Bioinformatic prediction of putative metallothioneins in non-ciliate protists

Abstract

Intracellular ligands that bind heavy metals (HMs) and thereby minimize their detrimental effects to cellular metabolism are attracting great interest for a number of applications including bioremediation and development of HM-biosensors. Metallothioneins (MTs) are short, cysteine-rich, genetically encoded proteins involved in intracellular metal-binding and play a key role in detoxification of HMs. We searched approximately 700 genomes and transcriptomes of non-ciliate protists for novel putative MTs by similarity and structural analyses and found 21 unique proteins playing a potential role as MTs. Most putative MTs derive from heterokonts and dinoflagellates and share common features such as (i) a putative metal-binding domain in proximity of the N-terminus, (ii) two putative MT-specific domains near the C-terminus and (iii) one to three CTCGXXCXCGXXCXCXXC patterns. Although the biological function of these proteins has not been experimentally proven, knowledge of their genetic sequences adds useful information on proteins that are potentially involved in HM-binding and can contribute to the design of future biomolecular assays on HM–microbe interactions and MT-based biosensors.

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royalsocietypublishing.org/journal/rsbl
Research
Cite this article: Balzano S, Sardo A. 2022
Bioinformatic prediction of putative
metallothioneins in non-ciliate protists. Biol.
Lett. 18: 20220039.
https://doi.org/10.1098/rsbl.2022.0039
Received: 1 February 2022
Accepted: 18 March 2022
Subject Areas:
bioinformatics, environmental science
Keywords:
heavy metals, pollution, metallothioneins,
non-ciliate protists
Author for correspondence:
Sergio Balzano
e-mail: sergio.balzano@szn.it
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.
c.5918300.
Marine biology
Bioinformatic prediction of putative
metallothioneins in non-ciliate protists
Sergio Balzano
1,2
and Angela Sardo
1,3
1
Stazione Zoologica Anton Dohrn Napoli (SZN), Department of Ecosustainable Marine Biotechnology,
via Ammiraglio Ferdinando Acton 55, 80133, Naples, Italy
2
NIOZ Royal Netherlands Institute for Sea Research, 1790AB Den Burg, The Netherlands
3
Istituto di Scienze Applicate e Sistemi Intelligenti –CNR, via Campi Flegrei 34, 80078 Pozzuoli, Naples, Italy
SB, 0000-0002-3172-1332
Intracellular ligands that bind heavy metals (HMs) and thereby minimize
their detrimental effects to cellular metabolism are attracting great interest
for a number of applications including bioremediation and development
of HM-biosensors. Metallothioneins (MTs) are short, cysteine-rich, geneti-
cally encoded proteins involved in intracellular metal-binding and play a
key role in detoxification of HMs. We searched approximately 700 genomes
and transcriptomes of non-ciliate protists for novel putative MTs by simi-
larity and structural analyses and found 21 unique proteins playing a
potential role as MTs. Most putative MTs derive from heterokonts and dino-
flagellates and share common features such as (i) a putative metal-binding
domain in proximity of the N-terminus, (ii) two putative MT-specific
domains near the C-terminus and (iii) one to three CTCGXXCXCGX
XCXCXXC patterns. Although the biological function of these proteins has
not been experimentally proven, knowledge of their genetic sequences
adds useful information on proteins that are potentially involved in HM-
binding and can contribute to the design of future biomolecular assays on
HM–microbe interactions and MT-based biosensors.
1. Introduction
Microorganisms inhabiting heavy metal (HM)-contaminated environments,
eventually incorporating contaminants within the cell, are biotechnologically
interesting because of their potential use for bioremediation [1]. Passive adsorp-
tion of cations onto cell walls and transport across cell membranes are the two
major mechanisms of HM uptake by living cells [2,3]. Subsequently, intracellu-
lar polypeptides such as enzymatically produced phytochelatins and
genetically encoded metallothioneins (MTs) limit the detrimental effect of
HMs by complexing and transporting them towards vacuoles, chloroplasts or
mitochondria [4,5].
MTs are low-molecular weight proteins exhibiting a low content of aromatic
amino acids and high proportions of cysteine residues (10% or more); they have
been characterized in great detail in multicellular organisms [6,7] as well as
in bacteria [8], yeasts and ciliates [9], and are currently classified in 15 families
that are not phylogenetically related but are likely to result from convergent
evolution [10]. Ciliate MTs are generally longer than average and, along with
MTs from metazoans and fungi, contain greater proportions of cysteine than
MTs from plants and bacteria [11]. In addition to classified proteins, MTs iso-
lated and characterized experimentally from the brown macroalga Fucus
vesiculosus [12], the excavate Trichomonas vaginalis [10] and different fungi and
metazoans [13], as well as HM-contaminated soils [14], could not be classified
and were suggested to make up novel MT families [13].
The broad genetic diversity spanning living organisms [15,16] and the scar-
city of known MTs in microbial eukaryotes other than fungi and ciliates [17]
© 2022 The Author(s) Published by the Royal Society. All rights reserved.
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Table 1. List of protein sequences predicted from eukaryotic genomes and transcriptomes as likely to play a role as MTs, as revealed by Interproscan analyses or motif search.
a
protein ID species class supergroup strain ID transcriptome ID database ID
no. identical
transcripts
b
stress condition
c
interproscan
domain code
d
EinvMT Entoamoeba invadens archamoeboe Amoebozoans IP1 NA XP_004259069 1 NA
AlanMT Armaparvus languidus vannellids Amoebozoans PRA-29 MMETSP0420 Tr3694 1 HL IPR035715
CsorMT Chlorella sorokiniana green algae Archaeplastida 1602 NA PRW44601.1 1 NA IPR002045
MconMT Micractinium condutrix green algae Archaeplastida SAG 241.80 NA PSC70917 1 NA
TvagMT Trichomonas vaginalis parabasalids Discoba ATCC PRA-98 NA XP_001321197 1 NA
CrotMT Chrysochromulina rotalis haptophytes Hacrobians UIO044 MMETSP0287 Tr26136 1 HL IPR001008
CowcMT Capsaspora owczarzaki filozoa Opisthokonts ATCC 30864 NA XP_011270693 1 NA
BbigMT Babesia bigemina apicomplexa SAR
e
NA XP_012768823 1 NA
BlasMT Blastocystis sp. bigyra SAR ATCC 50177 NA OAO13187 1 NA
EsilMT Ectocarpus siliculosus brown algae SAR NA CBJ32637 IPR001008
FvesMT Fucus vesiculosus brown algae SAR NA CAA06729 IPR001008
AglaMT1 Asterionellopsis glacialis diatoms SAR CCMP134 MMETSP0708 Tr19519 3 N-/P- IPR001008
AglaMT2 Asterionellopsis glacialis diatoms SAR CCMP1581 MMETSP1394 Tr220 1 N-/P-
CwaiMT Coscinodiscus wailesii diatoms SAR CCMP2513 MMETSP1066 Tr41518 1 HL IPR001008
DbriMT Ditylum brightwellii diatoms SAR GSO105 MMETSP0998 Tr22984 8 HL/No/N-/P- IPR001008
EspiMT Extubocellulus spinifer diatoms SAR CCMP396 MMETSP0697 Tr10701 1 Si-/No/HL
MpolMT Minutocellus polymorphus diatoms SAR NH13 MMETSP1070 Tr24663 2 No/HL
OaurMT Odontella aurita diatoms SAR Is-1302-5 MMETSP0015 Tr34634 2 HL
PdubMT Pseudodictyota dubia diatoms SAR CCMP147 MMETSP1175 Tr24667 1 HL IPR001008
SyneMT Synedropsis sp. diatoms SAR CCMP1620 MMETSP1176 Tr28518 2 HL IPR001008
TpseMT Thalassiosira pseudonana diatoms SAR CCMP1335 NA XP_002296843 1 NA
AcatMT Alexandrium catenella dinoflagellate SAR OF101 MMETSP0790 Tr99632 1 No
AmonMT Alexandrium monilatum dinoflagellate SAR CCMP3105 MMETSP0096 Tr45933 4 HL/P-
AzspMT Azadinium spinosum dinoflagellate SAR 3D9 MMETSP1037 Tr93697 2 HL IPR001008
GspiMT Gonyaulax spinifera dinoflagellate SAR CCMP409 MMETSP1439 Tr79705 1 HL
LpolMT Lingulodinium polyedrum dinoflagellate SAR CCMP1738 MMETSP1032 Tr14667 4 No/HL
AplaMT Aplanochytrium sp. labyrinthulids SAR PBS07 MMETSP0956 Tr7261 4 NA IPR001008
AstoMT Aplanochytrium stocchinoi labyrinthulids SAR GSBS06 MMETSP1349 Tr9377 4 NA IPR001008
AuanMT1 Aureococcus anophagefferens pelagophyceae SAR CCMP1850 MMETSP0917 Tr30268 3 N-
(Continued.)
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suggest that the real diversity of MTs as well as the number of
distinct families are likely to be broader than is currently
known. For example, less than 1% of proteins annotated as
MTs on GenBank belong to protists, they are mostly associ-
ated with parasitic genera (Babesia,Entoamoeba,Plasmodium
and Trichomonas), and other microorganisms including
microalgae are highly underrepresented [17]. MTs from
both eukaryotic and prokaryotic microbes have been recently
reviewed by Gutiérrez et al. [18] and while MTs from ciliates
and fungi have been characterized in detail and classified in
different families, little is known on MTs from non-
ciliate protists. Although some common features—such as a
prevalence of CXC motifs—were observed, MTs from non-
ciliate protists do not share a common evolutionary origin
and are likely to result from the convergent evolution of
different genes [18]. Overall, very little is known to date on
proteins from microalgae and, in general, from protists differ-
ent from ciliates. Here we predicted, through a bioinformatic
approach, novel potential MTs from eukaryotic microbial
genomes and transcriptomes.
2. Material and methods
We searched 44 genomes [19] and 636 transcriptomes [20] for
novel MTs of non-ciliate protists. The amino acid sequences of
the proteins predicted from the genomes were downloaded from
GenBank (electronic supplementary material, table S1), whereas
a re-assembled version of the proteins predicted from the
marine microbial eukaryote transcriptome sequencing project
(MMETSP) database (electronic supplementary material, table
S2) was downloaded from iMicrobe [21,22]. We carried out struc-
tural analyses of the proteins predicted from the abovementioned
databases using InterProScan [23] with default parameters
(https://interproscan-docs.readthedocs.io/en/latest/HowToRun.
html); proteins found to possess regions identified as MT-domains
with a score (e-value) of less than 5 × 10
−5
were retained for down-
stream analyses (electronic supplementary material, table S3).
GPS-Prot software [24] was used to plot the position of the differ-
ent domains within each protein. The resulting proteins were
aligned using MAFFT-linsy [25] and analyses revealed the pres-
ence of one to three highly conserved CTCGXXCXCGXXCXC
XXC patterns in most proteins. We then searched for other pro-
teins possessing the CTCGXXCXCGXXCXCXXC pattern within
the abovementioned databases and results were then added to
the previous alignments (electronic supplementary material,
figure S1). A sequence logo of the abovementioned pattern was
generated using WebLogo [26].
3. Results and discussion
Functional analyses of genomes and transcriptomes
sequenced from non-ciliate protists yielded 10 unique
proteins possessing putative MT-specific domains (table 1;
electronic supplementary material, table S4). AlanMT protein
(Armaparvus languidus, amoebozoan and excavate) possesses
a region sharing similarities with a domain present in yeast
MTs (IPR035715), whereas all the other proteins found here
contain two adjacent regions sharing similarities with
known MT domains from molluscs (IPR001008). Most
(8 out of 10) proteins also contain a putative HM-associated
domain (HMA, IPR006121) located in proximity of the
N-terminus (figure 1), one to three conserved cysteine-rich
patterns 18 AA long (CTCGXXCXCGXXCXCXXC), and
have been originally isolated from species affiliated to the
Table 1. (Continued.)
protein ID species class supergroup strain ID transcriptome ID database ID
no. identical
transcripts
b
stress condition
c
interproscan
domain code
d
AuanMT2 Aureococcus anophagefferens pelagophyceae SAR CCMP1984 NA XP_009037419 1 NA IPR001008
PsubMT Pelagococcus subviridis pelagophyceae SAR CCMP1429 MMETSP0883 Tr17315 3 N-
PcalMT Pelagomonas calceolata pelagophyceae SAR RCC969 MMETSP1328 Tr480 4 NA
a
Known MTs identified in previous studies are in bold.
b
In many cases, 2 or more identical proteins possessing MT-specific domain or resulting from keyword searches were found from different transcriptomes of the same strain.
c
Stress condition at which the strain was maintained prior to transcriptome sequencing. ‘No’refers to transcriptomes derived from strains cultured at standard conditions.
In some cases identical sequences were obtained from different transcriptomes reflecting either different stress treatments or both stress and non-stress conditions. Abbreviations: NA, not available; N-, nitrogen deprivation (<2 μM); P-,
phosphorus deprivation (<0.5 μM); Si-, silica deprivation (<0.5 μM); HL, high light (>300 μEm
−2
s
−1
).
d
The sequences without an InterProScan code were identified by keyword search of the conserved CTCGXXCXCGXXCXCXXC motif.
e
Stramenopiles-Alveolata-Rhizaria.
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Stramenopile–Alveolata–Rhizaria (SAR) supergroup. Twelve
additional unique proteins containing the same 18 AA
pattern were subsequently found in other SAR species (elec-
tronic supplementary material, table S5). Overall, structural
analyses and pattern search allowed the identification of 21
unique proteins (table 1), 19 of which derive from SAR
species and possess a highly conserved cysteine-rich pattern,
that are likely to play a role as MTs (figure 2). Thirteen
putative MTs are present in more than one transcriptome of
the MMETSP database being thus very unlikely to result
from contaminations or sequencing errors. Interestingly, in
many cases, our putative MTs derive from transcriptomes
sequenced out of specimens collected under stress conditions
such as high light irradiance (greater than 300 µE m
−2
s
−1
)or
under nitrogen (less than 2 µM) or phosphorus (less than
0.5 µM) limitation (table 1). Both high light irradiance and
nutrient starvation can generate oxidative stress [27,28] that
has been reported to induce MT biosynthesis [4,29]. Current
data thus suggest that the proteins found here are more
likely to be expressed while microorganisms thrive under
oxidative stress conditions, coherently with a potential role
as MTs.
Little is known on metal-binding mechanisms in microal-
gal MTs. MTs are generally known to have affinities with
monovalent and divalent ions, with each cation coordinated
by 3 to 4 cysteine residues, and each residue coordinating
one or two cations [7,30,31]. The number of monovalent or
divalent metal cations that can be coordinated by the putative
260
161
180
6
68
202
212
312
127
146
156
177
4
66
193
132
151 187
46
106
204
122
141
20
58
162
172
255
161
180
203
213
52
92
258
167
186
208
218
3
76
264
142
153
170
191
14
84
208
163
173
120
139
43
98
116
80
107
112
52
71
83
104
Aplanochytrium sp. MMETSP0956_Tr7261
Aplanochytrium stocchinoi MMETSP1349_Tr_9377
Coscinodiscus wailesii MMETSP1066_Tr41518
Asterionellopsis glacialis MMETSP0708_Tr_19519
Dytilum brightwellii MMETSP0998_Tr22984
Synedropsis sp. MMETSP1176_Tr28518
Pseudictyota dubia MMETSP1175_Tr24667
Azadinium spinosum MMETSP1037_Tr93697
Chrysochromulina rotalis MMETSP0287_Tr26136
Armaparvus languidus MMETSP0420_Tr3694
labyrinthulids
diatoms
dinoflagellates
haptophytes
amoebozoans
177
Figure 1. Proteins from different microbial eukaryotes containing MT-specific domains as found by structural analyses using InterProScan [23]. Numbers indicate
protein length and the position of the different domains. Domains specific for MTs are in black (Mollusc MTs, IPR001008; crustacean MTs, IPR002045; eukaryotic MT,
PF12809), whereas HM-associated domains (HMA, IPR006121) are in grey. Species name and sequence identifiers are indicated on the left of each putative MTs,
whereas class names are on the right.
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MTs found here cannot be predicted in silico but needs to be
evaluated experimentally. It has been suggested that a MT is
able to chelate a number of monovalent cations slightly
higher than half of its cysteine residues and a number of diva-
lent cations lower than 50% of its cysteine residues [7,30,31].
Short putative MTs such as AlanMT,CrotMT or EspiMT can
coordinate around 5–10 cations, whereas the longest proteins
found here such as AplaMT (258 AA), AstoMT (264) and
SyneMT (255) can coordinate up to 30 cations.
Current results strongly suggest that at least the proteins
that possess an HMA domain along with two adjacent MT
domains (figure 1) that were found here from SAR represen-
tatives are likely to play a role as MTs. HMA domains have
previously been found in proteins involved in HM transport
and detoxification in mammals [32,33], and two adjacent
MT-domains typically occur in known MTs from plants [7],
mammals [34] and ciliates [35]. Proteins found here from
SAR representatives are longer than most known MTs
(table 1), ranging from 189 (DbriMT) to 320 AA (OaurMT).
The presence of multiple, conserved cysteine-rich patterns
(figure 2), and the fact that such proteins are longer than
average, suggest that putative SAR MTs might have resulted
from gene duplication of shorter MTs, similarly to what has
been hypothesized for very long MTs in fungi [36], molluscs
[37] and T. vaginalis [10].
The cysteine content found in our putative MTs is lower
than that of most known MTs, ranging from 8% (AlanMTs)
to 19% (CrotMT and CwaiMT) and was highly variable even
within SAR-derived proteins (table 2). Histidine content is
very low (less than 2%) in all proteins except for CrotMT
(3.6%) and SyneMT (3.9%); aromatic amino acids account
for less than 5% in most proteins, whereas lysine contribution
ranges from 0.9% (CrotMT) to 10% (AlanMTs). Overall, puta-
tive SAR MTs found here, along with the known MT
AuanMT2, exhibit a similar domain distribution (figure 1),
contain cysteine residues mostly clustered in CXC motifs
and share one to three conserved 18 AA patterns (figure 2).
Gutiérrez et al. [18] observed a predominance of CXC
motifs, especially CKC, in MTs from non-ciliate protists.
However, while some known MTs like BlasMT, CowcMT
and TvagMT are indeed rich (more than 8) in CKC motifs,
this does not seem to be a common feature among the puta-
tive MTs found here in non-ciliate protists. For example,
AuanMTs and MconMT do not contain such motifs, whereas
only one CKC motif occurs in BbigMT,CsorMT and TpseMT
(table 2). Similarly, among our putative MTs, a CKC motif
occurs five times in OaurMT, but it is repeated three times
or less in the other proteins. In general, CTC and CQC
motifs are more common than CKC motifs in our putative
SAR MTs (table 2). Current data indicate that both proteins
with an experimentally proven HM-binding activity and
putative MTs found here via bioinformatic analyses exhibit
a highly variable content in CKC, CTC and CQC motifs.
The possible role of our SAR proteins as MTs is further
suggested by the presence in known MTs, from some metazo-
ans, amoebozoans, fungi and higher plants, of a region slightly
Figure 2. Alignment of the putative MTs from heterokonts (Labyinthulids, Pelagophyceae and diatoms) and dinoflagellates and sequence logo of the highly con-
served motif CTCGXXCXCGXXCXCXXC. Underlined sequence IDs correspond to putative MTs found in the present study, whereas IDs that are not underlined are related
known MTs from previous studies. Numbers reflect the amino acid position with respect to the longest protein found here (SyneMT from Synedropsis sp. CCMP1620).
Cysteine residues are highlighted in black while histidine residues, which might also be involved in HM binding, are in grey. Only the regions corresponding to the
HM-associated domains (HMA, IPR006121, positions 197 to 242) and those exhibiting the cysteine-rich motif CTCGXXCXCGXXCXCXXC are shown for clarity, whereas
the full alignment is shown in electronic supplementary material, figure S1. MTs predicted in this study are underlined, whereas MT activity has been previously
proven or predicted in the other proteins. The species, strain and treatment associated with each protein abbreviated here are reported in table 1. Sequence logo was
created using WebLogo (weblogo.berkeley.edu/logo.cgi).
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different from our 18 AA pattern. In this case, the threonine
residue on the second position is replaced by other polar or
positively charged amino acids (electronic supplementary
material, figure S2). Besides this difference, putative SAR
MTs share the same number and position of cysteine residues
with metal-binding domains in Type 1 MTs from plants [7],
copper and cadmium MTs in snails [38], and silver MTs in
fungi [39].
In spite of the similarities found, even putative SAR MTs,
possessing the shared 18 AA pattern, exhibit great differences
among each other, and we could not construct a meaningful
(i.e. bootstrap support greater than 30%, using neighbour
joining or maximum-likelihood algorithms) phylogenetic
tree from the alignment of such sequences. This variability
is likely to reflect the broad genetic diversity of non-ciliate
protists and suggests that, although SAR species share a
common evolutionary origin [16], their MTs are likely to
result from convergent evolution of different genes, in spite
of the shared 18 AA pattern.
Although the putative SAR MTs found here possess two
regions related to metal-binding domains of mollusc MTs
(figure 1) and a conserved 18 AA cysteine-rich region
(figure 2) that can be found, in part, in MTs from different
organisms (electronic supplementary material, figure S2),
none of the putative SAR MTs found here possesses the
motifs previously described for the 15 MT families [10,11]
Table 2. Main features and proportions of amino acids potentially involved in metal chelation, for the putative MTs found in the present study.
species protein ID length
amino acid
cysteine
a
(%)
residues
histidine (%)
aromatic
AA (%) CXC
specific
motifs
18 AA
b
CKC CTC
Alexandrium catenella AcatMT 189 9 1.6 3.2 6 2 1 1
Alexandrium monilatum AmonMT 196 10 1.0 5.1 6 3 1 1
Aplanochytrium sp. AplaMT 258 15 0.4 1.9 12 0 5 3
Aplanochytrium stocchinoi AstoMT 264 14 1.1 1.5 12 2 6 3
Armaparvus languidus AlanMT 116 8 1.7 7.7 3 0
Asterionellopsis glacialis AglaMT1 204 13 1.5 2.5 9 1 3 2
Asterionellopsis glacialis AglaMT2 196 14 1.0 2.6 9 1 4 1
Aureococcus anophagefferens AuanMT1 232 15 0.0 1.3 11 1 4 1
Azadinium spinosum AzspMT 208 11 0.5 4.8 6 3 1 1
Chrysochromulina rotalis CrotMT 112 19 3.6 6.3 6 1
Coscinodiscus wailesii CwaiMT 312 19 0.0 0.0 18 0 7 4
Ditylum brightwellii DbriMT 193 11 1.0 1.6 6 0 3 1
Extubocellulus spinifer EspiMT 129 12 0.8 1.6 4 0 2 1
Gonyaulax spinifera GspiMT 164 10 0.6 3.7 6 3 1
Lingulodinium polyedrum LpolMT 196 10 1.0 3.6 6 1 1 1
Minutocellus polymorphus MpolMT 203 11 1.0 1.5 6 0 3 1
Odontella aurita OaurMT 320 17 0.0 1.3 17 5 6 3
Pelagococcus subviridis PsubMT 207 11 0.5 2.4 6 3 2 1
Pelagomonas calceolata PcalMT 160 11 0.6 2.5 6 0 1 2
Pseudodictyota dubia PdubMT 260 19 0.0 0.8 15 3 5 3
Synedropsis sp. SyneMT 255 11 3.9 4.7 9 1 2 1
Aureococcus
anophagefferens
AuanMT2 171 18. 0.0 1.2 12 031
Babesia bigemina BbigMT 214 12 1.9 7.9 2 1
Blastocystis sp. BlasMT 207 40 0.0 0.0 33 29
Capsaspora owczarzaki CowcMT 176 27 0.0 0.0 15 11 1
Chlorella sorokiniana CsorMT 56 32 0.0 0.0 6 11
Entoamoeba invadens EinvMT 103 35 0.0 1.9 13 4
Micractinium condutrix MconMT 59 30 0.0 0.0 6 03
Thalassiosira pseudonana TpseMT 141 13 1.4 5.7 6 1
Trichomonas vaginalis TvagMT 308 30 6.2 2.3 41 9
a
Values refer to the numbers of amino acids in the sequence.
b
Specific, 18 amino acid motif (CTCGXXCXCGXXCXCXXC) identified in the putative MTs found in the present study.
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and thus do not belong to any family described to date.
Although ciliates are part of the SAR supergroup, MTs
from ciliates (Family 7) are shorter, contain greater pro-
portions of cysteine and differ in their amino acid sequence
from the putative SAR MTs found here [9]. In addition,
except for AuanMT2, known unclassified MTs from SAR
species (BbigMT, BlasMT, EsilMT, FvesMT and TpseMT)do
not possess the conserved 18AA pattern observed here
(figure 2), suggesting great differences even within SAR MTs.
MTs can contribute to the development of more efficient
HM-sensors. Whole-cell MT-based biosensors have been
developed in different microbes [40–42] and ciliates are cur-
rently considered as the most suitable candidates because of
the absence of cell wall [43,44]. However, testing the potential
of MTs from other microbes for the development of whole-
cell biosensors might yield some more efficient candidates.
Microalgae can be cultured autotrophically in simple sea-
water or freshwater enriched with basic nutrients, and
several green algae, diatoms, dinoflagellates and Eustigmato-
phyceae are commonly used for genetic editing. In particular,
lightly silicified diatoms, unarmoured dinoflagellates and
Chlorella spp. are known for their weak cell walls [45], and
cell wall-free mutants of Chlamydomonas spp. are currently
available (https://www.chlamycollection.org). Diatoms and
dinoflagellates typically dominate shallow benthic commu-
nities [46], including HM-contaminated sediments [47], and
might thus be suitable for the development of MT-based
sensors.
Bioinformatic mining of eukaryotic genomes and tran-
scriptomes thus contributed to predict putative MTs of 21
species, 19 of which derive from SAR representatives and
share an 18 amino acid-long cysteine-rich motif. The biologi-
cal function of these proteins remains to be experimentally
proven for a complete structural and functional in vivo charac-
terization, as well as for the quantification of MT expression
in polluted environments and in laboratory microcosms by
real-time PCR, and, finally, for the development of MT-based
biosensors. Furthermore, physiological assays of species tol-
erance to HMs can be combined with gene expression
determination to improve our understanding of microbe–
HM interactions.
Data accessibility. All of the GenBank and MMETSP IDs for the
sequences used in this study are included in the electronic sup-
plementary material, tables [48]. The electronic supplementary
material also includes the protein sequences used in these studies
and the same sequences aligned to identified conserved patterns.
Both files are available as fasta files.
Authors’contributions. S.B.: conceptualization, formal analysis, investi-
gation, writing—original draft and writing—review and editing;
A.S.: conceptualization, writing—original draft and writing—
review and editing.
Both authors gave final approval for publication and agreed to be
held accountable for the work performed therein.
Competing interests. We declare we have no competing interests.
Funding. We received no funding for this study.
Acknowledgements. The authors are grateful to M. Miralto and
L. Ambrosino (RIMAR, SZN) for their support in bioinformatic
data processing, and to G. Lanzotti (RIMAR, SZN) for graphical
assistance. Analyses were performed by using the SZN bioinfor-
matics server Falkor available at SZN (https://bioinfo.szn.it). The
authors received no financial support for the research and authorship
of this article.
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