Mitigación de las emisiones de metano: Estrategias microbiológicas y sus retos

Laboratorio de Biorremediación

Facultad de Ciencias Biológicas de la

Universidad Autónoma de Coahuila

Unidad Torreón, Coahuila

Nagamani Balagurusamy

Past and current global atmospheric concentrations

of principal greenhouse gases

GasPre-industrial

concentration

Current

atmospheric

concentration

Global

warming

potential

Carbon

dioxide

(CO2)

277 ppm 400 ppm 1

Methane

(CH4)600 ppb 1728 ppb 25

Nitrous

oxide (N2O)270-290 ppb 318 ppb 296

Introduction

Mexico accounts for

1.5% of the world

total GHG

emissions

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5

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15

20

25

% o

f th

e t

ota

l G

HG

em

iss

ion

s

Global gas emission by source(IPCC, 2007)

World Resources Institute, 2011

• Agricultural activities account for

14% of the total greenhouse

gases’ emissions.

• More than 8% of the global water

consumption is used by the

agricultural sector.

• Extensive areas are used to

grow pasture for livestock feed.

Introduction

Livestock sector plays an important role in

climate change contributing about 14.5% of

anthropogenic GHG emissions.

Beef and cattle milk production account for the

majority of these emissions, respectively

contributing 41 and 20 percent of the sector’s

emissions.

Mexico – rich in cattle wealth.

Manure map of Mexico

LIVESTOCK

45%

39%

10%6%

Livestock emission sources

Feed production and processing

Enteric fermentation

Manure storage and processing

Processing and transportation ofanimal products

LIVESTOCK

ENTERIC FERMENTATION

Significant amount of methane is produced bymethanogens residing within the rumen (87%),which is released principally through eructation,approximately 10–15% is emitted by normalrespiration and via flatus.

Feed digestion

Microbial activity

Hydrolysis

Fermentation

Acetogenesis

Methanogenesis

Produced VFAs are absorbed by the rumen and omasal walls

Mutualism Commensalism Syntrophy Competition Depredation

About 2–12% of gross energyintake produced in the rumenby fermentation is convertedto methane.

Anaerobic digestion

Microbial populations can be affected byfactors such as type and race of animal,age of the host, diets, feeds, farmingpracticing and geographical regions whichinfluence directly methane production.

MITIGATION STRATEGIES OF METHANE EMISSIONS FROM ENTERIC FERMENTATION

The main target of mitigation strategies ison methanogens by decreasing theirsubstrate availability either directly orindirectly. The main strategies are focusedon changes in dietary composition, and/orby supplementation of diet with chemicalinhibitors, lipids or plant compounds.

Emissions from manure are produced during theits decomposition by anaerobic microbialactivities.

Emissions depend on manure composition andquantity produced.

Although CH4 emissions from entericfermentation are higher than those frommanure, manures also contribute to N2Oemissions.

GHG EMISSIONS BY MANURE MANAGEMENT

In Mexico there are 33, 502,623heads of cattle which producearound 763,187.28 tons of manureper day.

749,580 heads of cattle

ANIMAL MANURE PRODUCTION IN MEXICO

Fig. 1. Animal manure map based on the wet waste generated per day in Mexico.

(Hernandez-De Lira et al., 2015; SAGARPA, 2016)

STRATEGIES TO REDUCE METHANE EMISSIONS FROM MANURE

Nutritional management

Fertilizer

-Composting

-Bioslurry

Treatment options

BiodigestersReduction ofwastes andbioenergyproduction

Comarca Lagunera. Mexico’slargest Milk Producer

Sustainabletechnology

MANURE

RESIDUALS

BIOGAS

BIODIGESTERS IN COMARCA LAGUNERA

• 380 Farms• 60 Lagoon type Biodigesters with a capacity

of 20,000-30,000 m3

REAL TIME MONITORING OF BIODIGESTERS

Although anaerobic digestion is a well-known and consolidated technology, the key players of methaneproduction and their associations and functioning are not completely understood yet. Thus, it is critical tomonitor the community structure and variability to better understand the biochemical reactions involved inanaerobic digestion and to optimize operational conditions. In our lab we employ three main strategies tomonitor biodigesters:

Metagenomic analysis

Biochemical approach

Gene expression

Research into anaerobic processes is currently undergoing a reawakening due to thedevelopment of techniques suitable for mechanistic linking of whole community function andphylogeny (Vanwonterghem et al., 2014).

By knowing the microbial composition inside the biodigesters, operators could have

more control over the outcome of the process.

23,000 m3 mesophilic

Biodigester. Fed with

380-400 m3 d-1 of dairy

manure & under

operation for 3 years.

Good performance

8,000 m3 mesophilic

Biodigester. Fed with 250-

300 m3 d-1 of dairy manure

& under operation for 1

year.

Bad Performance

OUR STUDY

METAGENOMIC ANALYSIS

Bad Performance Biodigester

Good Performance Biodigester

Biodigesters Performance during 12 months

METAGENOMIC ANALYSIS

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10%

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60%

70%

80%

90%

100%

GPS GPW GPI GPE BPS BPW BPI BPE

Re

lati

ve A

bu

nd

ance

SamplesProteobacteria Firmicutes Synergistetes Verrucomicrobia Unclassified Bacteria Thermotogae

Chloroflexi WS3 Spirochaetes Acidobacteria Lentisphaerae Hyd24-12

OD1 Bacteroidetes NKB19 LD1 Planctomycetes Tenericutes

OP3 OP8 Actinobacteria TM7 Chlorobi WS1

[Caldithrix] TM6 Chlamydiae Euryarchaeota Thermi OP9

OP1 Fibrobacteres Phylum <0.1%

Syntrophobacterales 8%

Clostridiales 21%

Synergistales 21%

Syntrophobacterales 6%

Clostridiales 19%

Synergistales 21%

Syntrophobacterales13%

Clostridiales 13%

Synergistales 8%

Pseudomonales13%

Clostridiales 8%

Synergistales 10%

Syntrophobacterales 2%

Clostridiales 42%

Synergistales 3%

Pseudomonales17%

Clostridiales 12%

Synergistales3%

Syntrophobacterales12%

Clostridiales 6%

Thermotogales14%

Thermotogales38%

Clostridiales 6%

Pseudomonales38%

Bacterial Community Composition by PhylumMost Representative Orders

METAGENOMIC ANALYSIS

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100%

GPS GPW GPI GPE BPS BPW BPI BPE

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Samples

Methanogens Community Composition by Order

Unclassified Unclassified Euryarchaeota MethanobacterialesUnclassified Methanomicrobia Methanomicrobiales Methanosarcinales

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GPS GPW GPI GPE BPS BPW BPI BPE

Re

lati

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ance

Samples

Archaeal Community Composition by Phylum

Unclassified Crenarchaeota Euryarchaeota

METAGENOMIC ANALYSIS

Weighted UniFrac PCoA

Bacterial Sequences

GPBBPB

METAGENOMIC ANALYSIS

Weighted UniFrac PCoAArchaea Sequences

GPBBPB

METAGENOMIC ANALYSIS

SPATIAL VARIATIONS OF BACTERIAL COMMUNITIES IN A BIODIGESTER FED WITH CATTLE MANURE

Scheme of the lagoon type biodigester. Cross-sectional view showing the sites sampled.

Influent (1), beginning (2), middle (3), final (4) and effluent (5).

23,000 m3 mesophilic

Biodigester. Fed with

380-400 m3 d-1 of

dairy manure & under

operation for 3 years.

Analysis of bacterial communities in the different sites of abiodigester fed with cattle manure in order to determine anddecipher the microbial interactions and their role in theprocess of anaerobic digestion

o Our study

Relative abundance at phylum level among the different samples.

TAXONOMIC COMPOSITION

Total sequences: 25,336Total OTUs number:1,735Classification: 51 phyla, 127classes, 221 orders, 300 familiesand 424 genera

Hydrolytic and fermentative

VFAs oxidationSulfate reductionCo-nitrate reduction

Acetate oxidation

Carbohydrate degradation

-Distribution of bacterial communities was very variable.-Wastewater treatment plant (Ye and Zhang, 2013) -Mixed plug-flow-loop reactor (MPFLR) (Li et al., 2014)

Firmicutes (29.16%)Proteobacteria (18.08%)Verrucomicrobia (9.41%)Synergistetes (6.63%)

Higher diversity is often correlated with good-performing anaerobic reactors (Carballa et al., 2015).

PVC superphylum (Wagner and Horn, 2006):Planctomycetes, Verrucomicrobia, Chlamydiae, and Lentisphaerae

Relative abundance at order level among the different samples.

TAXONOMIC COMPOSITION Present in an anaerobic reactor addedwith phenol (Ju and Zhang, 2014)

Pathogen for humans and animalsdue to their ability to produce cancer(Eppinger et al., 2004) .

syntrophic acetate oxidation

Cellulolytic rumen bacteria

VENN ANALYSIS

Venn diagram representing the distribution of shared OTUs among the different points of

the biodigester. The numbers in the diagram represent the unique and shared OTUs.

InfluentBeginningMiddleFinalEffluent

PeptostreptococcaceaeClostridiaceaeChristensenellaceaeCampylobacteraceae

ProteobacteriaPlanctomycetesChloroflex

OD1(Parcubacteria)

The richness observed in this study can beconsidered high in comparison with a study of 21full-scale biodigesters fed with agriculturalwastes and animal manures in which the OTUsranged from 193 to 434 (Sundberg et al., 2013).

-Total biodigester richness: 1735 OTUs-364 to 656 OTUs per sample

Major quantity of unique OTUs

Major number of OTUs shared

BETA DIVERSITY

Weighted UniFrac PCoA Unweighted UniFrac PCoA

Principal Coordinate Analyses (PCoA) plot based on

weighted UniFrac distance between the different points

of the biodigester. The percentage variation explained

with first two principal components (P1, P2).

Principal Coordinate Analyses (PCoA) plot based on unweighted

UniFrac distance between the different points of the

biodigester. The percentage variation explained with first two

principal components (P1, P2).

• Oxidized coenzyme F420

shows an intense blue

fluorescence when

excited at 420 nm

(DiMarco et al., 1990;

Ashby et al., 2001).

Methanogens have specialized metabolism with

unique coenzymes such as F420 and F430, which

can emit fluorescence on exposure to UV

radiation.

o Methanogens

BIOCHEMICAL APPROACH

o Methanogens

Obligate anaerobic archaea dependent on

fermentative bacteria.

Chemoheterotrophic:

Aceticlastic

Acetate to CO2 and methane

Chemoautotrophic:

H2 or formate as energy & electron

source

CO2 to methane and other cellular

components

Selective characteristics of representative genera of methanogens

Genus Morphology %G+C Wall composition Gram

reaction

Motility Methanogenic

substrates used

Order Methanobacteriales

Methanobacterium Long rods or

filaments

32-61 Pseudomurein + to

variable

- H2+CO2, formate

Methanothermus Straight to

slightly curved

rods

33 Pseudomurein

with an outer

protein S-layer

+ + H2+CO2

Order Methanococcales

Methanococcus Irregular cocci 29-34 Protein - - H2+CO2, formate

Order Methanomicrobiales

Methanomicrobium Short curved

rods

45-49 Protein - + H2+CO2, formate

Methanogenium Irregular cocci 52-61 Protein or

glycoprotein

- - H2+CO2, formate

Methanospirillum Curved rods or

spirilla

45-50 Protein - + H2+CO2, formate

Methanosarcina Irregular cocci,

packets

36-43 Heteropolysacch

aride or protein

+ to

variable

- H2+CO2, methanol,

methylamines, acetate

BIOCHEMICAL APPROACH

Variables Levels

Strain ID AB11a1 AB11a2

Substrate Acetate Formate Methano

l

Concentration (mM) 50 100 150 200 250

Temperature (ºC) 30 37 45 55

pH 5 6 7 8

Variables Levels

Strain ID AB11a1 AB11a2

Substrate Acetate Formate Methanol

Concentration (mM) 100 200

Temperature (ºC) 30 37

pH 7

o Our study

Is there any relation to the fluorescence intensity

of methanogens to their culture conditions, growth

phases and methane formation?

BIOCHEMICAL APPROACH

Specific Fluorescence

intensity (FIU/

microgram of cellular

of protein) of AB11a1

at different acetate

concentrations and at

different temperatures

FIU/ microgram of cellular protein

Temperature (°C)

0 5 10 15 20 25 30 35

Substr

ate

concentr

ation (

mM

)

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50

100

150

200

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70

o Our study

BIOCHEMICAL APPROACH

Temperature (°C)

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pe

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Me

tha

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Activity

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50

100

150

200

CH

4*u

g-1

SM

AF

CH

4 (

ppm

)/ F

IU

Specific Methanogenic

Activity in terms of

Fluorescence intensity

[CH4 (ppm)/ FIU] and

Specific Methanogenic

Activity (CH4 (ppm)/

microgram of cellular

protein) of AB11a1 at

different acetate

concentrations and at

different temperatures

o Our study

BIOCHEMICAL APPROACH

o Our study

Treatments

Principal Component

Analysis

(PCA) with PS1 (46.7 %) Y

PS2 (20 %) for 4 samples.

Sampling dates [0-35] are

identified

30 °C/ 200 mM

30 °C/ 100 mM

37 °C/ 200 mM

37 °C/ 100 mM

BIOCHEMICAL APPROACH

mRNA has been used in the diagnosis of several

diseases (Rybaczyk et al., 2008)

Environmental monitoring technologies based on

differential gene expression.

Changes in the performance of the

biodigesters happens way before they are

observed.

If we'd be able to spot these changes,

preventive actions could be taken.

Waste water treatment systems

and biodigesters are highly

dynamic environments: 1000

OUT’s (Hess et al., 2011).

o Our studies

GENE EXPRESSION

o mcrA

Methanogens are divided in two main groups according to their principal

pathways; hydrogenotrophic and aceticlastic

Methanogenesis

from acetate

Methyl-coenzyme M (CH3-S-

CoM) is a key factor in both

pathways

Hinderberger et al., 2008; Hedderich

and Whitman, 2013.

Methanogenesis

from H2/CO2

GENE EXPRESSION

mcrA gene

Molecular surface representation of methyl-CoM reductase. Subunits α and α’ are represented in red andorange, subunits β and β’ in dark green and light green, and subunits γ and γ’ in dark blue and light blue.

o mcrA

GENE EXPRESSION

Extracción de ARN

Síntesis de cDNA

qPCR

- Protocolo de fenol ácido

- RevertAid H Minus First Strand cDNA Synthesis KitThermo Scientific

- Maxima SYBR Green/ROX qPCR Master Mix (2X) Thermo Scientific

Primers

Name Target Sequences(5’-3’) Reference

ARC787FArchaea

ATTAGATACCCSBGTAGTCC

(Yuetal.,2005)

ARC1059R GCCATGCACCWCCTCT

MBT857FMethanobacteriales

CGWAGGGAAGCTGTTAAGTMBT1196R TACCGTCGTCCACTCCTTMSL812F

MethanosarcinalesGTAAACGATRYTCGCTAGGT

MSL1159R GGTCCCCACAGWGTACCqmcrA

mcrAgene

TTCGGTGGATCDCARAGRGC (Denmanetal.,2007)

mcrA-rev CGTTCATBGCGTAGTTVGGRTAGT(SteinbergandRegan,

2008)

-PCR-Tratamiento con DNAsa

LightCycler 480 Real Time PCR System SW 1.5

(Palacio-Molina et al., 2013)

METHODOLOGY

0

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Ex

pre

són

mc

rA(%

)

Transcrito MBT

Transcrito MSL

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Po

bla

cio

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s (%

)

MBT

MSL

MBT transcripts MSL transcriptsMBT MSL

mcrA

exp

ressio

n (

%)

Po

pu

latio

n (

%)

GENE EXPRESSION

0

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222 422 622 822 244 444 644 844

Rel

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222 244 444

Rel

ativ

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Samples

24/10

22/09

Relationship between methanogenic activity and

transcript in samples of anaerobic reactor fed with

dairy cow manure

Sample1. Carcamo2. Influent3. EffluentNúmero de muestra

0 1 2 3 4 5 6 7

Tra

nscrito

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4

Activid

ad M

eta

nogénic

a (

ppm

)

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1e+5

2e+5

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5e+5

6e+5

7e+5

AM

/t

0.0

2.0e+5

4.0e+5

6.0e+5

8.0e+5

1.0e+6

1.2e+6

1.4e+6

Methanogenic activity – transcript ratio

Methanogenic activity (ppm)

Transcript

Número de muestra

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nscrito

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Activid

ad M

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nogénic

a (

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1.4e+6

Methanogenic activity – transcript ratio

Methanogenic activity (ppm)

Transcript

Número de muestra

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nscrito

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Activid

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Número de muestra

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nscrito

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Activid

ad M

eta

nogénic

a (

ppm

)

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AM

/t

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Sample1.- UASB reactor fed with animal manures2.- UASB reactor fed with brewery wastewaters

Relationship between methanogenic

activity and transcript in samples of

UASB reactors.

Methanogenic activity - Transcript

GENE EXPRESSION

POTENTIAL OF ELECTRICAL ENERGY GENERATION (GWH) FOR DIFFERENT STATES OF MEXICO DUE TO METHANE PRODUCTION FROM LIVESTOCK MANURE

CONCLUSIONS

Microbial communities play a key role in GHG emissions since their metabolicfunctions and interactions drive methane production. Thus, strategiesfocused on alter microbial groups can help to minimize methane emissionsfrom livestock.

These communities can be used to reduce emissions from manures andincrease carbon recovery in the form of methane to produce bioenergy.

Nevertheless, the complexity of these groups are affected by several factorsand is necessary to develop integrated approach consisting of engineering,biochemistry, microbiology, molecular biology principles to guarantee theoptimum production of bioenergy and reduce GHG emissions.

Bioinformatics

http://bioremlaguna.blogspot.com/

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Methanogenesis, F420 & mcrA gene expression

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Metagenomics of biodigestores

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Economics of renewable energy