Peng Zhou, Xing-Lou Yang, Xian-Guang Wang, Ben Hu, Lei Zhang, Wei Zhang, Hao-Rui Si, Yan Zhu, Bei Li, Chao-Lin Huang, Hui-Dong Chen, Jing Chen, Yun Luo, Hua Guo, Ren-Di Jiang, Mei-Qin Liu, Ying Chen, Xu-Rui Shen, Xi Wang, Xiao-Shuang Zheng, Kai Zhao, Quan-Jiao Chen, Fei Deng, Lin-Lin Liu, Bing Yan, Fa-Xian Zhan, Yan-Yi Wang, Gengfu Xiao, Zheng-Li Shi

Since the SARS outbreak 18 years ago, a large number of severe acute respiratory syndrome related coronaviruses (SARSr-CoV) have been discovered in their natural reservoir host, bats. Previous studies indicated that some of those bat SARSr-CoVs have the potential to infect humans. Here we report the identification and characterization of a novel coronavirus (nCoV-2019) which caused an epidemic of acute respiratory syndrome in humans, in Wuhan, China. The epidemic, started from December 12th, 2019, has caused 198 laboratory confirmed infections with three fatal cases by January 20th, 2020. Full-length genome sequences were obtained from five patients at the early stage of the outbreak. They are almost identical to each other and share 79.5% sequence identify to SARS-CoV. Furthermore, it was found that nCoV-2019 is 96% identical at the whole genome level to a bat coronavirus. The pairwise protein sequence analysis of seven conserved non-structural proteins show that this virus belongs to the species of SARSr-CoV. The nCoV-2019 virus was then isolated from the bronchoalveolar lavage fluid of a critically ill patient, which can be neutralized by sera from several patients. Importantly, we have confirmed that this novel CoV uses the same cell entry receptor, ACE2, as SARS-CoV.

Michael C Letko, Vincent Munster

Over the past 20 years, several coronaviruses have crossed the species barrier into humans, causing outbreaks of severe, and often fatal, respiratory illness. Since SARS-CoV was first identified in animal markets, global viromics projects have discovered thousands of coronavirus sequences in diverse animals and geographic regions. Unfortunately, there are few tools available to functionally test these novel viruses for their ability to infect humans, which has severely hampered efforts to predict the next zoonotic viral outbreak. Here we developed an approach to rapidly screen lineage B betacoronaviruses, such as SARS-CoV and the recent 2019-nCoV, for receptor usage and their ability to infect cell types from different species. We show that host protease processing during viral entry is a significant barrier for several lineage B viruses and that bypassing this barrier allows several lineage B viruses to enter human cells through an unknown receptor. We also demonstrate how different lineage B viruses can recombine to gain entry into human cells and confirm that human ACE2 is the receptor for the recently emerging 2019-nCoV.

C. Shan Xu, Michal Januszewski, Zhiyuan Lu, Shin-ya Takemura, Kenneth J. Hayworth, Gary Huang, Kazunori Shinomiya, Jeremy Maitin-Shepard, David Ackerman, Stuart Berg, Tim Blakely, John A. Bogovic, Jody Clements, Tom Dolafi, Philip Hubbard, Dagmar Kainmueller, William Katz, Takashi Kawase, Khaled Khairy, Laramie Leavitt, Peter H. Li, Larry Lindsey, Nicole Neubarth, Donald J. Olbris, Hideo Otsuna, Eric T. Troutman, Lowell Umayam, Ting Zhao, Masayoshi Ito, Jens Goldammer, Tanya Wolff, Robert Svirskas, Philipp Schlegel, Erika R Neace, Christopher J. Knecht, Chelsea X. Alvarado, Dennis Bailey, Samantha Ballinger, Jolanta A Borycz, Brandon Canino, Natasha Cheatham, Michael Cook, Marisa Dreyer, Octave Duclos, Bryon Eubanks, Kelli Fairbanks, Samantha Finley, Nora Forknall, Audrey Francis, Gary Patrick Hopkins, Emily M. Joyce, SungJin Kim, Nicole A. Kirk, Julie Kovalyak, Shirley A. Lauchie, Alanna Lohff, Charli Maldonado, Emily A. Manley, Sari McLin, Caroline Mooney, Miatta Ndama, Omotara Ogundeyi, Nneoma Okeoma, Christopher Ordish, Nicholas Padilla, Christopher Patrick, Tyler Paterson, Elliott E. Phillips, Emily M. Phillips, Neha Rampally, Caitlin Ribeiro, Madelaine K. Robertson, Jon Thomson Rymer, Sean M. Ryan, Megan Sammons, Anne K. Scott, Ashley L. Scott, Aya Shinomiya, Claire Smith, Kelsey Smith, Natalie L. Smith, Margaret A. Sobeski, Alia Suleiman, Jackie Swift, Satoko Takemura, Iris Talebi, Dorota Tarnogorska, Emily Tenshaw, Temour Tokhi, John J. Walsh, Tansy Yang, Jane Anne Horne, Feng Li, Ruchi Parekh, Patricia K. Rivlin, Vivek Jayaraman, Kei Ito, Stephan Saalfeld, Reed George, Ian A. Meinertzhagen, Gerald M. Rubin, Harald Hess, Louis Scheffer, Viren Jain, Stephen M. Plaza

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

Stefan Kurtenbach, J. William Harbour

While there are sophisticated resources available for displaying NGS data, including the Integrative Genomics Viewer (IGV) and the UCSC genome browser, exporting regions and assembling figures for publication remains challenging. In particular, customizing track appearance and overlaying track replicates is a manual and time-consuming process. Here, we present SparK, a tool which auto-generates publication-ready, high-resolution, true vector graphic figures from any NGS-based tracks, including RNA-seq, ChIP-seq, and ATAC-seq. Novel functions of SparK include averaging of replicates, plotting standard deviation tracks, and highlighting significantly changed areas. SparK is written in Python 3, making it executable on any major OS platform. Using command line prompts to generate figures allows later changes to be made very easy. For instance, if the genomic region of the plot needs to be changed, or tracks need to be added or removed, the figure can easily be re-generated within seconds without the manual process of re-exporting and re-assembling everything. After plotting with SparK, changes to the output SVG vector graphic files are simple to make, including text, lines, and colors. SparK is publicly available on GitHub: https://github.com/harbourlab/SparK.

Matthew R Olm, Alexander Crits-Christoph, Keith Bouma-Gregson, Brian Firek, Michael Morowitz, Jillian Banfield

Coexisting microbial cells of the same species often exhibit genetic differences that can affect phenotypes ranging from nutrient preference to pathogenicity. Here we present inStrain, a program that utilizes metagenomic paired reads to profile intra-population genetic diversity (microdiversity) across whole genomes and compare populations in a microdiversity-aware manner, dramatically increasing genomic comparison accuracy when benchmarked against existing methods. We use inStrain to profile >1,000 fecal metagenomes from newborn premature infants and find that siblings share significantly more strains than unrelated infants, although identical twins share no more strains than fraternal siblings. Infants born via cesarean section harbored Klebsiella with significantly higher nucleotide diversity than infants delivered vaginally, potentially reflecting acquisition from hospital versus maternal microbiomes. Genomic loci showing diversity within an infant included variants found in other infants, possibly reflecting inoculation from diverse hospital-associated sources. InStrain can be applied to any metagenomic dataset for microdiversity analysis and rigorous strain comparison.

Siruo Wang, Tyler H McCormick, Jeffrey T. Leek

Many modern problems in medicine and public health leverage machine learning methods to predict outcomes based on observable covariates. In an increasingly wide array of settings, these predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes post-prediction inference . In this paper, we develop methods for correcting statistical inference using outcomes predicted with an arbitrary machine learning method. Rather than trying to derive the correction from the first principles for each machine learning tool, we make the observation that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for the post-prediction inference that naturally fits into the standard machine learning framework. We estimate the relationship between the observed and predicted outcomes on the testing set and use that model to correct inference on the validation set and subsequent statistical models. We show our postpi approach can correct bias and improve variance estimation (and thus subsequent statistical inference) with predicted outcome data. To show the broad range of applicability of our approach, we show postpi can improve inference in two totally distinct fields: modeling predicted phenotypes in re-purposed gene expression data and modeling predicted causes of death in verbal autopsy data. We have made our method available through an open-source R package: https://github.com/SiruoWang/postpi

Tadepall Lakshmikanth, Sayyed Auwn Muhammad, Axel Olin, Yang Chen, Jaromir Mikes, Linn Fagerberg, Anders Gummesson, Goran Bergstrom, Mathias Uhlen, Petter Brodin

The human immune system varies extensively between individuals, but variation within individuals over time has not been well characterized. Systems-level analyses allow for simultaneous quantification of many interacting immune system components, and the inference of global regulatory principles. Here we present a longitudinal, systems-level analysis in 99 healthy adults, 50 to 65 years of age and sampled every 3rd month during one year. We describe the structure of inter-individual variation and characterize extreme phenotypes along a principal curve. From coordinated measurement fluctuations, we infer relationships between 115 immune cell populations and 750 plasma proteins constituting the blood immune system. While most individuals have stable immune systems, the degree of longitudinal variability is an individual feature. The most variable individuals, in the absence of overt infections, exhibited markers of poor metabolic health suggestive of a functional link between metabolic and immunologic homeostatic regulation.

Caleb Weinreb, Alejo Rodriguez-Fraticelli, Fernando Camargo, Allon M. Klein

A challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Though the development of single cell assays allows for the capture of progenitor cell states in great detail, these assays cannot definitively link those molecular states to their long-term fate. Here, we use expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. Additionally, we find that the monocyte lineage differentiates through two distinct transcriptional and clonal routes, leaving a persistent imprint on mature cells. Finally, we use our approach to reflect on current methods of dynamics inference from single-cell snapshots. We find that for in vitro hematopoiesis, published fate prediction algorithms do not detect lineage priming in early progenitors, and provide evidence that there are hidden properties that influence cell fate but are not detectable with current single-cell sequencing methods.

Delaney L Milller, Eric A Smith, Irene Newton

Fungi are the leading cause of insect disease, contributing to the decline of wild and managed populations1,2. For ecologically and economically critical species, such as the European honey bee (Apis mellifera), the presence and prevalence of fungal pathogens can have far reaching consequences, endangering other species and threatening food security3,4,5. Our ability to address fungal epidemics and opportunistic infections is currently hampered by the limited number of antifungal therapies6,7. Novel antifungal treatments are frequently of bacterial origin and produced by defensive symbionts (bacteria that associate with an animal/plant host and protect against natural enemies 89. Here we examined the capacity of a honey bee-associated bacterium, Bombella apis, to suppress the growth of fungal pathogens and ultimately protect bee brood (larvae and pupae) from infection. Our results showed that strains of B. apis inhibit the growth of two insect fungal pathogens, Beauveria bassiana and Aspergillus flavus, in vitro. This phenotype was recapitulated in vivo; bee brood supplemented with B. apis were significantly less likely to be infected by A. flavus. Additionally, the presence of B. apis reduced sporulation of A. flavus in the few bees that were infected. Analyses of biosynthetic gene clusters across B. apis strains suggest antifungal production via a Type I polyketide synthase. Secreted metabolites from B. apis alone were sufficient to suppress fungal growth, supporting this hypothesis. Together, these data suggest that B. apis protects bee brood from fungal infection by the secretion of an antifungal metabolite. On the basis of this discovery, new antifungal treatments could be developed to mitigate honey bee colony losses, and, in the future, could address fungal epidemics in other species.

Kay Robbins, Jonathan Touryan, Tim Mullen, Christian Kothe, Nima Bigdely-Shamlo

EEG preprocessing approaches have not been standardized, and even those studies that follow best practices contain variations in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures. Signal measures include recording-level channel amplitudes, study-level channel amplitude dispersion, and recording spectral characteristics. Event-related methods include ERPs and ERSPs and their correlations across methods for a diverse set of stimulus events. Our analysis also assesses differences in residual signals both in the time and spectral domains after blink artifacts have been removed. Using fully automated pipelines, we evaluate these measures across 17 EEG studies for two ICA-based preprocessing approaches (LARG, MARA) plus two variations of Artifact Subspace Reconstruction (ASR). Although the general structure of the results is similar across these preprocessing methods, there are significant differences, particularly in the low-frequency spectral features and in the residuals left by blinks. These results argue for detailed reporting of processing details and for using a federation of processing pipelines to quantify effects of processing choices.

Jennifer N. Cash, Sarah Kearns, Yilai Li, Michael A. Cianfrocco

Recent advances in single-particle cryo-electron microscopy (cryo-EM) data collection utilizes beam-image shift to improve throughput. Despite implementation on well-aligned 300 keV cryo-EM instruments, it remains unknown how well beam-image shift data collection affects data quality on 200 keV instruments and whether any aberrations can be computationally corrected. To test this, we collected and analyzed a cryo-EM dataset of aldolase at 200 keV using beam-image shift. This analysis shows that beam tilt on the instrument initially limited the resolution of aldolase to 5.6Å. After iterative rounds of aberration correction and particle polishing in RELION, we were able to obtain a 2.8Å structure. This analysis indicates that software correction of microscope misalignment can provide a dramatic improvement in resolution.

Kurt G Schilling, Justin Blaber, Colin Hansen, Baxter Rogers, Adam W Anderson, Seth Smith, Praitayini Kanakaraj, Tonia Rex, Susan M Resnick, Andrea T. Shafer, Laurie Cutting, Neil Woodward, David Zald, Bennett A. Landman

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U- nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

Nina Luhmann, Guillaume Holley, Mark Achtman

BlastFrost is a highly efficient method for querying 100,000s of genome assemblies. It builds on Bifrost, a recently developed dynamic data structure for compacted and colored de Bruijn graphs from bacterial genomes. BlastFrost queries a Bifrost data structure for sequences of interest, and extracts local subgraphs, thereby enabling the efficient identification of the presence or absence of individual genes or single nucleotide sequence variants. Here we describe the algorithms and implementation of BlastFrost. We also present two exemplar practical applications. In the first, we determined the presence of the individual genes within the SPI-2 Salmonella pathogenicity island within a collection of 926 representative genomes in minutes. In the second application, we determined the existence of known single nucleotide polymorphisms associated with fluoroquinolone resistance in the genes gyrA , gyrB and parE among 190,209 Salmonella genomes.

Jiming Li, Like Fokkens, Lee James Conneely, Martijn Rep

During host colonization, plant pathogenic fungi secrete proteins, called effectors, to facilitate infection. Collectively, effectors may defeat the plant immune system, but usually not all effectors are equally important for infecting a particular host plant. In Fusarium oxysporum f.sp. lycopersici, all known effector genes – also called SIX genes – are located on a single accessory chromosome which is required for pathogenicity and can also be horizontally transferred to another strain. To narrow down the minimal region required for virulence, we selected partial pathogenicity chromosome deletion strains by fluorescence-assisted cell sorting of a strain in which the two arms of the pathogenicity chromosome were labelled with GFP and RFP, respectively. By testing the virulence of these deletion mutants, we show that the complete long arm and part of the short arm of the pathogenicity chromosome are not required for virulence. In addition, we demonstrate that smaller versions of the pathogenicity chromosome can also be transferred to a non-pathogenic strain and they are sufficient to turn the non-pathogen into a pathogen. Surprisingly, originally non-pathogenic strains that had received a smaller version of the pathogenicity chromosome were much more aggressive than recipients with a complete pathogenicity chromosome. Whole genome sequencing analysis revealed that partial deletions of the pathogenicity chromosome occurred mainly close to repeats, and that spontaneous duplication of sequences in accessory regions is frequent both in chromosome deletion strains and in horizontal transfer (recipient) strains.

C. Titus Brown, Dominik Moritz, Michael O'Brien, Felix Reidl, Taylor Reiter, Blair Sullivan

Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome. We apply this system to recover missing content from genome bins and show that substantial genomic sequence variation is present in a real metagenome. Our software implementation is available at https://github.com/spacegraphcats/spacegraphcats under the 3-Clause BSD License.

Braden T. Tierney, Yixuan He, George Church, Eran Segal, Aleksandar D. Kostic, Chirag J. Patel

Over the past decade, studies of the human genome and microbiome have deepened our understanding of the connections between human genes, environments, microbes, and disease. For example, the sheer number of indicators of the microbiome and human genetic common variants associated with disease has been immense, but clinical utility has been elusive. Here, we compared the predictive capabilities of the human microbiome versus human genomic common variants across 13 common diseases. We concluded that microbiomic indicators outperform human genetics in predicting host phenotype (overall Microbiome-Association-Study [MAS] area under the curve [AUC] = 0.79 [SE = 0.03], overall Genome-Wide-Association-Study [GWAS] AUC = 0.67 [SE = 0.02]). Our results, while preliminary and focused on a subset of the totality of disease, demonstrate the relative predictive ability of the microbiome, indicating that it may outperform human genetics in discriminating human disease cases and controls. They additionally motivate the need for population-level microbiome sequencing resources, akin to the UK Biobank, to further improve and reproduce metagenomic models of disease.

N. M. Doll, S. Royek, S. Fujita, S. Okuda, A. Stintzi, Thomas Widiez, M. Hothorn, A. Schaller, N. Geldner, G. Ingram

The plant embryonic cuticle is a hydrophobic barrier deposited de novo by the embryo during seed development. At germination it protects the seedling from water loss and is thus critical for survival. Embryonic cuticle formation is controlled by a signaling pathway involving the protease ALE1 and the receptor-like kinases GSO1 and GSO2. We show that a sulfated peptide, TWISTED SEED1 (TWS1) acts as a GSO1/GSO2 ligand. Cuticle surveillance depends on the action of ALE1 which, unlike TWS1 and GSO1/2, is not produced in the embryo but in the neighboring endosperm. Cleavage of an embryo-derived TWS1 precursor by ALE1 releases the active peptide, triggering GSO1/2-dependent cuticle reinforcement in the embryo. A bidirectional molecular dialogue between embryo and endosperm thus safeguards cuticle integrity prior to germination.

Kyaw Thu Minn, Yuheng C Fu, Shenghua He, Steven C George, Mark A Anastasio, Samantha A Morris, Lilianna Solnica-Krezel

During mammalian gastrulation, germ layers arise and are shaped into the body plan while extraembryonic layers sustain the embryo. Human embryonic stem cells, cultured with BMP4 on extracellular matrix micro-discs, reproducibly differentiate into gastruloids, expressing markers of germ layers and extraembryonic cells with radial organization. Single-cell RNA sequencing and cross-species comparisons with mouse and cynomolgus monkey gastrulae suggest that gastruloids contain seven major cell types, including epiblast, ectoderm, mesoderm, endoderm, and extraembryonic trophoblast- and amnion-like cells. Upon gastruloid dissociation, single cells reseeded onto micro-discs were motile and aggregated with the same but segregated from distinct cell types. Ectodermal cells segregated from endodermal and extraembryonic cells but mixed with mesodermal cells. Our work demonstrates that the gastruloid system supports primate-specific features of embryogenesis, and that gastruloid cells exhibit evolutionarily conserved sorting behaviors. This work generates a resource for transcriptomes of human extraembryonic and embryonic germ layers differentiated in a stereotyped arrangement.

Patricia J. Ahl, Richard A. Hopkins, Wen Wei Xiang, Bijin Au, Nivashini Kaliaperumal, Anna-Marie Fairhurst, John E Connolly

A complex interaction of anabolic and catabolic metabolism underpins the ability of leukocytes to mount an immune response. Their capacity to respond and adapt to changing environments by metabolic reprogramming is crucial to their effector function. However, current methods lack the ability to interrogate this network of metabolic pathways at the single cell level within a heterogeneous population. Here we present Met-Flow, a novel flow cytometry-based method that captures the metabolic state of immune cells by targeting key proteins and rate-limiting enzymes across multiple pathways. We demonstrate the ability to simultaneously measure divergent metabolic profiles and dynamic remodeling in human peripheral blood mononuclear cells. Using Met-Flow, we discovered that glucose restriction and metabolic remodeling drive the expansion of an inflammatory central memory T cell subset. This method captures the complex metabolic state of any cell as it relates to its phenotype and function, leading to a greater understanding of the role of metabolic heterogeneity in immune responses.

Katerina Kalemaki, XiaXia Xu, Ileana L. Hanganu-Opatz, Kyriaki Sidiropoulou, Domna Karagogeos

The prefrontal cortex (PFC) is characterized by delayed maturation that extends until adulthood. Although the adolescent PFC has been well investigated, the cellular mechanisms controlling the early development of prefrontal circuits are still largely unknown. Our study delineates the developmental cellular processes that are on-going in the mouse medial PFC (mPFC) during the second and third postnatal weeks and compares them to those in the barrel cortex (BC). We show that basal synaptic transmission decreases from the second to the third postnatal week in both brain areas due to increased spontaneous inhibitory currents and reduced excitatory ones. Furthermore, increasing GABAA receptor (GABAAR) activity leads to increased basal synaptic response of neonatal mPFC, but not BC. Additionally, the K-Cl co-transporter 2 (KCC2) expression is decreased in the neonatal mPFC compared to the pre-juvenile one as well as to the neonatal and pre-juvenile BC, suggesting that GABAAR function in the neonatal mPFC is non-inhibitory. Moreover, the intrinsic properties of both interneurons and pyramidal cells change with age and relate to augmented network activity across development.