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Strategies to cope with methanogens in hydrogenproducing UASB reactors: Community dynamics

Juli�an Carrillo-Reyes a, Lourdes B. Celis b, Felipe Alatriste-Mondrag�on a,Lilia Montoya c,1, Elías Razo-Flores a,*

a Divisi�on de Ciencias Ambientales, Instituto Potosino de Investigaci�on Científica y Tecnol�ogica, Camino a la Presa

San Jos�e 2055, Lomas 4a Secci�on, C.P. 78216, San Luis Potosí, SLP, Mexicob Divisi�on de Geociencias Aplicadas, Instituto Potosino de Investigaci�on Científica y Tecnol�ogica, Camino a la Presa

San Jos�e 2055, Lomas 4a Secci�on, C.P. 78216, San Luis Potosí, SLP, Mexicoc Divisi�on de Biología Molecular, Instituto Potosino de Investigaci�on Científica y Tecnol�ogica, Camino a la Presa San

Jos�e 2055, Lomas 4a Secci�on, C.P. 78216, San Luis Potosí, SLP, Mexico

a r t i c l e i n f o

Article history:

Received 10 March 2014

Received in revised form

14 May 2014

Accepted 15 May 2014

Available online xxx

Keywords:

Dark fermentation

Hydrogen

Methane

Homoacetogenesis

Organic shock load

Heat treatment

* Corresponding author. Tel.: þ52 444 834202E-mail address: erazo@ipicyt.edu.mx (E. R

1 Present address: Laboratorio 11, Simulac# 1001, Colonia Chamilpa, 62209 Cuernavaca

Please cite this article in press as: Carrilreactors: Community dynamics, Inj.ijhydene.2014.05.099

http://dx.doi.org/10.1016/j.ijhydene.2014.05.00360-3199/Copyright © 2014, Hydrogen Ener

a b s t r a c t

Methane occurrence is a common concern in hydrogen producing reactors. This study

presents the analysis of the microbial community structure during the application of

operational strategies to decrease methane production, in three different up-flow anaer-

obic sludge blanket hydrogen-producing reactors. Cloning and denaturing gradient gel

electrophoresis approach were used to establish the presence of homoacetogens, metha-

nogens and hydrogen producers. The results showed that homoacetogenic organisms

related to Blautia hydrogenotrophica and Oscillibacter valericigenes, and the hydrogen producer

Enterobacter aerogenes where favored during pH decreasing strategies (5.6 to 4.5). The

increment of the organic loading rate from 20 to 30 g chemical oxygen demand/L-d,

selected hydrogen producers similar to Clostridium tyrobutyricum, Citrobacter freundii and

E. aerogenes; further increments caused inhibition of hydrogen production due to the high

undissociated acids concentration. Methane production was inhibited completely only

when the biomass of the reactor was heat treated for a second time, this strategy selected

hydrogen producers capable to sporulate, but homoacetogens were also favored. In all

reactors the methanogenic activity was attributed to hydrogenotrophs related to the

genera Methanobrevibacter and Methanobacterium.

Copyright © 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights

reserved.

Introduction

Hydrogen is considered a clean fuel, which can be used

directly in energy fuel cells to produce electricity and has the

greatest energy content among any fuel. Biohydrogen can be

6; fax: þ52 444 8342010.azo-Flores).

i�on de Atm�osferas Planeta, Morelos, Mexico.

lo-Reyes J, et al., Strategternational Journal

99gy Publications, LLC. Publ

produced by dark fermentation of organic wastes, which may

be considered a promising technology for renewable energy

source [1].

The continuous hydrogen production by dark fermentation

is more feasible using mixed cultures than pure cultures.

rias, Centro de Investigaciones Químicas, UAEM, Av. Universidad

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

ished by Elsevier Ltd. All rights reserved.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y x x x ( 2 0 1 4 ) 1e1 02

Mixed cultures do not require sterile conditions and have an

increased adaptation capacity, which allows the use of com-

plex substrates [2,3]. Nevertheless, the major drawback of

using mixed cultures, such as anaerobic granular sludge, is

the proliferation of microorganisms that eventually consume

the hydrogen produced (e.g., methanogens and homoace-

togens), decreasing the system efficiency. This issue is a

common problem in high biomass retaining reactors, such as

the up-flow anaerobic sludge blanket (UASB), despite the fact

that the anaerobic sludge used as inoculum had been sub-

jected to a pretreatment for the selection of hydrogen pro-

ducing microorganisms [4e6]. Another source of undesirable

microorganisms that can affect the hydrogen production po-

tential is the indigenous microorganisms load of the sub-

strate, mainly in complex substrates such as cheese whey [7],

or other readily fermentable wastes such as vinasses from the

beverage industry [8].

Some strategies to reduce methane production in hydro-

genogenic reactors are: i) diminishing the pH below the

methanogenic optimal (6e7) [9], ii) applying a shock load, to

promote an imbalance of the methanogenic step due to the

accumulation of volatile fatty acids [6,10] and iii) repeated

heat treatments of the biomass [11], to enrich the spore

forming microorganisms and eliminate the methanogens.

Recently, Luo et al. [3] analyzed the effect of different

pretreatments of the inoculum: acid treatment, heat treat-

ment, and shock load in repeated batch tests, and concluded

that the inhibition of methanogenesis and homoacetogenesis

depends on the fermentation conditions rather than on the

inoculum pretreatment. However, in that work the meth-

anogenic community structure was not evaluated, only the

bacterial one. Abreu et al. [2] carried out a similar study in

expanded granular sludge bed (EGSB) reactors, they evaluated

the effect of different pretreatments (heat treatment, 2-

bromoethanesulfonate (BES) and BES þ chloroform) in the

performance of the reactor and in the bacterial community.

Such treatments inhibited completely the methanogens but

prompted the homoacetogenic activity, which is hydrogen

consuming too.

In our research group we have observed that during the

operation of hydrogen producing UASB reactors, for at least 80

days, hydrogen was produced at acceptable rates

(1.12 ± 0.19 L H2/L-d) but methane was eventually produced at

rates around 0.5 L/L-d despite the inoculum was heat treated

[4]. It has been proposed that methane occurrence in

hydrogen producing reactors is exclusively due to hydro-

genotrophic activity [12]. Studies in environmental samples

from saturated wetland soils, showed that hydrogenotrophic

methanogens are tolerant to acidic conditions (pH from 3.8 to

5.0), more than the acetoclastic ones; the identified hydro-

genotrophic methanogens belonged to the Methanomicrobiales

order, although DNA sequences of acetoclastic methanogens

were also found [13]. In contrast, in fermentative hydrogen

producing systems the microorganisms responsible of meth-

anogenic activity are usually not identified, therefore it cannot

be assumed that methane production in hydrogen producing

reactors is only due to hydrogenotrophic methanogenic

activity.

In this sense, there is a lack of studies that evaluate the

archeal and bacterial community in hydrogen producing

Please cite this article in press as: Carrillo-Reyes J, et al., Strategreactors: Community dynamics, International Journalj.ijhydene.2014.05.099

continuous reactorswith the eventual production ofmethane.

From our point of view, it is necessary to establish: i) if there is

a link between the community structure and reactor perfor-

mance and ii) if any species is determinant in the methane

occurrence after the application of strategies to control the

methane production.

The aim of this work was to evaluate the microbial com-

munity dynamics during the application of different strategies

to inhibit the methanogenic activity in three hydrogen pro-

ducing UASB reactors. The reactors were in operation for

extended periods of time (more than 80 days) before the

strategies of methane suppression were applied. The use of

16S rDNA-based methods such as denaturing gradient gel

electrophoresis (DGGE) [14], molecular cloning and

sequencing [15] can provide an accurate estimation of the

bacterial and archeal community composition, distribution,

and diversity in those reactors.

Materials and methods

Reactors and inoculum

Three different UASB reactors were used to produce hydrogen

using cheese whey powder (CWP) solution as a synthetic dairy

wastewater. The working volume of the reactors was between

0.47 and 1.3 L, and were inoculated with heat treated anaer-

obic sludge, from a confectionery factory in San Luis Potosí,M�exico, to reach a solids concentration from 13 to 20 g/L of

volatile suspended solids. The reactorswere operatedwith the

main objective of hydrogen production, although eventually

during their operation methane started to be produced

concomitant to hydrogen, for periods not less than 20 days

before applying the operational strategies to suppress

methane production.

The CWP was purchased from Grande Custom Ingredients

Group (Wisconsin, USA). The medium contained a known

amount of CWP-chemical oxygen demand (COD) and was

supplemented with a nutrient solution previously described

[4]. Additional Na2HPO4 was added to increase the buffer ca-

pacity of the medium to control the pH at the desired value.

Experimental set-up

The microbial community dynamics was analyzed during the

different methane decreasing operational strategies applied

to the three UASB hydrogen-producing reactors. Fig. 1 shows

the operational conditions and the hydrogen and methane

response during each strategy and the previous conditions

(before day 0), details are as follows:

Reactor 1A pH reduction strategy was tested by decreasing the reactor

pH from 5.63 to 5.0 and 4.5, and reestablishing the pH again to

5.0. The organic loading rate (OLR) and hydraulic retention

time (HRT) were kept at 20 g COD/L-d and 6 h respectively.

Reactor 2An organic shock load strategy was tested by increasing the

OLR from 20 to 30 and finally to 40 g COD/L-d. The OLR was

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

Fig. 1 e Operational time and performance of the UASB

reactors, with the following methane suppression

strategies: A) pH reduction, B) Organic shock load and C)

HRT decrement and heat treatment, for Reactor 1 to 3,

respectively. The box plots show hydrogen (gray) or

methane (white) production rates, showing the mean,

median, maximum, minimum values and percentiles. The

mean value is represented by a square inside the box plot.

The inverse triangles (;) show the periods where biomass

was sampled for themicrobial community characterization

with PCR-DGGE and cloning techniques. pH (- - -); Organic

loading rate (d); Hydraulic retention time (⋯); HT, Heat

treatment.

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Please cite this article in press as: Carrillo-Reyes J, et al., Strategreactors: Community dynamics, International Journalj.ijhydene.2014.05.099

increased by the COD influent concentration, keeping con-

stant the HRT at 6 h. The effect of the organic shock load was

further tested by operating at the previous OLR (40 g COD/L-d),

but by means of decreasing both the HRT (at 3 h) and the COD

influent concentration.

Reactor 3The effect of the HRT reduction was evaluated keeping con-

stant the OLR at 48 g COD/L-d, decreasing the HRT from 10 to

8 h. Afterwards, the whole content of granules in the reactor

was harvested, heat treated (boiled 1 h) and re-inoculated to

evaluate the effect of a second heat treatment, keeping the

same operational parameters.

The pH in reactors 2 and 3 was maintained between values

of 5.3 and 5.5. The objective of decreasing the HRT, in reactors

2 and 3, was to evaluate in different systems the effect of the

HRT reduction, keeping constant the OLR. In order to carry out

the microbial community analysis, biomass samples were

withdrawn from the three reactors in all the operational

conditions evaluated, except in Reactor 1 where samples were

only analyzed at the pH values of 5.63 and 5, as is shown in

Fig. 1.

Analytical methods

Hydrogen, CH4 and CO2 were measured by gas chromatog-

raphy (GC, 6890N Network GC System, Agilent Technologies,

Waldbronn, Germany) equipped with a thermal conductivity

detector and a Hayesep D column (Alltech, Deerfield, Illinois,

USA). Lactose and volatile fatty acids (VFA) were analyzed by

capillary electrophoresis (Agilent 1600A, Waldbronn, Ger-

many) using a basic anion buffer (Agilent, pH ¼ 12.1) and a

fused silica capillary column (Agilent, id ¼ 50 mm, L ¼ 80.5 cm,

effective length ¼ 72 cm). Ethanol was quantified by gas

chromatography (Agilent, Wilmington, USA) and a capillary

column HP-Innowax, equipped with a flame ionization de-

tector (FID). Further analytical details are described in Ref. [4].

Gas production and composition, as well as the influent and

effluent sampling were done daily along all the reactors

operation periods (Fig. 1). The biogas volumes are reported at

standard conditions (273.15 K and 1 atm).

Statistical analysis

In order to explore the effects of the operational strategies

(pH, HRT, OLR and repeated heat treatment) on the gas and

metabolites production as response variables (hydrogen and

methane biogas content, hydrogen and methane production

rates, acetate, propionate, butyrate and ethanol effluent

concentration), all data collected from the daily sampling

were analyzed using a cross-correlation matrix of the

Spearman's rank correlation coefficients [16]; this correlation

was evaluated individually for each reactor. Then, the sig-

nificant correlations (P < 0.05) were tested with the non

parametric multivariate analysis of variance (MANOVA) [17],

in order to elucidate the magnitude of such relations. The

relationships between the different significant response var-

iables and the independent variables were analyzed with

generalized linear models (GLM), using the deviance to

quantify the percentage of variance explained by the model.

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

Fig. 2 e Metabolites concentration produced in the UASB

reactors, with the followingmethane suppression strategies:

A) pH reduction, B) Organic shock load andC) HRT decrement

and heat treatment, for Reactor 1 to 3, respectively. The box

plots show themetabolites concentration for each condition

evaluated,showingthemean,median,maximum,minimum

values and percentiles. The mean value is represented by a

square inside the box plot. OLR, Organic loading rate; HRT,

Hydraulic retention time; HT, Heat treatment.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y x x x ( 2 0 1 4 ) 1e1 04

Please cite this article in press as: Carrillo-Reyes J, et al., Strategreactors: Community dynamics, International Journalj.ijhydene.2014.05.099

Normal plots and fits of the residuals were inspected to

ensure that the assumptions of the analysis methods were

met. All statistical analyses were performed with the R

environment [18]. Spearman correlations and non parametric

MANOVA were run using the R “Pspearman” and “Vegan”

packages, respectively [19,20].

Microbial community analyses

DNA extractionTen milliliters of sludge withdrawn from each operational

condition evaluated were kept at �20 �C. The samples are

represented as black triangles in Fig. 1. Genomic DNA was

extracted as described elsewhere [21].

PCR amplificationAmplification of the 16S rRNA gene from the purified DNA

preparations was carried out by PCR using Taq DNA poly-

merase (DONGSHENG, China). Almost complete bacterial 16S

rDNA was selectively amplified for cloning and sequencing

using primers 27-F and 1492-R [22]. For DGGE a specific region

of the 16S rDNA was amplified using the primer 357F- with a

GC clamp and the reverse primer 907R [23].

For Archaea, genomic DNA was amplified for cloning with

primers Arch109(K)-F and Uni1492-R. Primers A109(T)-F and

515-R with a GC clamp were used for the archeal DGGE anal-

ysis [24].

DGGE analysisDGGE was performed with a denaturing gradient (ure-

aeformamide) that ranged from 30 to 60% to obtain the bac-

terial community fingerprinting, as was described previously

(Carrillo-Reyes et al., 2012). DGGE gels were analyzed with the

Cross Checker v 2.91 software (Wageningen University, The

Netherlands) to create a binary matrix, corresponding den-

drograms showing the relationships between the DGGE pro-

files were constructed with PHYLIP version 3.69 [25], using the

unweighted pair group method with arithmetic mean

(UPGMA). The similarity between DGGE pair of lanes was

measured by Dice's coefficient.

CloningPCR products obtained with the primer pairs 27-F and 1492-R,

and Arch109(K)-F and Uni1492-R for each sample, were ligated

into pGEM-T vector using the pGEM Easy Vector Systems kit

(Promega), and introduced into competent Top10 Escherichia

coli cells. For each amplification, 12 positive transformants

were selected by blue/white screening, and grown in LBmedia

supplemented with ampicillin. After cell lysis, inserts were

amplified using the primer set M13 and the obtained PCR

products were analyzed in agarose gel (1%) in order to select

clones with the right insert fragments. Amplicons of the cor-

rect size were screened by amplified ribosomal DNA restric-

tion analysis (ARDRA), using the restriction enzymesMspI and

HinfI, incubated during 2 h at 37 �C. The restriction fragments

were analyzed by electrophoresis in 2.5% (w/v) agarose gels

and visualized with ethidium bromide. Amplification with

M13 primers of selected transformants, with different ARDRA

patterns were subjected to DNA sequence analysis. The PCR

products were sent to purification and sequencing to

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

Fig. 3 e Phylogenetic relationships of partial 16S rRNA of the representative OTUs gene sequences recovered from the

bacterial clone libraries (in bold). E. coli was used as the outgroup taxon. The Genbank accession numbers (in brackets) were

obtained from the 16S ribosomal RNA sequence databases. The scale bar represents 5% sequence divergence; circles at the

branch nodes represent bootstrap confidence level higher than 70 percent. A similarity threshold higher than 97% was used

for the same OTU assignment.

i n t e r n a t i o n a l j o u r n a l o f h yd r o g e n e n e r g y x x x ( 2 0 1 4 ) 1e1 0 5

“Laboratorio Nacional de Biotecnología Agrícola, M�edica y Ambi-

ental” (LANBAMA, IPICYT, Mexico). The M13 amplicons of the

sequenced clones were subject to a PCR amplification with the

DGGE primers, in order to relate the DGGE bands with the

identified clones.

Phylogenetic analysisBacterial 16S rRNA gene partial sequences were depurated

with the BioEdit V7.1.3 software package. Consensus se-

quences were checked for potential chimera artifacts by the

Pintail software V1.0 (Cardiff University, UK). Similarity

searches for the partial 16S rRNA gene sequences were per-

formed using the NCBI BLAST search program within the

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GenBank database. In order to dereplicate the sequences and

to group the ‘similar’ sequences together in operational

taxonomic units (OTU), the FastGroupII tool was used with a

similarity threshold of 97% [26]. 16S rRNA sequences were

further aligned by using the Clustal X V2.0 software [27]. The

resulting alignments were used for the construction of a 16S

rRNA gene-based phylogenetic tree, using the neighbor-

joining (Maximum Composite Likelihood) method with the

MEGA 5 package [28]. Bootstrapping (1000 times) was per-

formed to estimate the confidence levels for the tree nodes.

Tree was rooted using E. coli (accession number X80730). Due

to the low number of archeal sequences recovered, only the

bacterial phylogenetic tree is included.

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

Table 1 e Affiliation of the archaeal (Arc) OTUs identified showing the highest percentage of identity in the output resultfrom the analysis in the non-redundant nucleotide database from NCBI using the BLAST program, and using therepresentative OTUs gene sequences. The relative abundance considering all the archeal clones selected for all samples isshown.

Arc OTU Relative abundance (%) Clone Closest relative Percentage of identity

1 7 Clone1_ExpPH (KF644465) Methanobrevibacter arboriphilus (NR_042783) 99

2 22 Clone2_ExpOLR (KF644466) Methanobacterium congolense (NR_028175) 98

3 19 Clone3_ExpOLR (KF644467) Methanobacterium bryantii (NR_042781) 96

4 37 Clone4_ExpOLR (KF644468) Methanobacterium oryzae (NR_028171) 95

5 4 Clone5_ExpHRT (KF644469) Methanobacterium palustre (NR_041713) 96

6 11 Clone6_ExpOLR (KF644470) Methanobacterium congolense (NR_028175) 97

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The bacterial and the archeal 16s rDNA partial sequences

were deposited in the Genbank database under the accession

numbers KF484475 to KF484497, and KF644465 to KF644470,

respectively.

Results

In this study the microbial community dynamics was evalu-

ated during the application of different strategies to diminish

the methane production in UASB hydrogen-producing re-

actors: pH reduction, organic shock load, HRT decrement, and

second heat treatment to the biomass. The samples analyzed

were taken at each evaluated condition. The box plots pre-

sented in Fig. 1 show the hydrogen and methane production

rates during reactor operation. The metabolites concentra-

tions for the differentmethane reduction strategies are shown

in Fig. 2.

pH reduction strategy

The effect of pH reduction was evaluated in Reactor 1, after

changing the pH from 5.63 to 5.0, and then to 4.5 and again to

5.0. The average volumetric hydrogen production rate

diminished from 0.12 to 0.09 L/L-d; meanwhile, methane

production rate remained in a range between 0.09 and 0.43 L/

L-d (Fig. 1A). Considering all data of the pH strategy, the

Spearman's correlation coefficients showed a positive asso-

ciation between pH and the H2 production rate, acetate, and

ethanol production; as shown in Fig. 2A, where the highest

acetate (22.9 mM) and ethanol (11.7 mM) productions were

found at pH value of 5.63. In contrast, butyrate productionwas

associated negatively with the pH. According to the MANOVA

the associations found in the pH reduction strategy were

significant, explaining 36.6% of the variance.

The statistical analysis showed that methane production

was independent of the pH, however a significant negative

association resulted between acetate and propionate and the

volumetric methane production rate, explaining 37.9% of the

variance of the methane production, according to the devi-

ance analysis of the GLM. The similarity index of the bacterial

fingerprint showed 50% of similitude between pH 5.63 and the

last condition evaluated (pH ¼ 5.0) in Reactor 1, which may be

explained by a bacterial community selection (Fig. 4A). Among

all reactors, 23 bacterial OTUs (BacOTU)were identified, which

are shown in the phylogenetic tree indicating the closets

relative organisms in Fig. 3.

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The relation of BacOTUs sequenced and the DGGE profile,

showed that BacOTUs 1 and 2 prevailed during the pH

reduction strategy, and were related to Blautia hydro-

genotrophica and Oscillibacter valericigenes. A higher abundance

of OTUs related to Propionibacterium acidipropionici and Lacto-

bacillus casei, BacOTUs 5 and 6, respectively were identified at

pH 5.0; moreover, BacOTU 15 was related to Enterobacter aero-

genes. Table 1 shows the archeal OTUs (ArcOTU) identified in

Reactors 1 to 3. In the case of Reactor 1, all the ArcOTU were

present, and were related to the Methanobacteriaceae family,

from the genera Methanobrevibacter and Methanobacterium,

both associated to hydrogenotrophic activity.

Organic shock load strategy

The shock load strategy was evaluated increasing the OLR

from 20 to 30 and then to 40 g COD/L-d. According to the

Spearman's correlations, the increase in the OLR had a sig-

nificant effect in hydrogen, acetate, and propionate produc-

tion (Figs. 1 and 2), as was expected due to the increment in

the substrate concentration. Fig. 1B shows that only the first

increment of the OLR, from 20 to 30 g COD/L-d, had a positive

effect in the hydrogen production rate. Considering the OLR as

independent variable, the MANOVA explained a significant

correlation, 14.7% of the variance observed in Reactor 2. Spe-

cifically, considering only the volumetric hydrogen produc-

tion rate, acetate, propionate and ethanol concentrations as

response variables, according to the significant Spearman'scorrelations, the OLR explained 21.1% of the variance in

Reactor 2.

The dendrogram (Fig. 4B) shows that the bacterial com-

munities at 20 and 40 g COD/L-d were clustered together. In

these conditions the hydrogen production rate was similar

with a mean value of 0.27 L/L-d (Fig. 1B) but the average

methane production rate decreased from 0.71, at an OLR of

20 g COD/L-d, to 0.22 L/L-d at an OLR of 40 g COD/L-d, which in

fact was the objective of the strategy. The DGGE profile

showed similar dominant bacterial ribotypes between the

samples at 20 and 40 g COD/L-d (Fig. 4A). The DGGE bacterial

fingerprint from Reactor 2 at 30 g COD/L-d was clustered

separately from the community of the other conditions eval-

uated. At this condition the highest hydrogen production rate

(1.39 L/L-d) and the lowest average methane production rate

(0.18 L/L-d) were observed.

At an OLR of 20 g COD/L-d, BactOTUs 5 and 6 related to P.

acidipropionici and L. casei were identified. In addition, the

BactOTU 4 was related to Butyricimonas synergistic and

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

Fig. 4 e Bacterial DGGE community fingerprints and corresponding similarity dendrograms. The scale correspond to Dice'scoefficient. A) Reactor 1, B) Reactor 2 and C) Reactor 3. The numbers correspond to the different bacterial OTUs associated

with the DGGE fingerprint. The pH, OLR (Organic loading rate), HRT (Hydraulic retention time) and HT (Heat treatment) refers

to the different conditions evaluated at which samples were obtained.

i n t e r n a t i o n a l j o u r n a l o f h yd r o g e n e n e r g y x x x ( 2 0 1 4 ) 1e1 0 7

Parabacteroides johnsonii. When the OLR was increased to

30 g COD/L-d the dominant BacOTUs 9, 14 and 8 were recov-

ered and related to Clostridium tyrobutyricum, Citrobacter

freundii and E. aerogenes.

In the last condition (40 g COD/L-d), beside the presence of

BacOTUs 9, 8 and 11 related to C. tyrobutyricum, E. aerogenes

and Clostridium ramosum, the BacOTU 22 was also identified

and related to Clostridium ljungdahlii.

According to the Spearman's correlations, only the me-

tabolites concentration had a significant effect in themethane

production; among the metabolites analyzed, the GLM

showed that butyrate had a significant effect over the volu-

metric methane production rate, and explained 43.7% of the

variance.

In Reactor 2 all the ArcOTUs included in Table 1 were

identified, providing evidence that methane was produced by

hydrogenotrophic activity. It is worth to recall that methane

production decreased with the organic shock load strategy

from 20 to 30 g COD/L-d. The Spearman's correlations showed

a significant negative relation between acetate and butyrate

concentration (Fig. 2) and the volumetricmethane production,

confirming statistically this association. The last OLR incre-

ment to 40 g COD/L-d did not affect the methane production

compared to 30 g COD/L-d, in spite of the low hydrogen pro-

ductivity (Fig. 1B).

HRT decrements and heat treatment strategy

In order to evaluate the effect of the HRT in Reactors 2 and 3

(Fig. 1B and C), the HRT was diminished from 6 to 3 and from

10 to 8 h, respectively. The Spearman's correlation showed a

significant negative association between HRT and the

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volumetric methane production rate. However, according to

the MANOVA, the HRT decrements only explained 19.7% of

the variance observed in those experiments. For the purpose

of seeking significant associations between the parameters

evaluated, the MANOVA showed that a combined effect of the

HRT and OLR explained 38.2% of the variance observed in the

hydrogen, methane and metabolites production in Reactor 2,

which is a higher percentage than their individual effects.

The bacterial DGGE fingerprint corresponding to the HRT

decrement in Reactor 2, had 60% of similitude with the pre-

vious HRT evaluated (Fig. 4B). This HRT decrement had a

positive effect in both the hydrogen and methane production

(Fig. 1B), favored by the low substrate concentration. Accord-

ing to the MANOVA, the HRT explained 19.7% of the experi-

ment variance in a significant way. Important changes in the

community due to the HTR reduction were the occurrence of

the BacOTU 6 (L. casei) and the disappearance of the BacOTU 11

(C. ramosum).

The bacterial community fingerprint during the HRT

decrement to 8 h in Reactor 3 had 60% of similitude with the

community at the previous condition, 48 g COD/L-d at 10 h of

HRT (Fig. 4C). The BacOTUs 20 and 14 identified in this

experiment were related to Lactococcus lactis and C. tyrobutyr-

icum. The ArcOTUs identified in both reactors (2 and 3) for the

HRT decrements were all those listed in Table 1. Conse-

quently, the HRT did not affect the methane community

richness, only affected themethanogenic activity in Reactor 2.

The repeated heat treatment in Reactor 3 was the only

strategy that inhibited completely the methane production,

increasing the average hydrogen production rate from 0.49 to

1.09 L/L-d (Fig. 1C). Three samples of biomass were withdrawn

from the reactor during this strategy (after the heat treatment

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y x x x ( 2 0 1 4 ) 1e1 08

was applied) at days 50, 65 and 80. The bacterial DGGE

fingerprint shows that the samples taken at day 65 and 80

were clustered together with 82% of similitude (Fig. 4B).

The BacOTUs identified at day 50, corresponded to the

hydrogen producing bacteria Clostridium butyricum (BacOTU

21) and to the homoacetogenic C. ljungdahlii (BacOTU 22). The

DGGE profile (Fig. 4C) shows that the abundance of BacOTU 22

diminished at day 65, and the presence of the E. aerogenes can

be inferred, corresponding to BacOTU 15, a hydrogen producer

microorganism. At the end of the experiment (day 80), the

bands corresponding to the BacOTUs 15 and 22 (E. aerogenes

and C. ljungdahlii) disappeared. The BacOTUs 21 and 23, related

to C. butyricum and Lactobacillus rhamnosus (a lactic acid pro-

ducer), prevailed after the heat treatment.

Discussion

The present study demonstrated that the operational strate-

gies to control the methane production in UASB reactors

caused changes in the microbial community, explaining the

reactor performance.

The pH reduction strategy significantly decreased the

hydrogen production, promoting different metabolic path-

ways and caused a diminution in the average acetate pro-

duction (Fig. 2A), which in turn favored the average methane

production (Fig. 1A). Similar results regarding pH and

hydrogen production were found by Fang and Liu [29] in a

CSTR at pH values from 4.0 to 7.0.

The low hydrogen production during the pH decrement

strategy may be also related to the presence of ribotypes

similar to the homoacetogens B. hydrogenotrophica and O.

valericigenes [30]. The presence of homoacetogenic activity at

pH values below 5.5 has been observed before, even though

this is not the most favorable condition for this type of

metabolism [31]. Beside the homoacetogenic activity at pH 5,

the presence of microorganisms related to P. acidipropionici

and L. casei, which are producers of reduced compounds [32],

could contribute to the low hydrogenogenic activity, due to

the use of the available electrons to produce propionic and

lactic acid, respectively. As observed in Fig. 2A, the propionic

concentration showed higher values at pH 5.0 than at pH 5.63.

Even though the low hydrogen production can be explained by

the putative presence of hydrogen consuming and non

hydrogen producing bacteria, microorganisms related to E.

aerogenes, a hydrogen producer [33], were still found in the last

condition (pH 5.0) evaluated in Reactor 1.

As Fig. 1A shows, the volumetric methane production rate

in Reactor 1 was affected at pH 4.5, probably by the low

hydrogen production; at pH values of 5.5, methane was pro-

duced at similar rates. In completely mixed and fixed biomass

reactors for hydrogen production, methane activity has been

only found at pH above 5.0 [9,34]. Nevertheless, methane ac-

tivity was observed at pH values between 4.0 and 5.3 in an

anaerobic digester, which can be explained by an acclimated

consortium and the presence of micro environments [35]. In

previous reports, methanogenic communities observed under

acidic conditions were related to the order Methanomicrobiales

[13,36]; in the present study, the retrieved sequences belonged

to the familyMethanobacteriaceaewithin theMethanobacteriales

Please cite this article in press as: Carrillo-Reyes J, et al., Strategreactors: Community dynamics, International Journalj.ijhydene.2014.05.099

order. Both orders of methanogens are hydrogenotrophic,

which highlights the selection of hydrogenotrophic metha-

nogens at acidic pH; moreover, in the present study any

sequence corresponded to acetoclastic methanogens.

With respect to the shock load applied in Reactor 2, this

strategy enhanced the hydrogen production rate due to the

selection of hydrogen producing microorganisms. At the

lowest OLR of 20 g COD/L-d, the presence of P. acidipropionici

and L. casei could reduce the hydrogen production efficiency,

as was discussed above. Other ribotypes related to anaerobic

fermentative bacteria such as B. synergistic and P. johnsonii

were identified, both reported as non-hydrogen producers

[37,38]. The increment to 30 g COD/L-d caused a change in the

dominant ribotypes, related to C. tyrobutyricum, C. freundii

and E. aerogenes, all hydrogen producers [33,39,40], corre-

sponding to the highest hydrogen production rate in Reactor

2. In the last OLR evaluated (40 g COD/L-d), beside the

hydrogen producers identified at 30 g COD/L-d, C. ramosum,

another hydrogen producer, also prevailed. Despite the

presence of hydrogen producers at the organic load of

40 g COD/L-d, the low hydrogenogenic activity could be

explained by the presence of the clone related to C. ljung-

dahlii,which is an homoacetogen [41]. Homoacetogens use H2

and CO2 to produce acetate, explaining the highest acetate

concentration (Fig. 2B) without the expected hydrogen pro-

duction increment.

Another factor that contributed to the low hydrogen pro-

duction at 40 g COD/L-d was the undissociated VFA concen-

tration of 13.4 mM. According to Castro-Villalobos et al. [42], a

10 mM concentration of undissociated fermentation by-

products caused a biomass growth inhibition in a hydrogen

producing system. In Reactor 2, the HRT decrement implied a

decrease of the inlet substrate concentration from 10 to

5 g COD/L, which resulted in a decrement of the undissociated

acids concentration below the inhibition threshold for

hydrogen production [42], explaining the slight increment in

the hydrogen production rate (Fig. 1B). In contrast, in Reactor 3

the reduction of the substrate concentration was from 20 to

16 g COD/L, therefore the undissociated acids concentration

was above the inhibition threshold, showing similar hydrogen

and methane production rates.

Regarding to the second biomass heat treatment, applied

to reactor 3, this strategy favored the presence of spore

forming bacteria related to C. butyricum and the homoace-

togen C. ljungdahlii. The DGGE profile of day 80 in Reactor 3

showed that the band related with the non-spore bacterium E.

aerogenes disappeared.

In Reactor 3, another interesting finding after the heat

treatment was related to the presence of L. rhamnosus (a lactic

acid producer), which is commonly found in dairy effluents

such as the substrate used in the present work [5]. The pres-

ence of Lactobacillus genus has been widely reported in

hydrogen producing systems, but their capacity to produce

hydrogen has to be investigated due to contradictory results

[43]. The fact that L. rhamnosus is a non-spore forming bacte-

rium and prevailed after the heat treatment, highlights its

possible presence in the feeding substrate, as it has been

previously found [7].

The homoacetogenic activity in Reactor 3 was identified in

batch test and increased over the time, from 0 to

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

i n t e r n a t i o n a l j o u r n a l o f h yd r o g e n e n e r g y x x x ( 2 0 1 4 ) 1e1 0 9

18.29 mL H2 consumed/g VS-d during 30 days. Once the heat

treatment was applied, the hydrogen production decreased

from 2.4 L/L-d to an average of 1.02 L/L-d in the last 15 days of

reactor operation (Fig. 1C). The gradual diminution in the

hydrogen production caused an increment in acetate con-

centration by homoacetogenic activity, reflected in the wide

acetate concentration dispersion shown in Fig. 2C. Due to the

spore forming capacity of homoacetogenic bacteria, these

could survive to the heat treatment with no problems [3],

replacing the methanogenic activity in detriment of the

hydrogen production potential.

Despite the lack of methanogenic activity, archeal 16S

rDNAwas amplified and cloned, possibly explained by the low

DNA degradation in the reactor [44] or the low methanogens

concentration in the biomass sampled. Several works show

that methane can be produced even though a previously

adapted hydrogen producer community is used as inoculum

[29,45], which emphasizes the relevance to define strategies

that could control the methanogenic activity. The possibility

to change a methane producing reactor into a hydrogen pro-

ducing one, only by controlling the operational parameters,

has been proposed recently [34].

The link between the microbial community and the

methanogenic control strategies showed in the present work

highlights the relevance of the organic shock load,whichhad a

major effect in the control of methanogenesis. This observa-

tion is in accordance with the results obtained by Spagni et al.

[6]. With a second heat treatment the methanogenic activity

was successfully suppressed, nevertheless it selected homo-

acetogenic bacteria, favored by the long solid retention time in

the reactor and the high hydrogen partial pressure [46].

Conclusions

The strategies applied to decrease the methane production

had an effect in the microbial community. Among the oper-

ational strategies (pH reduction, shock load and HRT reduc-

tion), the shock load enhanced the hydrogen production rate

due to the selection of hydrogen producing microorganisms.

However, extended operation times selected organisms that

diverted the available electrons to the production of more

reduced compounds such as propionate or lactate. The

organic shock load strategy had a limitation, related to the

high concentration of undissociated acids resulting in the

inhibition of hydrogen production. Only a second heat treat-

ment applied to the biomass, completely inhibited the meth-

anogenic activity. However, the sporulation capacity selected

both hydrogen producing and homoacetogenic bacteria, the

latter reducing the hydrogen producing potential of the

reactor. The presence of microorganisms of the genus Lacto-

bacillus (non-spore forming) after the heat treatment, high-

lighted the importance of the indigenous microorganisms

load present in the substrate, indicating that the relevance of

this microorganisms in the reactors performance has to be

investigated.

The archeal community characterization confirms that

hydrogenotrophic methanogens were responsible for

diminish the hydrogen production potential in the three re-

actors, andwere able to survive at acidic pH conditions, as low

Please cite this article in press as: Carrillo-Reyes J, et al., Strategreactors: Community dynamics, International Journalj.ijhydene.2014.05.099

as 4.5. The tolerance of methanogens is an important issue in

fermentative hydrogen production reactors, which are not

operate under sterile conditions and are potentially vulner-

able to methanogenic contamination.

This study shows that operational strategies to decrease

methane production can select hydrogen-producing bacteria,

providing suitable alternatives for hydrogen production using

unsterile industrial waste products with incoming microor-

ganisms; high organic loading rate based on increments in the

substrate concentration seems to be the optimal strategy for

hydrogen production when methane is observed.

Acknowledgments

This research was financially supported by the project SEP-

CONACYT 132483. The authors acknowledge the technical

assistance of Dulce Partida Guti�errez, Guillermo Vidriales

Escobar, Juan Pablo Rodas Ortíz and María del Carmen Rocha

Medina, and the use of the analytical infrastructure of the

“Laboratorio Nacional de Biotecnología Agrícola, M�edica y

Ambiental (LANBAMA)”. The authors also acknowledge to Dr.�Angel Gabriel Alpuche Solís and Dr. Gerardo Rafael Arguello

Astorga for the use of the “Laboratorio de Biología Molecular

de Plantas” facilities and the technical assistance of Salvador

Ambríz Granados during the molecular microbial analysis,

and the helpful advice of Dr. Arturo Carrillo-Reyes for the

statistical analysis.

r e f e r e n c e s

[1] Nath K, Das D. Improvement of fermentative hydrogenproduction: various approaches. Appl Microbiol Biotechnol2004;65:520e9.

[2] Abreu AA, Alves JI, Pereira MA, Sousa DZ, Alves MM.Strategies to suppress hydrogen-consuming microorganismsaffect macro and micro scale structure and microbiology ofgranular sludge. Biotechnol Bioeng 2011;108:1766e75.

[3] Luo G, Karakashev D, Xie L, Zhou Q, Angelidaki I. Long-termeffect of inoculum pretreatment on fermentative hydrogenproduction by repeated batch cultivations:homoacetogenesis and methanogenesis as competitors tohydrogen production. Biotechnol Bioeng 2011;108:1816e27.

[4] Carrillo-Reyes J, Celis LB, Alatriste-Mondrag�on F, Razo-Flores E. Different start-up strategies to enhancebiohydrogen production from cheese whey in UASB reactors.Int J Hydrogen Energy 2012;37:5591e601.

[5] Castello E, García y Santos C, Iglesias T, Paolino G, Wenzel J,Borzacconi L, et al. Feasibility of biohydrogen productionfrom cheese whey using a UASB reactor: links betweenmicrobial community and reactor performance. Int JHydrogen Energy 2009;34:5674e82.

[6] Spagni A, Casu S, Farina R. Effect of the organic loading rateon biogas composition in continuous fermentative hydrogenproduction. J Environ Sci Health Part e Tox Hazard Subst EnvEng 2010;45:1475e81.

[7] Stamatelatou K, Antonopoulou G, Tremouli A, Lyberatos G.Production of gaseous biofuels and electricity from cheesewhey. Ind Eng Chem Res 2011;50:639e44.

[8] Buitr�on G, Carvajal C. Biohydrogen production from Tequilavinasses in an anaerobic sequencing batch reactor: effect ofinitial substrate concentration, temperature and hydraulicretention time. Bioresour Technol 2010;101:9071e7.

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y x x x ( 2 0 1 4 ) 1e1 010

[9] Wang Y, Mu Y, Yu HQ. Comparative performance of twoupflow anaerobic biohydrogen-producing reactors seededwith different sludges. Int J Hydrogen Energy2007;32:1086e94.

[10] CordRuwisch R, Mercz TI, Hoh CY, Strong GE. Dissolvedhydrogen concentration as an on-line control parameter forthe automated operation and optimization of anaerobicdigesters. Biotechnol Bioeng 1997;56:626e34.

[11] Duangmanee T, Padmasiri SI, Simmons JJ, Raskin L, Sung S.Hydrogen production by anaerobic microbial communitiesexposed to repeated heat treatments. Water Environ Res2007;79:975e83.

[12] Kim IS, Hwang MH, Jang NJ, Hyun SHSH, Lee ST. Effect of lowpH on the activity of hydrogen utilizing methanogen in bio-hydrogen process. Int J Hydrogen Energy 2004;29:1133e40.

[13] Steinberg LM, Regan JM. Phylogenetic comparison of themethanogenic communities from an acidic, oligotrophic fenand an anaerobic digester treating municipal wastewatersludge. Appl Environ Microbiol 2008;74:6663e71.

[14] Davila-Vazquez G, Cota-Navarro CB, Rosales-Colunga LM, deLe�on-Rodríguez A, Razo-Flores E. Continuous biohydrogenproduction using cheese whey: improving the hydrogenproduction rate. Int J Hydrogen Energy 2009;34:4296e304.

[15] Abreu AA, Alves JI, Pereira MA, Karakashev D, Alves MM,Angelidaki I. Engineered heat treated methanogenicgranules: a promising biotechnological approach for extremethermophilic biohydrogen production. Bioresour Technol2010;101:9577e86.

[16] Ramette A. Multivariate analyses in microbial ecology. FEMSMicrobiol Ecol 2007;62:142e60.

[17] Anderson MJ. A new method for non-parametricmultivariate analysis of variance. Austral Ecol 2001;26:32e46.

[18] R Development Core Team. R: A language and environmentfor statistical computing. Vienna, Austria: R Foundation forStatistical Computing; 2009.

[19] Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR,O’Hara RB, et al. Vegan community ecology package. Rpackage version 2.0-5, http://CRAN.R-project.org/package¼vegan; 2012.

[20] Savicky P. Pspearman: Spearman's rank correlation test. Rpackage version 0.2-5, http://CRAN.R-project.org/package¼pspearman; 2009.

[21] Wisotzkey JD, Jurtshuk P, Fox GE. PCR amplification of 16SrDNA from lyophilized cell cultures facilitates studies inmolecular systematics. Curr Microbiol 1990;21:325e7.

[22] Martin-Laurent F, Philippot L, Hallet S, Chaussod R,Germon JC, Soulas G, et al. DNA extraction from soils: oldbias for new microbial diversity analysis methods. ApplEnviron Microbiol 2001;67:2354e9.

[23] Sanchez O, Gasol JM, Massana R, Mas J, Pedros-Alio C.Comparison of different denaturing gradient gelelectrophoresis primer sets for the study of marinebacterioplankton communities. Appl Environ Microbiol2007;73:5962e7.

[24] Sousa DZ, Pereira MA, Smidt H, Stams AJM, Alves MM.Molecular assessment of complex microbial communitiesdegrading long chain fatty acids in methanogenicbioreactors. FEMS Microbiol Ecol 2007;60:252e65.

[25] Felsenstein J. Phylogeny inference package. Seattle, WA:University of Washington; 2009.

[26] Yu Y, Breitbart M, McNairnie P, Rohwer F. FastGroupII: a web-based bioinformatics platform for analyses of large 16S rDNAlibraries. BMC Bioinform 2006;7:57.

[27] Larkin MA, Blackshields G, Brown NP, Chenna R,McGettigan PA, McWilliam H, et al. Clustal W and Clustal Xversion 2.0. Bioinformatics 2007;23:2947e8.

[28] Tamura K, Peterson D, Peterson N, Stecher G, Nei M,Kumar S. MEGA5: molecular evolutionary genetics analysis

Please cite this article in press as: Carrillo-Reyes J, et al., Strategreactors: Community dynamics, International Journalj.ijhydene.2014.05.099

using maximum likelihood, evolutionary distance, andmaximum parsimony methods. Mol Biol Evol2011;28:2731e9.

[29] Fang HHP, Liu H. Effect of pH on hydrogen production fromglucose by a mixed culture. Bioresour Technol 2002;82:87e93.

[30] Fonty G, Joblin K, Chavarot M, Roux R, Naylor G, Michallon F.Establishment and development of ruminalhydrogenotrophs in methanogen-free lambs. Appl EnvironMicrobiol 2007;73:6391e403.

[31] Calli B, Zhao J, Nijssen E, Vanbroekhoven K. Significance ofacetogenic H2 consumption in dark fermentation andeffectiveness of pH. Water Sci Technol 2008;57:809e14.

[32] Zhang A, Yang S-T. Engineering Propionibacteriumacidipropionici for enhanced propionic acid tolerance andfermentation. Biotechnol Bioeng 2009;104:766e73.

[33] Zhang C, Ma K, Xing X-H. Regulation of hydrogen productionby Enterobacter aerogenes by external NADH and NADþ. Int JHydrogen Energy 2009;34:1226e32.

[34] Liu D, Zeng RJ, Angelidaki I. Effects of pH and hydraulicretention time on hydrogen production versusmethanogenesis during anaerobic fermentation of organichousehold solid waste under extreme-thermophilictemperature (70�C). Biotechnol Bioeng 2008;100:1108e14.

[35] Taconi KA, Zappi ME, French WT, Brown LR. Methanogenesisunder acidic pH conditions in a semi-continuous reactorsystem. Bioresour Technol 2008;99:8075e81.

[36] Ban Q, Li J, Zhang L, Zhang Y, Jha AK. Phylogenetic diversityof methanogenic archaea and kinetics of methaneproduction at slightly acidic conditions of an anaerobicsludge. Int J Agric Biol 2013;15:347e51.

[37] Sakamoto M, Kitahara M, Benno Y. Parabacteroides johnsoniisp. nov., isolated from human faeces. Int J Syst Evol Microbiol2007;57:293e6.

[38] Sakamoto M, Takagaki A, Matsumoto K, Kato Y, Goto K,Benno Y. Butyricimonas synergistica gen. nov., sp. nov. andButyricimonas virosa sp. nov., butyric acid-producing bacteriain the family “Porphyromonadaceae”isolated from rat faeces.Int J Syst Evol Microbiol 2009;59:1748e53.

[39] Jo JH, Lee DS, Park D, Park JM. Biological hydrogen productionby immobilized cells of Clostridium tyrobutyricum JM1 isolatedfrom a food waste treatment process. Bioresour Technol2008;99:6666e72.

[40] Kumar GR, Vatsala TM. Hydrogen production from glucoseby Citrobacter freundii. Indian J Exp Biol 1989;27:824e5.

[41] K€opke M, Held C, Hujer S, Liesegang H, Wiezer A, Wollherr A,et al. Clostridium ljungdahlii represents a microbial productionplatform based on syngas. Proc Natl Acad Sci2010;107:13087e92.

[42] Castro-Villalobos MC, Garcia-Morales JL, Fernandez FJ. By-products inhibition effects on bio-hydrogen production. Int JHydrogen Energy 2012;37:7077e83.

[43] Castello E, Perna V, Wenzel J, Borzacconi L, Etchebehere C.Microbial community composition and reactor performanceduring hydrogen production in a UASB reactor fed with rawcheese whey inoculated with compost. Water Sci Technol2011;64:2265e73.

[44] Borin S, Crotti E, Mapelli F, Tamagnini I, Corselli C,Daffonchio D. DNA is preserved and maintains transformingpotential after contact with brines of the deep anoxichypersaline lakes of the Eastern Mediterranean Sea. SalineSyst 2008;4:10.

[45] Oh Y-K, Kim SH, Kim M-S, Park S. Thermophilic biohydrogenproduction from glucose with trickling biofilter. BiotechnolBioeng 2004;88:690e8.

[46] Clark IC, Zhang RH, Upadhyaya SK. The effect of lowpressure and mixing on biological hydrogen production viaanaerobic fermentation. Int J Hydrogen Energy2012;37:11504e13.

ies to cope with methanogens in hydrogen producing UASBof Hydrogen Energy (2014), http://dx.doi.org/10.1016/