title: Mapping single-cell atlases throughout Metazoa unravels cell type evolution

abstract: Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.


title: Reconsider phase reconstruction in chronobiological research from the modern signal processing perspective

abstract: Phase is the most fundamental physical quantity when we study an oscillatory time series. There are many tools aiming to estimate phase, most of them are developed based on the analytic function model. Unfortunately, this approach might not be suitable for modern signals with intrinsic nonstartionary structure, including multiple oscillatory components, each with time-varying frequency, amplitude, and non-sinusoidal oscillation, e.g., biomedical signals. Specifically, due to the lack of consensus of model and algorithm, phases estimated from signals simultaneously recorded from different sensors for the same physiological system from the same subject might be different. This fact might challenge reproducibility, communication, and scientific interpretation and thus we need a standardized approach with theoretical support over a unified model. In this paper, after summarizing existing models for phase and discussing the main challenge caused by the above-mentioned intrinsic nonstartionary structure, we introduce the adaptive non-harmonic model (ANHM), provide a definition of phase called fundamental phase, which is a vector-valued function describing the dynamics of all oscillatory components in the signal, and suggest a time-varying bandpass filter (tvBPF) scheme based on time-frequency analysis tools to estimate the fundamental phase. The proposed approach is validated with a simulated database and a real-world database with experts labels, and it is applied to two real-world databases, each of which has biomedical signals recorded from different sensors, to show how to standardize the definition of phase in the real-world experimental environment. Specifically, we report that the phase describing a physiological system, if properly modeled and extracted, is immune to the selected sensor for that system, while other approaches might fail. In conclusion, the proposed approach resolves the above-mentioned scientific challenge. We expect its scientific impact on a broad range of applications.

category: physiology


title: Reconsider phase reconstruction in signals with dynamic periodicity from the modern signal processing perspective


title: Immune Lag Is a Major Cost of Prokaryotic Adaptive Immunity During Viral Outbreaks

abstract: CRISPR-Cas adaptive immune systems enable bacteria and archaea to efficiently respond to viral pathogens by creating a genomic record of previous encounters. These systems are broadly distributed across prokaryotic taxa, yet are surprisingly absent in a majority of organisms, suggesting that the benefits of adaptive immunity frequently do not outweigh the costs. Here, combining experiments and models, we show that a delayed immune response which allows viruses to transiently redirect cellular resources to reproduction, which we call "immune lag", is extremely costly during viral outbreaks, even to completely immune hosts. Critically, the costs of lag are only revealed by examining the early, transient dynamics of a host-virus system occurring immediately after viral challenge. Lag is a basic parameter of microbial defense, relevant to all intracellular, post-infection antiviral defense systems, that has to-date been largely ignored by theoretical and experimental treatments of host-phage systems.

category: microbiology


title: Novel Chlamydiae and Amoebophilus endosymbionts are prevalent in wild isolates of the model social amoebae Dictyostelium discoideum

abstract: Amoebae interact with bacteria in diverse and multifaceted ways. Amoeba predation can serve as a selective pressure for the development of bacterial virulence traits. Bacteria may also adapt to life inside amoebae, resulting in symbiotic relationships (pathogenic or mutualistic). Indeed, particular lineages of obligate bacterial endosymbionts have been found in different amoebae. Here, we screened an extensive collection of Dictyostelium discoideum wild isolates for the presence of such bacterial symbionts using PCR primers that identify these endosymbionts. This is the first report of obligate symbionts in this highly-studied amoeba species. They are surprisingly common, identified in 42% of screened isolates (N=730). Members of the Chlamydiae phylum are particularly prevalent, occurring in 27% of the host strains. They are novel and phylogenetically distinct. We also found Amoebophilus symbionts in 8% of screened isolates (N=730). Antibiotic-cured amoebae behave similarly to their endosymbiont-infected counterparts, suggesting that endosymbionts do not significantly impact host fitness, at least in the laboratory. We found several natural isolates were co-infected with multiple endosymbionts, with no obvious fitness effects of co-infection under laboratory conditions. The high prevalence and novelty of amoeba endosymbiont clades in the model organism D. discoideum opens the door to future research on the significance and mechanisms of amoeba-symbiont interactions.


title: Genomic structure predicts metabolite dynamics in microbial communities

abstract: The metabolic function of microbial communities has played a defining role in the evolution and persistence of life on Earth, driving redox reactions that form the basis of global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolic dynamics from genomic structure remains elusive. Here we show, for the process of denitrification, that community metabolism is predictable from the genes each member of the community possesses. Machine learning reveals a sparse and generalizable mapping from gene content to metabolite dynamics across a genomically-diverse library of bacteria. A consumer-resource model correctly predicts community metabolism from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community function, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.

category: ecology


title: Theoretical and practical considerations when using retroelement insertions to estimate species trees in the anomaly zone

abstract: A potential shortcoming of concatenation methods for species tree estimation is their failure to account for incomplete lineage sorting. Coalescent methods address this problem but make various assumptions that, if violated, can result in worse performance than concatenation. Given the challenges of analyzing DNA sequences with both concatenation and coalescent methods, retroelement insertions (RIs) have emerged as powerful phylogenomic markers for species tree estimation. Here, we show that two recently proposed quartet-based methods, SDPquartets and ASTRAL BP, are statistically consistent estimators of the unrooted species tree topology under the coalescent when RIs follow a neutral infinite-sites model of mutation and the expected number of new RIs per generation is constant across the species tree. The accuracy of these (and other) methods for inferring species trees from RIs has yet to be assessed on simulated data sets, where the true species tree topology is known. Therefore, we evaluated eight methods given RIs simulated from four model species trees, all of which have short branches and at least three of which are in the anomaly zone. In our simulation study, ASTRAL BP and SDPquartets always recovered the correct species tree topology when given a sufficiently large number of RIs, as predicted. A distance-based method (ASTRID BP) and Dollo parsimony also performed well in recovering the species tree topology. In contrast, unordered, polymorphism, and Camin-Sokal parsimony typically fail to recover the correct species tree topology in anomaly zone situations with more than four ingroup taxa. Of the methods studied, only ASTRAL BP automatically estimates internal branch lengths (in coalescent units) and support values (i.e. local posterior probabilities). We examined the accuracy of branch length estimation, finding that estimated lengths were accurate for short branches but upwardly biased otherwise. This led us to derive the maximum likelihood (branch length) estimate for when RIs are given as input instead of binary gene trees; this corrected formula produced accurate estimates of branch lengths in our simulation study, provided that a sufficiently large number of RIs were given as input. Lastly, we evaluated the impact of data quantity on species tree estimation by repeating the above experiments with input sizes varying from 100 to 100 000 parsimony-informative RIs. We found that, when given just 1 000 parsimony-informative RIs as input, ASTRAL BP successfully reconstructed major clades (i.e clades separated by branches > 0.3 CUs) with high support and identified rapid radiations (i.e. shorter connected branches), although not their precise branching order. The local posterior probability was effective for controlling false positive branches in these scenarios.

category: evolutionary biology


title: Eusociality through conflict dissolution

abstract: Eusociality, where largely unreproductive offspring help their mothers reproduce, is a major form of social organization. An increasingly documented feature of eusociality is that mothers induce their offspring to help by means of hormones, pheromones, or behavioral displays, with evidence often indicating that offspring help voluntarily. The co-occurrence of maternal influence and offspring voluntary help may be explained by what we call the converted helping hypothesis, whereby maternally manipulated helping subsequently becomes voluntary. Such hypothesis requires that parent-offspring conflict is eventually dissolved--for instance, if the benefit of helping increases sufficiently over evolutionary time. We show that help provided by maternally manipulated offspring can enable the mother to sufficiently increase her fertility to transform parent-offspring conflict into parent-offspring agreement. This conflict-dissolution mechanism requires that helpers alleviate maternal life-history trade-offs, and results in reproductive division of labor, high queen fertility, and honest queen signaling suppressing worker reproduction--thus exceptionally recovering diverse features of eusociality. As such trade-off alleviation seemingly holds widely across eusocial taxa, this mechanism offers a potentially general explanation for the origin of eusociality, the prevalence of maternal influence, and the offsprings willingness to help. Overall, our results explain how a major evolutionary transition can happen from ancestral conflict.


title: Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

abstract: Lifes fundamental processes involve multiple molecules operating in close proximity within cells. To probe the composition and kinetics of molecular clusters confined within small (diffraction-limited) regions, experiments often report on the total fluorescence intensity simultaneously emitted from labeled molecules confined to such regions. Methods exist to enumerate total fluorophore numbers (e.g., step counting by photobleaching). However, methods aimed at step counting by photobleaching cannot treat photophysical dynamics in counting nor learn their associated kinetic rates. Here we propose a method to simultaneously enumerate fluorophores and determine their individual photophysical state trajectories. As the number of active (fluorescent) molecules at any given time is unknown, we rely on Bayesian nonparametrics and use specialized Monte Carlo algorithms to derive our estimates. Our formulation is benchmarked on synthetic and real data sets. While our focus here is on photophysical dynamics (in which labels transition between active and inactive states), such dynamics can also serve as a proxy for other types of dynamics such as assembly and disassembly kinetics of clusters. Similarly, while we focus on the case where all labels are initially fluorescent, other regimes, more appropriate to photoactivated localization microscopy, where fluorophores are instantiated in a non-fluorescent state, fall within the scope of the framework. As such, we provide a complete and versatile framework for the interpretation of complex time traces arising from the simultaneous activity of up to 100 fluorophores.

category: biophysics


title: Harmonicity aids hearing in noise

abstract:
Hearing in noise is a core problem in audition, and a challenge for hearing-impaired listeners, yet the underlying mechanisms are poorly understood. We explored whether harmonic frequency relations, a signature property of many communication sounds, aid hearing in noise for normal hearing listeners. We measured detection thresholds in noise for tones and speech synthesized to have harmonic or inharmonic spectra. Harmonic signals were consistently easier to detect than otherwise identical inharmonic signals. Harmonicity also improved discrimination of sounds in noise. The largest benefits were observed for two-note up-down "pitch" discrimination and melodic contour discrimination, both of which could be performed equally well with harmonic and inharmonic tones in quiet, but which showed large harmonic advantages in noise. The results show that harmonicity facilitates hearing in noise, plausibly by providing a noise-robust pitch cue that aids detection and discrimination.

Significance statementNoise is ubiquitous, and being able to hear in noise is critical to real-world behavior. We report that hearing in noise is aided by sensitivity to the harmonic frequency relations that occur in vocal communication signals and music: harmonic sounds are easier to hear in noise than inharmonic sounds. This effect was present in both musicians and non-musicians and for synthetic as well as natural sounds, suggesting a role in everyday hearing.

category: neuroscience


title: A computational examination of the two-streams hypothesis: which pathway needs a longer memory?

abstract: The two visual streams hypothesis is a robust example of neural functional specialization that has inspired countless studies over the past four decades. According to one prominent version of the theory, the fundamental goal of the dorsal visual pathway is the transformation of retinal information for visually-guided motor behavior. To that end, the dorsal stream processes input using absolute (or veridical) metrics only when the movement is initiated, necessitating very little, or no, memory. Conversely, because the ventral visual pathway does not involve motor behavior (its output does not influence the real world), the ventral stream processes input using relative (or illusory) metrics and can accumulate or integrate sensory evidence over long time constants, which provides a substantial capacity for memory. In this study, we tested these relations between functional specialization, processing metrics, and memory by training identical recurrent neural networks to perform either a viewpoint-invariant object classification task or an orientation/size determination task. The former task relies on relative metrics, benefits from accumulating sensory evidence, and is usually attributed to the ventral stream. The latter task relies on absolute metrics, can be computed accurately in the moment, and is usually attributed to the dorsal stream. To quantify the amount of memory required for each task, we chose two types of neural network models. Using a long-short-term memory (LSTM) recurrent network, we found that viewpoint-invariant object categorization (object task) required a longer memory than orientation/size determination (orientation task). Additionally, to dissect this memory effect, we considered factors that contributed to longer memory in object tasks. First, we used two different sets of objects, one with self-occlusion of features and one without. Second, we defined object classes either strictly by visual feature similarity or (more liberally) by semantic label. The models required greater memory when features were self-occluded and when object classes were defined by visual feature similarity, showing that self-occlusion and visual similarity among object task samples are contributing to having a long memory. The same set of tasks modeled using modified leaky-integrator echo state recurrent networks (LiESN), however, did not replicate the results, except under some conditions. This may be because LiESNs cannot perform fine-grained memory adjustments due to their network-wide memory coefficient and fixed recurrent weights. In sum, the LSTM simulations suggest that longer memory is advantageous for performing viewpoint-invariant object classification (a putative ventral stream function) because it allows for interpolation of features across viewpoints. The results further suggest that orientation/size determination (a putative dorsal stream function) does not benefit from longer memory. These findings are consistent with the two visual streams theory of functional specialization.


title: AutoCoEv: a high-throughput in silico pipeline for predicting novel protein-protein interactions

abstract: Protein-protein communications govern cellular processes via complex regulatory networks, that are still far from being understood. Thus, identifying novel interactions between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Co-evolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, evolutionary analysis of large arrays of proteins, in silico, is a highly time-consuming effort, which has limited the usage of this method to protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly computational pipeline for the search of co-evolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting co-evolution, AutoCoEv achieves seamless automation and parallelization of the workflow. Importantly, we provide a patch to CAPS2 source code to strengthen its statistical output, allowing for multiple comparisons correction and enhanced analysis of the results. We apply the pipeline to inspect co-evolution among 324 proteins identified to locate at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple strong coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, available at https://github.com/mattilalab/autocoev, can be used to predict functional interactions from large datasets in a time and cost-efficient manner.

category: bioinformatics


title: AutoCoEv -- a high-throughput in silico pipeline for predicting inter-protein co-evolution


title: Mapping the complex transcriptional landscape of the phytopathogenic bacterium Dickeya dadantii

abstract:
Dickeya dadantii is a phytopathogenic bacterium that causes soft rot in a wide range of plant hosts worldwide, and a model organism for studying virulence gene regulation. The present study provides a comprehensive and annotated transcriptomic map of D. dadantii obtained by a computational method combining five independent transcriptomic datasets: (i) paired-end RNA-seq data for a precise re-construction of the RNA landscape; (ii) DNA microarray data providing transcriptional responses to a broad variety of environmental conditions; (iii) long-read Nanopore native RNA-seq data for isoform-level transcriptome validation and determination of transcription termination sites; (iv)dRNA-seq data forthe precise mapping of transcription start sites; (v) in planta DNA microarray data for a comparison of gene expression profiles between in vitro experiments and the early stages of plant infection. Our results show that transcription units sometimes coincide with predicted operons but are generally longer, most of them comprising internal promoters and terminators that generate alternative transcripts of variable gene composition. We characterise the occurrence of transcriptional read-through at terminators, which might play a basal regulation role and explain the extent of transcription beyond the scale of operons. We finally highlight the presence of noncontiguous operons and excludons in the D. dadantii genome, novel genomic arrangements that might contribute to the basal coordination of transcription. The highlighted transcriptional organisation may allow D. dadantii to finely adjust its gene expression programme for a rapid adaptation to fast changing environments.

IMPORTANCEThis is the first transcriptomic map of a Dickeya species. It may therefore significantly contribute to further progress in the field of phytopathogenicity. It is also one of the first reported applications of long-read Nanopore native RNA-seq in prokaryotes. Our findings yield insights into basal rules of coordination of transcription that might be valid for other bacteria, and may raise interest in the field of microbiology in general. In particular, we demonstrate that gene expression is coordinated at the scale of transcription units rather than operons, which are larger functional genomic units capable of generating transcripts with variable gene composition for a fine-tuning of gene expression in response to environmental changes. In line with recent studies, our findings indicate that the canonical operon model is insufficient to explain the complexity of bacterial transcriptomes.


title: Reconfiguring primase DNA-recognition sequences by using a data driven approach

abstract: DNA-protein interactions are essential in all aspects of every living cell. Understanding of how features embedded in the DNA sequence affect specific interactions with proteins is challenging but important, since it may contribute to finding the means to regulate metabolic pathways involving DNA-protein interactions. Using a massive experimental benchmark dataset of binding scores for DNA sequences and a machine learning workflow, we describe the binding to DNA of T7 primase, as a model system for specific DNA-protein interactions. Effective binding of T7 primase to its specific DNA recognition sequences triggers the formation of RNA primers that serve as Okazaki fragment start sites during DNA replication.

category: biochemistry


title: Making sense of the linear genome, gene function and TADs

abstract:
BackgroundTopologically associating domains (TADs) are thought to act as functional units in the genome. TADs co-localise genes and their regulatory elements as well as forming the unit of genome switching between active and inactive compartments. This has led to the speculation that genes which are required for similar processes may fall within the same TADs, allowing them to share regulatory programs and efficiently switch between chromatin compartments. However, evidence to link genes within TADs to the same regulatory program is limited.

ResultsWe investigated the functional similarity of genes which fall within the same TAD. To do this we developed a TAD randomisation algorithm to generate sets of "random TADs" to act as null distributions. We found that while pairs of paralogous genes are enriched in TADs overall, they are depleted in TADs with CCCTC-binding factor (CTCF) ChIP-seq peaks at both boundaries. By assessing gene constraint as a proxy for functional importance we found that genes which singly occupy a TAD have greater functional importance than genes which share a TAD, and these genes are enriched for developmental processes. We found little evidence that pairs of genes in CTCF bound TADs are more likely to be co-expressed or share functional annotations than can be explained by their linear proximity alone.

ConclusionsThese results suggest that algorithmically defined TADs consist of two functionally different groups, those which are bound by CTCF and those which are not. We detected no association between genes sharing the same CTCF TADs and increased co- expression or functional similarity, other than that explained by linear genome proximity. We do however find that functionally important genes are more likely to fall within a TAD on their own suggesting that TADs play an important role in the insulation of these genes.

category: genomics


title: Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana

abstract: A genome encodes two types of information, the "what can be made" and the "when and where". The "what" are mostly proteins which perform the majority of functions within living organisms and the "when and where" is the regulatory information that encodes when and where DNA is transcribed. Currently, it is possible to efficiently predict the majority of the protein content of a genome but nearly impossible to predict the transcriptional regulation. This regulation is based upon the interaction between transcription factors and genomic sequences at the site of binding motifs1,2,3. Information contained within the motif is necessary to predict transcription factor binding, however, it is not sufficient4, as experimentally verified binding sites are substantially scarcer than the corresponding binding motif. Thus, it remains challenging to derive regulational information from binding motifs. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same binding motif. Our results contribute to the understanding of protein-DNA recognition and demonstrate the extraction of binding site features beyond the binding sequence. We observed that those features were individually weighted for each transcription factor, even if they shared the same binding sequence. We show that the gained insights enable a more robust prediction of binding behavior regarding novel, not-in-genome motif sequences. Understanding transcription factor binding as a combination of motif sequence and motif shape brings us closer to predicting gene expression from promoter sequence.

category: plant biology


title: Soil microbial diversity impacts plant microbiomes more than herbivory

abstract: Interactions between plants and microbiomes play a key role in ecosystem functioning and are of broad interest due to their influence on nutrient cycling and plant protection. However, we do not yet have a complete understanding of how plant microbiomes are assembled. Here, we tested and quantified the effect of different factors driving the diversity and composition of plant-associated microbial communities. We manipulated soil microbial diversity (high or low diversity), plant species (Solanum tuberosum or Solanum vernei), and herbivory (presence or absence of a phloem-feeding insect Macrosiphum euphorbiae), and found that soil microbial diversity influenced the herbivore-associated microbiome composition, but also plant species and herbivory influenced the soil microbiome composition. We quantified the relative strength of these effects and demonstrated that the initial soil microbiome diversity explained the most variation in plant- and herbivore-associated microbial communities. Our findings strongly suggest that soil microbial community diversity is a driver of the composition of multiple associated microbiomes (plant and insect), and this has implications for the importance of management of soil microbiomes in multiple systems.


title: Connecting structure and function from organisms to molecules in small animal symbioses through chemo-histo-tomography

abstract:
Our understanding of metabolic interactions between small symbiotic animals and bacteria or parasitic eukaryotes that reside within their body is extremely limited. This gap in knowledge originates from a methodological challenge, namely to connect histological changes in host tissues induced by beneficial and parasitic (micro)organisms to the underlying metabolites. To close this gap, we developed chemo-histo-tomography (CHEMHIST), a culture-independent approach to connect anatomic structure and metabolic function in millimeter-sized symbiotic animals. CHEMHIST combines spatial metabolomics based on mass spectrometry imaging (MSI) and microanatomy-based micro-computed X-ray tomography (microCT) on the same animal. Both high-resolution MSI and microCT allowed us to correlate the distribution of metabolites to the same animals three-dimensional (3D) histology down to sub-micrometer resolutions. Our protocol is compatible with tissue specific DNA sequencing and fluorescence in situ hybridization (FISH) for the taxonomic identification and localization of the associated micro(organisms). Building CHEMHIST upon in situ imaging, we sampled an earthworm from its natural habitat and created an interactive 3D model of its physical and chemical interactions with bacteria and parasitic nematodes in its tissues. Combining MSI and microCT, we introduce a workflow to connect metabolic and anatomic phenotypes of small symbiotic animals that often represent keystone species for ecosystem-functioning.

SignificanceMetabolites mediate the establishment and persistence of most inter-kingdom symbioses. Still, to pinpoint the metabolites each partner displays upon interaction remains the biggest challenge in studying multi-organismal assemblages. Addressing this challenge, we developed a correlative imaging workflow to connect the in situ production of metabolites with the organ-scale and cellular 3D distributions of mutualistic and pathogenic (micro)organisms in the same host animal. Combining mass spectrometry imaging and micro-computed X-ray tomography provided a culture-independent approach, which is essential to include the full spectrum of naturally occurring interactions. To introduce the potential of combining high-resolution tomography with metabolite imaging, we resolve the metabolic interactions between an invertebrate host, its symbiotic bacteria and tissue parasites at unprecedented detail for model and non-model symbioses.


title: Robust and independent metabolic transcriptional components in 34,494 patient-derived samples and cell lines: a resource for translational cancer research

abstract: Patient-derived expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Therefore, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. We, therefore, performed consensus Independent Component Analyses (c-ICA) with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. c-ICA enabled us to create a transcriptional metabolic landscape in which many robust metabolic transcriptional components and their activation score in individual samples were defined. Here we demonstrate how this landscape can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.


title: The discriminator sequence is a primary determinant in the supercoiling response of bacterial promoters

abstract: DNA supercoiling acts as a global transcriptional regulator, which contributes to the rapid transcriptional response of bacteria to many environmental changes. Although a large fraction of promoters from distant species respond to superhelical variations, the sequence or structural determinants of this behaviour remain elusive. Here, we propose the sequence of the "discriminator" element located downstream of the -10 hexamer to play an important role in this response, by modulating the facility of open-complex formation during transcription initiation. We develop a quantitative model of this regulatory mechanism relying on known parameters of DNA thermodynamics, and show that its predictions quantitatively match the in vitro and in vivo supercoiling response of stable RNA promoters previously measured, as well as the in vivo response of selected mRNA promoters with mutated discriminator sequences. We then test the universality of this mechanism by statistical analysis of promoter sequences in transcriptomes of phylogenetically distant bacteria under conditions of supercoiling variations, (1) by gyrase inhibitors, (2) by environmental stresses, (3) inherited in the longest-running evolution experiment. In all cases, we identify a robust and significant sequence signature in the discriminator region, suggesting that promoter opening underpins an ubiquitous regulatory mechanism in the prokaryotic kingdom, based on the fundamental mechanical properties of DNA.


title: Social relationships among captive male pygmy slow lorises (Nycticebus pygmaeus): Is forming male same-sex pairs a feasible management strategy?

abstract: Little is known about the social behavior of pygmy slow lorises, in particular, the social relationships of same-sex individuals have rarely been investigated. The Slow Loris Conservation Center was built at the Japan Monkey Center to enhance the welfare of confiscated slow lorises, promote their conservation, improve public education and perform scientific research on the species. In the course of improving housing conditions, several same-sex pairs of pygmy slow lorises were formed. We monitored their behaviors and fecal glucocorticoid metabolite (FGM) levels to understand whether male same-sex pairings could be a feasible management strategy. The subjects were 10 male and 6 female lorises for comparison, all of whom were over five years old. We successfully formed five pairs of male lorises after eight formation attempts. Male pairs initially showed some aggressive behaviors; however, the rate decreased approximately 10 days after introduction. All of the male pairs eventually exhibited extensive affiliative social behaviors, including allogrooming and social play, during the dark (active) phase, and sleep site sharing during the light (inactive) phase. The rate of sleep site sharing during the light phase was higher than expected, suggesting that the pairs preferred to stay near each other. There was no evidence of increased stress after a long period of male-male social housing. Female same-sex pairs and male-female pairs demonstrated a high level of affiliative behaviors right after introduction. These results highlight the flexibility and high sociability of this species and indicate that such same-sex pairings are a feasible option for their social management.

category: zoology


title: New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

abstract: A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly due to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6-1.2m, height: 30-46m, AGB: 3960-18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42-62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry, and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% vs. 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% vs. 15%). Further, whilst allometric error increased four-fold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.


title: Information-theoretic clustering for single-cell transcriptomics

abstract: Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information. Here, we introduce a formal framework for assessing the amount of information obtained from a sc-Seq experiment, which can be used throughout the sc-Seq analysis pipeline, including for feature selection, differential expression analysis, unsupervised clustering and semi-supervised cell annotation. Moreover, we show how this framework leads to a biologically intuitive definition of cell  type that can be used in common cell classification tasks. Based on this theory we describe an efficient method for the automatic unsupervised or semi-supervised identification of cell types from sc-Seq data, and provide an R package implementation. Our information-theoretic method performs comparably to the state-of-the-art on a series of benchmark sc-Seq data sets, and additionally provides a simple, biological interpretation of identified clusters.


title: The stability landscape of de novo TIM barrels explored by a modular design approach

abstract:
The ability to design stable proteins with custom-made functions is a major goal in biochemistry with practical relevance for our environment and society. High conformational stability lowers protein sensitivity to mutations and changes in the environment; thus, understanding and manipulating protein stability will expand the applications of de novo proteins. Since the ({beta}/)8-barrel or TIM-barrel fold is one of the most common functional scaffolds, in this work we designed a collection of stable de novo TIM barrels (DeNovoTIMs), using a computational fixed-backbone and modular approach based on improved hydrophobic packing of sTIM11, the first validated de novo TIM barrel. DeNovoTIMs navigate a region of the stability landscape previously uncharted by natural TIM barrels, with variations spanning 60 degrees in melting temperature and 22 kcal per mol in conformational stability throughout the designs. Significant non-additive or epistatic effects were observed when stabilizing mutations from different regions of the barrel were combined. The molecular basis of epistasis in DeNovoTIMs appears to be related to the extension of the hydrophobic cores. This study is an important step towards the fine-tuned modulation of protein stability by design.

Significance StatementDe novo protein design expands our knowledge about protein structure and stability. The TIM barrel is a highly relevant fold used in nature to host a rich variety of catalytic functions. Here, we follow a modular approach to design and characterize a collection of de novo TIM barrels and subjected them to a thorough folding analysis. Non-additive effects modulate the increase in stability when different regions of the barrel are mutated, showing a wide variety of thermodynamic properties that allow them to navigate an unexplored region of the stability landscape found in natural TIM barrels. The design of stable proteins increases the applicability of de novo proteins and provides crucial information on the molecular determinants that modulate structure and stability.

One Sentence SummaryA family of designed TIM barrels with diverse thermodynamic properties shows epistatic effects on its stability landscape.


title: Assembly factors chaperone rRNA folding by isolating helical junctions that are prone to misfolding

abstract: While RNAs are known to misfold, the underlying molecular causes remain unclear, and focused on alternative secondary structures. Conversely, how RNA chaperones function in a biological context to promote folding beyond duplex annealing, remains unknown. Here we show in a combination of DMS-MaPseq, structural analyses, biochemical experiments, and yeast genetics that three-way junctions are prone to misfolding during assembly of the small ribosomal subunit in vivo. We identify ubiquitous roles for ribosome assembly factors in chaperoning their folding by preventing the formation of tertiary interactions. In the absence of these assembly factors, tertiary interactions kinetically trap misfolded three-way junctions, thereby blocking further progress in the assembly cascade. While these protein chaperones act indirectly by binding the interaction partners, our analyses also suggest direct roles for snoRNAs in binding and chaperoning three-way junctions during transcription. This work furthermore shows that the dissociation of assembly factors renders reversible folding steps irreversible, thereby setting up a timer that regulates not just the flux of assembly but also dictates the propensity of misfolded intermediates to escape quality control. Finally, the data demonstrate how RNA chaperones act locally to unfold specific tertiary interactions, in contrast to protein chaperones, which globally unfold misfolded proteins.


title: Synergistic interactions between detritivores disappear under reduced rainfall

abstract: Understanding the consequences of altered rainfall patterns on litter decomposition is critical to predicting the feedback effect of climate change on atmospheric CO2 concentrations. While their effect on microbial decomposition received considerable attention, their effect on litter fragmentation by detritivores, the other dominant decomposition pathway, remains largely unexplored. Particularly, it remains unclear how different detritivore species and their interactions respond to changes in rainfall quantity and frequency. To fill this knowledge gap, we determined the contribution to litter decomposition of two detritivore species (millipede and isopod), separately and in combination, under contrasting rainfall quantity and frequency in a temperate forest. Although halving rainfall quantity and frequency decreased top-soil moisture by 7.8 and 13.1%, respectively, neither millipede- nor isopod-driven decomposition were affected by these changes. In contrast, decomposition driven by both detritivore species in combination was 65.5% higher than expected based on monospecific treatments under high rainfall quantity, but unchanged or even lower under low rainfall quantity. This indicates that while detritivore activity is relatively insensitive to changes in rainfall patterns, large synergistic interactions between detritivore species may disappear under future rainfall patterns. Incorporating interspecific interactions between decomposers thus seems critical to evaluate the sensitivity of decomposition to altered rainfall patterns.


title: Does Pisum sativum exhibit a response to neighbour and pot volume manipulations? A bayesian hierarchical meta-analysis

abstract: Plant-plant competition is ubiquitous in nature. However, studying below ground behaviour of roots has always posed certain difficulties. Pea (Pisum sativum L.) has become a sort of model species for ecological studies about how plant roots respond to neighbouring plant roots and barriers in soil. However, published results point in several different directions. This has sometimes been interpreted as pea having sophisticated context dependent responses that can change in complex ways depending on its surroundings. To explore this further, here, we combine the result of five new experiments with published results to examine 18 unique experiments from 7 different studies for a total of 254 replicates. We used a Bayesian hierarchical meta-analysis approach to estimating the likely effect size from the available data, as well as quantify heterogeneity among different experiments, studies and cultivars. The posterior distributions show that, at the coarsest possible scale of total root production, it is unlikely that P. sativum root growth is influenced by either neighbours or barriers to root growth imposed by the walls of pots that vary in volume. We suggest that further work should consider repeating experiments that reported finer scale root plasticity in pea at the rhizosphere scale, and also consider alternative model species for the study of plant root responses to external cues.


title: Is Pisum sativum a good model species for the study of root responses to neighbours and barriers in soil? A bayesian hierarchical meta-analysis


title: QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

abstract:
PurposeTo create a realistic in-silico head phantom for the second Quantitative Susceptibility Mapping (QSM) Reconstruction Challenge and for future evaluations of processing algorithms for QSM.

MethodsWe created a digital whole-head tissue property phantom by segmenting and post-processing high-resolution (0.64mm isotropic), multi-parametric MRI data acquired at 7T from a healthy volunteer. We simulated the steady-state magnetization at 7T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: repetition time and echo time, voxel size, background fields, and RF phase biases. Diffusion weighted imaging phantom data is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms.

ResultsThe brain-part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantoms properties, including the possibility of generating phase data with non-linear evolution over echo time due to partial volume effects or complex distributions of frequency shifts within the voxel.

ConclusionThe presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.


title: A preferential role for ventromedial prefrontal cortex in assessing the value of the whole in multi-attribute object evaluation

abstract:
Everyday decision-making commonly involves assigning values to complex objects with multiple value-relevant attributes. Drawing on object recognition theories, we hypothesized two routes to multi-attribute evaluation: assessing the value of the whole object based on holistic attribute configuration or summing individual attribute-values. In two samples of healthy human participants undergoing eye-tracking and fMRI while evaluating novel pseudo-objects, we found evidence for both forms of evaluation. Fixations to, and transitions between attributes differed systematically when value of pseudo-objects was associated with individual attributes or attribute configurations. Ventromedial prefrontal cortex (vmPFC) and perirhinal cortex were engaged when configural processing was required. These results converge with our recent findings that individuals with vmPFC lesions were impaired in decisions requiring configural evaluation, but not when evaluating "the sum of the parts". This suggests that multi-attribute decision-making engages distinct evaluation mechanisms relying on partially dissociable neural substrates, depending on the relationship between attributes and value.

SIGNIFICANCE STATEMENTDecision neuroscience has only recently begun to address how multiple choice-relevant attributes are brought together during evaluation and choice among complex options. Object recognition research makes a crucial distinction between individual attribute and holistic/configural object processing, but how the brain evaluates attributes and whole objects remains unclear. Using fMRI and eye-tracking, we found that the vmPFC and the perirhinal cortex contribute to value estimation specifically when it emerged from the whole objects i.e. predicted by the unique configuration of attributes, and not when value was predicted by the sum of individual attributes. This novel perspective on the interactions between subjective value and object processing mechanisms bridges an important gap between the fields of object recognition and reward-guided decision-making.


title: Novel players and large-scale protein dynamics of BCR activation revealed by APEX2 proximity labelling of lipid rafts

abstract: Successful B cell activation, critical for high-affinity antibody production, is controlled by the B cell antigen receptor (BCR). While the main components of the BCR signalling machinery are relatively well-characterised, we still lack a comprehensive protein-level view of the very dynamic multi-branched cellular events triggered by antigen binding. In this study, we employed APEX2 proximity biotinylation to study BCR signalling-induced changes at the vicinity of the plasma membrane. APEX2 provides critical spatial and kinetic resolution, 20 nm and 1 min, to trace early protein dynamics upon antigen receptor triggering. We identified a total of 1677 proteins to locate in the proximity of APEX2 at the plasma membrane lipid raft domains, where the BCR enriches upon activation. Our data provides unprecedented insights into the composition of lipid raft environment in B cells and the dynamic changes triggered upon IgM BCR cross-linking. In addition, the data provides new insights into the behaviour of proteins known to be involved in the proximal antigen receptor signalling and simultaneously triggered processes, such as actin cytoskeleton remodelling and endocytosis. Interestingly, our differential enrichment analysis identified dynamic responses in various proteins previously not linked to early B cell activation. Furthermore, we validated Golga3 and Vti1b as novel proteins responding to BCR activation, confirming that our dataset serves as a valuable tool for future studies.

category: immunology


title: Adult spiny mice (Acomys) exhibit endogenous cardiac recovery in response to myocardial infarction

abstract: Complex tissue regeneration is extremely rare among adult mammals. An exception, however, is the superior tissue healing of multiple organs in spiny mice (Acomys). While Acomys species exhibit the remarkable ability to heal complex tissue with minimal scarring, little is known about their cardiac structure and response to cardiac injury. In this study, we first examined baseline Acomys cardiac anatomy and function in comparison with the commonly used laboratory Mus strains (C57BL6 and SWR). Our results demonstrated comparable cardiac anatomy and function between Acomys and Mus, but Acomys exhibited a higher percentage of cardiomyocytes exhibiting immature characteristics. In response to myocardial infarction, all animals experienced a comparable level of initial cardiac damage. However, Acomys demonstrated superior ischemic tolerance and cytoprotection in response to injury as evidenced by cardiac functional stabilization, higher survival rate and smaller scar size 50 days after injury compared to the inbred and outbred mouse strains. Overall, these findings demonstrate augmented myocardial preservation in spiny mice post-MI and establish Acomys as a new adult mammalian model for cardiac research.

category: developmental biology


title: Deep-fUS: functional ultrasound imaging of the brain using deep learning and sparse data

abstract: Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an end-to-end image reconstruction approach based on deep learning that significantly reduces the amount of data necessary while retaining the imaging performance. We trained a convolutional neural network to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with a compression factor up to 95%, using high-quality images from in vivo acquisitions in rats. We tested the imaging performance in a functional neuroimaging application. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.

category: bioengineering


title: Sexually divergent cortical control of affective-autonomic integration

abstract: Depression and cardiovascular disease reduce quality of life and increase mortality risk. These conditions commonly co-occur with sex-based differences in incidence and severity. However, the biological mechanisms linking the disorders are poorly understood. In the current study, we hypothesized that the infralimbic (IL) prefrontal cortex integrates mood-related behaviors with the cardiovascular burden of chronic stress. In a rodent model, we utilized optogenetics during behavior and in vivo physiological monitoring to examine how the IL regulates affect, social motivation, neuroendocrine-autonomic stress reactivity, and the cardiac consequences of chronic stress. Our results indicate that IL glutamate neurons increase socio-motivational behaviors specifically in males. IL activation also reduced endocrine and cardiovascular stress responses in males, while increasing reactivity in females. Moreover, prior IL stimulation protected males from subsequent chronic stress-induced sympatho-vagal imbalance and cardiac hypertrophy. Our findings suggest that cortical regulation of behavior, physiological stress responses, and cardiovascular outcomes fundamentally differ between sexes.


title: Recruitment of Neuronal KCNQ2/3 Channels to Membrane Microdomains Depends on Palmitoylation of Alzheimer's Disease-Related Protein BACE1

abstract: {beta}-secretase 1 ({beta}-site amyloid precursor protein (APP)-cleaving enzyme 1, BACE1) plays a crucial role in the amyloidogenesis of Alzheimers Disease (AD). BACE1 was also discovered to act like an auxiliary subunit to modulate neuronal KCNQ2/3 channels independent of its proteolytic function. BACE1 is palmitoylated at its carboxyl-terminal region, which brings BACE1 to ordered, cholesterol-rich membrane microdomains (lipid rafts). However, the physiological consequences of this specific localization of BACE1 remain elusive. Using spectral Forster Resonance Energy Transfer (FRET), BACE1 and KCNQ2/3 channels were confirmed to form a signaling complex, a phenomenon that was relatively independent of the palmitoylation of BACE1. Nevertheless, palmitoylation of BACE1 was required for recruitment of KCNQ2/3 channels to lipid-raft domains. Two fluorescent probes designated L10 and S15, were used to label lipid-raft and non-raft domains of the plasma membrane, respectively. Coexpressing BACE1 substantially elevated the FRET between L10 and KCNQ2/3 whereas the BACE1-4C/A quadruple mutation failed to produce this effect. In contrast, BACE1 had no significant effect on the FRET between S15 probes and KCNQ2/3 channels. A reduction of BACE1-dependent FRET between raft-targeting L10 probes and KCNQ2/3 channels by applying cholesterol-extracting reagent methyl-{beta}-cyclodextrin (M{beta}CD), raft-disrupting general anesthetics, or pharmacological inhibitors of palmitoylation all supported our hypothesis of the palmitoylation-dependent and raft-specific localization of KCNQ2/3 channels. Furthermore, mutating the four carboxyl-terminal cysteines (4C/A) of BACE1 abolished the BACE1-dependent increase of FRET between KCNQ2/3 and a lipid raft-specific protein caveolin 1. Collectively, we propose how the AD-related protein BACE1 underlies the localization of a neuronal potassium channel.


title: DDX3X and DDX3Y are redundant in protein synthesis

abstract: DDX3 is a DEAD-box RNA helicase that regulates translation and is encoded by the X- and Y-linked paralogs DDX3X and DDX3Y. While DDX3X is ubiquitously expressed in human tissues and essential for viability, DDX3Y is male-specific and shows lower and more variable expression than DDX3X in somatic tissues. Heterozygous genetic lesions in DDX3X mediate a class of developmental disorders called DDX3X syndrome, while loss of DDX3Y is implicated in male infertility. One possible explanation for female-bias in DDX3X syndrome is that DDX3Y encodes a polypeptide with different biochemical activity. In this study, we use ribosome profiling and in vitro translation to demonstrate that the X- and Y-linked paralogs of DDX3 play functionally redundant roles in translation. We find that transcripts that are sensitive to DDX3X depletion or mutation are rescued by complementation with DDX3Y. Our data indicate that DDX3X and DDX3Y proteins can functionally complement each other in human cells. DDX3Y is not expressed in a large fraction of the central nervous system. These findings suggest that expression differences, not differences in paralog-dependent protein synthesis, underlie the sex-bias of DDX3X-associated diseases.

category: molecular biology


title: The cerebellar clock: predicting and timing somatosensory touch

abstract:
0The prediction of sensory feedback is known to involve the cerebellum, but its precise nature and timing have remained unclear due to the scarcity of time-sensitive cerebellar neuroimaging studies. We here, using magnetoencephalography in human participants, investigated the working hypothesis that one function of the cerebellum is to predict exactly when rhythmic stimuli are expected to impinge on sensory receptors.

We compared the cerebellums response between somatosensory omissions embedded in perfectly rhythmic versus jittered trains of stimulation. At the precise moment that an omitted stimulus would have appeared, the cerebellum exhibited a beta band (14-30 Hz) response that was stronger when preceded by a perfectly rhythmic sequence. Meanwhile, the rhythm of new stimulation sequences induced theta band (4-7 Hz) activity in the cerebellum.

Our results provide evidence that the cerebellum acts as a clock that entrains to rhythmic stimuli, likely for the purpose of detecting any deviations from that rhythm.


title: Selection for increased tibia length in mice alters skull shape through parallel changes in developmental mechanisms

abstract: Bones in the vertebrate cranial base and limb skeleton grow by endochondral ossification, under the control of growth plates. Mechanisms of endochondral ossification are conserved across growth plates, which increases covariation in size and shape among bones, and in turn may lead to correlated changes in skeletal traits not under direct selection. We used micro-CT and geometric morphometrics to characterize shape changes in the cranium of the Longshanks mouse, which was selectively bred for longer tibiae. We show that Longshanks skulls became longer, flatter, and narrower in a stepwise process. Moreover, we show that these morphological changes likely resulted from developmental changes in the growth plates of the Longshanks cranial base, mirroring changes observed in its tibia. Thus, indirect and non-adaptive morphological changes can occur due to developmental overlap among distant skeletal elements, with important implications for interpreting the evolutionary history of vertebrate skeletal form.


title: Wiring diagram of the oxytocin system in the mouse brain

abstract:
Oxytocin (OT) neurons regulate diverse physiological responses via direct connections with different neural circuits. However, the lack of comprehensive input-output wiring diagrams of OT neurons and their quantitative relationship with OT receptor (OTR) expression presents challenges to understanding circuit specific OT functions. Here, we establish a whole-brain distribution and anatomical connectivity map of OT neurons, and their relationship with OTR expression using cell type specific viral tools and high-resolution 3D mapping methods. We utilize a flatmap to describe OT neuronal expression in four hypothalamic domains including under-characterized OT neurons in the tuberal nucleus. OT neurons in the paraventricular hypothalamus (PVH) broadly project to nine functional circuits that control cognition, brain state, and somatic visceral response. In contrast, OT neurons in the supraoptic (SO) and accessory nuclei have limited central projection to a small subset of the nine circuits. Surprisingly, quantitative comparison between OT output and OTR expression showed no significant correlation across the whole brain, suggesting abundant indirect OT signaling in OTR expressing areas. Unlike output, OT neurons in both the PVH and SO receive similar mono-synaptic inputs from a subset of the nine circuits mainly in the thalamic, hypothalamic, and cerebral nuclei areas. Our results suggest that PVH-OT neurons serve as a central modulator to integrate external and internal information via largely reciprocal connection with the nine circuits while the SO-OT neurons act mainly as unidirectional OT hormonal output. In summary, our OT wiring diagram provides anatomical insights about distinct behavioral functions of OT signaling in the brain.

Significance StatementOxytocin (OT) neurons regulate diverse physiological functions from pro-social behavior to pain sensation via central projection in the brain. Thus, understanding detailed anatomical connectivity of OT neurons can provide insight on circuit specific roles of OT signaling in regulating different physiological functions. Here, we utilize high resolution mapping methods to describe the 3D distribution, mono-synaptic input and long-range output of OT neurons, and their relationship with OT receptor (OTR) expression across the entire mouse brain. We found OT connections with nine functional circuits controlling cognition, brain state, and somatic visceral response. Furthermore, we identified a quantitatively unmatched OT-OTR relationship, suggesting broad indirect OT signaling. Together, our comprehensive OT wiring diagram advances our understanding of circuit specific roles of OT neurons.


title: A deep learning framework for characterization of genotype data

abstract:
Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. Among frequently used methods are PCA, which is a linear transform that often misses more fine-scale structures, and neighbor-graph based methods which focus on local relationships rather than large-scale patterns.

Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this paper, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data.

Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to PCA, while preserving global geometry to a higher extent than t-SNE and UMAP. We also discuss the use of the methodology for more general characterization of genotype data, showing that models of a similar architecture can be used as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.


title: Multiscale communication in cortico-cortical networks

abstract: Signaling in brain networks unfolds over multiple topological scales. Areas may exchange information over local circuits, encompassing direct neighbours and areas with similar functions, or over global circuits, encompassing distant neighbours with dissimilar functions. Here we study how the organization of cortico-cortical networks mediate localized and global communication by parametrically tuning the range at which signals are transmitted on the white matter connectome. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity. We find that unimodal regions show preference for local communication and multimodal regions show preferences for global communication. We show that these preferences manifest as region- and scale-specific structure-function coupling. Namely, the functional connectivity of unimodal regions emerges from monosynaptic communication in small-scale circuits, while the functional connectivity of transmodal regions emerges from polysynaptic communication in large-scale circuits. Altogether, the present findings reveal how functional hierarchies emerge from hidden but highly structured multiscale connection patterns.


title: Probing universal protein dynamics using residue-level Gibbs free energy

abstract:
Hydrogen Deuterium Exchange Mass Spectrometry is a powerful monitor of protein intrinsic dynamics, yet the interpretation, visualization and cross-comparison of HDX-MS datasets is challenging. Here we present PyHDX, an open-source python package and web server, that batch-extracts the universal quantity Gibbs free energy at residue level over multiple protein conditions and homologues. {Delta}G values relate to protein normal modes and together provide a universal measure of protein flexibility.

AvailabilityPyHDX source code is released under the MIT license and can be accessed on GitHub.


title: Nonlinear post-selection inference for genome-wide association studies

abstract:
To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual single nucleotide polymorphisms (SNPs) into gene statistics. However, using all SNPs mapped to a gene, including those with low association scores, can mask the association signal of a gene.

We therefore propose a new two-step strategy, consisting in first selecting the SNPs most associated with the phenotype within a given gene, before testing their joint effect on the phenotype. The recently proposed kernelPSI framework for kernel-based post-selection inference makes it possible to model non-linear relationships between features, as well as to obtain valid p-values that account for the selection step.

In this paper, we show how we adapted kernelPSI to the setting of quantitative GWAS, using kernels to model epistatic interactions between neighboring SNPs, and post-selection inference to determine the joint effect of selected blocks of SNPs on a phenotype. We illustrate this tool on the study of two continuous phenotypes from the UKBiobank.

We show that kernelPSI can be successfully used to study GWAS data and detect genes associated with a phenotype through the signal carried by the most strongly associated regions of these genes. In particular, we show that kernelPSI enjoys more statistical power than other gene-based GWAS tools, such as SKAT or MAGMA.

kernelPSI is an effective tool to combine SNP-based and gene-based analyses of GWAS data, and can be used successfully to improve both statistical performance and interpretability of GWAS.


title: History modulates early sensory processing of salient distractors

abstract:
To find important objects, we must focus on our goals, ignore distractions, and take our changing environment into account. This is formalized in models of visual search whereby goal-driven, stimulus-driven and history-driven factors are integrated into a priority map that guides attention. Stimulus history robustly influences where attention is allocated even when the physical stimulus is the same: when a salient distractor is repeated over time, it captures attention less effectively. A key open question is how we come to ignore salient distractors when they are repeated. Goal-driven accounts propose that we use an active, expectation-driven mechanism to attenuate the distractor signal (e.g., predictive coding), whereas stimulus-driven accounts propose that the distractor signal is attenuated due to passive changes to neural activity and inter-item competition (e.g., adaptation). To test these competing accounts, we measured item-specific fMRI responses in human visual cortex during a visual search task where trial history was manipulated (colors unpredictably switched or were repeated). Consistent with a stimulus-driven account of history-based distractor suppression, we found that repeated singleton distractors were suppressed starting in V1, and distractor suppression did not increase in later visual areas. In contrast, we observed signatures of goal-driven target enhancement that were absent in V1, increased across visual areas, and were not modulated by stimulus history. Our data suggest that stimulus history does not alter goal-driven expectations, but rather modulates canonically stimulus-driven sensory responses to contribute to a temporally-integrated representation of priority.

Significance StatementVisual search refers to our ability to find what we are looking for in a cluttered visual world (e.g., finding your keys). To perform visual search, we must integrate information about our goals (e.g.,  find the red key-chain), the environment (e.g., salient items capture your attention), and changes to the environment (i.e., stimulus history). Although stimulus history impacts behavior, the neural mechanisms that mediate history-driven effects remain debated. Here, we leveraged fMRI and multivariate analysis techniques to measure history-driven changes to the neural representation of items during visual search. We found that stimulus history influenced the representation of a salient  pop-out distractor starting in V1, suggesting that stimulus history operates via modulations in early sensory processing rather than goal-driven expectations.


title: Sensory experience selectively reorganizes the late component of evoked responses

abstract: In response to sensory stimulation, the cortex exhibits an early transient response followed by a late and slower activation pattern. Recent studies suggest that the early component represents features of the stimulus while the late component is associated with stimulus perception. In this work we study how patterns of evoked and spontaneous activity are modified by experience at mesoscale level using voltage and extracellular glutamate transient recordings over widespread regions of mice dorsal neocortex. We find that repeated tactile or auditory stimulation selectively modifies the spatiotemporal patterns of activity mainly of the late evoked response. This modification lasted up to 60 minutes and results not only in an increase in amplitude of the late response after repeated stimulation, but also the spatiotemporal pattern of the late evoked activity becomes more similar to the pattern of the early evoked response. These changes occur only for the evoked responses of the sensory modality that received the repeated stimulation and they persisted up to one hour. Thus, this selective long-lasting spatiotemporal modification of the cortical activity patterns might provide new insights about how perception-related cortical activity changes with sensory experience.


title: Stabilization demands of walking modulate the vestibular contributions to gait

abstract: Stable walking relies critically on motor responses to signals of head motion provided by the vestibular system, which are phase-dependent and modulated differently within each muscle. It is unclear, however, whether these vestibular contributions also vary according to the stability of the walking task. Here we investigate how vestibular signals influence muscles relevant for gait stability (medial gastrocnemius, gluteus medius and erector spinae) - as well as their net effect on ground reaction forces - while humans walked normally, with mediolateral stabilization, wide and narrow steps. We estimated local dynamic stability of trunk kinematics together with coherence of electrical vestibular stimulation (EVS) with muscle activity and mediolateral ground reaction forces. Walking with external stabilization increased local dynamic stability and decreased coherence between EVS and all muscles/forces compared to normal walking. Wide-base walking also decreased vestibulomotor coherence, though local dynamic stability did not differ. Conversely, narrow-base walking increased local dynamic stability, but produced muscle-specific increases and decreases in coherence that resulted in a net increase in vestibulomotor coherence with ground reaction forces. Overall, our results show that while vestibular contributions may vary with gait stability, they more critically depend on the stabilization demands (i.e. control effort) needed to maintain a stable walking pattern.


title: Sequence-space expansion is driven by the emergence of new signaling pathways

abstract: This report presents multi-genomes evidence that sequence-space expansion occurs when pathways with incompatible functions emerge. It is speculated that as pathways evolve within the sequence-space of highly paralogous signaling systems, they insulate against unwanted interactions, and can expand into new sequence-space. To prove that sequence-space expansion is driven by pathway insulation against emergent pathways, we develop a comprehensive, holistic (whole genomes) new pathway discovery pipeline. The holistic pipeline, which uses t-distributed stochastic neighbor embedding algorithms and phylogeny to map the extant sequence-space of thousands of bacterial two-component systems, reveals a new signaling pathway, resultant from domain swapping. By employing functional genomics screens in our model system Pseudomonas putida KT2440, we provide empirical evidence that descendents of the functionally discordant domain swapped pathway and its nearest neighbors in sequence-space are paralogous. We conclude that the emergence of this new pathway necessitated pathway insulation and drove sequence-space expansion.


title: Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer

abstract: Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.


title: Inference of high-resolution trajectories in single cell RNA-Seq data from RNA velocity

abstract: Trajectory inference methods are used to infer cell developmental trajectories in a continuous biological process, for example, stem cell differentiation. Most of the current trajectory inference methods infer the developmental trajectories based on transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like linear structure or tree structure, and the directions of the trajectory can only be determined when the root cell is provided. On the other hand, RNA velocity inference method is shown to be a promising alternative in predicting short term cell developmental direction from the sequencing data. Here by we present CellPath, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPath is able to find multiple high-resolution cell developmental paths instead of a single backbone trajectory obtained from traditional trajectory inference methods, and it no longer constrains the trajectory structure to be of any specific topology. The direction information provided by RNA-velocity also allows CellPath to automatically detect the root cell and the direction of the dynamic process. We evaluate CellPath on both real and synthetic datasets, and show that CellPath finds more accurate and detailed trajectories compared to the state-of-the-art trajectory inference methods.


title: EEG-based decoding and recognition of imagined music

abstract:
The authors have withdrawn their manuscript because they discovered an error in the analysis code after publication of the preprint, which turns out to have a major impact on the main results in the paper. The results on the imagination data become non-significant after correcting for the mistake. Significant results on the perception data are preserved, although the classification results are worse than what is reported.

Therefore, the authors do not wish this work to be cited as a reference.

If you have any questions, please contact the last author.


title: Positive but variable effects of crop diversification on biodiversity and ecosystem services

abstract: Ecological theory suggests that biodiversity has a positive and stabilizing effect on the delivery of ecosystem services. Yet, the impacts of increasing the diversity of cultivated crop species or varieties in agroecosystems are still under scrutiny. The empirical evidence available is scattered in scope, agronomic and geographical contexts and impacts on ecosystem services may depend on the type of diversification strategy used. To robustly assess the effects of crop diversification in agroecosystems, we compiled the results of 95 meta-analyses integrating 5,156 experiments conducted over 84 experimental years and representing more than 54,500 paired observations on 120 crop species in 85 countries. Overall, our synthesis of experimental data from across the globe shows that crop diversification enhances not only crop production (median effect +14%), but also the associated biodiversity (+24%, i.e. the biodiversity of non-cultivated plants and animals), and several supporting and regulating ecosystem services including water quality (+51%), pest and disease control (+63%) and soil quality (+11%). However, there was substantial variability in the results for each individual ecosystem service between different diversification strategies like agroforestry, intercropping, cover-crops, crop rotation or variety mixtures. Agroforestry is particularly effective in delivering multiple ecosystem services, i.e. water use and quality, pest and diseases regulation, associated biodiversity, long-term soil productivity and quality. Variety mixtures, instead, provide the lowest benefits, while the other strategies show intermediate results. Our results highlight that while increasing the diversity of cultivated crop species or varieties in agroecosystems represents a very promising strategy for more sustainable land management, contributing to enhanced yields, enhanced biodiversity and ecosystem services, some crop diversification strategies are more effective than others in supporting key ecosystem services.


title: Evolution of anticipatory effects mediated by epigenetic changes

abstract: Anticipatory effects mediated by epigenetic changes occur when parents modify the phenotype of their offspring by making epigenetic changes in their gametes guided by information from an environmental cue. To investigate when do anticipatory effects mediated by epigenetic changes evolve in a fluctuating environment, I use an individual based simulation model with explicit genetic architecture. The model allows for the population to respond to environmental changes by evolving plasticity, bet-hedging, or by tracking the environment with genetic adaptation, in addition to the evolution of anticipatory effects. The results show that anticipatory effects evolve when the environmental cue provides reliable information about the environment and the environment changes at intermediate rates, provided that fitness costs of anticipatory effects are rather low. Moreover, evolution of anticipatory effects is quite robust to different genetic architectures when reliability of the environmental cue is high. Anticipatory effects always give smaller fitness benefits than within generation plasticity, suggesting a possible reason for generally small observed anticipatory effects in empirical studies.


title: Flexible analysis of animal behavior via time-resolved manifold embedding

abstract: Uncovering relationships between neural activity and behavior represents a critical challenge, one that would benefit from facile tools that can capture complex structures within large datasets. Here we demonstrate a generalizable strategy for capturing such structures across diverse behaviors: Time-REsolved BehavioraL Embedding (TREBLE). Using data from synthetic trajectories, adult and larval Drosophila, and mice we show how TREBLE captures both continuous and discrete behavioral dynamics, can uncover variation across individuals, detect the effects of optogenetic perturbation in unbiased fashion, and reveal structure in pose estimation data. By applying TREBLE to moving mice, and medial entorhinal cortex (MEC) recordings, we show that nearly all MEC neurons encode information relevant to specific movement patterns, expanding our understanding of how navigation is related to the execution of locomotion. Thus, TREBLE provides a flexible framework for describing the structure of complex behaviors and their relationships to neural activity.

category: animal behavior and cognition


title: Can Systems Neuroscientists Understand an Artificial Neural Network?

abstract: Network neuroscience has yielded crucial insights into the systems-level organisation of the brain, however the indirect nature of neuroimaging recordings has rendered the discovery of generative mechanisms for a given function inherently challenging. In parallel, neural network machine-learning models have exhibited breakthrough performance in tackling a range of complex problems, however the principles that govern learning-induced modifications to network structure remain poorly understood, in part due to a lack of analytic tools to quantify the dynamics of network structure. While the question of how network reconfiguration supports learning is mirrored in machine learning and network neuroscience, the different contexts of these fields provides a timely opportunity to bring them together synergistically to investigate the problem. Here we combine these two approaches to reveal connections between the brains network structure and the emerging network structure of an artificial neural network. Specifically, we train a shallow, feedforward neural network to classify handwritten digits and then used a combination of systems neuroscience and information theoretic tools to perform  virtual brain analytics on the resultant edge weights and activity patterns of each node. We identify three distinct phases of network reconfiguration across learning, each of which are characterised by unique topological and information-theoretic signatures. Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding layers (as relevant). We also observe a process of low-dimensional category separation in the network as a function of learning. Our results offer a systems-level perspective of how artificial neural networks function - in terms of multi-stage reorganization of edge weights and activity patterns to effectively exploit the information content of input data during edge-weight training - while simultaneously enriching our understanding of the methods used by systems neuroscience.


title: Nonlinear Reconfiguration of Network Edges, Topology and Information Content During an Artifical Learning Task


title: A Hilbert-based method for processing respiratory timeseries

abstract:
In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline.

Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas).

HighlightsO_LIWe introduce a new estimator for respiratory volume per unit time from respiratory recordings.
C_LIO_LIWe demonstrate how this is able to accurately characterise atypical breathing events.
C_LIO_LIThis removes significantly more variance when used as a confound regressor for fMRI data.
C_LIO_LIOur implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas.
C_LI


title: Intrusive Experiences In Post-Traumatic Stress Disorder: Treatment Response Induces Changes In The Effective Connectivity Of The Anterior Insula

abstract:
BackgroundOne of the core features of posttraumatic stress disorder (PTSD) is reexperiencing the trauma. The anterior insula (AI) was proposed to play a crucial role in these intrusive experiences. However, the dynamic function of the AI in reexperiencing trauma, as well as its putative modulation by effective therapy, still need to be specified.

MethodsThirty PTSD patients were enrolled and exposed to traumatic memory reactivation therapy. Resting-state fMRI scans were acquired before and after treatment. To explore AI directed influences over the rest of the brain, we referred to a mixed-model using pre/post Granger causality analysis seeded on the AI as a within-subject factor and treatment response as a between-subject factor. To further identify correlates of reexperiencing trauma, we investigated how intrusive severity affected: (i) causality maps and (ii) the spatial stability of other intrinsic brain networks.

ResultsWe observed dynamic changes in AI effective connectivity in PTSD patients. Many within- and between-network causal paths were found to be less influenced by the AI after effective therapy. Insular influences were found positively correlated with flashback severity, while reexperiencing was linked with a stronger default mode network (DMN) and more unstable central executive network (CEN) connectivity.

ConclusionWe showed that directed changes in AI signaling to the DMN and CEN at rest may underlie the degree of intrusive symptoms in PTSD. A positive response to treatment further induced changes in network-to-network anticorrelated patterns. Such findings may guide targeted neuromodulation strategies in PTSD patients not suitably improved by conventional treatment.


title: TRPA1 is a source of calcium-mediated cardiac mechano-arrhythmogenesis

abstract: SUMMARY PARAGRAPHMaintenance of cardiac function involves a regulatory loop in which electrical excitation causes the heart to contract through excitation-contraction coupling (ECC),1 and the mechanical state of the heart directly affects its electrical activity through mechano-electric coupling (MEC).2 However, in pathological states such as acute ischaemia that alter early or late electro-mechanical coordination (i.e., disturbances in ECC or repolarisation-relaxation coupling, RRC), MEC may contribute to the initiation and / or sustenance of arrhythmias (mechano-arrhythmogenesis).3 The molecular identity of specific factor(s) underlying mechano-arrhythmogenesis in acute ischaemia, however, remain undefined.4 By rapid stretch of rabbit single left ventricular cardiomyocytes, we show that upon ATP-sensitive potassium channel-induced alterations of RRC, overall vulnerability to mechano-arrhythmogenesis is increased, with mechano-sensitive5-11 transient receptor potential kinase ankyrin 1 (TRPA1) channels12 acting as the molecular driver through a Ca2+-mediated mechanism. Specifically, TRPA1 activation drives stretch-induced excitation and creates a substrate for self-sustained arrhythmias, which are maintained by increased cytosolic free Ca2+ concentration ([Ca2+]i) and spontaneous [Ca2+]i oscillations. This TRPA1-dependent mechano-arrhythmogenesis involves microtubules, and can be prevented by block of TRPA1 or buffering of [Ca2+]i. Thus, in cardiac pathologies with disturbed RRC dynamics and / or augmented TRPA1 activity, TRPA1 may represent an anti-arrhythmic target with untapped therapeutic potential.13-17


title: The cardiac TRPA1 channel drives calcium-mediated mechano-arrhythmogenesis


title: Plasmodium falciparum Atg18 localizes to the food vacuole via interaction with the multi-drug resistance protein 1 and phosphatidylinositol 3-phosphate

abstract: Autophagy is a lysosome-dependent degradative process involving over 35 Atg proteins. The autophagy repertoire in malaria parasites is limited and does not appear to be a major degradative process. To better understand the autophagy process, we investigated Plasmodium falciparum Atg18 (PfAtg18), a PROPPIN family protein, whose members like S. cerevisiae Atg18 (ScAtg18) and human WIPI2 are essential for autophagy. Wild type and mutant PfAtg18 were expressed in P. falciparum and assessed for localization, the effect of various inhibitors and antimalarials on PfAtg18 localization, and identification of PfAtg18-interacting proteins. PfAtg18 is expressed in asexual erythrocytic stages and localized to the food vacuole, which was also observed with other Plasmodium Atg18 proteins, indicating that food vacuole localization is a conserved feature. Interaction of PfAtg18 with the food vacuole-associated PI3P is essential for localization, as PfAtg18 mutants of PI3P-binding motifs neither bound PI3P nor localized to the food vacuole. Interestingly, ScAtg18 showed complete cytoplasmic localization despite binding with PI3P, indicating additional requirement for PfAtg18 localization. The food vacuole multi-drug resistance protein 1 (MDR1) was consistently identified in the PfAtg18 immunoprecipitate, and also interacted with PfAtg18. In contrast to PfAtg18, ScAtg18 did not interact with the MDR1, which, in addition to PI3P, could play a critical role in localization of PfAtg18. Chloroquine and amodiaquine greatly affected PfAtg18 localization, suggesting that these quinolines target PfAtg18 or the proteins that might be involved in its localization. Thus, PI3P and MDR1are critical mediators of PfAtg18 localization, and PfAtg18 may modulate MDR1 activity.

category: cell biology


title: The economics of organellar gene loss and endosymbiotic gene transfer

abstract:
The endosymbiosis of the bacterial progenitors of mitochondrion and the chloroplast are landmark events in the evolution of life on earth. While both organelles have retained substantial proteomic and biochemical complexity, this complexity is not reflected in the content of their genomes. Instead, the organellar genomes encode fewer than 5% of genes found in living relatives of their ancestors. While many of the 95% of missing organellar genes have been discarded, others have been transferred to the host nuclear genome through a process known as endosymbiotic gene transfer. Here we demonstrate that the difference in the per-cell copy number of the organellar and nuclear genomes presents an energetic incentive to the cell to either delete genes or transfer them to the nuclear genome. We show that, for the majority transferred genes, the energy saved by nuclear-transfer exceeds the costs incurred from importing the encoded protein into the organelle where it can provide its function. Finally, we show that the net energy saved by endosymbiotic gene transfer can constitute an appreciable proportion of total cellular energy budgets, and is therefore sufficient to impart a selectable advantage to the cell. Thus, reduced cellular cost and improved energy efficiency likely played a role in the reductive evolution of mitochondrial and chloroplast genomes and the transfer of organellar genes to the nuclear genome.

Significance statementThe endosymbioses of the mitochondrion and the chloroplast were each followed by substantial gene loss and transfer of organellar genes to the nuclear genome. Here we show that the high per-cell copy number of these organellar genomes creates an energetic incentive for the cell to discard genes or transfer them to the nuclear genome. Thus, organellar gene loss and endosymbiotic gene transfer can be intrinsically advantageous to the cell.


title: Bacterial surface properties influence the activity of the TAT-RasGAP317-326 antimicrobial peptide

abstract: Antibiotic resistance is an increasing threat for public health, underscoring the need for new antibacterial agents. Antimicrobial peptides (AMPs) represent an alternative to classical antibiotics. TAT-RasGAP317-326 is a recently described AMP effective against a broad range of bacteria, but little is known about the conditions that may influence its activity. Using RNA-sequencing and screening of mutant libraries, we show that Escherichia coli and Pseudomonas aeruginosa respond to TAT-RasGAP317-326 by regulating metabolic and stress response pathways, possibly implicating two-component systems. Our results also indicate that bacterial surface properties, in particular integrity of the lipopolysaccharide layer, influence peptide binding and entry. Finally, we found differences between bacterial species with respect to their rate of resistance emergence against this peptide. Our findings provide the basis for future investigation on the mode of action of this peptide and its potential clinical use as an antibacterial agent.