Earth Sciences Division (ESD) Department of Energy (DOE) Lawrence Berkeley National Laboratory (LBNL)

Earth Sciences Division Staff: Maude David

Maude David

Maude  M. David

Postdoctoral Fellow

Ecology Department

 

 

Phone: 510-486-6154

Fax: 510-486-7152

Email: mmdavid@lbl.gov

Additional Information

 

 

 

 

 

 

 

 

 

 

Biographical Summary & Research Interests

From Sept of 2010 to January 2014, I have been a postdoctoral fellow in Prof. Janet K. Jansson’s laboratory at Lawrence Berkeley National Laboratory. I received my PhD in December 2009 from the Ecole Centrale de Lyon, University of Lyon, France, with Prof. T.M. Vogel. I now work at Stanford University and study the impact of the gut microbiome on the brain using crowdsourced trinical trials, with Dennis Wall.

My expertise is in microbilogy, bioinformatics and biochemistry, and more specifically in molecular biology, utilizing metagenomics, metatranscriptomics and metaproteomics to understand microbial community functions in the environment. My grad-school work focused on the bacterial adaptation to chlorinated compounds, at the genome level (evolution mechanisms) and community (bioremediation). My research is now looking at the impact of climate change such as altered precipitation on the carbon cycle in soil.

Research Project at LBL

Meta-"Omics" Analysis of Microbial Carbon Cycling Responses to Altered Rainfall Inputs in Native Prairie Soils

In this project we are exploring the impact of climate change on the carbon cycle of native prairie soil in the US. Our goal is to harness the power of multiple omics tools to understand the functioning of whole-soil microbial communities and their role in C cycling. => more about this project

My contribution 

I addressed several tasks within that project.

incubation of Arthrobacter Chlorophenolicus in wheat rhizosphere

First, I worked on improving the database needed to apply the omics methods chosen in this project.  The Kansas prairie soil presents a very high biomass and important diversity of microorganisms, which makes it difficult to study. Indeed, if the latest technology of sequencing allowed to produce a deep sequencing of that soil, only a few % could be assembled to be used as a database in order to map the metatranscriptome and perform proteomic searches. I am working on significantly reducing the diversity of this complex ecosystem by labeling active microbes.
In addition, I did the analysis and the annotation of the metagenome and transcriptome data by building a comprehensive database where specific-to-soil functions of proteins have been manually selected and organized. In addition we turned each KEGG KO set of sequences into Markov models. This work is performed in collaboration with Kostas Mavrommatis at JGI.

Epifluorescence image of acridine orange stained Arthrobacter phenolicus sp. A6 tagged with gfp at 100X magnification.  Image captured with an Optronics MacroFire CCD camera with a FITC filter on a Zeiss Azioskop microscope

In order to optimize the DNA, RNA and protein extractions protocols and to practice the

integration of those data, we used the gfp (green fluorescent protein)-tagged soil microbe Arthrobacter chlorophenolicus. We aimed to study which A. chlorophenolicus genes are specifically transcribed and expressed in soil amended with different substrates and in the rhizosphere. This eco-systems biology approach enabled us to compare the activity and expression of this microorganism in different environmental samples and to determine what functions were important for survival in the different conditions and environments tested. This work is performed in collaboration with Steve Lindow laboratory, UC Berkeley.

 

Arthrobacter jars

LTQ Velos A. chlorophenolicus A6 incubation test run Maude M. David

 

 

 

 

 

 

 

 

Finally, I will use the methods optimized during three first tasks to perform the integration of the field and meta-Omics data with the help of the partners of the project.

 

Technical Skills

  • Bioinformatics: Next generation sequencing trimming. Implementation of functional gene database (using KEGG and MetaCyc). Functional annotation with BLAST and Hidden Markov Models. Integration of DNA, RNA and protein datasets (multivariate statistics with R, package ade4, python libraries for data visualization.) Ribosomal annotation with Silva and Greengenes database, analysis with CREST and megan (LCAclassifier).
  • Molecular biology and genetic engineering: in vivo and in vitro DNA shuffling, quantitative PCR and reverse transcription qPCR (sybr green, taqman), DNA labeling with bromodeoxyuridine, cloning (expression vector, screening), nucleic acids extraction, sequencing (Sanger, Roche 454, illumina GAII, Hiseq).
  • Proteomic: 2d-LC-MS/MS on an LTQ Velos or Velos orbitrap mass spectrometer
  • Microbiology: bacterial cultures (E. coli, Arthrobacter chlorophenolicus, Sphingobium francense...)