The coronavirus SARS-CoV-2 can survive in wastewater for several days with a potential risk of waterborne human transmission, hence posing challenges in containing the virus and reducing its spread. Herein, we report on an active biohybrid microrobot system that offers highly efficient capture and removal of target virus from various aquatic media. The algae-based microrobot is fabricated by using click chemistry to functionalize microalgae with angiotensin-converting enzyme 2 (ACE2) receptor against the SARS-CoV-2 spike protein. The resulting ACE2-algae-robot displays fast (>100 μm/s) and long-lasting (>24 h) self-propulsion in diverse aquatic media including drinking water and river water, obviating the need for external fuels. Such movement of the ACE2-algae-robot offers effective "on-the-fly" removal of SARS-CoV-2 spike proteins and SARS-CoV-2 pseudovirus. Specifically, the active biohybrid microrobot results in 95% removal of viral spike protein and 89% removal of pseudovirus, significantly exceeding the control groups such as static ACE2-algae and bare algae. These results suggest considerable promise of biologically functionalized algae toward the removal of viruses and other environmental threats from wastewater.
The coronavirusSARS-CoV-2 can survive in wastewater for several days with a potential risk of waterborne human transmission, hence posing challenges in containing the virus and reducing its spread. Herein, we report on an active biohybrid microrobot system that offers highly efficient capture and removal of target virus from various aquatic media. The algae-based microrobot is fabricated by using click chemistry to functionalize microalgae with angiotensin-converting enzyme 2 (ACE2) receptor against the SARS-CoV-2spike protein. The resulting ACE2-algae-robot displays fast (>100 μm/s) and long-lasting (>24 h) self-propulsion in diverse aquatic media including drinking water and river water, obviating the need for external fuels. Such movement of the ACE2-algae-robot offers effective "on-the-fly" removal of SARS-CoV-2spike proteins and SARS-CoV-2 pseudovirus. Specifically, the active biohybrid microrobot results in 95% removal of viral spike protein and 89% removal of pseudovirus, significantly exceeding the control groups such as static ACE2-algae and bare algae. These results suggest considerable promise of biologically functionalized algae toward the removal of viruses and other environmental threats from wastewater.
As an emerging coronavirus associated with formidable infectiousness and lethality, severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread through multiple
transmission routes, including direct airborne transmission from respiratory droplets or
aerosols and indirect fomite transmission upon contacting contaminated subjects or
surfaces.[1] While SARS-CoV-2 is known to infect the respiratory tract,
it can also infect the gastrointestinal tract with a prolonged residence in fecal
samples.[2] In addition to its presence in stool samples, SARS-CoV-2 has
also been detected in domestic wastewater in sewage and downstream
rivers.[3−5] These reports have raised
concerns that wastewater could be a potential route of SARS-CoV-2 infection via fecal-oral
transmission.[6,7] To
minimize the risk of secondary transmission to humans, there remains an urgent need to
develop wastewater treatment strategies for the effective removal of SARS-CoV-2. Several
physical, chemical, and biological processes, including sedimentation, filtration,
disinfection with UV or oxidants, and enzymatic degradation, have been proposed to tackle
SARS-CoV-2 decontamination in wastewater.[8] While these conventional
wastewater purification approaches are effective in general, it is still necessary to
explore new techniques that are easy, fast, and effective in resolving the wastewater
contamination issue of SARS-CoV-2.We present here a biohybrid microrobot for the efficient removal of SARS-CoV-2 from
contaminated aquatic media. Because of their robust self-propulsion ability and facile
surface functionalization, microrobots offer a dynamic and powerful strategy for rapid
decontamination of water matrices from a wide range of environmental pollutants, including
dyes,[9,10] heavy
metals,[11,12]
oil,[13] pathogenic organisms,[14,15] nitroaromatic explosives,[16] and chemical
and biological warfare agents.[17] Such movement of functional microrobots
provides an enhanced collision/contact and adsorption of the target contaminants along with
localized self-mixing as compared to their static counterparts, thus enabling efficient and
rapid decomposition and accelerated “on-the-fly” removal of
pollutants.[18,19]
However, widespread environmental and defense applications of current synthetic microrobotic
platforms have been hampered by their short life span, need for toxic fuels, or complex
external actuation equipment, and restricted operating media. To address these challenges
faced by synthetic microrobots, biohybrid microrobots, combining self-propelled
microorganisms with functional biomaterials, have recently demonstrated significant promise
for large-scale environmental remediation.[20−22]The new microrobotic strategy for SARS-CoV-2 removal relies on angiotensin-converting
enzyme 2 (ACE2) receptor functionalized algae microrobot (denoted
“ACE2-algae-robot”). The ACE2 receptor is responsible for the recognition of
the target virus with a high binding affinity to the S1 subunit of the viral spike protein
and has been reported as an effective cellular receptor for SARS-CoV-2 toward diverse
virus-related sensing,[23] therapeutic,[24] and
neutralization[25] applications. Algae have been used for wastewater
treatment[26] but not in connection to active microrobots or toward the
management of SARS-CoV-2 contaminated water. Here we select Chlamydomonas
reinhardtii as a model algae because of their attractive properties, including
easy large-scale production, fast motion in diverse aqueous environments, long life span,
and facile surface functionalization.[27,28] As illustrated in Figure a,
the ACE2-algae-robot is fabricated using a click chemistry reaction for anchoring the ACE2
receptor onto the algae surface. The resulting ACE2-algae-robot displays fast movement
(>100 μm/s) in various media, without compromising the intrinsic mobility of
unmodified algae. Using SARS-CoV-2spike protein (S protein) and pseudovirus as model
contaminants, the moving ACE2 receptor on the algae surface leads to remarkable binding to
the targets, enabling about 95% removal of the S protein and about 89% removal of SARS-CoV-2
pseudovirus from various testing wastewater (Figure b). The pseudovirus bears the same spike protein as the live SARS-CoV-2 virus and
has been shown extremely useful for developing SARS-CoV-2 detection and neutralization
technologies.[29] These results clearly illustrate the feasibility of
using the biohybrid microrobot for large-scale “on-the-fly” decontamination of
coronavirus and possibly other diverse environmental threats in wastewater.
Figure 1
Fabrication and characterization of ACE2-functionalized algae microrobot (denoted
“ACE2-algae-robot”). (a) Schematic of the functionalization of microalgae
with ACE2 receptor. (b) Schematic depicting the use of the ACE2-algae-robot for the
binding and removal of spike protein and SARS-CoV-2 virus. (c) Brightfield, fluorescent,
and merged images of the ACE2-algae-robot after immunostaining. Autofluorescence of
natural algae chloroplast in Cy5 channel; immunostaining of Fluor488-conjugated
anti-ACE2 antibody in GFP channel. Scale bar: 50 μm. (d) Enlarged fluorescence
image from panel (c) clearly shows the full coverage of the ACE2 receptor onto the algae
surface. Red core: chloroplast of the algae; green: Fluor488-conjugated anti-ACE2
antibody. Scale bar: 5 μm. (e) GFP fluorescence intensity of bare algae, bare
algae with Fluor488-conjugated anti-ACE2 antibody, and the ACE2-algae-robot with
Fluor488-conjugated anti-ACE2 antibody. (f,g) Pseudocolored scanning electron microscopy
(SEM) images of the ACE2-algae-robot (f) before and (g) after contact with the virus.
Green: ACE2-algae-robot; red: SARS-CoV-2 pseudovirus. Scale bar: 2 μm. Zoom-in
images show the surface morphology of the ACE2-algae-robot (f) before and (g) after
contact with the virus. Scale bar: 200 nm.
Fabrication and characterization of ACE2-functionalized algae microrobot (denoted
“ACE2-algae-robot”). (a) Schematic of the functionalization of microalgae
with ACE2 receptor. (b) Schematic depicting the use of the ACE2-algae-robot for the
binding and removal of spike protein and SARS-CoV-2 virus. (c) Brightfield, fluorescent,
and merged images of the ACE2-algae-robot after immunostaining. Autofluorescence of
natural algae chloroplast in Cy5 channel; immunostaining of Fluor488-conjugated
anti-ACE2 antibody in GFP channel. Scale bar: 50 μm. (d) Enlarged fluorescence
image from panel (c) clearly shows the full coverage of the ACE2 receptor onto the algae
surface. Red core: chloroplast of the algae; green: Fluor488-conjugated anti-ACE2
antibody. Scale bar: 5 μm. (e) GFP fluorescence intensity of bare algae, bare
algae with Fluor488-conjugated anti-ACE2 antibody, and the ACE2-algae-robot with
Fluor488-conjugated anti-ACE2 antibody. (f,g) Pseudocolored scanning electron microscopy
(SEM) images of the ACE2-algae-robot (f) before and (g) after contact with the virus.
Green: ACE2-algae-robot; red: SARS-CoV-2 pseudovirus. Scale bar: 2 μm. Zoom-in
images show the surface morphology of the ACE2-algae-robot (f) before and (g) after
contact with the virus. Scale bar: 200 nm.
Results and Discussion
Fabrication and Characterization of the ACE2-Algae-Robots
Figure schematically displays (a) the
fabrication process of the ACE2 modified algae and (b) the targeting and removal of S
protein and SARS-CoV-2 pseudovirus from wastewater. In the study, the ACE2 receptors were
conjugated to the algae surface using a click chemistry approach. Specifically, the azide
and dibenzocyclooctyne (DBCO) groups were conjugated to the ACE2 receptors and the algae,
respectively, via the N-hydroxysuccinimide (NHS) ester reaction. The
conjugated N3 on the ACE2 receptors will then react effectively with the DBCO
on the algae, resulting in the formation of the ACE2-algae-robot. Next, immunostaining was
performed to visualize the ACE2 receptors attached on the algae surface. Here,
Fluor488-conjugated anti-ACE2 antibody was used to label the ACE2 receptors on the algae.
As illustrated in Figure c, the signals from
immunostaining of fluorescent anti-ACE2 antibody in the GFP channel colocalized with those
of the autofluorescence of algae in the Cy5 channel. The enlarged image in Figure d further indicates the coverage of ACE2
receptors on the algae surface. In addition, the unmodified algae incubated with
fluorescent anti-ACE2 antibody exhibited a negligible change of GFP fluorescence intensity
as compared to the bare algae alone (Figure e
and Figure S1). In contrast, the ACE2-algae-robot incubated with fluorescent
anti-ACE2 antibody showed a significant increase in fluorescent intensity as compared to
the two control groups, reflecting the effective conjugation of ACE2 receptors onto the
algae via click chemistry. The pseudocolored scanning electron microscopy (SEM) images
further illustrate the ACE2-algae-robot before (Figure f and Figure S2i) and after (Figure g
and Figure S2ii) contact with SARS-CoV-2 pseudovirus. The bound virus particles
can be clearly observed on the ACE2-algae-robot from the inset of SEM images in Figure g.
Motion Behavior of ACE2-Algae-Robots in Aquatic Media
We next investigated the motion behavior of the biohybrid microrobot. After confirming
the DBCO-NHS ester conjugation (Figure S3), the ACE2-algae-robot was fabricated by mixing DBCO modified
algae with the azide-ACE2 receptor in DI water for 2 h. The speed of the DBCO-modified
algae and the ACE2-algae-robot were measured to be 112 μm/s and 108 μm/s
(∼11 body length/s), respectively, compared to 115 μm/s of the bare algae,
indicating that the functionalization process has a negligible effect on the motion of the
algae (Figure a and Video S1). Once prepared in DI water, the ACE2-algae-robot was transferred
into various aqueous media to test their mobility. As illustrated in Figure b and Video S2, the ACE2-algae-robot displayed efficient motion (>100
μm/s) in TAP medium, 0.1 × PBS, drinking water, and river water without a need
of any external fuel to propel the robot. In addition, the ACE2-algae-robot demonstrated
long-lasting motion in both drinking and river water matrices (Figure
c,d and Video S3), indicating its capability for extended operation toward the
removal of SARS-CoV-2S protein and pseudovirus. The images in Figure
e,f (along with Videos S4 and S5) illustrate representative tracking trajectories of the individual
ACE2-algae-robot, over a 2.5 s interval, at different times [0 h (i) and 24 h (ii)] in
drinking and river water, respectively, reflecting the highly stable algae motion in these
media.
Figure 2
Motion behavior of the ACE2-algae-robot. (a) Effect of the algae functionalization
upon its swimming behavior: speeds of bare algae, DBCO-modified algae, and the
ACE2-algae-robot (n = 6; mean + s.d.). (b) Movement of the
ACE2-algae-robot in different media: tris-acetate-phosphate medium (TAP), 0.1 ×
phosphate-buffered saline (PBS; diluted from 1 × PBS with DI water), drinking
water, and river water (n = 6; mean + s.d.). (c,d) Speed of the
ACE2-algae-robot at different time points (0, 1, 4, 12, and 24 h) in (c) drinking
water and (d) river water (n = 6; mean ± s.d.). (e,f)
Representative optical trajectories of the movement of the ACE2-algae-robot over 2.5 s
motion in (e) drinking water and (f) river water obtained at (i) 0 h and (ii) 24 h.
Scale bar: 40 μm.
Motion behavior of the ACE2-algae-robot. (a) Effect of the algae functionalization
upon its swimming behavior: speeds of bare algae, DBCO-modified algae, and the
ACE2-algae-robot (n = 6; mean + s.d.). (b) Movement of the
ACE2-algae-robot in different media: tris-acetate-phosphate medium (TAP), 0.1 ×
phosphate-buffered saline (PBS; diluted from 1 × PBS with DI water), drinking
water, and river water (n = 6; mean + s.d.). (c,d) Speed of the
ACE2-algae-robot at different time points (0, 1, 4, 12, and 24 h) in (c) drinking
water and (d) river water (n = 6; mean ± s.d.). (e,f)
Representative optical trajectories of the movement of the ACE2-algae-robot over 2.5 s
motion in (e) drinking water and (f) river water obtained at (i) 0 h and (ii) 24 h.
Scale bar: 40 μm.
Removal of SARS-CoV-2 Spike Protein by ACE2-Algae-Robots
Figure a illustrates that the presence of viral
S protein in the drinking water (red) and river water (blue) did not affect the mobility
or the lifespan of the ACE2-algae-robot, which showed highly stable motion in both media
for over 24 h operation. This was supported by the tracking trajectories of about 200
individual ACE2-algae-robots (during 0.6 s) after moving for 24 h in drinking water and
river water, respectively (Figure b,c). The
corresponding speed distribution indicates that 80% of algae moved faster than 100
μm/s (Figure S4). Such fast and continuous motion of the ACE2-algae-robot could
accelerate its collision with viral S protein and thus improve the specific binding and
removal of the target protein. Figure d examines
the effect of ACE2-algae-robot density on the kinetic removal efficiency of S protein from
drinking water. As expected, the speed and efficiency of the removal process increased
upon increasing the density of the microrobots, with 5 × 107
ml–1 ACE2-algae-robot removing 95% of 2.88 ng/mL S protein from the
water sample within 6 h. To compare the S protein removal capability between different
algae groups, the same density of algae (5 × 107 ml–1)
from different control groups, including the ACE2-algae-robot, static ACE2-algae
(deflagellated algae with the ACE2 receptor modification), bare algae, and cell wall
deficient algae, was added to 500 μL drinking water containing 2.88 ng/mL S protein.
The ACE2-algae-robot shows highly efficient binding and 95% removal efficiency after 6 h
continuous motion (Figure e,(i). In comparison,
the active bare algae lacking ACE2 (Figure e,(ii) and static ACE2-algae (Figure e,(iii) displayed only 46% and 23% removal efficiency after 16 h operation,
respectively, indicating the critical role of the ACE2 receptor modification and algae
motion on the speed and efficiency of the S protein removal. The S protein removal by the
bare algae is likely attributed to nonspecific binding associated with the presence of
diverse functional groups (e.g., carboxyl or amino groups) on the algae surface.[30] The images shown in Figure S5 represent a homogeneous mixture of the ACE2-algae-robot and S
protein after 6 h incubation compared to clear sediment of static ACE2-algae after 6 h
incubation. These results explain further the fast and efficient removal using the
ACE2-algae-robot in Figure e. The
ACE2-algae-robot also exhibits effective S protein removal in various media, including DI
water, drinking water, and river water, as indicated by the similar kinetic profiles in
Figure f. These results reveal that the
ACE2-algae-robot, with long-lasting motion and ACE2 receptor for S protein recognition,
represents an attractive system to enhance environmental remediation in complex aqueous
surroundings.
Figure 3
Use of the ACE2-algae-robot for the removal of SARS-CoV-2 spike protein. (a) Speed
comparison of the ACE2-algae-robot in drinking water (red) and river water (blue)
containing the spike protein (n = 6; mean ± s.d.). (b,c) Motion
trajectory of the ACE2-algae-robot in (b) drinking water and (c) river water samples
containing the spike protein. (d) Effect of the ACE2-algae-robot density on the spike
protein removal kinetic profile in the drinking water. Algae input density: (i) 5
× 107 ml–1, (ii) 2 × 107
ml–1, and (iii) 5 × 106 mL–1
(n = 3; mean ± s.d.). (e) Kinetic profile of the spike protein
removal efficiency from drinking water with the treatment of the (i) ACE2-algae-robot,
(ii) bare algae, (iii) static ACE2-algae, or (iv) cell wall deficient algae. (f)
Kinetic profile of the spike protein removal efficiency by employing the
ACE2-algae-robot in different media, including drinking water, DI water, and river
water (n = 3; mean ± s.d.).
Use of the ACE2-algae-robot for the removal of SARS-CoV-2spike protein. (a) Speed
comparison of the ACE2-algae-robot in drinking water (red) and river water (blue)
containing the spike protein (n = 6; mean ± s.d.). (b,c) Motion
trajectory of the ACE2-algae-robot in (b) drinking water and (c) river water samples
containing the spike protein. (d) Effect of the ACE2-algae-robot density on the spike
protein removal kinetic profile in the drinking water. Algae input density: (i) 5
× 107 ml–1, (ii) 2 × 107
ml–1, and (iii) 5 × 106 mL–1
(n = 3; mean ± s.d.). (e) Kinetic profile of the spike protein
removal efficiency from drinking water with the treatment of the (i) ACE2-algae-robot,
(ii) bare algae, (iii) static ACE2-algae, or (iv) cell wall deficient algae. (f)
Kinetic profile of the spike protein removal efficiency by employing the
ACE2-algae-robot in different media, including drinking water, DI water, and river
water (n = 3; mean ± s.d.).
Removal of SARS-CoV-2 Pseudovirus by ACE2-Algae-Robots
Following the effective removal of viral S protein, the next set of experiments examined
the ability of the ACE2-algae-robot to remove SARS-CoV-2 pseudoviruses, which represents
an effective alternative of live humanSARS-CoV-2 virus for research to evaluate new
antiviral technologies.[29]Figure a schematically illustrates the efficient
removal of the SARS-CoV-2 pseudovirus from water samples using the ACE2-algae-robot. After
ACE2-algae-robot treatment, the pseudoviruses are largely captured and removed from the
solution, validated, and visualized by an NL-20 cell-based assay. The NL-20 cells remained
nonfluorescent following 24 h incubation in pseudovirus contaminated water samples upon
ACE2-algae-robot treatment, confirming the highly effective pseudovirus removal by the
robot. In comparison, without the ACE2-algae-robot treatment, the cells displayed bright
fluorescence, reflecting the pseudovirus entry and significant expression of fluorescent
protein in the host cells. Figure b and
S6 show the motion behavior (speed and lifespan) of the ACE2-algae-robot in
various water matrices containing the SARS-CoV-2 pseudovirus. The biohybrid microrobot
displays similar speed in these aqueous media, which is similar to that observed in Figure b without the pseudovirus, indicating that
the presence of the pseudovirus does not hamper the movement of the ACE2-algae-robot.
Figure 4
Use of the ACE2-algae-robot for the removal of SARS-CoV-2 pseudovirus. (a) Schematic
illustrating the viral removal by the ACE2-algae-robot, which blocks the virus from
entering cells. Inset: viral infection was indicated by expression of green
fluorescent protein, whereas the virus treated by the ACE2-algae-robot shows low viral
infection. (b) Speed comparison of the ACE2-algae-robot in TAP, DI water, drinking
water, and river water containing 1 × 108 VG of the virus,
(n = 3; mean ± s.d.). (c) Effect of the ACE2-algae-robot
density on the efficiency of virus removal in drinking water. (d) Representative
fluorescent images of NL-20 cells infected with virus treated by different densities
of the ACE2-algae-robot. Scale bar: 100 μm. (e) Efficiency of the virus removal
in drinking water with the treatment of the ACE2-algae-robot, bare algae, static
ACE2-algae, or cell wall deficient algae. (f) Representative fluorescent images of
NL-20 cells infected with the virus treated by the ACE2-algae-robot, bare algae,
static ACE2-algae, and cell wall deficient algae. Scale bar: 100 μm. (g) Kinetic
profile of the virus removal efficiency in drinking water with the treatment of the
ACE2-algae-robot, bare algae, and static ACE2-algae (n = 3; mean
± s.d.). (h) Representative fluorescent images of NL-20 cells infected with the
virus, which is treated by the ACE2-algae-robot for different times (0, 1, 2, and 4
h). Scale bar: 100 μm.
Use of the ACE2-algae-robot for the removal of SARS-CoV-2 pseudovirus. (a) Schematic
illustrating the viral removal by the ACE2-algae-robot, which blocks the virus from
entering cells. Inset: viral infection was indicated by expression of green
fluorescent protein, whereas the virus treated by the ACE2-algae-robot shows low viral
infection. (b) Speed comparison of the ACE2-algae-robot in TAP, DI water, drinking
water, and river water containing 1 × 108 VG of the virus,
(n = 3; mean ± s.d.). (c) Effect of the ACE2-algae-robot
density on the efficiency of virus removal in drinking water. (d) Representative
fluorescent images of NL-20 cells infected with virus treated by different densities
of the ACE2-algae-robot. Scale bar: 100 μm. (e) Efficiency of the virus removal
in drinking water with the treatment of the ACE2-algae-robot, bare algae, static
ACE2-algae, or cell wall deficient algae. (f) Representative fluorescent images of
NL-20 cells infected with the virus treated by the ACE2-algae-robot, bare algae,
static ACE2-algae, and cell wall deficient algae. Scale bar: 100 μm. (g) Kinetic
profile of the virus removal efficiency in drinking water with the treatment of the
ACE2-algae-robot, bare algae, and static ACE2-algae (n = 3; mean
± s.d.). (h) Representative fluorescent images of NL-20 cells infected with the
virus, which is treated by the ACE2-algae-robot for different times (0, 1, 2, and 4
h). Scale bar: 100 μm.Next, we investigated the effect of ACE2-algae-robot density on the removal efficiency of
SARS-CoV-2 pseudovirus from drinking water. As shown in Figure c, the results demonstrated a gradual increase of the removal
efficiency, 19% to 90%, upon increasing ACE2-algae-robot concentrations from 5 ×
104 mL–1 to 5 × 107 mL–1.
The pseudovirus removal efficiency was also visualized by fluorescent imaging. As
expected, the highest robot concentration resulted in a minimal fluorescent virus signal
(Figure d and S7). The ACE2-algae-robot also offers significant improvement (89%) in the
removal of SARS-CoV-2 pseudovirus compared with control groups, including static
ACE2-algae (31%), bare algae (63%), and cell-wall deficient algae (21%) (Figure e). These experiments were performed by immersing 2.5
× 105 of algae in a 50 μL drinking water sample containing 2 ×
109 VG ml–1 (VG: viral genomes) pseudovirus for 16 h. In
the control experiments, the viral binding capability of the bare algae is possibly
attributed to both biosorption[30] and physical entrapment of the virus
into the porous structure of the algae cell wall.[31] The fluorescence
images in Figure f and Figure S8 illustrate the minimal fluorescent virus signal with the
ACE2-algae-robot treatment, corresponding to the virus removal efficiency data of Figure e. Furthermore, we evaluated and compared the
virus removal kinetic profiles in drinking water treated with the ACE2-algae-robot, bare
algae, or static ACE2-algae (Figure g). These
data illustrate that the ACE2-algae-robot can effectively remove 85% of pseudovirus after
4 h treatment, compared to 60% and 18% for bare algae and static ACE2-algae, respectively.
Therefore, each ACE2-algae-robot can bind to ∼340 VG of virus. Such results
indicate the significant contributions of both the ACE2 receptor for virus targeting and
the fast microrobot motion for contacting the virus toward highly efficient viral binding
and removal. The representative fluorescence images also show the progression of the viral
infection over time with samples treated with the ACE2-algae-robot (Figure h and Figure S9). Furthermore, the ACE2-algae-robot can be reused to reach 90%
viral removal efficiency after five repeated cycles (Figure S10). A post-treatment of the ACE2-algae-robot by flocculant was
performed to clean the robot in the water sample (Figure S11). The flocculant can isolate the ACE2-algae-robot from the water
matrices without affecting the virus removal efficiency, holding considerable potential
for practical future applications.
Conclusions
In summary, we have developed an effective biohybrid microrobotic method to actively remove
the SARS-CoV-2spike protein and pseudovirus from various water matrices using ACE2
receptor-modified microalgae. The ACE2-algae-robot can be readily fabricated by an efficient
click chemistry approach, without compromising the motion behavior of algae and the function
of the ACE2 receptor. The resulting ACE2-algae-robot displays excellent motion ability in
various water matrices and offers considerable potential to clean contaminated water
samples. The binding and removal capability of the ACE2-algae-robot were demonstrated and
characterized using both SARS-CoV-2spike protein and SARS-CoV-2 pseudovirus, resulting in
95% and 89% removal efficiency, respectively, at the experimental conditions. Given the high
concentration of viral load (2 × 109 VG ml–1) in our
experiment, the removal efficiency of 89% and binding capability of ∼340 VG
algae–1 represent a high efficacy compared to the traditional wastewater
viral treatments.[32] The enhanced removal of the viral spike protein and
pseudovirus is attributed to the continuous self-propulsion of the ACE2-algae-robot and
corresponding mixing and collision with the target protein and virus. The reusability and
post-treatment of the microrobot were also studied, demonstrating considerable promise for
removing waterborne pathogenic viruses from contaminated water. Although current platform
can reach 89% viral removal, it is difficult to directly convert viral concentration in
wastewater to disease prevalence in hosts.[33] However, any reduction of
viral load would have a positive correlation with the control of viral infectivity and the
illness of patients.[34] The algae microrobots are expected to have greater
mobility and longer lifetime in an open wastewater reservoir when compared to a confined
test tube and thus hold considerable potential for future scaling-up applications. Given
that new emerging SARS-CoV-2 variants show a similar binding mechanism and higher binding
affinity with the ACE2 receptor,[35,36] the functionalized algae microrobotic platform is expected to
efficiently remove different virus variants from wastewater. Overall, by relying on click
chemistry to attach protein receptors onto natural algae surfaces, such a functionalized
algae-based microrobot offers an attractive strategy for a variety of environmental
remediation applications.
Authors: Anum Glasgow; Jeff Glasgow; Daniel Limonta; Paige Solomon; Irene Lui; Yang Zhang; Matthew A Nix; Nicholas J Rettko; Shoshana Zha; Rachel Yamin; Kevin Kao; Oren S Rosenberg; Jeffrey V Ravetch; Arun P Wiita; Kevin K Leung; Shion A Lim; Xin X Zhou; Tom C Hobman; Tanja Kortemme; James A Wells Journal: Proc Natl Acad Sci U S A Date: 2020-10-22 Impact factor: 11.205
Authors: Milad Mousazadeh; Işık Kabdaşlı; Sara Khademi; Miguel Angel Sandoval; Seyedeh Parvin Moussavi; Fatemeh Malekdar; Vishakha Gilhotra; Marjan Hashemi; Mohammad Hadi Dehghani Journal: J Water Process Eng Date: 2022-08-17