Biology subjects

Graham, N. A.

Publications and source records attributed to Graham, N. A..

27 records · Page 2Linked to original sources

bioRxiv2020

Global gut content data synthesis and phylogeny delineate reef fish trophic guilds

The diversity of life on our planet has produced a remarkable variety of biological traits that characterize different species. Such traits are widely employed instead of taxonomy to increase our understanding of biodiversity and ecosystem functioning. However, for species trophic niches, one of the most critical aspects of organismal ecology, a paucity of empirical information has led to inconsistent definitions of trophic guilds based on expert opinion. Using coral reef fishes as a model, we show that experts often disagree on the assignment of trophic guilds for the same species. Even when broad categories are assigned, 60% of the evaluated trait schemes disagree on the attribution of trophic categories for at least 20% of the species. This disagreement greatly hampers comparability across studies. Here, we introduce a quantitative, unbiased, and fully reproducible framework to define species trophic guilds based on empirical data. First, we synthesize data from community-wide visual gut content analysis of tropical coral reef fishes, resulting in trophic information from 13,961 individuals belonging to 615 reef fish species across all ocean basins. We then use network analysis to cluster the resulting global bipartite food web into distinct trophic guilds, resulting in eight trophic guilds, and employ a Bayesian phylogenetic model to predict trophic guilds based on phylogeny and maximum body size. Our model achieved a misclassification error of 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, which can be updated as new information becomes available. Although our case study is for reef fishes, the most diverse vertebrate consumer group, our approach can be applied to other organismal groups to advance reproducibility in trait-based ecology. As such, our work provides an empirical and conceptual advancement for trait-based ecology and a viable approach to monitor ecosystem functioning in our changing world.

ecology↗

bioRxiv2020

Improved discrimination of asymmetric and symmetric arginine dimethylation by optimization of the normalized collision energy in LC-MS proteomics

Protein arginine methylation regulates diverse biological processes including signaling, metabolism, splicing, and transcription. Despite its important biological roles, arginine methylation remains an understudied post-translational modification. Partly, this is because the two forms of arginine dimethylation, asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA), are isobaric and therefore indistinguishable by traditional mass spectrometry techniques. Thus, there exists a need for methods that can differentiate these two modifications. Recently, it has been shown that the ADMA and SDMA can be distinguished by the characteristic neutral loss (NL) of dimethylamine and methylamine, respectively. However, the utility of this method is limited because the vast majority of dimethylarginine peptides do not generate measurable NL ions. Here, we report that increasing the normalized collision energy (NCE) in a higher-energy collisional dissociation (HCD) cell increases the generation of the characteristic NL that distinguish ADMA and SDMA. By analyzing both synthetic and endogenous methyl-peptides, we identify an optimal NCE value that maximizes NL generation and simultaneously improves methyl-peptide identification. Using two orthogonal methyl peptide enrichment strategies, high pH strong cation exchange (SCX) and immunoaffinity purification (IAP), we demonstrate that the optimal NCE increases improves NL-based ADMA and SDMA annotation and methyl peptide identifications by 125% and 17%, respectively, compared to the standard NCE. This simple parameter change will greatly facilitate the identification and annotation of ADMA and SDMA in mass spectrometry-based methyl-proteomics to improve our understanding of how these modifications differentially regulate protein function.

biochemistry↗

bioRxiv2020

Splicing of the SynGAP Carboxyl-Terminus Enables Isoform-Specific Tuning of NMDA Receptor Signaling Linked to Cognitive Function

SynGAP-1 is a splice variant of the neurodevelopmental disorder risk gene, SYNGAP1/Syngap1. 1 encodes the C-terminal PDZ binding motif (PBM) that promotes liquid-liquid phase separation, a candidate process for postsynaptic density organization within excitatory synapses. However, it remains unknown how the endogenous SynGAP PBM regulates synapse properties and related cognitive functions. We found that a major PBM function in mice is to limit the mobility of SynGAP-1 in response to NMDA receptor activation. Genetic disruption of the PBM increased SynGAP-1 mobility to levels consistent with other non-PBM-containing C-terminal isoforms. This resulted in a lowering of the threshold for NMDA receptor-dependent signaling required for plasticity, leading to aberrant strengthening of excitatory synapses in spontaneously active neurons. PBM-deficient animals also exhibited a lower seizure threshold, disrupted LTP, and impaired cognition. Thus, the PBM enables isoform-specific SynGAP gating of NMDA receptor function, a mechanism linking synaptic signaling dynamics to network excitability and cognition.

neuroscience↗

bioRxiv2020

Quantitative single cell heterogeneity profiling of patient derived tumor initiating gliomaspheres reveals unique signatures of malignancy and response

BackgroundGlioblastoma is a deadly brain tumor with median patient survival of 14.6 months. At the core of this malignancy are rare, highly heterogenous malignant stem-like tumor initiating cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR signal transduction pathways are common oncogenic drivers in these cells. Though gene-level clustering has determined the importance of the EGFR signaling pathway as a treatment indicator, multiparameter protein-level analyses are necessary to discern functional attributes of signal propagation. Multiparameter single cell analyses is emerging as particularly useful in identifying such attributes. MethodsSingle cell targeted proteomic analysis of EGFR-PTEN-AKT-mTOR proteins profiled heterogeneity in a panel of fifteen patient derived gliomaspheres. A microfluidic cell array chip tool served as a low cost methodology to derive high quality quantitative single cell analytical outputs. Chip design specifications produced extremely high signal-to-noise ratios and brought experimental efficiencies of cell control and minimal cell use to accommodate experimentation with these rare and often slow-growing cell populations. Quantitative imaging software generated datasets to observe similarities and differences within and between cells and patients. Bioinformatic self-organizing maps (SOMs) and hierarchical clustering stratified patients into malignancy and responder groups which were validated by phenotypic and statistical analyses. ResultsFifteen patient dissociated gliomaspheres produced 59,464 data points from 14,866 cells. Forty-nine molecularly defined signaling phenotypes were identified across samples. Bioinformatics resolved two clusters diverging on EGFR expression (p = 0.0003) and AKT/TORC1 activation (p = 0.08 and p = 0.09 respectively). TCGA status of a subset showed genetic heterogeneity with proneural, classical and mesenchymal subtypes represented in both clusters. Phenotypic validation measures indicated drug responsive phenotypes to EGFR blocking were found in the EGFR expressing cluster. EGFR expression in the subset of drug-treated lines was statistically significant (p<.05). The EGFR expressing cluster was of lower tumor initiating potential in comparison to the AKT/TORC1 activated cluster. Though not statistically significant, EGFR expression trended with improved patient prognosis while AKT/TORC1 activated samples trended with poorer outcomes. ConclusionsQuantitative single cell heterogeneity profiling resolves signaling diversity into meaningful non-obvious phenotypic groups suggesting EGFR is decoupled from AKT/TORC1 signalling while identifying potentially valuable targets for personalized therapeutic approaches for deadly tumor-initiating cell populations.

cancer biology↗

bioRxiv2019

MOTS-c is an Exercise-Induced Mitochondrial-Encoded Regulator of Age-Dependent Physical Decline and Muscle Homeostasis

Healthy aging can be promoted by enhancing metabolic fitness and physical capacity (1, 2). Mitochondria are chief metabolic organelles with strong implications in aging (3-8). In addition to their prominent role in bioenergetics, mitochondria also coordinate broad physiological functions by communicating to other cellular compartments or distal cells using multiple factors (9, 10), including peptides that are encoded within their own independent genome (11, 12). However, it is unknown if aging is actively regulated by factors encoded in the mitochondrial genome. MOTS-c is a mitochondrial-encoded peptide that regulates metabolic homeostasis (13, 14), in part, by translocating to the nucleus to regulate adaptive nuclear gene expression in response to cellular stress (15-17). Here, we report that MOTS-c is an exercise-induced mitochondrial-encoded peptide that significantly enhanced physical performance when administered to young (2 mo.), middle-aged (12 mo.), and old (22 mo.) mice. In humans, we found that endogenous MOTS-c levels significantly increased in response to exercise in skeletal muscle (11.9-fold) and in circulation (1.5-fold). Systemic MOTS-c treatment in mice significantly enhanced the performance on a treadmill of all age groups (~2-fold). MOTS-c regulated (i) nuclear genes, including those related to metabolism and protein homeostasis, (ii) glucose and amino acid metabolism in skeletal muscle, and (iii) myoblast adaptation to metabolic stress. Late-life (23.5 mo.) initiated intermittent MOTS-c treatment (3x/week) improved physical capacity and trended towards increasing lifespan. Our data indicate that aging is regulated by genes that are encoded not only in the nuclear genome (18, 19), but also in the mitochondrial genome. Considering that aging is the major risk factor for multiple chronic diseases (20, 21), our study provides new grounds for further investigation into mitochondrial-encoded regulators of healthy lifespan.

cell biology↗

bioRxiv2019

Differential Gene Set Enrichment Analysis: A statistical approach to quantify the relative enrichment of two gene sets

Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, to our knowledge, there exists no method for examining the enrichment of two gene sets relative to one another. Here, we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that assesses the relative enrichment of two gene sets. Using the metabolic pathways glycolysis and oxidative phosphorylation as an example, we demonstrate that DGSEA accurately captures the hypoxia-induced shift towards glycolysis. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Furthermore, we demonstrate that DGSEA identifies novel metabolic dependencies not found by GSEA in cancer cell lines. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of two gene sets.

bioinformatics↗

bioRxiv2019

AKT but not MYC promotes reactive oxygen species-mediated cell death in oxidative culture

Oncogenes can generate metabolic vulnerabilities in cancer cells. Here, we tested how AKT and MYC affect the ability of cells to shift between respiration and glycolysis. Using immortalized mammary epithelial cells, we discovered that constitutively active AKT but not MYC induced cell death in galactose culture, where cells must rely on oxidative phosphorylation for energy generation. However, the negative effects of AKT were short-lived, and AKT-expressing cells recommenced growth after ~15 days in galactose. To identify the mechanisms regulating AKT-mediated cell death, we used metabolomics and found that AKT cells dying in galactose upregulated glutathione metabolism. Next, using proteomics, we discovered that AKT-expressing cells dying in galactose upregulated nonsense-mediated mRNA decay, a marker of sensitivity to oxidative stress. We therefore measured levels of reactive oxygen species (ROS) and discovered that galactose induced ROS in cells expressing AKT but not MYC. Additionally, ROS were required for the galactose-induced death of AKT-expressing cells. We then tested whether these findings could be replicated in breast cancer cell lines with constitutively active AKT signaling. Indeed, we found that galactose induced rapid cell death in breast cancer cell lines and that ROS were required for galactose-induced cell death. Together, our results demonstrate that AKT but not MYC induces a metabolic vulnerability in cancer cells, namely the restricted flexibility to use oxidative phosphorylation.\n\nImplicationsThe discovery that AKT but not MYC restricts the ability to utilize oxidative phosphorylation highlights that therapeutics targeting tumor metabolism must be tailored to the individual genetic profile of tumors.

systems biology↗

bioRxiv2019

NADPH consumption by L-cystine reduction creates a metabolic vulnerability upon glucose deprivation

The consequences of metabolic reprogramming in cancer can include an increased dependence on metabolic substrates such as glucose for survival. As such, the vulnerability of cancer cells to glucose deprivation creates an attractive opportunity for therapeutic intervention. Because it is not possible to starve tumors of glucose in vivo, we sought to identify the mechanisms regulating cancer cell death upon glucose deprivation and then design combinations of inhibitors to mimic glucose deprivation-induced cell death. Using metabolomic profiling, we found that cells undergoing glucose deprivation-induced cell death exhibited dramatic accumulation of intracellular L-cysteine and its oxidized dimer, L-cystine, and depletion of the antioxidant glutathione. Building on this observation, we show that glucose deprivation-induced cell death is driven not by lack of glucose but rather by L-cystine import. Following glucose deprivation, the import of L-cystine and subsequent reduction to L-cysteine depleted both NADPH and glutathione, thereby allowing toxic accumulation of reactive oxygen species. Consistent with this model, we found that the glutamate/cystine antiporter, xCT, was required for sensitivity to glucose deprivation. We searched for glycolytic enzymes whose expression is essential for survival of cancer cells with high xCT expression and identified the glucose transporter GLUT1. We therefore tested a drug combination co-targeting GLUT1 and glutathione synthesis and found that these drugs induced synthetic lethal cell death in high xCT-expressing cell lines susceptible to glucose deprivation. These results indicate that co-targeting GLUT1 and glutathione synthesis is a potential therapeutic approach in tumors dependent on glucose for survival.

systems biology↗

bioRxiv2018

Computational model of chimeric antigen receptors explains site-specific phosphorylation kinetics

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phospho-proteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the ten tyrosine sites on the CD28-CD3{zeta} CAR is phosphorylated by LCK with distinct kinetics. The addition of CD28 at the N-terminal of CD3{zeta} increases the overall rate of CD3{zeta} phosphorylation. Our computational model identifies that LCK phosphorylates CD3{zeta} through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3{zeta} through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

systems biology↗