extfieldsofstudy: Medicine Chemistry

text:
Elevated ocular pressure reduces voltage-gated sodium channel NaV1.2 protein expression in retinal ganglion cell axons

Glaucoma is an age-related neurodegenerative disease that is commonly associated with sensitivity to intraocular pressure. The disease selectively targets retinal ganglion cells (RGCs) and constituent axons. RGC axons are rich in voltage-gated sodium channels, which are essential for action potential initiation and regeneration. Here, we identified voltage-dependent sodium channel, NaV1.2, in the retina, examined how this channel contributes to RGC light responses, and monitored NaV1.2 mRNA and protein expression in the retina during progression of modeled glaucoma. We found NaV1.2 is predominately localized in ganglion cell intraretinal axons with dispersed expression in the outer and inner plexiform layers. We showed Phrixotoxin-3, a potent NaV1.2 channel blocker, significantly decreased RGC electrical activity in a dose-dependent manner with an IC50 of 40 nM. Finally, we found four weeks of raised intraocular pressure (30% above baseline) significantly increased NaV1.2 mRNA expression but reduced NaV1.2 protein level in the retina up to 57% (p < 0.001). Following prolonged intraocular pressure elevation, NaV1.2 protein expression particularly diminished at distal sections of ganglion cell intraretinal axons (p ≤ 0.01). Our results suggest NaV1.2 might be a therapeutic target during disease progression to maintain RGC excitability, preserving presynaptic connections through action potential backpropagation. Glaucoma (glaucomatous optic neuropathy) is an age-related neurodegenerative disease worsened by sensitivity to intraocular pressure, which is the symptom typically treated (Heijl et al., 2002; Calkins, 2012). The defining feature of glaucoma is selective degeneration of retinal ganglion cells (RGCs) and their axons. Prior to outright RGC death, axons show degenerative signatures, including anterograde transport deficits, neurofilament loss, reduced optic nerve compound action potentials, and axon dropout (Buckingham et al., 2008; Baltan et al., 2010; Crish et al., 2010). Ganglion cell intraretinal axons are particularly sensitive to pressure due to their tortuous path. Intraretinal axons collect into axon bundles and exit the optic disk at right angles, forming the optic nerve head (Soto et al., 2008). RGC intraretinal axons are also vulnerable to stress due to high energy demands of voltagegated sodium channels within unmyelinated axon sections (Calkins, 2012). Of the 10 identified voltage-sensitive sodium (NaV1.1-NaVx) channel isoforms, NaV1.1, NaV1.2, NaV1.3, and NaV1.6 have been found in various tissues of the central nervous system, including the retina (Fjell et al., 1997; Caldwell et al., 2000; Boiko et al., 2003; Van Wart et al., 2005, 2007; Mojumder et al., 2007; Catterall, 2012). Previously, we reported expression of NaV1.6, which is essential for action potential initiation and propagation, transiently increased following modest short-term intraocular pressure elevation (Rush et al., 2005; Hu et al., 2009; Risner et al., 2018). Prolonged (four weeks) ocular hypertension led to reduced NaV1.6 expression (Risner et al., 2018). Here, we examined how modeled glaucoma affects NaV1.2, which has been shown to colocalize with NaV1.6 within ganglion cell intraretinal axons and mediate action potential backpropagation (Boiko et al., 2003; Hu et al., 2009). We found NaV1.2 is expressed predominately within intraretinal ganglion cell axons. The NaV1.2 blocker, Phrixotoxin-3, significantly suppressed light responses of physiologically-identified αONsustained RGCs in a dose-dependent manner. Raising intraocular pressure for four weeks reduced NaV1.2 protein but increased mRNA expression in the retina. Our results, taken together, confirm the architectural expression of NaV1.2 in the retina, show NaV1.2 significantly contributes to RGC electrical activity, and implicates NaV1.2 as a potential therapeutic target in glaucoma. All methods employed in this study were approved by the https://doi.org/10.1016/j.exer.2019.107873 Received 5 August 2019; Received in revised form 16 October 2019; Accepted 13 November 2019 Abbreviations: RGC, Retinal ganglion cell; NaV, Voltage-gated sodium; Phx3, Phrixotoxin-3; mRNA, messenger ribonucleic acid ∗ Corresponding author. Vanderbilt University Medical Center, 11435 MRB IV, 2215B Garland Avenue, Nashville, TN, 37232, USA. E-mail address: david.j.calkins@vumc.org (D.J. Calkins). Experimental Eye Research 190 (2020) 107873 Available online 14 November 2019 0014-4835/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). T Vanderbilt University Medical Center Institutional Animal Care and Use Committee. We modeled glaucoma in adult (2 months old) C57Bl/6 male mice (C57, Charles River Laboratory) by raising intraocular pressure (IOP) using the microbead occlusion model (Sappington et al., 2010; Calkins et al., 2018). Polystyrene microbeads (1.5 μL volume, 15 μm bead size, Invitrogen, Carlsbad CA) were injected into the anterior chamber of one eye and an equal volume of saline into the fellow eye while animals were anesthetized (2.5% isoflurane). Intraocular pressure was measured in anesthetized animals 2–3 times per week for up to 4 weeks by rebound tonometry (Tonopen XL, Medtronic Solan, Waltham MA). Microbead injection significantly raised eye pressure up to 30% in the cohort of animals used for immunohistochemical analysis of protein level (two week cohort (mean ± sem): 19.1 ± 2 vs 14.6 ± 1.7 mmHg; four week cohort: 19 ± 1.3 vs 14.9 ± 1.3 mmHg; see Supplementary Results in (Risner et al., 2018)). Microbead injection also significantly raised intraocular pressure up to 30% in animals used for mRNA expression quantification (two-week cohort (mean ± sem): 21.5 ± 1.3 vs 14.9 ± 1.1 mmHg, three-week cohort: 22.75 ± 1.25 vs 16.6 ± 1 mmHg, four-week cohort: 22.3 ± 1.9 vs 16.7 ± 1.6 mmHg). For immunohistochemical experiments, animals were transcardially perfused with 4% paraformaldehyde in PBS and retinas were dissected out of the eyes. Retinas from each cohort (two and four week) and condition (microbead and saline) were vertically cryosectioned (10 μm thick) or kept whole. Retinal sections were labeled with anti-NaV1.2 (1:200, Alomone Labs, Jerusalem Israel), anti-choline acetyltransferase (ChAT, 1:100, Millipore, Burlington MA), and mounted using Fluoromount medium with DAPI (SouthernBiotech, Birmingham AL). Whole retinas were labeled with anti-NaV1.2, anti-Pou domain transcription factor (Brn3a, 1:200 Santa Cruz Biotechnology, Dallas TX), and anti-neurofilament H (SMI32, 1:1000, BioLegend, San Diego CA). Retinal sections and whole mounts were imaged (0.72 μm z slices) using an Olympus FV-1000 confocal microscope with a 60X objective (Vanderbilt Cell Imaging Shared Resource). Confocal laser transmissivity and exposure settings (4 μs/pixel) were the same for all imaging sessions: SMI32 = 488 nm 5% transmissivity, NaV1.2 = 543 nm 25% transmissivity, Brn3a = 633 nm 25% transmissivity. Individual RGCs, marked by SMI32 labeling, were imaged using a 60X objective with 3X magnification. Images were analyzed using ImageJ (Fuji, Version 1.51i). Colocalization analysis was performed using the Coloc2 plugin within ImageJ. We measured the proportion of colocalization between SMI32 and NaV1.2 (tM1) and NaV1.2 and SMI32 (tM2 (Manders et al., 1993)). We also assessed NaV1.2 intensity distributions along SMI32labeled axon proximal segments. NaV1.2 intensity profiles along each separate axon segment were normalized relative to the total length of that segment. Normalized segments were binned into 0.02 sections. NaV1.2 intensities were then averaged within each bin (Duflocq et al., 2011). We also assessed the influence of elevated intraocular pressure on NaV1.2 mRNA expression. Total RNA was extracted from each retina according to the SV Total RNA Isolation System kit protocol (Promega, Madison, WI). RNA concentration and purity were determined by measuring the absorbance at 260 nm and 280 nm using a NanoDrop 8000 (Thermo Scientific, Wilmington, DE). mRNA was then reversetranscribed into cDNA using the 1st Strand cDNA Synthesis System for quantitative RT-PCR (Origene, Rockville, MD) following the manufacturer's instructions. Reaction mixtures were diluted 2-fold and real-time PCR was performed using QuantStudio 3 Real Time PCR System (Applied Biosystem, Waltham, MA) using PowerUp SYBR Green master mix (Applied Biosystem) and the following primers: NaV1.2 (Fw) 5′-GGGAACGCCCATCAAAGAAG-3’ (Rev) 5′-ACGCTATCGTAGGAAGG TGG-3’; GAPDH (Fw) 5′-TCCATGACAACTTTGGCATTG-3’ (Rev) 5′-CAGTCTTCTGGGTGGCAGTGA-3’ (Laedermann et al., 2014). Cycling conditions were: 95 °C for 2 min and 40 cycles of 95 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s. Samples were run in triplicate and analysis was performed using the comparative CT method, using GAPDH mRNA as endogenous controls. In a subset of experiments, we examined NaV1.2 channel contribution to the light-evoked spike rate of RGCs from naïve C57 mice (1.5–2 months old). Retinas were dissected out of the eye and placed in a physiological chamber continually perfused (2 mL/min) with oxygenated Ames’ medium (pH = 7.4, Osmolarity = 290, U.S. Biologicals, Salem MA), supplemented with 20 mM glucose and heated to 35 °C (TC344C, Warner Instruments). RGCs with large somas (> 18 μm in diameter) were targeted for recording with pipettes filled with (in mM) 125 K-Gluconate, 10 KCl, 10 HEPES, 10 EGTA, 4 Mg-ATP, and 1 NaGTP (pH = 7.35, Osmolarity = 285). Pipette resistance ranged from 4 to 8 MΩ. Electrical signals were amplified (MultiClamp 700B, Molecular Devices, Sunnyvale CA), digitized (DigiData 1550A, Molecular Devices), and sampled at 50 kHz. Light responses were evoked using a full-field flash produced by a 365 nm 300 μW/cm lightemitting diode (Roithner Laserteknik, Vienna Austria). NaV1.2 channels were modulated by bath application of 25–400 nM Phrixotoxin-3 (Alomone labs (Bosmans

Glaucoma (glaucomatous optic neuropathy) is an age-related neurodegenerative disease worsened by sensitivity to intraocular pressure, which is the symptom typically treated (Heijl et al., 2002;Calkins, 2012). The defining feature of glaucoma is selective degeneration of retinal ganglion cells (RGCs) and their axons. Prior to outright RGC death, axons show degenerative signatures, including anterograde transport deficits, neurofilament loss, reduced optic nerve compound action potentials, and axon dropout Baltan et al., 2010;Crish et al., 2010). Ganglion cell intraretinal axons are particularly sensitive to pressure due to their tortuous path. Intraretinal axons collect into axon bundles and exit the optic disk at right angles, forming the optic nerve head (Soto et al., 2008). RGC intraretinal axons are also vulnerable to stress due to high energy demands of voltagegated sodium channels within unmyelinated axon sections (Calkins, 2012).
All methods employed in this study were approved by the Vanderbilt University Medical Center Institutional Animal Care and Use Committee. We modeled glaucoma in adult (2 months old) C57Bl/6 male mice (C57, Charles River Laboratory) by raising intraocular pressure (IOP) using the microbead occlusion model Calkins et al., 2018). Polystyrene microbeads (1.5 μL volume, 15 μm bead size, Invitrogen, Carlsbad CA) were injected into the anterior chamber of one eye and an equal volume of saline into the fellow eye while animals were anesthetized (2.5% isoflurane). Intraocular pressure was measured in anesthetized animals 2-3 times per week for up to 4 weeks by rebound tonometry (Tonopen XL, Medtronic Solan, Waltham MA). Microbead injection significantly raised eye pressure up to 30% in the cohort of animals used for immunohistochemical analysis of protein level (two week cohort (mean ± sem): 19.1 ± 2 vs 14.6 ± 1.7 mmHg; four week cohort: 19 ± 1.3 vs 14.9 ± 1.3 mmHg; see Supplementary Results in (Risner et al., 2018)). Microbead injection also significantly raised intraocular pressure up to 30% in animals used for mRNA expression quantification (two-week cohort (mean ± sem): 21.5 ± 1.3 vs 14.9 ± 1.1 mmHg, three-week cohort: 22.75 ± 1.25 vs 16.6 ± 1 mmHg, four-week cohort: 22.3 ± 1.9 vs 16.7 ± 1.6 mmHg).
We also assessed the influence of elevated intraocular pressure on NaV1.2 mRNA expression. Total RNA was extracted from each retina according to the SV Total RNA Isolation System kit protocol (Promega, Madison, WI). RNA concentration and purity were determined by measuring the absorbance at 260 nm and 280 nm using a NanoDrop 8000 (Thermo Scientific, Wilmington, DE). mRNA was then reversetranscribed into cDNA using the 1st Strand cDNA Synthesis System for quantitative RT-PCR (Origene, Rockville, MD) following the manufacturer's instructions. Reaction mixtures were diluted 2-fold and real-time PCR was performed using QuantStudio 3 Real Time PCR System (Applied Biosystem, Waltham, MA) using PowerUp SYBR Green master mix (Applied Biosystem) and the following primers: NaV1.2 (Fw) 5′-GGGAACGCCCATCAAAGAAG-3' (Rev) 5′-ACGCTATCGTAGGAAGG TGG-3'; GAPDH (Fw) 5′-TCCATGACAACTTTGGCATTG-3' (Rev) 5′-CAGTCTTCTGGGTGGCAGTGA-3' (Laedermann et al., 2014). Cycling conditions were: 95°C for 2 min and 40 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 30 s. Samples were run in triplicate and analysis was performed using the comparative CT method, using GAPDH mRNA as endogenous controls.
NaV1.2 is localized within RGC axon initial segments of adult mice (Boiko et al., 2003). To confirm this finding, we assessed NaV1.2 expression within individual intraretinal ganglion cell axons. RGCs and their axons were identified by NaV1.2 and SMI32 immunolabeling, and axons were distinguished from dendrites by the axon hillock and z-axis depth. RGC proximal axon segments were clearly labeled by anti-NaV1.2 ( Fig. 1A dashed line). We examined the expression pattern of NaV1.2 within RGC axon proximal segments by manually tracing the axon, using the segmented line tool in ImageJ. The line width was 0.7 μm. The average axon proximal segment length was 35 ± 1.7 μm, which is similar to axon initial segment lengths previously reported (up to 30 μm (Boiko et al., 2003)). The intensity profile of NaV1.2 labeling appeared to form a normal distribution (Fig. 1B). We also found NaV1.2 expression within distal RGC axons (Fig. 1A arrowheads). NaV1.2 is particularly localized within axon varicosities of distal RGC intraretinal axons, as evidenced by the spatial pattern of NaV1.2 immunolabeling and the overlap of NaV1.2 and SMI32 signals ( Fig. 1C-D). In addition, we found NaV1.2 labeling defined the perimeter of many RGC somas (Fig. 1E). NaV1.2 intensity profiles show NaV1.2 staining outlined the cell body, reduced labeling in surrounding vicinities, and sparse NaV1.2 localization within Brn3a-positive areas (Fig. 1F).
NaV1.2 has previously been shown to distinctively promote action potential backpropagation in pyramidal neurons of the rodent prefrontal cortex (Hu et al., 2009). Action potential backpropagation significantly regulates dendritic synaptic strength (Eilers and Konnerth, 1997). RGC dendritic remodeling and pruning is observed in several models of glaucoma (Della Santina et al., 2013;El-Danaf and Huberman, 2015;Ou et al., 2016;Risner et al., 2018). Thus, NaV1.2 channels might mediate RGC dendritic organization via action potential backpropagation during disease progression. However, it is unknown if NaV1.2 channels significantly modulate RGC electrical activity.
To address the functional contribution of NaV1.2 channels on RGC light responses, we measured the influence of the NaV1.2 channel blocker, Phrixotoxin-3 (Phx3). We targeted RGCs with large cell bodies (20 ± 0.3 μm diameter) that produced a sustained spike train during light presentation (Fig. 1G, cell n = 10, animal n = 3). These cells have been categorized as αON-sustained (αON-S) RGCs (Pang et al., 2003;Risner et al., 2018). We assessed each cell's light response by counting the number of spikes during the 3 s of light onset and dividing by the number of trials (≥6 trials) and time (i.e., referred to as spike rate or spikes/s). αON-S RGCs produced 51 ± 9.5 spikes/s during light onset.
To determine the potency of Phx3 on blocking spike activity, we bathapplied the drug from 25 to 400 nM for 2 min and measured the effects of the agent on the light response for each concentration tested (Fig. 1H). Application of just 25 nM Phx3 significantly decreased light-evoked activity by 38% (p = 0.001). Raising the concentration of Phx3 to 50 nM suppressed light-induced firing by 67% (p < 0.001). Lightevoked spiking was completely abolished by 400 nM Phx3 (Fig. 1I). We determined the I C 50 of Phx3 on the light-evoked spike rate using interpolated values from a best-fit exponential decay function. This analysis revealed 40 nM of Phx3 reduced the light-evoked spike rate by half (Fig. 1I), interestingly this is the concentration used to probe NaV1.2 channels in pyramidal neurons of the prefrontal cortex (Yin et al., 2017). Following Phx3 washout (> 20 min), light responses returned 72 ± 7% of pre-drug firing rates for all but one cell, which failed to recover from the highest dose of Phx3. This reduction in light-evoked activity following drug washout may be due to cell run-down as experiments typically lasted about 1.5 h, including all washout cycles.
In a previous report, we found two weeks of modest intraocular pressure elevation (30% above baseline) led to a transient increase in NaV1.6 expression within RGC intraretinal axons. After four weeks of ocular hypertension, NaV1.6 abundance decreased (Risner et al., 2018). Here, we explore how modeled glaucoma influences NaV1.2 mRNA and protein expression within the retina.
We first examined the potential influence of raising eye pressure on NaV1.2 mRNA expression in the retina. We found NaV1.2 expression is dependent on the duration of raised intraocular pressure. Elevating (I) Light-evoked spike rate was normalized to pre-drug light response for each cell (n = 10). Spike rate was significantly (*, p ≤ 0.04) reduced by application of 25 nM Phx3, and spiking was completely abolished by 400 nM for all cells analyzed. Solid line is the best-fit exponential decay (R 2 = 0.88). Based on the exponential decay function 40 nM Phx3 would reduce the light-evoked spike rate by 50% (I C 50, dashed gray line). The heavy solid gray horizontal lines in G and H indicates light duration (3 s). Data represented as mean ± sem. Statistics: One-Way ANOVA, Tukey Post-hoc comparisons. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) pressure for two weeks did not alter NaV1.2 mRNA levels (p = 0.81). Three weeks of intraocular pressure elevation decreased NaV1.2 mRNA abundance by 37% (p = 0.33). Raising pressure for four weeks, however, increased NaV1.2 mRNA expression by 93% compared to retinas of saline-injected eyes (p = 0.06). Given that mRNA expression appears to be altered by the duration of raised pressure, we compared NaV1.2 levels between each time point. We observed an approximate 3-fold increase in NaV1.2 mRNA expression between three and four weeks of raised eye pressure (*, p = 0.039 Fig. 2A).
Next, we assessed the impact of intraocular pressure elevation on NaV1.2 protein expression within the outer and inner plexiform layers of the retina (OPL, IPL). Following IOP elevation, retinal cryosections were immunolabeled for NaV1.2, ChAT, and DAPI. The outer plexiform layer was identified based on the lack of DAPI staining between the outer and inner nuclear layers. Similar to Mojumder et al. (2007), we find dispersed NaV1.2 labeling within the OPL (Fig. 2B). We quantified NaV1.2 expression by manually outlining the OPL within each retinal section (n = 4 sections/retina, animal n = 6) and computing the average channel intensity for the region of interest using ImageJ. Raising eye pressure modestly decreased NaV1.2 expression within the OPL at both two (25%, p = 0.12) and four weeks (35%, p = 0.18). Next, we assessed NaV1.2 expression within the IPL, which was identified by ChAT labeling and lack of DAPI staining. Comparable to the OPL, the IPL exhibited dispersed NaV1.2 labeling. NaV1.2 expression within the IPL was unaffected by two weeks of elevated pressure (p = 0.33). However, following four weeks of elevated pressure, NaV1.2 staining within the IPL was significantly reduced by 39% (p = 0.02, Fig. 2C). We then broadly evaluated the influence of intraocular pressure on NaV1.2 channel protein localization within RGC axon and soma sublayers from large patches (212 μm 2 ) of whole-mounted retinas from all quadrants (n = 4 images/retina, animal n = 6). The axon layer, marked by predominate SMI32 staining of axon bundles, possesses intense NaV1.2 expression, colocalizing with SMI32 by 72 ± 3% (Fig. 2D, F). The RGC soma layer appears weak compared to the axon layer but more punctate relative to the plexiform layers. Following four weeks of intraocular pressure elevation, NaV1.2 appears to be reduced (Fig. 2D, right). We separately measured NaV1.2 expression within RGC soma and axon layers by identifying the plane with the densest SMI32 (axon layer) and Brn3a (soma layer) labeling and computing the average intensity of the NaV1.2 channel (Fig. 2E). Following two weeks of raised pressure, we did not detect a change in NaV1.2 protein expression within RGC axon or soma layers (axon layer p = 0.37, soma layer p = 0.16, Fig. 2G, left). However, after four weeks of elevated pressure, NaV1.2 expression significantly decreased in both RGC axon (38%, p = 0.003) and soma layers (48%, p = 0.002, Fig. 2G, right).
To confirm our analysis of the impact of ocular hypertension on NaV1.2 expression within RGC somas and axons from large retinal images, we examined NaV1.2 localization in individual SMI32-positive RGCs from the same images (n ≥ 19 cells for each condition). Visually, NaV1.2 intensity was drastically reduced after four weeks of elevated IOP (Fig. 2H). NaV1.2 channel expression was analyzed in ImageJ using the freehand draw function to outline somas and the line segment tool (0.7 μm line width) to mark axons. NaV1.2 channel mean intensity was computed for each region of interest. There was no significant difference in proximal axon segment lengths between microbead-and salineinjected eyes (saline = 35 ± 1.7 μm, microbead = 39 ± 1.8 μm, p = 0.09). Like the results from our analysis of large retinal patches (Fig. 2D, G), NaV1.2 expression is unaltered after two weeks of elevated pressure in either RGC somas (p = 0.29) or axons (p = 0.84). Four weeks of raised intraocular pressure, however, significantly reduced NaV1.2 intensity within RGC somas (54%, p < 0.001) and their axons (57%, p < 0.001, Fig. 2I). To further analyze where the NaV1.2 dropout occurs within axon proximal segments, we plotted the distribution of NaV1.2 intensities normalized by length. NaV1.2 expression significantly decreased within distal sections of the axon following four weeks of elevated pressure (p ≤ 0.01, Fig. 2J).
Our results confirm NaV1.2 expression within the retina, and we identify dense NaV1.2 localization within ganglion cell intraretinal axons (Boiko et al., 2003). Furthermore, for the first time, we show NaV1.2 channels significantly contribute to RGC light-evoked spiking. Finally, we find retinal NaV1.2 mRNA expression is increased but protein is reduced after prolonged intraocular pressure elevation.
At steady-state, mRNA concentration is a weak predictor of protein abundance (Gygi et al., 1999;Vogel and Marcotte, 2012). Protein availability is determined by dynamically linked processes controlling production and degradation of mRNA and protein, including translation rates, modification of protein turnover, protein synthesis time, protein localization, and cellular state. When the cellular environment is perturbed, the relationship between mRNA levels and protein abundance is often temporally discordant (Vogel et al., 2011;Liu et al., 2016;Martin-Perez and Villén, 2017).
Here, we found elevating pressure for two weeks does not change NaV1.2 mRNA relative expression and protein in the retina ( Fig. 2A, C,  G). After three weeks of pressure elevation, we found a modest decrease in mRNA expression. Following four weeks of increased intraocular pressure, NaV1.2 mRNA increases while local protein expression decreases ( Fig. 2A, C, G, I). Based on our current results, we suggest the modest decrease in NaV1.2 mRNA expression noted after three weeks of raised pressure contribute to the reduction of protein observed following four weeks of ocular hypertension, and the significant increase in NaV1.2 mRNA at four weeks post microbead injection might reveal an on-going compensatory reaction to IOP-related reduction in NaV1.2 protein. A lengthy delay between changes in transcription and protein levels is possible given the reported half-life of NaV channels is approximately 30 h (Maltsev et al., 2008).
Previously, we found four to five weeks of elevated IOP decreased RGC spike activity and decreased RGC dendritic complexity, suggesting a positive correlation between spike rate and dendritic arbor maintenance (Weitlauf et al., 2014;Risner et al., 2018). In our previous studies, intracellular recordings of action potentials were performed from RGC somas, which is a measure of spike backpropagation. Recent evidence suggests a direct relationship between NaV1.2-mediated backpropagation and synaptic strength (Shin et al., 2019;Spratt et al., 2019).
Since NaV1.2 channels support action potential electrogenesis, backpropagation, and consequently, synaptic strength, our current results suggest if RGC electrical activity can be maintained during the early stages of modeled glaucoma, synaptic integrity may be preserved. Previously, we have found four weeks of raised IOP led to significant RGC dendritic pruning, but did not cause outright RGC degeneration (Risner et al., 2018). However, two and four weeks of raised IOP decreases anterograde transport of cholera toxin subunit B to the superior colliculus, indicating progression of axonopathy (Risner et al., 2018). Following eight weeks of microbead-induced elevated pressure, we find significant RGC cell body dropout and axon loss in the optic nerve Chen et al., 2011). These results suggest RGCbipolar cell synapse loss precedes RGC death and axon dropout. Activating NaV1.2 channels might be one way to maintain the backwardpropagating spread of depolarization in order maintain presynaptic connections in addition to pressure-lowering treatment.


s2fieldsofstudy:
Biology
Agricultural And Food Sciences

text:
Parasitism in pupae synanthropic flies in Caldas Novas, Gois

T his study reports on the prevalence of parasitism in synanthropic flies collected at the altitudes of 740 and 1000 meters in Caldas Novas, State of Goiás. T he substrates utilized were human feces, fruits, chicken viscera, fish and bovine kidneys. T he pupae were obtained via the flotation method and were individually placed in gelatin capsules until the emergence of the adults or their parasitoids. From the altitude of 740 meters, 1255 muscoid Diptera were collected, from which 211 parasitoids emerged, while from the altitude of 1000 meters, 1691 parasitoids were collected, from which 1196 parasitoids emerged. T he total prevalence of parasitism found was 10.3%. T he prevalence of parasitism at 740 and 1000 meters was 13.5% and 9.1%, respectively. At the altitude of 740 meters, the greatest prevalence of parasitism found was of the parasitoid Spalangia drosophilae Ashmead (Hymenoptera: Pteromalidae), in Peckia chrysostoma (Wiedemann) (Diptera: Sarcophagidae), with a prevalence of 17.4%. At the altitude of 1000 meters, the greatest prevalence of parasitism found was of the parasitoid Gnathopleura quadridentata Wharton (Hymenoptera: Braconidae), in Sarcodexia lambens (Wiedemann) (Diptera: Sarcophagidae), with a prevalence of 56.0%.

Introduction Introduction
T he flies included in the Muscomorpha infra-order (Guimarães et al., 2001) are of medical and veterinary importance, since they can produce myiasis and act on the transmission of pathogens to humans and animals (Ferreira, 1978;Marcondes, 2001). T hey have been found carrying more than 100 species of disease-causing organisms such as bacteria, protozoa and helminths (Greenberg, 1971). Qeios, CC-BY 4.0 · Article, November 26, 2019 Qeios ID: 349368 · https://doi.org/10.32388/349368 1/9 In addition to chemical technique through insecticides, so-called natural regulators of various pests can be used as a means of controlling these insects in both agriculture and animal husbandry (Silveira et al., 1989).
Parasitoid hymenopterans are an important element of neotropical fauna because of their role in controlling the population of other insects that directly or indirectly and not yet well-qualified interfere with the trophic chains of most ecosystems (Perioto et al., 2004).
Aiming at an effective and safe method of fly control for humans and the environment, biological control studies of synanthropic flies are performed worldwide (Carvalho et al., 2004 T he flies were collected by using traps, made of dark cans measuring 19 cm in height and 9 cm in diameter, with two openings resembling blinders, located in the lowest third of the can, to allow flies to enter. T he top of the can was connected to a nylon funnel that was open at both ends, with the base pointing down. T his was wrapped in plastic bags, so that when they were removed, the flies and parasitoids could be collected. T he following items were used as baits: human feces, cattle kidneys, cattle liver, chicken, fruit and fish which were placed inside the cans, over a layer of earth. . 10 traps were used for the altitude of 740 meters (17º 46'04 "S -48º 39 '35" W) and 10 traps for the altitude of 1000 meters (17º 46' 52 "S -48º 41 '14" W), two for each type of bait that were hung on trees 1 meter from the ground, 2 meters from each other. T he collected individuals were taken to the laboratory, sacrificed with ethyl ether and preserved in 70% alcohol for later identification.
T o obtain the parasitoids, the contents of the traps were placed in plastic containers with a layer of sand for use as a substrate for transformation of the larvae into pupae. T his sand was sifted after being in the fields for 15 days and the pupae were extracted from it and were individually placed in gelatin capsules (number 00) in order to obtain the flies and/or parasitoids.
T he total percentage parasitism was calculated by means of the number of pupae parasitized, divided by the total number of pupae collected, and multiplied by 100. T he percentage parasitism of each parasitoid species was calculated by means of the number of pupae parasitized per species of parasitoid, divided by the total number of pupae from that host, and multiplied by 100.
T he preference of parasitoids for their hosts was tested by Chi-Square at 5% significance level. Spalangia drosophilae is cited in the literature as parasitoid of pups of small dipterans of the families Chloropidae, Drosophilidae, Muscidae, Sarcophagidae and Sepsidae (Marchiori et al., 2001;Marchiori et al., 2002).

Results and Discussion
At the altitude of 1000 meters the highest percentage of parasitism was found by the parasitoid Gnathopleura quadridentata Wharton (Hymenoptera: Braconidae) in Sarcodexia lambens (Wiedemann) (Diptera: Sarcophagidae) with 56.0% (T able 2).
Gnathopleura quadridentata has a preference for muscoid dipterans, especially for Sarcophagidae (Shenefelt, 1974). T his parasitoid is solitary, and emerges from the host puparium. Species of the genus Gnatopleura have been used for biological control of Sarcophagidae and Muscidae (Wharton, 1979). Spalangia drosophilae and G.
quadridentata presented the highest percentage of parasitism at 740 and 1000 meters, Regarding substrate, the highest percentage of parasitism at 740 meters was obtained in human feces with 6.7% (84/1255) and at 1000 meters also in human feces with 60.0% (1013/1591) (T able 3).
Studies on the faunal composition of parasitoid hymenoptera in the Neotropical region are scarce, despite the diversity, biological, ecological and economic importance of these insect groups (Perioto et al., 2003).


extfieldsofstudy: Medicine Biology

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High prevalence and genetic diversity of Haemoproteus columbae (Haemosporida: Haemoproteidae) in feral pigeons Columba livia in Cape Town, South Africa

In this study, we explore blood parasite prevalence, infection intensity, and co-infection levels in an urban population of feral pigeons Columba livia in Cape Town. We analyze the effect of blood parasites on host body condition and the association between melanin expression in the host’s plumage and parasite infection intensity and co-infection levels. Relating to the haemosporidian parasite itself, we study their genetic diversity by means of DNA barcoding (cytochrome b) and show the geographic and host distribution of related parasite lineages in pigeons worldwide. Blood from 195 C. livia individuals was collected from April to June 2018. Morphometric measurements and plumage melanism were recorded from every captured bird. Haemosporidian prevalence and infection intensity were determined by screening blood smears and parasite lineages by DNA sequencing. Prevalence of Haemoproteus spp. was high at 96.9%. The body condition of the hosts was negatively associated with infection intensity. However, infection intensity was unrelated to plumage melanism. The cytochrome b sequences revealed the presence of four Haemoproteus lineages in our population of pigeons, which show high levels of co-occurrence within individual birds. Three lineages (HAECOL1, COLIV03, COQUI05) belong to Haemoproteus columbae and differ only by 0.1% to 0.8% in the cytochrome b gene. Another lineage (COLIV06) differs by 8.3% from the latter ones and is not linked to a morphospecies, yet. No parasites of the genera Leucocytozoon and Plasmodium were detected.

Introduction
Wild feral pigeon populations of Columba livia f. domestica Gmelin, 1789 are typical inhabitants of urban landscapes, where they are associated with humans due to the availability of suitable nesting sites and anthropogenic food. These factors have enabled feral pigeons and many of its parasites to colonize cities across the globe (Johnston and Janiga 1995). The undomesticated relative is the rock pigeon Columba livia, whose native range is restricted to Western and Southern Europe. Archeological evidence suggests that rock pigeons were domesticated several thousand years ago (Shapiro and Domyan 2013) and have been used as a food source as early as 10,000 years ago (Blasco et al. 2014). Since then, feral pigeons have successfully expanded their range and established wild populations worldwide, mainly in urban environments. The feral pigeon is not native to South Africa but was introduced as a domestic species in the seventeenth century (Brooke 1981;Dean 2000). Cape Town is a hotspot of biodiversity (Myers et al. 2000) and of special interest as an urban study location. Not only is the literature body comparatively scarce in the "Global South" in comparison to the "Global North" (Hedblom and Murgui 2017), but urban environments are also rapidly changing in the Southern Hemisphere (Swilling 2006). Feral pigeons are likely the most successful avian urban exploiters that manage very well in urban and suburban environments. Furthermore, they are also a model organism in many research disciplines, i.e., parasitology, behavioral science, and physiology (i.e., Abs 1983; Harbison et al. 2009;Klopfleisch et al. 2006).
Avian haemosporidians are comprised of apicomplexan protozoans of the genera Plasmodium, Haemoproteus, and Leucocytozoon. Haemosporidians are the most diverse group of avian blood parasites with several hundred species infecting birds all over the world (Valkiūnas 2004). The genus Haemoproteus contains two subgenera: Haemoproteus and Parahaemoproteus (Valkiūnas 2004). Haemoproteus comprises about ten parasite species described from Columbidae hosts and one species each from Laridae and Fregatidae hosts. Parahaemoproteus comprises the majority of Haemoproteus parasite specialized on passerines (Levin et al. 2012;Levin et al. 2011;Valkiūnas 2004;Valkiūnas et al. 2013;Valkiūnas et al. 2010). The two Haemoproteus subgenera form distinct clades in molecular phylogenetic analyses (Galen et al. 2018;Pacheco et al. 2017) and depend on different dipteran groups as vectors. Species of the subgenus Haemoproteus are transmitted by dipterans of the family Hippoboscidae, whereas species of the subgenus Parahaemoproteus are transmitted by biting midges of the family Ceratopogonidae (genus Culicoides).
Haemoproteus columbae Kruse, 1890 was described from C. livia and is transmitted horizontally by blood-sucking insects, predominantly by the ectoparasitic pigeon louse fly Pseudolynchia canariensis Macquart, 1839 (Sol et al. 2000). Blood stages of Haemoproteus species are quite conspicuous in blood smears (Valkiūnas 2004), but species determination is complicated due to the limited number of distinct morphological features. More recently, DNA sequencing using PCR has been established to identify blood parasite lineages (Fallon et al. 2003) and has also been used as a more accurate method for identifying blood parasite prevalence, especially at low infection intensities (Fallon and Ricklefs 2008;Fallon et al. 2003;Garamszegi 2010). Furthermore, genetic sequencing enables the identification of closely related blood parasite lineages, which cannot be differentiated through microscopic examination. Sequencing also allows the determination of coinfections of multiple haemosporidian lineages, which commonly occur (Alizon et al. 2013;Poulin and Morand 2000;Silva-Iturriza et al. 2012). To distinguish avian haemosporidian species or lineages, the mitochondrial cytochrome b (cytb) is often used as a genetic marker as it incorporates the so-called "DNA barcode" region for haemosporidians (Bensch et al. 2009;Dimitrov et al. 2014). Single and multiple infections of several blood parasite lineages can have different effects on the hosts and have been associated with negative effects on their body conditions (Marzal et al. 2008), lower hematocrit values (Palinauskas et al. 2011), and lower host survival (Pigeault et al. 2018). However, in studies on house martins Delichon urbica, birds with double infections showed better reproductive performance (Marzal et al. 2008;Pigeault et al. 2018).
Generally, H. columbae infections are not considered to be very detrimental to their hosts Sol et al. 2003). However, high infection intensity can be associated with negative physiological ) and behavioral (Markus and Oosthuizen 1972) changes. In some cases, infections can even be lethal, especially in young individuals that have not yet developed an adequate immune response (Sol et al. 2003). Due to their successful expansion across the globe, feral pigeons act as vectors for H. columbae and other haemosporidian parasites. They can facilitate the spread into naïve new hosts and can potentially threaten local columbid bird species (Chagas et al. 2016;Foronda et al. 2004;Lee-Cruz et al. 2016;Peirce et al. 1997;Swinnerton et al. 2005;van Riper et al. 1986). Specifically in Cape Town, feral pigeons regularly form mixed flocks with the native speckled pigeon Columba guinea and might allow the spread of feral pigeonspecific blood parasites to naïve hosts (Earle and Little 1993;Little 1994). In an earlier study, Haemoproteus has been identified in speckled pigeons; however, this study was based on blood smear analysis only. In the absence of DNA sequencing, it remains unknown whether these represent blood parasite lineages originating from feral pigeons or whether they are unique to native speckled pigeons. Furthermore, feral pigeons were identified as potential vectors of novel parasites for endemic and endangered columbid species native in the Canary Islands (Foronda et al. 2004). Haemoproteus columbae has not yet been found in a non-columbid host and might be specific to pigeon and dove hosts (Valkiūnas 2004).
The domesticated feral pigeon is color polymorphic, whereby different morphs are independent of age or sex, are interbreeding freely, and can be present within the same population (Huxley 1955). The species features a wide range of plumage colorations ranging from completely white to almost fully melanistic (Haase et al. 1992;Jacquin et al. 2011Jacquin et al. , 2013Johnston and Janiga 1995). Plumage phenotypes are determined by eumelanin and pheomelanin that are known for their pleiotropic effects on the immune system through the melanocortin system (Ducrest et al. 2008), whereby the gene coding the phenotype also controls the expression of several different traits, unrelated to color, such as immunocompetence. Plumage variation in the feral pigeon is associated with a stronger immune system (Jacquin et al. 2011), and white pigeons (i.e., lacking melanin) were found to have a higher blood parasite prevalence with increasing levels of urbanization (Jacquin et al. 2013). In turn, darker individuals showed a stronger cortisone stress response in rural areas than in urban habitats (Corbel et al. 2016).
In the present study, (1) we describe H. columbae prevalence and infection intensity in a wild feral pigeon population in the city of Cape Town, South Africa. Additionally, (2) we assess blood parasite diversity by PCR screening for H. columbae lineages and by sequencing a section of the mitochondrial cytb gene. (3) We show the geographic and host distribution of H. columbae and related parasite lineages in pigeons worldwide. (4) We test for potential health consequences of blood parasite infection by exploring whether higher infection intensity and co-infection with different Haemoproteus lineages reduce individual body condition. We hypothesize that those blood parasites have a negative effect on the health of the host and thus predict that individual body condition decreases with increasing infection intensity or infection by multiple parasite species. Lastly, (5) we explore for potential pleiotropic effects of color polymorphism by correlating infection intensity and co-infection with the expression of eumelanin. Here, we hypothesize that eumelanin expression influences an individual's health. Therefore, we predict that feral pigeons with darker plumage coloration have lower haemosporidian infection intensity and a lower number of co-infections.

Study site and feral pigeons
For this study, 195 wild feral pigeons were caught in walk-in traps in suburban areas of Cape Town, South Africa (33.92°S, 18.42°E) from April to June 2018, during the austral autumn and winter. We attempted to obtain data for all birds on morphometric measurements, blood samples for smears, and DNA extraction; however, some data is missing from some birds due to time constraints in processing several individuals in a short period of time. Sample sizes (n) are given in brackets below throughout; for a complete sample list, see Appendix Table S1.
Before blood sampling, pigeons were routinely housed communally in a pigeon coop at Eagle Encounters, Stellenbosch (33.97°S, 18.78°E), for a maximum of 6 days prior to being part of an unrelated behavioral experiment (Nebel et al. 2019). A maximum of 1 ml blood was taken from the brachial vein of each individual, and these blood samples were then immediately use to prepare blood smears on microscope slides and air-dried. Remaining blood was stored in EDTA buffer at 4°C. We measured the tarsus length with a caliper (to the closest 0.1 mm) and weighed the pigeon on a scale (in g). These measures were taken to derive individual body condition and were obtained from 187 individuals in total. The age of the pigeons was classified depending on the color of the eyes and cere (immature, < 6 months of age, n = 12, or adult, > 6 months, n = 183; following Kautz and Seamans 1986). Sex was not determined. All pigeons were individually marked and released back at the source of capture.

Assessment of plumage phenotype
Plumage coloration in the feral pigeon is determined by eumelanin and pheomelanin deposited in the feathers (Haase et al. 1992). Although both eumelanin and pheomelanin are associated with the melanocortin system, and both can have beneficial effects on the immune system (Ducrest et al. 2008;Jacquin et al. 2011), in this study, we focus on pigeons showing a variation of eumelanin and excluded all individuals with a pheomelanin-based plumage (i.e., reddish plumage; n = 3) due to the small sample size. Systematic photographs were taken (showing the belly, back with one wing spread and the head) to document the plumage of every individual. The feral pigeons' phenotypes were determined in a twofold approach. First, melanin content of the wing was quantified by using the software ImageJ (Abramoff et al. 2004) and then categorized according to levels of melanism by author AP following established protocols of Johnston and Janiga (1995) and Jacquin et al. (2011): phenotypes 0 ("white," n = 1), 1 ("blue bar," n = 40), 2 ("checker," < 50% dark feathers on the wing surface, n = 57), 3 ("T-pattern," > 50% dark feathers on the wing surface, n = 65), and 4 (completely melanistic, n = 28). The plumage of one pigeon could not be scored, because no photos were taken during processing. One individual showing mainly white feathers (score 0) was excluded from all analyses due to low sample size. Ten percent of all individuals were randomly re-scored, and the assignment was highly reproducible.

Assessment of infection intensity by blood slide screening
Blood smears of 192 individual C. livia were fixed with methanol and stained with Giemsa stain following the standard protocol of Hemacolor ® Rapid staining of blood smear kit (Merck, Darmstadt, Germany). The slides were first scanned under the microscope at × 400 magnification to determine the presence or absence of haemosporidian blood parasites. For infected individuals, the intensity of infection was then determined by scanning each slide at × 1000 magnification with an oil immersion lens and counting the number of parasites seen within 10,000 erythrocytes.

Molecular genetics: assessment parasite lineages and co-infections
Molecular screening for blood parasites and identification of Haemoproteus lineages were performed on 144 feral pigeon samples of which aliquots of blood were stored in EDTA buffer (in 51 cases, all obtained blood was used up, preparing the blood smears). DNA was extracted with the DNeasy Blood & Tissue Kit (Qiagen, Venlo, Netherlands) following the standard protocol for isolation from blood and using equivalents of about 10 μl blood from each sample. We amplified an 886-bp section of the mitochondrial cytb using the primers CytB_HPL_intF1 (5′-GAGA ATTATGGAGTGGATGGTG-3′) and CytB_HPL_intR1 (5′-ATGTTTGCTTGGGAGCTGTAATC-3′) (Harl et al. 2019), which covers the so-called DNA barcode region for avian haemosporidians. CytB_HPL_intF1 binds at nucleotide po sitions 17 4 t o 195 o f the cy tb , and CytB_HPL_intR1 binds at a conserved site 13-bp from the 3′ end of the cytb. PCRs were performed with the GoTaq Long PCR Master Mix (Promega, Madison, Wisconsin, USA) on a peqSTAR 2X Universal Gradient thermocycler (VWR, Radnor, Pennsylvania, USA). Each 25 μl master mix contained 12.5 μl GoTaq Long PCR Master Mix, 9.5 μl H 2 O, each 1 μl of 10 mM primer, and 1 μl of DNA template. The PCRs started with an initial denaturation for 2 min at 94°C, followed by 35 cycles with 30 s at 94°C, 30 s at 53°C, 1 min at 65°C, and a final extension for 10 min at 72°C. PCR products were visualized on 1% agarose gels, and positive samples were purified with the QIAquick PCR Purification Kit (Qiagen). Sequencing was performed at Microsynth Austria GmbH (Vienna, Austria) using the PCR primer CytB_HPL_intR1. Raw sequences were inspected by eye with BioEdit v.7.0.8 (Hall 1999). Sequences of samples with double and triple infections featured ambiguous sites with wobble bases, which were consequently checked in the electropherograms. After the first inspection, the sequences were sorted by similarity and all aberrant sites were rechecked. Identification (and unphasing) of parasite lineages in samples with multiple infections was straightforward, because the number of different haplotypes in our sample was low. Haplotype networks, based on a 478-bp cytb fragment, were calculated with two different datasets, the first comprising haplotypes related to H. columbae and the second comprising the lineage of a yet unidentified Haemoproteus species (COLIV06) and related haplotypes. To visualize the geographic and host distribution, we included already published sequences from GenBank (NCBI) in the networks. These were retrieved by performing BLAST searches and including all samples with sequence similarity of 97% to 100% to haplotypes found in the present study. Only those BLAST hits were kept, which did not contain ambiguities and which covered the 476-bp DNA barcode region of the cytb. The respective lineage names of the haplotypes were retrieved by BLAST searches against the MalAvi database (Bensch et al. 2009). The sequences were aligned with MAFFT v.7 (Katoh and Standley 2013) and trimmed to the size of the barcode region. Median-Joining networks were calculated with Network v.5.1.0.0 (Fluxus Technology Ltd., Suffolk, England) applying the default settings. In order to reduce unnecessary median vectors, the networks were post-processed with the maximum parsimony (MP) option. Networks were graphically prepared and supplemented by information on host species and geographic region in Network Publisher v.2.1.1.2 (Fluxus Technology Ltd.) and finalized in Adobe Illustrator CC v.19.0.0 (San Jose, California, USA). Coinfection was determined based on DNA sequencing and reflects the number of Haemoproteus lineages (1 = monoinfection, 2 = double-infection, and 3 = triple-infection).

Statistical analysis
We explored whether individual body condition was associated with blood parasite parameters (i.e., infection intensity or co-infection level, models a and b) using linear models (LMs), and whether blood parasite parameters were associated with plumage phenotype using LM (model c) and a multinomial logistic regression (model d). Linear models were fit with the stats package (version 3.4.0) and the multinomial regression nnet package (7.3-12) (Ripley et al. 2016) in the R environment, version 3.4.4 (R Core Team 2018). All quantitative variables (body mass, tarsus length, and infection intensity) were scaled beforehand. Six individuals that showed no prevalence in the blood smear analysis (even if they were positives in the PCR screening) were excluded from all models featuring infection intensity. However, four of these were included in all models exploring the effects of co-infection on body condition and the effect of melanism expression on co-infection, because sequence data were available.
Previous research has shown that juvenile individuals are often suffering from higher infection intensities and are in lower body condition than adults (Sol et al. 2003). Therefore, before running the main analyses, we explored the influence of agespecific differences on individual body condition and infection intensity. Two linear models were fitted: one with body condition and a second with infection intensity (log-transformed to ensure normally distributed residuals) as response variables. Age was the key explanatory variable (immature or adult), and the length of the tarsus was added as a control variable in the model exploring body condition to control for individual variation in size. Due to a significant difference between immature and adult pigeons (see the "Results" section) and small sample size, immature individuals (n = 12) were excluded from the main models introduced below.
In the first set of the main analysis, we explored the effect of infection intensity (model a, n = 169) and co-infection (model b, n = 125) on individual condition and we fitted a linear model with body mass as the response variable and infection intensity (%) or co-infection of Haemoproteus spp. lineages (categorical variable, mono-, double-, or triple-infection) as explanatory variables. The length of the tarsus and the phenotype were also added as covariates to control for the individual size of the pigeon and to take the effect of different melanin expression levels into account. This approach was generally favored, rather than fitting residuals of a regression between body mass and tarsus as a body condition response in the model (García-Berthou 2001;Smith 1999).
In a second set of analysis, we explored the effect of plumage phenotype on blood parasite parameters, infection intensity (model c, n = 169), and co-infection (model d, n = 130). To do so, we fitted a linear model with log-transformed infection intensity as a response in order to fulfill the requirement of normally distributed residuals. In all linear models, residuals were visually inspected to ensure a normal distribution. Additionally, we explored whether pigeons of different phenotypes were infected by different numbers of blood parasites by fitting a multinomial logistic regression with the number of lineages as the response variable. Infection with only one parasite was the reference. p values were obtained by performing a Wald test (z test). In both models, the degree of melanism (phenotypes 1-4) was fitted as a continuous explanatory variable. As infection intensity might be influenced by multiple competing parasites (Clark et al. 2016), we fitted the co-infection variable as a fixed effect in explorative models a and c, and co-infection did not improve the model fit (by AIC) and was returned as not significant. It was thus removed from the final models.

Haemosporidian parasite infection intensity and prevalence
Blood smears were prepared for 192 feral pigeons and scored for haemosporidian parasite infections. In the blood smear analysis, only six individuals, out of 192, were identified as uninfected (no parasites detected in 10,000 erythrocytes, 96.9% infection prevalence). However, four of these six birds did score positive in the PCR screening. Molecular screening could not be performed for one sample due to insufficient blood volume, and for a second one, the repeated PCR was negative. All positive samples were infected with Haemoproteus spp., whereas Leucocytozoon spp. and Plasmodium spp. were absent in our study population. The mean infection intensity of Haemoproteus spp. positive individuals was 1.3% (SD 2.9), ranging from 0.007 and 20.4% of erythrocytes being infected. The distribution of infection intensity was skewed, and the median was lower than the mean (median = 0.3%).

Molecular analysis of haemosporidian parasites
Molecular screening for blood parasite lineages was performed on 144 DNA samples isolated from pigeon blood.
PCRs for all but one sample were positive, and products were sequenced from 143 samples. Almost all samples contained lineages belonging to H. columbae (HAECOL1, COLIV03, COQUI05), which is a common parasite of pigeons, and only four samples contained another Haemoproteus lineage (COLIV06) not yet linked to a morphospecies. A high number of pigeons were infected with more than one parasite lineage. From the 143 PCR positive samples, 81 (56.6%) featured a single lineage, 51 (35.7%) two lineages, and 11 (15.7%) three lineages. The most common lineage HAECOL1 was found in 133 pigeons. We actually found two haplotypes matching 100% with HAECOL1, but differing in a single site at position 1003 of the cytb. The first variant (HAECOL1a) was found in 110 specimens and the second one (HAECOL1b) in 37 birds, and both haplotypes co-occurred in 16 individual pigeons. In the following, we refer to both of the latter haplotypes as lineage HAECOL1. The MalAvi lineages COLIV03 and COQUI05 were found in 44 and 36 samples, respectively. At the 478-bp DNA barcode section, HAECOL1 and COQUI05 differ from each other in three sites (0.6% uncorrected p distance), HAECOL1 and COLIV03 in four sites (0.8%), and COLIV03 and COQUI05 in five sites (1%). Only four pigeons were infected with the lineage COLIV06 belonging to an unidentified Haemoproteus species, differing from the H. columbae lineages by about 10%. The relationship of haplotypes found in the present study and already published data is displayed in Median-Joining networks (Fig. 1). All cytb sequences are available at NCBI GenBank under the accession numbers MN065190-MN065419.
We found no relationship between the levels of coinfection of Haemoproteus spp. (mono-, double-, or tripleinfection) and body condition (Table 1). We also found no relationship between plumage phenotype and infection intensity or co-infection levels (Table 1). Feral pigeons with a paler plumage had similar levels of infection intensity and coinfection as conspecifics with darker melanic plumage (Table 2).

Discussion
Based on the blood smear analysis, the prevalence of Haemoproteus spp. was high in our study population with a 96.9% infection rate. Only six individuals did not show any infection with haemosporidian parasites based on the blood smear screening, but four of these tested positive through molecular screening. Screenings of blood smears often underestimate infection prevalence-especially when infection intensity is low-whereas PCR is more sensitive (Valkiūnas et al. 2008). Our results thus show that almost all feral pigeons sampled in suburban Cape Town were infected with H. columbae. This high rate is in line with results of a study conducted in Pretoria in the 1970s with 100% (Markus and Oosthuizen 1972), but much higher than previously reported in Cape Town in the 1990s with 72.7% infection rate (Earle and Little 1993). Both studies used only blood smear screenings and did not apply any PCR-based methods. Similarly high prevalence values were found in cities outside of South Africa, i.e., 97-100% in Madrid, Barcelona, and Granollers (Spain) (Sol et al. 2000;Vázquez et al. 2010); 100% in Sao Paulo (Brazil) (Chagas et al. 2016); or 100% in Hyattsville (Maryland, USA) (Knisley and Herman 1967). Haemoproteus spp. infection intensities of C. livia have been studied in other Sub-Saharan African countries with prevalence levels varying greatly both between and within countries. Haemoproteus spp. prevalence was high at 75-80% in Sebele (Botswana), Owerri (Nigeria), and Kampala (Uganda) (Dranzoa et al. 1999;Mushi et al. 2000;Opara et al. 2012 (Johnston and Janiga 1995). Possibly, the high infection prevalence is depending on population density. Both Haemoproteus spp. prevalence and feral pigeon population density are generally high in suburban and urban studies (Chagas et al. 2016;Dranzoa et al. 1999;Karamba et al. 2012;Knisley and Herman 1967;Mushi et al. 2000;Opara et al. 2012;Sacchi et al. 2002;Sol et al. 2000;Vázquez et al. 2010). It can be assumed that these populations of feral pigeons are more likely to become infected by parasites and diseases because of their larger population densities. However, since natural populations of C. livia are poorly investigated, there is no data to compare with.
Haemoproteus columbae does not infect non-columbid birds and thus is not a threat to other bird groups. However, breeding pigeons or native, naïve pigeon species are prone to become infected as the close proximity between wildlife, livestock, and pets could facilitate cross-species disease transmission in suburban and urban environments (Earle and Little 1993).
Although not much sequence data on avian haemosporidians of pigeons has been published so far, the four cytb lineages found in the present study seem to be common in feral pigeons. Exactly the same lineages were isolated from feral pigeons in the Sao Paolo Zoo (Chagas et al. 2016). The authors of the latter study confirmed that the lineages HAECOL1, COQUI05, and COLIV03 morphologically belong to H. columbae. The similarity in parasite lineage composition should be viewed in a historical context, since feral pigeons were introduced into both South Africa and South America by humans (Johnston and Janiga 1995), most likely together with their louse fly (Hippoboscidae) vectors. Apart from the four H. columbae lineages, only two other haplotypes are so far published for the feral pigeon: COLIV07 from Peru (Pacheco et al. 2017), differing in one site from HAECOL01, and an unnamed haplotype isolated from a pigeon in Japan (LC325859; published on GenBank only), differing in a single site from COQUI05. Related haplotypes have been isolated from common wood pigeon Columba palumbus in Europe (Dunn et al. 2017), African olive pigeon Columba arquatrix in Malawi (Lutz et al. 2015), Amboyna cuckoo-dove Macropygia amboinensis in Papua New Guinea  (Lutz et al. 2015); and in island bronze-naped pigeon Columba malherbii in Sao Tome and Principe (KT376899; published on GenBank only). In our study, young pigeons had a significantly lower body condition and higher infection intensity than older individuals, a pattern often reported in different bird species (Sol et al. 2003). In adults, individual body condition was negatively correlated with infection intensity of Haemoproteus-implying that there may well be a physiological cost associated with being heavily infected. Parasites might drain energy from their host (Price et al. 1986), or infection could directly impair foraging activities (Marzal et al. 2005) and thus negatively affect body condition. To our knowledge, no correlation between Haemoproteus spp. and body condition in feral pigeons has been reported to date. The influence of different Parahaemoproteus parasites and infection intensity in noncolumbids on body condition is frequently reported; however, these associations are not consistent. Negative effects of infection intensity on body condition have been found in American kestrel Falco sparverius (females only) (Dawson and Bortolotti 2000), but not in Eurasian kestrel Falco tinnunculus (Korpimäki et al. 1995) nor for European blackcap Sylvia atricapilla (during the first days after infection with Haemoproteus belopolskyi) (Valkiūnas et al. 2006). Furthermore, no significant negative correlations between infection intensity and body condition were documented in several other passerine species (Bennett et al. 1988;Granthon and Williams 2017), including during migration (correlation with body fat scores) (Ashford 1971). In the present study, coinfections with several Haemoproteus lineages did not affect the body condition more than infections with a single lineage only, which is in line with findings of Palinauskas et al. (2011) and Marzal et al. (2008). For future work, it would be interesting to investigate whether closely related lineages interact with each other. This interaction could be positive and favor co-infections, or it could be negative and closely related parasite lineages could compete within the host or within the vector. An example of a positive interaction was found in experimentally infected passerines. Two Plasmodium spp. acted synergetically during primary infections and were found to be highly virulent (Palinauskas et al. 2011). In a cross-genus study, microfilariae parasites were enhancing the co-infection of certain, but not all haemosporidians (Clark et al. 2016). In the present study, sequences were obtained by standard PCR and Sanger sequencing. Mixed infections were identified by the presence of wobble bases in the electropherograms, but we did not quantify the amount of each lineage in samples with mixed infections. It should be mentioned that with standard PCR and Sanger sequencing, parasites at much higher parasitemia (quantity) may be amplified preferentially, masking the fact that other parasites are present. In order to overcome this problem in future studies, deep amplicon sequencing would provide a possibility to accurately determine the percentage of two or more pathogen lineages in samples with mixed infections (Olmstead et al. 2019).
The feral pigeons caught for this study are part of a wild population in an area with large populations of native avian predator species (e.g. black sparrowhawk Accipiter melanoleucus (Martin et al. 2014;Rose et al. 2017); peregrine falcon Falco peregrinus (Jenkins 2000)), which may explain the absence of individuals with extremely high infection intensities of 50% or higher, since such birds might appear physically "sick" and might be rapidly removed from the system Markus and Oosthuizen 1972). Most The sample size (n) available for this model is 130. Significant effects are indicated in italic 1 ANOVA III was used to derive χ 2 values, but it is not available for the intercept in multinomial logistic regression individuals sampled are likely exhibiting chronic infections rather than primary infections that are usually characterized by higher infection intensities (Asghar et al. 2012). Individuals during primary infections that show extreme infection intensities are more likely to die due to predation (Møller and Nielsen 2007;Temple 1987) or through the effects of the parasite themselves, like anemia . Our results show that H. columbae infection intensity has negative effects on a body condition which could lower individual fitness (Chastel et al. 1995;Cichoiń et al. 1998). However, our study does not include any long-term effects on individual survival, mate choice, or reproductive success and no such studies have been conducted to date on the H. columbae-C. livia parasite-host system. However, longterm fitness correlations have been studied in other species, for example female Eurasian kestrels mating with males infected with Haemoproteus (Parahaemoproteus) tinnunculi laid later and smaller clutches than females mating with uninfected males (Korpimäki et al. 1995 (Dawson and Bortolotti 2000). On the contrary, white-crowned sparrow Zonotrichia leucophrys oriantha females infected with Haemoproteus (Parahaemoproteus) beckeri had higher survival rates and about twice as high lifetime reproductive success than uninfected females (Zylberberg et al. 2015). Interestingly, other avian malaria parasites like Leucocytozoon spp. and Plasmodium spp. were absent in our study population. Leucocytozoon spp. have been reported in two male C. livia in South Africa in the 1990s, one from the southwestern Cape and one from Stromberg, Eastern Cape (Earle and Little 1993). The absence of Leucocytozoon spp. and Plasmodium spp. in feral pigeons has also been reported from Sao Paulo (Brazil) (Chagas et al. 2016) and Santiago (Chile) (Toro et al. 2010); however, in the latter study, no Haemoproteus spp. were found in the feral pigeon population either. Leucocytozoon spp. and Plasmodium spp. use different dipteran vectors than Haemoproteus spp. with Leucocytozoon spp. being mainly transmitted by blackflies (Simulium spp., Simuliidae, Diptera) and Plasmodium spp. by mosquitoes (Culicidae, Diptera) (Njabo et al. 2009;Russell and Mohan 1942;Ventim et al. 2012). Blackflies need running, fastflowing streams (Rivers-Moore et al. 2007), which might be absent in highly urbanized areas in Cape Town, as suggested in a haemosporidian parasite study in black sparrowhawks (Suri et al. 2017). In that study, prevalence of Leucocytozoon spp. significantly decreased with a higher degree of urbanization; however, the prevalence for Haemoproteus spp. was similar along the urban gradient (Suri et al. 2017). The absence of vectors seems unlikely to cause the absence of Plasmodium in this study, and feral pigeons might not be native hosts for Plasmodium parasites (but see the experimentally infected C. livia with Plasmodium (Giovannolaia) gabaldoni and Plasmodium (Novyella) columbae) (Valkiūnas 2004). Generally, Plasmodium species were reported only rarely in wild feral pigeons (see exceptions, i.e., Gupta et al. 2011;Natala et al. 2009;Opara et al. 2012), but none of these records was confirmed by molecular screening methods and Haemoproteus and Plasmodium can easily be confused depending on the development stage of the parasite and the quality of the blood slide (Valkiūnas 2004).
Lastly, no effect of eumelanism on the infection intensity was found. Higher levels of eumelanism are hypothesized to have beneficial effects on the immune system (Ducrest et al. 2008) and have been shown to be correlated with improved immunocompetence in other species (Baeckens and Van Damme 2018;Figuerola et al. 1999;Galeotti and Sacchi 2003;Galván et al. 2010;Roulin et al. 2001). Lei et al. (2013) found for the color-polymorphic black sparrowhawk that darker individuals showed lower infection intensity. In the barn owl Tyto alba, individuals with more melanistic spots in their plumage had a smaller bursa of Fabricius, suggesting that they were less parasitized (Roulin et al. 2001). In feral pigeons, darker individuals are assumed to show stronger immune response, but blood parasite intensity was independent of melanism in a study by Jacquin et al. (2011), a result that is in line with our finding. Differences of blood parasite intensity and melanism were found only along an urban gradient, and only when completely white individuals were considered (Jacquin et al. 2013), which were largely absent in our study system.
To conclude, our study represents the largest dataset to date that combines sequences of mitochondrial cytochrome b (cytb) of haemosporidian parasites in feral pigeons, and provides evidence for a negative association between host body condition and parasite infection with Haemoproteus spp. However, it would be interesting to understand the longterm fitness consequences of infection by Haemoproteus spp. on feral pigeons, a species that is closely associated with humans, and has been introduced to several cities across the globe. This is especially the case, given that there is the potential for it to introduce its parasites to naïve native avifauna.
Author contributions Originally, the idea for this descriptive study came from JH, AA and PS. AP analyzed the blood slides and undertook the statistical analysis. JH carried out the molecular laboratory work and DNA sequence analyses. HW provided support in the preparation and interpretation of the data. The manuscript was prepared by CN, JH, AA, and PS and approved by all co-authors. Data availability The dataset is available as electronic supplement (Appendix Table S1), and cytochrome b sequences are available on NCBI GenBank under the accession numbers MN065190-MN065419.

Compliance with ethical standards
Competing interests The authors declare that they have no competing interests.
Ethical approval and consent to participate This study was conducted under a CapeNature Permit (CN44-30-4175) and was approved by the University of Cape Town's Science Faculty Animal Ethics Committee (Permit number 2018/v5/AA).    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.  The table lists individual details (age, body morphometrics) and its parasite inflection parameters. Pigeon ID is the individual identifier given each individual. Date refers to the date of collection and age to the age class (adult > 6 months, immature < 6 months). Body morphometrics are tarsus length ("tarsus") and weight ("body mass"). The phenotype score (0-4) can be found in the "plumage" column and was obtained following the protocol established by Johnston and Janiga (1995) and Jacquin et al. (2011). Infection intensity is given in percentages. The prevalence score is binary (either 0 for uninfected or 1 for infected). Both infection intensity and prevalence are based on microscopy analysis. Additional binary codes (0 for not present, 1 for present) are given for the Haemoproteus spp. lineages detected in this study. The "co-infection" column refers whether multiple lineages are present or not