title: A developmental transition in the sensory coding of limb kinematics in primary motor cortex

abstract: Primary motor cortex (M1) undergoes protracted development in mammals, functioning initially as a sensory structure. Throughout the first postnatal week in rats, M1 is strongly activated by self-generated forelimb movements--especially by the twitches that occur during active sleep. Here, we quantify the kinematic features of forelimb movements to reveal receptive-field properties of individual units within the forelimb region of M1. At postnatal day (P) 8, nearly all units were strongly modulated by movement amplitude, especially during active sleep. By P12, only a minority of units continued to exhibit amplitude-tuning, regardless of behavioral state. At both ages, movement direction also modulated M1 activity, though to a lesser extent. Finally, at P12, M1 population-level activity became more sparse and decorrelated, along with a substantial alteration in the statistical distribution of M1 responses to limb movements. These findings reveal a transition toward a more complex and informationally rich representation of movement long before M1 develops its motor functionality.


title: Soft sweeps predominate recent positive selection in bonobos (Pan paniscus) and chimpanzees (Pan troglodytes)

abstract: Two modes of positive selection have been recognized: 1) hard sweeps that result in the rapid fixation of a beneficial allele typically from a de novo mutation and 2) soft sweeps that are characterized by intermediate frequencies of at least two haplotypes that stem from standing genetic variation or recurrent de novo mutations. While many populations exhibit both hard and soft sweeps throughout the genome, there is increasing evidence that soft sweeps, rather than hard sweeps, are the predominant mode of adaptation in many species, including humans. Here, we use a supervised machine learning approach to assess the extent of completed hard and soft sweeps in the closest living relatives of humans: bonobos and chimpanzees (genus Pan). We trained convolutional neural network classifiers using simulated data and applied these classifiers to population genomic data for 71 individuals representing all five extant Pan lineages, of which we successfully analyzed 60 individuals from four lineages. We found that recent adaptation in Pan is largely the result of soft sweeps, ranging from 73.1 to 97.7% of all identified sweeps. While few hard sweeps were shared among lineages, we found that between 19 and 267 soft sweep windows were shared by at least two lineages. We also identify novel candidate genes subject to recent positive selection. This study emphasizes the importance of shifts in the physical and social environment, rather than novel mutation, in shaping recent adaptations in bonobos and chimpanzees.

category: genomics


title: DeepCellState: an autoencoder-based framework for prediction of cell type specific transcriptional states induced by drug treatment

abstract: Drug treatment induces cell type-specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.

category: bioinformatics


title: Informing NMR experiments with molecular dynamics simulations to characterize the dominant activated state of the KcsA ion channel

abstract: As the first potassium channel with a X-ray structure determined, and given its homology to eukaryotic channels, the pH-gated prokaryotic channel KcsA has been extensively studied. Nevertheless, questions related in particular to the allosteric coupling between its gates remain open. The many currently available X-ray crystallography structures appear to correspond to various stages of activation and inactivation, offering insights into the molecular basis of these mechanisms. Since these studies have required mutations, complexation with antibodies, and substitution of detergents in place of lipids, examining the channel under more native conditions is desirable. Solid-state NMR (SSNMR) can be used to study the wild-type protein under activating conditions (low pH), at room temperature, and in bacteriomimetic liposomes. In this work, we sought to structurally assign the activated state present in SSNMR experiments. We used a combination of molecular dynamics (MD) simulations, chemical shift prediction algorithms, and Bayesian inference techniques to determine which of the most plausible X-ray structures resolved to date best represents the activated state captured in SSNMR. We first identified specific nuclei with simulated NMR chemical shifts that differed significantly when comparing partially open vs. fully open ensembles from MD simulations. The simulated NMR chemical shifts for those specific nuclei were then compared to experimental ones, revealing that the simulation of the partially open state was in good agreement with the SSNMR data. Nuclei that discriminate effectively between partially and fully open states belong to residues spread over the sequence and provide a molecular level description of the conformational change.

category: biophysics


title: STUB1 is an intracellular checkpoint for interferon gamma sensing

abstract:
Immune checkpoint blockade (ICB) leads to durable and complete tumour regression in some patients but in others gives temporary, partial or no response. Accordingly, significant efforts are underway to identify tumour-intrinsic mechanisms underlying ICB resistance.

Results from a published CRISPR screen in a mouse model suggested that targeting STUB1, an E3 ligase involved in protein homeostasis, may overcome ICB resistance but the molecular basis of this effect remains unclear. Herein, we report an under-appreciated role of STUB1 to dampen the interferon gamma (IFN{gamma}) response. Genetic deletion of STUB1 increased IFNGR1 abundance on the cell surface and thus enhanced the downstream IFN{gamma} response as showed by multiple approaches including Western blotting, flow cytometry, qPCR, phospho-STAT1 assay, immunopeptidomics, proteomics, and gene expression profiling. Human prostate and breast cancer cells with STUB1 deletion were also susceptible to cytokine-induced growth inhibition.

Furthermore, blockade of STUB1 protein function recapitulated the STUB1-null phenotypes. Despite these encouraging in vitro data and positive implications from clinical datasets, we did not observe in vivo benefits of inactivating Stub1 in mouse syngeneic tumour models - with or without combination with anti-PD-1 therapy. However, our findings elucidate STUB1 as a barrier to IFN{gamma} sensing, prompting further investigations to assess if broader inactivation of human STUB1 in both tumors and immune cells could overcome ICB resistance.

category: cancer biology


title: The dorsal hippocampus is required for the formation of long-term duration memories in rats

abstract: Interval timing--the perception of durations mainly in seconds or minutes--is a ubiquitous behavior in organisms. Animal studies have suggested that the hippocampus plays an essential role in duration memory; however, the memory processes involved are unclear. To clarify the role of the dorsal hippocampus in the acquisition of long-term duration memories, we adapted the "time-shift paradigm" to a peak-interval procedure. After a sufficient number of training with an initial target duration (20 s), the rats underwent "shift sessions" with a new target duration (40 s) under a muscimol (0.5 g per side) infusion into the bilateral dorsal hippocampus. The memory of the new target duration was then tested in drug-free "probe sessions," including trials in which no lever presses were reinforced. In the probe sessions, the mean response rate distribution of the muscimol group was located leftward to the control group, but these two response rate distributions were superimposed on the standardized time axis, suggesting a scalar property. In the session-by-session analysis, the mean peak time (an index of timing accuracy) of the muscimol group was lower than that of the control group in the probe sessions, but not in the shift sessions. These findings suggest that the dorsal hippocampus is required for the formation of long-term duration memories within the range of interval timing.

category: neuroscience


title: Multiscale and integrative single-cell Hi-C analysis with Higashi

abstract: The advent of single-cell Hi-C (scHi-C) technologies offers an unprecedented opportunity to unveil cell-to-cell variability of 3D genome organization. However, the development of computational methods that can effectively enhance scHi-C data quality and extract 3D genome features in single cells remains a major challenge. Here, we report Higashi, a new algorithm that achieves state-of-the-art analysis of scHi-C data based on hypergraph representation learning. Extensive evaluations demonstrate that Higashi significantly outperforms existing methods for embedding and imputation of scHi-C data. Higashi is uniquely able to identify multiscale 3D genome features (such as compartmentalization and TAD-like domain boundaries) in single cells, allowing markedly refined delineation of cell-to-cell variability of 3D genome features. By applying to a scHi-C dataset from human prefrontal cortex, Higashi reveals complex cell types as well as new connections between 3D genome features and cell type-specific gene regulation. Higashi provides an end-to-end solution to scHi-C data analysis and is applicable to studying single-cell 3D genomes in a wide range of biological contexts.


title: EMT changes actin cortex rheology in a cell-cycle dependent manner

abstract:
The actin cortex is a key structure for cellular mechanics and cellular migration. Accordingly, cancer cells were shown to change their actin cytoskeleton and their mechanical properties in correlation with different degrees of malignancy and metastatic potential. Epithelial-Mesenchymal transition (EMT) is a cellular transformation associated with cancer progression and malignancy. To date, a detailed study of the effects of EMT on the frequency-dependent viscoelastic mechanics of the actin cortex is still lacking. In this work, we have used an established AFM-based method of cell confinement to quantify the rheology of the actin cortex of human breast, lung and prostate epithelial cells before and after EMT in a frequency range of 0.02 - 2 Hz. Interestingly, we find for all cell lines opposite EMT-induced changes in interphase and mitosis; while the actin cortex softens upon EMT in interphase, the cortex stiffens in mitosis. Our rheological data can be accounted for by a rheological model with a characteristic time scale of slowest relaxation. In conclusion, our study discloses a consistent rheological trend induced by EMT in human cells of diverse tissue origin reflecting major structural changes of the actin cytoskeleton upon EMT.

Significance statementThe actin cortex is a key structure for cellular mechanics and cellular migration. Correspondingly, migratory cancer cells were shown to change their mechanical properties to a softer phenotype. EMT is a cellular transformation associated with cancer progression and malignancy. To date, a detailed study of the effects of EMT on the mechanics of the actin cortex is still lacking. In this work, we provide such a study for human breast, lung and prostate epithelial cells in dependence of the cell cycle stage. We observe a softening of the actin cortex in interphase but stiffening in mitosis upon EMT. In conclusion, our study discloses a consistent mechanical trend induced by EMT in human cells of diverse tissue origin.


title: Computational model for migration of human osteoblasts in direct current electric field

abstract:
Under both physiological (development, regeneration) and pathological conditions (cancer metastasis), cells migrate while sensing environmental cues in the form of mechanical, chemical or electrical stimuli. Although it is known that osteoblasts respond to exogenous electric fields, the underlying mechanism of electrotactic collective movement of human osteoblasts is unclear. Theoretical approaches to study electrotactic cell migration until now mainly used reaction-diffusion models, and did not consider the effect of electric field on single-cell motility, or incorporate spatially dependent cell-to-cell interactions. Here, we present a computational model that takes into account cell interactions and describes cell migration in direct current electric field. We compare this model with in vitro experiments in which human primary osteoblasts are exposed to direct current electric field of varying field strength. Our results show that cell-cell interactions and fluctuations in the migration direction together lead to anode-directed collective migration of osteoblasts.

Author summaryElectrotactic migration of cells involves directed movement of a large number of single cells under the influence of external electric field. Influencing the migration behaviour of osteoblasts by external direct current electric field offers a promising approach towards building highly effective implants for bone regeneration. We present a computational model for electrotactic migration of osteoblasts subject to external direct current electric field. Our model considers individual cells that interact with each other and the external electric field, and, replicates the experimental observations, based on single-cell analysis, of the response of osteoblasts to electrical stimulation of varying strengths for 7 hours. Our results suggest that tracking trajectories of individual cells provide a way of determining the role of various interactions of a cell in collective migration. Our model provides a framework that links single cell response to the large scale collective dynamics.


title: Analysis of Amino Acid Variants in Malignant Melanoma Cells Resistant to BRAF inhibition

abstract: Analysis of patient-specific nucleotide variants is a cornerstone of personalised medicine. Although only 2% of the genomic sequence is protein-coding, mutations occurring in these regions have the potential to influence protein structure and may have severe impact on disease aetiology. Of special importance are variants that affect modifiable amino acid residues, as protein modifications involved in signal transduction networks cannot be analysed by genomics. Proteogenomics enables analysis of proteomes in context of patient- or tissue-specific non-synonymous nucleotide variants. Here, we developed a proteogenomics workflow and applied it to study resistance to serine/threonine-protein kinase B-raf (BRAF) inhibitor (BRAFi) vemurafenib in malignant melanoma cell line A375. This approach resulted in high identification and quantification of non-synonymous nucleotide variants and (phospho)proteins. We integrated multi-omic datasets to reconstruct the perturbed signalling networks associated with BRAFi resistance and to predict drug therapies with the potential to disrupt BRAFi resistance mechanism in A375 cells. Notably, we showed that aurora kinase A (AURKA) inhibition is effective and specific against BRAFi resistant A375 cells. Furthermore, we investigated nucleotide variants that interfere with protein post-translational modification (PTM) status and potentially influence cell signalling. Mass spectrometry (MS) measurements confirmed variant-driven PTM changes in 12 proteins; among them was the runt-related transcription factor 1 (RUNX1) displaying a variant on a known phosphorylation site S(Ph)276L. We confirmed the loss of phosphorylation site by MS and demonstrated the impact of this variant on RUNX1 interactome.


title: Diverse modes of binocular interactions in the mouse superior colliculus

abstract: The superior colliculus (SC) integrates visual and other sensory information to regulate critical reflexive and innate behaviors, such as prey capture. In the mouse, the vast majority of retinal ganglion cells (RGCs) innervate the SC, including inputs from both the contralateral (contra-RGCs) and ipsilateral (ipsi-RGCs) eye. Despite this, previous studies revealed minimal neuronal responses to ipsilateral stimulation and few binocular interactions in the mouse SC. More recent work suggests that ipsi-RGC function and innervation of the SC are critical for efficient prey capture, raising the possibility that binocular interactions in the mouse SC may be more prevalent than previously thought. To explore this possibility, we investigated eye-specific and binocular influences on visual responses and tuning of SC neurons, focusing on the anteromedial region. While the majority of SC neurons were primarily driven by contralateral eye stimulation, we observed that a substantial proportion of units were influenced or driven by ipsilateral stimulation. Clustering based on differential responses to eye-specific stimulus presentation revealed five distinct putative subpopulations and multiple modes of binocular interaction, including facilitation, summation, and suppression. Each of the putative subpopulations exhibited selectivity for orientation, and differences in spatial frequency tuning and spatial summation properties were observed between subpopulations. Further analysis of orientation tuning under different ocular conditions supported differential modes of binocular interaction between putative subtypes. Taken together, these data suggest that binocular interactions in the mouse SC may be more prevalent and diverse than previously understood.


title: Decoding neurobiological spike trains using recurrent neural networks; a case study with electrophysiological auditory cortex recordings

abstract: Recent advancements in multielectrode methods and spike-sorting algorithms enable the in vivo recording of the activities of many neurons at a high temporal resolution. These datasets offer new opportunities in the investigation of the biological neural code, including the direct testing of specific coding hypotheses, but they also reveal the limitations of present decoder algorithms. Classical methods rely on a manual feature extraction step, resulting in a feature vector, like the firing rates of an ensemble of neurons. In this paper, we present a recurrent neural-network based decoder and evaluate its performance on experimental and artificial datasets. The experimental datasets were obtained by recording the auditory cortical responses of rats exposed to sound stimuli, while the artificial datasets represent preset encoding schemes. The task of the decoder was to classify the action potential timeseries according to the corresponding sound stimuli. It is illustrated that, depending on the coding scheme, the performance of the recurrent-network based decoder can exceed the performance of the classical methods. We also show how randomized copies of the training datasets can be used to reveal the role of candidate spike-train features. We conclude that artificial neural network decoders can be a useful alternative to classical population vector based techniques in studies of the biological neural code.


title: Theory of (antimicrobial) relativity: When competitors determine a species' drug sensitivity.

abstract: Microbes associate in nature forming complex communities, but they are often studied in purified form. Here I show that differences in antibiotic sensitivity between co-existing bacterial species can modify the diffusion of antibiotic molecules, and alter the drug efficacy measured in conventional in vitro assays. I developed a model, validated experimentally using an artificial two-species community, showing antibiotic molecules re-distribute evenly across all species if they have similar drug sensitivity. The result is lower drug efficacy with respect to conventional in vitro assays. However, when species have different sensitivities, the re-distribution of antibiotic is uneven and most sensitive species accumulate more drug. The result is increased drug efficacy against these species. Despite this discrepancy with conventional laboratory assays, my data suggests the changes in drug efficacy are predictable and provides insight into how communities can affect the diffusion of other molecules in the environment.

category: microbiology


title: Predictability of drug sensitivity in multi-species environments


title: Re-distribution of antibiotic within a community is behind the unpredictable change in efficacy observed in classic laboratory assays


title: Predicting the re-distribution of antibiotic molecules caused byinter-species interactions in microbial communities


title: Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria

abstract: The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, metabolomics, ...). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairs. In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by 1H NMR the bio-indicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris area (France). In the present study, we further investigate on the same samples the great potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome remarkably allows clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations beyond the obvious genetic differences between the two species, and seem to be influenced by the production of noxious metabolites by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches is constituting an innovative bio-indicative tool for ecological stress assessment, such as toxigenic cyanobacterial blooms, and aim at being further follow up.

category: ecology


title: Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans

abstract:
Genetic data can provide insights into population history, but first we must understand the patterns that complex histories leave in genomes. Here, we consider the admixed human population of Cabo Verde to understand the patterns of genetic variation left by social and demographic processes. First settled in the late 1400s, Cabo Verdeans are admixed descendants of Portuguese colonizers and enslaved West African people. We consider Cabo Verdes well-studied historical record alongside genome-wide SNP data from 563 individuals from 4 regions within the archipelago. We use genetic ancestry to test for patterns of nonrandom mating and sex-specific gene flow, and we examine the consequences of these processes for common demographic inference methods and for genetic patterns. Notably, multiple population genetic tools that assume random mating underestimate the timing of admixture, but incorporating non-random mating produces estimates more consistent with historical records. We consider how admixture interrupts common summaries of genomic variation such as runs-of-homozygosity (ROH). While summaries of ROH may be difficult to interpret in admixed populations, differentiating ROH by length class shows that ROH reflect historical differences between the islands in their contributions from the source populations and post-admixture population dynamics. Finally, we find higher African ancestry on the X chromosome than on the autosomes, consistent with an excess of European males and African females contributing to the gene pool. Considering these genomic insights into population history in the context of Cabo Verdes historical record, we can identify how assumptions in genetic models impact inference of population history more broadly.

Author SummaryPatterns of genetic variation are often used to infer human population histories; however population-genetic models make a variety of simplifying assumptions that often neglect the demographic and social dynamics of human populations. Here, we use the population of Cabo Verde as a case study to understand the patterns of genetic variation left by social and demographic processes. The islands of Cabo Verde were first settled in the late 1400s by Portuguese colonizers and enslaved West African people. We consider genomic data from four regions within the archipelago alongside historical records that document settlement patterns, timing, and sociocultural dynamics within the islands. We use genetic ancestry to test for nonrandom mating and sex-specific demography. We show that these sociocultural processes may bias inference of population history parameters and distributions of shared ancestry in the region. Overall, by providing insights into the patterns of genetic variation social processes leave in human genomes for a population with a well-studied historical record, we highlight processes to consider in order to more accurately understand the history of populations without extensive records.

category: evolutionary biology


title: Widespread introgression across a phylogeny of 155 Drosophila genomes

abstract: Genome-scale sequence data have invigorated the study of hybridization and introgression, particularly in animals. However, outside of a few notable cases, we lack systematic tests for introgression at a larger phylogenetic scale across entire clades. Here we leverage 155 genome assemblies, from 149 species, to generate a fossil-calibrated phylogeny and conduct multilocus tests for introgression across nine monophyletic radiations within the genus Drosophila. Using complementary phylogenomic approaches, we identify widespread introgression across the evolutionary history of Drosophila. Mapping gene-tree discordance onto the phylogeny revealed that both ancient and recent introgression has occurred across most of the nine clades that we examined. Our results provide the first evidence of introgression occurring across the evolutionary history of Drosophila and highlight the need to continue to study the evolutionary consequences of hybridization and introgression in this genus and across the Tree of Life.


title: Interplay between intraocular and intracranial pressure effects on the optic nerve head in vivo

abstract:
Intracranial pressure (ICP) has been proposed to play an important role in the sensitivity to intraocular pressure (IOP) and susceptibility to glaucoma. However, the in vivo effects of simultaneous, controlled, acute variations in ICP and IOP have not been directly measured. We quantified the deformations of the anterior lamina cribrosa (ALC) and scleral canal at Bruchs membrane opening (BMO) under acute elevation of IOP and/or ICP.

Four eyes of three monkeys were imaged in vivo with OCT under four pressure conditions: IOP and ICP either at baseline or elevated. The BMO and ALC were reconstructed from manual delineations. From these, we determined canal area at the BMO (BMO area), BMO aspect ratio and planarity, and ALC median depth relative to the BMO plane. To better account for the pressure effects on the imaging, we also measured ALC visibility as a percent of the BMO area. Further, ALC depths were analyzed only in regions where the ALC was visible in all pressure conditions. Bootstrap sampling was used to obtain mean estimates and confidence intervals, which were then used to test for significant effects of IOP and ICP, independently and in interaction.

Response to pressure manipulation was highly individualized between eyes, with significant changes detected in a majority of the parameters. Significant interactions between ICP and IOP occurred in all measures, except ALC visibility. On average, ICP elevation expanded BMO area by 0.17mm2 at baseline IOP, and contracted BMO area by 0.02 mm2 at high IOP. ICP elevation decreased ALC depth by 10m at baseline IOP, but increased depth by 7 m at high IOP. ALC visibility decreased as ICP increased, both at baseline (-10%) and high IOP (-17%). IOP elevation expanded BMO area by 0.04 mm2 at baseline ICP, and contracted BMO area by 0.09 mm2 at high ICP. On average, IOP elevation caused the ALC to displace 3.3 m anteriorly at baseline ICP, and 22 m posteriorly at high ICP. ALC visibility improved as IOP increased, both at baseline (5%) and high ICP (8%).

In summary, changing IOP or ICP significantly deformed both the scleral canal and the lamina of the monkey ONH, regardless of the other pressure level. There were significant interactions between the effects of IOP and those of ICP on LC depth, BMO area, aspect ratio and planarity. On most eyes, elevating both pressures by the same amount did not cancel out the effects. Altogether our results show that ICP affects sensitivity to IOP, and thus that it can potentially also affect susceptibility to glaucoma.

Research Highlights- In vivo ONH deformations caused by acute, controlled, simultaneous changes in IOP and/or ICP can be directly visualized and measured in the monkey eye using OCT.
- Acute changes of either IOP or ICP significantly deformed both the scleral canal and the lamina cribrosa, regardless of the other pressure level.
- Pressures interacted, meaning that the effects of one pressure depended significantly on the level of the other pressure.
- Elevating both pressures did not cancel out the effects of one of them being elevated.
- Our results show that ICP affects sensitivity to IOP, and thus that it can potentially also affect susceptibility to glaucoma.

category: bioengineering


title: Immunogenomic profile of colorectal cancer response to immune checkpoint blockade

abstract: Colorectal cancers (CRCs) show variable response to immune checkpoint blockade, which can only partially be explained by the variability of tumour mutational burden. To dissect the cellular and molecular determinants of response we performed a multi-omic screen of 721 cancer regions from patients treated with Pembrolizumab (KEYNOTE 177 clinical trial) or Nivolumab. Multi-regional whole exome, RNA and T-cell receptor sequencing show that, within hypermutated CRCs, response to both anti-PD1 agents is not positively associated with tumour mutational burden but with high clonality of immunogenic mutations, expanded T cells, low activation of the WNT pathway and active immune escape mechanisms. Coupling high-dimensional imaging mass cytometry with multiplexed immunofluorescence and computational spatial analysis, we observe that responsive hypermutated CRCs are rich in cytotoxic and proliferating PD1-expressing CD8 cells interacting with high-density clusters of PDL1-expressing antigen presenting macrophages. We propose that anti-PD1 agents release the PD1-PDL1 interaction between CD8 T cells and macrophages thus promoting cytotoxic anti-tumour activity.


title: Pitfalls in Post Hoc Analyses of Population Receptive Field Data

abstract:
Data binning involves grouping observations into bins and calculating bin-wise summary statistics. It can cope with overplotting and noise, making it a versatile tool for comparing many observations. However, data binning goes awry if the same observations are used for binning (selection) and contrasting (selective analysis). This creates circularity, biasing noise components and resulting in artifactual changes in the form of regression towards the mean. Importantly, these artifactual changes are a statistical necessity. Here, we use (null) simulations and empirical repeat data to expose this flaw in the field of population receptive field modeling. In doing so, we reveal factors modulating the appearance of such artifactual changes and highlight that some factors result in artifactual changes even without circular data binning. These pitfalls might have led to erroneous claims about changes in population receptive fields in previous work and can be mitigated by using independent data for selection purposes. Our evaluations highlight the urgency for us researchers to make the validation of analysis pipelines standard practice.

Graphical abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/422942v2_ufig1.gif" ALT="Figure 1">
View larger version (33K):
org.highwire.dtl.DTLVardef@149b091org.highwire.dtl.DTLVardef@e7338aorg.highwire.dtl.DTLVardef@e8ce6aorg.highwire.dtl.DTLVardef@47b377_HPS_FORMAT_FIGEXP  M_FIG C_FIG HighlightsO_LICircular data binning produces artifactual changes in the form of regression towards the mean
C_LIO_LISeveral factors modulate these artifactual changes
C_LIO_LISome factors produce artifactual changes even without circular data binning
C_LIO_LIThese pitfalls can lead to faulty claims about changes in population receptive fields
C_LI


title: Widespread premature transcription termination of Arabidopsis thaliana NLR genes by the spen protein FPA

abstract: Genes involved in disease resistance are some of the fastest evolving and most diverse components of genomes. Large numbers of nucleotide-binding, leucine-rich repeat receptor (NLR) genes are found in plant genomes and are required for disease resistance. However, NLRs can trigger autoimmunity, disrupt beneficial microbiota or reduce fitness. It is therefore crucial to understand how NLRs are controlled. Here we show that the RNA-binding protein FPA mediates widespread premature cleavage and polyadenylation of NLR transcripts, thereby controlling their functional expression and impacting immunity. Using long-read Nanopore direct RNA sequencing, we resolved the complexity of NLR transcript processing and gene annotation. Our results uncover a co-transcriptional layer of NLR control with implications for understanding the regulatory and evolutionary dynamics of NLRs in the immune responses of plants.

category: molecular biology


title: Mathematical function for predicting recombination from map distance

abstract: Map distance, measured in centimorgans, is used in genetics to represent the average number of crossovers between two loci in a gamete and can be translated to recombination frequency with mapping functions. Commonly used mapping functions, such as Haldane or Kosambi functions, are single-variable functions and yield one recombination prediction per map distance. Here we show that these approaches contain systematic error and formulate a new function to predict recombination frequency from the map distance. Our new function is paradigm shifting as it takes into account the variation in the number of crossovers and their localisation, yielding context-specific estimates of recombination frequency that can vary for a given map distance. The function is chromosome-specific, making it generalizable for different sexes and organisms. We validate the new approach using empirical data from stickleback fishes and humans and show that it outperforms the conventional mapping functions and produces more accurate predictions about recombination frequency between loci.

category: genetics


title: The interaction of curiosity and reward on long-term memory in younger and older adults

abstract: Long-term memory is sensitive to both intrinsic and extrinsic motivation, but little is known about the relative influence of these two sources of motivation on memory performance across the adult lifespan. The study examined the effects of extrinsic motivation, manipulated via monetary reward, and curiosity, a form of intrinsic motivation, on long-term memory in healthy younger and older adults. During the incidental encoding phase on Day 1, 60 younger and 53 older participants viewed high- and low-curiosity trivia items as well as unrelated face stimuli. Half of the participants in each age group received financial rewards for correctly guessing trivia answers. On Day 2, participants completed a trivia recall test and an old-new recognition test for the face stimuli. Both curiosity and reward were associated with enhanced trivia recall, but the effects were interactive, such that only low-curiosity items benefitted from monetary reward. Neither curiosity nor reward affected face recognition performance in either age group. These findings indicate that individual and joint effects of intrinsic and extrinsic motivation on long-term memory are relatively preserved in healthy aging, a finding that highlights the viability of motivational strategies for memory enhancement into old age. Identifying conditions under which memory for unrelated information benefits from motivational spillover effects in younger and older adults is a priority for future research.

category: animal behavior and cognition


title: HISAT-3N: a rapid and accurate three-nucleotide sequence aligner

abstract: Nucleotide conversion sequencing technologies such as bisulfite-seq and SLAM-seq are powerful tools to explore the intricacies of cellular processes. In this paper, we describe HISAT-3N (hierarchical indexing for spliced alignment of transcripts - 3 nucleotides), which rapidly and accurately aligns sequences consisting of nucleotide conversions by leveraging powerful hierarchical index and repeat index algorithms originally developed for the HISAT software. Tests on real and simulated data sets demonstrate that HISAT-3N is over 7 times faster, has greater alignment accuracy, and has smaller memory requirements than other modern systems. Taken together HISAT-3N is the ideal aligner for use with converted sequence technologies.


title: A diencephalic circuit for opioid analgesia but not positive reinforcement

abstract: Mu opioid receptor (MOR) agonists are potent analgesics, but also cause sedation, respiratory depression, and addiction risk. The epithalamic lateral habenula (LHb) signals aversive states including pain and we found it is a potent site for MOR-agonist analgesia. Importantly, LHb MOR activation is not reinforcing in the absence of noxious input. The LHb receives excitatory inputs from multiple sites including the ventral tegmental area, lateral hypothalamus, entopeduncular nucleus, and the lateral preoptic area of the hypothalamus (LPO). Here we report that LHb-projecting glutamatergic LPO neurons are excited by noxious stimulation and are preferentially inhibited by MOR selective agonists. Critically, optogenetic stimulation of LHb-projecting LPO neurons produces an aversive state that is relieved by LHb MOR activation, and optogenetic inhibition of LHb-projecting LPO neurons relieves the aversiveness of ongoing pain. Therefore, targeting this MOR sensitive forebrain circuit can relieve pain yet lower the risk of misuse by pain free individuals.


title: AdRoit: an accurate and robust method to infer complex transcriptome composition

abstract: Bulk RNA sequencing technology provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method infer the cell composition from transcriptome data comprised of multiple cell types. AdRoit uses gene expression profile obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to correct the sequencing technique difference between the single cell data and the bulk or spatial transcriptome data, enabling cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its superior sensitivity and specificity compared to other existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than many existing methods.


title: Ribosome profiling elucidates differential gene expression in bundle sheath and mesophyll cells in maize

abstract: The efficiencies offered by C4 photosynthesis have motivated efforts to understand its biochemical, genetic and developmental basis. Reactions underlying C4 traits in most C4 plants are partitioned between two cell types, bundle sheath (BS) and mesophyll (M) cells. RNA-seq has been used to catalog differential gene expression in BS and M cells in maize and several other C4 species. However, the contribution of translational control to maintaining the distinct proteomes of BS and M cells has not been addressed. In this study, we used ribosome profiling (ribo-seq) and RNA-seq to describe translatomes, translational efficiencies, and microRNA abundance in BS and M-enriched fractions of maize seedling leaves. A conservative interpretation of our data revealed 182 genes exhibiting cell-type dependent differences in translational efficiency, 31 of which encode proteins with core roles in C4 photosynthesis. Our results suggest that non-AUG start codons are used preferentially in upstream open reading frames of BS cells, revealed mRNA sequence motifs that correlate with cell type-dependent translation, and identified potential translational regulators that are differentially expressed. In addition, our data expand the set of genes known to be differentially expressed in BS and M cells, including genes encoding transcription factors and microRNAs. These data add to the resources for understanding the evolutionary and developmental basis of C4 photosynthesis and for its engineering into C3 crops.

category: plant biology


abstract:
Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins.

We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus-Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.

AvailabilityWe make all of our data and code available on GitHub https://github.com/QData/DeepVHPPI.

ACM Reference FormatJack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, and Yanjun Qi. 2021. Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences. In Proceedings of ACM Conference (ACM-BCB). ACM, New York, NY, USA, 10 pages. https://doi.org/??


title: Information flow, cell types and stereotypy in a full olfactory connectome

abstract: The hemibrain connectome provides large scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter - versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.


title: Plasmodium falciparum serology: A comparison of two protein production methods for analysis of antibody responses by protein microarray

abstract:
The evaluation of protein antigens as putative serologic biomarkers of infection has increasingly shifted to high-throughput, multiplex approaches such as the protein microarray. In vitro transcription/translation (IVTT) systems - a similarly high-throughput protein expression method - are already widely utilised in the production of protein microarrays, though purified recombinant proteins derived from more traditional whole cell based expression systems also play an important role in biomarker characterisation. Here we have performed a side-by-side comparison of antigen-matched protein targets from an IVTT and purified recombinant system, on the same protein microarray. The magnitude and range of antibody responses to purified recombinants was found to be greater than that of IVTT proteins, and responses between targets from different expression systems did not clearly correlate. However, responses between amino acid sequence-matched targets from each expression system were more closely correlated. Despite the lack of a clearly defined relationship between antigen-matched targets produced in each expression system, our data indicate that protein microarrays produced using either method can be used confidently, in a context dependent manner, though care should be taken when comparing data derived from contrasting approaches.

Statement of significance of the studyProtein microarray technology is increasingly being realised as a powerful tool in disease biomarker identification. Protein-based, serologic arrays are already well utilised in the characterisation of antibody responses to the malarial Plasmodium spp., drastically improving throughput and efficiency compared to more typical experimental assays. Such approaches have commonly made use of in vitro transcription/translation (IVTT) protein expression systems, though other protein expression methods are regularly employed. In this study we have directly compared antibody responses in a malaria endemic population, to matched protein antigens derived from both an IVTT and purified bacterial recombinant system in the same protein microarray. We demonstrated that the magnitude of measured antibody response to matched protein antigens tends to be greater to purified recombinants, rather than IVTT products. Further, our analysis showed highly variable levels of correlation of response between antigen-matched targets derived from each expression system, although relationships in antibody response were stronger between proteins with overlapping amino acid sequences. This study highlights the importance of considering the strengths and weaknesses of each expression system in the context of a protein microarray according to experimental hypotheses, and illustrates the need for attention when comparing data generated by these different methodologies.

category: immunology


title: Microbial Species Abundance Distributions Guide Human Population Size Estimation from Sewage Microbiomes

abstract:
The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxas relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome.

Author summaryWastewater-based epidemiology (WBE) is an emerging field that employs sewage as aggregated samples of human populations. This approach is particularly promising for tracking diseases that can spread asymptomatically in large populations, such as the COVID-19. As a new type of biological data, sewage has its own unique challenges to utilize. While wastewater samples are usually assumed to represent large populations, it is not guaranteed, because of stochasticity in toilet flushes; unlike epidemiological experiments collecting data from individuals, sample size, i.e., the human population size represented by a wastewater sample, is a fundamental yet difficult-to-characterize parameter for sewage samples. Researchers would need to aggregate data from large areas and week-long collection to stabilize data, during which, important spikes in small areas or short time scales may be lost. It also remains challenging to turn viral titers into case prevalences, evaluating representativeness, or comparing measurements across sites/studies.

This study provides a framework to estimate human population size from sewage utilizing human gut-associated microorganisms. Through analysis, we demonstrate that variance of taxon abundances and single-nucleotide polymorphism as two variables that change with population size. We provide a new tool MicrobiomeCensus that performs population size estimation from microbial taxon abundances. MicrobiomeCensus outperforms generic algorithms in terms of computational efficiency while at comparable or better accuracy. Using MicrobiomeCensus, we detected population size differences in sewage samples taken in Cambridge, MA, under two sampling approaches, i.e., "grab" or "composite" sampling. This study provides a framework to utilize individual-level microbiomes to learn from sewage, paving the way to prevalence estimation and improved spatio-temporal resolutions in WBE..


title: Reassessing associations between white matter and behaviour with multimodal microstructural imaging

abstract:
Several studies have established specific relationships between White Matter (WM) and behaviour. However, these studies have typically focussed on fractional anisotropy (FA), a neuroimaging metric that is sensitive to multiple tissue properties, making it difficult to identify what biological aspects of WM may drive such relationships. Here, we carry out a pre-registered assessment of WM-behaviour relationships in 50 healthy individuals across multiple behavioural and anatomical domains, and complementing FA with myelin-sensitive quantitative MR modalities (MT, R1, R2*).

Surprisingly, we only find support for predicted relationships between FA and behaviour in one of three pre-registered tests. For one behavioural domain, where we failed to detect an FA-behaviour correlation, we instead find evidence for a correlation between behaviour and R1. This hints that multimodal approaches are able to identify a wider range of WM-behaviour relationships than focusing on FA alone.

To test whether a common biological substrate such as myelin underlies WM-behaviour relationships, we then ran joint multimodal analyses, combining across all MRI parameters considered. No significant multimodal signatures were found and power analyses suggested that sample sizes of 40 to 200 may be required to detect such joint multimodal effects, depending on the task being considered.

These results demonstrate that FA-behaviour relationships from the literature can be replicated, but may not be easily generalisable across domains. Instead, multimodal microstructural imaging may be best placed to detect a wider range of WM-behaviour relationships, as different MRI modalities provide distinct biological sensitivities. Our findings highlight a broad heterogeneity in WMs relationship with behaviour, suggesting that variable biological effects may be shaping their interaction.

HighlightsO_LIPre-registered testing of microstructural imaging across modalities (FA, MT, R1, R2*) to test WM-behaviour relationships.
C_LIO_LIPartial support for FA-behaviour relationships hypothesised based on previous literature.
C_LIO_LIMultimodal approaches can help detect WM-behaviour relationships that are not detected with FA alone.
C_LIO_LISample sizes of 40 to 200 may be needed to detect myelin-behaviour relationships in joint multimodal analyses.
C_LIO_LIVariable biological effects may be shaping WM-behaviour relationships.
C_LI


title: Ageing and MPTP-sensitivity depend on molecular and ultrastructural signatures of astroglia and microglia in mice nigra

abstract:
Both astroglia and microglia show region-specific distribution in CNS and often maladapt to age-associated alterations within their niche. Studies on autopsied substantia nigra of Parkinsons disease (PD) patients and experimental models propose gliosis as a trigger for neuronal loss. Epidemiological studies propose an ethnic bias in PD prevalence, since Caucasians are more susceptible than non-whites. Similarly, different mice strains are variably sensitive to MPTP. We had earlier likened divergent MPTP-sensitivity of C57BL/6J and CD-1 mice with differential susceptibility to PD, based on differences in neuronal numbers.

Here we examined whether the variability was also incumbent to inter-strain differences in glial features of male C57BL/6J and CD-1 mice. Stereological counts showed more microglia and fewer astrocytes in the substantia nigra of MPTP-susceptible normal C57BL/6J mice, suggesting persistence of an immune-vigilant state. MPTP-induced microgliosis and astrogliosis in both strains, suggests their involvement in pathogenesis. ELISA of pro-inflammatory cytokines in the ventral-midbrain revealed augmentation of TNF- and IL-6 at middle-age in both strains that reduced at old-age, suggesting middle-age as a critical, inflamm-aging associated time-point. TNF- levels were persistently high in C57BL/6J, through ageing and post-MPTP; while IL-6 and IL-1{beta} were upregulated at old-age. CD-1 had higher levels of anti-inflammatory cytokine TGF-{beta}. MPTP-challenge caused upregulation of enzymes MAO-A, MAO-B and iNOS in both strains. Post-MPTP enhancement in fractalkine and hemeoxygenase-1, may be neuronal compensatory signals. Lastly, ultrastructural observations of elongated astroglial/microglial mitochondria vis-a-vis the shrunken ones in neurons, suggest upscaling of their functions with neurotoxic consequences. Thus, astroglia and microglia modulate ageing and PD-susceptibility.

Significance statementPeople of Caucasians ancestry are more susceptible to Parkinsons disease, than the Asians, for reasons not completely understood. We designed a disease model around two different laboratory mice i.e. C57BL/6 and CD-1 mice and extrapolated the results to the ethnicities, using a neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP). Our study provides objective evidence that astroglia are inherently more and microglia fewer in the mice that resist MPTP. They secrete low levels of neuroinflammatory proteins and their gut microbiota is typical. The astrocytic and microglia mitochondria may hold the key to cure neurodegeneration.


title: CRISPR knockdown of GABAA alpha3 subunits on thalamic reticular neurons enhances deep sleep

abstract:
Identification of mechanisms which increase deep sleep could lead to novel treatments which promote the restorative effects of sleep. Here, knockdown of the 3 GABAA-receptor subunit from parvalbumin neurons in the thalamic reticular nucleus using CRISPR-Cas9 gene editing increased the thalamocortical delta oscillations implicated in many health-promoting effects of sleep. Inhibitory synaptic currents were strongly reduced in vitro. Effects were most pronounced in the mouse sleep (light) period. Further analysis identified a novel deep-sleep state in mice prior to NREM-REM transitions which was preferentially affected by deletion of 3 subunits. Our results identify a functional role for GABAA receptors on Thalamic Reticular Nucleus neurons and suggest antagonism of 3 subunits as a strategy to enhance deep sleep.

One Sentence SummarySelective genetic knockdown of the major  subunit of GABAA receptors present in the thalamic reticular nucleus enhanced deep sleep in mice.


title: Experiencing surprise: the temporal dynamics of its impact on memory

abstract:
To efficiently process information, the brain shifts between encoding and retrieval states, prioritising bottom-up or top-down processing accordingly. Expectation violation before or during learning has been shown to trigger an adaptive encoding mechanism, resulting in better memory for unexpected events. Using fMRI we explored (1) whether this encoding mechanism is also triggered during retrieval, and if so, (2) what the temporal dynamics of its mnemonic consequences are. Male and female participants studied object images, then, with new objects, they learned a contingency between a cue and a semantic category. Rule-abiding (expected) and violating (unexpected) targets and similar foils were used at test. We found interactions between previous and current similar events expectation, such that when an expected event followed a similar but unexpected event, its performance was boosted, underpinned by activation in the hippocampus, midbrain, and occipital cortex. In contrast, a sequence of two unexpected similar events also triggered occipital engagement, however, this did not enhance memory performance. Taken together, our findings suggest that when the goal is to retrieve, encountering surprising events engages an encoding mechanism, supported by bottom-up processing, that may enhance memory for future related events.

Significance statementOptimising the balance between new learning and the retrieval of existing knowledge is an ongoing process, at the core of human cognition. Previous research into memory encoding suggests experiencing surprise leads to the prioritisation of the leaning of new memories, forming an adaptive encoding mechanism. We examined whether this mechanism is also engaged when the current goal is to retrieve information. Our results demonstrate that an expectation-driven shift towards an encoding state, supported by enhanced perceptual processing, is beneficial for the correct identification of subsequent expected similar events. These findings have important implications for our understanding of the temporal dynamics of the adaptive encoding of information into memory.


title: Brain rhythms define distinct interaction networks with differential dependence on anatomy

abstract: What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.


title: The role of anatomical connection strength for interareal communication in macaque cortex


title: Increased connectivity among sensory and motor regions during visual and audiovisual speech perception

abstract: In everyday conversation, we usually process the talkers face as well as the sound of their voice. Access to visual speech information is particularly useful when the auditory signal is degraded. Here we used fMRI to monitor brain activity while adult humans (n = 60) were presented with visual-only, auditory-only, and audiovisual words. The audiovisual words were presented in quiet and several signal-to-noise ratios. As expected, audiovisual speech perception recruited both auditory and visual cortex, with some evidence for increased recruitment of premotor cortex in some conditions (including in substantial background noise). We then investigated neural connectivity using psychophysiological interaction (PPI) analysis with seed regions in both primary auditory cortex and primary visual cortex. Connectivity between auditory and visual cortices was stronger in audiovisual conditions than in unimodal conditions, including a wide network of regions in posterior temporal cortex and prefrontal cortex. In addition to whole-brain analyses, we also conducted a region-of-interest analysis on the left posterior superior temporal sulcus (pSTS), implicated in many previous studies of audiovisual speech perception. We found evidence for both activity and effective connectivity in pSTS for visual-only and audiovisual speech, although these were not significant in whole-brain analyses. Taken together, our results suggest a prominent role for cross-region synchronization in understanding both visual-only and audiovisual speech that complements activity in "integrative" brain regions like pSTS.


title: The Gradient Clusteron: A model neuron that learnsvia dendritic nonlinearities, structural plasticity, and gradient descent

abstract: Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via the nonlinear voltage-dependence of NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm (Mel 1991) takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of "under-performing" synapses on a model dendrite during learning ("structural plasticity"), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location- dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the All-vs-All MNIST task ([~]85%) approaches that of logistic regression ([~]93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron ("functional plasticity") and show that a G-clusteron that utilizes the weight update rule can achieve [~]89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions.


title: The ratio versus difference optimization and its implications for optimality theory

abstract: Among the classical models of optimization, some models maximize the ratio of returns per investment, others maximize the difference between returns and investment. However, the question under what conditions use of the ratio model is appropriate and under what conditions a difference model should be used remained unaddressed until recently. The question is important because the strategies indicated by ratio optimum can be substantially different than the ones suggested by difference optimum. We make a general case here for the set of conditions for appropriate use of ratio versus difference optimum. When the investable amount is perceived as limiting but not the investment opportunities a ratio optimum is appropriate and when the investment opportunities are perceived to be limiting but not the investable amount, difference optimum is appropriate. Taking examples of Concorde fallacy, parental investment, r and K selection, nectar production, pollinator behavior, protein synthesis and stability, viral burst size and human economic behavior we show that the ratiodifference distinction in optimization models resolves many long standing debates and conundrums in evolution, behavior and economics.


title: Activity Budget and Postural Behaviours in Orangutans on Bukit Merah Orang Utan Island for Assessing Captive Great Ape Welfare

abstract: Assessments of the welfare status of captive and semi-captive animals often compare how their expression of natural behaviours differs from that of free-ranging conspecifics. Bukit Merah Orang Utan Island (BMOUI) is the only orangutan rehabilitation and conservation centre in the Malay Peninsula. We recorded and analyzed the activity budget and postural behaviours of orangutans moving freely in the enclosures on BMOUI to evaluate their welfare status. From December 2015 to December 2016, we observed three individuals: an adult male, an adult female and a subadult male, and collected 252 hours of focal data (84 hr/individual). The orangutans activity budget was dominated by resting (60%), feeding (13%), playing (14%) and moving (9%). The study individuals heavily relied on the artificial foods (79.2%), and they spent majority of their time on the ground (85.1%) with occasional arboreal observations like using the wooden tree platform or a rope. Despite some significant individual differences, behavioural categories followed a similar trend: resting > feeding > moving > playing, except that the subadult male spent significantly more time playing (35%) than the two adults (3-4%). The most predominant posture was sitting (47.0%), followed by pronograde standing (29.4%), lying (10.5%) and clinging (4.5%). Our results suggest that orangutans on BMOUI engage in less feeding but more resting, and show less postural diversity, than free-ranging individuals. We propose that appropriate interventions to shift activity budgets, especially feeding vs. resting, and postural behaviours of captive orangutans towards those found in free-ranging orangutans might be beneficial for their welfare and survival; however, the conclusions we can draw are limited due to the small sample size, and thus until the captive behaviours of a larger number of orang-utans has been described, these results must be considered preliminary and just a case study.


title: Quantitative Genetic Scoring, or how to put a number on an arbitrary genetic region

abstract:
MotivationWith the increasing availability of genome-wide genetic data, methods to combine genetic variables with other sources of data in statistical models are required. This paper introduces quantitative genetic scoring (QGS), a dimensionality reduction method to create quantitative genetic variables representing arbitrary genetic regions.

MethodsQGS is defined as the sum of absolute differences in the genetic sequence between a subject and a reference population. QGS properties such as distribution and sensitivity to region size were examined, and QGS was tested in six different existing genomic data sets of various sizes and various phenotypes.

ResultsQGS can reduce genetic information by >98% yet explain phenotypic variance at low, medium, and high level of granularity. Associations based on QGS are independent of both size and linkage disequilibrium structure of the underlying region. In combination with stability selection, QGS finds significant results where a traditional genome-wide association approaches struggle. In conclusion, QGS preserves phenotypically significant genetic variance while reducing dimensionality, allowing researchers to include quantitative genetic information in any type of statistical analysis.

Availabilityhttps://github.com/machine2learn/QGS

Contactgido.schoenmacker@radboudumc.nl

Supplemental informationSupplemental data are available online.


title: Predicting compound activity from phenotypic profiles and chemical structures

abstract: Recent advances in deep learning enable using chemical structures and phenotypic profiles to accurately predict assay results for compounds virtually, reducing the time and cost of screens in the drug-discovery process. We evaluate the relative strength of three high-throughput data sources--chemical structures, images (Cell Painting), and gene-expression profiles (L1000)--to predict compound activity using a sparse historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity with high accuracy in 6-10% of assays tested; replacing million-compound physical screens with computationally prioritized smaller screens throughout the pharmaceutical industry could yield major savings. Furthermore, the three profiling modalities are complementary, and in combination they can predict 21% of assays with high accuracy, and 64% if lower accuracy is acceptable. Our study shows that, for many assays, predicting compound activity from phenotypic profiles and chemical structures might accelerate the early stages of the drug-discovery process.


title: Do Motoneurons Slow With Aging? A Systematic Review And Meta-Analysis With Meta-Regression

abstract: Nervous system maladaptation is linked to the loss of maximal strength production and motor control with aging. Motor unit discharge rates are a critical determinant of force production; thus, lower discharge rates could be a mechanism underpinning maximal strength and motor control losses during aging. This meta-analysis summarized the findings of studies comparing motor unit discharge rates between young and older adults, and examined the effect of distinct muscles and contraction intensities on the magnitude of discharge rates difference between these two groups. Eligible studies were combined in a meta-analysis, including tested contraction intensities and muscles in different levels, to investigate whether there were differences in discharge rates between younger and older adults. Motor unit discharge rates were higher in younger adults compared to older adults, with a pooled standardized mean difference (SMD) for all studies of 0.63 (95%CI= 0.27 to 0.99). Contraction intensity had a significant effect on the pooled SMD, with a 1% increase in intensity associated with a 0.009 (95%CI= 0.003 to 0.015) change in the pooled SMD. These findings suggest that the reductions in motor unit discharge rates, especially at higher contraction intensities, may be an important mechanism underpinning age-related losses in maximal strength production.

category: physiology


title: The rapid, massive growth of COVID-19 authors in the scientific literature

abstract: We examined the extent to which the scientific workforce in different fields was engaged in publishing COVID-19-related papers. According to Scopus (data cut, August 1, 2021), 210,183 COVID-19-related publications included 720,801 unique authors, of which 360,005 authors had published at least 5 full papers in their career and 23,520 authors were at the top 2% of their scientific subfield based on a career-long composite citation indicator. The growth of COVID-19 authors was far more rapid and massive compared with cohorts of authors historically publishing on H1N1, Zika, Ebola, HIV/AIDS and tuberculosis. All 174 scientific subfields had some specialists who had published on COVID-19. In 109 of the 174 subfields of science, at least one in ten active, influential (top-2% composite citation indicator) authors in the subfield had authored something on COVID-19. 52 hyper-prolific authors had already at least 60 (and up to 227) COVID-19 publications each. Among the 300 authors with the highest composite citation indicator for their COVID-19 publications, most common countries were USA (n=67), China (n=52), UK (n=32), and Italy (n=18). The rapid and massive involvement of the scientific workforce in COVID-19-related work is unprecedented and creates opportunities and challenges. There is evidence for hyper-prolific productivity.

category: scientific communication and education


title: Leveraging spiking deep neural networks to understand neural mechanisms underlying selective attention

abstract: Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neurons input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity, and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.


title: Leveraging spiking deep neural networks to understand the neural mechanisms underlying selective attention


title: A Guide to Pre-Processing High-Throughput Animal Tracking Data

abstract:
O_LIModern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animals step size, leading to mis-estimation of key movement metrics such as speed.  Cleaning the data to reduce location errors prior to analyses is one of the main ways movement ecologists deal with noisy data, and has the advantage of being more scalable to massive datasets than more complex methods. Though data cleaning is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth, inclusive uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is still rather scarce.
C_LIO_LIA pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficient signal vs. noise screening, in which location errors are rejected without discarding valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods, while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time consuming, and to boost reproducibility, a robust pre-processing pipeline must be automated.
C_LIO_LIIn this article we provide guidance on building pipelines for pre-processing high-throughput animal tracking data in order to prepare it for subsequent analysis. Our recommended pipeline, consisting of removing outliers, smoothing the filtered result, and thinning it to a uniform sampling interval, is applicable to many massive tracking datasets. We apply this pipeline to simulated movement data with location errors, and also show a case study of how large volumes of cleaned data can be transformed into biologically meaningful  residence patches, for quick biological inference on animal space use. We use calibration data to illustrate how pre-processing improves its quality, and to verify that the residence patch synthesis accurately captures animal space use. Finally, turning to tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example.
C_LIO_LITo help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals movement capacity can be used to reduce location errors. The atlastools function is easy to use for beginners, while providing a template for further development. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and leads to better inferences from data.
C_LI


title: Bait-ER: a Bayesian method to detect targets of selection in Evolve-and-Resequence experiments

abstract:
For over a decade, experimental evolution has been combined with high-throughput sequencing techniques in so-called Evolve-and-Resequence (E&R) experiments. This allows testing for selection in populations kept in the laboratory under given experimental conditions. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER - a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model.

Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data.

We implemented and released our method in a new open-source software package that can be accessed at https://github.com/mrborges23/Bait-ER.


title: Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines

abstract: Pairs of paralogs may share common functionality and hence display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However only a biased subset of human paralog pairs has been tested for synthetic lethality to date. Here, by analysing genome-wide CRISPR screens and molecular profiles of over 700 cancer cell lines, we identify features predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier based on these features to predict which paralog pairs are most likely to be synthetic lethal and to explain why. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific.

category: systems biology


title: Utilization of the repeated squat-stand model for studying the directional sensitivity of the cerebral pressure-flow relationship

abstract:
Hysteresis in the cerebral pressure-flow relationship describes the superior ability of the cerebrovasculature to buffer cerebral blood flow changes when mean arterial pressure (MAP) acutely increases compared to when MAP acutely decreases. This phenomenon can be evaluated by comparing the change in middle cerebral artery mean blood velocity (MCAv) per change in MAP during either acute increases or decreases in MAP induced by repeated squat-stands (RSS). However, no real baseline can be employed for this particular protocol as there is no true stable reference point. Herein, we characterized a novel metric using the greatest MAP oscillations induced by RSS without using an independent baseline value and adjusted for time intervals ({Delta}MCAvT/{Delta}MAPT). We also examined whether this metric during each RSS transition were comparable between each other over a 5-min period. {Delta}MCAvT/{Delta}MAPT was calculated using the minimum to maximum MCAv and MAP for each RSS performed at 0.05 Hz and 0.10 Hz. We compared averaged {Delta}MCAvT/{Delta}MAPT during MAP increases and decreases in 74 healthy subjects [9 women; 32 {+/-} 13 years]. {Delta}MCAvT/{Delta}MAPT was lower for MAP increases than MAP decreases at 0.10 Hz RSS only (0.91 {+/-} 0.34 vs. 1.01 {+/-} 0.44 cm{middle dot}s-1/mmHg; p = 0.0013). For both frequency and MAP direction, time during RSS had no effect on {Delta}MCAvT/{Delta}MAPT. This novel analytical method supports the use of the RSS model to evaluate the directional sensitivity of the pressure-flow relationship. These results contribute to the importance of considering the direction of MAP changes when evaluating dynamic cerebral autoregulation.

News & NoteworthyRepeated squat-stand maneuvers are able to examine the directional sensitivity of the cerebral pressure-flow relationship. These maneuvers induce stable physiological cyclic changes where brain blood flow changes with blood pressure increases are buffered more than decreases. These results highlight the importance of considering directional blood pressure changes within cerebral autoregulation.


title: Modeling intrahippocampal effects of anterior hippocampal hyperactivity relevant to schizophrenia using chemogenetic excitation of long axis-projecting mossy cells in the mouse dentate gyrus

abstract:
BackgroundThe anterior hippocampus of individuals with early psychosis or schizophrenia is hyperactive, as is the ventral hippocampus in rodent models for schizophrenia risk. Hyperactive ventral hippocampal projections to extrahippocampal brain regions contribute to schizophrenia symptoms, but less is known about the functional effects of hyperactive projections within the hippocampal formation long axis. We approached this question by testing whether hyperactivation of ventral dentate gyrus (DG) mossy cells (MCs), which densely project intrahippocampally to the dorsal DG, influences spatial memory, a cognition dependent on intact dorsal DG function.

MethodsIn CD-1 mice, we targeted dorsal DG-projecting ventral DG MCs using an adeno-associated virus intersectional strategy. In vivo fiber photometry recording of ventral DG MCs was performed during exploratory behaviors. We targeted excitatory chemogenetic constructs to ventral DG MCs and tested whether their hyperactivation impaired encoding in a spatial memory task.

ResultsVentral DG MCs were activated during behavior related to environmental information gathering (rearing) but not during non-exploratory motor behaviors. Ventral DG MCs made functional monosynaptic inputs to dorsal DG granule cells, with chemogenetic activation of ventral DG MCs leading to increased activity of dorsal DG granule cells. Finally, chemogenetic activation of ventral DG MCs during the encoding phase of an object location memory task impaired retrieval 24 hours later, without effects on locomotion or other exploratory behaviors.

ConclusionsThese data suggest that localized hippocampal hyperactivity may have longitudinal intrahippocampal functional consequences, supporting study of longitudinal circuits as targets to mitigate cognitive deficits associated with schizophrenia.


title: Python Interfaces for the Smoldyn Simulator

abstract:
MotivationSmoldyn is a particle-based biochemical simulator that is frequently used for systems biology and biophysics research. Previously, users could only define models using text-based input or a C/C++ applicaton programming interface (API), which were convenient, but limited extensibility.

ResultsWe added a Python API to Smoldyn to improve integration with other software tools such as Jupyter notebooks, other Python code libraries, and other simulators. It includes low-level functions that closely mimic the existing C/C++ API and higher-level functions that are more convenient to use. These latter functions follow modern object-oriented Python conventions.

AvailabilitySmoldyn is open source and free, available at http://www.smoldyn.org, and can be installed with the Python package manager pip. It runs on Mac, Windows, and Linux.

Contactsteven.s.andrews@gmail.com

Supplementary informationDocumentation is available at http://www.smoldyn.org/SmoldynManual.pdf and https://smoldyn.readthedocs.io/en/latest/python/api.html.


title: Improved prime editors enable pathogenic allele correction and cancer modelling in adult mice

abstract: Prime editors (PEs) mediate genome modification without utilizing double-stranded DNA breaks or exogenous donor DNA as a template. PEs facilitate nucleotide substitutions or local insertions or deletions within the genome based on the template sequence encoded within the prime editing guide RNA (pegRNA). However, the efficacy of prime editing in adult mice has not been established. Here we report an NLS-optimized SpCas9-based prime editor that improves genome editing efficiency in both fluorescent reporter cells and at endogenous loci in cultured cell lines. Using this genome modification system, we could also seed tumor formation through somatic cell editing in the adult mouse. Finally, we successfully utilize dual adeno-associated virus (AAVs) for the delivery of a split-intein prime editor and demonstrate that this system enables the correction of a pathogenic mutation in the mouse liver. Our findings further establish the broad potential of this genome editing technology for the directed installation of sequence modifications in vivo, with important implications for disease modeling and correction.


title: uPTI: uniaxial permittivity tensor imaging of intrinsic density and anisotropy

abstract: Biological architecture is intrinsically tensorial. The permittivity tensor (PT) of biological material reports the density, angular anisotropy, symmetry, and 3D orientation of biomolecules. High-resolution measurement of PT can enable quantitative and label-free analysis of organelle, cell, and tissue architecture, but remains challenging. We report uniaxial permittivity tensor imaging (uPTI), a label-free computational imaging method for volumetric measurement of PT with diffraction-limited resolution. uPTI encodes the components of PT into intensity modulations using oblique illumination and polarization-resolved imaging. The high-dimensional data is decoded with a vectorial image formation model and a multi-channel convex optimization, assuming that the molecular distribution in each voxel has uniaxial symmetry. We describe a modular implementation of uPTI that can be multiplexed with complementary imaging modalities. We report volumes of uPT in mouse brain tissue, SARS-CoV-2 infected cardiomyocytes, RSV infected A549 cells, H&E stained tissue sections, isotropic beads, and anisotropic glass targets. uPTI enabled volumetric imaging of the 3D orientation and symmetry of organelles, cells, and tissue components with higher spatio-angular resolution than current vectorial tomography, ptychography, and light-field microscopy methods. We provide an open source implementation of the image formation model and reconstruction algorithms.


title: A standardized non-visual behavioral event is broadcasted homogeneously across cortical visual areas without modulating visual responses

abstract:
Multiple recent studies have shown that motor activity greatly impacts the activity of primary sensory areas like V1. Yet, the role of this motor related activity in sensory processing is still unclear.

Here we dissect how these behavior signals are broadcast to different layers and areas of the visual cortex. To do so, we leveraged a standardized and spontaneous behavioral fidget event in passively viewing mice. Importantly, this behavior event had no relevance to any ongoing task allowing us to compare its neuronal correlate with visually relevant behavior like running.

A large two-photon Ca2+ imaging database of neuronal responses uncovered four neural response types during fidgets that were surprisingly consistent in their proportion and response patterns across all visual areas and layers of the visual cortex. Indeed, the layer and area identity could not be decoded above chance level based only on neuronal recordings. In contrast to running behavior, fidget evoked neural responses were independent to visual processing.

The broad availability of visually orthogonal standardized behavior signals could be a key component in how the cortex selects, learns and binds local sensory information with motor outputs. Contrary to relevant motor outputs, irrelevant motor signals would use a separate neural subspaces.

Significance StatementRecent studies have shown contextual and behavioral variables to dominate brain-wide activity, but yet it is unknown how this information is broadcast across cortical layers and areas. Using a large two-photon dataset collected in mice passively viewing a battery of visual stimuli, we characterized the neuronal response of neurons of the visual cortex to a standardized fidget behavior. We found that as much as 47% of excitatory neurons show significant co-activity with fidgets. Throughout all areas and layers we recorded from, those responses were distributed across surprisingly consistent three neural response types. Further analysis showed no interaction between fidget neural events and cells visual stimulus responses, contrasting with feedback neural signals induced by running. These contrasting response distribution patterns suggest that behavioral neuronal correlates are broadly available but will modulate sensory responses depending on their relevance to local sensory inputs.


title: When to retrieve and encode episodic memories: a neural network model of hippocampal-cortical interaction

abstract: Recent human behavioral and neuroimaging results suggest that people are selective in when they encode and retrieve episodic memories. To explain these findings, we trained a memory-augmented neural network to use its episodic memory to support prediction of upcoming states in an environment where past situations sometimes reoccur. We found that the network learned to retrieve selectively as a function of several factors, including its uncertainty about the upcoming state. Additionally, we found that selectively encoding episodic memories at the end of an event (but not mid-event) led to better subsequent prediction performance. In all of these cases, the benefits of selective retrieval and encoding can be explained in terms of reducing the risk of retrieving irrelevant memories. Overall, these modeling results provide a resource-rational account of why episodic retrieval and encoding should be selective and lead to several testable predictions.


title: Interactions between auditory statistics processing and visual experience emerge only in late development

abstract: The human auditory system relies on both detailed and summarized representations to recognize different sounds. As local features can exceed the storage capacity, average statistics are computed over time to generate more compact representations at the expense of temporal details availability. This study aimed to identify whether these fundamental sound analyses develop and function exclusively under the influence of the auditory system or interact with other modalities, such as vision. We employed a validated computational synthesis approach allowing to control directly statistical properties embedded in sounds. To address whether the two modes of auditory representation (local features processing and statistical averaging) are influenced by the availability of visual input in different phases of development, we tested samples of sighted controls (SC), congenitally blind (CB), and late-onset (> 10 years of age) blind (LB) individuals in two separate experiments which uncovered auditory statistics computations from behavioral performances. In experiment 1, performance relied on the availability of local features at specific time points; in experiment 2, performance benefited from computing average statistics over longer durations. As expected, when sound duration increased, detailed representation gave way to summary statistics in SC. In both experiments, the sample of CB individuals displayed a remarkably similar performance revealing that both local and global auditory processes are not altered by blindness since birth. Conversely, LB individuals performed poorly compared to the other groups when relying on local features, with no impact on statistical averaging. The dampening in the performance was not associated with the onset and duration of visual deprivation. Results provide clear evidence that vision is not necessary for the development of the auditory computations tested here. Remarkably, a functional interplay between acoustic details processing and vision emerges at later developmental phases. Findings are consistent with a model in which the efficiency of local auditory processing is vulnerable in case sight becomes unavailable. Ultimately results are in favor of a shared computational framework for auditory and visual processing of local features, which emerges in late development.


title: A double negative post-transcriptional regulatory circuit underlies the virgin behavioral state

abstract: The survival and reproductive success of animals depends on the ability to harmonize their external behaviors with their internal states. For example, females conduct numerous social programs that are distinctive to virgins, compared to post-mated and/or pregnant individuals. In Drosophila, the fact that this post-mating switch is initiated by seminal factors implies that the default state is virgin. However, we recently showed that loss of miR-iab-4/8-mediated repression of the transcription factor Homothorax (Hth) within the abdominal ventral nerve cord (VNC) causes virgin females to execute mated behaviors. To elucidate new components of this post-transcriptional regulatory circuit, we used genomic analysis of mir-iab-4/8 deletion and hth-miRNA binding site mutants (hth[BSmut]) to elucidate doublesex (dsx) as a critical downstream factor. While Dsx has mostly been studied during sex-specific differentiation, its activities in neurons are little known. We find that accumulation of Dsx in the CNS is highly complementary to Hth, and downregulated in miRNA/hth[BSmut] mutants. Moreover, virgin behavior is highly dose-sensitive to developmental dsx function. Strikingly, depletion of Dsx in SAG-1 cells, a highly restricted set of abdominal neurons, abrogates female virgin conducts in favor of mated behavioral programs. Thus, a double negative post-transcriptional pathway in the VNC (miR-iab-4/8 -| Hth -| Dsx) specifies the virgin behavioral state.