Adina Howe, ABE Faculty Retreat, January 8, 2015

Measurements of health and productivity

Biological sequencing, chemical characterization, yield / growth / weight, climate data, structured & unstructured

Unifying heterogeneous

datasets

Moving beyond the Big Data craze:

Background

Purdue University, BSME,

Mechanical Engineering

Purdue University, MS,

Environmental Engineering

(Sustainability)

University of Iowa, PhD,

Environmental Engineering

(Microbiology/Bioremediatio

n)

Michigan State University

NSF Postdoc Math and Biology Fellow (cross-

training)

Computational Biologist

Microbiology / Microbial Ecology

GERMS Lab (Genomics &

Environmental Research in Microbial

Systems)

Jin Choi, PhD, University of Tennessee, ChemE

Ryan Williams, PhD, Iowa State, Ecology Evolution

Dan Shea, MS, Northeastern, Bioinformatics

Website: germslab.org

GERMS Mission

We are changing the

environment that we live in.

To preserve our

environmental integrity, we

must understand and

manage the impacts of global

change.

Scientific research must

inform our decisions and

policy.

Therefore, we use innovative

scientific methods to evaluate

and understand our complex

and changing natural world.

Towards this Mission: Microbes as lens into

understanding global change in the natural

world

MICROBES

IN

ECOSYSTEMS

NATURE

AIR

WATER

SOIL

MICROBIOMES

HUMANS/ANIMAL

ENGINEERED

BIOREACTORS

WASTEWATER

GERMS Vision (5 year goals)

To provide scalable, quantitative tools to

monitor microbial responses in complex

environments

To identify the microbial drivers responding to

global change in complex environments (e.g.,

soils, waters, gut)

To predict and model the impacts of

microbial responses on ecosystem health and

servicesTo monitor, evaluate, and manage our microbial

partners and their services.

WATER project: Improved methods to

evaluating water quality

Scalable, quantitative tools to monitor microbial

responses in complex environments

Data Type Example

Cost per sample /

Frequency of sampling

Precision / Water quality information

Challenges

Water propertieschemical analysis of

water qualitynarrow range of information about services in ecosystem

Traditional integrity indicatorspresence of coliform bacteria

detection methods lack specificitity and are often imprecise

Phytoplankton community characterization

cyanotoxin detection through fractionation of ammonia

detection of toxicity may not reveal source

Microbial community characterization (16S rRNA)

abundance of genes present and assoiated with all cyanobacteria

characterization of microbial community structure may not reveal gene function; data volume large for public understanding

Proposed MAVeRiC genes (DNA)

abundance of genes present associated with specific source of pollution

identifying relevant genes of interest to water quality; DNA reveals genes present but not necessarily actively expressed

Proposed MAVeRiC genes (RNA)

abundance of genes expressed and present associated with specific source pollution

identifying relevant genes of interest to water quality

Scalable, quantitative tools to monitor microbial

responses in complex environments

Estimating risks from

pathogens

Biotic integrity of a healthy

water system

Sources of non point

pollution

Role of waters in stabilizing

climate change

Microbial genetic biomarkers can capture…

MicroArray Value and Risk Chip

(MAVeRIC)

$24 for 216 bioindicators/sample, estimates gene abundance of biological signals,

quantitative PCR

PollutionPathogens

NutrientsToxicity

Biodiversity

Pollution

biomarkers:

Non point

pollution source

markers

Pathogen

biomarkers:

Specific

bacteria or

virus genes

Nutrient cycling

biomarkers:

Carbon, nitrogen,

phosphorus

metabolic genes

Toxicity biomarkers

Biodiversity biomarkers

A

B

C

D

Monitoring, Evaluating, Predicting

Scaling: Iowa Lake Waters (John

Downing and Chris Filstrup)

Integrate measurements of bioindicators with

water quality measurements in 132 lakes

sampled for a routine EPA-reported, lake water

quality assessment program.

Interdisciplinary

collaboration allowing for

evaluation and prediction

SOIL Project: Microbial drivers of

carbon cycling

Carbon cycling in agricultural soils

(in response to global change)

Collaboration with Kirsten Hofmockel, ANL, PNNL

THE DIRT ON SOIL

Biodiversity in the dark, Wall et al., Nature Geoscience, 2010 Jeremy Burgress

MAGNIFICENT BIODIVERSITY

THE DIRT ON SOIL

SPATIAL HETEROGENEITY

http://www.fao.org/ www.cnr.uidaho.edu

THE DIRT ON SOIL

DYNAMIC

THE DIRT ON SOIL

INTERACTIONS: BIOTIC, ABIOTIC, ABOVE, BELOW, SCALES

Philippot, 2013, Nature Reviews Microbiology

Strategy of breaking down complexity:

Identifying drivers of carbon degradation

Labeled Carbon (Cellulose)

Monitoring & Evaluating

Soil microbial communities

Communities assimilating carbonCutting edge

fluorescent cell

sorting

GUT Project: Identifying the

microbes that make us chubby and

sick

How do our microbial partners in our bodies

affect our stability and resilience to change?

Collaboration with ANL and University of Chicago (Eugene Chang and Daina Ringus

We have the

same genes, but

why are you a

rounder?

A fascination with viruses

Despite its ferocity in humans, Ebola is a life-form of mysterious

simplicity. ..If it were the size of a piece of spaghetti, then a

human hair would be about twelve feet in diameter and would

resemble the trunk of a giant redwood tree. (Michael Specter,

New Yorker)

80% unknown

Concluding thoughts

All my projects depend heavily on

collaborations

Unifying heterogeneous datasets – improved

resolutions, investigating diverse questions

Biological data: Rapid, high resolution, cheap

Effective integrations are POISED FOR

IMPACT.

Looking forward to the adventure together!