bluuebunny/crossref_metadata_2025 huggingface dataset:
Site-Randomized Controlled Trial of a Combined Cognitive Behavioral Therapy and a Medication Management Algorithm for Treatment of Depression Among Youth Living With HIV in the United States
Authors: Brown Larry K. , Chernoff Miriam , Kennard Betsy D. , Emslie Graham J. , Lypen Kathryn , Buisson Sarah , Weinberg Adriana , Whiteley Laura B. , Traite Shirley , Krotje Chelsea , Harriff Lauren , Townley Ellen , Bunch Amber , Purswani Murli , Shaw Ray , Spector Stephen A. , Agwu Allison , Shapiro David E. ,   for the IMPAACT 2002 team
Published: September 2021

Background:

Depression is frequent among youth living with HIV (YLWH). Studies suggest that manualized treatment guided by symptom measurement is more efficacious than usual care.

Setting:

This study evaluated manualized, measurement-guided depression treatment among YLWH, aged 12–24 years at 13 US sites of the International Maternal Pediatric Adolescent AIDS Clinical Trials Network.

Methods:

Using restricted randomization, sites were assigned to either a 24-week, combination cognitive behavioral therapy and medication management algorithm (COMB-R) tailored for YLWH or to enhanced standard of care, which provided standard psychotherapy and medication management. Eligibility included diagnosis of nonpsychotic depression and current depressive symptoms. Arm comparisons used t tests on site-level means.

Results:

Thirteen sites enrolled 156 YLWH, with a median of 13 participants per site (range 2–16). At baseline, there were no significant differences between arms on demographic factors, severity of depression, or HIV status. The average site-level participant characteristics were as follows: mean age of 21 years, 45% male, 61% Black, and 53% acquired HIV through perinatal transmission. At week 24, youth at COMB-R sites, compared with enhanced standard of care sites, reported significantly fewer depressive symptoms on the Quick Inventory for Depression Symptomatology Self-Report (QIDS-SR score 6.7 vs. 10.6, P = 0.01) and a greater proportion in remission (QIDS-SR score ≤ 5; 47.9% vs. 17.0%, P = 0.01). The site mean HIV viral load and CD4 T-cell level were not significantly different between arms at week 24.

Conclusions:

A manualized, measurement-guided psychotherapy and medication management algorithm tailored for YLWH significantly reduced depressive symptoms compared with standard care at HIV clinics.
The many functions of vocal learning
Authors: Carouso-Peck Samantha , Goldstein Michael H. , Fitch W. Tecumseh
Published: September 2021

The capacity to learn novel vocalizations has evolved convergently in a wide range of species. Courtship songs of male birds or whales are often treated as prototypical examples, implying a sexually selected context for the evolution of this ability. However, functions of learned vocalizations in different species are far more diverse than courtship, spanning a range of socio-positive contexts from individual identification, social cohesion, or advertising pair bonds, as well as agonistic contexts such as territorial defence, deceptive alarm calling or luring prey. Here, we survey the diverse usages and proposed functions of learned novel signals, to build a framework for considering the evolution of vocal learning capacities that extends beyond sexual selection. For each function that can be identified for learned signals, we provide examples of species using unlearned signals to accomplish the same goals. We use such comparisons to generate hypotheses concerning when vocal learning is adaptive, given a particular suite of socio-ecological traits. Finally, we identify areas of uncertainty where improved understanding would allow us to better test these hypotheses. Considering the broad range of potential functions of vocal learning will yield a richer appreciation of its evolution than a narrow focus on a few prototypical species.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
The multi-dimensional nature of vocal learning
Authors: Vernes Sonja C. , Kriengwatana Buddhamas Pralle , Beeck Veronika C. , Fischer Julia , Tyack Peter L. , ten Cate Carel , Janik Vincent M.
Published: September 2021

How learning affects vocalizations is a key question in the study of animal communication and human language. Parallel efforts in birds and humans have taught us much about how vocal learning works on a behavioural and neurobiological level. Subsequent efforts have revealed a variety of cases among mammals in which experience also has a major influence on vocal repertoires. Janik and Slater (Anim. Behav.60, 1–11. (doi:10.1006/anbe.2000.1410)) introduced the distinction between vocal usage and production learning, providing a general framework to categorize how different types of learning influence vocalizations. This idea was built on by Petkov and Jarvis (Front. Evol. Neurosci.4, 12. (doi:10.3389/fnevo.2012.00012)) to emphasize a more continuous distribution between limited and more complex vocal production learners. Yet, with more studies providing empirical data, the limits of the initial frameworks become apparent. We build on these frameworks to refine the categorization of vocal learning in light of advances made since their publication and widespread agreement that vocal learning is not a binary trait. We propose a novel classification system, based on the definitions by Janik and Slater, that deconstructs vocal learning into key dimensions to aid in understanding the mechanisms involved in this complex behaviour. We consider how vocalizations can change without learning, and a usage learning framework that considers context specificity and timing. We identify dimensions of vocal production learning, including the copying of auditory models (convergence/divergence on model sounds, accuracy of copying), the degree of change (type and breadth of learning) and timing (when learning takes place, the length of time it takes and how long it is retained). We consider grey areas of classification and current mechanistic understanding of these behaviours. Our framework identifies research needs and will help to inform neurobiological and evolutionary studies endeavouring to uncover the multi-dimensional nature of vocal learning.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
A researcher's guide to the comparative assessment of vocal production learning
Authors: Lattenkamp Ella Z. , Hörpel Stephen G. , Mengede Janine , Firzlaff Uwe
Published: September 2021

Vocal production learning (VPL) is the capacity to learn to produce new vocalizations, which is a rare ability in the animal kingdom and thus far has only been identified in a handful of mammalian taxa and three groups of birds. Over the last few decades, approaches to the demonstration of VPL have varied among taxa, sound production systems and functions. These discrepancies strongly impede direct comparisons between studies. In the light of the growing number of experimental studies reporting VPL, the need for comparability is becoming more and more pressing. The comparative evaluation of VPL across studies would be facilitated by unified and generalized reporting standards, which would allow a better positioning of species on any proposed VPL continuum. In this paper, we specifically highlight five factors influencing the comparability of VPL assessments: (i) comparison to an acoustic baseline, (ii) comprehensive reporting of acoustic parameters, (iii) extended reporting of training conditions and durations, (iv) investigating VPL function via behavioural, perception-based experiments and (v) validation of findings on a neuronal level. These guidelines emphasize the importance of comparability between studies in order to unify the field of vocal learning.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
Evolving the capacity for socially guided vocal learning in songbirds: a preliminary study
Authors: Carouso-Peck Samantha , Goldstein Michael H.
Published: September 2021

Socially guided vocal learning, the ability to use contingent reactions from social partners to guide immature vocalizations to more mature forms, is thought to be a rare ability known to be used only by humans, marmosets and two unrelated songbird species (brown-headed cowbirds and zebra finches). However, this learning strategy has never been investigated in the vast majority of species that are known to modify their vocalizations over development. We propose a novel, preliminary evolutionary modelling approach that uses ecological, reproductive and developmental traits to predict which species may incorporate social influences as part of their vocal learning system. We demonstrate our model using data from 28 passerines. We found three highly predictive traits: temporal overlap between sensory (memorization) and sensorimotor (practice) phases of song learning, song used for mate attraction, and social gregariousness outside the breeding season. Species with these traits were distributed throughout the clade, suggesting that a trait-based approach may yield new insights into the evolution of learning strategies that cannot be gleaned from phylogenetic relatedness alone. Our model suggests several previously uninvestigated and unexpected species as likely socially guided vocal learners and offers new insight into the evolution and development of vocal learning.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
The role of vocal learning in call acquisition of wild grey seal pups
Authors: Stansbury Amanda L. , Janik Vincent M.
Published: September 2021

Pinnipeds have been identified as one of the best available models for the study of vocal learning. Experimental evidence for their learning skills is demonstrated with advanced copying skills, particularly in formant structure when copying human speech sounds and melodies. By contrast, almost no data are available on how learning skills are used in their own communication systems. We investigated the impact of playing modified seal sounds in a breeding colony of grey seals (Halichoerus grypus) to study how acoustic input influenced vocal development of eight pups. Sequences of two or three seal pup calls were edited so that the average peak frequency between calls in a sequence changed up or down. We found that seals copied the specific stimuli played to them and that copies became more accurate over time. The differential response of different groups showed that vocal production learning was used to achieve conformity, suggesting that geographical variation in seal calls can be caused by horizontal cultural transmission. While learning of pup calls appears to have few benefits, we suggest that it also affects the development of the adult repertoire, which may facilitate social interactions such as mate choice.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
Neuroanatomy of the grey seal brain: bringing pinnipeds into the neurobiological study of vocal learning
Authors: Hoeksema Nienke , Verga Laura , Mengede Janine , van Roessel Corné , Villanueva Stella , Salazar-Casals Anna , Rubio-Garcia Ana , Ćurčić-Blake Branislava , Vernes Sonja C. , Ravignani Andrea
Published: September 2021

Comparative animal studies of complex behavioural traits, and their neurobiological underpinnings, can increase our understanding of their evolution, including in humans. Vocal learning, a potential precursor to human speech, is one such trait. Mammalian vocal learning is under-studied: most research has either focused on vocal learning in songbirds or its absence in non-human primates. Here, we focus on a highly promising model species for the neurobiology of vocal learning: grey seals (
Halichoerus grypus
). We provide a neuroanatomical atlas (based on dissected brain slices and magnetic resonance images), a labelled MRI template, a three-dimensional model with volumetric measurements of brain regions, and histological cortical stainings. Four main features of the grey seal brain stand out: (i) it is relatively big and highly convoluted; (ii) it hosts a relatively large temporal lobe and cerebellum; (iii) the cortex is similar to that of humans in thickness and shows the expected six-layered mammalian structure; (iv) there is expression of FoxP2 present in deeper layers of the cortex;
FoxP2
is a gene involved in motor learning, vocal learning, and spoken language. Our results could facilitate future studies targeting the neural and genetic underpinnings of mammalian vocal learning, thus bridging the research gap from songbirds to humans and non-human primates. Our findings are relevant not only to vocal learning research but also to the study of mammalian neurobiology and cognition more in general.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
Protophones, the precursors to speech, dominate the human infant vocal landscape
Authors: Oller D. Kimbrough , Ramsay Gordon , Bene Edina , Long Helen L. , Griebel Ulrike
Published: September 2021

Human infant vocalization is viewed as a critical foundation for vocal learning and language. All apes share distress sounds (shrieks and cries) and laughter. Another vocal type, speech-like sounds, common in human infants, is rare but not absent in other apes. These three vocal types form a basis for especially informative cross-species comparisons. To make such comparisons possible we need empirical research documenting the frequency of occurrence of all three. The present work provides a comprehensive portrayal of these three vocal types in the human infant from longitudinal research in various circumstances of recording. Recently, the predominant vocalizations of the human infant have been shown to be speech-like sounds, or ‘protophones’, including both canonical and non-canonical babbling. The research shows that protophones outnumber cries by a factor of at least five based on data from random-sampling of all-day recordings across the first year. The present work expands on the prior reports, showing the protophones vastly outnumber both cry and laughter in both all-day and laboratory recordings in various circumstances. The data provide new evidence of the predominance of protophones in the infant vocal landscape and illuminate their role in human vocal learning and the origin of language.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
Variation in vocal production learning across songbirds
Authors: Searcy William A. , Soha Jill , Peters Susan , Nowicki Stephen
Published: September 2021

Songbirds as a whole are considered to be vocal production learners, meaning that they modify the structure of their vocalizations as a result of experience with the vocalizations of others. The more than 4000 species of songbirds, however, vary greatly in crucial features of song development. Variable features include: (i) the normality of the songs of early-deafened birds, reflecting the importance of innate motor programmes in song development; (ii) the normality of the songs of isolation-reared birds, reflecting the combined importance of innate auditory templates and motor programmes; (iii) the degree of selectivity in choice of external models; (iv) the accuracy of copying from external models; and (v) whether or not learning from external models continues into adulthood. We suggest that because of this variability, some songbird species, specifically those that are able to develop songs in the normal range without exposure to external models, can be classified as limited vocal learners. Those species that require exposure to external models to develop songs in the normal range can be considered complex vocal learners.

This article is part of the theme issue ‘Vocal learning in animals and humans’.
Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks
Authors: Sani Omid G. , Pesaran Bijan , Shanechi Maryam M.
Published: September 2021

Understanding the dynamical transformation of neural activity to behavior requires modeling this transformation while both dissecting its potential nonlinearities and dissociating and preserving its nonlinear behaviorally relevant neural dynamics, which remain unaddressed. We present RNN PSID, a nonlinear dynamic modeling method that enables flexible dissection of nonlinearities, dissociation and preferential learning of neural dynamics relevant to specific behaviors, and causal decoding. We first validate RNN PSID in simulations and then use it to investigate nonlinearities in monkey spiking and LFP activity across four tasks and different brain regions. Nonlinear RNN PSID successfully dissociated and preserved nonlinear behaviorally relevant dynamics, thus outperforming linear and non-preferential nonlinear learning methods in behavior decoding while reaching similar neural prediction. Strikingly, dissecting the nonlinearities with RNN PSID revealed that consistently across all tasks, summarizing the nonlinearity only in the mapping from the latent dynamics to behavior was largely sufficient for predicting behavior and neural activity. RNN PSID provides a novel tool to reveal new characteristics of nonlinear neural dynamics underlying behavior.
ISL1 is necessary for auditory neuron development and contributes towards tonotopic organization
Authors: Filova Iva , Pysanenko Kateryna , Tavakoli Mitra , Vochyanova Simona , Dvorakova Martina , Bohuslavova Romana , Smolik Ondrej , Fabriciova Valeria , Hrabalova Petra , Benesova Sarka , Valihrach Lukas , Cerny Jiri , Yamoah Ebenezer N. , Syka Josef , Fritzsch Bernd , Pavlinkova Gabriela
Published: September 2021

A cardinal feature of the auditory pathway is frequency selectivity, represented in a tonotopic map from the cochlea to the cortex. The molecular determinants of the auditory frequency map are unknown. Here, we discovered that the transcription factor ISL1 regulates the molecular and cellular features of auditory neurons, including the formation of the spiral ganglion and peripheral and central processes that shape the tonotopic representation of the auditory map. We selectively knocked out Isl1 in auditory neurons using Neurod1Cre strategies. In the absence of Isl1, spiral ganglion neurons migrate into the central cochlea and beyond, and the cochlear wiring is profoundly reduced and disrupted. The central axons of Isl1 mutants lose their topographic projections and segregation at the cochlear nucleus. Transcriptome analysis of spiral ganglion neurons shows that Isl1 regulates neurogenesis, axonogenesis, migration, neurotransmission-related machinery, and synaptic communication patterns. We show that peripheral disorganization in the cochlea affects the physiological properties of hearing in the midbrain and auditory behavior. Surprisingly, auditory processing features are preserved despite the significant hearing impairment, revealing central auditory pathway resilience and plasticity in Isl1 mutant mice. Mutant mice have a reduced acoustic startle reflex, altered prepulse inhibition, and characteristics of compensatory neural hyperactivity centrally. Our findings show that ISL1 is one of the obligatory factors required to sculpt auditory structural and functional tonotopic maps. Still, upon Isl1 deletion, the ensuing central compensatory plasticity of the auditory pathway does not suffice to overcome developmentally induced peripheral dysfunction of the cochlea.
Parenteral glucose supply and pharmacological glycolysis inhibition determine the clinical fate of infected preterm newborns
Authors: Muk Tik , Brunse Anders , Henriksen Nicole L. , Aasmul-Olsen Karoline , Nguyen Duc Ninh
Published: September 2021

Preterm infants are susceptible to bloodstream infection that can lead to sepsis. High parenteral glucose supplement is commonly used to support their growth and energy expenditure, but may exceed endogenous regulation during infection, causing dysregulated immune response and clinical deterioration. Using a preterm piglet model of neonatal sepsis induced byStaphylococcus epidermidisinfection, we demonstrate the delicate interplay between immunity and energy metabolism to regulate the host infection response. Circulating glucose levels, glycolysis and inflammatory response to infection are closely connected across the states of tolerance, resistance and immunoparalysis. Further, high parenteral glucose provision during infection induces hyperglycemia, elevated glycolysis and inflammation, leading to lactate acidosis and sepsis, whereas glucose restricted individuals are clinically unaffected with increased gluconeogenesis to maintain moderate hypoglycemia. Finally, pharmacological glycolysis inhibition during normoglycemia enhances bacterial clearance and dampens inflammation but fails to prevent sepsis. Our results uncover how blood glucose controls immune cell metabolism and function, in turn determining the clinical fate of infected preterm neonates. This also questions the current practice of parenteral glucose supply for infected preterm infants.
The evolution of open habitats in North America revealed by deep learning models
Authors: Andermann Tobias , Strömberg Caroline , Antonelli Alexandre , Silvestro Daniele
Published: September 2021

Open vegetation today constitutes one of the most extensive biomes on earth, including temperate grasslands and tropical savannas. Yet these biomes originated relatively recently in earth history, likely replacing forested habitats as recently as the second half of the Cenozoic, although the timing of their origination and the dynamics of their expansion remain uncertain. Here, we present a new hypothesis of paleovegetation change in North America, showing that open habitats originated between 25 and 20 Ma in the center of the continent, and expanded rapidly starting 8 Ma to eventually become the most prominent vegetation type today. To obtain space-time predictions of paleovegetation, we developed a new Bayesian deep learning model that utilizes available information from fossil evidence, geologic models, and paleoclimate proxies. We compiled a large dataset of paleovegetation reconstructions from the peer-reviewed literature, which we used in combination with current vegetation data to train the model. The model learns to predict vegetation based on the learned associations between the vegetation at a given site and multiple biotic and abiotic predictors: fossil mammal occurrences, plant macrofossils, estimates of temperature and precipitation, latitude, and the effects of spatial and temporal autocorrelation. Our results provide a new, spatially detailed reconstruction of habitat evolution in North America and our deep learning model paves the way for a new quantitative approach to estimating paleovegetation changes.
Medullary vein architecture modulates the white matter BOLD cerebrovascular reactivity signal response to CO2: observations from high-resolution T2*weighted imaging at 7T
Authors: Bhogal Alex A.
Published: September 2021

Brain stress testing using blood oxygenation level-dependent (BOLD) MRI to evaluate changes in cerebrovascular reactivity (CVR) is of growing interest for evaluating white matter integrity. However, even under healthy conditions, the white matter BOLD-CVR response differs notably from that observed in the gray matter. In addition to actual arterial vascular control, the venous draining topology may influence the WM-CVR response leading to signal delays and dispersions. These types of alterations in hemodynamic parameters are sometimes linked with pathology, but may also arise from differences in normal venous architecture. In this work, high-resolution T2*weighted anatomical images combined with BOLD imaging during a hypercapnic breathing protocol were acquired using a 7 tesla MRI system. Hemodynamic parameters including base CVR, hemodynamic lag, lag-corrected CVR, response onset and signal dispersion, and finally ΔCVR (corrected CVR minus base CVR) were calculated in 8 subjects. Parameter maps were spatially normalized and correlated against an MNI-registered white matter medullary vein atlas. Moderate correlations (Pearson’s rho) were observed between medullary vessel frequency (MVF) and ΔCVR (0.52; 0.58 for total WM), MVF and hemodynamic lag (0.42; 0.54 for total WM), MVF and signal dispersion (0.44; 0.53 for total WM), and finally MVF and signal onset (0.43; 0.52 for total WM). Results indicate that, when assessed in the context of the WM venous architecture, changes in the response shape may only be partially reflective of the actual vascular reactivity response occurring further upstream by control vessels. This finding may have implications when attributing diseases mechanisms and/or progression to presumed impaired WM BOLD-CVR.
Improve consensus partitioning via a hierarchical procedure
Authors: Gu Zuguang , Hübschmann Daniel
Published: September 2021

Consensus partitioning is an unsupervised method widely used in high throughput data analysis for revealing subgroups and assigns stability for the classification. However, standard consensus partitioning procedures are weak to identify large numbers of stable subgroups. There are two main issues. 1. Subgroups with small differences are difficult to separate if they are simultaneously detected with subgroups with large differences. And 2. stability of classification generally decreases as the number of subgroups increases. In this work, we proposed a new strategy to solve these two issues by applying consensus partitionings in a hierarchical procedure. We demonstrated hierarchical consensus partitioning can be efficient to reveal more subgroups. We also tested the performance of hierarchical consensus partitioning on revealing a great number of subgroups with a DNA methylation dataset. The hierarchical consensus partitioning is implemented in the R package cola with comprehensive functionality for analysis and visualizations. It can also automate the analysis only with a minimum of two lines of code, which generates a detailed HTML report containing the complete analysis.
Forest wildflowers bloom earlier as Europe warms – but not everywhere equally
Authors: Willems Franziska M. , Scheepens J. F. , Bossdorf Oliver
Published: September 2021

Some of the most striking biological responses to climate change are the observed shifts in the timing of life-history events of many organisms. Plants, in particular, often flower earlier in response to climate warming, and herbarium specimens are excellent witnesses of such long-term changes. However, in large-scale analyses the magnitude of phenological shifts may vary geographically, and the data are often clustered, and it is thus necessary to account for spatial correlation to avoid geographical biases and pseudoreplication. Here, we analysed herbarium specimens of 20 spring-flowering forest understory herbs to estimate how their flowering phenology shifted across Europe during the last century. Our analyses show that on average these forest wildflowers now bloom over six days earlier than at the beginning of the last century. These changes were strongly associated with warmer spring temperatures. Plants flowered on average of 3.6 days earlier per 1°C warming. However, in some parts of Europe plants flowered earlier or later than expected. This means, there was significant residual spatial variation in flowering time across Europe, even after accounting for the effects of temperature, precipitation, elevation and year. Including this spatial autocorrelation into our statistical models significantly improved model fit and reduced bias in coefficient estimates. Our study indicates that forest wildflowers in Europe strongly advanced their phenology in response to climate change during the last century, with potential severe consequences for their associated ecological communities. It also demonstrates the power of combining herbarium data with spatial modelling when testing for long-term phenology trends across large spatial scales.
ProteinPrompt: a webserver for predicting protein-protein interactions
Authors: Canzler Sebastian , Fischer Markus , Ulbricht David , Ristic Nikola , Hildebrand Peter W. , Staritzbichler René
Published: September 2021

Motivation

Protein-protein interactions play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side-effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine-learning algorithms to calculate specific, currently unknown protein-protein interactions. Our tool is designed to quickly and reliably predict contacts based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.

Methods

We collected and thoroughly filtered a comprehensive database of known contacts from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for contacts.

Results

To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the random forest method achieved an accuracy rate of 0.88 and an area under curve of 0.95. The graph neural network achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of random forest and graph neural networks reduces the likelihood of errors. The consensus reached an accuracy of 0.89. ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt

The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/Proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.
Feed-forward inhibition fine-tunes response timing in auditory-vocal interactions
Authors: Norton Philipp , Benichov Jonathan , Pexirra Margarida , Schreiber Susanne , Vallentin Daniela
Published: September 2021

The ability to regulate vocal timing is a fundamental aspect of communicative interactions for many species, including conversational speech among humans, yet little is known about the neural circuitry that regulates the input-dependent timing of vocal replies. Exploring this topic in the zebra finch premotor area HVC, we identify feed-forward inhibition as a key regulator of vocal response timing. Based on a spiking network model informed by behavioral and electrophysiological data from communicating zebra finches, we predicted that two different patterns of inhibition regulate vocal-motor responses. In one scenario, the strength of production-related premotor inhibition translates into plasticity in vocal response delays. In the other scenario, fast transient interneuron activity in response to auditory input results in the suppression of call production while a call is heard, thereby reducing acoustic overlap between callers. Extracellular recordings in HVC during the listening phase confirm the presence of auditory-evoked response patterns in putative inhibitory interneurons, along with corresponding signatures of auditory-evoked activity suppression. The proposed model provides a parsimonious framework to explain how auditory-vocal transformations can give rise to vocal turn-taking and highlights multiple roles of local inhibition for behavioral modulation at different time scales.
DNA replication stress stratifies prognosis and enables exploitable therapeutic vulnerabilities of HBV-associated hepatocellular carcinoma: an in silico strategy towards precision oncology
Authors: Lu Xiaofan , Meng Jialin , Zhou Yujie , Wang Haitao , Ruan Xinjia , Chen Yi , Ye Yuqing , Su Liwen , Fan Xiaole , Yan Hangyu , Jiang Liyun , Yan Fangrong
Published: September 2021

Background

Hepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs.

Methods

A robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk.

Results

Compared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an ‘immune-cold’ phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS.

Conclusions

Overall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.
Network Modeling Predicts Personalized Gene Expression and Drug Responses in Valve Myofibroblasts Cultured with Patient Sera
Authors: Rogers Jesse D. , Aguado Brian A. , Watts Kelsey M. , Anseth Kristi S. , Richardson William J.
Published: September 2021

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals are transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ) along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in VICs cultured with AVS patient sera and treated with or without bosentan - a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.
Spectrum graph-based de-novo sequencing algorithm MaxNovo achieves high peptide identification rates in collisional dissociation MS/MS spectra
Authors: Gutenbrunner Petra , Kyriakidou Pelagia , Welker Frido , Cox Jürgen
Published: September 2021

We describe MaxNovo, a novel spectrum graph-based peptide de-novo sequencing algorithm integrated into the MaxQuant software. It identifies complete sequences of peptides as well as sequence tags that are incomplete at one or both of the peptide termini. MaxNovo searches for the highest-scoring path in a directed acyclic graph representing the MS/MS spectrum with peaks as nodes and edges as potential sequence constituents consisting of single amino acids or pairs. The raw score is a sum of node and edge weights, plus several reward scores, for instance, for complementary ions or protease compatibility. For search-engine identified peptides, it correlates well with the Andromeda search engine score. We use a particular score normalization and the score difference between the first and second-best solution to define a combined score that integrates all available information. To evaluate its performance, we use a human cell line dataset and take as ground truth all Andromeda-identified MS/MS spectra with an Andromeda score of at least 100. MaxNovo outperforms other software in particular in the high-sensitivity range of precision-coverage plots. We also identify incomplete sequence tags and study their statistical properties. Next, we apply MaxNovo to ion mobility-coupled time of flight data. Here we achieve excellent performance as well, except for potential swaps of the two amino acids closest to the C-terminus, which are not well resolved due to the low end of the mass range in MS/MS spectra in this dataset. We demonstrate the applicability of MaxNovo to palaeoproteomics samples with a Late Pleistocene hominin proteome dataset that was generated using three proteases. Interestingly, we did not use any machine learning in the construction of MaxNovo, but implemented expert domain knowledge directly in the definition of the score. Yet, it performs as good as or better than the leading deep learning-based algorithm.
Over-expression Screen of Interferon-Stimulated Genes Identifies RARRES3 as a Restrictor ofToxoplasma gondiiInfection
Authors: Rinkenberger Nicholas , Abrams Michael E. , Matta Sumit K. , Schoggins John W. , Alto Neal M. , Sibley David
Published: September 2021

Toxoplasma gondiiis an important human pathogen infecting an estimated 1 in 3 people worldwide. The cytokine interferon gamma (IFNγ) is induced during infection and is critical for restrictingT. gondiigrowth in human cells. Growth restriction is presumed to be due to the induction interferon stimulated genes (ISGs) that are upregulated to protect the host from infection. Although there are hundreds of ISGs induced by IFNγ, their individual roles in restricting parasite growth in human cells remain somewhat elusive. To address this deficiency, we screened a library of 414 IFNγ induced ISGs to identify factors that impactT. gondiiinfection in human cells. In addition to IRF1, which likely acts through induction of numerous downstream genes, we identified RARRES3 as a single factor that restrictsT. gondiiinfection by inducing premature egress of the parasite in multiple human cell lines. Overall, while we successfully identified a novel IFNγ induced factor restrictingT. gondiiinfection, the limited number of ISGs capable of restrictingT. gondiiinfection when individually expressed suggests that IFNγ mediated immunity toT. gondiiinfection is a complex, multifactorial process.
Hippo pathway and Bonus control developmental cell fate decisions in the Drosophila eye
Authors: Zhao Heya , Moberg Kenneth H. , Veraksa Alexey
Published: September 2021

The canonical function of the Hippo signaling pathway is regulation of organ growth. How this pathway controls cell fate determination is less well understood. Here, we uncover a function of the Hippo pathway in developmental cell fate decisions in the Drosophila eye-antennal disc exerted through the interaction of Yorkie (Yki) with the transcriptional regulator Bonus (Bon), an ortholog of mammalian Transcriptional Intermediary Factor 1/tripartite motif (TIF1/TRIM) family proteins. Instead of controlling tissue growth, Yki and Bon promote epidermal and antennal fates at the expense of the eye fate. Proteomic, transcriptomic, and genetic analyses reveal that Yki and Bon control these cell fate decisions by recruiting transcriptional and post-transcriptional co-regulators, and by activating epidermal differentiation genes and repressing Notch target genes. Our work expands the range of functions and regulatory mechanisms under Hippo pathway control.
Neurons in the dorsomedial hypothalamus promote, prolong, and deepen torpor in the mouse
Authors: Ambler Michael , Hitrec Timna , Wilson Andrew , Cerri Matteo , Pickering Anthony
Published: September 2021

Torpor is a naturally occurring, hypometabolic, hypothermic state engaged by a wide range of animals in response to imbalance between the supply and demand for nutrients. Recent work has identified some of the key neuronal populations involved in daily torpor induction in mice, in particular projections from the preoptic area of the hypothalamus (POA) to the dorsomedial hypothalamus (DMH). The DMH plays a role in thermoregulation, control of energy expenditure, and circadian rhythms, making it well positioned to contribute to the expression of torpor. We used activity dependent genetic TRAPing techniques to target DMH neurons that were active during natural torpor bouts in female mice. Chemogenetic reactivation of torpor-TRAPed DMH neurons in calorie-restricted mice promoted torpor, resulting in longer and deeper torpor bouts. Chemogenetic inhibition of torpor-TRAPed DMH neurons did not block torpor entry, suggesting a modulatory role for the DMH in the control of torpor. This work adds to the evidence that the POA and the DMH form part of a circuit within the mouse hypothalamus that controls entry into daily torpor.

Significance

Daily heterotherms such as mice employ torpor to cope with environments in which the supply of metabolic fuel is not sufficient for the maintenance of normothermia. Daily torpor involves reductions in body temperature, as well as active suppression of heart rate and metabolism. How the central nervous system controls this profound deviation from normal homeostasis is not known, but a projection from the preoptic area to the dorsomedial hypothalamus has recently been implicated. We demonstrate that the dorsomedial hypothalamus contains neurons that are active during torpor. Activity in these neurons promotes torpor entry and maintenance, but their activation alone does not appear to be sufficient for torpor entry.
ScanNet: An interpretable geometric deep learning model for structure-based protein binding site prediction
Authors: Tubiana Jérôme , Schneidman-Duhovny Dina , Wolfson Haim J.
Published: September 2021

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its functionin vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available fromhttp://bioinfo3d.cs.tau.ac.il/ScanNet/
Rest/Stress Myocardial Perfusion Imaging by Positron Emission Tomography with 18F-Flurpiridaz: A Feasibility Study in Mice
Authors: Bengs Susan , Warnock Geoffrey I. , Portmann Angela , Mikail Nidaa , Rossi Alexia , Ahmed Hazem , Etter Dominik , Gisler Livio , Jie Caitlin , Meisel Alexander , Keller Claudia , Schibli Roger , Mu Linjing , Buechel Ronny R. , Kaufmann Philipp A. , Ametamey Simon M. , Gebhard Catherine , Haider Ahmed
Published: September 2021

Background

Myocardial perfusion imaging by positron emission tomography (PET-MPI) is the current gold standard for quantification of myocardial blood flow. 18F-flurpiridaz was recently introduced as a valid alternative to currently used PET-MPI probes. Nonetheless, optimum scan duration and time interval for image analysis are currently unknown. Further, it is unclear whether rest/stress PET-MPI with 18F-flurpiridaz is feasible in mice.

Methods

Rest/stress PET-MPI was performed with 18F-flurpiridaz (0.6-3.0 MBq) in 29 mice aged 7-8 months. Regadenoson (0.1 μg/g) was used for induction of vasodilator stress. Kinetic modeling was performed using a metabolite-corrected arterial input function. Image-derived myocardial 18F-flurpiridaz uptake was assessed for different time intervals by placing a volume of interest in the left ventricular myocardium.

Results

Tracer kinetics were best described by a two-tissue compartment model. K1 ranged from 6.7-20.0 mL/cm3/min, while myocardial volumes of distribution (VT) were between 34.6 and 83.6 mL/cm3. Of note, myocardial 18F-flurpiridaz uptake (%ID/g) was significantly correlated with K1 at rest and following pharmacological stress testing for all time intervals assessed. However, while Spearman’s coefficients (rs) ranged between 0.478 and 0.672, R2 values were generally low. In contrast, an excellent correlation of myocardial 18F-flurpiridaz uptake with VT was obtained, particularly when employing the averaged myocardial uptake from 20-40 min post tracer injection (R2 ≥0.98). Notably, K1 and VT were similarly sensitive to pharmacological stress induction. Further, mean stress-to-rest ratios of K1, VT, and %ID/g 18F-flurpiridaz were virtually identical, suggesting that %ID/g 18F-flurpiridaz can be used to estimate CFR in mice.

Conclusion

Our findings suggest that a simplified assessment of relative myocardial perfusion and coronary flow reserve (CFR), based on image-derived tracer uptake, is feasible with 18F-flurpiridaz in mice, enabling high-throughput mechanistic CFR studies in rodents.