Biology subjects

Lopez, D.

Publications and source records attributed to Lopez, D..

24 records · Page 2Linked to original sources

bioRxiv2019

Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, gender and vital status of patients

Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer classification systems is that, even for tumors affecting a single organ, there is significant molecular heterogeneity. Precise molecular classification of tumors could be beneficial in personalizing patients therapy and predicting prognosis. To this end, here we propose to use molecular signatures to further refine cancer classification. Molecular signatures are collections of genes characterizing particular cell types, tissues or disease. Signatures can be used to interpret expression profiles from heterogeneous samples. Large collections of gene signatures have previously been cataloged in the MSigDB database. We have developed a web-based Signature Visualization Tool (SaVanT) to display signature scores in user-generated expression data. Here we have undertaken a systematic analysis of correlations between inflammatory signatures and cancer samples, to test whether inflammation can differentiate cancer types. Inflammatory response signatures were obtained from MsigDB and SaVanT and a signature score was computed for samples associated with 7 different cancer types. We first identified types of cancers that had high inflammation levels as measured by these signatures. The correlation between signature scores and metadata of these patients (gender, age at initial cancer diagnosis, cancer stage, and vital status) was then computed. We sought to evaluate correlations between inflammation with other clinical parameters and identified four cancer types that had statistically significant association (p-value < 0.05) with at least one clinical characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), and uveal melanoma (UVM). These results may allow future studies to use these approaches to further refine cancer subtyping and ultimately treatment.

cancer biology↗

bioRxiv2019

The Gene Expression Deconvolution Interactive Tool: Accurate Cell Type Quantification from Gene Expression Data

The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g. cell flow cytometry) are costly, time consuming, and can introduce bias. Computational approaches that infer cell type abundance from expression data offer an alternate solution. While these methods have gained popularity, most are limited to predicting hematopoietic cell types and do not produce accurate predictions for stromal cell types. Many of these methods are also limited to particular platforms, whether RNA-seq or specific microarrays. We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a tool that overcomes these limitations, compares favorably with existing methods, and provides superior versatility. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include a variety of platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from eight sources spanning a wide variety of stromal and hematopoietic types in both human and mouse. This reference database allows the user to obtain estimates for a wide variety of tissue samples without having to provide their own data. GEDIT also accepts user submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data is available. Author SummaryThe Gene Expression Deconvolution Interactive Tool (GEDIT) is a robust and accurate tool that uses gene expression data to estimate cell type abundances. Extensive testing on a variety of tissue types and technological platforms demonstrates that GEDIT provides greater versatility than other cell type deconvolution tools. GEDIT utilizes reference data describing the expression profile of purified cell types, and we provide in the software package a library of reference matrices from various sources. GEDIT is also flexible and allows the user to supply custom reference matrices. A GUI interface for GEDIT is available at http://webtools.mcdb.ucla.edu/, and source code and reference matrices are available at https://github.com/BNadel/GEDIT.

bioinformatics↗

bioRxiv2019

Dynamic evolution in the key honey bee pathogen Deformed wing virus

The impacts of invertebrate RNA virus population dynamics on virulence and infection outcomes are poorly understood. Deformed wing virus (DWV), the main viral pathogen of honey bees, negatively impacts bee health which can lead to colony death. Despite previous reports on the reduction of DWV diversity following the arrival of the parasitic mite Varroa destructor, the key DWV vector, we found high genetic diversity of DWV in infested United States (US) honey bee colonies. Phylogenetic analysis showed that the divergent US DWV genotypes are of monophyletic origin, which were likely generated as a result of diversification after a genetic bottleneck. To investigate the population dynamics of this divergent DWV, we designed a series of novel infectious cDNA clones corresponding to co-existing DWV genotypes, thereby devising a reverse genetic system for an invertebrate RNA virus quasispecies. Equal replication rates were observed for all clone-derived DWV variants in single infections. Surprisingly, individual clones replicated to the same high levels as their mixtures and even the parental highly diverse natural DWV population, suggesting that complementation between genotypes was not required to replicate to high levels. Mixed clone-derived infections showed a lack of strong competitive exclusion, suggesting that the DWV genotypes were adapted to co-exist. Mutational and recombination events were observed across clone progeny providing new insights into the forces that drive and constrain virus diversification. Accordingly, herein we propose a new model of Varroa-induced DWV dynamics whereby an initial selective sweep is followed by virus diversification fueled by negative frequency-dependent selection for new genotypes. This selection likely reflects the ability of rare lineages to evade host defenses, specifically antiviral RNA interference (RNAi). In support of this, we show that RNAi induced against one DWV strain is less effective against an alternate strain from the same population.\n\nAuthor SummaryVirulence of Deformed wing virus (DWV), a major pathogen of honey bees, showed a sharp and significant increase following the introduction of its vector, the mite Varroa destructor. Varroa vectoring resulted in genetic changes of DWV, including reduction of DWV diversity to nearly clonal levels in the UK and Hawaii. Contrary to the previous reports, we discovered that virulent DWV populations circulating across the Varroa-infested United States included many divergent genotypes generated following a strong bottleneck event. We designed a series of the full-length infectious cDNA clones that captured the diversity of a typical virulent DWV population from a declining Varroa-infested colony, effectively establishing first reverse genetic system for an invertebrate RNA virus quasispecies, in order to investigate interactions between the virus genotypes. We demonstrated that individual co-existing DWV genotypes and diverse natural DWV populations replicated equally well indicating that complementation between isolates was not required to enable DWV replication to high levels. Also, no obvious competitive exclusion was detected between genotypes in mixed infections suggesting DWV genotypes are adapted to co-exist to maintain overall population diversity. We suggest that introduction of Varroa resulted in an initial selective sweep of DWV diversity which was followed by DWV diversification driven by selection for new genotypes capable of evading host defenses, specifically antiviral RNA interference.

microbiology↗

bioRxiv2019

Microbial community-level features linked to divergent carbon flows during early litter decomposition in a constant environment

During plant litter decomposition in soils, carbon has two general fates: return to the atmosphere via microbial respiration or transport into soil where long-term storage may occur. Discovering microbial community features that drive carbon fate from litter decomposition may improve modeling and management of soil carbon. This concept assumes there are features (or underlying processes) that are widespread among disparate communities, and therefore amenable to modeling. We tested this assumption using an epidemiological approach in which two contrasting patterns of carbon flow in laboratory microcosms were delineated as functional states and diverse microbial communities representing each state were compared to discover shared features linked to carbon fate. Microbial communities from 206 soil samples from the southwestern United States were inoculated on plant litter in microcosms, and carbon flow was measured as cumulative carbon dioxide (CO2) and dissolved organic carbon (DOC) after 44 days. Carbon flow varied widely among the microcosms, with a 2-fold range in cumulative CO2 efflux and a 5-fold range in DOC quantity. Bacteria, not fungi, were the strongest drivers of DOC variation. The most significant community-level feature linked to DOC abundance was bacterial richness--the same feature linked to carbon fate in human-gut microbiome studies. This proof-of-principle study under controlled conditions suggests common features driving carbon flow in disparate microbial communities can be identified, motivating further exploration of underlying mechanisms that may influence carbon fate in natural ecosystems.

microbiology↗

bioRxiv2017

A Phylogenomic Study Quantifies Competing Mechanisms for Pseudogenization in Prokaryotes

BackgroundPseudogenes are non-functional sequences in the genome with homologous sequences that are functional (i.e. genes). They are abundant in eukaryotes where they have been extensively investigated, while in prokaryotes they are significantly scarcer and less well studied. Here we conduct a comprehensive analysis of the evolution of pseudogenes in prokaryotes. For this analysis we consider a broad range of bacteria, but also focus on the leprosy pathogen Mycobacterium leprae, which contains an unusually large number of pseudogenes which comprise approximately 40% of its entire genome.\n\nResultsWe have developed an informatics-based approach to characterize the evolution of pseudogenes. This approach combines tools from phylogenomics, genomics, and transcriptomics. The results we obtain suggest the presence of two mechanisms for pseudogene formation: failed horizontal gene transfer events and disruption of native genes.\n\nConclusionsWe conclude that while in most bacteria the former is most likely responsible for the majority of pseudogenization events, in mycobacteria, and in particular in M. leprae with its exceptionally high pseudogene numbers, the latter predominates. We believe that our study sheds new light on the evolution of pseudogenes in bacteria, by utilizing new methodologies that are applied to the unusually abundant M. leprae pseudogenes and their orthologs. As such, we anticipate that it will be of broad interest to both evolutionary biologists as well as microbiologists.

evolutionary biology↗

bioRxiv2017

Food Webs Over Time: Evaluating The Variability Of Degree Distribution On Ecological Networks

Although networks analysis has moved from static to dynamic, ecological networks are still analyzed as time-aggregated units where time-specific interactions are aggregated into one single network. As a result, several questions arise such as what is the functional form of and how variable is the topology of time-specific versus time-aggregated ecological networks? Furthermore, it is yet unknown to what extent the structure of time-aggregated networks is representative of the dynamics of the community. Here, we compared the topology of time-specific and time-aggregated networks by analyzing a set of intertidal networks containing more than 1,000 interactions, and assessed the spatiotemporal dynamics of their degree distributions. By fitting different distribution models, we found that the out-degree distributions of seasonal and time-aggregated networks were best described by an exponential model while the in-degree distributions were best described by a discrete generalized beta model. The degree distributions of the seasonal networks were highly temporally variable and are significantly different from those of time-aggregated networks. We observed that seasonal degree distributions converged toward time-aggregated network distributions after 1.5 years of sampling. Our results highlight the importance of understanding the dynamics of ecological networks, which can show topological characteristics significantly different from those of time-aggregated networks.

ecology↗