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At least 181 records · Page 10

Coupled Transcriptome and Proteome Analysis of Human Lymphotropic Tumor Viruses: Insights on the Detection and Discovery of Viral Genes

Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) are related human tumor viruses that cause primary effusion lymphomas (PEL) and Burkitt's lymphomas (BL), respectively. Viral genes expressed in naturally-infected cancer cells contribute to disease pathogenesis; knowing which viral genes are expressed is critical in understanding how these viruses cause cancer. To evaluate the expression of viral genes, we used high-resolution separation and mass spectrometry coupled with custom tiling arrays to align the viral proteomes and transcriptomes of three PEL and two BL cell lines under latent and lytic culture conditions. Results The majority of viral genes were efficiently detected at the transcript and/or protein level on manipulating the viral life cycle. Overall the correlation of expressed viral proteins and transcripts was highly complementary in both validating and providing orthogonal data with latent/lytic viral gene expression. Our approach also identified novel viral genes in both KSHV and EBV, and extends viral genome annotation. Several previously uncharacterized genes were validated at both transcript and protein levels. Conclusions This systems biology approach coupling proteome and transcriptome measurements provides a comprehensive view of viral gene expression that could not have been attained using each methodology independently. Detection of viral proteins in combination with viral transcripts is a potentially powerful method for establishing virus-disease relationships.

59 BASIC BIOLOGICAL SCIENCES↗

Unique aspects of fiber degradation by the ruminal ethanologen Ruminococcus albus 7 revealed by physiological and transcriptomic analysis

Bacteria in the genus Ruminococcus are ubiquitous members of the mammalian gastrointestinal tract. In particular, they are important in ruminants where they digest a wide range of plant cell wall polysaccharides. For example, Ruminococcus albus 7 is a primary cellulose degrader that produces acetate usable by its bovine host. Moreover, it is one of the few organisms that ferments cellulose to form ethanol at mesophilic temperatures in vitro . The mechanism of cellulose degradation by R. albus 7 is not well-defined and is thought to involve pilin-like proteins, unique carbohydrate-binding domains, a glycocalyx, and cellulosomes. We used a combination of comparative genomics, fermentation analyses, and transcriptomics to further clarify the cellulolytic and fermentative potential of R. albus 7 . A comparison of the R. albus 7 genome sequence against the genome sequences of related bacteria that either encode or do not encode cellulosomes revealed that R. albus 7 does not encode for most canonical cellulosomal components. Fermentation analysis of R. albus 7 revealed the ability to produce ethanol and acetate on a wide range of fibrous substrates in vitro . Global transcriptomic analysis of R. albus 7 grown at identical dilution rates on cellulose and cellobiose in a chemostat showed that this bacterium, when growing on cellulose, utilizes a carbohydrate-degrading strategy that involves increased transcription of the rare carbohydrate-binding module (CBM) family 37 domain and the tryptophan biosynthetic operon. Our data suggest that R. albus 7 does not use canonical cellulosomal components to degrade cellulose, but rather up-regulates the expression of CBM37-containing enzymes and tryptophan biosynthesis. This study contributes to a revised model of carbohydrate degradation by this key member of the rumen ecosystem.

09 BIOMASS FUELS↗

Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions

Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.

59 BASIC BIOLOGICAL SCIENCES↗

Assembled genomic and tissue-specific transcriptomic data resources for two genetically distinct lines of Cowpea (Vigna unguiculata (L.) Walp)

Cowpea (Vigna unguiculata (L.) Walp) is an important legume crop for food security in areas of low-input and smallholder farming throughout Africa and Asia. Genetic improvements are required to increase yield and resilience to biotic and abiotic stress and to enhance cowpea crop performance. An integrated cowpea genomic and gene expression data resource has the potential to greatly accelerate breeding and the delivery of novel genetic traits for cowpea. Extensive genomic resources for cowpea have been absent from the public domain; however, a recent early release reference genome for IT97K-499-35 (Vigna unguiculata v1.0, NSF, UCR, USAID, DOE-JGI, http://phytozome.jgi.doe.gov/) has now been established in a collaboration between the Joint Genome Institute (JGI) and University California (UC) Riverside. Here we release supporting genomic and transcriptomic data for IT97K-499-35 and a second transformable cowpea variety, IT86D-1010. The transcriptome resource includes six tissue-specific datasets for each variety, with particular emphasis on reproductive tissues that extend and support the V. unguiculata v1.0 reference. Annotations have been included in our resource to allow direct mapping to the v1.0 cowpea reference. Access to this resource provided here is supported by raw and assembled data downloads.

Spriggs, Andrew↗

Dynamics of Streptococcus mutans Transcriptome in Response to Starch and Sucrose during Biofilm Development

The combination of sucrose and starch in the presence of surface-adsorbed salivary a-amylase and bacterial glucosyltransferases increase the formation of a structurally and metabolically distinctive biofilm by Streptococcus mutans. This host-pathogen-diet interaction may modulate the formation of pathogenic biofilms related to dental caries disease. We conducted a comprehensive study to further investigate the influence of the dietary carbohydrates on S. mutanstranscriptome at distinct stages of biofilm development using whole genomic profiling with a new computational tool (MDV) for data mining. S. mutans UA159 biofilms were formed on amylase-active saliva coated hydroxyapatite discs in the presence of various concentrations of sucrose alone (ranging from 0.25 to 5% w/v) or in combination with starch (0.5 to 1% w/v). Overall, the presence of sucrose and starch (suc+st) influenced the dynamics of S. mutans transcriptome (vs. sucrose alone), which may be associated with gradual digestion of starch by surface-adsorbed amylase. At 21 h of biofilm formation, most of the differentially expressed genes were related to sugar metabolism, such as upregulation of genes involved in maltose/maltotriose uptake and glycogen synthesis. In addition, the groEL/groES chaperones were induced in the suc+stbiofilm, indicating that presence of starch hydrolysates may cause environmental stress. In contrast, at 30 h of biofilm development, multiple genes associated with sugar uptake/transport (e.g. maltose), two-component systems, fermentation/ glycolysis and iron transport were differentially expressed in suc+st-biofilms (vs. sucrose-biofilms). Interestingly, lytT (bacteria autolysis) was upregulated, which was correlated with presence of extracellular DNA in the matrix of suc+stbiofilms. Specific genes related to carbohydrate uptake and glycogen metabolism were detected in suc+st-biofilms in more than one time point, indicating an association between presence of starch hydrolysates and intracellular polysaccharide storage. Our data show complex remodeling of S. mutans-transcriptome in response to changing environmental conditions in situ, which could modulate the dynamics of biofilm development and pathogenicity.

59 BASIC BIOLOGICAL SCIENCES↗

Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding

Temporal analysis of genome-wide data can provide insights into the underlying mechanism of the biological processes in two ways. First, grouping the temporal data provides a richer, more robust representation of the underlying processes that are co-regulated. The net result is a significant dimensional reduction of the genome-wide array data into a smaller set of vocabularies for bioinformatics analysis. Second, the computed set of time-course vocabularies can be interrogated for a potential causal network that can shed light on the underlying interactions. The method is coupled with an experiment for investigating responses to high doses of ionizing radiation with and without a small priming dose. From a computational perspective, inference of a causal network can rapidly become computationally intractable with the increasing number of variables. Additionally, from a bioinformatics perspective, larger networks always hinder interpretation. Therefore, our method focuses on inferring the simplest network that is computationally tractable and interpretable. The method first reduces the number of temporal variables through consensus clustering to reveal a small set of temporal templates. It then enforces simplicity in the network configuration through the sparsity constraint, which is further regularized by requiring continuity between consecutive time points. We present intermediate results for each computational step, and apply our method to a time-course transcriptome dataset for a cell line receiving a challenge dose of ionizing radiation with and without a prior priming dose. Our analyses indicate that (i) the priming dose increases the diversity of the computed templates (e.g., diversity of transcriptome signatures); thus, increasing the network complexity; (ii) as a result of the priming dose, there are a number of unique templates with delayed and oscillatory profiles; and (iii) radiation-induced stress responses are enriched through pathway and subnetwork studies.

59 BASIC BIOLOGICAL SCIENCES↗

Use of a Capture-Based Pathogen Transcript Enrichment Strategy for RNA-Seq Analysis of the Francisella Tularensis LVS Transcriptome during Infection of Murine Macrophages

Francisella tularensis is a zoonotic intracellular pathogen that is capable of causing potentially fatal human infections. Like all successful bacterial pathogens, F. tularensis rapidly responds to changes in its environment during infection of host cells, and upon encountering different microenvironments within those cells. This ability to appropriately respond to the challenges of infection requires rapid and global shifts in gene expression patterns. In this study, we use a novel pathogen transcript enrichment strategy and whole transcriptome sequencing (RNA-Seq) to perform a detailed characterization of the rapid and global shifts in F. tularensis LVS gene expression during infection of murine macrophages. We performed differential gene expression analysis on all bacterial genes at two key stages of infection: phagosomal escape, and cytosolic replication. By comparing the F. tularensis transcriptome at these two stages of infection to that of the bacteria grown in culture, we were able to identify sets of genes that are differentially expressed over the course of infection. This analysis revealed the temporally dynamic expression of a number of known and putative transcriptional regulators and virulence factors, providing insight into their role during infection. In addition, we identified several F. tularensis genes that are significantly up-regulated during infection but had not been previously identified as virulence factors. These unknown genes may make attractive therapeutic or vaccine targets.

59 BASIC BIOLOGICAL SCIENCES↗

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. While tools for co-profiling single-cell genomes, transcriptomes, and epigenomes are available, accessing proteomes in parallel is more challenging. We developed nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells using RNA sequencing and mass spectrometry-based proteomics, respectively. nanoSPLITS can precisely quantify over 5000 genes, 2000 proteins, and 140 phosphopeptides per single cell and identify candidate cell markers from these modalities. By exploring Cdk1-mediated cell cycle arrest, we demonstrate how nanoSPLITS single-cell multiomics can provide comprehensive cellular characterization with insights into covarying protein/gene clusters, unique phosphorylation events, and mitotic pathways.

59 BASIC BIOLOGICAL SCIENCES↗

Effective Mechanisms for Improving Seed Oil Production in Pennycress (Thlaspi arvense L.) Highlighted by Integration of Comparative Metabolomics and Transcriptomics

Pennycress is a potentially lucrative biofuel crop due to its high content of long-chain unsaturated fatty acids, and because it uses non-conventional pathways to achieve efficient oil production. However, metabolic engineering is required to improve pennycress oilseed content and make it an economically viable source of aviation fuel. Research is warranted to determine if further upregulation of these non-conventional pathways could improve oil production within the species even more, which would indicate these processes serve as promising metabolic engineering targets and could provide the improvement necessary for economic feasibility of this crop. To test this hypothesis, we performed a comparative biomass, metabolomic, and transcriptomic analyses between a high oil accession (HO) and low oil accession (LO) of pennycress to assess potential factors required to optimize oil content. An evident reduction in glycolysis intermediates, improved oxidative pentose phosphate pathway activity, malate accumulation in the tricarboxylic acid cycle, and an anaplerotic pathway upregulation were noted in the HO genotype. Additionally, higher levels of threonine aldolase transcripts imply a pyruvate bypass mechanism for acetyl-CoA production. Nucleotide sugar and ascorbate accumulation also were evident in HO, suggesting differential fate of associated carbon between the two genotypes. An altered transcriptome related to lipid droplet (LD) biosynthesis and stability suggests a contribution to a more tightly-packed LD arrangement in HO cotyledons. In addition to the importance of central carbon metabolism augmentation, alternative routes of carbon entry into fatty acid synthesis and modification, as well as transcriptionally modified changes in LD regulation, are key aspects of metabolism and storage associated with economically favorable phenotypes of the species.

59 BASIC BIOLOGICAL SCIENCES↗

Rhythmic Mechanisms Governing CAM Photosynthesis in Kalanchoe fedtschenkoi : High-Resolution Temporal Transcriptomics

Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO 2 uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles.

09 BIOMASS FUELS↗

The stem cell-type transcriptome of bioenergy sorghum reveals the spatial regulation of secondary cell wall networks

Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type.

cell specificity↗

Defining the maize transcriptome de novo using deep RNA-Seq

De novo assembly of the transcriptome is crucial for functional genomics studies in bioenergy research, since many of the organisms lack high quality reference genomes. In a previous study we successfully de novo assembled simple eukaryote transcriptomes exclusively from short Illumina RNA-Seq reads [1]. However, extensive alternative splicing, present in most of the higher eukaryotes, poses a significant challenge for current short read assembly processes. Furthermore, the size of next-generation datasets, often large for plant genomes, presents an informatics challenge. To tackle these challenges we present a combined experimental and informatics strategy for de novo assembly in higher eukaryotes. Using maize as a test case, preliminary results suggest our approach can resolve transcript variants and improve gene annotations.

97 MATHEMATICS AND COMPUTING↗

Transcriptome Analysis of Manganese-deficient Chlamydomonas reinhardtii Provides Insight on the Chlorophyll Biosynthesis Pathway

The biosynthesis of chlorophyll and other tetrapyrroles is a vital but poorly understood process. Recent genomic advances with the unicellular green algae Chlamydomonas reinhardtii have created opportunity to more closely examine the mechanisms of the chlorophyll biosynthesis pathway via transcriptome analysis. Manganese is a nutrient of interest for complex reactions because of its multiple stable oxidation states and role in molecular oxygen coordination. C. reinhardtii was cultured in Manganese-deplete Tris-acetate-phosphate (TAP) media for 24 hours and used to create cDNA libraries for sequencing using Illumina TruSeq technology. Transcriptome analysis provided intriguing insight on possible regulatory mechanisms in the pathway. Evidence supports similarities of GTR (Glutamyl-tRNA synthase) to its Chlorella vulgaris homolog in terms of Mn requirements. Data was also suggestive of Mn-related compensatory up-regulation for pathway proteins CHLH1 (Manganese Chelatase), GUN4 (Magnesium chelatase activating protein), and POR1 (Light-dependent protochlorophyllide reductase). Intriguingly, data suggests possible reciprocal expression of oxygen dependent CPX1 (coproporphyrinogen III oxidase) and oxygen independent CPX2. Further analysis using RT-PCR could provide compelling evidence for several novel regulatory mechanisms in the chlorophyll biosynthesis pathway.

99 GENERAL AND MISCELLANEOUS↗

Single Cell Genomics and Transcriptomics for Unicellular Eukaryotes

Despite their small size, unicellular eukaryotes have complex genomes with a high degree of plasticity that allow them to adapt quickly to environmental changes. Unicellular eukaryotes live with prokaryotes and higher eukaryotes, frequently in symbiotic or parasitic niches. To this day their contribution to the dynamics of the environmental communities remains to be understood. Unfortunately, the vast majority of eukaryotic microorganisms are either uncultured or unculturable, making genome sequencing impossible using traditional approaches. We have developed an approach to isolate unicellular eukaryotes of interest from environmental samples, and to sequence and analyze their genomes and transcriptomes. We have tested our methods with six species: an uncharacterized protist from cellulose-enriched compost identified as Platyophrya, a close relative of P. vorax; the fungus Metschnikowia bicuspidate, a parasite of water flea Daphnia; the mycoparasitic fungi Piptocephalis cylindrospora, a parasite of Cokeromyces and Mucor; Caulochytrium protosteloides, a parasite of Sordaria; Rozella allomycis, a parasite of the water mold Allomyces; and the microalgae Chlamydomonas reinhardtii. Here, we present the four components of our approach: pre-sequencing methods, sequence analysis for single cell genome assembly, sequence analysis of single cell transcriptomes, and genome annotation. This technology has the potential to uncover the complexity of single cell eukaryotes and their role in the environmental samples.

59 BASIC BIOLOGICAL SCIENCES↗

A Study on Contrastive Graph Neural Network Pretraining for Predicting Transcriptome Profiles

We study graph neural network learning for transcriptomics with limited amount of labeled data. Our study reveals that simple GNN architectures perform well and do not suffer from over-fitting as the more sophisticated ones. Our study shows that although contrastive learning as a pretraining strategy has been successful in predicting properties such as formation and binding energy, it is not effective for transcriptomics.

Ma, Jiaji [University of Virginia]↗

Quantitative trait locus (QTL) mapping and transcriptome profiling identify QTLs and candidate genes associated with heat stress response during reproductive development in Camelina sativa

Camelina sativa (L.) Crantz is a low-input oilseed crop that has great potential in providing sustainable feedstock for biofuels and bioproducts. Climate change is threatening production of camelina with rising global temperatures. Elucidating the genetic response to high temperatures is essential for successful breeding of heat-tolerant camelina varieties. Here, we report a combinatorial approach to identifying candidate genes associated with heat stress by quantitative trait locus (QTL) mapping and comparative transcriptome profiling. A population of recombinant inbred lines (RILs) was grown in a controlled growth chamber under the high-temperature regimes for 14 days beginning at the onset of the reproductive stage. Several traits related to seed production were evaluated at maturity. The QTL analysis identified several regions with co-located traits on chromosomes 8, 10, and 12. Two RILs with contrasting phenotypic responses to heat stress were chosen for gene expression profiling via RNA sequencing. Multiple pathways and genes were found to be strongly affected by heat stress, and many genes expressed differently between the two RILs. Several genes identified within the QTL regions were considered strong candidates that may control heat tolerance during reproduction in camelina. These studies provide resources for future studies that may assist in improving the heat tolerance of camelina.

60 APPLIED LIFE SCIENCES↗

Integrating transcriptomic and metabolomic analysis of the oleaginous yeast Rhodosporidium toruloides IFO0880 during growth under different carbon sources

Rhodosporidium toruloides is an oleaginous yeast capable of producing a variety of biofuels and bioproducts from diverse carbon sources. Despite numerous studies showing its promise as a platform microorganism, little is known about its metabolism and physiology. In this work, we investigated the central carbon metabolism in R. toruloides IFO0880 using transcriptomics and metabolomics during growth on glucose, xylose, acetate, or soybean oil. These substrates were chosen because they can be derived from plants. Significant changes in gene expression and metabolite concentrations were observed during growth on these four substrates. We mapped these changes onto the governing metabolic pathways to better understand how R. toruloides reprograms its metabolism to enable growth on these substrates. One notable finding concerns xylose metabolism, where poor expression of xylulokinase induces a bypass leading to arabitol production. Collectively, these results further our understanding of central carbon metabolism in R. toruloides during growth on different substrates. Furthermore, they may also help guide the metabolic engineering and development of better models of metabolism for R. toruloides.

59 BASIC BIOLOGICAL SCIENCES↗

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10 –6 ) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10 –6 ) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10 –6 ) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

60 APPLIED LIFE SCIENCES↗