Sponge-mediated miR138-5p inactivation specifically in mouse parvalbumin (PV)-expressing interneurons impairs spatial recognition memory and enhances GABAergic synaptic input onto pyramidal neurons. Cellular and behavioral phenotypes associated with miR138-5p inactivation are paralleled by an upregulation of the schizophrenia (SCZ)-associated Erbb4, which we validated as a direct miR138-5p target gene. Our findings suggest that miR138-5p is a critical regulator of PV interneuron function in mice, with implications for cognition and SCZ. More generally, they provide evidence that microRNAs orchestrate neural circuit development by fine-tuning both excitatory and inhibitory synaptic transmission.The proper development and function of neuronal circuits rely on a tightly regulated balance between excitatory and inhibitory (E/I) synaptic transmission, and disrupting this balance can cause neurodevelopmental disorders, for example, schizophrenia. MicroRNA-dependent gene regulation in pyramidal neurons is important for excitatory synaptic function and cognition, but its role in inhibitory interneurons is poorly understood. Here, we identify  MicroRNAs (miRNAs) are short noncoding RNAs that act as negative regulators of mRNA translation and stability . Over thIn the rodent hippocampus, microcircuits of excitatory pyramidal neurons and local inhibitory interneurons provide an extensively studied model in the context of information processing, with specific implications for the control of spatial short-term and long-term memory . Among tmiR138-5p as an important regulator of excitatory synapse function in hippocampal pyramidal neurons  which allows expression of a miR138-5p inactivating sponge transcript harboring a lacZ coding sequence and six imperfect miR138-5p binding sites (6x-miR-138sponge) upon Cre-recombinase expression. Sponge transcripts sequester endogenous miRNA, thereby leading to miRNA inactivation and the derepression of cognate target genes  followed by two perfect miR-138 binding sites and a mCherry cds  in CA1 pyramidal neurons. Amplitude and frequency of mEPSCs were indistinguishable between 138-spongeub and control slices   were not differentially expressed between 138-spongeub and control mice (Gene ontology (GO) term analysis on DEGs from the RNA-seq analysis  revealedpocampus . Surpristivation . Accordirneurons . In control mice .miR138-5p expression at subcellular resolution. This analysis revealed strong expression of miR138-5p in both Camk2a-positive excitatory and Erbb4-positive inhibitory hippocampal neurons . Erbb4 is an SCZ risk gene that has been linked to the regulation of GABAergic synaptic transmission and short-term memory . rAAV expressing Dlx-138 sponge or DLX-control sponge co-localizes with Gad65/67 in primary rat hippocampal neurons, confirming interneuron-specific expression (Erbb4 RNA based on single-molecule fluorescence in situ hybridization (smFISH) was significantly elevated in Dlx-138 sponge compared to Dlx-control sponge infected interneurons  interneurons in the hippocampal circuit, frequency of miniature inhibitory postsynaptic current (mIPSC) was significantly increased in mice injected with the Dlx-138 sponge as compared to control sponge injected mice . In contmiR138-5p inactivation might be more robust in PV+ interneurons compared to other cell types. Thus, we again injected a miR-138 pbds sensor construct into the hippocampus of control or miR-138 spongeUb mice . Thus, the observed lack of regulation of miR138-5p targets in excitatory pyramidal neurons of 138-spongeUb mice is likely a result of inefficient sponge-mediated miR138-5p inhibition in this cell type. Since miR138-5p and alterations in PV+ interneuron function had recently been linked to SCZ  of PV-expressing inhibitory interneurons (PV-positive cells) . Our analysis revealed no significant difference in the number and intensity of PV-positive presynaptic boutons impinging onto the somata of CA1 pyramidal cells between 138-spongePV mice and their littermate controls  . On a becontrols . However and increases neurotransmitter release    or are dMiR-138 sponge was cloned into the BsrGI and HindIII restriction sites of a modified pAAV-6P-SEWB backbone, where the GFP has previously been replaced with dsRed . DiffereMiR-138 sponge imperfect binding site: 5′ CGGCCTGATTCGTTCACCAGT 3′; spacer sequence: 5′ TTTTT 3′; Control sponge sequence: 5′ TGTGACTGGGGGCCAGAGG 3′; spacer sequence: 5′ CAGTG 3′.Dlx-138 sponge and Dlx-control sponge were cloned into a modified mCaMKIIα-mCherry-WPRE-hGHp(A)  backbone where 138-sponge and 138-sponge reversed (control-sponge) were previously inserted using MiR-138 sponge vector as template. The backbone was opened with MluI (NEB: R0198S) and KpnI (NEB: R0142). pAAV-mDlx-GFP-Fishell-1  was used to cut out Dlx enhancer and HBB promoter with MluI and EheI (NEB: R0606S).5′ CACCACCTGTTCCTGTAGTGTAC 3′; 5′ CCAGAGGTTGATTATCGATAAGCTTAAC 3′.Primers for 138 and control sponge: AAV expressing Cre-recombinase under the control of the CMV promoter .The oligonucleotides were designed without any specific overhangs to allow blunt-end cloning in both directions for a perfect binding site reporter and an antisense control reporter. They were annealed and ligated into a pGL3 promoter vector  expressing firefly luciferase. For cloning, the pGL3 vector was before digested with XbaI and the ends were blunted to insert the binding sites via blunt-end cloning at the 3′end of the firefly open reading frame. After cloning, several clones were sequenced to determine sense and antisense reporter.5′CTAGACGGCCTGATTCACAACACCAGCTACCGGCCTGATTCACAACACCAGCTGGATCC 3′.5′CTAGCGGATCCAGCTGGTGTTGTGAATCAGGCCGGTAGCTGGTGTTGTGAATCAGGCCGT 3′.An EcoR1-BglII PCR fragment spanning the pCMV promoter, mCherry coding sequence, and SV40 poly adenylation signal were amplified using C1-mCherry  as template. The PCR product was cloned into the BGlII/EcoR1 sites of the AAV-hSyn-EGFP , upstream of the human Synapsin 1 promoter. Two miR-138 perfectly complementary binding sites were cloned into the SpeI and HIndIII restriction sites, between the EGFP coding sequence and WPRE element of AAV-hSYN-GFP, to generate the final AAV-138 sensor.5′CTAGACGGCCTGATTCACAACACCAGCTACCGGCCTGATTCACAACACCAGCTGGATCC 3′.5′CTAGCGGATCCAGCTGGTGTTGTGAATCAGGCCGGTAGCTGGTGTTGTGAATCAGGCCGT 3′.pmirGLO dual-luciferase expression vector reporter  was used to clone portions of 3′ untranslated regions of the investigated mRNAs. XhoI and SalI restriction enzymes were used.5′TTGAATGAAGCAATATGGAAGCAACCAGCAGATTAACTAATTTAAATACTTC 3′.5’TTGAATGAAGCAATATGGAAGCAgCatGaAGATTAACTAATTTAAATACTTC 3’.5’GCCTCAGTCACCAGCTCTGTACCAGCAATACTCACCCCTCCACCTCCCTGACTT 3’.5’GCCTCAGTgAtatcCTCTGTACCAGCAATACTCACCCCTCCACCTCCCTGACTT 3’.Cultures of dissociated primary cortical and hippocampal neurons from embryonic day 18 (E18) Sprague-Dawley rats  were prepared and cultured as described previously . Animal Transfection of primary neurons was performed using Lipofectamine 2000 . For each well of a 24-well plate, a total of 1 µg DNA was mixed with a 1:50 dilution of Lipofectamine in NB/NBP medium. After an incubation of 20 min at room temperature, it was further diluted 1:5 in NB/NBP medium and applied to the cells. Neurons were incubated for 2 hr with the mix. A 1:1000 dilution of APV (20 mM) in NB+/NBP+ was applied for 45–60 min afterward before exchanging with NB+/NBP+.Luciferase assays were performed using the dual-luciferase reporter assay system on a GloMax R96 Microplate Luminometer (Promega). pmirGLO dual-luciferase expression vector reporter  was used to clone portions of 3′ untranslated regions of the investigated mRNAs. PcDNA3 was used to balance all amounts to a total of 1 µg DNA per condition.tm2459 ArteGt(ROSA)26So  (hereafter named ‘138-floxed’) mouse lines were created at TaconicArtemis GmbH . The targeting strategy allowed the generation of a constitutive LacZ-miRNA138 Sponge Knock-In (KI) allele in the C57Bl/6 mouse ROSA26 locus via targeted transgenesis. The presence of the loxP-flanked transcriptional STOP cassette is expected to terminate the transcription from the CAG promoter and thus prevent the expression of the NLS-LacZ miRNA138 Sponge cDNA, which allows this line to be used as a control (138-floxed line). The constitutive KI allele was obtained after Cre-mediated removal of the loxP-flanked transcriptional STOP cassette from the conditional KI allele, by crossing the 138-flox line with a B6.C-Tg(CMV-Cre)1Cgn (CMV-CRE) line, which allows the expression of the sponge construct (138-spongeub mice). In all experiments, heterozygous male mice were used. The 138-spongePV line was generated by crossing 138-floxed line with a previously characterized B6;129P2-tm1(cre)ArbrPvalb/J (PV-CRE) line.The C57BL/6NTac-Animals were housed in groups of 3–5 per cage, with food and water ad libitum. All experiments were performed on adult male mice (3–5 months old).The animal house had an inverted light-dark cycle, all testing was done during the dark phase (8 a.m. to 8 p.m.). Mice were handled for 10 min for 5 days before the experiments began. All measures were analyzed by Noldus Ethovision xt 14, unless stated differently. During the experimental phase, mice were transported individually and allowed to acclimatize to the experimental room in a holding cage for at least 20 min before the beginning of the task. At the end of each experiment, they were transported back into the animal storage room in their holding cage and placed back into their original home cage with their littermates. 10 ml/L detergent  was used to clean equipment in between trials. Tasks which required the use of the same apparatus were scheduled at least 4 days apart. Two separate cohorts of mice were tested ( seven mice each). No cohort-specific differences were found. The behavioral essays were performed from the least to the most stressful: home cage activity, open field, y-maze, EPM, NOR, and CFC. All animal experiments were performed in accordance with the animal protection law of Switzerland and were approved by the local cantonal authorities (ZH017/18).OFT was performed as described previously . Each seSpontaneous alternation: mice were placed in a Y-shaped maze (8.5 cm width × 50 cm length × 10 cm height) for 5 min. They were free to explore the whole maze and the alternation between the arms was calculated.Novelty preference test: mice were given 5 min to explore two arms of the maze during the familiarization phase. A door made from the same Plexiglas used for the walls was used to prevent the access of the subjects into the third arm. At the end of the familiarization phase, mice were placed in their holding cage for 90 s, while the apparatus was cleaned to avoid olfactory trails. Mice were then placed back into the maze for the test phase, where all three arms were accessible. Preference ratio between the familiar and new arm was then scored based on the time spent in those arms.The maze was elevated 60 cm from the floor, with two arms enclosed by dark Plexiglas walls (5 cm width×30 cm length×15 cm height), two opposing open arms, and a central platform/intersection. Experiments were conducted in a homogenously illuminated room, with the maze placed in the center of the room. Mice were placed in the central platform of the EPM. The position and motion of the animals were automatically determined and recorded for 5 min. Time spent and distance traveled in the different arms were scored.Objects were based on the Nature protocol published previously . They weThis task was performed as previously described . BrieflyAdult male mice (4 months old) were anesthetized with isoflurane  and then quickly cervically dislocated and decapitated. Hippocampal tissue was dissected and freshly processed. RNA was extracted using mirVana microRNA Isolation Kit (Life Technologies) according to the manufacturer’s instructions.Stranded polyA+ enriched RNA sequencing libraries were prepared at the GENCORE  and sequenced on an Illumina HiSeq 2000 machine using a 50-nt paired-end protocol .We removed the sequencing adapter and quality-trimmed all short reads from the 3′end using FLEXBAR 2.5.3 and a Phred score cutoff >10. All reads longer than 18 bp were retained and rRNA reads were subtracted in silico using bowtie2 and an index of the complete repeating rRNA unit (BK000964.3). We used the STAR aligner 2.4.2a4 to map against the mouse genome (EnsEMBL 79 genome+annotations). We performed differential gene expression analysis using Cuffdiff 2.2.1.qPCR was performed as described earlier . Primer  5′GACTCCAATAGGAATCAGTTTGTC 3′.ErbB4 FW:5′ TACTGGAGCCTCTGGTATGG 3′.ErbB4 REV: 5′ GCATCAGCGGTGAGATCTGT 3′.Rims3 FW:  5’ CTGGGTCAAGCCGACGATAG 3’.Rims3 REV:Image acquisition was done with the experimenter being blinded to the different conditions. Pictures were taken on a confocal laser scanning microscope equipped with an Airyscan detector . Image analysis was carried out on Fiji (ImageJ).A 63× oil objective was used to take images from the CA1 hippocampal region immunostained with PV, VGAT and Hoechst were used to counterstain the nuclei (further details in the immunostaining section). The number of PV+ and VGAT+ en passant boutons around the nuclear perimeter were counted and normalized to the total length of the perimeter. vGAT puncta mean intensity: vGAT puncta mean intensity: ImageJ was used to measure the intensity of all the vGAT puncta in the pictures used to quantify PV boutons.2).A 20× objective was used and PV+ cells were counted on a maximum projection intensity of the CA1 region immunostained with PV . The number of PV + cells was normalized to a defined region of interest . To balance all amounts to a total of 1 µg DNA per condition, PcDNA3 was used. At 18 DIV, cells were fixed using 4% PFA for 15 min and mounted on glass slides using Aqua-Poly/Mount . The experimenter was blinded to all the conditions. The z-stack images of GFP-positive neurons exhibiting pyramidal morphology were taken with the 63× objective of a confocal laser scanning microscope . Eight consecutive optical sections of the dendrites were taken at a 0.4-μm interval with a 1024×1024 pixel resolution. Spine volumes were analyzed with the ImageJ software using the maximum intensity projections of the z-stack images. The GFP intensity of 150–200 spines per cell was measured and normalized to the total intensity of the dendritic tree. For each experimental condition, at least 18 representative neurons derived from three independent experiments were analyzed.ub mice were processed with FD Rapid GolgiStain Kit  according to the manufacturer’s protocol. Pictures were taken with a Zeiss axio-observer seven inverted widefield fluorescence microscope equipped with an Axiocam 702 mono Zeiss camera with a 100× oil objective. Dendritic spines were manually analyzed with Fiji (ImageJ).Brains from 3-month-old 138-floxed and 138-spongeub, and 138-spongePV mice (age: 6–8 weeks) and incubated at 34°C in sucrose-containing artificial cerebrospinal fluid  for 0.5 hr, and then held at room temperature until recording.Hippocampal slices (300-μm thick) were prepared at 4°C, as previously described , from 13Whole-cell patch-clamp recordings were performed at 32°C on an upright microscope (Olympus BX51WI) under visual control using infrared differential interference contrast optics. Data were collected with an Axon MultiClamp 700B amplifier and a Digidata 1550B digitizer and analyzed with pClamp 11 software . Signals were filtered at 2 kHz for mEPSCs and mIPSCs and digitized at 5 kHz. Stimulus evoked and unitary postsynaptic currents were filtered at 4 kHz, fast-spiking interneurons at 10 kHz, both were digitized at 100 kHz. Recording pipettes were pulled from borosilicate capillary glass  with a DMZ-Universal-Electrode-Puller (Zeitz) and had resistances between 2 and 3 MΩ.3, 1.25 NaH2PO4, 2 CaCl2, 2 MgCl2, and 10 glucose. For EPSCs measured in CA1 pyramidal cells and for fast-spiking interneurons, the intracellular solution was composed of 125 K-Gluconate, 20 KCl 0.5 EGTA, 10 HEPES, 4 Mg-ATP, 0.3 GTP, and 10 Na2-phosphocreatine (adjusted to pH 7.3 with KOH). Cells were held at –70 mV for mEPSCs and at –60 mV for stimulus evoked EPSCs (eEPSCs). 1 μM Gabazine was added to isolate AMPA-mediated postsynaptic currents, additionally 1 μM TTX for mEPSCs. For IPSCs intracellular solution was composed of 135 Cs-Gluconate, 5 KCl, 2 NaCl, 0.2 EGTA, 10 HEPES, 4 Mg-ATP, 0.3 GTP, and 10 Na2-phosphocreatine (adjusted to pH 7.3 with CsOH). Cells were held at +10 mV for mIPSCs and at –10 mV for stimulus evoked inhibitory postsynaptic currents (eIPSCs) and uIPSCs. 25 μM AP-5, 10 μM NBQX, and 10 μM SCH 50911 were added to isolate GABAa-mediated postsynaptic currents for eIPSCs, additionally 1 μM TTX for mIPSCs. Synaptic currents were evoked by mono-polar stimulation with a patch pipette filled with ACSF and positioned in the middle of CA1 stratum radiatum for eEPSCs and in stratum pyramidale for eIPSCs. Series resistance of CA1 pyramidal neurons  was monitored and recordings were discarded if series resistance changed by more than 20%. Membrane potentials were not corrected for liquid junction potential.The extracellular solution (ACSF) was composed of (in mM) 126 NaCl, 2.5 KCl, 26 NaHCOFor paired recordings, whole-cell configuration was first established in putative fast-spiking interneurons. Cells were selected based on morphological appearance in stratum pyramidale of hippocampal CA1, on fast-spiking properties and on input resistance characteristics for PV-positive interneurons . Subsequhttps://storage.googleapis.com/linnarsson-lab-loom/l5_all.loom, restricted it to hippocampus cells, and aggregated to the pseudo-bulk level using muscat  was used to perform GO enrichment analysis, essentially as in To sum up, genes were annotated with the ‘Cellular Component’ ontology and significantly changed genes subsequently tested against the expressed background. For the main figure, we plotted the Top10 GO-Terms ranked by significance (filtering out those with more than 200 annotated genes).miR138-5p. Putative miR-138 targets (including site-type information) were then aligned with the genes and log fold changes (logFC) as obtained by the differential expression analysis. Plotted is the cumulative proportion (logFC rank (–1)/number of genes (–1)) over the logFC.Mouse 3′UTR positions of predicted conserved microRNA binding sites were downloaded from Targetscan (version 7.2)  and filtsmFISH for miRNA detection on hippocampal neuron cultures was performed using the QuantiGene ViewRNA miRNA Cell Assay Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol with slight modifications. To preserve dendrite morphology, protease treatment was reduced to a dilution of 1:10,000 in phosphate-buffered saline (PBS) for 45 s. smFISH for mRNA detection was performed using the QuantiGene ViewRNA ISH Cell Assay Kit (Thermo Fisher Scientific) as previously described , but omi12 vector genomes/ml. Stereotactic brain injections were performed on 2- to 3-month-old 138-floxed and 138-spongeub mice as previously described ]rev-hSyn1-EGFP-2x (miR138-5p)-WPRE-SV40p(A)) was produced by the local Viral Vector Facility (VVF9 of the Neuroscience Center Zurich). The produced virus had a physical titer of 6.1×10eescribed . BrieflyPV mice) or overnight (138-flox and 138-spongeub mice) at 4°C. The fixed tissue was placed in sucrose solution (30% sucrose in PBS) for 24 hr and frozen in tissue mounting medium . The tissue was coronally sectioned at 50–60 µm thickness on a cryostat, immediately placed in ice-cold PBS, and subsequently conserved in cryoprotectant solution  at –20°C.Animals were sacrificed by intraperitoneal injection of pentobarbital (150 mg/kg). When in deep anesthesia, mice were perfused intracardially with ice-cold PBS pH 7.4, followed by perfusion with 4% paraformaldehyde in PBS pH 7.4. The brains were then isolated and postfixed for 2–3 hr : for immunofluorescence, cryosections were washed in ice-cold PBS for 30 min, placed on microscope slides , and air-dried for 5–10 min. Afterward, permeabilization was performed by incubating sections in permeabilization solution (0.5% Triton X-100 in PBS) for 30 min at room temperature, followed by a blocking step in blocking buffer  with the addition of blocking Reagent  for 1 hr at room temperature. Cryosections were washed in PBS two times for 10 min at room temperature and incubated with primary antibodies in blocking buffer overnight at 4°C. Subsequently, the sections were washed three times with blocking buffer, incubated with secondary antibodies and Hoechst 33342 in blocking buffer for 1 hr at room temperature, washed again three times in blocking buffer, washed in PBS, air-dried, and mounted with Aqua-Poly/Mount .Ex vivo (Ex vivo (vibratome sections): for immunofluorescence, 300 µM sections were sliced on a vibratome in ice-cold sucrose-containing artificial cerebrospinal fluid, and then fixed in 4% PFA overnight. Free-floating sections were washed in ice-cold 1× PBS for 30 min, permeabilized by incubation in 0.5% PBX (0.5% Triton X-100 in PBS) for 30 min at room temperature, followed by a quenching step in 0.1 M Glycine to reduce background autofluorescence. Sections were blocked in blocking buffer  for 1 hr at room temperature, and then incubated in primary antibodies for 36 hr at 4°C. Sections were washed in 0.05% PBX two times for 10 min at room temperature, and then incubated with secondary antibodies in 0.05% PBX with 10% normal goat serum at room temperature for 3 hr.Subsequently, the sections were washed three times with 0.05% PBX, incubated with Hoechst 33342 in 1× PBS for 10 min, and then washed again three times in PBS, mounted on SuperFrost + slides with Aqua-Poly/Mount . A 40-µM Z-stack (with 0.25 µM z-step) was acquired on a Zeiss LSM 880 confocal microscope, with a Plan-Apochromatic 63×/1.4NA DIC M27 oil objective and FastAiryScan detector settings in the hippocampal CA1 region.2O) for 20 min. Primary antibody was diluted in GDB and incubated for 1 hr at room temperature, followed by five washes with PBS . Secondary antibody was diluted in GDB and incubated in the dark, at room temperature for 1 hr. The cells were then washed again with PBS . In the second last wash, Hoechst 33342 was added to the PBS. The coverslips were mounted with Aqua-Poly/Mount  on microscope slides .In vitro: cells were washed once with NBP, fixed for 20 min with 4%PFA and, after five washes with PBS , permeabilized with GDB (20 mM sodium phosphate buffer (pH 7.4), 450 mM NaCl, 0.3% Triton X-100, 0.1% gelatin, and ddHStatistical analysis was performed on either GraphPad Prism 8.0 or RStudio. Plots were generated in R, mainly using the packages ggplot2, ggsci, ggrepel, and scales. For data sets with n>4, Box plots (Tukey style) were used. For data sets with n<5, average±s.d., including individual data points, is shown. Normal distribution of data was tested with the Shapiro-Wilk test. Based on the results, parametric  or nonparametric (e.g. Mann-Whitney and Kolmogorov-Smirnov) tests were used.The detailed parameters  for the statistical assessment of the data are provided in the figure legends. The authors provide evidence for a role of the micro RNA – mi-138 – in synaptic inhibition and behavior in mice. They designed a novel transgenic model in which a miR-138 sponge is conditionally expressed to sequester endogenous miR-138 in a cell-type specific manner. Using this tool they show that sequestration of miR-138 in a major, parvalbumin-expressing inhibitory cell type results in enhanced inhibitory transmission, and deficits in memory. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Review Commons.][Editors' note: this paper was reviewed by  Reviewer #1 ):Daswani et al. study the role of miR-138-5p, a microRNA the authors previously found to regulated excitatory synapse function, in mutant mice. To this end they overexpress a miR-138 sponge construct first ubiquitously and find that short term memory is impaired. Gene-expression analysis points to a specific role in inhibitory neurons, which the authors confirm by repeating key experiments in mutant mice in which the miR-138 sponge is expressed in PV-inhibitory neurons. This is a very interesting study that pioneers the analysis of microRNAs in a cell-type specific manner in the CNS. The following point represent helpful suggestion the authors could address before submission.- A question that may come up is related to the mutant mice the controls. One could argue that the better control would the transgenic mice expressing the "control sponge". At present the control group are mice that carry the construct but its not expressed, as far as I understood. The authors may want to address this issue upfront and discuss somewhere in the manuscript that their control animals are suitable.We agree with this reviewer that an independent line expressing a “control sponge” construct might represent a “gold standard” control. However, we decided to take the current approach for two reasons: (1) considering the RRR principles of animal research, including an independent mouse line would have doubled the number of animals needed for experiments. (2) although a control sponge line addresses potential specificity issues of expressing a sponge transcript, it also has its own limitations since behavioural experiments cannot be done with littermates, which could also introduce variability. Nevertheless, we have rigorously validated the specificity of the miR-138 sponge construct by comparing it to a highly similar miR-138 control sponge construct in several assays: (i) miR-138 perfect binding site luciferase sensor assay ; (ii) dendritic spine size assay ; (iii) mIPSC electrophysiological recordings from hippocampal slices of intracerebrally injected mice ; (iv) short-term memory assay in intracerebrally injected mice ; (v) Erbb4 FISH in primary rat hippocampal neurons infected with Dlx-138-sponge . As suggested by this reviewer, we have discussed our approach in more detail in the revised manuscript .- Moreover, as far as I understand it, the sponge construct will be expressed from early developmental stages in all cells of the body in the initial experiment. While the authors later study specifically PV neurons, this may also represent early neurodevelopmental effects. It might be helpful for the reader to explain the approach and rational in more detail. want the specifically focus on the role of miR-138 in neurodevelopmental diseases  in which case the approach would make sense. However, I am not aware that miR-138 has been genetically linked yet to neurodevelopmental disease but I might have missed this. This could clarified specifically in the introduction.We agree with this reviewer that the phenotypes we observe might have an early developmental origin, at least in the ubiquitous sponge mouse line. However, as pointed out by reviewer 2, expression of PV doesn’t kick in before the second postnatal week, arguing for a postnatal role of miR-138-5p in the PV-sponge model. Moreover, stereotactic injections of Dlx-138-sponge into the hippocampus  were performed in young adult mice, providing further support that perturbing miR-138-5p at adult stages impacts physiology and behaviour. Our findings are thus in line with a link to a disease like schizophrenia whose onset is either during adolescence (early-onset) or in adulthood (late-onset). In fact, miR-138 has been linked to cognitive functions, and a rare variant in the miR-138-2 gene has been associated with schizophrenia . We have now better clarified these links in the introduction and discussion of our revised manuscript. In addition, we have provided additional data showing a preferred deregulation of SCZ-associated genes in the miR-138 sponge model .- Figure 1B. To convincingly demonstrate the functionality of the construct in vivo it may help to assay the regulation of some confirmed targets at the mRNA/Protein level or point already here to the exp. later shown in Figure 2.Ub mice using Western blot . Unfortunately, we were unable to obtain conclusive Western blot results for Rims3 using several commercially available antibodies.We agree with this reviewer, that showing target gene regulation is imperical to confirm the functionality of the miR-138 loss-of-function approach. However, we want to stress that a number of experiments in this context were already included in our original manuscript. (1) Expression of the sponge in mice in vivo leads to a preferential upregulation of predicted miR-138 targets compared to non-targets , which provides strong evidence that expression of the construct leads to a specific loss-of-function of miR-138. (2) Validation by qPCR confirmed upregulation of PV-enriched genes at the RNA level, e.g. Rims3 and Erbb4. (3) In the revised manuscript, we further included protein data for Erbb4 upregulation in the hippocampus of miR-138-sponge- Supplemental Figure 1.: n=1 for pGL3 CTR may not be sufficient for statistical analysis. Panel (A) Please explain this assay in greater detail. What is measured here?In fact, this assay has been repeated 3 times. The lack of error bar in the control conditions is due to normalization. Statistical test could still be performed with a 1-sample t-test, which revealed a significant difference. The experiment measures the endogenous activity of miR-138 in hippocampal neurons (based on the extent of cleavage of the luciferase reporter).- Supplemental Fig1d: No scale bar. Why show a small section of the cerebellum when later hippocampus is analyzed? I suggest to show a selection of brain regions with high quality images.We agree and have now included a lacZ staining from the hippocampus in the revised manuscript .- Figure 1. The authors may want to consider that no effect in the contextual fear conditioning may not be sufficient to claim that specifically short-term memory is affected in mutant mice. A more thorough behavioral characterization may be necessary or it should be discussed why this is not necessary in the context of the current manuscript.In the revised manuscript, we have now further included CFC data from the PV-sponge mouse line , which also didn’t reveal a significant genotype-dependent difference. That said, we fully agree that results from only one behavioural paradigm (CFC) are not sufficient to make general statements about the specific types of memories affected by miR-138 loss-of-function. However, since our laboratory is currently not set up to perform an extensive behavioural characterization of memory function using different tests, we consider such experiments beyond the scope of this revision. We therefore decided to tone down the statements in the manuscript related to short-term memory specificity accordingly.- Figure 2: please explain in more detail (in the methods) the statistics behind the diff. expression analysis.A more detailed statistical description has been included in the methods section of the revised manuscript .- Figure 2d. To further strengthen the idea that DEG are enriched in inhibitory neurons, corresponding viol plots across cell types may help. The authors could plot for example selected genes are the eigen-vector expression of DEGs.We agree with this reviewer that an alternative presentation of the DEG data could be helpful. We have therefore added another panel  illustrating the distribution of expression of the sponge-upregulated-DEGs in the different cell types. In our view, this presentation is more easily interpretable compared to viol plots.- The selection of Erbb4 and Rims3 for further analysis could be explained better.Based on the available data, we have now decided to prioritize on Erbb4 regulation in the revised manuscript (Rims3 data was moved to the supplement). We have further provided a more detailed rationale for studying Erbb4 in the context of inhibitory synaptic transmission and short-term memory.- Figure 3B.Maybe show representative images.Representative pictures have now been included in the revised manuscript .- How about FC and other long-term memory assays in 138-spongePV´ mice?We have now additionally performed CFC in 138-sponge-PV mice, and found no significant difference in memory performance between 138-sponge and control mice . As outlined above, additional memory assays are beyond the scope of this study.- Please address the finding that behavior phenotypes are similar in 138-spongeub and 138-spongePV´ mice, while e-physiology differs (mini ipsc).Ub mice We assume that this is a misunderstanding, since we did not measure mIPSCs in pyramidal neurons of 138-sponge- I guess an obvious question could be to show the regulation of miR-138 target genes in PV neurons and /or study the transcriptome in PV-neurons as a result of sponge expression.We agree that assessing the transcriptome specifically of PV neurons would be a logical next step. However, this is technically rather challenging, either involving FACS sorting of PV-positive neurons or single-cell RNAseq approaches, both of which are currently not established in the laboratory. We therefore feel that such experiments, also considering the limited new insight that would be expected from them, are beyond the scope of this revision.- The discussion is comparatively short. The specific reference to miR-138 as a target for schizophrenia and ASD provokes the question of more detailed analysis, e.g behavioral phenotypes for ASD and schizophrenia in mutant mice or comparing the DEG to genes de-regulated in other mouse models for these diseases.We agree that making more functional connections to ASD/schizophrenia would be exciting. However, this would involve a battery of additional mouse behavioural experiments, which is in our view clearly beyond the scope of this revision. Concerning the second point, we have performed a comparison between DEGs deregulated in the miR138 sponge model and a previous study about schizophrenia . Excitingly, we found that the genes upregulated in the 138-sponge (at FDR<0.05) are significantly enriched for genes found upregulated in the brain of SCZ patients , which provides additional correlative evidence for an involvement of miR-138 in SCZ. Moreover, we have elaborated more on the role of miR-138 in cognition and SCZ in the discussion .- Given that the literature suggests of role for miR-138 also excitatory neurons, the phenotypes should be discussed in more detail. Why are mainly genes in inhibitory neurons affected in 138-spongeub mice?Based on our new results from a cell-type specific miR-138 sensor assay  we speculate that the reason for the preferential regulation in inhibitory neurons is the more efficient miR-138 inhibition in this cell type. This is now discussed in more detail . Please see also our comments to the other reviewers regarding this topic.Reviewer #1 (Significance (Required)): Generally this study addresses the cell-type specific role of microRNAs in the CNS, which is important to still innovative.The finding that miR-138 controls specific functions in inhibitory neurons and that this is linkned to behavioral alterations is novelThe data will be mainly intersting for the neuroscientific communityReviewer #2 ):Understanding the role of miRNAs in different neuronal cell types remains a paramount goal in neuroscience. Using a conditional miR-138 sponge transgenic line, Daswani and colleagues demonstrate that miR-138 in parvalbumin+ (PV) GABAergic neurons is necessary for short-term memory. Further, they identify a possible cellular mechanism underlying this behavioral deficit: CA1 pyramidal neurons receive more inhibition from neighboring PVs. This work is timely, impactful, and an important contribution to the field. However, there are important aspects that needs to be addressed before publication:Major:It is surprising that the miR-138 sponge has no effect in pyramidal neurons. The authors' laboratory has published beautiful work showing that miR-138 has important roles for synaptogenesis in this cell type. The authors hypothesize that the expression levels of the miR-138 sponge might not be high enough for pyramidal neurons (where miR-138 is highly expressed) and instead it is sufficient for PVs. However, sensor data in Figure 1b and spines data in Sup Figure 1c are from pyramidal neurons. For the miR-138 sponge to be credible, the authors must demonstrate that, as they claim, the sponge is effectively working in PVs and only partially in pyramidal neurons. This could be achieved by showing that miR-138 is expressed at different levels in the two cell types: (i) analysis of miR-138 expression levels from He et al. (2012) in different cell types; (ii) FACS of PVs and pyramidal neurons and assessing miR-138 levels; (iii) quantifying miRNA FISH in different cell types. In addition, the authors should demonstrate that the miR-138 has different levels of efficacy in the two cell types, i.e. analyzing sensor data in PNs vs PVs . If the results from this further analysis don't support the original hypothesis, the authors must find a plausible reason and validate it.Ub sponge mice.We fully agree with this reviewer that the absence of phenotype in pyramidal neurons is surprising and that we do not have a full explanation for this yet. To further test our hypothesis of an incomplete miR-138 inhibition in excitatory pyramidal neurons of the miR-138 sponge line, we have now performed a series of new experiments, largely following the suggestions made by this reviewer: (i) we monitored miR-138 expression from a published small RNA-seq study  . (Ii) we quantified miR-138 signal in already existing smFISH datasets from hippocampal neurons containing PNs and PVs . (Iii) we quantified mir-138 sensor activity in PNs and PVs in hippocampal slices stained for PN and PV markers . Altogether, this analysis suggests that while miR-138 is expressed to a similar level in PNs and PVs, its activity is markedly higher in PVs and more robustly inhibited in PVs by expression of a miR-138 sponge. Although the mechanistical underpinnings are unknown, these observations might explain why we preferentially observed miR-138-5p dependent regulation of interneuron-enriched genes in miR-138Minor:The sensor data are somewhat confusing. It would benefit to add schemes of the sensor and sponge constructs and to represent the data in Figure 1b with a bar graph. To further support the claims of incomplete effects of the miR-138 sponge, the authors should have a mutated MRE sensor (maximum de-repression) and express the effect of the sponge in different cell types as percentage of maximum de-repression. Please also add text explaining the sensor experiments in more detail so that the non-specialist can follow.As suggested by this reviewer, we have now added a scheme of the sensor and presented the data in Figure 1b as a bar graph. In addition, we have performed the sensor analysis separately for PNs and PVs . This analysis clearly shows that the degree of inhibition exerted by the 138-sponge is more pronounced in PV+ interneurons compared to PV- neurons, thereby supporting our model that transgenic expression of the miR-138 sponge preferentially de-represses miR-138-5p target genes in interneurons.The choice of PV-Cre is interesting. PV-Cre does not kick in until the second postnatal week, it is notoriously not a very efficient Cre line, and although it correlates well in the cortex with basket cells, it turns on also in cerebellar purkinje cells and in the peripheral nervous system. Some of these limitations should be discussed, considering that behavior is used as readout.The point about ectopic expression of PV-Cre in PV- cells in cerebellar purkinje cells and the peripheral nervous system is well taken. We therefore decided to perform a complementary approach where we delivered 138-sponge and control-sponge specifically to hippocampal interneurons by stereotactic injection of a rAAV-Dlx5/6 construct . Intriguingly, the miR-138-sponge associated behavioural and electrophysiological phenotypes were fully recapitulated by this approach. Together with our detailed electrophysiological characterization of hippocampal PV+ interneurons in paired recordings , we are confident that hippocampal PV dysfunction contributes in an important way to the observed phenotypes.The authors should remove emphasis from E/I. The physiology experiments performed don't assess E/I but E and I in isolation. It is the opinion of this reviewer that the work is impactful because it reveals novel miRNA mechanisms in GABAergic cells, no need to claiming anything else.We agree that our claims about E/I are currently overstated, and have now toned down the respective statements in the revised manuscript (e.g. by removing emphasis from E/I in the introduction).Sup Figure 1c. Should the y axes say something like Spines Density?We have added the y-axes label as suggested.Sup Figure 1d. Why did the authors choose the cerebellum to show CMV-Cre efficacy? The rest of the paper focuses on the hippocampus.We have now provided a new lacZ staining from the hippocampus to illustrate efficient Cre-mediated recombination in this region .In the introduction, the Tan et al. paper from the Schaefer lab should be mentioned, considering that miR-128 was knocked out in GABAergic cells (MSNs).We thank the reviewer for this reminder and have now referenced this paper in the introduction.Reviewer #2 (Significance (Required)): Identifying cell type-specific roles of miRNAs has eluded the field, specifically in the case of rare cell types, i.e GABAergic interneurons. The paper from Daswani and colleagues is an important step forward in this direction. Somewhat serendipitously, the authors have found that miR-138 targets preferentially genes enriched in GABAergic interneurons. Indeed, PV-specific knockout of miR-138 recapitulates behavioral deficits induced by global miR-138 knockout. The only similar contributions are well referenced in the introduction: (i) Tuncdemir et al. (Fishell lab) performed MGE-specific Dicer knockout and found defects in GABAergic interneuron survival, migration, and lamination; (ii) Qiu et al. (Miao He lab) showed that Dicer ko in VIP cells induces long-term dysfunction in GABAergic interneuron computations and survival.This manuscript should be of interest to both the miRNA field and the synaptic plasticity, learning and memory fields.I am a neurobiologist that studies the roles of miRNAs in instructing network formation.Reviewer #3 ):Daswani et al. report an interesting role for miR-138-5p in the control of short-term memory and inhibitory synaptic transmission. They notably built on an elegant transgenic model in which a miR-138 sponge is conditionally expressed using the Cre-Lox system and allows the sequestering of endogenous miR-138 in a cell-type specific manner. Using this model, the authors provide evidence that miR-138-5p expressed in parvalbumin-expressing neurons controls short-term memory as well as inhibitory synaptic input onto CA1 pyramidal neurons. Using RNAseq and qrtPCR, they identify presynaptic genes whose expression is regulated by miR-138 and possibly accounting for inhibitory synaptic alterations. Overall, the experiments are well conducted. The data are interesting, solid and logically presented and the methods are sufficiently described. However, there are some concerns that should be considered prior publication:Major concerns:(1) In the abstract, the authors' claim that "miR-138-5p regulates the expression of presynaptic genes in hippocampal parvalbumin-expressing inhibitory interneurons to control short-term memory" is overstated. Indeed, while the authors provide compelling evidence that miR-138 sponge controls short-term memory, the causal evidence to demonstrate that this is going through the regulation of presynaptic genes expression is missing. Moreover, because they are using a transgenic model in which the 138-sponge is constitutively expressed in any cell types and brain areas, the short-term memory deficits found by the authors are not necessarily resulting from hippocampal synaptic alterations. Therefore, the authors should either tone down this claim or perform additional experiments to support their model (see points 3 and 4).We largely agree with these statements and specifically address them under points 3 and 4.(2) While the authors provide convincing data demonstrating the efficiency of the miR-138 sponge both in cultured neurons and in vivo using sensor constructs and luciferase assays, the only evidence that the 138-sponge specifically inhibits miR-138 is by using a control sponge in cultured neurons and by measuring luciferase activity from sensors or spine size. To better support the specificity of the 138-sponge, it is important to demonstrate the absence of effect of the control sponge on inhibitory synaptic transmission, at least in cultured neurons.As suggested by this reviewer, we have now performed additional experiments to support the specificity of the 138-sponge in interneurons. Therefore, we generated rAAV expressing 138-sponge selectively in interneurons due to the presence of a Dlx5/6 promoter (Dlx-138-sponge). (i) expression of Dlx-138-sponge, but not Dlx-control-sponge, elevated Erbb4 expression in rat hippocampal interneurons in culture as assessed by smFISH ; (ii) stereotactic injection into the adult mouse hippocampus of Dlx-138-sponge, but not Dlx-control-sponge, impairs short-term memory  and inhibitory synaptic transmission in pyramidal neurons .Moreover, it would be reassuring to see that not all miRNAs are regulated by miR-138 (for instance by using sensors for distinct miRNAs).A priori, one cannot rule out that miR-138 inactivation indirectly regulates the expression and/or activity of other miRNAs, for example by regulating targets which control miRNA biogenesis. Therefore, the proposed sensor experiments for selected miRNA candidates are in our view not suitable to unequivocally judge the specificity of the miR-138 sponge approach. Moreover, such experiments would have required the stereotactic injection of at least two additional cohorts of mice, which was not covered by our currently approved animal experimentation license. Finally, only a low number of miRNAs could be screened with such an in vivo approach, making general conclusions difficult. By weighing all these arguments, we decided not to perform sensor experiments for additional miRNAs and instead added further experiments which support the specificity of the 138-sponge approach (see above).(3) The authors propose that miR-138 expression in the hippocampus regulates short-term memory. Although there is evidence from previous studies that hippocampal PV+ interneurons are involved in short-term memory, the data do not directly address this point. Specific involvement of miR-138 in hippocampal PV+ interneurons could be easily tested through stereotaxic injections of rAAVs carrying PV-Cre in 138-floxed mice in order to drive the expression of miR-138 sponge selectively in CA1 hippocampal interneurons.Ub and 138-spongePV mice. We did not pursue the PV-Cre injections into 138-floxed mice, since viral expression of PV-Cre (as opposed to transgenic expression in PV-Cre mice) apparently is not very specific and therefore not frequently used .We agree that the data presented in the original manuscript didn’t provide direct evidence for a role of the hippocampus in miR-138-dependent regulation of short-term memory. Therefore, we now inactivated miR-138-5p specifically in hippocampal interneurons by stereotactic injection of a Dlx-138-sponge rAAV construct . This led to a similar impairment in both short-term memory  and inhibitory synaptic transmission  as observed in 138-sponge(4) Although not required for the paper as it is, experiments addressing the causal link between (1) the miR-138-dependent regulation of ErbB4 and/or RimS3 in CA1 interneurons and (2) the role of miR-138 in inhibitory synaptic transmission and/or short-term memory would considerably strengthen the model proposed. To address that, the authors could alter the expression/function of the candidate genes in their transgenic model. Alternatively, strategies could be used to selectively protect putative target from the action of miR-138-5p .Addressing the molecular mechanism underlying impaired short-term memory and inhibitory transmission is clearly the next step in this project, but beyond the scope of the current manuscript, as also pointed out by this reviewer.(5) Description of statistics require clarifications: - n should be clearly defined in all figure legends .- The authors should indicate whether and how they tested for normal distribution of their data.—figure supplement Figure 1a-c and Suppl. Figure 3a : a non-parametric test should be used to compare the different conditions, considering the too low n number which does not allow to conclude on a normal distribution.We have now addressed these statistical issues in the revised manuscript. (i) we have now clearly defined n in all figure legends. (ii) normal distribution of the data was tested with the Shapiro-Wilk test in GraphPad (stated in the Methods section). When normal distribution was not observed, non-parametric tests were used . (iii) we agree with this reviewer that the n for the indicated experiments is too low to conclude on a normal distribution with a common test. However, visual inspection of the data suggested normal distribution for luciferase assays presented in Suppl. Figure 1a-c, considering our long-term experience with these assays. We therefore did not see the necessity to use non-parametric tests in this case. Concerning the PV+ cell density count , we now applied the Mann-Whitney test.Minor issues: (1) The fact that excitatory synapses onto pyramidal neurons are unaffected in 138-sponge mice is puzzling when considering the previous study from the same authors  as well as the results obtained in cultured neurons to validate the sponge . While the authors propose that 'the absence of changes in excitatory synaptic transmission in the hippocampus of 138-sponge-ub mice might be due to ineffective silencing of the highly abundant miR-138-5p in pyramidal neurons' they could actually refine their analysis to give support this hypothesis:- By quantifying the signal from single-molecule FISH for miR-138 in Camk2a+ neurons versus Erbb4+ neurons . In the images shown, it is not evident that excitatory neurons express more miR-138 in comparison to inhibitory ones.- By quantifying the degree of inhibition of miR-138 in pyramidal neurons versus interneurons using the dual sensor system . In Figure 1b, the authors do not discriminate between cell types. A lack of increase of the GFP signal in pyramidal cells may indicate an inability of those cells to express the sponge.- By checking whether APT1, a known miR-138 target that they previously identified in excitatory neurons  is also a regulated gene in 138-sponge mice. If APT1 expression remains unchanged, this could give support to the fact that the sponge is indeed ineffective in excitatory neurons.We agree with the comments made by this reviewer, which are also in agreement with those from reviewer 2 . Concerning the last point of reviewer 3, we now checked the expression APT1 (new nomenclature LYPLA1), as well as two other validated miR-138 targets from excitatory neurons  in our RNA-seq data. None of these targets was differentially expressed . Together, this data supports our hypothesis that miR-138 is not effectively inhibited by the sponge transgene in PNs.(2) The analysis of VGAT signal intensity in individual PV+ boutons may provide useful information about the content in vesicles per bouton. One could expect a higher VGAT intensity/bouton in 138-sponge mice, correlated with an increase number of release sites per bouton (as proposed by the authors) or a similar trend as for increased success rate or decreased PPR.PV and control mice, suggesting that the gross morphology of inhibitory release sites is unchanged. Further ultrastructural investigation of synaptic release sites will require super-resolution microscopy, which is currently not established in the laboratory and therefore beyond the scope of this revision.We agree that the analysis of VGAT signal intensity might provide additional information regarding the observed inhibitory synapse phenotype and therefore performed the respective analysis . However, this didn’t reveal any significant differences between miR-138-sponge(3) For the following data, titles of the graph axis are not accurate and the authors should indicate more precisely what are the exact parameters measured: Figure 1b, Suppl. Figure 1a, Suppl. Figure 1c, Suppl. Figure 3b.We have fixed these issues in the revised manuscript.(4) Figure 3j: "Coefficient of variance" should be corrected for "coefficient of variation".We have changed this expression accordingly.(5) Results – L70: the use of the miR-138 sponge is critical for the key findings of the study. It is therefore important to detail within the text how the specificity and the efficiency of the sponge were tested.Please refer to our comments for reviewer 1 and 2 regarding miR-138 sponge validation in vitro and in vivo. In addition, we have provided more discussion of the sponge strategy in the text .(6) Results – L75: the authors should introduce briefly how the dual sensor works and why an increase of the GFP signals indicate efficient sequestering of endogenous miR-138.We have now explained the sensor strategy in more detail in the Results section  and in addition provided a schematic (7) Supplementary figure 1d: to illustrate the 'penetrant expression' of the sponge, the authors should provide an image representative of the hippocampus not the cerebellum.We have now provided a representative picture of the hippocampus .(8) Suppl. Figure 1g and Suppl. Figure 3B: the use of a single two-way ANOVA test with a single P value is not clear to me as two different parameters are measured (distance and time).We apologize for the confusion. The two parameters have now been plotted as individual panels  and separately analyzed with heteroscedastic t-tests.(9) Suppl. Figure 1i,j and suppl. Figure 3d: the authors should mention whether a statistical test was used to compare the two conditions. n values (cells) should be indicated.The respective figures correspond to bar graphs in the main manuscript , for which a statistical test was provided. However, we now also provided a statistical assessment of the cumulative graphs (using KS-test) in the revised manuscript.Reviewer #3 (Significance (Required)): This study provides important findings that will be of interest for a large audience of neuroscientists interested in synaptic physiology and pathologies:(1) The authors developed an elegant transgenic model allowing to address the role of miR-138 in a cell specific manner and which allows to correlate synaptic alterations to the behavior.(2) While the majority of studies have investigated the role of miRNAs at excitatory synapses, this study provides important insight about the role of one identified miRNA, miR-138, at inhibitory synapses. Interestingly, because miR-138 was also shown to regulate excitatory synapses, the data suggest a central role of miR-138 in regulating E-I balance in hippocampal circuits.(3) The study further shows that alterations in inhibitory synaptic transmission resulting from miR-138 inhibition are correlated with deficits in working memory. Because miR-138 as well as its targets are associated with cognitive deficits and neuropsychiatric disorders such as autism and schizophrenia , this study thus suggests a critical role of miR-138 in disorders with altered E/I balance.I have expertise in the role of microRNAs in synaptic development and plasticity. While I have little expertise regarding behavioral investigations in general, I feel that the behavioral data shown in the present paper are easily accessible and straightforward.


Cell membrane fusion and multinucleation in macrophages are associated with physiologic homeostasis as well as disease. Osteoclasts are multinucleated macrophages that resorb bone through increased metabolic activity resulting from cell fusion. Fusion of macrophages also generates multinucleated giant cells (MGCs) in white adipose tissue (WAT) of obese individuals. For years, our knowledge of MGCs in WAT has been limited to their description as part of crown-like structures (CLS) surrounding damaged adipocytes. However, recent evidence indicates that these cells can phagocytose oversized lipid remnants, suggesting that, as in osteoclasts, cell fusion and multinucleation are required for specialized catabolic functions. We thus reason that WAT MGCs can be viewed as functionally analogous to osteoclasts and refer to them in this article as adipoclasts. We first review current knowledge on adipoclasts and their described functions. In view of recent advances in single cell genomics, we describe WAT macrophages from a ‘fusion perspective’ and speculate on the ontogeny of adipoclasts. Specifically, we highlight the role of CD9 and TREM2, two plasma membrane markers of lipid-associated macrophages in WAT, which have been previously described as regulators of fusion and multinucleation in osteoclasts and MGCs. Finally, we consider whether strategies aiming to target WAT macrophages can be more selectively directed against adipoclasts. Mycobacterium tuberculosis) or foreign materials. MGCs are derived from monocyte progenitors [Macrophages have a unique potential to fuse with themselves to form multinucleated giant cells (MGCs) . During genitors  but theigenitors , an obsegenitors . These oThe adipose tissue contains macrophages and during obesity, their number increases significantly  to correlate with metabolic dysfunction characterized by inflammation, fibrosis and insulin resistance –11. TheiThere is an intriguing association between lipids and macrophage fusion. Cholesterol-rich MGCs have been reported as a frequent and non-specific histological feature in lung biopsies . HistoriWe first describe the current knowledge on CLS and their proposed function. We then review recent advances in WAT single cell transcriptomics, with a specific focus on TREM2 and CD9, membrane receptors that have been previously described in macrophage fusion and multinucleation. We highlight the respective roles of TREM2 and CD9 in osteoclasts, in order to speculate on the adipoclasts’ origin and function. Finally, we discuss whether recent macrophage-targeting therapies in the fat may be beneficial or fine-tuned in targeting adipoclasts in obesity. The review does not cover the polarization of macrophages in adipose tissue nor the significance of WAT inflammation in insulin resistance and metabolic disorders in general—an area that is amply covered by excellent reviews ob/ob) [The infiltration of immune cells in the obese adipose tissue was shown in the 1960s , 25 and ob/ob) , 28. Froob/ob) , surrounob/ob) , 28. Todob/ob)  and the ob/ob) . A bead ob/ob) . Interesob/ob)  and thisob/ob) .In summary, while it is well-accepted that adipoclasts are specialized in efferocytosis of damaged adipocytes, many questions remain regarding the mechanisms underlying this process, as well as the other advantages that cell fusion and multinucleation may confer in the context of prolonged obesity. Furthermore, given the presence of mononucleated, often foamy macrophages in WAT, it is necessary to consider more trophic functions and crosstalk between macrophages and adipocytes  includinDuring prolonged obesity, adipose tissue remodelling is a well-described phenomenon that consists in depot-dependent adipocyte death associated with macrophage infiltration , 34. OurHence adipoclasts have been linked to adipocytes in different cellular states that describe broadly cellular stress and ultimately death. This raises the question of whether adipoclasts can ‘sense’ a particular adipocyte state and whether their fusion from mononuclear macrophages is triggered through adipocyte-derived markers of stress. For instance, using a co-culture setup, it was shown that adipocyte death triggers MGC formation in vitro . FurtherWhile it is accepted that obesity is associated with a shift toward pro-inflammatory macrophage function –47, WAT None of the single cell RNA-seq studies in the WAT distinguished multinucleated macrophages (i.e. adipoclasts) from other macrophage subsets. Although technically challenging, this could have been attempted by sorting LAMs with > 2 nuclei. The advantage of such an approach would have been the identification of potential precursors of adipoclasts, in order to make a distinction between ‘fusion-competent’ LAMs and adipoclasts, as well as the polarization state of each cell type. Nevertheless, the recent single cell transcriptomic approaches in human WAT suggest that adipoclasts and/or adipoclast precursors express TREM2 and CD9 –54.The existence of CD9+ and TREM2+ adipoclasts is worth highlighting from a macrophage fusion perspective, especially given the relevance of these two membrane proteins in osteoclast and MGC fusion and multinucleation.TREM2 (the triggering receptor expressed on myeloid cells 2) is essential for macrophage multinucleation as part of a signalling pathway that includes DAP12 and Syk [TREM2 are causally associated with Nasu–Hakola disease, a dementia associated with bone cystic lesions [TREM2 and DAP12 induce defective multinucleation in osteoclasts, resulting in impaired bone resorption [Trem2 is a trans-acting genetic regulator of a macrophage multinucleation gene co-expression network [Besides its widely studied role in microglial phagocytosis  and neur and Syk . TREM2 r and Syk –61 and a and Syk . Further lesions , 64. Impsorption . Trem2 i network , 66, whi network  and the  network , 68.Trem2 [ApoE) is a Trem2 ligand [Trem2 and ApoE are expressed by a subpopulation of tumour-associated macrophages [Trem2 in this process is yet to be found. The lipid sensing role of TREM2 has been shown as part of the microglia response [Ldlr-/-) mice identified macrophages with high Trem2 expression, specialized in lipid metabolism/catabolism and enriched in the osteoclast gene signature [Trem2 expressing macrophages accumulate lipids and become fusogenic, giving rise to adipoclast precursors and adipoclasts. Fusion and multinucleation could be considered as the final differentiation step of these precursors. However, the exact Trem2-dependent and lipid-related mechanisms allowing the transition from fusion-competent adipoclast precursors to adipoclasts remain to be identified, and in that sense, some parallels drawn from knowledge on osteoclast lipid metabolism may be of relevance. Cholesterol is indispensable for membrane fusion and osteoclast v-ATPase activity [Ldlr-/- mice have defective osteoclast fusion [Jaitin et al. identified TREM2, not only as a marker, but also as a driver of the LAM cell molecular program as lipid uptake and storage were abrogated in the absence of Trem2 . Interes2 ligand , 70 and rophages . Macrophrophages , but a presponse  but alsoresponse . This raresponse . Interesignature . If one activity , 77 and t fusion . Since ot fusion , adipoclt fusion  and palmt fusion . On the t fusion . This sut fusion . As the t fusion  can be aTetraspanins are a superfamily of membrane proteins, and among them, CD9 and CD81 are closely related and known to control cell–cell fusion as they negatively regulate fusion of osteoclasts and MGCs , 86. The+CD9+ adipoclasts or adipoclasts precursors seems to correlate with WAT inflammation and the severity of obesity-related pathologies , repres and CD9 .Fig. 2T+/K+-ATPase blockers such as ouabain [To date, it is well-accepted that obesity triggers the recruitment of monocytes into adipose tissue to promote inflammation, which itself may cause ectopic fat deposition in the liver and insulin resistance , 100. Th ouabain . A recen ouabain .Csf1r in rats and Trib1 in mice reduces adipose tissue mass [Trem2-/- and Marco-/- LAMs lose their efficacy in lipid buffering [Trem2) should be taken into consideration as it may influence metabolic health [When considering macrophage-targeted treatments in adipose tissue, it is crucial to keep in mind the heterogeneity and master regulatory role of macrophages in the development and homeostatic function of adipose tissue. It has become evident that macrophages express organ-specific genes in addition to canonical macrophage genes, a phenomenon referred to as niche-specific programming , 117. Thsue mass , 123, whuffering , 124. Ofuffering , 126 whic health . AltogetBased on current knowledge, adipoclasts are likely to form when relatively high numbers of macrophages infiltrate the adipose tissue due to prolonged obesity. It is still not clear whether adipoclasts are only homokaryons or whether they can also form by fusion of mononucleated macrophages and adipocytes. Here we argue that inhibiting adipoclast formation may improve insulin sensitivity. Rather than global approaches aiming to target adipose tissue macrophages, one can envisage inhibition of adipoclast formation. However, such therapies require a better understanding of adipoclast formation and the identification of novel markers that differentiate mononucleated precursors from multinucleated fused cells. Integrating transcriptomic, epigenetic and metabolic events that accompany cell fusion and multinucleation in the WAT will fine-tune cell-based therapies in obesity and metabolic syndrome.


We propose probabilistic spike propagation, an approach to optimize rate-coded SNNs by interpreting synaptic weights as probabilities, and utilizing these probabilities to regulate spike propagation. The approach results in 2.4–3.69× reduction in spikes propagated, leading to reduced time and energy consumption. We propose Probabilistic Spiking Neural Network Application Processor (P-SNNAP), a specialized SNN accelerator with support for probabilistic spike propagation. Our evaluations across a suite of benchmark SNNs demonstrate that probabilistic spike propagation results in 1.39–2× energy reduction with simultaneous speedups of 1.16–1.62× compared to the traditional model of SNN evaluation.Spiking neural networks (SNNs) have gained considerable attention in recent years due to their ability to model temporal event streams, be trained using unsupervised learning rules, and be realized on low-power event-driven hardware. Notwithstanding the intrinsic desirable attributes of SNNs, there is a need to further optimize their computational efficiency to enable their deployment in highly resource-constrained systems. The complexity of evaluating an SNN is strongly correlated to the spiking activity in the network, and can be measured in terms of a fundamental unit of computation,  Spiking Neural Networks (SNNs), often referred to as the third generation of neural networks Maass, , are attSNNs represent information as discrete spike events and follow an event-driven model of computation, where the work done  is proportional to the number of spike events. Further, they do not require multiplication to be performed when processing a spike, offering the prospect of reduced hardware complexity compared to conventional Artificial Neural Networks (ANNs). Due to these differences, SNNs are not well-suited to commodity hardware platforms like graphics processing units (GPUs). Further, in contrast to hardware accelerators for ANNs, which usually focus on exploiting regular data parallelism, hardware architectures for spiking networks  spike injection, (b) spike generation, and (c) spike propagation, as illustrated in A more detailed description of SNN evaluation is presented in vm is fetched and the bias is added to it (line 10). Next, it is checked for firing by comparing it with the threshold voltage vth (line 11). In case of firing, the neuron ID is pushed into a spike queue (line 12), and the membrane potential is reset and written back to the memory (line 13). If every neuron in the network is evaluated at every timestep, the above process will involve at least one memory access per neuron per timestep. Thus, the number of computations and memory accesses performed during spike generation are proportional to the number of neurons.The second phase, spike generation, is the process of evaluating each neuron and, based on a mathematical model of the neuron, determining whether it produces a spike. Neuron models with varying levels of bio-fidelity have been proposed. In this work, we use the Integrate-and-Fire (IF) neuron model for illustration, but the approach is largely independent of the underlying neuron model. The spike generation step, as described in The final phase, spike propagation, as described in viz. IBM TrueNorth  in an SNN, that are connected by a synapse. Neuron i is called presynaptic and neuron j postsynaptic if the output of i is used to drive the membrane potential of j. The magnitude of the weight associated with the synapse is wij, which we will assume  to be a real-valued number ∈ . This is a safe assumption since the effects of the weights are evaluated relative to a threshold value, and so it is possible to normalize all weights to this range of magnitudes. Note that negative weights would correspond to inhibitory synapses, which are modeled in exactly the same way as excitatory synapses, and only the magnitude of the weight matters for the discussion that follows.Consider two neurons  is the total number of times neuron i has spiked until time t. We term this process of spike propagation as deterministic, and denote it using the superscript d. It should be noted here that i has on the membrane potential of neuron j, while the spiking behavior of neuron j itself depends on the neuron model and its potentiation by other presynaptic neurons.where j by wij every time neuron i spikes, if we apply a weight of ŵij for a random subset of the spikes, the total potentiation becomesInstead of potentiating neuron Ŝi(t) is the random subset of spikes from neuron i that were propagated to neuron j and Ĉi(t) is the number of spikes in Ŝi(t) till time t. In other words, we propagate a spike from neuron i to neuron j with a probability of pij, wherep.We term this process of spike propagation as probabilistic, and denote it by the superscript j by neuron i as follows:We can define the average potentiation of neuron i. For the deterministic approach, the average potentiation is equal to the synaptic weight itself.It should be noted that the average potentiation is defined when there is at least one spike from neuron On the other hand, for the probabilistic approach corresponding to Equation (3), the average potentiation is a limit.pij and ŵij in the probabilistic approach. One interesting choice is to set pij = wij and ŵij = 1, which is simply an alternative interpretation of each weight as the probability of spike propagation. We highlight the effects of such a probabilistic approach through an example below.We hypothesize that, it is sufficient that the average potentiation for probabilistic spike propagation tends toward the average potentiation for the deterministic case, for the network to achieve similar levels of accuracy with both the probabilistic and deterministic approaches. This can be achieved by carefully choosing values for Consider the connectivity pattern illustrated in S3 for neuron 3 is shown in The spike patterns of neurons 1 and 2 are shown in Overall, when the spikes are propagated in a probabilistic fashion, the instantaneous membrane potential of neuron 3 may differ from that under deterministic propagation, but as more and more spikes are generated by the presynaptic neuron, the average potentiation for the probabilistic case converges to the deterministic one, which is essentially the synaptic weight. We introduced randomness into the process of spike propagation in a network that is otherwise deterministic, and allowed the temporal nature of the network to average it out.The crux of our hypothesis is that even though the introduced randomness alters the instantaneous state of the network, the variations will average out over time and result in a network-level equivalence with the deterministic evaluation scheme. In the following subsections, we describe how to take advantage of this randomness to develop efficient implementations of SNNs.From wij. The number of Bernoulli trials required, which in this case is the number of spikes, for an approximation with low relative error is inversely proportional to the probability of success .It is important to note that, for a given number of timesteps, the probability of spike propagation controls a trade-off relationship between cost and performance. The lower the probability, the lower the number of synaptic updates and poorer network performance. The higher the probability, the higher the number of synaptic updates and better network performance. We explore this trade-off in greater detail in section 6.wij. The probability of success of this Bernoulli trial isA probabilistic synapse can be realized by generating a uniformly distributed random number wij greater than rb.which is equal to the desired probability of propagation presented in the previous discussion. Hence, the spike can be propagated on every synapse that has a weight While this implements the probabilistic synapse, it requires fetching of the weight for each synapse from memory prior to the decision of propagation. This can be cheaper than the deterministic approach, as for the synapses that we don't propagate the spikes on, the post-synaptic neurons need not be updated. It should be noted that this random skipping of synapses can cause pipeline inefficiencies in a hardware implementation. In the probabilistic spike propagation process described in i has spiked. Assume that the weights of the outgoing synapses of neuron i in synaptic cluster b are ranked from 1 to rb (line 3). Since the weights are stored in sorted order, every synaptic update requires reading the index (line 5) and weight (line 7), but as soon as the comparison fails for one synapse, all the remaining synapses in the cluster can be skipped (line 10). The index j of the postsynaptic neuron can be determined with the indices of the presynaptic neuron and the synapse (line 6), based on the underlying connectivity pattern.Consider that neuron termination point of the spike propagation from neuron i, wij > rb. It is the rank of the smallest weight in the synaptic cluster that is greater than rb. In the straightforward method of determining tp described above, note that we need both the actual weight value and the index of the target neuron, potentially requiring twice the number of memory accesses.We define the i in synaptic cluster b with weights lesser than rb. Therefore, the termination point tp is essentially tp through a single memory access (line 4). Consequently, we can perform synaptic updates without fetching the synaptic weights.An alternate approach is to use a cumulative histogram of the outgoing weights of each neuron in each cluster, indicated by rb. For discrete values of rb, we can store the termination point tp directly and sample from these points. The number of discrete points correspond to the number of bins of the cumulative histogram and is a parameter of concern. It controls the trade-off between memory overhead and performance. The higher the number of bins, the better the fidelity of the termination point. The lower the resolution, the lower the memory overhead. In this work, we have implemented this approach in the hardware architecture and have studied its implications on accuracy and cost. The trade-off between accuracy and memory overhead has been studied in section 6.Another way to look at this is as a way of discretizing the space of random number generation for In summary, the proposed probabilistic spike propagation approach reduces the number of synaptic updates, and consequently the number of memory accesses in SNNs by introducing randomness into the process of spike propagation.To evaluate the system-level impact of probabilistic spike propagation, we develop P-SNNAP, an SNN accelerator based on SNNAP , the Eval unit and the global controller. It also contains three types of on-chip memories—the spike memory, the weight memory, and the state memory, for storing spikes, weights, and neuronal state variables, respectively.In a deterministic SNN evaluation, as performed in SNNAP, the neurons in every layer are evaluated at each timestep before moving on to the next timestep. However, it involves loading the neuronal state variables and weights for each layer into the on-chip memory repeatedly at every timestep. To avoid these repeated off-chip memory accesses and increase the reuse of loaded weight values, we evaluate one layer for the total number of timesteps before moving on to the next layer. The spikes generated at each timestep during the evaluation of one layer are stored in the spike memory and subsequently fetched during the evaluation of the next layer. Since a large number of modern deep networks are strictly feed-forward, this layer-wise evaluation scheme can be applied to reduce the required buffering. Specifically, all networks evaluated as part of this work are feed-forward networks. We note that this optimization is orthogonal to our proposal and is applied to both deterministic and probabilistic SNN evaluation.The Eval unit, similar to the Leak-and-Spike unit in SNNAP, is the module that performs neuron evaluation. It fetches the membrane potentials from the state memory, increments it by the bias value and compares it with the threshold. If the membrane potential exceeds the threshold, a spike is generated and communicated to the controller. The updated membrane potentials are written back to the state memory.The Controller orchestrates the functioning of the accelerator. It has two phases of operation—the first phase controls the SNPEs and the second phase controls the Eval unit. For each timestep, the controller goes through both phases. In the first phase, the controller fetches the spikes generated by the previous layer from the spike memory and sends them to the SNPEs. Once all the spikes are sent and the SNPEs finish their operations, the controller moves on to the second phase, in which the controller receives spikes from the Eval unit as it evaluates all the neurons in the layer and writes the spikes to the spike memory. Once all the neurons of the current layer are evaluated, the current timestep is completed and the controller moves on to the next timestep for the layer.Spike propagation is realized by an array of Spike Neural Processing Elements (SNPEs). The propagation along different outgoing synapses of neurons are parallelized and balanced across the 16 lanes of the SNPE array. Each lane has an SNPE coupled with two blocks of on-chip memory, one each for membrane potentials and weights. When a layer is evaluated, the controller fetches the spikes from the previous layer and sends them to the SNPEs. On receiving a spike, an SNPE uses the index of the spiking neuron to iterate through its outgoing synpases. For each synapse, the SNPE calculates the index of the post-synaptic neuron. This calculation depends on the connectivity pattern of the layer being evaluated. Next, for each post-synaptic neuron, its membrane potential and the weight of the corresponding synapse are fetched. The membrane potential is updated and written back.Both the lanes of SNPEs in the architecture and the synaptic clusters in the probabilistic approach group outgoing synapses of neurons. Despite the similarity, the grouping is done with different goals. While deciding the number of lanes, the primary concerns are inference speed and the required logic area and size of the on-chip memories, at the hardware level. On the other hand, while deciding the number of synaptic clusters, the concerns are computational effort and accuracy.When the number of synaptic clusters and lanes are chosen to be equal, a simple direct mapping is possible—the outer loop in The weight memory in each SNPE lane stores all the information required to perform probabilistic spike propagation, including the discretized cumulative histograms for the corresponding mapped synaptic clusters, the sorted synaptic indices and the maximum weight values.tp that each lane comes up with is random and thus, they perform different number of synaptic updates and end up taking unequal amounts of time, as illustrated in In the deterministic propagation of spikes, since the outgoing synapses of the spiked neuron are distributed equally among the lanes, each lane ends up performing an equal number of synaptic updates, which means that all the SNPEs take an equal amount of time, as shown in To address the aforementioned challenge, P-SNNAP implements asynchronous spike processing. Each SNPE is equipped with a queue as shown in In this section, we describe the experimental methodology and benchmarks used to evaluate the proposed concepts.The benefits of the proposed approach have been studied across a range of image classification networks trained on MNIST, SVHN, CIFAR10, CIFAR100, and ImageNet datasets, as listed in We refer to the deterministic evaluation of all synapses in a network as the baseline (BSL) approach in section 6. On the other hand, for the Probabilistic Spike Propagation (PSP) approach in section 6, we empirically choose between deterministic or probabilistic spike propagation at a layer-granularity for each network in the benchmark suite, with the goal of iso-timesteps operation. The probabilistic approach is beneficial only for layers with large numbers of synaptic connections and high activity. For instance, the CIFAR10-AllConv network in our benchmark suite is the All-CNN-C network from Springenberg et al. , that wamm2. The compute power consumption is 28.6 mW. A version of SNNAP without support for probabilistic spike propagation was implemented to act as the baseline in our comparisons. We observe that the probabilistic approach incurs a compute logic area overhead of 12% and compute logic power overhead of 23.5%. These hardware additions facilitate significant improvements in time and energy consumed to evaluate SNNs, as discussed in the following section.The P-SNNAP engine was designed at the Register Transfer Level and synthesized to the Nangate 15 nm technology using Synopsys Design Compiler. CACTI  in SNNs.In this subsection, we study the trade-off between classification accuracy of a network and the number of synaptic updates performed during its evaluation. Specifically, we record the classification accuracy and number of synaptic updates  at each timestep for both the deterministic and probabilistic propagation schemes. The results for the CIFAR10 all-convolutional network are presented in The benefits of PSP in terms of the reduction in the number of synaptic updates, total energy, and execution time on the P-SNNAP architecture are presented in As discussed in section 3.2, increasing the number of synaptic clusters causes the number of synapses affected by outlier weights to go down, and their probabilities of propagation go up.We observe that this improves the classification accuracy for iso-synaptic updates. In the extreme case, with 1 synapse per cluster, probabilistic propagation becomes identical to the deterministic approach. This dictates that the trade-off relationship between number of synaptic updates and accuracy has a sweet spot on the possible number of synaptic clusters.The all-convolutional CIFAR10 network has been studied to explore this relationship in more detail. The network is evaluated with different number of synaptic clusters and the accuracy and average number of synaptic updates per inference image are measured. The contour plot in It should be noted that this sweet spot is dependent, at a high level, on the number of synapses per cluster, which is decided by the size of the network. Ideally, the number of synaptic clusters could be determined at a per-neuron granularity. However, in this work, we have chosen it to be a network-level hyperparameter to reduce the overall search space.rb, as it affects the value of the termination point tp determined from the cumulative histogram. Therefore, it directly affects the degradation in classification accuracy. At the same time, reducing the number of bins reduces the memory footprint. It should be noted that, the number of accesses to determine tp is only one per lane per spike, irrelevant of the number of bins used in the cumulative histogram.The number of bins used in the cumulative histogram impacts the fidelity of the random number We specifically study the effect of the number of bins on the classification accuracy of CIFAR10 all-convolutional network. A cumulative histogram of 50 bins causes a memory overhead of 23.7% in the CIFAR10-AllConv network. While this can be considered to be significant, we note the followingThe memory overhead is much lower in larger models like CIFAR100-VGG16 (10.9%) and ImageNet-VGG16 (1.1%).Although the memory footprint is larger, the total number of memory accesses with PSP is substantially lower.The focus of this work is to improve the energy efficiency of spiking neural networks by utilizing a probabilistic approach to spike propagation for reducing the number of memory accesses. It can be directly applied to pre-trained spiking networks, without any structural or behavioral modifications. We now relate this to previously proposed approaches for improving SNN implementations and highlight the unique aspects of our approach.There have been several custom hardware accelerators designed expressly to implement spiking networks (Neil and Liu, Stochastic computation techniques apply randomness to the process of computation itself (Shanbhag et al., Ahmed et al.  considerPruning is a technique used to reduce memory footprint of neural networks. Rathi et al.  propose Approximate computing is well-known in the area of signal processing and neural network hardware, but has seen limited application to spiking networks. One example is Sen et al. , where nFinally, there are approaches that rely on the use of new and emerging technologies, such as spin-based computing (Sengupta et al., In this work, we introduce probabilistic spike propagation as a new approach for improving the energy efficiency of spiking neural networks. The proposed approach reduces the number of memory accesses during the spike propagation phase in SNNs by casting spike propagation as a probabilistic process. We show that the temporal nature of SNNs allows the network to regain any accuracy loss caused by this approach. We successfully apply the technique on pre-trained spiking networks without any network modifications or retraining and demonstrate significant reductions in the number of synaptic updates performed during evaluation while maintaining near iso-accuracy performance levels. We further develop a new hardware architecture, P-SNNAP, to realize probabilistic spike propagation in hardware and show that the proposed approach achieves considerable execution time and energy savings when compared to deterministic spike propagation.http://yann.lecun.com/exdb/mnist/; http://ufldl.stanford.edu/housenumbers/; https://www.cs.toronto.edu/~kriz/cifar.html; http://www.image-net.org/.Publicly available datasets were analyzed in this study. This data can be found at: AN implemented the experimental framework. All authors contributed to the conception of the ideas, design of the experiments, analysis of the results, and development of the manuscript.SS was employed at IBM Thomas J. Watson Research Center. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.