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BackgroundThe blue whale, Balaenoptera musculus , is the largest animal known to have ever existed. Body size is tightly coupled to cell metabolism and environmental adaptations. A high-quality genome assembly of this magnificent animal will aid our understanding of body size regulation and related biological processes.ResultsWe report a reference-quality, long read based assembly of the blue whale genome. We sequenced genomic DNA using PacBio long-read, Illumina short-read, and 10X Genomics synthetic long-read technologies. We also obtained long-range mapping information using Bionano optical mapping and Dovetail Hi-C. Additionally, we sequenced the transcriptome of blue whale fibroblasts using Illumina RNA-seq and PacBio Iso-seq technologies. We also measured genome size using a flow cytometry technique. We report on: (1) comparison of alternative long-read and short-read-based assemblies; (2) segmental duplications within the blue whale genome, which have resulted in dramatic amplifications of several genes; (3) sites in IGF1 gene correlated to body size in cetaceans; and (4) heterozygosity and historical demography of Pacific and Atlantic blue whale populations.ConclusionsThis is one of the first high-quality reference genomes of a cetacean and the first baleen whale sequenced using a long-read technology. A high quality, annotated blue whale genome will serve as an important resource for biology, evolution, and conservation research.

The vaquita is the most critically endangered marine mammal, with fewer than 19 remaining in the wild. First described in 1958, the vaquita has been in rapid decline for more than 20 years resulting from inadvertent deaths due to the increasing use of large‐mesh gillnets. To understand the evolutionary and demographic history of the vaquita, we used combined long‐read sequencing and long‐range scaffolding methods with long‐ and short‐read RNA sequencing to generate a near error‐free annotated reference genome assembly from cell lines derived from a female individual. The genome assembly consists of 99.92% of the assembled sequence contained in 21 nearly gapless chromosome‐length autosome scaffolds and the X‐chromosome scaffold, with a scaffold N50 of 115 Mb. Genome‐wide heterozygosity is the lowest (0.01%) of any mammalian species analysed to date, but heterozygosity is evenly distributed across the chromosomes, consistent with long‐term small population size at genetic equilibrium, rather than low diversity resulting from a recent population bottleneck or inbreeding. Historical demography of the vaquita indicates long‐term population stability at less than 5,000 (Ne) for over 200,000 years. Together, these analyses indicate that the vaquita genome has had ample opportunity to purge highly deleterious alleles and potentially maintain diversity necessary for population health., see also the Perspective by Annabel Whibley

In this work, we introduce the Growth Curve Analysis Tool (GCAT). GCAT is designed to enable efficient analysis of high-throughput microbial growth curve data collected from cultures grown in microtiter plates. GCAT is accessible through a web browser, making it easy to use and operating system independent. GCAT implements fitting of global sigmoid curve models and local regression (LOESS) model. We assess the relative merits of these approaches using experimental data. Additionally, GCAT implements heuristics to deal with some peculiarities of growth curve data commonly encountered in bioenergy research. GCAT server is publicly available at http://gcat-pub.glbrc.org/ . The source code is available at http://code.google.com/p/gcat-hts/ .

Microbial depolymerization of plant cell walls contributes to global carbon balance and is a critical component of renewable energy. The genomes of lignocellulose degrading microorganisms encode diverse classes of carbohydrate modifying enzymes, although currently there is a paucity of knowledge on the role of these proteins in vivo. We report the comprehensive analysis of the cellulose degradation system in the saprophytic bacterium Cellvibrio japonicus. Gene expression profiling of C. japonicus demonstrated that three of the 12 predicted β-1,4 endoglucanases (cel5A, cel5B, and cel45A) and the sole predicted cellobiohydrolase (cel6A) showed elevated expression during growth on cellulose. Targeted gene disruptions of all 13 predicted cellulase genes showed that only cel5B and cel6A were required for optimal growth on cellulose. Our analysis also identified three additional genes required for cellulose degradation: lpmo10B encodes a lytic polysaccharide monooxygenase (LPMO), while cbp2D and cbp2E encode proteins containing carbohydrate binding modules and predicted cytochrome domains for electron transfer. CjLPMO10B oxidized cellulose and Cbp2D demonstrated spectral properties consistent with redox function. Collectively, this report provides insight into the biological role of LPMOs and redox proteins in cellulose utilization and suggests that C. japonicus utilizes a combination of hydrolytic and oxidative cleavage mechanisms to degrade cellulose.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.

Highlight Reducing the function of PAL, the first enzyme in the phenylpropanoid pathway, in Brachypodium distachyon alters cell wall composition, increases fungal susceptibility, but minimally affects caterpillar herbivory and abiotic stress tolerance., The phenylpropanoid pathway in plants synthesizes a variety of structural and defence compounds, and is an important target in efforts to reduce cell wall lignin for improved biomass conversion to biofuels. Little is known concerning the trade-offs in grasses when perturbing the function of the first gene family in the pathway, PHENYLALANINE AMMONIA LYASE (PAL). Therefore, PAL isoforms in the model grass Brachypodium distachyon were targeted, by RNA interference (RNAi), and large reductions (up to 85%) in stem tissue transcript abundance for two of the eight putative BdPAL genes were identified. The cell walls of stems of BdPAL-knockdown plants had reductions of 43% in lignin and 57% in cell wall-bound ferulate, and a nearly 2-fold increase in the amounts of polysaccharide-derived carbohydrates released by thermochemical and hydrolytic enzymic partial digestion. PAL-knockdown plants exhibited delayed development and reduced root growth, along with increased susceptibilities to the fungal pathogens Fusarium culmorum and Magnaporthe oryzae. Surprisingly, these plants generally had wild-type (WT) resistances to caterpillar herbivory, drought, and ultraviolet light. RNA sequencing analyses revealed that the expression of genes associated with stress responses including ethylene biosynthesis and signalling were significantly altered in PAL knocked-down plants under non-challenging conditions. These data reveal that, although an attenuation of the phenylpropanoid pathway increases carbohydrate availability for biofuel, it can adversely affect plant growth and disease resistance to fungal pathogens. The data identify notable differences between the stress responses of these monocot pal mutants versus Arabidopsis (a dicot) pal mutants and provide insights into the challenges that may arise when deploying phenylpropanoid pathway-altered bioenergy crops.

A rise in resistance to current antifungals necessitates strategies to identify alternative sources of effective fungicides. We report the discovery of poacic acid, a potent antifungal compound found in lignocellulosic hydrolysates of grasses. Chemical genomics using Saccharomyces cerevisiae showed that loss of cell wall synthesis and maintenance genes conferred increased sensitivity to poacic acid. Morphological analysis revealed that cells treated with poacic acid behaved similarly to cells treated with other cell wall-targeting drugs and mutants with deletions in genes involved in processes related to cell wall biogenesis. Poacic acid causes rapid cell lysis and is synergistic with caspofungin and fluconazole. The cellular target was identified; poacic acid localized to the cell wall and inhibited β-1,3-glucan synthesis in vivo and in vitro, apparently by directly binding β-1,3-glucan. Through its activity on the glucan layer, poacic acid inhibits growth of the fungi Sclerotinia sclerotiorum and Alternaria solani as well as the oomycete Phytophthora sojae. A single application of poacic acid to leaves infected with the broad-range fungal pathogen S. sclerotiorum substantially reduced lesion development. The discovery of poacic acid as a natural antifungal agent targeting β-1,3-glucan highlights the potential side use of products generated in the processing of renewable biomass toward biofuels as a source of valuable bioactive compounds and further clarifies the nature and mechanism of fermentation inhibitors found in lignocellulosic hydrolysates.

The ability to quantitatively compare protein levels across different regions of the brain to identify disease mechanisms remains a fundamental research challenge. It requires both a robust method to efficiently isolate proteins from small amounts of tissue and a differential technique that provides a sensitive and comprehensive analysis of these proteins. Here, we describe a proteomic approach for the quantitative mapping of membrane proteins between mouse fore- and hindbrain regions. The approach focuses primarily on a recently developed method for the fractionation of membranes and on-membrane protein digestion, but incorporates off-line SCX-fractionation of the peptide mixture and nano-LC-MS/MS analysis using an LTQ-FT-ICR instrument as part of the analytical method. Comparison of mass spectral peak intensities between samples, mapping of peaks to peptides and protein sequences, and statistical analysis were performed using in-house differential analysis software (DAS). In total, 1213 proteins were identified and 967 were quantified; 81% of the identified proteins were known membrane proteins and 38% of the protein sequences were predicted to contain transmembrane helices. Although this paper focuses primarily on characterizing the efficiency of this purification method from a typical sample set, for many of the quantified proteins such as glutamate receptors, GABA receptors, calcium channel subunits, and ATPases, the observed ratios of protein abundance were in good agreement with the known mRNA expression levels and/or intensities of immunostaining in rostral and caudal regions of murine brain. This suggests that the approach would be well-suited for incorporation in more rigorous, larger scale quantitative analysis designed to achieve biological significance.

Electroconvulsive therapy (ECT) remains the treatment of choice for drug-resistant patients with depressive disorders, yet the mechanism for its efficacy remains unknown. Gene transcription changes were measured in the frontal cortex and hippocampus of rats subjected to sham seizures or to 1 or 10 electroconvulsive seizures (ECS), a model of ECT. Among the 3500–4400 RNA sequences detected in each sample, ECS increased by 1.5- to 11-fold or decreased by at least 34% the expression of 120 unique genes. The hippocampus produced more than three times the number of gene changes seen in the cortex, and many hippocampal gene changes persisted with chronic ECS, unlike in the cortex. Among the 120 genes, 77 have not been reported in previous studies of ECS or seizure responses, and 39 were confirmed among 59 studied by quantitative real time PCR. Another 19 genes, 10 previously unreported, changed by

Efficient microbial conversion of lignocellulosic hydrolysates to biofuels is a key barrier to the economically viable deployment of lignocellulosic biofuels. A chief contributor to this barrier is the impact on microbial processes and energy metabolism of lignocellulose-derived inhibitors, including phenolic carboxylates, phenolic amides (for ammonia-pretreated biomass), phenolic aldehydes, and furfurals. To understand the bacterial pathways induced by inhibitors present in ammonia-pretreated biomass hydrolysates, which are less well studied than acid-pretreated biomass hydrolysates, we developed and exploited synthetic mimics of ammonia-pretreated corn stover hydrolysate (ACSH). To determine regulatory responses to the inhibitors normally present in ACSH, we measured transcript and protein levels in an Escherichia coli ethanologen using RNA-seq and quantitative proteomics during fermentation to ethanol of synthetic hydrolysates containing or lacking the inhibitors. Our study identified four major regulators mediating these responses, the MarA/SoxS/Rob network, AaeR, FrmR, and YqhC. Induction of these regulons was correlated with a reduced rate of ethanol production, buildup of pyruvate, depletion of ATP and NAD(P)H, and an inhibition of xylose conversion. The aromatic aldehyde inhibitor 5-hydroxymethylfurfural appeared to be reduced to its alcohol form by the ethanologen during fermentation, whereas phenolic acid and amide inhibitors were not metabolized. Together, our findings establish that the major regulatory responses to lignocellulose-derived inhibitors are mediated by transcriptional rather than translational regulators, suggest that energy consumed for inhibitor efflux and detoxification may limit biofuel production, and identify a network of regulators for future synthetic biology efforts.

Enhancement of acyl-CoA–dependent triacylglycerol (TAG) synthesis in vegetative tissues is widely discussed as a potential avenue to increase the energy density of crops. Here, we report the identification and characterization of Chlamydomonas reinhardtii diacylglycerol acyltransferase type two (DGTT) enzymes and use DGTT2 to alter acyl carbon partitioning in plant vegetative tissues. This enzyme can accept a broad range of acyl-CoA substrates, allowing us to interrogate different acyl pools in transgenic plants. Expression of DGTT2 in Arabidopsis thaliana increased leaf TAG content, with some molecular species containing very-long-chain fatty acids. The acyl compositions of sphingolipids and surface waxes were altered, and cutin was decreased. The increased carbon partitioning into TAGs in the leaves of DGTT2-expressing lines had little effect on transcripts of the sphingolipid/wax/cutin pathway, suggesting that the supply of acyl groups for the assembly of these lipids is not transcriptionally adjusted. Caterpillars of the generalist herbivore Spodoptera exigua reared on transgenic plants gained more weight. Thus, the nutritional value and/or energy density of the transgenic lines was increased by ectopic expression of DGTT2 and acyl groups were diverted from different pools into TAGs, demonstrating the interconnectivity of acyl metabolism in leaves.

The highly conserved ANP32 proteins are proposed to function in a broad array of physiological activities through molecular mechanisms as diverse as phosphatase inhibition, chromatin regulation, caspase activation, and intracellular transport. On the basis of previous analyses of mice bearing targeted mutations of Anp32a or Anp32e , there has been speculation that all ANP32 proteins play redundant roles and are dispensable for normal development. However, more recent work has suggested that ANP32B may in fact have functions that are not shared by other ANP32 family members. Here we report that ANP32B expression is associated with a poor prognosis in human breast cancer, consistent with the increased levels of Anp32b mRNA present in proliferating wild-type (WT) murine embryonic fibroblasts and stimulated WT B and T lymphocytes. Moreover, we show that, contrary to previous assumptions, Anp32b is very important for murine embryogenesis. In a mixed genetic background, ANP32B-deficient mice displayed a partially penetrant perinatal lethality that became fully penetrant in a pure C57BL/6 background. Surviving ANP32B-deficient mice showed reduced viability due to variable defects in various organ systems. Study of compound mutants lacking ANP32A, ANP32B, and/or ANP32E revealed previously hidden roles for ANP32A in mouse development that became apparent only in the complete absence of ANP32B. Our data demonstrate a hierarchy of importance for the mammalian Anp32 genes, with Anp32b being the most critical for normal development.

A microarray study of chemostat growth on insoluble cellulose or soluble cellobiose has provided substantial new information on Clostridium thermocellum gene expression. This is the first comprehensive examination of gene expression in C. thermocellum under defined growth conditions. Expression was detected from 2,846 of 3,189 genes, and regression analysis revealed 348 genes whose changes in expression patterns were growth rate and/or substrate dependent. Successfully modeled genes included those for scaffoldin and cellulosomal enzymes, intracellular metabolic enzymes, transcriptional regulators, sigma factors, signal transducers, transporters, and hypothetical proteins. Unique genes encoding glycolytic pathway and ethanol fermentation enzymes expressed at high levels simultaneously with previously established maximal ethanol production were also identified. Ranking of normalized expression intensities revealed significant changes in transcriptional levels of these genes. The pattern of expression of transcriptional regulators, sigma factors, and signal transducers indicates that response to growth rate is the dominant global mechanism used for control of gene expression in C. thermocellum.

Once a model of the secondary structure of an RNA has been deduced, thermal melting analysis can be used to determine whether the model accounts for all intramolecular interactions of the RNA, or whether noncanonical and tertiary interactions make the structure more stable than predicted, or link parts of the structure in unexpected ways. It is also useful to determine the pH, salt, and temperature ranges under which the RNA adopts a stably folded structure, or to analyze unfolding pathways. This unit discusses sample preparation, instrumentation, and theoretical background. It also provide a sample analysis of tRNA unfolding.

Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24,540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

Background Hippocampal dentate granule neurons are altered in schizophrenia, but it is unknown if their gene expressions change in schizophrenia or other psychiatric diseases. Methods Laser-captured dentate granule neurons from two groups of schizophrenia and control cases and from major depression and bipolar disease cases were examined for alterations in gene expression using complementary DNA (cDNA) microarrays and reverse transcription polymerase chain reaction (RT-PCR). Results Compared with 24 control cases, the 22 schizophrenia patients in both groups revealed decreases in clusters of genes that encode for protein turnover (proteasome subunits and ubiquitin), mitochondrial oxidative energy metabolism (isocitrate, lactate, malate, nicotinamide adenine dinucleotide [NADH], and succinate dehydrogenases; cytochrome C oxidase; adenosine triphosphate [ATP] synthase), and genes associated with neurite outgrowth, cytoskeletal proteins, and synapse plasticity. These changes were not obtained in 9 bipolar cases or 10 major depression cases and were not associated with age, sex, brain weight, body weight, postmortem interval, or drug history. Brain pH contributed to the variance of some genes but was mostly independent of the disease effect. Conclusions Decreases in hippocampal neuron gene expression are consistent with brain imaging and microarray studies of the frontal cortex in schizophrenia. A mitochondrial and ubiquitin-proteasome hypofunctioning of dentate granule neurons may contribute to the deficits of schizophrenia.

The gene expression profiles of human postmortem parietal and prefrontal cortex samples of normal controls and patients with bipolar disease, or human neuroblastoma flat (NBFL) cells treated with the mood-stabilizing drug, valproate, were used to compare the performance of Affymetrix oligonucleotide U133A GeneChips ® and Agilent Human 1 cDNA microarrays. Among those genes represented on both platforms, the oligo array identified 26–53% more differentially expressed genes compared to the cDNA array in the three experiments, when identical fold change and t -test criteria were applied. The increased sensitivity was primarily the result of more robust fold changes measured by the oligonucleotide system. Essentially all gene changes overlapping between the two platforms were co-directional, and ranged from 4 to 19% depending upon the amount of biological variability within and between the comparison groups. Q-PCR validation rates were virtually identical for the two platforms, with 23–24% validation in the prefrontal cortex experiment, and 56% for both platforms in the cell culture experiment. Validated genes included dopa decarboxylase, dopamine beta-hydroxylase, and dihydropyrimidinase-related protein 3, which were decreased in NBFL cells exposed to valproate, and spinocerebellar ataxia 7, which was increased in bipolar disease. The modest overlap but similar validation rates show that each microarray system identifies a unique set of differentially expressed genes, and thus the greatest information is obtained from the use of both platforms.

Background: Epithelial ovarian cancer is the leading cause of death by gynecological malignancy. Due to inadequate screening modalities, a lack of characteristic presenting symptoms, limited treatments and a poor understanding of the molecular underpinnings of the disease, only 25% of ovarian cancers are diagnosed at an early stage. Current 5-year survival rates range from 80%, for disease diagnosed in Stage I to as low as 13% for Stage IV. Current screening for ovarian cancer involves measuring CA-125 levels. However, CA-125 testing has low sensitivity since it can be elevated in a variety of other gynecological diseases. Numerous studies have found molecular heterogeneity between the four histological subtypes of ovarian cancer (serous, endometrioid, clear cell and mucinous). However, treatments remain the same for all subtypes regardless of molecular heterogeneity. Thus, better treatment targets and biomarkers must be found for this disease. Methods:In our study 300 ovarian tumors will be genomically profiled using the Affymetrix Genome-Wide SNP Array 6.0 to identify loci and genes implicated in ovarian cancer. To date, 51 ovarian tumors have been analyzed using the SNP array. Results:Preliminary analysis of copy number variation in these tumors using Partek software has revealed a total of 978 loci. Known amplifications derived from the literature were seen at CCNE1 and ERBB2. Similarly, well known deletions ofp53 and RB1 in ovarian cancer were detected. Novel amplified loci at 18q11.2 and 4q33 were also detected. Novel deletions were detected at 7p13 and 8q22.2. Conclusion: Future work will include running the remaining 249 tumor samples on the SNP array and analyzing the complete dataset using Partek software. Future validation of identified genes in vitro and in vivo may provide insight and possible biomarkers that may be used clinically to benefit the ovarian cancer patient.