Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional organization of mouse auditory cortex in response to stimulus complexity and brain state

The functional organization of sensory cortices is modulated by both extrinsic events and intrinsic states. The present study aimed to assess the mouse auditory cortex (AC) responses under varying conditions of stimulus complexity and brain state. Using wide-field calcium imaging, our results suggest a complete outline of topographic maps of frequency and FM rate as well as highly responsive regions to mouse ultrasonic vocalizations (USVs) in both awake and anesthetized states. Three new regions responsive to high-frequency tones and four new gradients responsive to frequency modulations (FMs) were identified. These maps are highly replicable across weeks and between animals. In awake versus anesthetized states, cortical responsiveness to pure tones was stronger, and regions that preferentially responded to slow rate FMs were smaller. In both states, fast FM regions showed the greatest contribution to the processing of USVs. Finally, our modeling of how best tone frequency or FM rate changes as a function of distance along a topographic gradient resulted in a sigmoid function. Together, our findings provide a better understanding of mouse AC functional organization and how this organization is modulated by changes in stimulus complexity and brain state. The function of newly identified regions in higher-order auditory/vocal processing and animal behavior should be considered in future research.
Morphological Subprofile Analysis for Bioactivity Annotation of Small Molecules

Fast prediction of mode of action for bioactive compounds would immensely foster bioactivity annotation in compound collections and may early on reveal off-targets in chemical biology research and drug discovery. A variety of target-based assays is available for addressing the modulation of druggable proteins. However, they cannot precisely predict how a compound would influence cellular processes due to polypharmacology. Furthermore, non-protein targets are often not considered. Morphological profiling, e.g., using the Cell Painting assay that monitors hundreds of morphological features upon compound perturbation and staining of cellular components, offers a fast, unbiased assessment of compound activity on various targets and cellular processes in one single experiment. However, due to incomplete bioactivity annotation and unknown activities of reference (landmark) compounds, prediction of bioactivity is not straightforward. Here we introduce the concept of subprofile analysis to map the mode of action for both reference and unexplored compounds. We defined mode-of-action clusters for a group of reference compounds and extracted cluster subprofiles that contain only a subset of morphological features (i.e., subprofiles) to represent a consensus profile. Subprofile analysis allows for assignment of compounds to, currently, ten different targets or modes of action in one single assay and bypasses the need of exploring all biosimilar reference compounds for the generation of target hypothesis. This approach will enable rapid bioactivity annotation of compound collections, particularly of uncharacterized small molecules, and will be extended to further bioactivity clusters in future. The data is public accessible via https://github.com/mpimp-comas/2022_pahl_ziegler_subprofiles and the web app tool http://cpcse.pythonanywhere. com/.
Diversity of Ganglion Cell Responses to Saccade-Like Image Shifts in the Primate Retina

Saccades are a fundamental part of natural vision. They interrupt fixations of the visual gaze and rapidly shift the image that falls onto the retina. These stimulus dynamics can cause activation or suppression of different retinal ganglion cells, but how they affect the encoding of visual information in different types of ganglion cells is largely unknown. Here, we recorded spiking responses to saccade-like shifts of luminance gratings from ganglion cells in isolated marmoset retinas and investigated how the activity depended on the combination of presaccadic and postsaccadic images. All identified cell types, On and Off parasol and midget cells, as well as a type of Large Off cells, displayed distinct response patterns, including particular sensitivity to either the presaccadic or the postsaccadic image or combinations thereof. In addition, Off parasol and Large Off cells, but not On cells, showed pronounced sensitivity to whether the image changed across the transition. Stimulus sensitivity of On cells could be explained based on their responses to step changes in light intensity, whereas Off cells, in particular, parasol and the Large Off cells, seem to be affected by additional interactions that are not triggered during simple light-intensity flashes. Together, our data show that ganglion cells in the primate retina are sensitive to different combinations of presaccadic and postsaccadic visual stimuli. This contributes to the functional diversity of the output signals of the retina and to asymmetries between On and Off pathways and provides evidence of signal processing beyond what is triggered by isolated steps in light intensity. SIGNIFICANCE STATEMENT Sudden eye movements (saccades) shift our direction of gaze, bringing new images in focus on our retinas. To study how retinal neurons deal with these rapid image transitions, we recorded spiking activity from ganglion cells, the output neurons of the retina, in isolated retinas of marmoset monkeys while shifting a projected image in a saccade-like fashion across the retina. We found that the cells do not just respond to the newly fixated image, but that different types of ganglion cells display different sensitivities to the presaccadic and postsaccadic stimulus patterns. Certain Off cells, for example, are sensitive to changes in the image across transitions, which contributes to differences between On and Off information channels and extends the range of encoded stimulus features.
PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design

Protein is biology workhorse. Since the recent break-through of novel folding methods, the amount of available structural data is increasing, closing the gap between data-driven sequence-based and structure-based methods. In this work, we focus on the inverse folding problem that consists in predicting an amino-acid primary sequence from protein 3D structure. For this purpose, we introduce a simple Transformer model from Natural Language Processing augmented 3D-structural data. We call the resulting model PeTriBERT: Proteins embedded in tridimensional representation in a BERT model. We train this small 40-million parameters model on more than 350 000 proteins sequences retrieved from the newly available AlphaFoldDB database. Using PetriBert, we are able to in silico generate totally new proteins with a GFP-like structure. These 9 of 10 of these GFP structural homologues have no ressemblance when blasted on the whole entry proteome database. This shows that PetriBert indeed capture protein folding rules and become a valuable tool for de novo protein design.
K978C CFTR restores essential epithelial function with greater efficiency than wildtype CFTR when expressed in CF airway cells

Class Ia/b CFTR variants cause severe cystic fibrosis (CF) lung disease in ~10% of CF patients and are untreatable with small molecule pharmaceuticals. Genetic replacement strategies offer a potential cure for all patients but so far, have displayed limited efficiency in vivo. We hypothesised that increasing protein abundance and/or activity of introduced CFTRs would more effectively restore function to CF bronchial epithelial cells (CFBE) in the presence of CF sputum (CFS) than wildtype (WT)-CFTR. We investigated codon optimised CFTR (hCAI), increased open probability CFTR (K978C) and codon optimised plus K978C (h^K978C) as candidates for gene therapy. Transfection of HEK293T with hCAI and h^K978C produced ~10-fold more CFTR protein than WT or K978C CFTRs. hCAI and h^K978C also displayed ~4-fold greater anion transport than WT in a halide-sensitive YFP quenching assay. However, functionality of modified CFTR cDNAs expressed in CFBE were profoundly different. 10% transduction of CFBE with K978C, compared to 22% transduction with WT, restored Cl- transport to similar levels as that recorded from non-CF cells. K978C increased ASL height and pH more effectively than WT-CFTR, while hCAI and h^K978C had limited impact. Further investigation indicated that codon optimised CFTRs mis-localised in CFBE and compromised vectoral Cl- transport. These data provide further evidence that codon optimised CFTR cDNAs may be unsuitable for gene therapy practices that employ high activity promoters. However, increased activity CFTR cDNAs such as K978C, that potentially mimic the effect of potentiators, may provide more potent recovery of function than WT-CFTR cDNA in CF airways. Significance Statement Cystic fibrosis (CF) disease is associated with genetic malfunction of the Cl- channel CFTR, leading to dehydration and decreased pH in the fluid lining the airways. Replacement of CFTR by gene therapy/gene editing offers potential therapeutic benefit but efficiency is poor. We show that gain of activity K978C CFTR under the control of a high activity promoter fully restored Cl- transport, hydration and pH to CF bronchial epithelial cells (CFBE) in the presence of CF sputum and more efficiently than wild type CFTR. Codon optimised forms of CFTR were much less effective and proteins were mis-localised/mis-processed in CFBE. Thus, K978C could offer improved therapeutic potential.
Group B streptococcal membrane vesicles induce proinflammatory cytokine production and are sensed in an NLRP3 inflammasome-dependent mechanism in human macrophages

Group B Streptococcus (GBS) is a major cause of fetal and neonatal mortality worldwide. Many of the adverse effects associated with invasive GBS are associated with inflammation that leads to chorioamnionitis, preterm birth, sepsis, and meningitis; therefore, understanding bacterial factors that promote inflammation is of critical importance. Membrane vesicles (MVs), which are produced by many pathogenic and non-pathogenic bacteria, may modulate host inflammatory responses. In mice, GBS MVs injected intra-amniotically can induce preterm birth and fetal death. Although it is known that GBS MVs induce large-scale leukocyte recruitment into infected tissues, the immune effectors driving these responses are unclear. Here, we hypothesized that macrophages respond to GBS-derived MVs by producing proinflammatory cytokines and are recognized through one or more pattern recognition receptors. We show that THP-1 macrophage-like cells produce high levels of neutrophil- and monocyte-specific chemokines in response to MVs derived from different clinical isolates of GBS. Interleukin (IL)-1β was significantly upregulated in response to MVs, which was independent of NF-kB signaling but dependent on both caspase-1 and NLRP3. These data indicate that MVs contain one or more pathogen-associated molecular patterns that can be sensed by the immune system. Furthermore, this study identifies the NLRP3 inflammasome as a novel sensor of GBS MVs. Our data additionally indicate that MVs may serve as immune effectors that can be targeted for immunotherapeutics, particularly given that similar responses were observed across this subset of GBS isolates.
APE1 recruits ATRIP to ssDNA in an RPA-independent manner to promote the ATR DNA damage response

Cells have evolved the DNA damage response (DDR) pathways in response to DNA replication stress or DNA damage. In the ATR-Chk1 DDR pathway, it has been proposed that ATR is recruited to RPA-coated single-strand DNA (ssDNA) by direct ATRIP-RPA interaction. However, it remains elusive whether and how ATRIP is recruited to ssDNA in an RPA-independent manner. Here, we provide evidence that APE1 directly associates ssDNA to recruit ATRIP onto ssDNA in an RPA-independent fashion. The N-terminal motif within APE1 is required and sufficient for the APE1-ATRIP interaction in vitro and the distinct APE1-ATRIP interaction is required for ATRIP recruitment to ssDNA and the ATR-Chk1 DDR pathway activation in Xenopus egg extracts. In addition, APE1 directly associates with RPA70 and RPA32 via two distinct motifs. Taken together, our evidence identifies APE1 as a direct recruiter of ATRIP onto ssDNA independent of RPA in the activation of ATR DDR pathway. Summary APE1 associates with ssDNA and ATRIP directly via distinct motifs and thereby recruits ATRIP onto ssDNA independent of RPA to promote the ATR DDR. APE1 interacts with RPA via distinct two motifs in vitro but such RPA-APE1 interaction is dispensable for ATRIP recruitment onto ssDNA.
Small non-coding vault RNA1-2 modulates expression of cell membrane proteins through nascent RNA silencing

Gene expression can be regulated by transcriptional or post-transcriptional gene silencing. Recently, we described nuclear nascent RNA silencing (NRS) that is mediated by Dicer dependent tRNA-derived small RNA molecules. In addition to tRNA, RNA polymerase III also transcribes Vault RNA, a component of the ribonucleoprotein complex Vault. Here, we show that Dicer dependent small vault RNA1-2 (svtRNA1-2) associate with Argonaute 2 (Ago2). Whilst endogenous vtRNA1-2 is present mostly in cytoplasm, svtRNA1-2 localises predominantly in nucleus. Furthermore, in Ago2 and Dicer knockdown cells, a subset of genes which are upregulated at the nascent level were predicted to be targeted by svtRNA1-2 in the intronic region. Genomic deletion of vtRNA1-2 results in impaired cellular proliferation and the upregulation of genes associated with cell membrane physiology and cell adhesion. Silencing activity of svtRNA1-2 molecules is dependent on seed-plus-complementary-paired hybridisation features and the presence of a 5-nucleotide loop protrusion on target RNAs. Our data reveal a role for Dicer dependent svtRNA1-2, possessing unique molecular features, in modulation of expression of membrane associated proteins at the nascent RNA level.
DNARecords: An extensible sparse format for petabyte scale genomics analysis

Recent growth in population scale sequencing initiatives involve both cohort scale and proportion of genome surveyed, with a transition from genotyping arrays to broader genome sequencing approaches. The resulting datasets can be challenging to analyze. Here we introduce DNARecords a novel sparse-compatible format for large scale genetic data. The structure enables integration of complex data types such as medical images and drug structures towards the development of machine learning methods to predict disease risk and drug response. We demonstrate its speed and memory advantages for various genetics analyses. These performance advantages will become more pronounced as it becomes feasible to analyze variants of lower population allele frequencies. Finally, we provide an open-source software plugin, built on top of Hail, to allow researchers to write and read such records as well as a set of examples for how to use them.
Design, implementation, and functional validation of a new generation of microneedle 3D high-density CMOS multi-electrode array for brain tissue and spheroids

In the last decades, planar multi-electrode arrays (MEAs) have been widely used to record activity from in vitro neuronal cell cultures and tissue slices. Though successful, this technique bears some limitations, particularly relevant when applied to three-dimensional (3D) tissue, such as brain slices, spheroids or organoids. For example, planar MEAs signals are informative on just one side of a 3D-organized structure. This limits the interpretation of the results in terms of network functions in a complex structured and hyperconnected brain tissue. Moreover, the side in contact with the MEAs often shows lower oxygenation rates and related vitality issues. To overcome these problems, we empowered a CMOS high-density multi-electrode array (HD-MEA) with thousands of microneedles (μneedles) of 65-90 μm height, able to penetrate and record in-tissue signals, providing for the first time a 3D HD-MEA chip. We propose a CMOS-compatible fabrication process to produce arrays of μneedles of different widths mounted on large pedestals to create microchannels underneath the tissue. By using cerebellar and cortico-hippocampal slices as a model, we show that the μneedles efficiently penetrate the 3D tissue while the microchannels allow the flowing of maintenance solutions to increase tissue vitality in the recording sites. These improvements are reflected by the increase in electrodes sensing capabilities, the number of sampled neuronal units (compared to matched planar technology), and the efficiency of compound effects. Importantly, each electrode can also be used to stimulate the tissue with optimal efficiency due to the 3D structure. Furthermore, we demonstrate how the 3D HD-MEA can efficiently penetrate and get outstanding signals from in vitro 3D cellular models as brain spheroids. In conclusion, we describe a new recording device characterized by the highest spatio-temporal resolution reported for a 3D MEA and significant improvements in the quality of recordings, with a high signal-to-noise ratio and improved tissue vitality. The applications of this game-changing technique are countless, opening unprecedented possibilities in the neuroscience field and beyond.
Huntingtin-KIF1A-mediated axonal transport of synaptic vesicle precursors influences synaptic transmission and motor skill learning in mice

Neurotransmitters are released at synapses by synaptic vesicles (SVs), which originate from SV precursors (SVPs) that have traveled along the axon. Because each synapse maintains a pool of SVs, only a small fraction of which are released, it is unclear whether axonal transport of SVPs modifies synaptic function. Here, studying the corticostriatal network both in microfluidic devices and in mice, we find that phosphorylation of the Huntingtin protein (HTT) causes it to recruit the kinesin motor KIF1A, which in turn increases axonal transport of SVPs and synaptic glutamate release. In mice, constitutive HTT phosphorylation leads to SV over-accumulation at synapses, increases the probability of SV release, and impairs motor skill learning on the rotating rod. Silencing KIF1A in these mice restored SV transport and motor skill learning to wild-type levels. Axonal SVP transport within the corticostriatal network thus influences synaptic plasticity and motor skill learning.
Lipidomic Analysis Reveals Differences in Bacteroides Species Driven Largely by Plasmalogens, Glycerophosphoinositols and Certain Sphingolipids

There has been increasing interest in bacterial lipids in recent years due, in part, to their emerging role as molecular signalling molecules. Bacteroides thetaiotaomicron is an important member of the mammalian gut microbiota that has been shown to produce sphingolipids (SP) that pass through the gut epithelial barrier to impact host SP metabolism and signal into host inflammation pathways. B. thetaiotaomicron also produces a novel family of N-acyl amines (called glycine lipids) that are potent ligands of host Toll-like receptor 2 (TLR2). Here, we specifically examine the lipid signatures of 4 species of gut associated Bacteroides. In total we identify 170 different lipids and we report that the range and diversity of Bacteroides lipids is species-specific. Multi-variate analysis reveals that the differences in the lipid signatures are largely driven by the presence/absence of plasmalogens, glycerophosphoinositols and certain SP. Moreover, we show that, in B. thetaiotaomicron, mutations altering either SP or glycine lipid biosynthesis results in significant changes in the levels of other lipids suggesting the existence of compensatory mechanisms required to maintain the functionality of the bacterial membrane. Importance Bacteroides are important beneficial members of the gut microbiome that produce lipids that can function as cross-kingdom signalling molecules. We describe, for the first time, a comprehensive and qualitative comparison of the lipid signatures of 4 important Bacteroides species. We identify a group of Bacteroides core lipids and uncover species-specific differences in plasmalogen, glycerophospholipid and SP metabolism with more subtle differences observed in glycine lipid production. This data will provide a useful platform for the further characterisation of the lipid-based host-microbe dialogue and the influence of microbial lipids on host health and disease states.
Provable Learning-based Algorithm For Sparse Recovery

Recovering sparse parameters from observational data is a fundamental problem in machine learning with wide applications. Many classic algorithms can solve this problem with theoretical guarantees, but their performances rely on choosing the correct hyperparameters. Besides, hand-designed algorithms do not fully exploit the particular problem distribution of interest. In this work, we propose a deep learning method for algorithm learning called PLISA ( P rovable L earning-based I terative S parse recovery A lgorithm). PLISA is designed by unrolling a classic path-following algorithm for sparse recovery, with some components being more ﬂexible and learnable. We theoretically show the improved recovery accuracy achievable by PLISA . Furthermore, we analyze the empirical Rademacher complexity of PLISA to characterize its generalization ability to solve new problems outside the training set. This paper contains novel theoretical contributions to the area of learning-based algorithms in the sense that (i) PLISA is generically applicable to a broad class of sparse estimation problems, (ii) generalization analysis has received less attention so far, and (iii) our analysis makes novel connections between the generalization ability and algorithmic properties such as stability and convergence of the unrolled algorithm, which leads to a tighter bound that can explain the empirical observations. The techniques could potentially be applied to analyze other learning-based algorithms in the literature.
Factor Scores, Structure Coefficients, and Communality Coefficients: It's All One General Linear Model.

This paper explains the meaning and use of three important factor analytic statistics: factor scores, factor structure coefficients, and communality coefficients. For the discussion, 301 observations of junior high school students 11 measured variables from a previous study are analyzed. While factors provide the researcher with general information, the factor scores are weighted combinations of scores on a series of measured variables. Four methods of calculating factor scores are discussed. These are: (1) the regression method; (2) the Bartlett method (M. Bartlett, 1937), which uses least squares procedures to minimize the sum of squares of the other unique factors over the range of variables; (3) the Anderson-Rubin method (T. Anderson and H. Rubin, 1956), which is similar to the Bartlett method with an added condition requiring that factors scores be orthogonal; and (4) the Thompson method (B. Thompson, 1993). Calculating factor structure coefficients and communality coefficients is described, and what each brings to the analysis is outlined. In the example studied, the interpretable results were 5 extracted factors and 301 factor scores for each of the factors, 55 structure coefficients, and 11 communality coefficients. The combined evaluation of these statistics enables the researcher to formulate interpretations more accurately. An attachment contains the syntax reference for the analysis. (Contains 9 tables and 23 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. Running Head: GENERAL LINEAR MODEL PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY