposure before CDI onset in the community. Conclusion Our study results suggest that symptomatic CDI cases are not a substantial source of transmission to PD cases. Frequent antibiotic exposure in post-discharge CDI cases is an important target for surveillance and stewardship efforts.

Author(s): Arkin, Adam P; Cottingham, Robert W; Henry, Christopher S; Harris, Nomi L; Stevens, Rick L; Maslov, Sergei; Dehal, Paramvir; Ware, Doreen; Perez, Fernando; Canon, Shane; Sneddon, Michael W; Henderson, Matthew L; Riehl, William J; Murphy-Olson, Dan; Chan, Stephen Y; Kamimura, Roy T; Kumari, Sunita; Drake, Meghan M; Brettin, Thomas S; Glass, Elizabeth M; Chivian, Dylan; Gunter, Dan; Weston, David J; Allen, Benjamin H; Baumohl, Jason; Best, Aaron A; Bowen, Ben; Brenner, Steven E; Bun, Christopher C; Chandonia, John-Marc; Chia, Jer-Ming; Colasanti, Ric; Conrad, Neal; Davis, James J; Davison, Brian H; DeJongh, Matthew; Devoid, Scott; Dietrich, Emily; Dubchak, Inna; Edirisinghe, Janaka N; Fang, Gang; Faria, Jose P; Frybarger, Paul M; Gerlach, Wolfgang; Gerstein, Mark; Greiner, Annette; Gurtowski, James; Haun, Holly L; He, Fei; Jain, Rashmi; Joachimiak, Marcin P; Keegan, Kevin P; Kondo, Shinnosuke; Kumar, Vivek; Land, Miriam L; Meyer, Folker; Mills, Marissa; Novichkov, Pavel S; Oh, Taeyun; Olsen, Gary J; Olson, Robert; Parrello, Bruce; Pasternak, Shiran; Pearson, Erik; Poon, Sarah S; Price, Gavin A; Ramakrishnan, Srividya; Ranjan, Priya; Ronald, Pamela C; Schatz, Michael C; Seaver, Samuel MD; Shukla, Maulik; Sutormin, Roman A; Syed, Mustafa H; Thomason, James; Tintle, Nathan L; Wang, Daifeng; Xia, Fangfang; Yoo, Hyunseung; Yoo, Shinjae; Yu, Dantong

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

Immunoglobulin heavy chain (IGH) clonality testing by next-generation sequencing (NGS) offers unique advantages over current low-throughput methods in the assessment of B-cell lineage neoplasms. Clinical use remains limited because assays are not standardized and validation/implementation guidelines are not yet developed. Herein, we describe our clinical validation and implementation of NGS IGH clonality testing and summarize our experience based on extensive routine use. NGS-based clonality testing targeting IGH FR1, FR2, FR3, and the conserved leader sequence upstream of FR1 was validated using commercially available kits. Data were analyzed by commercial and in-house–developed bioinformatics pipelines. Performance characteristics were evaluated directly comparing with capillary electrophoresis (CE) assays (BIOMED-2 primers). Assays were monitored after implementation (>1.5 years), concurrently testing by CE methods. A total of 1189 clinical samples were studied (94 validation, 1095 postimplementation). NGS showed superior performance compared with CE assays. For initial assessment, clonality detection rate was >97% for all malignancy types. Concordance with CE was 96%; discordances were related to higher sensitivity/resolution of NGS and improved detection in cases with high somatic hypermutation. Routine NGS clonality assessment is feasible and superior to existing assays, enabling accurate and specific index clone assessment and future tracking of all rearrangements in a patient sample. Successful implementation requires new standardization, validation, and implementation processes, which should be performed as a multicenter and multidisciplinary collaboration.

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically-defined tumor types, coupled with an expanding portfolio of molecularly-targeted therapies, demands flexible and comprehensive approaches to profile clinically significant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative utilizing a comprehensive assay, MSK-IMPACT, through which we have compiled matched tumor and normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel non-coding alterations, and mutational signatures that were shared among common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

Organisms regulate gene expression in response to the environment to coordinate metabolic reactions. Clostridium thermocellum expresses enzymes for both lignocellulose solubilization and its fermentation to produce ethanol. One LacI regulator termed GlyR3 in C. thermocellum ATCC 27405 was previously identified as a repressor of neighboring genes with repression relieved by laminaribiose (a β-1,3 disaccharide). To better understand the three C. thermocellum LacI regulons, deletion mutants were constructed using the genetically tractable DSM1313 strain. DSM1313 lacI genes Clo1313_2023, Clo1313_0089, and Clo1313_0396 encode homologs of GlyR1, GlyR2, and GlyR3 from strain ATCC 27405, respectively. Growth on cellobiose or pretreated switchgrass was unaffected by any of the gene deletions under controlled-pH fermentations. Global gene expression patterns from time course analyses identified glycoside hydrolase genes encoding hemicellulases, including cellulosomal enzymes, that were highly upregulated (5- to 100-fold) in the absence of each LacI regulator, suggesting that these were repressed under wild-type conditions and that relatively few genes were controlled by each regulator under the conditions tested. Clo1313_2022, encoding lichenase enzyme LicB, was derepressed in a Δ glyR1 strain. Higher expression of Clo1313_1398, which encodes the Man5A mannanase, was observed in a Δ glyR2 strain, and α-mannobiose was identified as a probable inducer for GlyR2-regulated genes. For the Δ glyR3 strain, upregulation of the two genes adjacent to glyR3 in the celC-glyR3-licA operon was consistent with earlier studies. Electrophoretic mobility shift assays have confirmed LacI transcription factor binding to specific regions of gene promoters. IMPORTANCE Understanding C. thermocellum gene regulation is of importance for improved fundamental knowledge of this industrially relevant bacterium. Most LacI transcription factors regulate local genomic regions; however, a small number of those genes encode global regulatory proteins with extensive regulons. This study indicates that there are small specific C. thermocellum LacI regulons. The identification of LacI repressor activity for hemicellulase gene expression is a key result of this work and will add to the small body of existing literature on the area of gene regulation in C. thermocellum .

Genomic characteristics discriminating parasitic and mutualistic relationship of bacterial symbionts with plants are poorly understood. This study comparatively analyzed the genomes of 54 mutualists and pathogens to discover genomic markers associated with the different phenotypes. Using metabolic network models, we predict external environments associated with free-living and symbiotic lifestyles and quantify dependences of symbionts on the host in terms of the consumed metabolites. We show that specific differences between the phenotypes are pronounced at the levels of metabolic enzymes, especially carbohydrate active, and protein functions. Overall, biosynthetic functions are enriched and more diverse in plant mutualists whereas processes and functions involved in degradation and host invasion are enriched and more diverse in pathogens. A distinctive characteristic of plant pathogens is a putative novel secretion system with a circadian rhythm regulator. A specific marker of plant mutualists is the co-residence of genes encoding nitrogenase and ribulose bisphosphate carboxylase/oxygenase (RuBisCO). We predict that RuBisCO is likely used in a putative metabolic pathway to supplement carbon obtained heterotrophically with low-cost assimilation of carbon from CO2. We validate results of the comparative analysis by predicting correct phenotype, pathogenic or mutualistic, for 20 symbionts in an independent set of 30 pathogens, mutualists, and commensals.

Introduction: Next-generation sequencing (NGS)-based IGH clonal rearrangement assays can characterize and subsequently track disease-associated clonal sequences for lymphoid and plasma cell neoplasms, even at very low levels. As IGH PCR primers are used, the detected clonal sequences are usually reported as % of sequencing reads, roughly corresponding to % of B and plasma cells (PC) in samples, rather than % of total cellularity, hampering accurate disease burden assessment. In this study, we evaluated a method for calculating residual disease burden as % of total cellularity, with the aid of adding a known quantity of "spike-in" clonal sequence to the samples, and compared to concurrent 10-color flow cytometry (FC) quantitation of abnormal PC. Methods: DNA was extracted from 40 plasma cell myeloma patient marrow biopsies sent for disease monitoring purposes at Memorial Sloan Kettering Cancer Center (MSKCC), with previously-characterized clonal sequences specific to the patients' myelomas. All samples had concurrent FC analyses and aspirate differential counts performed. 100 cell equivalent of DNA with a known clonal sequence (LymphoQuant®, LQ) was added to 700ng of patient DNA (~100,000 cell equivalent), and testing was performed using LymphotrackTM, a NGS-based assay. Following PCR amplification using IGH FR1 primers, sequencing was performed on the Illumina MiSeqTM instruments at the molecular laboratory of MSKCC. Reproducibility studies were conducted on a subset of samples at the laboratory of Invivoscribe, Inc. using identical methodology. LymphoTrack MRD data analysis tool (MRDDAT) v.1.0.3 was used to search for both the myeloma-specific and LQ clonal sequences. Disease as # of cell equivalent was calculated as: (% reads for myeloma clonal sequence/% reads for LQ) X 100 cells. Disease as % of total cellularity was calculated as: (# of cell equivalent/100,000 cells) X 100%. Results: Disease as % of total cellularity calculated by LQ showed a median of 0.7576% cells (range: 0.000614% to 39.89%), compared to abnormal PC as % of total WBC by FC with a median of 0.355% cells (range: 0.00061% to 44.70%). Overall, a good correlation between disease quantitation by LQ and FC could be observed for cases with ≤10% total PC by aspirate count (r=0.79), while the correlation is lower for cases with >10% total PC (r=0.51). 12/40 samples were tested in two different laboratories, and showed excellent correlation in disease quantitation by LQ (r=0.94). As expected, detectable clonal sequences as % of sequencing reads (rather than as % of total cellularity) showed poor correlation with FC quantitation (r=0.32), due to variability of total B and plasma cell content in different samples. Conclusions: Disease as % of total cellularity calculated with the aid of a known "spike-in" sequence in the NGS-based assay showed good correlation with the quantitation of abnormal PC by FC, when total PC was ≤10% by aspirate count. The correlation between the two declines when total PC was >10%. When patient samples contain a high number of B and/or plasma cells, the PCR amplification efficiency of the very small amount of the admixed "spike-in" clonal sequence may be hampered, affecting accurate quantitation. Furthermore, FR1 primers may not anneal optimally to some patients' clonal sequences due to somatic hypermutations in binding sites, underestimating the % of disease clone. Utilization of a second "spike-in" sequence and other primer sets (FR2, FR3) may improve disease % calculations in some cases. Disclosures Ho: Invivoscribe, Inc.: Honoraria. Roshal:Celgene: Other: Provision of Services; Auron Therapeutics: Equity Ownership, Other: Provision of services; Physicians' Education Resource: Other: Provision of services. Huang:Invivoscribe, Inc.: Employment. Hutt:Invivoscribe, Inc.: Employment. Miller:Invivoscribe, Inc.: Employment. Landgren:Theradex: Other: IDMC; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Merck: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Arcila:Invivoscribe, Inc.: Consultancy, Honoraria.

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

Bacterial gene regulation involves transcription factors (TF) that bind to DNA recognition sequences in operon promoters. These recognition sequences, many of which are palindromic, are known as regulatory elements or transcription factor binding sites (TFBS). Some TFs are global regulators that can modulate the expression of hundreds of genes. In this study we examine global regulator half-sites, where a half-site, which we shall call a binding motif (BM), is one half of a palindromic TFBS. We explore the hypothesis that the number of BMs plays an important role in transcriptional regulation, examining empirical data from transcriptional profiling of the CRP and ArcA regulons. We compare the power of BM counts and of full TFBS characteristics to predict induced transcriptional activity. We find that CRP BM counts have a nonlinear effect on CRP-dependent transcriptional activity and predict this activity better than full TFBS quality or location.

Introduction After therapy or stem cell transplantation, multiple myeloma patients achieving complete response (CR) or stringent complete response (sCR) can still have a significant risk of disease relapse, illustrating the importance of using highly sensitive methods for minimal residual disease (MRD) detection and prognostication. Two techniques used clinically for MRD detection include multiparametric flow cytometry (FC), which has a sensitivity down to 2-6 X 10-6 of cells, and next-generation sequencing (NGS)-based assay for detection of patient-specific clonal IGH VDJ gene sequences associated with the neoplastic plasma cells (PC). We determined the clonal characterization success rate of plasma cell neoplasm samples from a single institution in a clinical lab, using a commercially available NGS-based assay, Lymphotrack® (Invivoscribe, San Diego, CA). The characterized clonal sequences were used for MRD detection in subsequent monitoring samples, and the results were compared to concurrent FC findings. Methods DNA was extracted from fresh marrow or formalin-fixed paraffin-embedded (FFPE) tissue, and amplified by PCR reactions using primers sets for IGH Leader, FR1, FR2, FR3 regions, and IGK. Sequencing was performed on the Illumina MiSeqTM Platform, and sequence analyses were performed using the Lymphotrack® software, and MSK-Lymphoclone, a software developed at our institution. Disease-associated clonal sequences were characterized based on predefined clonal calling criteria and stored. In subsequent samples sent for disease monitoring, a search for sequencing reads with high homology (>99%) to the patient-specific sequences was performed for MRD detection. 10-color FC for PC analyses were also performed on the same samples at our institution (Roshal M, et al. Blood Adv 2017;1(12):728-32), with a target minimum of 3 million cells for MRD analyses. Results Overall, clonal characterization was successful in 235/251 cases (93.6%), with no difference in number of sequencing reads between the successful and unsuccessful cases (p=0.24). Higher success rate was observed among cases with higher aspirate PC counts: ≥5% (95.6% success rate) and ≥10% (98.1% success rate). IGH FR1 and Leader primers together characterized 214/251 cases (85.3%), while the remaining cases required additional primers. The characterized clones showed high median somatic hypermutation (SHM) rate of 8.1% (range: 0.0-29.0%), as well as IGH V and J segment usage bias: V3 (50.2%), followed by V4 (20.3%); J4 (43.4%), followed by J6 (27.5%), concordant with prior literature. 187 samples from 124 unique patients were tested by the Lymphotrack® assay for monitoring purposes, of which the diagnostic clones were detected in 147/187 samples (78.6%), with no difference in number of sequencing reads between cases with and without detectable clone (p=0.35). Within the short median time interval of 9.5 months between the characterization and monitoring samples, most clonal sequences remained stable. In 2 cases, new clonal sequences emerged in subsequent samples. Overall, FC and Lymphotrack® showed high concordance rate for MRD detection (92.9%) (See figures). All discordant cases showed Conclusions Our study demonstrated high clonal characterization success rate for plasma cell neoplasms using the Lymphotrack® assay when multiple primers sets were used, and the assay showed concordance with FC in MRD detection for the majority of cases. MRD detection sensitivity can be limited by low sample concentration/volume. Furthermore, the presence of very low level neoplastic subclones in the characterization samples might hamper clonal calling and detection in subsequent samples. Disclosures Ho: Invivoscribe, Inc.: Honoraria. Landgren:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy; Karyopharm: Consultancy. Arcila:Invivoscribe, Inc.: Consultancy, Honoraria.

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

The PUMA2 system (available at http://compbio.mcs. 10 anl.gov/puma2) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of geno15 mic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mito20 chondrial and 1493 viral genomes and automated metabolic reconstructions for .200 organisms. Genomic data is annotated with information integrated from .20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both 25 automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families 30 and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 envir35 onment for further interactive sequence analysis and annotation.

72 Background: Clonality testing is an integral part of the initial assessment of B and T cell malignancies. The further use of conventional clonality assays for monitoring of disease post treatment is limited by low assay sensitivity and inability to track clones based on their unique sequences. Clonality assessment by next generation sequencing (NGS) provides increased diagnostic capabilities, allowing the tracking of disease specific clones as well as the full spectrum of clonal sequences that arise in response to therapy. Here we describe our initial experience using an NGS based assay for monitoring and our comparison to conventional clonality assays and flow cytometry. Methods: DNA was extracted from hematologic samples received for routine clonality assessment including diagnostic (DS) and follow-up post therapy (PT) samples. Clonality testing was performed by conventional PCR-based assays using biomed II primers and by NGS utilizing Lymphotrack IGH FR1 and TRG kits (Invivoscribe). The amplified products were sequenced on Illumina MiSeq. For MRD assessment, we created dedicated laboratory protocols as well as in-house developed software, MSK-LymphoClone (LC), containing a data analysis pipeline, analytical tools for clonality assessment and a signout portal for easy search and retrieval of pertinent clones. Results: Samples from 48 patients were included (48 DS and 60 PT) encompassing 12 plasma cell neoplasms, 11 acute lymphoblastic leukemias, 16 mature B-cell and 9 T-cell lymphomas. All diagnostic samples showed clonal rearrangement with 100% concordance between conventional and NGS assays. Residual disease was detected in 27/60 (45%) PT samples using conventional fragment size based assays, 27/57 (47%) using flow and 38/60 (63%) using NGS. Diagnostic clonal sequences were detected in as low as 0.0019% of total reads in PT samples tested by NGS. 18/60 (30.0%) PT samples (17 patients) were disease negative by all assays; 16 patients remained disease-free (median follow-up - 2.4 months). Conclusions: NGS clonality testing is a valuable tool for monitoring patients with B and T cell neoplasms showing higher sensitivity and specificity than conventional assays.

Sentra (http://compbio.mcs.anl.gov/sentra), a database of signal transduction proteins encoded in completely sequenced prokaryotic genomes, has been updated to reflect recent advances in understanding signal transduction events on a whole-genome scale. Sentra consists of two principal components, a manually curated list of signal transduction proteins in 202 completely sequenced prokaryotic genomes and an automatically generated listing of predicted signaling proteins in 235 sequenced genomes that are awaiting manual curation. In addition to two-component histidine kinases and response regulators, the database now lists manually curated Ser/Thr/Tyr protein kinases and protein phosphatases, as well as adenylate and diguanylate cyclases and c-di-GMP phosphodiesterases, as defined in several recent reviews. All entries in Sentra are extensively annotated with relevant information from public databases (e.g. UniProt, KEGG, PDB and NCBI). Sentra's infrastructure was redesigned to support interactive cross-genome comparisons of signal transduction capabilities of prokaryotic organisms from a taxonomic and phenotypic perspective and in the framework of signal transduction pathways from KEGG. Sentra leverages the PUMA2 system to support interactive analysis and annotation of signal transduction proteins by the users.

BackgroundThe thermophilic anaerobeClostridium thermocellumis a candidate consolidated bioprocessing (CBP) biocatalyst for cellulosic ethanol production. The aim of this study was to investigateC. thermocellumgenes required to ferment biomass substrates and to conduct a robust comparison of DNA microarray and RNA sequencing (RNA-seq) analytical platforms.ResultsC. thermocellumATCC 27405 fermentations were conducted with a 5 g/L solid substrate loading of either pretreated switchgrass orPopulus. Quantitative saccharification and inductively coupled plasma emission spectroscopy (ICP-ES) for elemental analysis revealed composition differences between biomass substrates, which may have influenced growth and transcriptomic profiles. High quality RNA was prepared forC. thermocellumgrown on solid substrates and transcriptome profiles were obtained for two time points during active growth (12 hours and 37 hours postinoculation). A comparison of two transcriptomic analytical techniques, microarray and RNA-seq, was performed and the data analyzed for statistical significance. Large expression differences for cellulosomal genes were not observed. We updated gene predictions for the strain and a small novel gene, Cthe_3383, with a putative AgrD peptide quorum sensing function was among the most highly expressed genes. RNA-seq data also supported different small regulatory RNA predictions over others. The DNA microarray gave a greater number (2,351) of significant genes relative to RNA-seq (280 genes when normalized by the kernel density mean of M component (KDMM) method) in an analysis of variance (ANOVA) testing method with a 5% false discovery rate (FDR). When a 2-fold difference in expression threshold was applied, 73 genes were significantly differentially expressed in common between the two techniques. Sulfate and phosphate uptake/utilization genes, along with genes for a putative efflux pump system were some of the most differentially regulated transcripts when profiles forC. thermocellumgrown on either pretreated switchgrass orPopuluswere compared.ConclusionsOur results suggest that a high degree of agreement in differential gene expression measurements between transcriptomic platforms is possible, but choosing an appropriate normalization regime is essential.

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.

Shewanellae are facultative gamma-proteobacteria whose remarkable respiratory versatility has resulted in interest in their utility for bioremediation of heavy metals and radionuclides and for energy generation in microbial fuel cells. Extensive experimental efforts over the last several years and the availability of 21 sequenced Shewanella genomes made it possible to collect and integrate a wealth of information on the genus into one public resource providing new avenues for making biological discoveries and for developing a system level understanding of the cellular processes. The Shewanella knowledgebase was established in 2005 to provide a framework for integrated genome-based studies on Shewanella ecophysiology. The present version of the knowledgebase provides access to a diverse set of experimental and genomic data along with tools for curation of genome annotations and visualization and integration of genomic data with experimental data. As a demonstration of the utility of this resource, we examined a single microarray data set from Shewanella oneidensis MR-1 for new insights into regulatory processes. The integrated analysis of the data predicted a new type of bacterial transcriptional regulation involving co-transcription of the intergenic region with the downstream gene and suggested a biological role for co-transcription that likely prevents the binding of a regulator of the upstream gene to the regulator binding site located in the intergenic region. Database URL: http://shewanella-knowledgebase.org:8080/Shewanella/ or http://spruce.ornl.gov:8080/Shewanella/

Shewanella oneidensis MR-1 is an important model organism for environmental research as it has an exceptional metabolic and respiratory versatility regulated by a complex regulatory network. We have developed a database to collect experimental and computational data relating to regulation of gene and protein expression and a visualization environment that enables integration of these data types. The regulatory information in the database was collected from the published literature and different Internet resources. It includes predictions of DNA regulator binding sites, sigma factor binding sites, transcription units, operons, promoters, and RNA regulators including non-coding RNAs, riboswitches, and different types of terminators. A visualization environment based on GBrowser was developed for accessing the collected information and for its overlaying with experimental data (experimental results from studies employing microarrays, proteomics, and/or gene mutagenesis) and other genome annotations.

The BioEnergy Science Center (BESC) is undertaking large experimental campaigns to understand the biosynthesis and biodegradation of biomass and to develop biofuel solutions. BESC is generating large volumes of diverse data, including genome sequences, omics data and assay results. The purpose of the BESC Knowledgebase is to serve as a centralized repository for experimentally generated data and to provide an integrated, interactive and user-friendly analysis framework. The Portal makes available tools for visualization, integration and analysis of data either produced by BESC or obtained from external resources. Availability: http://besckb.ornl.gov Contact: syedmh@ornl.gov

Background: An industrially robust microorganism that can efficiently degrade and convert lignocellulosic biomass into ethanol and next-generation fuels is required to economically produce future sustainable liquid transportation fuels. The anaerobic, thermophilic, cellulolytic bacterium Clostridium thermocellum is a candidate microorganism for such conversions but it, like many bacteria, is sensitive to potential toxic inhibitors developed in the liquid hydrolysate produced during biomass processing. Microbial processes leading to tolerance of these inhibitory compounds found in the pretreated biomass hydrolysate are likely complex and involve multiple genes. Methodology/Principal Findings: In this study, we developed a 17.5% v/v Populus hydrolysate tolerant mutant strain of C. thermocellum by directed evolution. The genome of the wild type strain, six intermediate population samples and seven single colony isolates were sequenced to elucidate the mechanism of tolerance. Analysis of the 224 putative mutations revealed 73 high confidence mutations. A longitudinal analysis of the intermediate population samples, a pan-genomic analysis of the isolates, and a hotspot analysis revealed 24 core genes common to all seven isolates and 8 hotspots. Genetic mutations were matched with the observed phenotype through comparison of RNA expression levels during fermentation by the wild type strain and mutant isolate 6 in various concentrations of Populus hydrolysate (0%, 10%, and 17.5% v/v). Conclusion/Significance: The findings suggest that there are multiple mutations responsible for the Populus hydrolysate tolerant phenotype resulting in several simultaneous mechanisms of action, including increases in cellular repair, and altered energy metabolism. To date, this study provides the most comprehensive elucidation of the mechanism of tolerance to a pretreated biomass hydrolysate by C. thermocellum. These findings make important contributions to the development of industrially robust strains of consolidated bioprocessing microorganisms. Citation: Linville JL, Rodriguez M, Land M, Syed MH, Engle NL, et al. (2013) Industrial Robustness: Understanding the Mechanism of Tolerance for the

GeNome Analysis Research Environment (GNARE) is a bioinformatics server that supports both automated and interactive expert-driven analysis of user-submitted genomes and metagenomes. These analyses include gene function prediction and development of organism-specific metabolic reconstructions from sequence data. GNARE provides a framework for comparative and evolutionary analysis as well as annotation of genomes and metabolic networks in the context of phenotypic and taxonomic information. Results of analyses and metabolic models are visualized and extensively annotated with information from public databases. GNARE uses automated workflows and a Gridbased computational backend to perform highthroughput analysis of genomes. This use of distributed computing allows the analysis of an average-sized prokaryotic genome in less than 5 h. GNARE is available at http://compbio.mcs.anl. gov/gnare/.