Genome-Wide Characterization of SARS-CoV-2 Cytopathogenic Proteins in the Search of Antiviral Targets

ABSTRACT Therapeutic inhibition of critical viral functions is important for curtailing coronavirus disease 2019 (COVID-19). We sought to identify antiviral targets through the genome-wide characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins that are crucial for viral pathogenesis and that cause harmful cytopathogenic effects. All 29 viral proteins were tested in a fission yeast cell-based system using inducible gene expression. Twelve proteins, including eight nonstructural proteins (NSP1, NSP3, NSP4, NSP5, NSP6, NSP13, NSP14, and NSP15) and four accessory proteins (ORF3a, ORF6, ORF7a, and ORF7b), were identified that altered cellular proliferation and integrity and induced cell death. Cell death correlated with the activation of cellular oxidative stress. Of the 12 proteins, ORF3a was chosen for further study in mammalian cells because it plays an important role in viral pathogenesis and its activities are linked to lung tissue damage and a cytokine storm. In human pulmonary and kidney epithelial cells, ORF3a induced cellular oxidative stress associated with apoptosis and necrosis and caused activation of proinflammatory response with production of the cytokines tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), and IFN-β1, possibly through the activation of nuclear factor kappa B (NF-κB). To further characterize the mechanism, we tested a natural ORF3a Beta variant, Q57H, and a mutant with deletion of the highly conserved residue, ΔG188. Compared with wild-type ORF3a, the ΔG188 variant yielded more robust activation of cellular oxidative stress, cell death, and innate immune response. Since cellular oxidative stress and inflammation contribute to cell death and tissue damage linked to the severity of COVID-19, our findings suggest that ORF3a is a promising, novel therapeutic target against COVID-19.

KEYWORDS SARS-CoV-2, ORF3a, viral therapeutic target, fission yeast, apoptosis and necrosis, oxidative stress, proinflammatory response, Schizosaccharomyces pombe T he ongoing pandemic of coronavirus disease 2019 , caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is unprecedented in its rapid spread, persistence, and high fatality. Although current vaccines are effective at preventing SARS-CoV-2 infection, breakthrough infections are not uncommon. Moreover, the emergence of new viral variants could undermine the effectiveness of current vaccines, as well as treatments. For example, of three approved monoclonal antibody treatments, only sotrovimab is effective against the Omicron variant (1). Antiviral drugs are an alternative, potentially effective means to treat SARS-CoV-2 infection and curtail COVID-19 disease, but currently such drugs are limited. The discovery of new antiviral agents is hampered by limited knowledge of the pathogenicity of the virus and of which viral protein(s) should be targeted. There is an urgent need to identify the viral proteins that cause harmful cytopathic effects, tissue damage, and cytokine storm, in order to advance the rational design of anti-COVID-19 therapies.
We carried out a genome-wide functional screening of SARS-CoV-2 proteins to identify viral targets that can be used for antiviral drug discovery and testing. We reasoned that SARS-CoV-2 protein therapeutic targets (i) should be essential for virus survival, (ii) should contribute to virus pathogenesis, (iii) should confer measurable cytopathic effects that could be used as endpoints for high-throughput drug screening, and (iv) could be either an established or a novel antiviral drug target. We adapted a unique integrated approach, viz., a well-established fission yeast cell-based system to conduct genomewide and functional analyses of the viral proteins (5,6), with the goal of identifying proteins that cause harmful cytopathic effects. Fission yeast (Schizosaccharomyces pombe) is a haploid single-cell eukaryotic organism that has been used extensively as a model to study human cancer biology (7) and viruses (5,6,8,9). Fission yeast is a well-tested model system for the study of highly conserved cellular activities (10,11), such as the cytopathic effects caused by viral proteins, including inhibition of cell proliferation, interruption of cell structural integrity, and induction of apoptosis and necrosis (8). Fission yeast cell-based systems also have been shown to be suitable for antiviral drug testing and drug discovery (5,12).
A total of 29 viral ORFs (4) representing the entire viral genome were cloned into the fission yeast gene expression system (6,13). The expression of each viral ORF was under the control of an inducible no message in thiamine (nmt1) promoter (13), allowing all SARS-CoV-2-specific viral protein effects to be measured specifically by gene induction versus those without gene induction (6,8). Using this unique integrated strategy, we report the identification of multiple possible viral therapeutic targets with a detailed description, as a proof-of-concept, of ORF3a. We demonstrate the potential of ORF3a to serve as a therapeutic target for the discovery and testing of antiviral drugs against COVID-19.

RESULTS
Identification of SARS-CoV-2 cytopathic proteins that prevent cellular proliferation and colony formation of fission yeast. A total of 29 viral ORFs representing all viral proteins produced by the SARS-CoV-2 genome were expressed using inducible nmt1 promoter-mediated transcription in a wild-type fission yeast strain, SP223. Fission yeast cells expressing an empty cloning plasmid, pYZ1N, were used as a cloning vector control (Ctr). The effect of the SARS-CoV-2 protein expression on the ability of fission yeast to form colonies, an indication of cell proliferation, was measured on minimal selective agar plates (12,13). All cells with viral genes repressed (gene-off) formed normal-sized colonies on the agar plates ( Fig. 1A; see Fig. S2A in the supplemental material). In contrast, 8 nonstructural viral protein-producing fission yeast cells (NSP1, NSP3, NSP4, NSP5, NSP6, NSP13, NSP14, and NSP15) and 4 accessory proteins (ORF3a, ORF6, ORF7a, and ORF7b) showed either complete inhibition or near-complete inhibition of yeast colony formation on minimal selective agar plates (Fig. 1A). In each case, either no colony or very small colonies were observed under viral gene-inducing conditions, FIG 1 Effect of SARS-CoV-2 expression on fission yeast colony formation (A), cellular growth (B), and summary of the relative cellular growth (15%, red; 25% blue) of each of the SARS-CoV-2 protein-expressing cells (C). The name of each SARS-CoV-2 protein is labeled above each agar plate. An empty pYZ1N vector (Ctr) was used as a control. Gene-off, no SARS-CoV-2 protein production; gene-on, the specific SARS-CoV-2 protein production was induced by triggering the nmt1 promoter-mediated gene transcription. Fission yeast colony formation was measured by growing SARS-CoV-2 protein-expressing fission yeast cells on the selective EMM agar plates and incubated at 30°C for 3 to 5 days before the pictures were taken. The cell proliferation analysis was carried out by comparing cellular growth between the SARS-CoV-2 protein-producing cells and the SARS-CoV-2 proteinsuppressing cells over time. Cell growth was measured by spectrophotometry (OD 650 ). Only the effect of those SARS-CoV-2 proteins that showed complete (NSP1, NSP4, NSP6, NSP13, NSP14, ORF3a, ORF6, and ORF7a) or nearly complete (NSP3, NSP5, and NSP15) inhibition of yeast colony formation is shown in A and thereafter. Complete data on cell proliferation are included in Fig. S2. Each experiment was repeated at least three times, and the standard errors of each time point were calculated.
Characterization of SARS-CoV-2 Cytopathogenic Proteins ® consistent with cellular growth inhibition. With the Ctr control, normal size colonies were observed on both gene-suppressing (gene-off) and gene-inducing (gene-on) plates, confirming the specificity of the viral protein effect observed. We named the 12 viral proteins the "cytopathic proteins." The production of the remaining 18 SARS-CoV-2 proteins showed either no effect or a reduced effect on the fission yeast colony formation (Fig. S2A).
To verify the effect of the 12 cytopathic proteins on cell proliferation, we measured the growth kinetics of the SARS-CoV-2-carrying fission yeast cells. Fission yeast cells were grown under the gene-off and gene-on conditions in the liquid minimal and selective Edinburgh minimal medium (EMM). Cellular growth was measured by the change in cell concentration over 48 h after gene induction (agi) using optical density at 650 nm (OD 650 )-based measurements. In the Vec-only control cultures, two indistinguishable growth curves with typical logarithmic kinetics were observed in the gene-off and gene-on pYZ1N-carrying yeast cells. In contrast, expression of NSP1, NSP4, NSP6, NSP13, ORF3a, ORF6, or ORF7a completely blocked cellular growth, whereas expression of NSP3, NSP5, NSP14, NSP15, or ORF7b significantly suppressed cellular growth (Fig. 1B). During the first 24 h agi, both the SARS-CoV-2 gene-off and gene-on cells grew at about the same rate, consistent with 16 h being required to fully produce a protein after the induction of genes under the control of the nmt1 promoter (14). However, thereafter, the growth of cells expressing the 12 viral proteins stopped or slowed compared with that of cells without viral gene expression. The relative cellular growth of each of the SARS-CoV-2 protein-expressing cells are summarized in Fig. 1C. Consistent with the data on colony formation, the production of the remaining 18 SARS-CoV-2 proteins showed either no effect or a reduced effect on fission yeast cellular growth (Fig. S2B).
SARS-CoV-2 cytopathic proteins induce fission yeast cell morphologic changes and hypertrophy. Changes in cell morphology, such as hypertrophy, are linked to SARS-CoV-2-induced cytopathic effects in human airway epithelial cells and myocytes (15). To determine whether the SARS-CoV-2 cytopathic proteins affect cell morphogenesis, we compared microscopic cell morphologies between fission yeast cells with or without viral protein production. Control cells containing the Vec-carrying plasmid appeared normal under both gene-suppressing and gene-inducing conditions. Cells were typically rod shaped with diameters of 3 to 4 mM and lengths of 7 to 14 mM. They were shiny on the edges, which is a sign of healthy cells ( Fig. 2A) (10). Normal cell morphologies also were observed in cells that carried the 12 cytopathic protein-encoding plasmids under the gene-suppressing conditions. In contrast, the expression of the 12 SARS-CoV-2 cytopathic proteins under the gene-inducing conditions resulted in various degrees of hypertrophic morphologies ( Fig. 2A, bottom rows of each panel). The common features of the abnormal cell morphologies included cell elongation and enlargement. We further analyzed the overall changes in cell morphology caused by the 12 cytopathic proteins. Both the forward-scattered light (FSC) and side-scattered light (SSC) were measured for each cell population of 10,000 cells using flow cytometric analysis (Fig. 2B). The FSC is relative to the cell surface area and thus measures cell size. The SSC is related to cell granularity and thus determines intracellular complexity. The combined analyses of FSC and SSC provide the overall architectures of cell shapes in a heterogeneous cell population. As shown in Fig. 2B, there were significant differences in the cells' overall architectures between the gene-off and gene-on cultures of cells with cytopathic proteins. In contrast, there were no clear differences between the gene-inducing and gene-repressing Vec-carrying fission yeast cells, suggesting the observed morphological changes were specific to SARS-CoV-2 proteins. The production of all other SARS-CoV-2 proteins did not induce clear morphologic changes (see Fig. S3A in the supplemental material) or cellular hypertrophy (Fig. S3B). Together, our observations suggest that the 12 cytopathic proteins cause various degrees of cellular hypertrophy.
Correlation of SARS-COV-2 protein-induced cell death and induction of cellular oxidative stress in fission yeast. Since all 12 cytopathic proteins reduce or prevent cell proliferation ( Fig. 1) and cause cellular morphologic changes (Fig. 2), these effects could be lethal. We tested whether the cytopathic proteins cause cell death. The inducible production of each viral protein was carried out as described above. Forty-eight hours agi, cell death was detected by trypan blue, a vital diazo dye that specifically detects dead cells (6,8). As shown in Fig. 3A, all cytopathic proteins induced cell death in the range of 10% to 60% (Fig. 3C, blue bars). In contrast, most of the other SARS-CoV-2 proteins did not kill cells or the extent of cell killings is not as strong as those of the 12 cytopathic proteins (see Fig. S4A in the supplemental material).
We explored the mechanism of cell death caused by the cytopathic proteins. Because a cellular oxidative stress response can trigger cell death and is known to play an important role in COVID-19 (16)(17)(18), we tested for intracellular oxidative stress by measuring the production of reactive oxygen species (ROS). A ROS-specific dye, dihydroethidium (DHE), that produces red fluorescence in the presence of ROS, was used to measure the presence of ROS 48 h agi (Fig. 3B). Except NSP14, other cytopathic proteins triggered the induction of ROS as quantified and shown by the red bars (Fig. 3C). These observations suggest that cell death induced by the cytopathic proteins, with the exception of NSP14, was at attributable least partially to the induction of intracellular oxidative stress. Note that 2 NSPs (NSP2 and NSP16) and 3 structural proteins (E, M, and S) also induced ROS production (Fig. S4B). However, the levels of the cell killings by these proteins were below the cutoff as shown by the 12 cytopathic proteins (Fig. 1C). Thus, these proteins are excluded from discussion. Characterization of SARS-CoV-2 Cytopathogenic Proteins ® ORF3a-induced apoptosis and necrosis are associated with the activation of mammalian cellular oxidative stress and immune proinflammatory responses. Of the identified cytopathic proteins, ORF3a was chosen for further study because it plays an important role in viral pathogenesis and its activities are linked to lung tissue damage and a cytokine storm in COVID-19 (19,20). Since we found that ORF3a-induced cell death was associated with the induction of cellular oxidative stress in fission yeast (Fig. 3), we tested whether a similar ORF3a effect is produced in mammalian cells, including human pulmonary epithelial A549 and Calu-3, as well as kidney epithelial 293T cell lines. After transfection of the ORF3a-carrying lentiviral pLVX451 EF1alpha- IRES-Puro plasmid vector into A549 and 293T cells, cell growth over time was measured. Cell viability and cell death were evaluated by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) method and trypan blue exclusion. As shown in Fig. 4A and B, the expression of ORF3a in either the A549 or the 293T cells significantly reduced cell growth, decreased cell viability, and increased cell death in comparison with the mock and the vector controls. Possible effects of ORF3a-induced cell death via apoptosis or necrosis were evaluated by a RealTime Glo annexin V apoptosis and necrosis assay. As shown in Fig. 4C, a to b, ORF3a-induced cell death involved both apoptosis and necrosis. ORF3a-induced apoptosis was mediated at least in part through the cleavage of caspase-3 ( Fig. 4C, a to c). In addition, the expression of ORF3a triggered a cellular oxidative stress response, as measured by DHE staining (Fig. 4D). Our results suggest that ORF3a-induced apoptosis and necrosis in mammalian cells are associated at least in part with the induction of a cellular oxidative stress response.
To test whether the induction of apoptosis and necrosis by ORF3a is related to a host cellular immune response, we first measured a well-recognized master regulator, nuclear factor kappa B (NF-k B), that mediates cellular proinflammatory responses to viral infection (21). A NF-k B luciferase (luc) reporter plasmid system (Stratagene, La Jolla, CA) was used to measure NF-k B-mediated transcriptional activities. The plasmid contains a firefly luc gene that is driven by 5-kb NF-k B binding and activating elements located upstream of the minimal TATA promoter. Upon the activation by proinflammatory cytokines, endogenous NF-k B transcription factors bind to the DNA response elements, inducing transcription of the luc reporter gene. Thus, the production of Luc indicates the activation of the NF-k B pathways. As shown in Fig. 4E, expression of ORF3a markedly elevated NF-k B-mediated transcriptional activities over time. Next, we measured the production of the cytokines, tumor necrosis factor alpha (TNF-a), interleukin-6 (IL-6), and their regulator NF-k B by quantitative reverse transcriptase PCR (qRT-PCR). The mRNA levels of each gene target were measured over time (Fig. 4F). The gene expression of both TNF-a and IL-6 increased moderately in Calu-3 and 293T cells at 72 h posttransfection (hpt). The level of NF-k B also increased slightly over time. These data suggest that ORF3a-induced apoptosis and necrosis could also contribute to the activation of a cellular proinflammatory response.
ORF3a-induced apoptosis and necrosis are affected by natural and artificial gene mutations. To further elucidate the molecular mechanism underlying ORF3ainduced apoptosis and necrosis (Fig. 5), we tested the effect of ORF3a mutation on the induction of apoptosis and necrosis. A natural mutant variant (Q57H) that is associated with the ongoing Beta variant and deletion of a highly conserved glycine residue (DG188) were tested. The DG188 mutant was chosen because the amino acid (aa) 188 is highly conserved among sarbecoviruses. G188 may be structurally important as it is one of the two glycine resides that separate two antiparallel b4 and b5 sheets, and both residues are in the proximity of two homodimers of ORF3a and the free C-terminal end of the protein (22). The same methods as described above (Fig. 4) were used to measure cell growth, cell viability, apoptosis, and necrosis with the ORF3a mutants. Compared with wild-type ORF3a, the DG188 mutant showed significant differences in its effects, including markedly reduced cellular growth and viability and increased cell death over time (Fig. 5A, a to c, red bars). In contrast, the Q57H mutant showed slightly improved cellular growth and viability and reduced cell death (Fig. 5A, a to c, blue bars). Compared with wild-type ORF3a, the DG188 mutant markedly increased apoptosis and necrosis, whereas the Q57H mutant showed slightly reduced apoptosis and necrosis. Consistent with the idea that ORF3a-induced cell death is mediated through the induction of cellular oxidative stress, a positive correlation also was seen between the wild-type and mutant ORF3a in the production of ROS (Fig. 5C, a to b). Based on these observations, ORF3a-induced apoptosis and necrosis are likely mediated through the induction of cellular oxidative stress, and the overall structure of the ORF3a protein appears to be important for the induction of cell death.   ORF3a-induced apoptosis and necrosis are correlated with the level of cellular proinflammatory response. Since ORF3a-induced cell death is associated with the activation of host cell proinflammatory response (Fig. 4E and F), we tested whether the level of ORF3a-induced cell death is correlated with the NF-k B-mediated transcriptional activities or the level of the cellular proinflammatory response. Both Q57H and DG188 mutants were tested in comparison with the wild-type ORF3a. While the Q57H mutant showed a similar immune expression profile as the wild-type ORF3a (Fig. 5D;  Fig. 5E-a, blue bars), sharp increases in NF-k B-mediated activities and transcription of TNF-a, IL-6, and NF-k B were observed with the DG188 mutant ( Fig. 5D; Fig. 5E-a, red bars), suggesting a strong correlation between the level of apoptosis and necrosis induced by the DG188 mutant and NF-k B-mediated transcription of TNF-a or IL-6. To further test whether other cytokines or pattern recognition receptors that recognize RNA virus also respond to the expression of ORF3a, we measured mRNA levels of type I interferon beta-1 (IFN-b1), Toll-like receptors 4 (TLR4), and Toll-Like receptors 3 (TLR3). Similar or slightly reduced activation of IFN-b1, TLR4, and TLR3 was observed in the Q57H mutant compared with the wild-type ORF3a (Fig. 5E-b), whereas sharp increases of IFN-b1, TLR4, and TLR3 were observed with the DG188 mutant ( Fig. 5E-b). Interestingly, there were temporal differences in the activation of the various cytokines and regulators. The increase in TNF-a began as early as at 24 hpt and was significantly reduced at 72 hpt ( Fig. 5E-a). In contrast, no significant increase of signals of NF-k B, TLR4, or TLR3 was seen at 24 hpt, but mRNA abundance gradually increased from 48 to 72 hpt, suggesting they might be late events. The two type I interferons IFN-b1 and IL-6 showed a significant spike at 48 hpt. These observations suggest that there is an active interaction between the expression of ORF3a and the host cellular innate immune responses.

DISCUSSION
Although current mRNA-based vaccines are generally effective at preventing SARS-CoV-2 infection, the effectiveness of those vaccines wane over time (23). In addition, the emergence of new viral variants could further undermine the effectiveness of those vaccines. Antiviral drugs are an alternative, potentially effective means to treat SARS-CoV-2 infection and curtail COVID-19 but currently such drugs are limited. Two antiviral drugs were granted recently by U.S. FDA for emergency use, which include a nirmatrelvir and ritonavir drug combination (Paxlovid) by Pfizer and molnupiravir (Lagevrio) by Merck. Molnupiravir is a nucleoside analogue that resembles cytidine in RNA. It inhibits viral reproduction by introducing nucleoside substitution b-D-N4-hydroxycytidine 59-triphosphate into viral RNA during viral replication that is mediated by RNA-directed RNA polymerase (24). Nirmatrelvir is a competitive 3C-like protease (3CL pro ) inhibitor (25). Ritonavir is a CYP3A enzyme inhibitor that is used to maintain the high plasma concentration of nirmatrelvir. However, the discovery of new antiviral agents is limited by our current knowledge on the viral pathogenicity of the virus and of which viral protein(s) should be targeted. Thus, there is a continued need to identify viral proteins that cause, e.g., harmful cytopathic effects, tissue damage, and cytokine storm, in order to advance the rational design of anti-COVID-19 drug therapies.
The goal of this study was to identify SARS-CoV-2 viral targets that can be used in high-throughput drug screening and antiviral drug discovery. Our criteria for appropriate targets were that the viral protein must contribute to viral pathogenesis and cause a measurable cytopathic effect that can be used in large-scale drug screening. Twelve viral proteins were identified that included 8 nonstructural proteins (NSP1, NSP3, NSP4, (E) Activation of cellular innate immune proinflammatory responses, as measured by qRT-PCR. Data are presented as mean 6 SE from three independent experiments. Statistical differences between control and ORF3a WT or mutants (indicated with #) or between WT and mutants (indicated with *) were evaluated. * or #, P , 0.05; ** or ##, P , 0.01; *** or ###, P , 0.001 (one-way ANOVA). NSP5, NSP6, NSP13, NSP14, and NSP15) and 4 accessory proteins (ORF3a, ORF6, ORF7a, and ORF7b). Overall, they all inhibited cellular growth (Fig. 1), caused cellular hypertrophy (Fig. 2), and induced cell death (Fig. 3).
Two major types of viral proteins were identified, namely, viral enzymes and viral proteins that participate in viral replication or transcription (26). Among the viral enzymes, NSP5 carries the major 3C-like protease, NSP3 produces a minor papain-like protease (PL2 pro ), NSP13 carries a viral helicase that unwinds duplex RNA or DNA with a 59!39 polarity, and NSP14 is a dual-function enzyme with N-terminal exoribonuclease (ExoN) and C-terminal N7-methyltransferase (N7-MTase) activities. All of these viral enzymes are highly conserved among coronaviruses and are essential for viral replication (26). Therefore, they might be ideal targets for antiviral drug discovery. Notably, viral proteases are proven antiviral targets against HIV-1 (27) and hepatitis C virus (HCV) infection (28). Some of the FDA-approved HIV-1 and HCV protease inhibitor drugs inhibit 3CL pro or PL2 pro (29,30). Although no clinical benefit has yet been shown from using those repurposed drugs, they nevertheless indicate the potential of using protease inhibitor drugs for treating COVID-19. The successful development of the first protease inhibitor antiviral drug Paxlovid (PF-07321332) against SARS-CoV-2 by Pfizer (31) attests this possibility. The fission yeast 3CL pro or PL2 pro cell-based assays described here could conceivably be used for high-throughput screening and testing of future SARS-CoV-2 protease inhibitors. This use is certainly feasible because our early study shows that all FDA-approved HIV-1 protease inhibitor drugs suppress HIV-1 protease in the fission yeast cell-based system (12). NSP13 also shows dNTPase and RNA 59-triphosphatase activities besides its helicase activity. NSP13 is the most conserved coronaviral protein, and it is essential for viral RNA transcription and replication. Therefore, it is another promising viral target for antiviral drug development (32).
Three of the identified viral candidates (NSP3, NSP4, and NSP6) form a core protein complex that is responsible for the formation of virus-induced double-membrane vesicles (DMVs). DMVs are part of the viral replication-transcription complex (RTC) in SARS-CoV-2infected cells that drives replication and transcription for virus reproduction (33,34). The RTC formed by NSP3/4/6 in SARS-CoVs links the cytoplasmic membrane with the endoplasmic reticulum lumen to induce membrane curvature and form unique DMVs (35). In addition, NSP3/4/6 interacts with E, M, and N proteins to facilitate viral assembly (36). Thus, interruption of the NSP3/4/6 protein complex and DMV formation could potentially be a critical viral target to inhibit viral replication and transcription.
ORF3a was chosen for mechanistic studies because it fulfills the predefined criteria as a potential therapeutic target. ORF3a is a multifunctional protein that has 275 aa (;31 kDa) and presents as a homodimer or tetramer (22). It has 3 transmembrane regions that span halfway across the membrane and cytosol that are linked to multifunctionalities, including virulence, infectivity, ion channel formation, and virus release (37)(38)(39)(40). ORF3a plays an important role in SARS-CoV-2-mediated viral pathogenesis, including induction of the inflammasome in damaged lung tissue and initiation of cytokine storm (20,41). High titers of the anti-ORF3a antibody were found in SARS-CoV-2-infected patients, suggesting its clinic significance (42). Deletion or transcription knockdown of ORF3a from either the SARS-CoV or the SARS-CoV-2 genome results in a major reduction of virus growth (43,44). SARS-CoV-2 ORF3a is essential for viral replication in the absence of the E protein (45,46). Consistent with our findings from the fission yeast studies, ORF3a prevented cell proliferation and induced apoptosis and necrosis in mammalian cells (Fig. 4A to C), in part through the induction of cellular oxidative stress (Fig. 4A to D). ORF3a-induced apoptosis has been reported previously (47). However, oxidative stress-induced cell death had not been reported in SARS-CoV-2 infection, and the viral protein(s) responsible for inducing cellular oxidative stress had not been identified. Here, we report for the first time that ORF3a induces ROS production leading to cellular oxidative stress, and this cellular stress response appears to be a common feature of all the viral proteins except NSP14 that link to cell death in fission yeast cells (Fig. 3C, shown by arrows). Whether the activation of cellular oxidative stress also contributes to the death of human cells caused by other viral proteins besides ORF3a remains to be elucidated. Oxidative stress is a common cellular response to viral infection (48). Sustained viral infection leads to the accumulation of the ROS in cells or tissues that may result in tissue damage and cell death (49,50). Increasing evidence suggests that excessive ROS production is a major cause of local or systemic tissue damage and contributes to the severity of COVID-19 (17,18). Therefore, focusing on these viral targets could potentially help ease the COVID-19 burden. It is unclear at present why NSP14-induced cell death did not trigger a cellular oxidative stress response. One possible explanation is that it functions as an enzyme that is part of normal cellular activity.
ORF3a-induced apoptosis and necrosis might also be attributed in part to the activation of a cellular proinflammatory response, as three of the common proinflammatory cytokines, namely, TNF-a, IL-6, and IFN-b1, were all elevated ( Fig. 4E and F;  Fig. 5D). Clinical studies suggest that high serum IL-6 and TNF-a levels are strong, independent predictors of patient survival (51,52) and that hyperinflammatory responses in patients with COVID-19 are a major cause of disease severity and death (51,52). ORF3a-induced apoptosis and necrosis might be one of the viral factors that contributes to the elevation of IL-6 and TNF-a observed in these patients. Our data show that the elevated production of IL-6 and TNF-a could be mediated through NF-k B, a wellrecognized master regulator that mediates cellular proinflammatory responses to viral infection (21). In addition, TLR3 and TLR4 might also be involved in these elevations, as they are also activated (Fig. 5E). Both TLR3 and TLR4 recognize RNA viruses and trigger the antiviral production of type I IFNs and proinflammatory cytokines. TLR4 is a cell membrane receptor (53) and TLR3 resides on the endosomal membrane in epithelial cells (54). TLR3 induces proinflammatory cytokine production through TRIF (54), whereas TLR4-mediated proinflammatory cytokine production is likely through NF-k B. Both TLR3-and TLR4-mediated proinflammatory responses contribute to the severity of COVID-19 (55)(56)(57). TLR4-mediated production of IL-6 and TNF-a is associated with the severity of COVID-19 in patients with cardiometabolic comorbidities (55). Inhibition of TLR3 by famotidine decreases IL-6 (56) and reduces the risk of intubation and death in patients hospitalized with COVID-19 and alleviates symptoms in nonhospitalized patients with COVID-19 (57). Both cellular inflammation and oxidative stress contribute to the severity of the COVID-19 (58). In addition, ORF3a-induced apoptosis and necrosis are highly conserved cellular responses among yeast (this study), Drosophila (59), and human cells (47). Therefore, targeting ORF3a-induced apoptosis and necrosis could be a promising antiviral strategy to fight against COVID-19. Fission yeast could potentially be used as a surrogate system for large-scale drug screening and testing of anti-ORF3a inhibitors.
The DG188 mutant triggered a much stronger activation of the cellular oxidative stress and innate immune responses than wild-type ORF3a (Fig. 4 and 5). An early protein structural analysis suggests that the G188 residue may be structurally important, as it is one of the two highly conserved glycine resides among sarbecoviruses that separate two antiparallel b4 and b5 sheets, and both residues are in the proximity of two homodimers of ORF3a and the free C-terminal end of the protein (22). The DG188 mutant may interrupt dimerization of the protein or the structure of the two antiparallel b4 and b5 sheets at the C-terminal end where it affects ion channel activity or interaction of ORF3a with host cellular proteins (22). The ORF3a activity has been linked to a number of sequence motifs from the signal peptide (aa 1 to 13) of the N terminus to a di-acidic motif (aa 171 to 173) (40,47). This study is the first demonstration of the functional relevance of the G188 residue at the b4/b5 junction of the C terminus of ORF3a in the activation of cellular stress, innate immune response, and induction of apoptosis and necrosis. Additional mutagenesis studies are needed to confirm our findings. One possible explanation for the stronger effect of the DG188 mutant than that of the wild type is that the wild-type ORF3a effect might be restricted by a host restriction cellular protein(s) through a direct protein-protein interaction, as shown in other viral infections (60,61). If this is indeed the case, interruption of the ORF3a protein with the host restriction factor(s) by the DG188 mutation could release the ORF3a resulting enhanced triggering of host cellular stress and innate immune response and leading to stronger cell death. This scenario is further suggested by the fact that the wild-type ORF3a completely kills fission yeast cells but only partially kills human cells. Note that ORF3a-induced cell killing in fission yeast is not an artifact caused by protein overproduction, as viral proteins with similar molecular weights, such as NSP8 and seventeen other viral proteins, did not cause cell killing nor do they induce the ROS under the same experimental conditions. It would be of interest to identify the binding partner(s) of the ORF3a in human cells.
ORF3a mutations are associated with disease progression (39,40) and high mortality in patients with COVID-19 (62). The Q57H mutation (40,62) was found recently in the emerging Beta variant (63), and the Q57H variant was suspected of contributing to a surge of SARS-CoV-2 infection in Hong Kong (64). Our data show that the Q57H mutant activities are comparable to those of the wild-type ORF3a regarding the induction of cellular oxidative stress, innate immune responses, and apoptosis and necrosis ( Fig. 4 and 5). However, our data cannot rule out the possibility that the Q57H mutant may have other effects on viral pathogenesis. For example, the Q57H mutant was predicted to bind the S protein, whereas the wild type does not (65). It would be of interest to test whether the Q57H mutant contributes to viral entry. A number of new ORF3a mutations were found to associate with the emergence of the new viral variants. It would be necessary to test the effect of those natural ORF3a variants on their interactions with host cellular stress and innate immune responses to better understand the role of ORF3a in COVID-19.
In summary, through genome-wide characterization, 12 viral proteins were identified to exert cytopathic effects. Since these viral proteins are essential for viral survival and contribute to viral pathogenesis, they could serve as therapeutic targets for future antiviral drug discovery. In accordance with this finding, we demonstrated here that ORF3a induces apoptosis and necrosis through the activation of cellular oxidative stress and host innate immune proinflammatory responses. Since these ORF3a activities are linked to viral pathogenesis, disease progression, and severity of COVID-19, it would be desirable to develop a fission yeast cell-based high-throughput system to identify anti-ORF3a drugs to battle COVID-19.

MATERIALS AND METHODS
Cell and growth media. A wild-type fission yeast SP223 strain (h-, ade6-216, leu1-32, and ura4-294) was used to test the cytopathic effect of SARS-CoV-2 viral proteins in this study (8,14). Standard yeast extract-sucrose (YES) complete, minimal EMM, or Pombe Glutamate medium (PMG) selective media supplemented with adenine, uracil, leucine, or thiamine (20 mM) was used to grow fission yeast cells or to select for plasmid-carrying cells. Luria-Bertani (LB) medium supplemented with ampicillin (100 mg/mL) was used for growing New England BioLabs (NEB) stable Escherichia coli (NEB catalog [cat] no. C3040H) or DH5a cells and for DNA transformation.
SARS-CoV-2 reference strain and plasmids. A U.S. SARS-CoV-2 reference strain, USA-WA1/2020 (GenBank accession no. MN985325), was used for the genome-wide functional analysis of the SARS-CoV-2 viral proteins. A fission yeast gene cloning system, which includes a pYZ1N gene expression plasmid, has been described previously (6,13). pYZ1N carries an inducible no message in the thiamine (nmt1) promoter. Through the regulation of this promoter, viral gene expression can be either induced or repressed in the absence (gene-off) or presence (gene-on) of 20 mM thiamine, respectively (13). The fission yeast strain that carries the pYZ1N-SARS-CoV-2 ORF was maintained in the minimal EMM with the selection of the Leu2 gene carried on the plasmid. For the mammalian ORF3a study, a lentiviral constitutive expression vector pLVX-EF1alpha-IRES-Puro (TaKaRa) that carries the ORF insert (provided by Nevan J. Krogan of University of California San Francisco [UCSF]) was used (4). The ORF3a mutant variants (Q57H and DG188) were generated by overlapping PCR with mutant-specific primers (see Table S1 in the supplemental material) and cloned onto the same pLVX-EF1alpha-IRES-Puro plasmid via the Gibson assembly method. All final constructs were verified by Sanger sequencing.
Characterization of SARS-CoV-2 Cytopathogenic Proteins ® Molecular cloning of SARS-CoV-2 ORFs in fission yeast. A total of 29 SARS-CoV-2 viral ORFs were cloned into a fission yeast pYZ1N gene expression vector system using methods as described previously (6,8) (see Fig. S1A in the supplemental material). Among them, 27 of the viral ORFs were described previously (4). NSP3 or NSP16 was obtained from Zhe Han of University of Maryland, Baltimore, or Fritz Roth of University of Toronto (Addgene plasmid no. 141269), respectively. Briefly, each SARS-CoV-2 protein-encoding nucleotide was PCR amplified with a pair of primers that contained specific restriction enzyme sites (Table S1; Fig. S1B) for molecular cloning and cloned into the pYZ1N expression vector for the functional analysis. The SARS-CoV-2 viral gene inserts were verified by restriction digestions and Sanger sequencing.
Fission yeast plasmid transformation and inducible SARS-CoV-2 gene expression. The SARS-CoV-2 gene-carrying pYZ1N plasmids were transformed into a wild-type fission yeast SP223 strain by electroporation (8,66). The plasmid transformants were selected for the presence of the Leu2 gene on a minimal selective PMG medium. Successful transformation of the respective SARS-CoV-2-containing plasmids was verified by colony PCR with gene-specific primers (Table S1). Successful expression of the respective SARS-CoV-2 ORF mRNA transcripts was measured by RT-PCR using gene-specific primers (Table S1) and a SuperScript III one-step RT-PCR system (Invitrogen; cat no. 12574-030). To measure SARS-CoV-2 genespecific activities, the PCR-confirmed yeast colony, which carries the desired and specific SARS-CoV-2 gene-containing plasmid, was grown to log phase in liquid EMM supplemented with 20 mM of thiamine. Cells were then washed three times with distilled water to remove thiamine. Finally, 2 Â 10 4 cells/mL, which was quantified using a TC20 automated cell counter (Bio-Rad), was reinoculated into fresh liquid EMM without thiamine to induce gene expression (gene-on) or with thiamine to suppress gene expression (gene-off). The cell cultures were grown at 30°C with constant shaking before the observation.
Measurement of fission yeast cell-specific activities. The effects of a SARS-CoV-2 protein on fission yeast cellular growth were measured by several methods, which include a yeast colony-forming assay to measure cell proliferation, a cell viability assay (8,12), and the cellular growth kinetics to quantify cellular growth (8,14). Briefly, the fission yeast cultures were prepared as described above. A total of 50 mL of liquid cultures with approximately 1 Â 10 3 cells was spread onto the selective EMM agar plates with (gene-off) and without (gene-on) thiamine. The agar plates were incubated at 30°C for 4 to 6 days before the observation for the presence or absence of fission yeast colony formations and the sizes of the forming colonies. The absence of colonies on the agar plates indicates complete inhibition of cellular proliferation. Fewer colonies with smaller colony sizes than the normal control typically suggest reduced cellular proliferation.
To quantify the level of growth inhibition caused by the expression of a SARS-CoV-2 protein in fission yeast, 100 mL of gene-on or gene-off liquid cultures (2 Â 10 4 cells/mL) was grown in the 96-well microtiter plate in the selective EMM over time at 30°C in an incubator with moisture. Cellular growth kinetics was measured at OD 650 over 48 hours by using a Synergy H1M monochromator-based multimode microplate reader (BioTek).
The methods used to test the effect of each viral protein production on fission yeast cell morphology have been described previously (8,14). Briefly, fission yeast cell morphology was observed using a BZX fluorescence microscope (Keyence) under the bright field 48 h agi. The overall cell morphology was evaluated by flow cytometry using forward-scattered analysis (8,14). Ten thousand cells were analyzed on a FACSCanto II flow cytometer (Becton, Dickinson). The forward-scattered light (FSC) and side-scattered light (SSC) were measured for each cell population. FSC is proportional to the cell surface area and thus measures cell size. SSC determines intracellular complexity because it is proportional to cell granularity.
SARS-CoV-2 protein-induced cell death was detected by trypan blue staining (8). Trypan blue is a dye that specifically detects dead cells. The activation of fission yeast cellular oxidative stress by SARS-CoV-2 proteins was monitored by the production of ROS, which can be detected by a ROS-specific dye, DHE (Sigma). DHE generates red fluorescence in the presence of ROS (8,67). DHE was added in a final concentration of 5 mg/mL. Cellular oxidative stress was measured 48 h agi.
Measurement of mammalian cell-specific activities. A total of 2 Â 10 4 293T or 1 Â 10 4 A549 and Calu-3 cells/well were seeded onto a 96-well plate and cultured at 37°C and 5% CO 2 overnight. The plasmid was transfected into cells using the Lipofectamine 3000 reagent (ThermoFisher) following the manufacturer's protocol. Cellular growth or cell death at the indicated time was quantified by cell number counting and trypan blue staining. Briefly, cells were trypsinized, and 10 mL of the cell suspension was mixed with an equal volume of trypan blue. Within 5 minutes of mixing, the total cells and dead cells were counted using a TC20 automated cell counter. Cell viability was determined by the MTT assay as described (68). At the time of the measurement, 10 mL of 5 mg/mL MTT was added to each well of the 96-well plate and incubated at 37°C for 2 to 5 h. After the medium was removed, 100 mL of dimethyl sulfoxide (DMSO) was added to each well and mixed by pipetting. The plates were agitated gently for 15 minutes, and the absorbance was measured at 570 nm using the H1M microplate reader.
Cell apoptosis and necrosis were measured by a RealTime-Glo annexin V apoptosis and necrosis assay (Promega) (69). At 24 h after plasmid transfection, the apoptosis and necrosis detection reagents were added into each well of a 96-well plate. Following incubation at 37°C and 5% CO 2 at the indicated time, luminescence (relative light units [RLU]) and fluorescence (relative fluorescence units [RFU]; 485 nm excitation/520 to 530 nm emission) were used to measure apoptosis and necrosis, respectively, with the H1M microplate reader.
The activation of cellular oxidative stress in mammalian cells was monitored using a ROS detection cellbased assay kit (Cayman Chemical; cat no. 601290). The fluorescence of DHE staining was visualized using a BZX fluorescence microscope (Keyence). The intensity was measured using the H1M microplate reader.
The Western blot analysis was carried out as described previously (68). Total proteins were extracted from transfected 293T cells using radioimmunoprecipitation assay (RIPA) lysis buffer (Millipore Sigma) at 48 hpt. Equal amounts of total protein were separated on an SDS-PAGE gel by electrophoresis and transferred to a polyvinylidene difluoride (PVDF) membrane (Bio-Rad). Target proteins were detected using the following antibodies: mouse anti-Strep tag II (Millipore Sigma; cat no. 71590); rabbit anti-Caspase 3 (Cell Signaling; cat no. 9662); rabbit anti-Cleaved-Caspase 3 (Cell Signaling; cat no. 9664), and mouse anti-GAPDH antibody (Cell Signaling; cat no. 2118).
For the NF-k B luciferase assay to measure NF-k B-mediated transcriptional activities, 2 Â 10 4 293T cells/well was cultured in a white 96-well plate overnight. A total of 0.05 mg of the plasmid with the gene of interest was cotransfected with 0.05 mg of pNF-k B-Luc and 0.005 mg of pRL-SV40. The activities of firefly luciferase and Renilla luciferase were measured at the indicated time using the dual luciferase reporter assay system (Promega; cat no. E1910) and H1 plate reader (BioTek). The signal of firefly luciferase was normalized with Renilla luciferase, and the fold changes compared to empty vector control were calculated.
Reverse transcriptase quantitative PCR (qRT-PCR) was carried out as we described previously (69). Briefly, a total of 1 mg of extracted total RNA was used for the synthesis of first-strand cDNA using a high-capacity RNA-to-cDNA kit (Thermo Fisher Scientific) according to the manufacturer's instructions. Quantitative PCR was performed on a QuantStudio 3 real-time PCR system using gene-specific primers (see Table S2 in the supplemental material) and 2Â SYBR green qPCR master mix (Bimake). The amplification conditions were 40 cycles of 95°C for 10 s and 60°C for 30 s, followed by melting curve analysis. The fold change in mRNA expression was quantified by calculating the threshold cycle (2 2DDCT ) value, with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA as an endogenous control.

SUPPLEMENTAL MATERIAL
Supplemental material is available online only.


s2fieldsofstudy: Biology Psychology

text:
Tuned in to communication sounds: Neuronal sensitivity in the túngara frog midbrain to frequency modulated signals

For complex communication signals, it is often difficult to identify the information-bearing elements and their parameters necessary to elicit functional behavior. Consequently, it may be difficult to design stimuli that test how neurons contribute to communicative processing. For túngara frogs (Physalaemus pustulosus), however, previous behavioral testing with numerous stimuli showed that a particular frequency modulated (FM) transition in the male call is required to elicit phonotaxis and vocal responses. Modeled on such behavioral experiments, we used awake in vivo recordings of single units in the midbrain to determine if their excitation was biased to behaviorally important FM parameters. Comparisons of stimulus driven action potentials revealed greatest excitation to the behaviorally important FM transition: a downward FM sweep or step that crosses ~600 Hz. Previous studies using long-duration acoustic exposure found immediate early gene expression in many midbrain neurons to be most sensitive to similar FM. However, those data could not determine if FM coding was accomplished by the population and/or individual neurons. Our data suggest both coding schemes could operate, as 1) individual neurons are more sensitive to the behaviorally significant FM transition and 2) when single unit recordings are analytically combined across cells, the combined code can produce high stimulus discrimination (FM vs. noise driven excitation), approaching that found in behavioral discrimination of call vs. noise.

Introduction
Across animal taxa, receivers are often most sensitive to the stimulus parameters of their species-specific mating signals [1,2]. This specificity forms the basis of the matched filter hypothesis, which predicts that signaling and sensory processing coevolve, resulting in sensory responses 'tuned' to the signal parameters mediating functional behavior [3][4][5]. For acoustic signals, Suga [6] proposed that information in signals used during recognition and torus semicircularis (TS) or its mammalian homologue the inferior colliculus (IC) [38][39][40]. For example, in bats and rodents the IC contains neurons sensitive to FM stimuli, including specializations for the FM bandwidth, rate, and sweep direction of functionally relevant sounds [41][42][43][44][45]. Based on anatomy and neural tuning, the frog TS exhibits similar evidence for convergence, with extensive complex sensitivity, including stimulus combination sensitivity, integrated frequency channels, and temporal tuning [18, [46][47][48][49][50][51][52]. With regard to FM stimuli, however, there are relatively few tests of single neuron sensitivity in the frog TS compared to those in mammals [40]. Ponnath et al. [50] found evidence for frequency specific adaptation in phasic TS units: compared to the phasic response to tones, FM stimulation elicited a more tonic response due to the change in frequency in the FM stimuli. In experiments using more functionally relevant sounds, TS neurons in Eleutherodactylus coqui exhibit different excitatory responses for upward and downward FM sweeps [53]. Neither of those datasets, however, investigated the extent to which individual cells code the specific FM parameters required to elicit behavior. In contrast to single cell recordings, multiple studies have taken an indirect approach to evaluate TS coding of species-specific FM parameters in anatomical nuclei. For example, in response to several minutes of acoustic stimulation, immediate early gene (IEG, egr-1 mRNA; also called ZENK, zif268, NGFI-A, and krox-24) induction shows that cells in all TS subdivisions in túngara frogs exhibit increased response to stimuli containing the FM whine [54]. These IEG responses are indicative of neuronal excitability [55] and could predict action potential responses in individual TS neurons increase with stimulation by the parameters in the whine's FM [30]. By using many of the same stimuli generating the model by Wilczynski et al.
[30], we tested whether excitation in isolated TS units is indeed tuned to whine stimulation and if that tuning is based on the same FM transition critical to behavior. Additionally, we use signal detection theory to assess whether action potential responses to the FM transition is sufficient to account for behavioral discrimination of whine and noise, the phonotactic test that determined the information-bearing parameters in the FM whine [30].

Ethics statement
All animal care and experimental procedures were performed in accordance with and approved by the institutional animal care and use committee (IACUC) at Louisiana State University Health Sciences Center (IACUC #3542) and the University of Texas-Austin (AUP-2015-00051).

Preparation and recording
The protocol for extracellular recordings of isolated TS units in awake túngara frogs is similar to that used previously [49,50]. Under general (SQ injection MS-222; 0.16 mg/g) and topical anesthesia (dibucaine cream; 0.9%), the midbrain was exposed by resecting a piece of skull dorsal to the optic tectum. After 24 h of recovery from anesthesia, immobilized frogs (i.m. succinylcholine chloride; 22 μg/g body weight; [56]) were mounted dorsal side up on an air table in a foam (Tecnifoam 4 in.; NRC 1.21 at 500 Hz) lined Faraday cage and kept moist to maintain cutaneous respiration. All recordings were carried out at 19.5-22.5˚C. Extracellular electrophysiological activity was recorded using thin-walled glass micropipettes filled with 4 M NaCl (3)(4)(5)(6)(7)(8)(9)(10). After amplification (GRASS P511 with high impedance head stage), neural responses were digitized (100 μs sampling period) using a TDT AD3 and System II array processor with custom written software. TS auditory units were isolated (peak action potential voltage was >20 dB above the noise at recording start) using a series of search stimuli covering the frequency range of auditory sensitivity and call spectra (~0.1-5 kHz) [57]. These stimuli included a 60-ms Gaussian noise, 30-ms tones, and a 20-ms band limited noise centered at 2 kHz. Call stimuli were not used in the search to avoid any potential of adaption to test stimuli. The recording site was recovered using the stereotactic position of the electrode, which was calibrated by current injection lesion and nissl stain [49,50]. Electrodes were inserted in the central and medial portions of the tectum with recording sites ranging across the dorsal-ventral depths of the midbrain, with most recordings in the principle nucleus of the TS.

Acoustic stimulus production
Acoustic stimuli were generated and amplified using a TDT II DA3 16 bit D-A converter (40 μs sample period) and a Harmon/Kardon integrated amplifier, respectively. Stimulus amplitude was controlled with TDT PA2 programmable attenuators. Acoustic stimuli were presented from a single Fostek #FE127 broadband speaker positioned at 0˚normal to the front of the frog (30 cm distance). The speaker was calibrated at the position of the recording site, directly between and dorsal to the two tympana using a Bruel and Kjaer B&K 2608 measuring amplifier with a B&K model 4133 1/2-in. microphone and a B&K 4220 pistonphone calibrator. All frequency components of the chamber's ambient noise were � 21.5 dB SPL (peak ambient noise range 120-230 Hz).

Frequency tuning and relative sensitivity to call parameter stimuli
Isolated cells were used in one or both of two experiments. Experiment 1 determined single unit frequency tuning from 0.2 and 5.1 kHz (50 or 100 Hz steps) using 200 ms tone pulses (1 ms cosine ramps) and an adaptive procedure (�3/5 down, �2/5 up; ±3 dB resolution). Subsequently, the flanks of the tuning curves were modeled using a least-squares fit of a rounded exponential filter function. Functions with significantly fitting (P<0.05) enabled calculation of the equivalent rectangular bandwidth (ERB) of the tuning curve filters [15,50,[58][59][60]. With respect to descriptive measures of frequency tuning, two sample t-tests were used to compare the difference in the means of ERBs and the best frequency thresholds for the low and high frequency (AP and BP) channels. Experiment 2 was modeled after the one used to assess which acoustic parameters of the whine elicit phonotaxis [30]. The experiment only included whine sensitive units: after recording isolation using the search stimuli, several natural whine stimuli (see below) were presented. If action potentials were elicited, then the battery of test stimuli ensued. The envelope and duration (325 ms) of all test stimuli were those of the natural túngara whine call (or timereversed; see below), which was calculated (Hilbert transform; [59]) and smoothed using a Savitzky-Golay FIR smoothing filter (span 81, degree 5) in MATLAB. The following are the 14 test stimuli (examples in Fig 1). The natural whine: recorded from one of 50 calling males in Gamboa, Panama, it is the call closest to the population mean of 15 acoustic variables [61]. This whine has been used previously to elicit robust female phonotaxis [20,22,62,63]. Reverse whine: the time-reversed natural whine. Noise: broadband noise with the whine envelope. Reverse noise: broadband noise with time-reversed whine envelope. Single tones at frequencies that are components of the dominant frequencies of the FM whine: 430, 500, and 800 Hz. Sequential frequency transitions: consist of two sequential tones with an instantaneous frequency step at a sinusoidal zero crossing, which prevented spectral splatter. The transition (step) times match the relative times used in behavioral tests [30]. The time is noted relative to stimulus onset: 900 to 430 Hz (162 ms), 430 to 900 Hz (162 ms), 800 to 500 Hz (109 ms), 500 to 800 Hz (109 ms), and 700 to 550 Hz (109 ms). Because the natural call used here was slightly longer than the synthetic one used by Wilczynski et al. [30], these transition times are slightly delayed (~10 ms) from theirs. Simultaneous tones: concurrent presentation without FM step of 900+430 Hz or 800+500 Hz. Stimuli were presented in pseudorandom order, with whine and reverse whine always tested in the first two blocks before moving to the other 12 stimuli. This ensured that responses to these first stimuli were recorded, as they form the basis of the analysis of sensitivity both to the FM transition parameters and the effects of envelope shape. All stimuli were presented in 20 repetition blocks with 2 s stimulus period. As part of signal detection theory analysis (see below), we assessed how responses correlated to particular ongoing FM stimulus components. Thus, stimulus spectrograms were calculated using short-time Fourier transforms (MATLAB). The dominant frequency in the natural whine was determined from the power spectral density matrix of the spectrogram as the frequency component with the highest amplitude.
After isolating a unit, the order of the two experiments (tuning curves using tones; series of complex FM stimuli) was pseudo-randomized. Depending on the ability to hold the isolated recording, some cells were tested in both experiments and others in only one. Typical of midbrain recordings, success rate (number of isolated units) varied between subjects, with some frogs yielding more units than others. Similar to work using in vivo recordings in frog TS, we have pooled units across subjects to understand the variance in processing capabilities at this auditory nucleus [49,50,[64][65][66][67].

Analysis of responses to varied call parameters
Responses to stimuli with varied call parameters were quantified as the mean number of spikes per stimulus for 20 repetitions. The capture buffer duration was 1000 ms. Stimulus driven responses within cells were corrected for spontaneous activity by subtracting the cell's mean spontaneous rate (mean of 20 repetitions of silence [49]). Note that this correction was minimal because spontaneous activity was low, as only two cells showed >2 spontaneous spikes per 'silent' sweep (they were 2.2 and 4.7 spikes/silent stimulus presentation).
For each cell, responses to the different stimuli represent repeated measures. Additionally, the design is unbalanced, as the number of stimuli per cell differed because not all cells completed recordings for all test stimuli. Thus, a linear mixed effect model is fitted to compare whether excitation differed between the battery of test stimuli. The model has a random intercept to account for the variation across cells. With covariates, sex, stimulus ID, and their interaction, we chose compound symmetry (CS) covariance structure due to the facts that convergence criteria were not met with unstructured covariance, and that it is not reasonable to assume a covariance structure other than CS because there is not a natural order in stimuli. Furthermore, with AR(1) covariance structure (autoregressive), the Akaike Information Criterion and Bayesian Information Criterion are about the same as those using CS structure. The variance of the random intercept is significantly different from zero (p = 0.0008), and the covariance in CS structure is significantly different from zero (p < 0.0001). With this model, we concluded that both sex (p = 0.8962) and the sex � stimulus ID interaction (p value = 0.5655) have negligible effect and were removed from the model. The responses of cells from males and females are thus pooled to compare stimulus responses.
To compare the mean responses to the whine and those to the other test stimuli, we obtained Huber robust estimates of standard errors, and then used Dunnett-Hsu method to adjust p values for multiple comparison correction with whine as the control. The whine is used as the standard in all but one comparison because the whine is already known to contain the information-bearing element (in the form of FM) necessary and sufficient to elicit phonotaxis [30]. The comparison between noise and reverse noise was carried out separately by using a contrast test. All the computation was carried out in SAS 9.4 (Cary, North Carolina, USA), employing the MIXED procedure.
Note that the use of 20 repetitions for each stimulus creates a potential effect of adaptation. Thus, repetition number was analyzed by a nonparametric Friedman test and associated rank sum with correction for multiple comparisons. The test evaluated if the repetition number (1 to 20) was associated with the amount of excitation.

Results
The search stimuli enabled isolation of 110 units. The sample sizes for tests of frequency tuning to tones and call parameter stimuli were 65 and 87 cells, respectively. Whereas 42 cells contributed to both experiments, 23 contributed only to frequency tuning to tones and 45 cells contributed only to call parameter testing (yielding 110 total cells). The distribution of recording positions primarily covered areas known to contain auditory cells [54,68]; most were located in nuclei of the torus, but also included some positions in the optic tectum and tegmentum (Fig 2).

Frequency tuning characteristics
Data here are the first to show single unit recordings in the túngara frog midbrain, as previous work used gross multiunit recordings [69]. Frequency tuning curves were measured using tones in 65 cells, with best sensitivity represented by three general shapes: low frequency only (<1.2 kHz; 25 cells) (Fig 3A-3D), high frequency only (>1.2 kHz; 1 cell) (Fig 3L), and low-high frequency or a combination of the two bands, creating 'W' shaped tuning curves (39 cells)( Fig  3E-3K). The latter shape means cells can have two sensitivity peaks (i.e., two 'best' frequencies), allowing for analysis of their contribution to low and high frequency band sensitivity. Averaging all tuning curves reveals the contribution of these two sensitivity bands to overall tuning ( Fig 4A), likely reflecting integration of peripheral input from the amphibian (AP) and basilar papillae (BP) (low and high frequency channels, respectively; [69]). The boundary of the two bands, measured as the highest midpoint threshold between the two lowest thresholds, is 1.2 kHz. The low frequency band (best frequency at 700 Hz) was more sensitive than the high frequency band (best frequency at 2.1 kHz) with mean (±s.e.) best frequency threshold of 60.8 (±2.55) and 73.6 (±2.21) dB SPL, respectively ( Fig 4A). There was extensive overlap of tuning in the low frequency band, with most cells exhibiting best frequencies near 700 Hz. In contrast, best frequencies across cells in the high frequency band were more broadly distributed ( Fig 4B). Both bands exhibited a wide range of thresholds at the best frequencies, ranging over 30-40 dB (Fig 4C and 4D). Fitting the low frequency (AP) and high frequency (BP) components of each cell's tuning curves with a rounded exponential filter function enabled calculation of equivalent rectangular bandwidth (ERB). ERB of tuning was narrower in the low frequency than the high frequency channel (Fig 4E-4F; ERB Low = 334.7 ±25.4 Hz, N = 55; High = 674.3 ±86.0 Hz, N = 33, t-test; p<0.00053), raising the possibility of a positive correlation between BF and ERB [70]. When including low and high frequency BFs, linear regression analysis revealed such a relationship (N = 88 ERBs, Coef. = 0.257, intercept = 172.8 Hz, R 2 = 0.303; p<0.0000001). However, the relationship was driven by the wide bandwidth data from the BP-like band. If those best frequencies and ERBs are removed so that regression only includes ERBs with BFs <1.2 kHz, then there is no significant BF vs. ERB correlation in the AP-like channel alone (N = 55, Coef. = 0.104, intercept = 274.7 Hz, R 2 = 0.008, p = 0.507). Note, as described above, the single cell exhibiting only high frequency sensitivity ( Fig 3L) was unusual in our sample. It was not sensitive to the whine and thus not included in the analysis of FM sensitivity. Finally, with respect to the relationship between frequency sensitivity and anatomy, there was no correlation (linear regression) between recording depth and the single best frequency for each cell's tuning curve (R 2 = 0.00109; p = 0.846).

Response to varied call parameters
Using tones to measure tuning in the previous dataset does not allow for assessment of whether modulation in stimulus spectrum alters excitability. Due to its effectiveness in eliciting phonotaxis, the natural whine, an FM signal, was used as a standard for comparisons of the effect of frequency modulation on spike count. In general, stimuli with descending FM, especially the natural whine, but including downward tone steps, were most excitatory ( Table 1). The whine elicits significantly greater responses than nearly all stimuli even when the spectral content was the same. For example, time-reversing the whine, which changes the temporal direction of the FM and envelope, significantly reduces the spike response (e.g., Fig 5). The whine is also more excitatory than tones and ascending FM steps. But, interestingly, it was not as strongly different from descending FM step stimuli (Table 1). In particular, response to the 700 Hz to 550 Hz step is not significantly different. Finally, spike number is not affected by reversing the envelope shape, which was isolated by comparing responses to noise with the natural whine envelope to responses to noise with the envelope in reverse. Different from  Overall tuning from all recordings. Filled circles are the mean (±s.e.) threshold for each frequency. Red and blue curves are the least squares fits of the rounded exponential function to the mean thresholds for the low and high frequency channels (1.2 kHz boundary), respectively [69]. Dotted line shows the lowest threshold at each frequency across all cells. Grey curves are previous threshold data measured using multiunit recordings from large areas of the TS from Ryan et al. [69]. (B) Histogram showing the distribution best frequencies in the low and high frequency channels, revealing two distributions with no best frequencies from 1-1.4 kHz (histogram bin width = 100 Hz). (C-D) Histograms showing the distribution of best thresholds at frequencies below and above 1.2 kHz (blue and red, respectively). (E) Relationship between best frequency and equivalent rectangular bandwidth (ERB) size. Red and blue are ERBs centered at best frequencies below and above 1.2 kHz, respectively. (F) Mean (±s.e.) ERB size in the low and high frequency bands (amphibian papilla, AP and basilar papilla, BP). reversing the temporal sequence of FM, as in the reverse whine, the use of noise removes frequency modulation while keeping the broad spectrum (Table 1). Graphic representation of within-cell comparisons of spike count are presented in Fig 6. How is the greater excitation to the whine (and potentially whine-like FM) manifested in the spike train? Fig 7 shows evidence for increased excitation associated with an FM transition (appearing as a histogram 'bump'). This increased excitation or 'bump' could be functionally relevant: in behavioral choices against noise, whereas the 800 Hz to 500 Hz step elicits whinelike preferences, 800 Hz alone, 500 Hz alone, and the 500 Hz to 800 Hz stimuli do not [30]. Thus, subsequent analysis determined: whether such a 'bump' occurs in response to the whine (rather than an FM step stimulus), what frequency transition in the whine creates the largest histogram 'bump', and if that FM transition matches that critical to behavior [30]. Finally, signal detection theory was used to compare histograms to determine if the discrimination index value of the 'bump' is sufficient to explain discrimination of whine (signal) vs. noise.

Signal detection theory analysis of responses to whine vs. noise
The mixed model analysis assesses the overall response (i.e., number of spikes in the recording buffer) to stimuli and does not address whether a particular part of the histogram, and thus part of the stimulus, is responsible for the whine's dominance in Table 1. However, combining the responses of all cells to create an experimental multi-unit response (with mean and variance) in a single spike histogram enables calculation of an ongoing discrimination index for two stimuli (e.g., d'). Spike times for all cells (N = 45) that completed stimulation by the natural whine and noise were binned in peristimulus time histograms (i.e., one histogram each for whine and noise; Fig 8B and 8C). Each bin represents the mean number of spikes to the whine or noise. Taking the difference in the histograms (Fig 8D) reveals an increase in spikes during the 150-250 ms time frame of the whine, like the phenomenon found for FM steps (Fig 7). The whine elicits an initial burst of spikes (first~40 ms of stimulus), followed by a slight decrease. Subsequently, a large 'bump' or increase occurs (at 150-250 ms), followed by a decay. In contrast, the noise stimulus elicits an initial burst followed by a decay. The call envelope (Fig 8A) appears to explain the initial burst, as reversed noise, which lacks the high amplitude beginning, does not elicit the same initial response (Fig 8E-8G). Furthermore, response to the reversed noise also shows that the 'bump' in the whine histogram cannot be produced by a rising envelope, as the reversed noise's increasing amplitude is unable to elicit a large secondary 'bump'. It is also important to note that the 'bump' or spike increase for the 150-250 ms time frame of the whine is not an artifact of a particular histogram bin width. Whether the histogram is plotted as absolute or relative (to each cell's max bin count) mean number of spikes/bin, the 'bump' remains for bin widths from 1 ms to~50 ms (Fig 9). No such 'bump' occurs for noise, as those histograms follow the stimulus envelope, regardless of bin width (Fig 9). Although the absolute difference between the values in each bin (mean spike number) in the whine and noise histograms shows several large differences (especially in the 150-250 ms range; Fig 8D), a relative difference is required to determine the extent to which the 'bump' is sufficient to discriminate whine (signal) from noise responses. Thus, using the relative bin counts (Fig 9) we calculated an ongoing (for each bin) discrimination index that accounts for potential unequal variances in each bin's spike response: the difference in mean response per bin (whine bin-noise bin) divided by the average variance of the two bins [71][72][73]. This index, referred to as d a (rather than d'), enables the calculation of the per bin discrimination index for the two stimuli. Using a 1 ms bin width, the large 'bump' in the whine response histogram generates a peak in discrimination when compared to the noise response. Using the mean first spike latency (23.5 ms) to sync bin responses to the whine, the maximum d a (0.721) occurs at the time when the whine fundamental frequency is 601 Hz (Fig 10). Thus, FM transition through this frequency elicits the largest discrimination against noise, predicting 69-70% behavioral choice of whine over noise. Note that the increased number of action potentials near the 600 Hz portion in the whine's FM transition cannot be explained by the spectral tuning of the cells. Many cells are indeed tuned to frequencies near 600 Hz, potentially increasing responses as the modulated fundamental 'passed through' this low threshold area of tuning curves. However, such an effect would be equally likely for the reverse whine and reversed step FM stimuli. This increase in spike number was not found, and thus more consistent with downward FM sensitivity. It is important to point out that there is no biological justification for using 1 ms bins in this analysis. This was chosen to provide the sharpest resolution of stimulus frequency at the highest d a .  Fig 9 suggests that other bin widths may yield similar stimulus times of high discrimination. When d a is calculated at other bin widths, although reduced in value, the times of highest discrimination largely overlap the behaviorally relevant FM transition around 600 Hz (Fig 11).
It is possible that the increased response (including the 'bump') to the whine over noise is due to greater adaptation to noise across the 20 stimulus presentations. Because of frequency specific adaptation [50], noise stimuli may be expected to elicit greater adaptation: noise continuously stimulates all critical bands, whereas stimulation of a critical band by the whine is limited to the time when modulated frequencies pass through them. Although adaptation was potentially present in the design of the previous behavioral experiments, too, and thus incorporated in the phonotactic responses [30], measurements here at the neural level enable analysis of such an effect. Within individuals that received all stimuli, each stimulus presentation was ranked by spike count, enabling analysis of the effect of presentation number. There was significant adaptation to the whine (earlier presentations had more spikes; p< 0.005), but not for the noise (p> 0.434; Fig 12): rank of stimulus presentation number (i.e., 1 to 20) decreased only for the whine. This is the opposite of that predicted if adaptation were to explain our finding of greater responses to the whine than noise responses. Thus, greater sensitivity to the whine does not appear to be due to a relative lack of adaptation.

Discussion
Acoustic sexual signals exhibit inter-and intraspecific variance in multiple physical dimensions (e.g., time, amplitude, and frequency), enabling recognition and choice behavior [2, 74,

Fig 6. Graphic comparison of each cell's response to call stimuli with varied parameters.
Each point is the mean number of spikes per stimulus for the two stimuli shown on the X and Y axes. In all but panel D, the Whine is the reference stimulus. Lines are that predicted if the responses are equal. Note that panel E is an expansion of the axes in panel A so that data close to the origin can be viewed.
https://doi.org/10.1371/journal.pone.0268383.g006 75]. Whereas measuring signal variance is often straightforward, measurements of a signal's information-bearing elements and how the information-bearing parameters in those elements are processed by receivers are often difficult. The robust phonotaxis behavior in P. pustulosus (C) 800 Hz to 500 Hz step FM; D) 500 Hz to 800 Hz step FM. Downward FM elicits a large mid-stimulus response, less so for upward FM, and not evident for tones. All stimuli had 325 ms duration and the natural whine envelope (Fig 1). The FM step transition is 109 ms after stimulus onset (at 25 ms). Histogram bin width is 1 ms. N = 52 cells. https://doi.org/10.1371/journal.pone.0268383.g007

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frogs previously enabled isolation of the signal parameters mediating recognition behavior [30]. As described above, those behavioral data framed our tests of whether individual units in the P. pustulosus auditory midbrain match behavioral sensitivity and if those units, when summed in an experimental population, could predict behavioral discrimination of the call from noise.

Neural correlates to behavior
Prior to the data in this study, neuronal mechanisms of call processing and discrimination in P. pustulosus frogs were assayed at a gross level. In response to long term exposure to stimuli (i.e., minutes), measurements of immediate early gene expression indirectly determined whether the amount of activity across many cells in an anatomical nucleus varied with stimulus type [54,[76][77][78]. These experiments guided our recordings, as they revealed a medial-tolateral area of the torus that exhibits increased gene expressions for conspecific over

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heterospecific whines and other stimulus variants [68,76]. However, even though IEG expression is correlated to excitation, the gene expression data cannot determine within-cell changes in responses, only that levels of expression across cells (and individual frogs) are increased for one stimulus and not for the other. The isolated recordings in our study overcame this limitation and further addressed whether changes in activity in individual cells were generated at a particular point in the stimulus, revealing a neural correlate to phonotaxis. Responses were significantly more sensitive to the whine, with closest responses to whine-like FM steps. Based on recording position, our sample of whine sensitive cells matches well the positions of cells exhibiting gene expression change in response to conspecific calls, as the medial-to-lateral area here overlaps the area of call-sensitive cells composing the laminar nucleus and dorsal area of the principle nucleus of the torus [68,76]. This match offers independent confirmation of the call-sensitive area and suggests that the previously found call-induced change in IEG expression could be explained by within-cell changes in activity.
Does this bias towards whine sensitivity in these cells predict phonotactic decisions? Overall stimulus responses (Table 1) are grossly indicative of behavioral responses, including that the only response not significantly different from the whine's was that to the 700-550 Hz FM step.  Assessing the potential contribution of adaption to greater whine than noise response. Mean response rank (N = 45 cells) for each of the stimulus presentations. There is evidence for adaption to the whine, but not to noise. Thus, adaptation in this experimental design cannot explain increased excitation to the whine relative to the noise. Note that because the number of action potentials per stimulus was often consistent, response rank included ties, compressing the range of ranks on the y-axis. https://doi.org/10.1371/journal.pone.0268383.g012

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However, aspects of the overall response do not align with behavior. For example, some of the largest overall discrimination values (d a , Table 1) should have been found for whine versus reverse whine, as the reverse whine does not elicit phonotaxis [30]. Instead, due to the large variance in these particular responses, this comparison had a small d a , smaller even than that for the whine vs. downward FM steps. Because comparing overall stimulus responses (Table 1) may include coding of non information-bearing parameters, we explored whether subsets of the response were indicative of behavior. Pooling the recordings across cells can address this question by creating ongoing response distributions that enable analysis of discriminability at the stimulus times predicted to be important for behavior. That is, the analysis asks in what part of the stimulus is discriminability highest. Experiments using phonotactic responses showed that the high-to-low frequency transition required for whine recognition occurs across an 80 Hz wide boundary: between 640-560 Hz [30]. For example, FM step stimuli, such as 700-550 Hz and 800-500 Hz, elicit significant whine-like behavioral choices when competing with noise. Consequently Wilczynski et al. [30] concluded, "stimulation anywhere between 900 and 560 Hz, followed at least 50 ms later by stimulation between 640 and 500 Hz in a natural whine is necessary for call recognition, although there is no single frequency in either region that must be stimulated for recognition to occur." Interestingly, for the highest resolution bin width (1 ms) the neural responses to whine verses noise showed peak discriminability (highest d a ) when the FM whine fundamental was at 601 Hz, which is centered in the behaviorally important FM transition zone. Additionally, the experimental stimulus that fit best into the behaviorally predicted range, the 700 Hz to 550 Hz FM step, was the only stimulus in which overall neural excitability did not differ from that of the whine (Table 1). These results support the conclusion that this random sample of whine sensitive cells is processing the information-bearing parameter for call recognition and is not simply exhibiting a more general increased sensitivity to whines, such as more action potentials across the whole call. For example, d a values for whole responses were smaller than those around the FM transition at 600 Hz (see Table 1). This FM stimulus similarity between best neural discrimination and best phonotactic responses is somewhat tempered, however, by the value of the neural discrimination index, which at best was lower than that predicted from behavior. Here, for 1 ms bins d a = 0.72, predicting 69-70% [79] whine versus noise responses. However, P. pustulosus females exhibit 100% (confidence interval to 83%) whine responses vs. noise [30], which would correspond to a much larger discrimination index (d a > 3). This difference allows us to raise several caveats regarding whether these cells could mediate whine recognition as part of the sensorymotor interface of the midbrain [18,39]. First, the sample population is undoubtably incomplete, likely missing cells that could have different response types as part of the recognition circuit. Second, the sample population could have included cells not involved in behavior, causing increased variance and/or reduced response difference. Both effects would decrease d a . Currently, however, there is no way to identify such cells for exclusion. Third, if the sample population is small compared to the endogenous circuitry, sampling error could affect variance, reducing discrimination. Indeed, larger neural sample sizes can increase neural discrimination towards that found in behavior [80]. Using the means (effect size) and variances of the 1 ms bin with the highest d a , we estimate that a sample size of >70 cells would be needed [81] to achieve the discrimination rates in behavior (>95%). The sample size here was 45. Fourth, there may be variance in sensitivity when frogs are under different endocrine states [82][83][84].
Although it is unlikely that females in the study were reproductive (none were gravid or had recently dropped eggs), male status was unknown. Reproductive modulation could increase sensitivity and potentially cause sexually dimorphic responses, although our data did not show this. Finally, these cells are recorded sequentially and across individuals. Thus, we cannot directly confirm how representative the recorded sample is using this 'one-cell-at-a-time' approach. However, given that the summed tuning curve from our sample population is nearly identical to those measured for responses of the entire torus and brainstem (Fig 3A [69 , 85]), it appears the search technique did sample all auditory sensitivity in the frequency domain. Furthermore, the anatomy covered in our search area attempted to match the location of known midbrain auditory processing hubs [68,76,86]. When the tuning and recording locations are taken together with the sensitivity to the important FM transition, it suggests that a significant portion of the auditory midbrain exhibits specialized FM processing for the information-bearing parameter, as there was no evidence for generalized or varied FM sensitivity.
Midbrain sensitivity that is specialized to call features has been found across anuran taxa, including sensitivity to call spectrum [47,48,87]; amplitude modulation (AM) rates [40]; and sensitivity to interpulse intervals, enabling counting of call pulses [52,[88][89][90]. These specializations are correlated to behavior, likely contributing to signal recognition and discrimination decisions. Wilczynski and Ryan [17] noted that in spite of these specializations, responses of midbrain auditory neurons are not exclusive to calls (or other signals), as they often exhibit some, albeit less, response to many other stimuli and thus cannot explain phonotactic decisions that are exclusive to particular stimuli. Thus, such decisions were hypothesized to potentially be mediated by neurons at later processing nodes with greater integration and filtering (e.g., the thalamus; [91,92]) and/or by the output of a network of cells (e.g., population response across anatomical nuclei). It is still unknown in P. pustulosus if cells with near categorical responses exist upstream, such as in the thalamus. However, sensitivity to the whine's FM transition found here is arguably already that strong. Accounting for individual cell variance, comparisons showed significantly greater responses to the whine's FM (the informationbearing parameter, in particular), suggesting extensive integration prior to the di-and telencephalon stages. This within-cell specialization raises the question of whether a standard model for population coding applies to whine processing in the túngara midbrain. Such models often include a population of neurons with different peak, yet overlapping, sensitivities across a range of a stimulus parameter, such as different FM. Because each cell's sensitivity range is overlapping in such a model of population coding, a stimulus at one point on that range creates a distribution of responses across many cells. That response distribution across the population (i.e., its vector) creates the code for that stimulus [93]. A priori, we considered such a population vector as one possible outcome from our recordings: cells would show a range of FM sensitivity, including to different stimuli and even different parts of the whine. This would potentially create a population vector for whine stimuli. Hoke et al. [54], using egr-1 induction, found that expression patterns across several midbrain nuclei were more effective in distinguishing acoustic stimuli than expression patterns in any one nucleus, which is consistent with a population code yielding a stimulus specific vector. However, we found little variance in cell sensitivity: the whine elicited the largest response across cells, suggesting that tuning in the P. pustulosus midbrain is strongly biased to whine FM and not a variety of FM types; the latter would be expected with population vector coding. Thus, in this sample of cells, there appears to be only one dominant sensitivity vector: one for the functional signal. More conclusive evidence for a lack of population coding would require more direct testing, however, such as using multi-unit recordings within individuals, which we did not do.
The FM tuning previously revealed through behavior and its neural correlate here appear most suitable for whine recognition behavior and less so for intraspecific call discrimination, as between-male variance in whine FM is small compared to the parameters of FM sensitivity. Ryan and Rand [61] analyzed over 300 calls from 50 males recorded in the field. Of 15 spectral and temporal call parameters, whine spectral components have relatively low coefficients of variation. And, the distributions of the initial and end frequencies of the whine's FM sweep are on either sides of the important FM transition boundary. Thus, there appears to be little evidence that conspecific calls in the wild would not meet the criteria for FM sensitivity found here and in behavior [30].

FM processing
Considering whine processing in a larger comparative context, FM sensitivity, including to functionally important sounds, is well known at the level of the vertebrate midbrain [37,45,94]. For mammals, FM sensitivity in the IC has been found in rodents [34,43], but most data come from bats. Many units in the IC of bats are tuned to FM stimuli, with sensitivity varying with spectral and temporal structure, and mediated by multiple mechanisms residing potentially within and prior to the IC [35,[95][96][97]. Although many species use FM sounds, there are fewer data for non-mammalians. Comparatively, when added to those in mammals, our data and those in birds [41] strongly support the hypothesis that across-channel or integrative processing in the auditory midbrain is potentially expressed throughout vertebrate systems. Indeed, FM sensitivity in P. pustulosus expands understanding of spectral integration by units of frogs' TS, including sensitivity to disparate frequency combinations important to call processing [87]. While it is still unknown if the mechanisms generating FM sensitivity in túngara frogs are the same as those in other taxa, responses here are consistent with varied temporal excitation and inhibition for different frequency channels, for example. GABAergic inhibition does shape the relationship between excitatory and inhibitory frequency tuning in the frog TS [98]. However, direct experimentation on whine sensitive units is needed to assess FM mechanisms. We would predict at least two benefits to such research. First, from a comparative point of view, it would address whether there are common solutions to creating FM sensitivity across disparate taxa. Second, unlike intraspecific whine FM variance, across species of túngara frogs there is prominent interspecific variance in the FM parameters of the whine-like component of the male calls, to which females can discriminate: conspecific versus heterospecific [99]. If FM sensitivity is mediated by asymmetric excitation and inhibition for frequency input, species-specific divergence in sensitivity could be mediated by shifting the integration across frequency channels [100].

Conclusion
This study builds on extensive behavioral and gene expression assays of call sensitivity in P. pustulosus. Using isolated recordings of single cells in awake frogs, we found specialized sensitivity for the whine component of the complex male call. Functionally, this specialization was significant, as it matched what Suga [6] termed the information-bearing parameter of this species' FM male call. Because each cell's responses were dominated by this parameter (i.e., statistically significant excitation compared to other stimuli), our data suggest that population coding of the whine may not be necessary for stimulus recognition. However, based on signal detection theory analysis, we speculate that given such sensitivity by single cells, combining responses across cells would improve discrimination.


s2fieldsofstudy: Biology Chemistry

text:
GAK and PRKCD kinases regulate basal mitophagy

ABSTRACT The removal of mitochondria in a programmed or stress-induced manner is essential for maintaining cellular homeostasis. To date, much research has focused upon stress-induced mitophagy that is largely regulated by the E3 ligase PRKN, with limited insight into the mechanisms regulating basal “housekeeping” mitophagy levels in different model organisms. Using iron chelation as an inducer of PRKN-independent mitophagy, we recently screened an siRNA library of lipid-binding proteins and determined that two kinases, GAK and PRKCD, act as positive regulators of PRKN-independent mitophagy. We demonstrate that PRKCD is localized to mitochondria and regulates recruitment of ULK1-ATG13 upon induction of mitophagy. GAK activity, by contrast, modifies the mitochondrial network and lysosomal morphology that compromise efficient transport of mitochondria for degradation. Impairment of either kinase in vivo blocks basal mitophagy, demonstrating the biological relevance of our findings. Abbreviations: CCCP: carbonyl cyanide-m-chlorophenyl hydrazone; DFP: deferiprone; GAK: cyclin G associated kinase; HIF1A: hypoxia inducible factor 1 subunit alpha; PRKC/PKC: protein kinase C; PRKCD: protein kinase C delta; PRKN: parkin RBR E3 ubiquitin protein ligase.

Damaged or aged mitochondria are constantly being removed and replenished to maintain an active healthy pool of mitochondria. Mitochondria can also be removed in a programmed manner, such as in the absence of oxygen when they are no longer needed for the generation of ATP by oxidative phosphorylation. The cell has developed several mechanisms for mitochondrial quality control, including detection, disposal, and recycling of superfluous or nonfunctional parts of or whole mitochondria by autophagy, commonly referred to as mitophagy.
Mitophagy can be induced through multiple triggers and pathways. The rapid depolarization of the mitochondrial membrane potential indicates loss of mitochondrial integrity and leads to recruitment of PRKN (parkin RBR E3 ubiquitin protein ligase) and other components to clear mitochondria in a ubiquitin-dependent manner. On the contrary, environmental cues such as hypoxia or iron depletion can trigger mitochondrial turnover in a PRKNindependent manner that appears to be dependent upon HIF1A (hypoxia inducible factor 1 subunit alpha) and its response genes. Interestingly, "housekeeping" levels of mitophagy do not require PRKN, suggesting that the majority of basal mitophagy is regulated in a PRKNindependent manner.
We aimed to identify new regulators of PRKNindependent mitophagy in the hope that this may highlight regulators of basal mitophagy in vivo, using the iron chelator deferiprone (DFP) that mimics a HIF1A response. Given the membrane dependence of the autophagy pathway, we focused specifically on proteins that contain domains known to interact with lipids (e.g., FYVE, PX, C1, C2 domains, etc.). Carrying out an image-based siRNA screen in U2OS cells expressing a mitochondrial matrix localized EGFP-mCherry reporter, we determined that the kinases GAK (cyclin G associated kinase) and PRKCD (protein kinase C delta) act as positive regulators of DFP-induced mitophagy [1]. We validated their effect on mitophagy through several means, including biochemical activity assays, Western blot, and proteomic-based approaches. GAK and PRKC/PKC (protein kinase C) kinase inhibitors recapitulate the effects of targeted siRNA treatment, showing that their kinase activities are key for their roles as positive regulators of PRKN-independent mitophagy. In contrast, the same kinase inhibitors do not affect depolarization-induced (using carbonyl cyanide-m-chlorophenyl hydrazone [CCCP]) PRKN-dependent mitophagy in cells with PRKN-overexpression or serum and amino acid starvation (using Earle's balanced salt solution/EBSS)induced autophagy. Thus, GAK and PRKCD are specific to PRKN-independent mitophagy with no general effect on autophagy.
We discovered that PRKCD localizes to mitochondria irrespective of its lipid-binding domains and that it becomes degraded upon DFP-or CCCP-mediated mitophagy, indicating it remains localized to mitochondria during mitophagy. By analysis of the early autophagy markers ULK1 (unc-51 like autophagy activating kinase 1), ATG13 (autophagy related 13), and WIPI2 (WD repeat domain, phosphoinositide interacting 2), we found that PRKC inhibitors strongly prevent the formation of initiation sites during DFP treatment, likely explaining the observed inhibition of mitophagy.
GAK does not localize to mitochondria and its kinase inhibition results in more profound mitochondrial morphology changes, with mitochondrial networks clumping together into parallel stagnant networked layers. Cotreatment with CCCP causes mitochondrial fragmentation, indicating functional mitochondrial fission machinery. GAK inhibition also induces phosphorylation changes in glycolytic proteins that may indicate metabolic pathway changes. In addition, large autolysosome-like structures with multiple membrane layers are observed by electron microscopy, indicating that GAK affects lysosomal morphology. However, acidic LysoTracker-positive compartments that retain degradative ability are still observed in cells treated with the GAK inhibitor during CCCP or nutrient-starvation treatments, indicating that GAK activity is specifically required for DFP-induced mitophagy.
Having observed a role for PRKCD and GAK in PRKNindependent mitophagy at a cellular level, we wanted to examine whether either kinase was important for mitophagy on a whole organism level. GAK is an essential gene due to the presence of a C-terminal J-domain that mediates clathrin uncoating. C. elegans offered a unique opportunity to test a possible in vivo function of GAK due to the presence of two homologs to GAK, GAKH-1 that contains the kinase domain, and DNJ-25 that contains the essential J-domain. Selective silencing of gakh-1 results in inhibition of basal mitophagy in the muscle cell wall, confirming that the kinase activity is key to mediating GAK's effect on mitophagy. This finding is of additional interest given that GAK mutations are also a risk factor for the development of Parkinson disease.
PRKCD has two homologs in zebrafish (prkcda and prkcdb) that are expressed across neuronal compartments. Using an antibody recognizing zebrafish Prkcda and Prkcdb, we identified an enriched expression in the hindbrain region of zebrafish larvae. Examination of mitophagy in this region showed that Figure 1. The regulation of PRKN-independent mitophagy by GAK and PRKCD. Several subtypes of autophagy and mitophagy pathways exist, including PRKNindependent mitophagy (DFP-induced), PRKN-dependent mitophagy (CCCP-induced), and starvation-induced autophagy (EBSS-induced). PRKCD localizes to mitochondria independent of its C1 or C2 domains and becomes degraded upon induction of mitophagy pathways. Recruitment of ATG13-ULK1-RB1CC1/FIP200-ATG101 to phagophore initiation sites upon induction of PRKN-independent mitophagy is blocked by PRKCD siRNA (siPRKCD) or PRKC kinase inhibitors (PRKCi). Treatment with GAK siRNA (siGAK) or GAK kinase inhibitor (GAKi) causes mitochondrial clustering and an accumulation of swollen multi-lamellar lysosomes. In vivo examination of the hindbrain region of zebrafish (D. rerio) demonstrates basal mitophagy, which is reduced by dual knockout of prkcda and prkcdb (homologs of PRKCD). Similarly, RNAimediated depletion of gakh-1 (GAK homolog) in worms (C. elegans) reduces basal mitophagy levels in muscle cell walls.
both basal and Hif1a-dependent mitophagy pathways are inhibited by CRISPR-Cas9-mediated knockout of both prkcda and prkcdb. Furthermore, double-knockout larvae display anxiety-dependent locomotory defects that may indicate neuronal abnormalities that require further investigation.
This work identifies two kinases as playing distinct roles in PRKN-independent mitophagy and offers exciting insights into the regulation of basal mitophagy in vivo (Figure 1). Further work is now required to fully understand mechanistically how each kinase orchestrates its role in mitophagy and identify downstream phosphorylated substrates. This will potentially enable the specific targeting and regulation of PRKN-independent mitophagy, independently of other cellular autophagy pathways.

Disclosure statement
M.J.M. is now an employee of AstraZeneca. The remaining authors declare no competing interests.