# Presentation: 791918

## Rapid quantification and taxonomic classification of a complex consortium of rDNA amplicons from both prokaryotic and eukaryotic origins using a microarray.

- CEB - ESD - LBNL
- Todd DeSantis, Sonya Murray, Jordan Moberg, Gary Andersen
- Carol Stone (DSTL, U.K.)

- What bugs are in my sample?

## The ponderings of a toddler

**The ponderings of a toddler**

- Why must Mom confiscate my “Hello Kitty” blanket on laundry day?

- Will the swings be wet at the park?

- How will this sausage impact the diversity in my lower G.I. bacterial community?

- Will I inhale any archaeal microorganisms when I visit the hot springs?

- Gianna DeSantis

## Every discarded water sample, geological core, or spent air filter is lost data.
But who wants to do all the work?
Culture? Anaerobes? non-cultivable? Safety?
Analysis of nucleic acids isolated from environment
Must classify or sort heterogeneous nucleic acids into bins.
Restriction Fragment Length Polymorphisms (RFLP)
Single Stranded Conformation Polymorphisms (SSCP)
Temp/Denat Gradient Gel Electrophoresis (T/DGGE)
Sequencing
Provides taxonomic nomenclature 
estimates the relative abundance 
Need to create, clone, & process hundreds of samples 
Can we create a simple, quantitative, comprehensive microbial test?

- But who wants to do all the work?
- Culture? Anaerobes? non-cultivable? Safety?
- Analysis of nucleic acids isolated from environment
- Must classify or sort heterogeneous nucleic acids into bins.
- Restriction Fragment Length Polymorphisms (RFLP)
- Single Stranded Conformation Polymorphisms (SSCP)
- Temp/Denat Gradient Gel Electrophoresis (T/DGGE)
- Sequencing
- Provides taxonomic nomenclature
- estimates the relative abundance
- Need to create, clone, & process hundreds of samples
- Can we create a simple, quantitative, comprehensive microbial test?

## Outline

- Goals
- Experimental Approach
- Organization of rDNA sequences into* taxa (CASCADE-P)*
- Assigning sets of probes for each *taxa*
- Using 16S GeneChip for quantitative aerosol analysis

## Project Overview

- Goal
- Create a single microarray capable of detecting and quantifying bacterial and/or archaeal organisms in a complex sample.
- Approach
- Combinatorial power of  multiple probes for sequence-specific hybridization

## 16S rRNA gene (16S rDNA)

- Used to identify and classify organisms by gene sequence variations.
- Variations have been used in design of DNA probes for the detection of:
- taxonomic domains, divisions, groups ...
- specific organisms

## The Ribosome

**The Ribosome**

- rDNA

- rRNA (functional molecule)

- LSU

- SSU
- 16s or 18s

## The Ribosome

**The Ribosome**

- Folded secondary structure
- Essential functional component
- Conserved spans
- structure must be retained for viability
- targeted for universal/group-specific PCR primers and probes
- Variable regions
- spans not fundamental to the folded structure
- receive less pressure from natural selection
- probed for genus and species level discrimination

## What could be amplified?

- Universal 16S PCR primers  complex population of amplicons.
- Must define the targets to consider as the Potential Amplicon Set.

- Variable

## First generation rDNA Array uses 85-basehighly variable region of ribosomal DNA

**5’**

**3’**

**1390**

**1507**

**Region interrogated on chip**

**pA**

**Ccomp**

**1492R**

**20 base DNA signature **
**segments on chip = probe set**

- Sample reacts only with complementary signature sequences on chip

**SSU rDNA**

**First generation rDNA Array uses 85-base****highly variable region of ribosomal DNA**

## http://greengenes.llnl.gov/16S

**C**omprehensive **A**ligned **S**equence **C**onstruction for **A**utomated **D**esign of **E**ffective **P**robes
- Igor Dubosarskiy
- Java implementations
- Tim Harsch
- RDBMS consultations
- Lisa Corsetti
- Apache module management
- Kevin Melissare
- Graphics

## Hierarchical Phylocodes

**Hierarchical**** Phylocodes**

## Chip Taxa

- Avoid groupings based on historical nomenclature.
- Sequence-dependent classification by transitive similarity clustering.
- Each sequence must end up in exactly 1 taxon.

**if   x R y   &   y R z  ********   x R z **

## Assigning Probes for GeneChip Microarray

- Select probe sets for each taxon
- Ideal Probe
- Present in all sequences of the taxon
- Not present outside the taxon
- Unable to X-hybe with seqs in other taxa
- Ideal Mis-match Control Probe
- Unable to X-hybe to any sequence

## Finding groupings

| seq | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| B |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| C |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| E |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| F |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| G |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| H |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| I |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| J |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| K |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| L |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| M |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| N |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| O |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

- sequences

- probes

- Consider A – O to be 16S sequences.
- Consider 1 – 24 to be probes already embedded on the chip.
- First, associate all available probes with all available sequences.
- Let probe similarities drive sequence groupings.

## Finding groupings

| seq | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| B |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| C |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| E |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| F |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| G |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| H |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| I |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| J |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| K |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| L |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| M |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| N |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| O |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

- Consider A – O to be 16S sequences.
- Consider 1 – 24 to be probes already embedded on the chip.
- First, associate all available probes with all available sequences.
- Let probe similarities drive sequence groupings.

## Finding groupings

| seq | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| B |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| C |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| E |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| F |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| G |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| H |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| I |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| J |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| K |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| L |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| M |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| N |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| O |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

- Consider A – O to be 16S sequences.
- Consider 1 – 24 to be probes already embedded on the chip.
- First, associate all available probes with all available sequences.
- Let probe similarities drive sequence groupings.

## Finding groupings

| seq | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| B |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| C |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| E |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| F |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| G |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| H |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| I |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| J |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| K |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| L |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| M |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| N |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| O |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

- Consider A – O to be 16S sequences.
- Consider 1 – 24 to be probes already embedded on the chip.
- First, associate all available probes with all available sequences.
- Let probe similarities drive sequence groupings.

## Finding groupings

| seq | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| B |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| C |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| E |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| F |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| G |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| H |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| I |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| J |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| K |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| L |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| M |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| N |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| O |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

- Consider A – O to be 16S sequences.
- Consider 1 – 24 to be probes already embedded on the chip.
- First, associate all available probes with all available sequences.
- Let probe similarities drive sequence groupings.

## Progressive Transitive Clustering

**DEFINE:**
-  *upp* (useful probe pair): a PM,MM pair where the 20-mer PM complements all intra-cluster sequences AND the central 16-mer of PM  does not complement any extra-cluster sequences AND the central 16-mer of the MM does not complement any sequence.  Probe pairs are reassessed whenever the sequence clusters are altered.
*nGB­upp*: number of *upp*s for a cluster, these probe pairs globally differentiate a cluster from all other sequences.
*L*:* *the value of *nGB­upp* which must be met for a cluster to be locked.
*nPW uppA*: number of useful probe pairs which pair-wise differentiate *clustA* from *clustB*
*nPW uppB*: number of useful probe pairs which pair-wise differentiate *clustB* from *clustA*
*m*: the value of *nPW upp *which must be met to inhibit two clusters from merging.

**FOR** *L* (11 .. 4) DO
**   ****FOR** *m* (1 .. 10) DO
-       Determine *nGB­upp *for each cluster;
-       Lock all clusters where *nGB­upp *≥ *L *;
-       Pair-wise compare non-locked clusters (*clustA, clustB*);
-       **UNLESS **(*nPW uppA *≥ *m *AND *nPW uppB *≥ *m)*
-          Merge sequences of *clustA* and *clustB* into one cluster;
-       **END UNLESS**
**   ****END FOR**
-    Uncluster non-locked clusters;
**END FOR**

- 650 clusters found

## Approach:  Custom Affymetrix GeneChip

**cctagcatg****C****attctgcata**
**cctagcatg****G****attctgcata**

**MATCH**

**MISMATCH**

**Approach:  Custom Affymetrix******** GeneChip**

** ****Massive parallelism – Up to 500,000 probes in a 1.28 cm****2**** array**
** ****Identification of multiple species in a mixed population**
** ****Single nucleotide mismatch resolution**

## General Protocol

**General Protocol**

**Air**
**Soil**
**Feces**
**Blood**
**Water**

- rRNA

- gDNA

- Universal 16S rDNA PCR

- Contains probes adhered to glass surface in grid pattern.

## Locating Hybridization Events

- 50 μ

- 50 μ

**A**
**C**
**G**
**G**
**T**
**C**
**G**
**A**

**A**
**C**
**G**
**G**
**T**
**C**
**G**
**A**

**A**
**C**
**G**
**G**
**T**
**C**
**G**
**A**

**A**
**C**
**G**
**G**
**T**
**C**
**G**
**A**

**A**
**C**
**G**
**G**
**T**
**C**
**G**
**A**

**Hybridize**

**PCR Amplify DNA**

**Fractionate DNA**

**Biotin End-label**

**Locating Hybridization Events**

## Frankia sp. str. G48

**Positive fraction****	****	****	****1.00****	****	****0.64**
**Average difference****	****	****3720****	****	****625**

***Frankia sp*****. str. G48**

**  ****PM MM**** **

***Clostridium butyricum***

## Can the chip detect more than one analyte?

**Can the chip detect more than one analyte?**

## Combinatorial scoring of “Probe Sets” are able to categorize mixed samples.

*S. aureus *spike

*B. anthracis *spike

**Can the chip detect more than one analyte?**

## Combinatorial scoring of “Probe Sets” are able to categorize mixed samples.

- Percent of probe-pairs scored positive for each probe set in the Staphylococcus Group.

- Hybridization results from spike-in experiment done in triplicate.
- Sonya Murray
- Aubree Hubbel

**Can the chip detect more than one analyte?**

**Combinatorial scoring of “Probe Sets” are able to categorize mixed samples.**

## Application Example

- Does air filter sample processing affect detection?
- Method 1
- Wash particles from filter with SDS
- Digest particles with lysozyme
- Purify DNA using Qiagen kit
- Method 2
- Pulverize filter and particles with bead mill, SDS, P:C:ISA
- Purify DNA using MoBio kit and Sephacryl column

## Bead beating allowed greater diversity to be detected.

## Quantitative Analysis

- Could the concentration of each amplicon in a sample be measured by fluorescence intensity?
- Experimental setup for 20 point Latin Square calibration:

| Experiment
 | Oc.oenos | Fer.nod | Sap.grand | M.neuro |  | H20 | Environmental amplicons* |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 5 | 13 | 31 | 74 |  | No | Yes |
| 2 | 13 | 31 | 74 | 143 |  | No | Yes |
| 3 | 31 | 74 | 143 | 5 |  | No | Yes |
| 4 | 74 | 143 | 5 | 13 |  | No | Yes |
| 5 | 143 | 5 | 13 | 31 |  | No | Yes |
| 6 | 0 | 0 | 0 | 0 |  | Yes | Yes |

- * 18uL of products from 30 cycle universal 16S PCR of gDNA extracted from U.K. air sample.

- SPIKE CONCENTRATION  (pM in Hybridization Solution)

- Sonya Murray
- Carol Stone

-                         Oo              	Fn              	Sg              	Mn
- 1                       5 (5474)        	13 (16069)      	31 (31805)      	74 (124732)
- 2                       13 (7885)      	31 (61185)      	74 (81107)      	143 (115237)
- 3                       31 (58912)      	74 (70317)      	143 (98235)     	5 (8759)
- 4                       74 (101803)     	143 (69529)     	5 (7789)        	13 (11530)
- 5                       143 (149869)    	5 (4534)        	13 (16228)      	31 (56103)
- 6                       n.a.     		n.a        		 n.a.       		n.a.
- Final concentration of spike in hybridization in pM.  Values in parentheses are the resulting hybridization signal in arbitrary units (a.u.) obtained from the Latin Square experiments. All spikes were added to 18μL of products of 30 cycle universal SSU PCR of gDNA extracted from air samples using Method 2.

## Log2 transformed

- Linear Least Squares Regression
- Pearson’s corr coeff was significant (df=18)
- 95% confidence intervals calculated according to: National Measurement System’s _V_alid _A_nalytical _M_easurement Programme (VAM)

## Environmental community is measured with confidence intervals.

- Conf Interval: Conc(*t*(RSE)/b)(1/m+1/n+((Y-y)2) / (b2(n-1)sx2))
- b = slope from regression
- Y = mean of 6 replicate measurements
- m = number of repeat measurements = 6
- y = mean of the HybScores for the 20 points used for calibration
*t *= critical value obtained from *t-*table for 18 d.f. for 95% = 1.734
- RSE = residual standard error of calibration points = 0.56
- sx = standard deviation of the conc. for the 20 points used for calibration

## Summary

**The SSU microarray was able to rapidly quantify and taxonomically classify of a complex consortium of rDNA amplicons from both prokaryotic and eukaryotic orgins.**

## Acknowledgements

- Gary Andersen – group Leader
- Carol Stone – sample collection, hybridization Sonya Murray - hybridizations

## Slide 36