bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimated limits of organism-specific training for epitope prediction
Authors: Ashford Jodie , Campelo Felipe
Published: November 2021

Background

The identification of linear B-cell epitopes remains an important task in the development of vaccines, therapeutic antibodies and several diagnostic tests. Machine learning predictors are trained to flag potential epitope candidates for experimental validation and currently, most predictors are trained as generalist models using large, heterogeneous data sets. Recently, organism-specific training has been shown to improve prediction performance for data-rich organisms. Unfortunately, for most organisms, large volumes of validated epitope data are not yet available. This article investigates the limits of organism-specific training for epitope prediction. It explores the validity of organism-specific training for data-poor organisms by examining how the size of the training data set affects prediction performance. It also compares the performance of organism-specific training under simulated data-poor conditions to that of models trained using traditional large heterogeneous and hybrid data sets.

Results

This work shows how models trained on small organism-specific data sets can outperform similar models trained on (potentially much larger) heterogeneous and mixed data sets. The results reported indicate that as few as 20 labelled peptides from a given pathogen can be sufficient to generate models that outperform widely-used predictors from the literature, which are trained on heterogeneous data. Models trained using more than about 100 to 150 organism-specific peptides perform consistently better than most generalist models across a wide variety of performance measures, and in some cases can even approach the performance of organism-specific models trained on considerably larger data sets.

Conclusions

Organism-specific training improves linear B-cell epitope prediction performance even in situations when only small training sets are available, which opens new possibilities for the development of bespoke, high-performance predictive models when studying data-poor organisms such as emerging or neglected pathogens.
Dopamine mediates the bidirectional update of interval timing
Authors: Jakob Anthony M.V. , Mikhael John G. , Hamilos Allison E. , Assad John A. , Gershman Samuel J.
Published: November 2021

The role of dopamine as a reward prediction error signal in reinforcement learning tasks has been well-established over the past decades. Recent work has shown that the reward prediction error interpretation can also account for the effects of dopamine on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a read-out of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of reinforcement learning and interval timing.
Single molecule tracking of bacterial cell surface cytochromes reveals dynamics that impact long-distance electron transport
Authors: Chong Grace W. , Pirbadian Sahand , Zhao Yunke , Zacharoff Lori A. , Pinaud Fabien , El-Naggar Mohamed Y.
Published: November 2021

Using a series of multiheme cytochromes, the metal-reducing bacteriumShewanella oneidensisMR-1 can perform extracellular electron transfer (EET) to respire redox-active surfaces, including minerals and electrodes outside the cell. While the role of multiheme cytochromes in transporting electrons across the cell wall is well established, these cytochromes were also recently found to facilitate long-distance (micrometer-scale) redox conduction along outer membranes and across multiple cells bridging electrodes. Recent studies proposed that long-distance conduction arises from the interplay of electron hopping and cytochrome diffusion, which allows collisions and electron exchange between cytochromes along membranes. However, the diffusive dynamics of the multiheme cytochromes have never been observed or quantifiedin vivo, making it difficult to assess their hypothesized contribution to the collision-exchange mechanism. Here we use quantum dot labeling, total internal reflection fluorescence microscopy, and single-particle tracking to quantify the lateral diffusive dynamics of the outer membrane-associated decaheme cytochromes MtrC and OmcA, two key components of EET inS. oneidensis. We observe confined diffusion behavior for both quantum dot-labeled MtrC and OmcA along cell surfaces (diffusion coefficientsDMtrC= 0.0192 ± 0.0018 μm2/s,DOmcA= 0.0125 ± 0.0024 μm2/s) and the membrane extensions thought to function as bacterial nanowires. We find that these dynamics can trace a path for electron transport via overlap of cytochrome trajectories, consistent with the long-distance conduction mechanism. The measured dynamics inform kinetic Monte Carlo simulations that combine direct electron hopping and redox molecule diffusion, revealing significant electron transport rates along cells and membrane nanowires.

Significance

Multiheme cytochromes inShewanella oneidensisMR-1 transport electrons across the cell wall, in a process called extracellular electron transfer. These electron conduits can also enable electron transport along and between cells. While the underlying mechanism is thought to involve a combination of electron hopping and lateral diffusion of cytochromes along membranes, these diffusive dynamics have never been observedin vivo. Here, we observe the mobility of quantum dot-labeled cytochromes on living cell surfaces and membrane nanowires, quantify their diffusion with single-particle tracking techniques, and simulate the contribution of these dynamics to electron transport. This work reveals the impact of redox molecule dynamics on bacterial electron transport, with implications for understanding and harnessing this process in the environment and bioelectronics.
Biological Factors and Self-Perception of Stress Predict Human Freeze-Like Responses in the Context of Self-Defence Training and Personal Experience with Violence
Authors: Lenart Peter , Vít Michal , Marečková Klára , Novák Jan , Zlámal Filip , Mikl Michal , Reguli Zdenko , Bugala Martin , Čihounková Jitka , Přecechtěl Pavel , Malčík Vojtěch , Vojtíšek Tomáš , Kučera Jan , Kučerová Jana Fialová , Hajnová Veronika , Tomandlová Marie , Šíp Radek , Ráčková Lucie , Grulichová Markéta , Tomandl Josef , Brázdil Milan , Bienertova-Vasku Julie
Published: November 2021

Many animals react to threatening stimuli such as a predator attacks by freezing. However, little experimental research investigated freeze response in humans. Here, we have employed practices commonly used in self-defence training to create two unique scenarios simulating armed physical attacks. Sixty healthy men volunteers divided into three groups of twenty (untrained, trained but unexperienced, trained and experienced) underwent these scenarios accompanied by measurement of biochemical, physiological, and psychological markers of stress. All participants also underwent an fMRI session during which they observed neutral and negative images from the International Affective Picture System (IAPS). Our results show that scenarios simulating physical attacks can induce a freeze-like response in men. In addition, we demonstrate that while electrodermal activity (EDA), subjective stress perception, and brain activity in fMRI predict freeze-like response in men, their effect on freeze-like response is entirely dependent on the level of training and experience of a given individual.
Demographic history shapes genomic variation in an intracellular parasite with a wide geographic distribution
Authors: Angst Pascal , Ebert Dieter , Fields Peter D.
Published: November 2021

Analyzing variation in a species’ genomic diversity can provide insights into its historical demography, biogeography and population structure, and thus, its ecology and evolution. Although such studies are rarely undertaken for parasites, they can be highly revealing because of the parasite’s coevolutionary relationships with hosts. Modes of reproduction and transmission are thought to be strong determinants of genomic diversity for parasites and vary widely among microsporidia (fungal-related intracellular parasites), which are known to have high intraspecific genetic diversity and interspecific variation in genome architecture. Here we explore genomic variation in the microsporidium Hamiltosporidium, a parasite of the freshwater crustacean Daphnia magna, looking especially at which factors contribute to nucleotide variation. Genomic samples from 18 Eurasian populations and a new, long-read based reference genome were used to determine the roles that reproduction mode, transmission mode and geography play in determining population structure and demographic history. We demonstrate two main H. tvaerminnensis lineages and a pattern of isolation-by-distance, but note an absence of congruence between these two parasite lineages and the two Eurasian host lineages. We suggest a comparatively recent parasite spread through Northern Eurasian host populations after a change from vertical to mixed-mode transmission and the loss of sexual reproduction. While gaining knowledge about the ecology and evolution of this focal parasite, we also identify common features that shape variation in genomic diversity for many parasites, e.g., distinct modes of reproduction and the intertwining of host–parasite demographies.
Retinal OFF ganglion cells allow detection of quantal shadows at starlight
Authors: Westö Johan , Martyniuk Nataliia , Koskela Sanna , Turunen Tuomas , Pentikäinen Santtu , Ala-Laurila Petri
Published: November 2021

Perception of light in darkness requires no more than a handful of photons and this remarkable behavioral performance can be directly linked to a particular retinal circuit – the retinal ON pathway. However, the neural limits to shadow detection in very dim light have remained unresolved. Here, we unravel the neural mechanisms that determine the sensitivity of mice to the dimmest light decrements by measuring signals from the most sensitive ON and OFF retinal ganglion cell types and by correlating their signals with visually guided behavior. We show that mice can detect shadows when only a few photon absorptions are missing among thousands of rods. Behavioral detection of “quantal” shadows relies only on the retinal OFF pathway and is limited by noise and losses of single-photon signals in retinal processing. Thus, in the dim-light regime, light increments and decrements are encoded separately via the ON and OFF retinal pathways, respectively.
MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates
Authors: Declercq Arthur , Bouwmeester Robbin , Hirschler Aurélie , Carapito Christine , Degroeve Sven , Martens Lennart , Gabriels Ralf
Published: November 2021

Immunopeptidomics aims to identify Major Histocompatibility Complex-presented peptides on every cell that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the non-tryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS2PIP and retention time predictions by DeepLC, have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS2PIP was tailored towards tryptic peptides, we have here retrained MS2PIP to include non-tryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides, but also yield further improvements for tryptic peptides. We show that the integration of new MS2PIP models, DeepLC, and Percolator in one software package, MS2Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS2Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Integration of immunopeptide MS2PIP models, DeepLC, and Percolator into MS2Rescore thus allows substantial improved identification of novel epitopes from existing immunopeptidomics workflows.
Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry
Authors: Crook Oliver M. , Chung Chun-wa , Deane Charlotte M.
Published: November 2021

A protein’s structure is state-specific and a key determinant of its function. Proteins can undergo subtle structural changes when binding to another protein, small molecule or as a result of environmental perturbations. Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a technique to explore differential protein structure by examining the rate of deuterium incorporation for specific peptides. This rate will be altered upon structural perturbation and detecting significant changes to this rate requires a statistical test. To determine rates of incorporation, HDX-MS measurements are frequently made over a time course. However, current statistical testing procedures ignore the correlations in the temporal dimension of the data. Using tools from functional data analysis, we develop a testing procedure that explicitly incorporates a physical model of hydrogen deuterium exchange. To further improve statistical power, we develop an empirical Bayes version of our method, allowing us to borrow information across peptides and stabilise variance estimates for low sample sizes. Our approach has increased power, reduces false positives and improves interpretation over linear model-based approaches. Due to the improved flexibility of our method, we can apply it to a multi-antibody epitope-mapping experiment where current approaches are inapplicable due insufficient flexibility. Hence, our approach allows HDX-MS to be applied in more experimental scenarios and reduces the burden on experimentalists to produce excessive replicates. Our approach is implemented in the R-package “hdxstats”: https://github.com/ococrook/hdxstats.
LRH-1/NR5A2 regulates the PTGS2-PGE2-PTGER1 signalling axis contributing to islet survival and antidiabetic actions of the agonist BL001
Authors: Vázquez Eugenia Martin , Cobo-Vuilleumier Nadia , Legido Raquel Araujo , Nola Emanuele , Bermudo Lucia López , Crespo Alejandra , Romero-Zerbo Silvana Y. , García-Fernández Maria , Montalvo Alejandro Martin , Rojas Anabel , Comaills Valentine , Bérmudez-Silva Francisco J. , Gannon Maureen , Martin Franz , Lorenzo Petra I. , Gauthier Benoit R.
Published: November 2021

We have previously described a role of LRH-1/NR5A2 in islet morphogenesis during postnatal development and reported that the treatment with BL001, an agonist of LRH-1/NR5A2, protects islets against-stress induced apoptosis and reverts hyperglycemia in 3 mouse models of Type 1 Diabetes Mellitus (T1DM). Islet transcriptome profiling revealed that most differentially expressed genes after BL001 treatment are involved in immunomodulation, among them, the increase in PTGS2/COX2 expression. Herein, we dissected the cellular and molecular branches of the BL001/LRH-1/NR5A2 signalling axis in order to chart the mode of action confering beta cell protection and hyperglycaemia reversion. We found that constitutive LRH-1/NR5A2 ablation within the insulin expression domain (RIP-Cre mouse model) caused a significant beta cell mass reduction characterized by blunted proliferation correlating with animal growth retardation, weight loss and hypoglycemia, leading to lethality before weaning. Using an inducible approach (pdx1PBCreER™ mouse model), specific deletion of LRH-1/NR5A2 in adult beta cells abolished the anti diabetic effect of BL001 in streptozotocin treated mice, correlating with complete beta-cell mass destruction. Additionally, BL001 induced Ptgs2 expression, was blunted in islets lacking LRH-1/NR5A2. The combined BL001/cytokine treatment did not further stimulate Ptgs2 expression above levels detected with cytokine alone yet secreted PGE2 levels were increased 5-fold. Inactivation of PTGS2 blunted induction of the target and its product PGE2 in islets treated with cytokines alone or with BL001. Importantly, PTGS2 inactivated islets were refractory to the BL001 protective effect under cytokine attack as evidenced by increased Bax expression levels, cytochrome C release and cleaved PARP. The PTGER1 antagonist ONO-8130, but not the PTGER4 antagonist L-161,982, negated BL001-mediated islet survival. Our results establish that the beneficial properties of BL001 against stress-induced cell death are specifically conveyed by LRH-1/NR5A2 activation in beta cells and downstream stimulation of the PTGS2-PGE2/PTGER1 signalling axis.
Structural variant-based pangenome construction has low sensitivity to variability of haplotype-resolved bovine assemblies
Authors: Leonard Alexander S. , Crysnanto Danang , Fang Zih-Hua , Heaton Michael P , Vander Ley Brian L. , Herrera Carolina , Bollwein Heinrich , Bickhart Derek M. , Kuhn Kristen L. , Smith Timothy PL. , Rosen Benjamin D. , Pausch Hubert
Published: November 2021

Advantages of pangenomes over linear reference assemblies for genome research have recently been established. However, potential effects of sequence platform and assembly approach, or of combining assemblies created by different approaches, on pangenome construction have not been investigated. We generated haplotype-resolved assemblies from the offspring of three bovine trios representing increasing levels of heterozygosity that each demonstrate a substantial improvement in contiguity, completeness, and accuracy over the currentBos taurusreference genome. Diploid coverage as low as 20x for HiFi or 60x for ONT was sufficient to produce two haplotype-resolved assemblies meeting the standards set by the Vertebrate Genome Project. Structural variant-based pangenomes created from the haplotype-resolved assemblies demonstrated significant consensus regardless of sequence platform, assembler algorithm, or coverage. Inspecting pangenome topologies identified 90 thousand structural variants including 931 overlapping with coding sequences; this approach revealed variants affectingQRICH2, PRDM9, HSPA1A, TAS2R46, andGCthat have potential to affect phenotype.
Bacterial colonisation dynamics of household plastics in a coastal environment
Authors: Lear Luke , Padfield Daniel , Dowsett Tirion , Jones Maia , Kay Suzanne , Hayward Alex , Vos Michiel
Published: November 2021

Accumulation of plastics in the marine environment has widespread detrimental consequences for ecosystems and wildlife. Marine plastics are rapidly colonised by a wide diversity of bacteria, including human pathogens, posing potential risks to human health. Here, we investigate the effect of polymer type, residence time and estuarine location on bacterial colonisation of common household plastics, including pathogenic bacteria. To do so, we submerged five main household plastic types: low-density PE (LDPE), high-density PE (HDPE), polypropylene (PP), polyvinyl chloride (PVC) and polyethylene terephthalate (PET) at an estuarine site in Cornwall (U.K.) and tracked bacterial colonisation dynamics. Using both culture-dependent and culture-independent approaches, we found that bacteria rapidly colonised plastics irrespective of polymer type. While biofilm community composition changed with colonisation time, no difference was observed between polymer types. Likewise, the presence of pathogenic bacteria, quantified using the insect modelGalleria mellonella, increased over a five-week period, with no consistent differences observed between polymer types. Pathogens isolated from plastic biofilms usingGalleriaenrichment includedSerratiaandEnterococcusspecies and harboured a wide range of antimicrobial resistance genes. Our findings show that plastics in coastal waters are rapidly colonised by a wide diversity of bacteria, including known human pathogens, independent of polymer type.
Learning from ingroup experiences changes intergroup impressions
Authors: Zhou Yuqing , Lindström Björn , Soutschek Alexander , Kang Pyungwon , Tobler Philippe N. , Hein Grit
Published: November 2021

Living in multicultural societies, humans form impressions towards individuals of their own social groups (ingroup members) and of different social groups (outgroup members). Some psychological theories predict that intergroup impressions are mainly shaped by experiences with outgroup individuals (“outgroup focused theories”), while other theories predict that ingroup experiences play a dominant role (“ingroup focused theories”). Here we test predictions from these two psychological theories by estimating how intergroup impressions are dynamically shaped when people learn from both ingroup and outgroup experiences. Participants expected to receive painful shocks but were saved from pain by different ingroup or outgroup members in 75% of all trials. We measured neural responses using functional magnetic resonance imaging (fMRI), and participants rated their social closeness as well as impressions towards the ingroup and the outgroup. Behavioral results showed an initial ingroup bias in impression ratings which was significantly reduced over the course of learning. Computational learning models revealed that these changes in intergroup impressions were predicted by the weight given to ingroup prediction errors. The weight of the ingroup prediction error and its effect on intergroup impression change was stronger the more individuals identified with their ingroup. On the neural level, the left inferior parietal lobule (IPL) encoded more negative prediction errors for the ingroup compared to the outgroup. Moreover, stronger weight for ingroup prediction errors was related to increased neural coupling between the left IPL and the anterior insula (AI). This coupling further predicted learning-related changes in intergroup impressions. Together, our work provides computational and neural evidence for “ingroup focused theories”, highlighting the importance of ingroup experiences in shaping social impressions in intergroup settings.
Re-expressing coefficients from regression models for inclusion in a meta-analysis
Authors: Linakis Matthew W. , Van Landingham Cynthia , Gasparini Alessandro , Longnecker Matthew P.
Published: November 2021

Meta-analysis poses a challenge when original study results have been expressed in a non-uniform manner, such as when regression results from some original studies were based on a log-transformed key independent variable while in others no transformation was used. Methods of re-expressing regression coefficients to generate comparable results across studies regardless of data transformation have recently been developed. We examined the relative bias of three re-expression methods using simulations and 15 real data examples where the independent variable had a skewed distribution.

Regression coefficients from models with log-transformed independent variables were re-expressed as though they were based on an untransformed variable. We compared the re-expressed coefficients to those from a model fit to the untransformed variable. In the simulated and real data, all three reexpression methods usually gave biased results, and the skewness of the independent variable predicted the amount of bias. Meta-analyses stratified by whether the key independent variable was log-transformed and synthesis of the stratified results without meta-analysis appear to be preferable to use of the re-expression methods examined.
Sex and Circuit Specific Dopamine Transporter Regulation Underlies Unique Behavioral Trajectories of Functional SLC6A3 Coding Variation
Authors: Stewart Adele , Mayer Felix P. , Gowrishankar Raajaram , Davis Gwynne L. , Areal Lorena B. , Gresch Paul J. , Katamish Rania M. , Peart Rodeania , Stilley Samantha E. , Spiess Keeley , Rabil Maximilian J. , Diljohn Faakhira A. , Wiggins Angelica E. , Vaughan Roxanne A. , Hahn Maureen K. , Blakely Randy D.
Published: November 2021

Virtually all neuropsychiatric disorders display sex differences in prevalence, age of onset, and/or clinical symptomology. In sex-biased disorders, one sex is often suggested to harbor protective mechanisms, rendering them resilient to genetic and/or environmental risk factors. Here, we demonstrate sex-biased molecular, pharmacological and behavioral effects induced by the dopamine (DA) transporter (DAT) coding variant Ala559Val, previously identified in subjects diagnosed with the male-biased disorders attention-deficit/hyperactivity disorder and autism spectrum spectrum disorder. In DAT Val559 mice, we identified sex differences in response to psychostimulants, social behavior, and cognitive traits. We reveal a sex by circuit dissociation in D2-type autoreceptor (D2AR) regulation of DAT wherein D2AR-dependent DAT phosphorylation and trafficking, detectable in the male dorsal striatum, does not occur in females but rather is a property of the ventral striatum, predicting sex-specific changes in behavior. Consequently, we found that a subset of altered behaviors can be normalized using the D2R antagonist sulpiride in DAT Val559 mice. Our studies provide a cogent example of how sex shapes the behavioral trajectory of DA signaling perturbations and identify the sex-dependent, locality-selective capacity for D2AR regulation of DAT as an unrecognized determinant of this trajectory. Rather than identifying one sex as resilient, we find that sex can drive alterative behavioral patterns from shared signaling perturbations that may result in females being underreported. Our work underscores the utility of model systems to study the functional intrusions of rare genetic variation to gain insights into pathways underlying normal and perturbed trait domains associated with common neuropsychiatric conditions.
Multiple hybridization events punctuate the evolutionary trajectory of Malassezia furfur
Authors: Theelen Bart , Mixão Verónica , Ianiri Giuseppe , Pei Zhen Joleen Goh , Dijksterhuis Jan , Heitman Joseph , Dawson Thomas L. , Gabaldón Toni , Boekhout Teun
Published: November 2021

Malassezia species are important fungal skin commensals and are part of the normal microbiota of humans and other animals. However, under certain circumstances these fungi can also display a pathogenic behaviour. For example, Malassezia furfur is a common commensal of human skin, and yet is often responsible for skin disorders but also systemic infections. Comparative genomics analysis of M. furfur revealed that some isolates have a hybrid origin, similar to several other recently described hybrid fungal pathogens. Because hybrid species exhibit genomic plasticity that can impact phenotypes, we sought to elucidate the genomic evolution and phenotypic characteristics of M. furfur hybrids in comparison to their parental lineages. To this end, we performed a comparative genomics analysis between hybrid strains and their presumptive parental lineages, and assessed phenotypic characteristics. Our results provide evidence that at least two distinct hybridization events occurred between the same parental lineages, and that the parental strains may have originally been hybrids themselves. Analysis of the mating-type locus reveals that M. furfur has a pseudobipolar mating system, and provides evidence that after sexual liaisons of mating compatible cells, hybridization involved cell-cell fusion leading to a diploid/aneuploid state. This study provides new insights into the evolutionary trajectory of M. furfur and contributes with valuable genomic resources for future pathogenicity studies.

Importance

Malassezia furfur is a common commensal member of human/animal microbiota that is also associated with several pathogenic states. Recent studies report involvement of Malassezia species in Crohn’s disease, a type of inflammatory bowel disease, pancreatic cancer progression, and exacerbation of Cystic fibrosis. A recent genomics analysis of M. furfur revealed the existence of hybrid isolates and identified their putative parental lineages. In this study, we explored the genomic and phenotypic features of these hybrids in comparison to their putative parental lineages. Our results revealed the existence of a pseudobipolar mating system in this species and showed evidence for the occurrence of multiple hybridization events in the evolutionary trajectory of M. furfur. These findings significantly advance our understanding of the evolution of this commensal microbe and are relevant for future studies exploring the role of hybridization in the adaptation to new niches or environments, including the emergence of pathogenicity.
Screening for axon regeneration promoting compounds with human iPSC-derived motor neurons
Authors: Ho Tammy Szu-Yu , Hees J. Tabitha , Xu Zhuqiu , Kawaguchi Riki , Biscola Natalia P. , Taub Daniel G , Chen Kuchuan , Chen Xirui , Barrett Lee B. , Cheng Long , Gabel Christopher V. , Havton Leif A. , Geschwind Daniel H. , Woolf Clifford J.
Published: November 2021

CNS neurons do not regenerate after injury, leading to permanent functional deficits. Although sensory and motor neuron axons do regrow after peripheral nerve injury, functional outcome is limited due to the incomplete and slow regrowth. The lack of human-relevant assays suitable for large-scale drug screens has limited neuro-repair therapy discovery. To address this we developed a phenotypic screening strategy using human induced pluripotent stem cell-derived motor neurons to identify axon-regeneration promoting compounds and targets. The screens involve both re-plating human motor neurons on chondroitin sulfate proteoglycans and measuring regeneration responses to laser axotomy in spot cultures, and from them we identified multiple hits that promote injured axon regrowth. The top hit blebbistatin, a non-muscle myosin II inhibitor, accelerated axon regeneration and functional recovery after sciatic nerve injury in vivo. Human “injury in a dish” assays are suitable, therefore, to screen for therapeutic interventions that can induce or accelerate axon regeneration.
Time-of-day influences on skill acquisition and consolidation after physical and mental practices
Authors: Truong Charlène , Hilt Pauline M. , Bouguila Fatma , Bove Marco , Lebon Florent , Papaxanthis Charalambos , Ruffino Célia
Published: November 2021

Time-of-day influences both physical and mental performance. Its impact on motor learning is, however, not well established yet. Here, using a finger tapping-task, we investigated the time-of-day effect on skill acquisition (i.e., immediately after a physical or mental practice session) and consolidation (i.e., 24 hours later). Two groups (one physical and one mental) were trained in the morning (10 a.m.) and two others (one physical and one mental) in the afternoon (3 p.m.). We found an enhancement of motor skill following both types of practice, whatever the time of the day, with a better acquisition for the physical than the mental group. Interestingly, there was a better consolidation for both groups when the training session was scheduled in the afternoon. Overall, our results indicate that the time-of-day positively influences motor skill consolidation and thus must be considered to optimize training protocols in sport and clinical domains to potentiate motor (re)learning.
In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner
Authors: Novello Lisa , Henriques Rafael Neto , Ianuş Andrada , Feiweier Thorsten , Shemesh Noam , Jovicich Jorge
Published: November 2021

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusion Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mappings in neural tissue. However, the specificity of DKI is limited as different microstructural sources can contribute to the total diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to intravoxel diffusion anisotropy (Kaniso), and microscopic kurtosis (μK) related to restricted diffusion and/or microstructural disorder. The latter in particular is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible. Recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation and revealed non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the kurtosis sources in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of the relative importance of μK in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented. We find that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. We further show that ignoring μK - as done by many contemporary methods based on multiple gaussian component approximation for kurtosis source estimation - biases the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

Highlights

Correlation Tensor MRI (CTI) was recently proposed to resolve kurtosis sources

We implemented CTI on a 3T scanner to study kurtosis sources in the human brain

Isotropic, anisotropic, and microscopic kurtosis sources were successfully resolved

Microscopic kurtosis (μK) significantly contributes to overall kurtosis in human brain

μK provides a novel source of contrast in the human brain in vivo
Drug targeting Nsp1-ribosomal complex shows antiviral activity against SARS-CoV-2
Authors: Afsar Mohammad , Narayan Rohan , Akhtar Md Noor , Rahil Huma , Eswarappa Sandeep M , Tripathi Shashank , Hussain Tanweer
Published: November 2021

The SARS-Cov-2 non-structural protein 1 (Nsp1) contains an N-terminal domain and C-terminal helices connected by a short linker region. The C-terminal helices of Nsp1 (Nsp1-C-ter) from SARS-Cov-2 bind in the mRNA entry channel of the 40S ribosomal subunit and block the entry of mRNAs thereby shutting down host protein synthesis. Nsp1 suppresses the host immune function and is vital for viral replication. Hence, Nsp1 appears to be an attractive target for therapeutics. In this study, we have in silico screened Food and Drug Administration (FDA)-approved drugs against Nsp1-C-ter and find that montelukast sodium hydrate binds to Nsp1-C-ter with a binding affinity (KD) of 10.8±0.2 μM in vitro and forms a stable complex with it in simulation runs with a binding energy of −76.71±8.95 kJ/mol. The drug also rescues the inhibitory effect of Nsp1 in host protein synthesis as demonstrated by the expression of firefly luciferase reporter gene in cells. Importantly, montelukast sodium hydrate demonstrates antiviral activity against SARS-CoV-2 with reduced viral replication in HEK cells expressing ACE2 and Vero-E6 cells. We therefore propose montelukast sodium hydrate may help in combatting SARS-CoV-2 infection.
Steady state flux balance analysis game
Authors: Iyengar Garud , Perry Mitch
Published: November 2021

Flux balance analysis (FBA) for microbial communities often assumes a global objective function that all species cooperatively maximize in addition to maximizing their own growth. Combining community FBA with dynamic FBA to understand the time course and steady states of communities typically entails discretizing time and solving a community FBA model at each time point, a time-intensive process. We propose a dynamic community FBA model where species compete for metabolites to grow off of without needing to cooperate to maximize a community-level objective. An efficient method for computing steady state community compositions is provided, as well as methods for determining the stability of a steady state community to perturbations in biomass and invasion by species outside the community. The model and methods are applied to a model of four E. coli mutants with elements of competition (for shared metabolites) and cooperation (via mutants being auxotrophic for metabolites exported by other mutants), as well as a nine-species gut microbiome model.
Population genomics analysis with RAD, reprised: Stacks 2
Authors: Rivera-Colón Angel G. , Catchen Julian
Published: November 2021

Restriction enzymes have been one of the primary tools in the population genetics toolkit for fifty years, being coupled with each new generation of technology to provide a more detailed view into the genetics of natural populations. Restriction site-Associated DNA protocols, which joined enzymes with short-read sequencing technology have democratized the field of population genomics, providing a means to assay the underlying alleles in scores of populations. More than ten years on, the technique has been widely applied across the tree of life and served as the basis for many different analysis techniques. Here, we provide a detailed protocol to conduct a RAD analysis from experimental design to de novo analysis – including parameter optimization – as well as reference-based analysis, all in Stacks version 2, which is designed to work with paired-end reads to assemble RAD loci up to 1000 nucleotides in length. The protocol focuses on major points of friction in the molecular approaches and downstream analysis, with special attention given to validating experimental analyses. Finally, the protocol provides several points of departure for further analysis.
A flexible method for estimating tip diversification rates across a range of speciation and extinction scenarios
Authors: Vasconcelos Thais , O’Meara Brian C. , Beaulieu Jeremy M.
Published: November 2021

Estimates of diversification rates at the tips of a phylogeny provide a flexible approach for correlation analyses with multiple traits and to map diversification rates in space, while also avoiding the uncertainty of deep time rate reconstructions. Available methods for tip rate estimation make different assumptions, and thus their accuracy usually depends on characteristics of the underlying model generating the tree. Here we introduce MiSSE, a trait-free, state-dependent speciation and extinction approach that can be used to estimate varying speciation, extinction, net-diversification, turnover rates, and extinction fraction at the tips of the tree. We compare the accuracy of tip rates inferred by MiSSE against similar methods and demonstrate that, due to certain characteristics of the model, the error is generally low across a broad range of speciation and extinction scenarios. MiSSE can be used alongside regular phylogenetic comparative methods in trait related diversification hypotheses, and we also describe a simple correction to avoid pseudoreplication from sister tips in analyses of independent contrasts. Finally, we demonstrate the capabilities of MiSSE, with a renewed focus on classic comparative methods, to examine correlation between plant height and turnover rates in eucalypts, a species-rich lineage of flowering plants.
Plasticity in Ligand Recognition at Somatostatin Receptors
Authors: Robertson Michael J. , Meyerowitz Justin G. , Panova Ouliana , Borrelli Kenneth , Skiniotis Georgios
Published: November 2021

Somatostatin is a signaling peptide that plays a pivotal and wide-ranging role in physiologic processes relating to metabolism and growth through its actions at somatostatin receptors (SSTRs). Members of the somatostatin receptor subfamily, particularly SSTR2, are key drug targets for neuroendocrine neoplasms, with several synthetic peptide agonists currently in clinical use. Here, we show the cryogenic electron microscopy (cryo-EM) structures of active-state SSTR2 in complex with heterotrimeric Gi protein and either the endogenous ligand SST14 or the FDA-approved drug octreotide.

Complemented by biochemical assays and molecular dynamics simulations, these structures reveal key details of ligand recognition, receptor activation, and subtype-selectivity at somatostatin receptors. We find that SSTR ligand recognition is highly diverse, as demonstrated by ligand-induced conformational changes in ECL2, substantial sequence divergence across subtypes in extracellular regions, and loss of ligand binding upon several structurally homologous substitutions between subtypes. Despite this complexity, we rationalize several previously described sources of SSTR subtype selectivity and identify an additional key interaction for SSTR2/3/5 specific binding. These results shed light on the basis of ligand recognition by somatostatin receptors and provide valuable insights for structure-based drug discovery at these targets.
Validating the wearable MUSE headset for EEG spectral analysis and Frontal Alpha Asymmetry
Authors: Cannard Cédric , Wahbeh Helané , Delorme Arnaud
Published: November 2021

EEG power spectral density (PSD), the individual alpha frequency (IAF) and the frontal alpha asymmetry (FAA) are all EEG spectral measures that have been widely used to evaluate cognitive and attentional processes in experimental and clinical settings, and that can be used for real-world applications (e.g., remote EEG monitoring, brain-computer interfaces, neurofeedback, neuromodulation, etc.). Potential applications remain limited by the high cost, low mobility, and long preparation times associated with high-density EEG recording systems. Low-density wearable systems address these issues and can increase access to larger and diversified samples. The present study tested whether a low-cost, 4-channel wearable EEG system (the MUSE) could be used to quickly measure continuous EEG data, yielding similar frequency components compared to research a grade EEG system (the 64-channel BIOSEMI Active Two). We compare the spectral measures from MUSE EEG data referenced to mastoids to those from BIOSEMI EEG data with two different references for validation. A minimal amount of data was deliberately collected to test the feasibility for real-world applications (EEG setup and data collection being completed in under 5 min). We show that the MUSE can be used to examine power spectral density (PSD) in all frequency bands, the individual alpha frequency (IAF; i.e., peak alpha frequency and alpha center of gravity), and frontal alpha asymmetry. Furthermore, we observed satisfying internal consistency reliability in alpha power and asymmetry measures recorded with the MUSE. Estimating asymmetry on PAF and CoG frequencies did not yield significant advantages relative to the traditional method (whole alpha band). These findings should advance human neurophysiological monitoring using wearable neurotechnologies in large participant samples and increase the feasibility of their implementation in real-world settings.
Epithelial-mesenchymal transition is the main driver of intrinsic metabolism in cancer cell lines
Authors: Cherkaoui Sarah , Durot Stephan , Bradley Jenna , Critchlow Susan , Dubuis Sebastien , Masiero Mauro Miguel , Wegmann Rebekka , Snijder Berend , Othman Alaa , Bendtsen Claus , Zamboni Nicola
Published: November 2021

A fundamental feature of cancer cells is genomic heterogeneity. It is a main driver of phenotypic differences, including the response to drugs, and therefore a key factor in therapy selection. Motivated by the increasing role attributed to metabolic reprogramming in tumor development, we wondered how genomic heterogeneity affects metabolic phenotype. To this end, we profiled the intracellular metabolome of 180 cancer cell lines grown in similar conditions to exclude environmental factors. For each cell line, we estimate activity for 49 pathways across the whole metabolic network. Upon clustering of activity data, we found a convergence into only two major metabolic types. These were further characterized by 13C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. These experiments revealed differences in lipid, mitochondrial, and carbohydrate metabolism between the two major types. Finally, a thorough integration of our metabolic data with multiple omics data revealed a strong association with markers of epithelial-mesenchymal transition (EMT). Our analysis indicates that in absence of variations imposed by the microenvironment, the metabolism of cancer cell lines falls into only two major classes despite genetic heterogeneity.
Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy
Authors: Zhu Danqing , Brookes David H. , Busia Akosua , Carneiro Ana , Fannjiang Clara , Popova Galina , Shin David , Donohue Kevin. C. , Chang Edward F. , Nowakowski Tomasz J. , Listgarten Jennifer , Schaffer David. V.
Published: November 2021

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for clinical gene therapy, but they need improvement. AAVs with enhanced properties, such as more efficient and/or cell-type specific infection, can be engineered by creating a large, diverse starting library and screening for desired phenotypes, in some cases iteratively. Although this approach has succeeded in numerous specific cases, such as infecting cell types from the brain to the lung, the starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a general prerequisite for engineering any gene delivery goal. Herein, we develop and showcase a machine learning (ML)-based method for systematically designing more effective starting libraries — ones that have broadly good packaging capabilities while being as diverse as possible. Such carefully designed but general libraries stand to significantly increase the chance of success in engineering any property of interest. Furthermore, we use this approach to design a clinically-relevant AAV peptide insertion library that achieves 5-fold higher packaging fitness than the state-of-the-art library, with negligible reduction in diversity. We demonstrate the general utility of this designed library on a downstream task to which our approach was agnostic: infection of primary human brain tissue. The ML-designed library had approximately 10-fold more successful variants than the current state-of-the-art library. Not only should our new library serve useful for any number of other engineering goals, but our library design approach itself can also be applied to other types of libraries for AAV and beyond.