Reactive morphology of dividing microglia following kainic acid administration

The microglial response to a pathological microenvironment is hallmarked by a change in cellular morphology. Following a pathological stimulus, microglia become reactive and simultaneously divide to create daughter cells. Although a wide array of microglial morphologies has been observed, the exact functions of these distinct morphologies are unknown, as are the morphology and reactivity status of dividing microglia. In this study, we used kainic acid to trigger microglial activation and cell division. Following a cortical kainic acid injection, microglial morphology and proliferation were examined at 3 days post-injection using immunohistochemistry for ionized calcium binding adapter molecule 1 (Iba1) to stain for microglia, and KI67 as a marker of cell division. Individual microglial cells were isolated from photomicrographs and skeletal and fractal analyses were used to examine cell size and spatial complexity. We examined the morphology of microglia in both wildtype and microglia-specific tumor necrosis factor (TNF)-α knockout mice. Data were analyzed using generalized linear mixed models or a two-way ANOVA. We found that dividing microglia had a more reactive morphology (larger cell body area, longer cell perimeter, and less ramification) compared to microglia that were not dividing, regardless of microglial release of TNF-α. However, we also observed dividing microglia with a complex, more ramified morphology. Changes in microglial morphology and division were greatest near the kainic acid injection site. This study uses robust and quantitative techniques to better understand microglial cell division, morphology, and population dynamics, which are essential for the development of novel therapeutics that target microglia.

The microglial response to a pathological microenvironment is hallmarked by a change in cellular morphology. Following a pathological stimulus, microglia become reactive and simultaneously divide to create daughter cells. Although a wide array of microglial morphologies has been observed, the exact functions of these distinct morphologies are unknown, as are the morphology and reactivity status of dividing microglia. In this study, we used kainic acid to trigger microglial activation and cell division. Following a cortical kainic acid injection, microglial morphology and proliferation were examined at 3 days post-injection using immunohistochemistry for ionized calcium binding adapter molecule 1 (Iba1) to stain for microglia, and KI67 as a marker of cell division. Individual microglial cells were isolated from photomicrographs and skeletal and fractal analyses were used to examine cell size and spatial complexity. We examined the morphology of microglia in both wildtype and microglia-specific tumor necrosis factor (TNF)-α knockout mice. Data were analyzed using generalized linear mixed models or a two-way ANOVA. We found that dividing microglia had a more reactive morphology (larger cell body area, longer cell perimeter, and less ramification) compared to microglia that were not dividing, regardless of microglial release of TNF-α. However, we also observed dividing microglia with a complex, more ramified morphology. Changes in microglial morphology and division were greatest near the kainic acid injection site. This study uses robust and quantitative techniques to better understand microglial cell division, morphology, and population dynamics, which are essential for the development of novel therapeutics that target microglia.

Introduction
Microglia are critical immune cells localized to the central nervous system (CNS) (Kreutzberg, 1996). Through fast movement of their branches, microglia actively survey the CNS (Davalos et al., 2005;Nimmerjahn et al., 2005). This scanning activity allows microglia to rapidly detect abnormal perturbations in the microenvironment that are associated with many types of injury or infection (Morrison et al., 2017;George et al., 2019;Mbagwu et al., 2019;Green et al., 2021). Upon detecting neuropathological stimuli, microglia quickly respond and attempt to protect the brain from further injury (Wake et al., 2011;Eyo et al., 2021). The microglial response is initially beneficial in helping clear cellular destruction caused by an injury or infection. However, a chronic microglial response is often pathological and can cause additional damage to the brain via excessive release of pro-inflammatory intermediates (Badoer, 2010;Lull and Block, 2010;Loane and Kumar, 2016).
The microglial response to a pathological microenvironment is hallmarked by a change in cellular morphology and proliferation (Morrison et al., 2017;Savage et al., 2019). Reactive microglia retract their processes and adopt a less complex branching structure and an enlarged cell Abbreviations: ANOVA, analysis of variance; CI, confidence intervals; CNS, central nervous system; Db, box-dimension; Iba1, ionized binding adapter molecule 1; IP, intraperitoneal; KO, Knockout; NDS, Normal donkey serum; PBS, phosphate buffered saline; SEM, standard error of the mean; TNFR, Tumor necrosis factor alpha receptor; TNF-α, tumor necrosis factor alpha; WT, wildtype.
body (Doorn et al., 2014;Morrison et al., 2017;York et al., 2018;Savage et al., 2019;Green et al., 2021). In severe cases, microglia adopt an ameboid morphology, much like circulating macrophages (Doorn et al., 2014). Because of these distinctive morphological changes, microglial morphology is often used as a marker of inflammation and severity of tissue damage (Davis et al., 1994;Minlebaev et al., 2013;Morrison et al., 2017). Although a wide array of microglial morphologies has been documented, the exact functions of these distinct morphologies are unknown, as are the morphology and reactivity status of microglia undergoing cell division. To reduce some of the harmful effects caused by chronic microglial reactivity, the population dynamics and morphologies of reactive and dividing microglia must first be investigated using robust and quantitative techniques. Precise microglial morphological data will allow specific therapeutic targeting of pathology-associated microglia and provide an accurate measurable outcome for preclinical drug screening.
Tumor necrosis factor-α (TNF-α) is a key cytokine that microglia release in response to inflammatory stimuli (Kuno et al., 2005;Smith et al., 2012). TNF-α can act on microglia via an autocrine mechanism that increases activation and the subsequent release of pro-inflammatory cytokines, including additional TNF-α (Kuno et al., 2005). Initial release of TNF-α by microglia may be beneficial, as TNF-α supports neuronal function, upregulates inflammatory pathways, and promotes neuroprotection (Carlson et al., 1999;Stellwagen and Malenka, 2006;Morrison et al., 2017). However, the chronic release of TNF-α caused by acquired neurological Frontiers in Neuroscience 02 frontiersin.org injuries, neurodegenerative disease, and infections, exacerbates neuropathology and worsens outcomes (Badoer, 2010;Lull and Block, 2010;Smith et al., 2012;Loane and Kumar, 2016). This secondary damage can include a prolonged inflammatory response, increased permeability of the blood-brain barrier, cytotoxicity, apoptosis, excitotoxity, and demyelination (Kuno et al., 2005), all of which prolong microglial reactivity (Smith et al., 2012). TNF-α is also involved with signaling pathways that control cell division in a variety of cell types (Cao et al., 2021;Niu et al., 2021;Skartsis et al., 2021). We hypothesized that eliminating TNF-α from microglia by genetic knockout (KO) would reduce microglial cell division and activation following an inflammatory trigger.
In this study, we used kainic acid to trigger microglial reactivity and cell division (Chang et al., 2006;Christensen et al., 2006;Akahoshi et al., 2007;Avignone et al., 2008;Ulmann et al., 2013;Bosco et al., 2018;Morin-Brureau et al., 2018;Araki et al., 2020;Di Nunzio et al., 2021). We followed a procedure of unilateral intracortical kainic acid injection that was developed as a model of temporal lobe epilepsy with hippocampal sclerosis (Bedner et al., 2015). Following a cortical kainic acid injection, microglial morphology and division were examined using quantitative measurements of cell size and spatial complexity. Microglial cells were stained with anti-ionized binding adapter molecule 1 (Iba1), a specific marker of microglia, and dividing cells were stained with anti-KI67. KI67, a protein that is essential to form the perichromosomal layer during mitosis (Pyo et al., 2016;Hayashi et al., 2017), was used to label cells in the growth 2 (G2) and mitosis phases of the cell cycle (Uxa et al., 2021). Microglial morphology and division were quantified, in both TNF-α KO and wildtype (WT) mice, to investigate whether the genetic elimination of microglia-produced TNFα altered microglial reactivity. We hypothesized that dividing microglia would be reactive, with a less ramified morphology, compared to non-dividing microglia after kainic acid induced inflammation. Furthermore, we hypothesized that there would be less microglial reactivity and division in TNF-α KO mice.

Rigor
To ensure all experiments were carried out under blinded conditions, animal numbers were re-labeled by an investigator not associated with the experiments and were revealed after all data collection was completed. All experiments were approved by the ethics committee of Languedoc Roussillon n • 36 and the French Ministry of Research (APAFIS#9899-2017042514488653 v4). All experiments followed the guidelines of the European Union for the care and use of laboratory animals (council directive 2010/63/EU). The Animal Research: Reporting of in vivo Experiments guidelines were followed in the preparation of this manuscript. Pre-determined exclusion criteria included exclusion of mice that lost > 20% of their body weight or had unmanageable pain; however, none of the mice in the study met these criteria and, therefore, none were excluded. A total of 268 microglia from five TNF-α KO mice, and 210 microglia from four WT mice were randomly selected from photomicrographs (using coordinates and a random number generator) and were used for analyses. Microglia were selected regardless of whether the cell was KI67 + or KI67 − .

Animals
Adult (90-120 days old) male and female WT and TNFα KO mice (C57BL/6J) from our in-house colony were used for all experiments. Mice were housed in a 12-h light: 12h dark cycle at constant temperature with food and water available ad libitum. CX3CR1-CreER mice (Yona et al., 2013) were crossed with TNF-α flox/flox mice (Grivennikov et al., 2005) to generate Cx3cr1 creER/+ :TNF-α +/+ and Cx3cr1 creER/+ :TNFα flox/flox littermates. CreER mediated silencing of TNF-α was induced by intraperitoneal (IP) injection of tamoxifen (Sigma-Aldrich T5648) dissolved in corn oil, every 24 h for 3 days, at a dose of 100 mg/kg body weight (Jahn et al., 2018). Mice were used for experiments 3 weeks after the last tamoxifen injection. Following the tamoxifen injection and post-surgery, mice were monitored post-operatively for pain and changes in weight.

Surgical preparation and kainic acid injection
Mice were anesthetized with Domitor (1.2 mg/kg) and ketamine (80 mg/kg) via IP injection. Mice were secured in a stereotaxic headframe and a midline incision was made to expose the skull. A 1-mm craniectomy was performed at 2 mm posterior to bregma and 1.5 mm from the sagittal suture. A 10 µl beveled tip microsyringe (NanoFil, NF33BV, 33GA) attached to a microsyringe pump controller (Micro4) was lowered and 70 nL of 20-mM kainic acid (1 nmol, Sigma-Aldrich; dissolved in 0.9% NaCl) was injected above the left dorsal hippocampus, 1.7 mm beneath the skull surface at a constant rate (70 nl per minute). The needle was left in place for an additional 3 min after injection to reduce backflow through the needle tract. Collectively, these methods ensured all animals received kainic acid in precisely the same injection site. Incisions were sutured and the mice recovered on a heating pad until ambulatory. The mice were visually monitored for seizure-like behavior for the full duration of the post-operative period. Seizurelike behaviors were observed post-injection in all kainic acidtreated mice.

Perfusion
At 3 days post-injection, a lethal dose of Euthasol R (340 mg/kg) was administered. Mice were transcardially perfused with phosphate buffered saline (PBS, 10 ml), followed by 4% paraformaldehyde (PFA; 10 ml). Brains were harvested from the skulls and drop fixed in 4% PFA for 24 h. Brains were then transferred to 0.01% sodium azide solution for the following 24 h. Brains were sectioned at 40 µm using a vibratome and collected in wells of 0.01% sodium azide, where they were stored until they were stained.

Immunohistochemistry
Regions of interest Three brain sections per mouse were randomly selected for each region of interest. The injection site was identified using a brightfield microscope (total area of 120 µm per brain was analyzed). Kainic acid injections are commonly used in epilepsy research to induce seizure-like activity in mice (Le Duigou et al., 2005Duigou et al., , 2008Avignone et al., 2008;Li and Liu, 2019). In this study, kainic acid was used to trigger microglia both locally and in response to seizure-like activity. Therefore, regions of interest included: (1) the cortex near the injection site, because of its high exposure to kainic acid; (2) the hippocampus (CA1), a key area involved in kainic acid-induced seizures which consequently becomes sclerotic (Bedner et al., 2015); and (3) the perirhinal cortex, as a distal region with a lower exposure to activating stimuli ( Figure 1A).

Iba1 and KI67
Anti-Iba1 antibody was used to mark all microglia in the brain, regardless of their stage of the cell cycle (Morrison et al., 2017;Green et al., 2021). Anti-KI67 antibody was used to stain all cells undergoing cell division (Kuno et al., 2005;Inwald et al., 2013;Pyo et al., 2016). Cells that were co-labeled for Iba1 and KI67 were considered "dividing microglia" in this study. To maximize scientific rigor and allow accurate comparisons between samples, all staining was performed in a single round with the same aliquots of reagents. Tissue slices were selected and placed into 1 ml of 1X PBS in a 24 well plate. Slices were washed three times for 10 min on an agitator. Blocking solution [4% normal donkey serum (NDS, Sigma-Aldrich) and 0.2% Triton in 1X PBS] was applied for 2 h under agitation at room temperature. Primary antibodies [anti-Iba1 (WAKO rabbit Iba1, NC1801858) at 1:500 concentration and anti-KI67 (R&D systems AF7649) at 1:250, 2% NDS, and 0.2% triton in PBS] was applied to tissue and left to incubate overnight at 4 • C. Tissue was then washed in PBS 3 times for 10 min, and incubated at room temperature with secondary antibody solution for 2 h [donkey anti-rabbit (Alexa 488 1:250, Invitrogen, A-110391:250), donkey anti-sheep (NorthernLights 557 1:250, R&D systems NL010), 2% NDS in 1X PBS]. Tissue was washed in PBS 3 times for 10 min under agitation and the tissue slices were mounted onto permafrost slides and coverslipped using DAKO fluorescent mounting medium.
Imaging Z-stacked images (15 slices per image) in pre-determined regions of interest were taken using a 40× objective lens and a ZEISS ApoTome.2 with consistent exposure, numerical aperture, and apotome function (3 ApoTome grid shifts per image to increase axial resolution). Nyquist theorem was followed to ensure the signal adequately represented the biological samples. For full brain images, the ZEISS Axioscan Z1 was used to acquire Z-stack images (5 Z-stack planes) with a 20× objective. Microscope settings were pre-determined, and all tissue was imaged in one session under identical conditions. A total of 162 images were used for the 40× field of view analyses. We used 27 full brain slices for hippocampal cell counts (Supplementary Figure 1).

Quantitative microglia morphology analyses
Zen Blue software was used to count the cells in the representative slices of the hippocampus from full brain images (Supplementary Figure 1). The 40× field of view cell counts were performed manually in ImageJ, and images were displayed using the Zen Blue 2.5-dimension function to ensure KI67 could be clearly visualized ( Figure 1B). For all individual microglia morphological analyses, 40× Z-stacks ( Figure 1C) were analyzed in ImageJ. For single cell analyses, images were binarized and three individual microglia were selected per image using image coordinates and a random number generator ( Figure 1D). Using the region of interest tool, we isolated each selected cell from the binary image. We then used the paintbrush tool to remove any fragments that were not attached to the cell and connect any branches that had become fragmented because of image processing, using the original photomicrograph for reference. Isolated microglia were then converted to a skeleton ( Figure 1E) and an outline ( Figure 1F). The skeletal analysis plugin was run on individual skeletonized cells to measure the number of branches, branch length, and endpoints per microglia. Additionally, the ImageJ FracLac plugin, with added hull and circle results, was used to analyze the outlines of individual cells ( Figure 1G; Morrison et al., 2017). The outcome measures of fractal analysis are: (1) density of pixels per individual cell; (2) fractal dimension (box-dimension; Db) defined as a statistical measure of the complexity of the microglial branches (Karperien et al., 2013); (3) circularity where a score of 1 represents a perfect circle; (4) lacunarity which is a geometric measure of the pattern complexity created by the branches and gaps to assess cell Methods for individual microglia ImageJ analyses. (A) Three regions of interest were selected, the cortex adjacent to the injection site, the CA1, and the perirhinal cortex. Scale bar = 1000 µm. (B) Iba1 + /KI67 + co-labeled cells. Images were also displayed at 2.5 renderings to ensure all co-labeled cells were counted. Scale bar = 50 µm. (C) Representative image of 40× Z-stacked photomicrograph of Iba1 and KI67 staining used for single cell morphology analyses. Scale bar = 50 µm. (D) Photomicrographs were converted to binary and (E) 3 randomly selected microglia were isolated. (F) For skeletal analyses, cells were converted to skeletons and analyzed. (G) For fractal analyses, the isolated microglia were converted to outlines and analyzed. Red = KI67 staining; Green = Iba1 stain.
complexity (Karperien et al., 2013);and (5) span ratio used to measure cell shape and elongation which is calculated as the convex hull longest length divided by convex hull longest width (Morrison et al., 2017). The multipoint area selection tool in ImageJ was used to measure microglial cell body perimeter and area (Green et al., 2021).

Statistical analyses
Prior to statistical analyses, Grubb's outlier tests were run using the GraphPad outlier calculator and data points that were statistically significant (p < 0.05) outliers were excluded from further analyses. All measurements from isolated single cell microglia analyses had the following group sizes: WT KI67 − 140 microglia, TNF-α KO KI67 − 182 microglia, WT KI67 + 72 microglia, TNF-α KO KI67 + 88 microglia. For isolated single cell analyses, the microglia were randomly selected, independent of KI67 staining, resulting in differences in groups sizes between KI67 + and KI67 − groups.
Microglial cell count and cell body perimeter and area data from each region of interest (where an average value calculated from three photomicrographs per animal was considered) were analyzed using a two-way ANOVA followed by a Tukey's multiple comparisons test when appropriate. Prior to ANOVAs, groups were assessed for equal variances to ensure the primary assumption of homoscedasticity was met. ANOVAs were conducted using GraphPad Prism 9.3.1.
For single cell data, we fit generalized linear mixed models to test for effects of KI67, genotype, and brain region (Stroup, 2012;Faraway, 2016). Because 54 data points were obtained for each mouse, we specified random intercepts for individual mice in all models to account for clustering and potential correlation among observations for a given mouse. For each outcome, we fit three mixed models: (1) a contralateral hemispherespecific model that included a three-way interaction among KI67, genotype, and brain region; (2) an ipsilateral hemispherespecific model that included a three-way interaction among KI67, genotype, and brain region; and (3) a non-hemispherespecific and non-region-specific model (i.e., hemisphere and region data combined) that included a two-way interaction between KI67 and genotype. Collectively, these three models allowed us to investigate differences between hemispheres, among regions within hemispheres, between genotypes within and among regions within each hemisphere, and between KI67 positive and negative within and between genotypes within and among regions within each hemisphere. We did not fit a model with a four-way interaction among KI67, genotype, brain region, and hemisphere because such a model would have been overparameterized relative to the design effective sample size.
Exploratory analyses suggested that, depending on the outcome variable, different error distributions were needed in the mixed models to accurately reflect the data scales and to obtain reliable estimates. The fractal dimension outcome was approximately normally distributed, so we specified Gaussian error distributions. In contrast, span ratio was severely leftskewed, so we specified a Gamma error distribution with a log link function (i.e., multiplicative arithmetic mean model) (Faraway, 2016). Because lacunarity, density, and circularity were all proportions bounded between zero and one, we specified Beta error distributions (Ferrari and Cribari-Neto, 2004). Point voxels, number of branches, branch lengths, cell areas, and cell perimeters were all count variables that exhibited overdispersion (dispersion range: 5.78-51.47), so we specified negative-binomial error distributions (Hardin and Hilbe, 2018). We fit all models via the glmmTMB package in the R statistical computing environment (Brooks et al., 2017;R Core Team., 2022). We present results as the estimated marginal means with corresponding 95% confidence intervals (CI), as well as p-values for contrasts that were obtained following Tukey's adjustments for multiple comparisons (Dunn, 1961), all of which we produced using the R package emmeans (Lenth, 2021).
Single cell multivariate data were presented as a heatmap using ClustVis (Metsalu and Vilo, 2015). All cells were included in the heatmap regardless of region or hemisphere and were separated by whether they expressed KI67. Five outcome measures that represent microglial reactivity were included in the heatmap: Endpoints per microglia, branches per microglia, total branch length per microglia, cell body area, and cell body perimeter. We observed no statistical differences in these outcome measures between genotypes, so we included all isolated microglia (KI67 − n = 320, KI67 + n = 158), regardless of genotype. All continuous data were standardized to have mean of zero and unit variance prior to plotting.
Prior to statistical analyses, a Type I error rate of α < 0.05 was pre-determined as an acceptable threshold. All data analyzed with two-way ANOVAs are presented as individual data points with the mean ± standard error of the mean (SEM). In contrast, all data analyzed with mixed models are displayed as individual data points with the point estimates and corresponding 95% confidence intervals.

Results
The number of dividing microglia was greatest near the injection site We quantified the number, the morphology and the proliferation of microglia in three different brain regions 3 days after a unilateral intracortical injection of kainic acid in mice. There were more Iba1 + microglia per field of view in the perirhinal cortex of TNF-α KO mice compared to WT mice (Figure 2A; see Table 1 for corresponding p-values and F-statistics). There were no differences in the total number of microglia in the perirhinal cortex (Figure 2A) or the CA1 (Figure 2B) between ipsilateral and contralateral hemispheres. However, there were more microglia in the ipsilateral cortex/injection site compared to the contralateral in WT mice ( Figure 2C). There were no differences in the total number of Ki67 + dividing cells between hemisphere or genotype in the perirhinal cortex or CA1 (Figures 2D-E). There were more dividing cells in the ipsilateral cortex/injection site of WT mice than in the contralateral hemisphere ( Figure 2F). KI67 labels all dividing cells and is not specific to dividing microglia. Therefore, KI67 cell counts represent any cell type that may divide after a kainic acid injection (e.g., astrocytes, oligodendrocytes, pericytes). There were no differences in the number of dividing microglia (Iba1 + , KI67 + ) between hemisphere or genotype in the perirhinal cortex ( Figure 2G) or the CA1 (Figure 2H). However, in the cortex/injection site, there were more dividing microglia on the ipsilateral side than the contralateral side ( Figure 2I) in both WT and TNF-α KO mice. There were no differences in the number of microglia, total number of dividing cells, or number of dividing microglia between hemisphere or genotype when four 40 µm slices of the hippocampus were counted for Iba1 + , KI67 + , and Iba1 + and KI67 + cells (Supplementary Figures 1A-C).  Table 1). Statistically significant post hoc comparisons are indicated by bars with corresponding p-values. WT n = 4; TNF-α KO n = 5. Dividing microglia had a larger cell body area and longer perimeter compared to microglia that were not dividing The perimeter and area of randomly selected microglia were measured (Figure 3A). In individual microglia isolated from 40× images, dividing microglia had longer cell body perimeters than non-dividing microglia in both WT and TNF-α KO mice (Figure 3B; see Table 2 for corresponding coefficient estimates and p-values). Microglial cell body area was also larger in dividing microglia compared to non-dividing microglia in WT and TNF-α KO mice ( Figure 3C). No differences in the average cell body perimeter of microglia between hemisphere or genotype were found in the perirhinal cortex ( Figure 3D), CA1 (Figure 3E), or cortex/injection site ( Figure 3F; see Table 3 for corresponding p-values and F-statistics). There were no differences in the average microglia cell body area between genotype or hemisphere in the perirhinal ( Figure 3G) or CA1 (Figure 3H) regions. However, cell body areas were larger in the cortex/injection site of the ipsilateral cortex compared to the contralateral cortex in both WT and TNF-α KO ( Figure 3I).

The morphology of dividing microglia was less complex
We compared the branching pattern and the morphological complexity of dividing and non-dividing microglia ( Figure 4A). Dividing microglia had fewer branches per microglia ( Figure 4B), a shorter total branch length (Figure 4C), and fewer branch endpoints per microglia ( Figure 4D) compared to microglia that were not dividing (see Table 4 for corresponding coefficient estimates and p-values). There were no differences in the density of pixels per microglial cell between dividing microglia and microglia that were not dividing ( Figure 4E; see Table 5 for coefficient estimates and p-values). Dividing microglia had a lower fractal dimension than microglia that were not dividing ( Figure 4F). There were no differences in the circularity of microglia ( Figure 4G). In both WT and TNF-α KO mice, dividing microglia had a lower lacunarity than microglia that were not dividing ( Figure 4H). There were no differences in span ratio (cell shape/elongation) of microglia ( Figure 4I). Although we found strong support that most dividing microglia had a more reactive morphology than microglia that were not dividing, the dispersion of the data points shown in Figure 4 also indicates that not all dividing microglia had a reactive morphology some microglia had a more ramified morphology than others during division.

Microglial morphology was most reactive near the injection site
There was more microglial reactivity in the ipsilateral hemisphere (Figures 5A-C) than in the contralateral hemisphere (Figures 5D-F and see Supplementary Tables 1-3 for corresponding coefficient estimates and p-values). Within the ipsilateral hemisphere, microglia had the fewest number of branches and endpoints in the cortex (Figures 5A,B). Microglia in the ipsilateral CA1 had a longer total branch length than in the cortex and perirhinal regions ( Figure 5C). In the contralateral hemisphere, there were no differences in branches per microglia, endpoints per microglia or total branch length per microglia across regions (Figures 5D-F).

Discussion
It is critical that microglial cell division (i.e., mitosis) and reactivity are understood. Microglia can become reactive and release pro-inflammatory mediators after an insult or injury, which worsens functional outcomes. The reactivity and cell division of microglia rely on molecular pathways that include TNF-α (Kuno et al., 2005;Kong et al., 2019;Jang et al., 2021). Therefore, we investigated the morphology of dividing microglia and the effect of TNF-α on these morphologies. To date, a detailed, quantitative account of dividing microglial morphology has not been published. We found that most dividing microglia had a less complex morphology following administration of kainic acid. Classically, this less complex morphology has been associated with microglial reactivity in response to inflammatory stimuli (Badoer, 2010;Loane and Kumar, 2016;Morrison et al., 2017;Green et al., 2021). These data support published studies that found microglia divide in a reactive/amoeboid morphology (Alliot et al., 1999;Zusso et al., 2012). We also observed dividing microglia with complex, more ramified morphologies. We speculate that dividing microglial morphologies occur on a continuum, which may explain the range in morphologies we observed in dividing microglia (Figure 6).
Interestingly, in the contralateral perirhinal cortex, the region furthest away from the injection site, we found a greater range in skeletal analysis data from KI67 + microglia, compared to KI67 + microglia from the CA1 and cortex near the injection site. This large range in the number and length of microglial branches, and number of endpoints per microglia, indicates that dividing microglia had varying morphological complexities. We conclude that remote to the injection site, some dividing microglia had a reactive morphology, while other dividing microglia had a more ramified morphology. We must also consider that microglial division may be accompanied by Dividing microglia had larger cell body areas and longer perimeters compared to microglia that were not dividing. (A) Iba1 + microglia were randomly selected, and the area and perimeter were traced and calculated in ImageJ (represented by black lines around the cell bodies). Scale bar = approximately 10 µm. (B) Cell body perimeter and (C) cell body area of individual microglia. (B,C) Individual data points are presented as dots and were analyzed using linear mixed models. Results are presented as point estimates with 95% confidence intervals (see Table 2).  Table 3). Statistically significant post hoc comparisons are indicated by bars with corresponding p-values. WT n = 4; TNF-α KO n = 5. morphological changes, regardless of their reactivity (Askew et al., 2017). These results are in agreement with two divergent hypotheses, that microglia can undergo cell division in both a ramified morphology (Glenn et al., 1992;Kloss et al., 1997) and a reactive/ameboid morphology (Alliot et al., 1999;Zusso et al., 2012). Under non-inflammatory conditions, microglia have a constant turnover rate (Réu et al., 2017). The population of microglia in the brain is dynamic and continuous turnover occurs to maintain the microglial population (Wirenfeldt et al., 2007;Askew et al., 2017). This maintenance involves a fine balance of cell division and apoptosis (Wirenfeldt et al., 2007;Askew et al., 2017). After an inflammatory insult, this balance is offset, and the number of microglia rapidly increases (Streit, 2006). It is possible that the ramified dividing microglia, found predominantly in cortices remote to the injection site, are dividing as part of this normal microglial turnover. However, under non-inflammatory conditions, the proportion of dividing microglia that have a ramified morphology versus ameboid morphology is unknown and this research question is an important future direction. An enduring debate among researchers is whether microglia can self-renew via cell division, or their population is replenished by infiltrating immune cells from the periphery that differentiate into microglia (Ginhoux et al., 2013). In agreement with previous research, our data suggest that microglia can divide to maintain their population (Ajami et al., 2007;Askew et al., 2017;Réu et al., 2017;Tay et al., 2017;Huang et al., 2018). However, we did not directly test if there was any contribution from infiltrating peripheral cells, CNS progenitor cells (Elmore et al., 2014), or perivascular macrophages (Masuda et al., 2022), which warrants additional research on microglia division. Our findings support that kainic acid is a potent inflammatory trigger that results in microglia reactivity and division (Chang et al., 2006;Christensen et al., 2006;Akahoshi et al., 2007;Avignone et al., 2008;Ulmann et al., 2013;Bosco et al., 2018;Morin-Brureau et al., 2018;Feng et al., 2019;Araki et al., 2020;Di Nunzio et al., 2021). Not only can kainic acid injection induce seizures, which are associated with microglial reactivity, in vitro studies have shown that microglia can express kainic acid receptors (Noda et al., 2000). Therefore, there are multiple mechanisms through which kainic acid administration can cause microglial reactivity. As expected, we found the greatest number of dividing microglia in the cortex near the injection site, which may reflect a multimodal stimulation of microglial reactivity: the inflammation caused by the needle insertion, microglial reactivity following seizures, and reactivity induced directly by the kainic acid through microgliaspecific receptors. Also, microglia near the injection site had larger cell bodies and a less ramified (i.e., reactive) morphology. In other studies that use an intraperitoneal injection of kainic acid, microglia were activated in the hippocampus when measured using microglia cell counts and percentage coverage of microglia (Avignone et al., 2008;Fox et al., 2020). A higher number of microglial cells corresponds to a higher percentage coverage of Iba1 + staining, which is interpreted as greater microglial reactivity. Our findings, using the intracerebral injection of kainic acid, did not produce the same conclusion as these intraperitoneal studies. This discrepancy in results may be because we did not consider the total expression of the marker Iba1, but instead used detailed quantitative analyses of morphology. This demonstrates how different methods can affect the outcomes of a study. Theoretically, microglia in the hippocampus were exposed to epileptiform activity caused by the kainic acid injection, which we hypothesize may have resulted in a different temporal and morphological response compared to microglia in the cortex near the injection site. Microglia near the injection site were exposed to both kainic acid and tissue injury caused by the insertion of the needle. We expect that the needle insertion disrupted tissue and increased reactivity and proliferation of local microglia. We postulate that discrepancies in the pathological stimuli between the CA1, and the cortex near the injection site, may account for the differences observed in microglial morphologies. We found TABLE 3 ANOVA results from perimeter and area data (Figure 3) separated by brain region, histological stain, and effect. Dividing (KI67 + ) microglia had less complex, activated morphologies compared to microglia that were not dividing (KI67 − ).   a greater number of cells in the ipsilateral cortex, compared to the contralateral cortex, but we did not find a difference in the number of microglia in the hippocampus between hemispheres at 3 days post-injection. However, the microglia could have been reactive across both hemispheres. We postulate that both hemispheres of the hippocampus had similar levels of microglial reactivity because seizures propagate from the ipsilateral hippocampus, the epicenter of epileptiform activity after kainic acid injection, to the contralateral hemisphere of the hippocampus (Pernot et al., 2011;Bedner et al., 2015). The microglial reactivity induced by epileptic activity in the hippocampus could also explain discrepancies in the morphology of hippocampal microglial compared to cortical microglia. It is possible that the frequency and severity of epileptiform activity could contribute to the reactivity and proliferation of microglia. However, we had low variability in the reactivity of microglia in the WT KI67 + group, which suggests either that there was a low variability in the severity of seizures or that microglia morphological reactivity may not be dependent on the severity of epileptiform activity. Nevertheless, future studies using electroencephalogram recordings or seizure scoring are warranted to investigate the relationship between epileptiform activity and microglial reactivity (Feng et al., 2019;Wu W. et al., 2020). Kainic acid administration increases microglial reactivity and proliferation (Ulmann et al., 2013;Feng et al., 2019). Microglial reactivity has been shown by elevated levels of CD11b staining, in the cortex and hippocampus but not cerebellum (Bockstael et al., 2014). Measuring the gross expression level of CD11b does not allow for morphometric analyses on Iba1 stained microglia, but our results are complimentary to this study as both show greater reactivity of microglia within close proximity to the injection site. Our results are also in agreement with studies that report microglial reactivity after kainic acid administration using a visual scoring system of microglial reactivity (Chen et al., 2005) and quantification of percentage area of microglia (Abraham et al., 2012).

Effect
A lack of sensitive and reliable methods to screen microglial morphology has prevented full comprehension of how microglial morphology differs spatially throughout the brain in response to an insult or injury. Technological advances, such as single cell RNA sequencing, have provided a better understanding of microglial gene expression after different inflammatory triggers (Masuda et al., 2020), but detailed histological analyses are less common. Detailed histological data can be collected using visual scoring systems in which a researcher provides a score based on observed microglial morphology (Woollacott et al., 2020). Although the data generated from that approach are categorical or qualitative, they still provide insight about the reactivity status of microglia (Woollacott et al., 2020). Furthermore, many studies use a percentage coverage of microglia to quantify microglial activation (Abraham et al., 2012;Norden et al., 2016;Feng et al., 2020;Fox et al., 2020;Swanson et al., 2020). This method can show useful information about changes in microglia activation associated markers (e.g., Iba1, CD11b), but do not provide morphometric data. As an alternative approach, we produced a heatmap that conveys the quantitative differences between KI67 + and KI67 − microglia. We observed the greatest Microglial activation was greatest near the kainic acid injection site. Microglia were isolated from the perirhinal cortex, CA1, and cortex/injection site and data were analyzed across regions from both the ipsilateral and contralateral hemispheres. (A) Branches, (B) endpoints, and (C) total branch length per individual microglia isolated from the ipsilateral hemisphere. (D) Branches, (E) endpoints, and (F) total branch length per individual microglia isolated from the contralateral hemisphere. All data were analyzed using linear mixed models and results are presented as point estimates with 95% confidence intervals (see Supplementary Tables 1-3). TNF-α KO KI67 − n = 181 microglia; WT KI67 − n = 139 microglia; TNF-α KO KI67 + n = 87 microglia; WT KI67 + n = 71 microglia. microglial reactivity in the cortex near the injection site of the ipsilateral cortex. We found lower reactivity in the ipsilateral perirhinal cortex, remote to the injection site, and the lowest reactivity in the contralateral perirhinal cortex.
These expected results help validate skeletal and fractal analysis methods for identifying microglial reactivity through morphometric data collection and analyses. Our data indicate that fractal and skeletal analysis techniques, applied to single Frontiers in Neuroscience 13 frontiersin.org Summary Figure showing that KI67 + microglia have a more reactive morphology than KI67-microglia. Heatmap showing branch and cell body data clustered using correlation distance and average linkage. Unit variance scaling was applied to all continuous variables. Columns represent individual microglial cells. A score of -1 (blue) denotes a lower number and a score of 5 a higher number in the respective row. The majority KI67 − microglia had higher scores for skeletal analysis measurements (endpoints/microglia, branches/microglia and total branch length/microglia) and lower scores for cell body area and perimeter. Most KI67 + cells (red) had lower scores for skeletal analysis measurements and higher scores for cell body area and perimeter, indicating that dividing microglia had less ramification and a larger cell body. TNF-α KO KI67 − n = 181 microglia; WT KI67 − n = 139 microglia; TNF-α KO KI67 + n = 87 microglia; WT KI67 + n = 71 microglia.
microglia isolated from photomicrographs, are effective in differentiating between microglial morphologies (summarized in Figure 6). TNF-α levels sharply increase in the cortex and hippocampus in the hours following a kainic acid injection (Minami et al., 1991;. Further, the level of TNF-α has been shown to be higher in the ipsilateral hippocampus, than in the contralateral hippocampus 24 h after unilateral intracortical kainic acid injection (Nikolic et al., 2018). Therefore, we investigated whether knocking out microglial release of TNF-α affected microglial reactivity. We found few effects on microglial division and activation in TNF-α KO mice compared to WT mice. This suggests that the TNF-α, released from microglia, does not play a critical role in microglial cell division after an inflammatory stimulus using kainic acid. As many cytokines are released after a kainic acid injection (Minami et al., 1991;Oprica et al., 2006;Caracciolo et al., 2011), the effect of TNF-α on cell division may be dampened by high levels of synergistic pro-inflammatory cytokines. Previous studies reported that pharmacologically targeting TNF-α release from microglia using quercetin inhibited microglial cell reactivity both in vivo and in vitro . Potential discrepancies between those findings and ours could be that different techniques were used to measure microglial reactivity, as well as potential off-target neuroprotective effects of quercetin. It is possible that knocking out TNF-α receptor (TNFR)1 and/or TNFR2 on microglia may yield a more robust response than knocking out TNF-α. The KO model utilized in our study allowed for TNF-α from other sources to bind the receptors on the microglial cell surface. However, the KO of TNFR1 on microglia enhanced microglial reactivity after administration of kainic acid (Lu et al., 2008). Together, these data suggest that TNF-α is involved with many cellular outcomes, including inflammation, tissue degeneration, cell survival, apoptosis, and necroptosis, and completely eliminating TNF-α signaling may be pathological (Jang et al., 2021).
Histological studies come with the limitation that some cells may be sliced in different planes so that the entire cell body is not captured in the image. To combat this, we took Z-stacked images that spanned the 40 µm tissue. We cut the tissue at 40 µm because microglia typically have a 30 µm width/diameter (Kettenmann and Verkhratsky, 2011), with the exception of rod microglia that have a longer cell body (Taylor et al., 2014). We only collected tissue at 3 days post-kainic acid injection to ascertain whether the morphology of dividing microglia changes temporally after a kainic acid injection. A limitation of our study is the lack of a saline control; however, we wanted to quantify and measure the morphology of reactive and dividing microglia triggered by an inflammatory stimulus, as seen in many clinical conditions. We used the perirhinal cortex, remote to the injection site, in the contralateral hemisphere to obtain less reactive microglia that could be compared to microglia near the injection site, where we expected a higher reactivity of microglia. Using three regions of interest with different proximities to the injection site allowed for the isolation of microglia with an extensive range of morphologies and ramification. However, when considering the regions used in this study, it should be noted that microglia have been shown to vary in density and morphology throughout different regions of the brain. Such morphological variability should be considered as a limitation of our self-control study design and future studies are warranted to investigate further comparisons made to a saline control.
In conclusion, skeletal and fractal analysis methods are valid for identifying microglial reactivity through morphometric data collection. We collected data using sensitive and quantitative microglia morphology analyses and found support for our hypothesis. Dividing microglia had an activated, less ramified morphology, compared to non-dividing microglia after kainic acid-induced inflammation. These findings contribute to the gap in knowledge about microglial morphologies and their population dynamics. Understanding microglial morphologies and division after an inflammatory stimulus are essential for the development of new therapeutics that target microglia in diseased states, such as acquired neurological injuries, neurodegenerative diseases, and aging.

Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement
All experiments were approved by the Ethics Committee of Languedoc Roussillon n • 36 and the French Ministry of Research (APAFIS#9899-2017042514488653 v4).

Author contributions
TG executed the experiments, led the data collection, constructed the figures, and led manuscript writing. SM analyzed data and reviewed and edited the manuscript. MM-M performed the surgeries, kainic acid injections, and reviewed and edited the manuscript. EA was responsible for conceptualization of the study design, execution of the experiments, and reviewing and editing the manuscript. RR assisted with data analysis, visualization, conceptualization of the study design, and writing and editing the manuscript. All authors contributed to the article and approved the submitted version.

Funding
TG was supported by the Chateaubriand Fellowship offered by the Embassy of France in the United States and EA was funded, in part, by a grant from the Agence Nationale de la Recherche (ANR-20CE16-0003-02).


Finite element modeling of slow water filtering

Slow sand filtration is most appropriate where there is funding to subsidize the initial cost of the filter, available training for use and maintenance, locally available sand, and a transportation network capable of moving the filter. Since the experimental study of slow filtration is taking a long time, theoretical analysis, modelling and simulation studies become important. Theoretical models of slow filtration are not yet sufficiently developed. Several reasons are hindering modelling and simulation: due to the complexity of the interaction of water and pollution with filters and the lack of detailed values for the kinetic coefficients in elementary processes. Multiphysics modelling using Comsol, where a complex set of Navier-Stokes equations is numerically solved together, with the molecular awareness for the kinetic coefficients, was used to studying various geometries and construction types of slow filtration.

INTRODUCTION
The goal of the paper: provide scientific substantiation and development of effective technological processes, technologies for purification of natural waters and improvement of the quality of drinking water in the regions of Kazakhstan.
The main efforts are to address theory, modelling and simulation of the slow filtration technology at low-capacity facilities for natural water purification.
Slow filtration technology was originally applied in the first half of the 19th century ( [1], Chelsea, 1829). At a time when surface waters weren't heavily polluted and standards weren't as stringent, slow filtration seemed like the ideal "ecological" treatment, mimicking the natural way 2 spring water was produced, because the water was filtered through the filter media without any added chemicals.
The company has built two reservoirs in Green Park, near Buckingham Palace. A third reservoir was built in Hyde Park. The water came from the River Thames. However, the river proved to be an unstable source of good drinking water. Increasing industrialization in 19th century polluted the river as sewers and factories dumped a constant stream of waste into its waters. The basic principle of the slow filtration process is simple. Polluted water flows through a layer of sand, where it is not only physically filtered, but also biologically treated. This removes deposits and pathogens. This process is based on the ability of microorganisms to remove disease-causing microbes. In the treatment process, the raw water underwent only physical pre-treatment (microdeformation or rapid pre-filtration through gravel and/or sand in "pre-filters", at a rate of approximately 5 m/d. Modern fast filters operate 30-50 times faster. Slow filtration sometimes uses only bacteria. However, it often happens that after a certain period of maturation, a complex biocenosis arises on the surface of the sand, consisting of algae, bacteria and zooplankton. The latter acts simply as a predator and has a limiting effect on the first two. In this environment (the biological membrane) a complex symbiosis occurs between algae and bacteria similar to that found in a natural lagoon. The various modes of slow filtration biomass can be summarized as follows: mechanical retention and filtering effect in two successive media: the biological membrane and the actual sand; bioflocculation using exopolymers secreted by algae and bacteria; bacterial oxidation due to dissolved O2 present in raw water and O2 produced by algae; nitrogen-ammonia nitrification, mineralization of biodegradable organic matter; binding (heavy metals), bioaccumulation (detergents, pesticides), metabolism (phenols, pesticides).
Elimination of pathogenic bacteria through the "fight for life" system, bioflocculation, predation, antibiosis (some green algae, such as Chlorella, are able to secrete bactericidal substances).
The pressure loss changes very slowly due to the low filtration rate, and on average the filters are washed once a month.
Rinsing is usually done manually (using pressurized water jets) and sometimes mechanized (descaling). After the wash is completed, the filtered water will not always be satisfactory and will have to be drained down the drain until the biological membrane is restored.
This process takes several days.
Slow filtration clarifies water well at low suspended solids content in raw water. As the solids content increases, the pre-treatment systems, gratings and pre-filters become inadequate and the turbidity of the treated water may well exceed the limit set by the standards.
In addition, these filters are sensitive to high levels of planktonic algae in raw water (surface contamination). Some species have been shown to create particularly high levels of pollution (eg Asterionella diatom and Pediastrum green algae, in microorganisms for which fresh water is their natural habitat).
Disadvantages of slow sand filtration: • Not fully effective against viruses • Lack of residual protection -may lead to re-infection.
• Regular cleaning can damage the biolayer and reduce efficiency.
• Difficult to transport due to weight -high initial cost Slow sand filtration is most appropriate where there is funding to subsidize the initial cost of the filter, available training for use and maintenance, locally available sand, and a transportation network capable of moving the filter.
Theoretical models of slow filtration are not sufficiently developed. First of all, due to the complexity of the interaction of water and pollution with filters and the lack of detailed values for the kinetic coefficients in elementary processes.
Multiphysics modelling using Comsol gives a powerful tool to researchers for studying various geometries and construction types for slow filtration.

Darcy's law.
The flow in porous materials is characterized by the Darcy's law, whereas the linear relationship between the velocity field u (m/s) and the gradient of the pressure p (Pa) can be written as follows [2], (Darcy 1856): with k (m 2 ) the permeability of the porous medium and (Pa·s) the dynamic viscosity of the fluid, respectively.
The permeability can be obtained from the Kozeny-Carman relationship for the packed beds or granular soils [3], (Kozeny 1927): Here, (m) is the average granular diameter. Darcy's linear flow is only valid for very low velocities or at low Reynolds numbers (Re < 10).

Transport of dilute species in porous media.
This transport model is a sub-model of the physical model including transport of chemical species. It is used for the study of solute transport in porous media for the Darcy's law. The solute transport is directly related to the nature of the porous media and model includes reaction rate expressions and solute sources. This interface used to compute the concentration and transport of species in a free and porous media [4], (Comsol Interface).
where  is the bulk density, c is the concentration, t is time, u is the fluid velocity, R is the retardation factor, S is the saturation factor, D is the hydrodynamic dispersion coefficient, N is the average pore water velocity, and Γ is the gamma function.

Numerical solution.
Using the COMSOL Multiphysics software, we solved contaminated water transfer through a porous sorbent and ceramic medias with the porosities of 70% where the linear Darcy flow is dominating the flow.
This model is based on the ceramic water filter model [5], (Comsol works), which contained activated carbon core as sorbent and we have modified it under our problem to show the efficiency of the sorbent for water filtration. Sorbent has a large surface area and is mostly used in granular form for water filters. The advantage of granular sorbent compared to powderedsorbent is the smaller pressure drop due to its relatively large particle size.
At the same time this results in a smaller surface area available for reaction and adsorption. results. Also, we consider displacing fluid is pure water. The concentration of displaced fluid is calculated from the initial concentrative properties of aqueous solution. Transport properties used in our simulations are given in Table 1. The computational domain was a three-dimensional (3D) as shown in Figure 1    It is interesting to note that the complete removal of the chlorine concentration doesn't depend on sorbent granular size as well as different filtration times. Comparing mercury, chlorine can be easily disposed of even at large sorbent granular sizes and short filtration times ( Figures 6, 7).

CONCLUSIONS
Theoretical models of slow filtration are not yet fully developed. Several reasons are hindering modelling and simulation. Due to the complexity of the interaction of water and contamination with sorbents and filters and the lack of detailed kinetic coefficients of the elemental processes. In this paper, the kinetic coefficients were obtained from experiment in this work and in Ref. [6]. Multiphysics modelling using Comsol, where a complex set of Navier-Stokes equations is numerically solved together, with the atomistic awareness of the kinetic coefficients, was used to studying various geometries and construction types of slow filtration.
Future work would include partially or fully the following tasks. We already started to consistently generate kinetic coefficients using multiscale approach using the Lammps package.
Assessment of the current state of drinking water supply to settlements of the Republic of Kazakhstan; the current state of water treatment facilities for the purification of natural waters; build a new design of water intake and treatment facilities; theory, experiments of the technology of slow filtration at low-capacity facilities for the purification of natural waters; theory and experiments for the development of modified sorbents from natural minerals of Kazakhstan, for the purification of natural waters. Development of natural water purification technology using a


s2fieldsofstudy: Biology Medicine

text:
Antimicrobial Susceptibility Pattern and Serotype Distribution of Streptococcus pneumoniae Isolates From a Hospital-Based Study in Chandigarh, North India

Streptococcus pneumoniae (pneumococcus) causes significant infection-related morbidity and mortality worldwide. The genome plasticity of pneumococcus is an essential factor in antibiotic resistance, serotype switching, and the emergence of nonvaccine serotypes. Information regarding the serotype distribution as well as antimicrobial susceptibility in pneumococcus clinical isolates responsible for various infections in Northern India is limited. Here, we have explored the antibiotic resistance and serotype pattern associated with S. pneumoniae infections from both invasive and noninvasive sites of patients of all ages, visiting out-patient department of a tertiary care hospital (PGIMER, Chandigarh, India). This study was carried out on 68 S. pneumoniae isolates and the isolates exhibited the highest resistance (76.5%) to cotrimaxozole followed by resistance toward tetracycline (36.8%) and erythromycin (23.5%). All isolates showed vancomycin susceptibility and 86.8% of isolates showed sensitivity to chloramphenicol. Multidrug resistance was found in 32% (n=22) of the S. pneumoniae isolates showing resistance toward three different antibiotics. Serotype 19F was found to be the most prevalent serotype (39%) followed by serotypes 6A/B/C (19%) and 1 (12%). These data shed light on the latest trends in antibiotic susceptibility and prevalent serotype patterns of hospital-based S. pneumoniae isolates. This information can be helpful in designing future disease-preventive strategies.

Introduction
Streptococcus pneumoniae is responsible for causing various invasive as well as noninvasive diseases like pneumonia, bacteremia, otitis media, sepsis, and meningitis [1]. Infections caused by pneumococcus are responsible for significant morbidity and mortality worldwide. According to an SRMGBD (2016) study by Troeger C (2018) [2], Streptococcus pneumoniae was responsible for 1,189,937 deaths with 95% uncertainty index (690 ,445-1,770, 660). Most of the pneumococcal-related cases are reported from Asia and Africa, in which India accounts for 27% of the cases [3]. In India, the number of deaths associated with pneumococcal infections is estimated to be around 68,700 (an uncertainty range of 44,600-86,000) [4]. Emerging drug resistance in the case of pneumococcus is a serious problem that is responsible for the increased incidence of the novel, nonvaccine serotype, and multidrug-resistant strains.
Globally, a relatively high prevalence of strains resistant to β -lactams (penicillin) and macrolides has been observed [5]. More than a hundred different serotypes of pneumococcus have been identified. The pneumococcal serotype distribution has shown geographical variation globally, with lower-to-middle income countries (LMIC) and densely populated countries like India bearing the significant burden of infections and serotype variation [6,7]. The currently available vaccines exhibit limited coverage of serotypes and fail to account for the phenomenon of capsular replacement. These vaccines have also been shown to confer poor mucosal immunity and are not easily accessible to the economically poorer countries due to their high costs [8][9][10]. This increase in the number of novel, antibiotic-resistant strains, and the overall increase in the number of serotypes have necessitated the need for continued sentinel surveillance. Also, data on the serotypes circulating in the community have been reported but information on serotypes causing serious infections that lead to hospital visits and subsequent hospital admissions is scarce [11]. In our hospital-based study, the distribution of serotypes and the antibiotic resistance patterns of S. pneumoniae isolates collected from invasive and noninvasive sites is reported. This study profiled the sample-wise serotype prevalence and susceptibility trends and analyzed the vaccine coverage of the detected serotype, which is crucial to define the prevention and treatment strategies for S. pneumoniae infections.  (Union Territory), a tertiary care center catering to the population of union territory and nearby states, and after that, those samples were collected from the Bacteriology unit of Department of Medical Microbiology. These samples were received from the out-patient department of PGIMER Hospital, Chandigarh. Isolates from the patients diagnosed with any other bacterial infection or illness or having any genetically inherited diseases were excluded.

Antibiotic susceptibility testing
All S. pneumoniae isolates were cultured and maintained for further susceptibility testing and evaluation of serotypes. Kirby Bauer disc diffusion technique was used for screening of all S. pneumoniae isolates as per Clinical and Laboratory Standard Institute (CLSI) guidelines [12,13]. The antibiotics used for screening included oxacillin (OXA; 1 µg), erythromycin (ERY; 15 µg), co-trimoxazole (SXT; 25 µg), tetracycline (TET; 30 µg), vancomycin (VAN; 30 µg), ciprofloxacin (CIP; 5 µg), cefotaxime (CTX; 30 µg), amoxicillin (AMX; 10 µg), levofloxacin (LVX; 5 µg), and chloramphenicol (CHL; 30 µg). The overnight grown cultures were spread on blood agar plates and antibiotic discs were placed at appropriate distances on the plates. The plates were incubated at 37ºC for 18 h and a zone size of each antibiotic was studied. Results were interpreted as resistant, susceptible, or intermediate according to the CLSI standards of interpretative criteria. The isolates showing a zone ≤19 mm around the OXA disk were interpreted for minimum inhibitory concentration (MIC) of penicillin by E-test (AB Biodisk). The categorical interpretations of MIC (µg/mL) were done using CLSI susceptibility breakpoints and isolates were labeled as S, I, or R to penicillin if MIC was <2.0 µg/mL, 4 µg/mL, or ≥8.0 µg/mL, respectively ("CLSI," 2012). The isolates resistant to three or more classes of antibiotics were labeled as multi-drug resistant (MDR).

Results
To check the antimicrobial susceptibility and serotype distribution, samples like blood (48), CSF (2), sputum (6), and nasopharynx (12) of patients suffering from lower respiratory tract infection were included in this study.

Antibiotic susceptibility
The in vitro susceptibility of pneumococcal isolates was checked against 10 antimicrobial agents to assess their susceptibility patterns ( Figure 1). Fifty-nine isolates showed antibiotic resistance to at least one of the tested antimicrobials while three were completely susceptible to tested antimicrobials including one standard strain. MIC of these isolates against benzylpenicillin E-strips ranged from 0.125 to 12 µg/mL. Penicillin resistance was detected in only five pneumococcal isolates, while the rest of the isolates showed penicillin susceptibility with MIC ≤2.0 µg/mL. Susceptibility profiling of isolates showed maximum resistance to SXT (76.5%) and complete sensitivity to VAN (100%). Besides this, many of the isolates also showed resistance to TET (36.8%) followed by ERY (23.5%) and CIP (17.6%). The antibiotic susceptibility patterns were further analyzed on the basis of invasive and noninvasive sources of S. pneumoniae isolates ( Table 1). All nasopharyngeal (NP) isolates were resistant to SXT, whereas only 68.8% (n=50) of the blood isolates were resistant to it. The resistance to ERY was highest (50%; n=6) in NP isolates. Sputum isolates showed maximum resistance to SXT (83%; n=5), CIP, and TET (66.6%; n=4). Ten isolates (nine blood and one NP) showed resistance for AMX and three isolates from blood were found to be resistant to CTX. The resistance to LVX was observed in three sputum as well as three blood isolates and a single isolate from NP. Likewise, CHL resistance was also detected in only seven blood and two sputum isolates. Interestingly, isolates from CSF consistently exhibited susceptibility for AMX, CTX, CHL, and LVX.

Invasive (blood and CSF)
Noninvasive (NP and sputum)

Serotypes of S. pneumoniae isolates
A total of six different serotypes/groups were found to be distributed among pneumococcal isolates. Serotyping was done for 68 isolates (Figure 2) in which 49 isolates were typed against 36 available primers of known serotypes and 19 isolates remain untyped due to the non-availability of primers of the remaining serotypes.  Sequencing at cps loci in 10 randomly chosen isolates covering seven different serotypes further confirmed the serotypes detected in the multiplex PCR method. The obtained sequence-typing nucleotide data were analyzed for homology using BLAST and hits with the highest bit score were compared with PCR-based serotypes. All sequence-derived serotypes matched exactly with PCR serotypes as per criteria mentioned elsewhere [16]. One isolate of each serogroup 6A/B/C and 7C/B was further resolved into serotypes 6A and 7C, respectively. The rest of the isolates bearing 11A/D, 22F/A, and 7C/B serogroups showed consistent outcomes upon sequence typing.

Assessment of vaccine coverage of serotypes
Analysis of the detected serotypes among clinical strains of pneumococcus for the percentage serotype coverage of various commercially vaccines ( Table 2) showed that the PPSV 23 vaccine offered maximum coverage (96%) followed by PCV11 and PCV 13 (77.5%) and PCV 7, PCV 9, and PCV 10 (59.1%) [17,18].

Discussion
S. pneumoniae frequently colonizes the upper respiratory tract in humans and is responsible for causing diseases that can be minor, to life-threatening diseases like pneumonia, sepsis, and meningitis under appropriate conditions [19]. Despite the accessibility of antibiotics and the development of many vaccines, the burden of pneumococcal infections in many LMIC including India still remains high and no concrete preventive strategies have been adopted so far [7]. The genome plasticity of S. pneumoniae and the uneven global prevalence of serotypes limit the success of vaccines [20] and have also resulted in the development of high resistance to commonly used antibiotics. Further, immunological and economical reasons restrict the effective management of pneumococcal infections. Therefore, it is imperative to study the locally prevalent serotypes and susceptibility trends in clinical isolates that are driving pneumococcal infections. S. pneumoniae isolate used in this study broadly represents the noninvasive (NP and sputum) and invasive (blood and CSF) pneumococcal infections. The data obtained in this hospital-based study demonstrate maximum resistance to SXT and TET. This is supported by earlier reports from India, which also stated high resistance to these antibiotics in invasive and carrier strains [21][22][23]. Importantly, the resistance to these antibiotics has been found to be higher in Indian communities in comparison to other global populations [24]. Besides, resistance to antibiotics like ERY is also concerning as it is broadly used for the treatment of acute respiratory infections. We have observed its higher resistance rate in colonizer NP strains. This was also correlated in various other studies carried out in India [23,24]. In relation to the different antibiotic classes, maximum resistance was observed in sulfonamide (76.5%) followed by TET and macrolides. These results are uniform with respect to other similar studies [17,18,23]. In context, in this study, no variation in the antibiotic susceptibility trends was exhibited by the clinical isolates when compared to the previously reported results from different parts of India. The prevalence of S. pneumoniae serotypes affects the choice of preventive measures utilized, which makes serotyping an absolute necessity. Hence, we used a multiplex-PCR typing approach for the serotyping of isolates and its accuracy was ensured by sequence typing. We found the predominance of serotype 19F among the isolates, which was followed by serotypes 6A/B/C, 1, 10A, 3, 7C/B, 11A/D, 14, 2, and 22F/A. Prevalence of 19F was also noted in a recent study carried out in Asian countries [5]. Various studies have also reported serotype 6 as most predominant in different hospital as well as day-care setups [21,22]. Earlier, invasive bacterial infection surveillance also implicated 1 and 19 as the most invasive disease-causing serotypes [25].  [16,18] covered 96% of the prevalent serotype followed by PCV13 (77.5%). This vaccine has been shown to provide coverage against serotypes like 19F, 14, and 23F, which are becoming increasingly resistant to antibiotics [18]. These findings can be correlated to our study as serotypes like 19F, 6B, and 3 were also found among our isolates. Further, the high mortality rates have been associated with infection of S. pneumoniae of serotypes 3, 6B, and 19F [27,28]. Our study has few limitations in terms of the number of isolates profiled. The number of strains included in this study is directly impacted by the inclusion and exclusion criteria. As per our exclusion criteria, we excluded patients diagnosed with any other bacterial infection or illness or a genetically inherited disease. A proportion (19%) of serotypes in this study were labeled as untyped as the current PCRbased method limits the range of serotypes (38 serogroups) as that can be detected. The use of conventional serotyping methods such as Quellung serotyping remains expensive [29]. Untyped isolates, if resolved, may help us detect more variation in the distribution of serotypes. Finally, we can say that this study contributes to our understanding of the recent antibiotic susceptibility and serotype patterns in a North Indian Union Territory. Further, this information can potentially help us in the development of better therapeutic and management strategies against pneumococcal infections.
PG11-ITRG/11384 & INT/IEC/2018/1081), PGIMER, Chandigarh. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.