bluuebunny/crossref_metadata_2025 huggingface dataset:
Microbial reaction rate estimation using proteins and proteomes
Authors: McCain J. Scott P. , Britten Gregory L. , Hackett Sean R. , Follows Michael J. , Li Gene-Wei
Published: August 2024

Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (medianR2= 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurementsin situto estimate the rates of microbially mediated reactions in natural systems.

Significance

One of the most basic phenotypes of a microbe is its set of associated reaction rates, but quantifying these ratesin situremains extremely challenging, especially in natural systems. We used molecular data and statistical models to estimate microbial rates in steady state cultures. We found that many reaction rates are highly predictable using proteomic data, though single proteins are typically not informative for their associated reaction rates. This result suggests that gene expression data from complex microbial communities could be used to estimatein situreaction rates, providing new clues into the lives and environmental function of microbes.
Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus
Authors: Currin Christopher B. , Burman Richard J. , Fedele Tommaso , Ramantani Georgia , Rosch Richard E. , Sprekeler Henning , Raimondo Joseph V.
Published: August 2024

Seizures that continue for beyond five minutes are classified as status epilepticus (SE) and constitute a medical emergency. Benzodiazepines, the current first-line treatment, attempt to terminate SE by increasing the conductance of chloride-permeable type-A GABA receptors (GABAARs). Despite their widespread use, benzodiazepines are ineffective in over a third of cases. Previous research in animal models has demonstrated that changes in intraneuronal chloride homeostasis and GABAAR physiology may underlie the development of benzodiazepine resistance in SE. However, there remains a need to understand the effect of these changes at a network level to improve translation into the clinical domain. Therefore, informed by data from human EEG recordings of SE and experimental brain slice recordings, we used a large spiking neural network model that incorporates chloride dynamics to investigate and address the phenomenon of benzodiazepine resistance in SE. We found that the GABAAR reversal potential (EGABA) sets SE-like bursting and determines the response to GABAAR conductance modulation, with benzodiazepines being anti-seizure at low EGABAand ineffective or pro-seizure at high EGABA. The SE-like activity and EGABAdepended on a non-linear relationship between the strength of Cl-extrusion and GABAAR conductance, but not on the initial EGABAof neurons. Independently controlling Cl-extrusion in the pyramidal and interneuronal cell populations revealed the critical role of pyramidal cell Cl-extrusion in determining the severity of SE activity and the response to simulated benzodiazepine application. Finally, we demonstrate the model’s utility for considering improved therapeutic approaches for terminating SE in the clinic.
Allopolyploidy expanded gene content but not pangenomic variation in the hexaploid oilseedCamelina sativa
Authors: Bird Kevin A. , Brock Jordan R. , Grabowski Paul P. , Harder Avril M. , Shu Shengqiang , Barry Kerrie , Boston LoriBeth , Daum Christopher , Guo Jie , Lipzen Anna , Walstead Rachel , Grimwood Jane , Schmutz Jeremy , Lu Chaofu , Comai Luca , McKay John K. , Pires J. Chris , Edger Patrick P. , Lovell John T. , Kliebenstein Daniel J.
Published: August 2024

Ancient whole-genome duplications (WGDs) are believed to facilitate novelty and adaptation by providing the raw fuel for new genes. However, it is unclear how recent WGDs may contribute to evolvability within recent polyploids. Hybridization accompanying some WGDs may combine divergent gene content among diploid species. Some theory and evidence suggest that polyploids have a greater accumulation and tolerance of gene presence-absence and genomic structural variation, but it is unclear to what extent either is true. To test how recent polyploidy may influence pangenomic variation, we sequenced, assembled, and annotated twelve complete, chromosome-scale genomes ofCamelina sativa, an allohexaploid biofuel crop with three distinct subgenomes. Using pangenomic comparative analyses, we characterized gene presence-absence and genomic structural variation both within and between the subgenomes. We found over 75% of ortholog gene clusters are core inCamelina sativaand <10% of sequence space was affected by genomic structural rearrangements. In contrast, 19% of gene clusters were unique to one subgenome, and the majority of these were Camelina-specific (no ortholog in Arabidopsis). We identified an inversion that may contribute to vernalization requirements in winter-type Camelina, and an enrichment of Camelina-specific genes with enzymatic processes related to seed oil quality and Camelina’s unique glucosinolate profile. Genes related to these traits exhibited little presence-absence variation. Our results reveal minimal pangenomic variation in this species, and instead show how hybridization accompanied by WGD may benefit polyploids by merging diverged gene content of different species.
Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells
Authors: Rylaarsdam Lauren E. , Nichols Ruth V. , O’Connell Brendan L. , Coleman Stephen , Yardımcı Galip Gürkan , Adey Andrew C.
Published: August 2024

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to describe a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts.
When monkeys meet an ANYmal robot in the wild
Authors: Canteloup Charlotte , Lee Joonho , Zimmermann Samuel , Alvino Morgane , Montenegro Markus , Hutter Marco , van de Waal Erica
Published: August 2024

Animal-robot interaction studies have been of increasing interest in research, but most of these studies have involved robots interacting with insects, birds, and frogs in laboratory settings. To date, only two studies used non-human primates and no behavioral study has tested the social integration of a robot in a group of wild primates. To fill this gap, we studied the interactions between the quadruped ANYmal robot and a group of 37 wild vervet monkeys in South Africa. The ANYmal robot is a remote-controlled sheep-sized robot with an open box of food on its back. We gradually introduced the robot to the monkeys following five different steps over 6 days for a total exposition time of about 10h. The monkeys habituated to the robot very quickly with six individuals eating the food in the robot’s box from the second day. A few individuals, mostly juveniles, emitted alarm calls towards the robot. In total, seven individuals, high rankers, spent time near the robot and 21 monkeys approached the robot from a greater distance. High rankers displayed significantly more vigilant and self-centered behaviors and they, with females and juveniles, ate more food in the robot’s box compared to low rankers, males, and adults conversely. This study offers exciting perspectives on the phenomena of social acceptance of machines in mammalian societies and the automation of field data collection.
Quantifying conformational changes in the TCR:pMHC-I binding interface
Authors: McMaster Benjamin , Thorpe Christopher , Rossjohn Jamie , Deane Charlotte , Koohy Hashem
Published: August 2024

T cells form one of the key pillars of adaptive immunity. Using their surface bound T cell antigen receptors (TCRs), these cells, screen millions of antigens presented by major histocompatibility complex (MHC) or MHC-like molecules. In other protein families, the dynamics of protein-protein interactions have important implications for protein function. Case studies ofTCR:class I peptide-MHCs (pMHC-Is) structures have reported mixed results on whether the binding interfaces undergo conformational change during engagement and no robust statistical quantification has been done to generalise these results. It thus remains an open question if movement occurs in the binding interface that enables recognition and activation of T cells. In this work, we quantify the conformational changes in the TCR:pMHC-I binding interface by creating a dataset of 358 structures, comprising 25 TCRs, 20 MHC alleles, and 58 peptide structures in both unbound (apo) and bound (holo) conformations. In support of some case studies, we demonstrate that all complimentary determining region (CDR) loops move to a certain extent but only CDR3α and CDR3β loops modify their shape when binding pMHC-Is. We also map out the contacts between TCRs and pMHC-Is, generating a novel fingerprint of TCRs on MHC molecules and show that the CDR3α tends to bind the N-terminus of the peptide and the CDR3β tends to bind the C-terminus of the peptide. Finally, we show that the presented peptides can undergo conformational changes when engaged by TCRs, as has been reported in past literature, but novelly show these changes depend on how the peptides are anchored in the MHC binding groove. Our work has implications in understanding the behaviour of TCR:pMHC-I interactions and providing insights that can be used for modelling T cell antigen specificity, an ongoing grand challenge in immunology.
In Silicostudies provide new structural insights intotrans-dimerization of β1and β2subunits of the Na+,K+-ATPase
Authors: Ramírez-Salinas Gema , Rosas-Trigueros Jorge L. , Huerta Christian Sosa , Shoshani Liora , Martínez-Archundia Marlet
Published: August 2024

The Na+,K+-ATPase is an electrogenic transmembrane pump located in the plasma membrane of all animal cells. It is a dimeric protein composed of α and β subunits and has a third regulatory subunit (γ) belonging to the FXYD family . This pump plays a key role in maintaining low concentration of sodium and high concentration of potassium intracellularly. The α subunit is the catalytic one while the β subunit is important for the occlusion of the K+ions and plays an essential role in trafficking of the functional αβ complex of Na+,K+-ATPase to the plasma membrane. Interestingly, the β1and β2(AMOG) isoforms of the β subunit, function as cell adhesion molecules in epithelial cells and astrocytes, respectively. Early experiments suggested a heterotypic adhesion for the β2. Recently, we reported a homotypic trans-interaction between β2-subunits expressed in CHO cells. In this work we useIn Silicomethods to analyze the physicochemical properties of the putative homophilic trans-dimer of β2subunits and provide insights about thetrans-dimerization interface stability. Our structural analysis predicts a molecular recognition mechanism of atrans-dimeric β2-β2subunit and permits designing experiments that will shed light upon possible homophilic interactions of β2subunits in the nervous system.

Author summary

The adhesion molecule on glia (AMOG) is the β2isoform of the β-subunit of the Na+-pump that is localized in the nervous system, specifically in astrocytes. It was shown that it mediates Neuron-Astrocyte interaction, promoting neurite outgrowth and migration during brain development. In recent years we have shown that the ubiquitous β1isoform is a homophilic adhesion molecule in epithelia and therefore we hypothesized that β2could also interact as a homophilic adhesion protein. In a previous work we show that fibroblasts (CHO) transfected with the human β2subunit of the Na+-pump become adhesive. Moreover, protein-protein interaction assay in a co-culture of cells transfected with β2tagged with two different markers (His6and YFP) reveal a positive interaction between the β2-subunits. In the present work, we apply bioinformatics methods to analyze and discuss the formation of atrans-dimer of β2-subunits. OurIn Silicostudy predicts a relatively stable dimer with an interface that involves the participation of four out of the seven N-glycosylation sites. Nevertheless, interacting interface and the dynamics of the β2-β2trans-dimer is different from that of the β1-β1dimer; it involves different surfaces and therefore it explains why β-subunits can not form mixed (β1-β2)trans-dimers.
Longitudinal metabolomics data analysis informed by mechanistic models
Authors: Li Lu , Hoefsloot Huub , Bakker Barbara M. , Horner David , Rasmussen Morten A. , Smilde Age K. , Acar Evrim
Published: August 2024

Motivation

Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. In this paper, we introduce a novel data analysis approach where data-driven methods are guided by prior information through joint analysis of simulated data generated using a human metabolic model and real metabolomics measurements.

Results

We arrange time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000cohort as a third-order tensor:subjectsbymetabolitesbytime samples. Simulated challenge test data generated using a human whole-body metabolic model is also arranged as a third-order tensor:virtual subjectsbymetabolitesbytime samples. Real and simulated data sets are coupled in themetabolitesmode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Our experiments demonstrate that joint analysis of simulated and real data has a better performance in terms of pattern discovery achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males while in females, the performance is comparable. We also demonstrate the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information.

Availability

The code for joint analysis of real and simulated metabolomics data sets is released as a GitHub repository. Simulated data can also be accessed using the GitHub repo. Real measurements of plasma samples are not publicly available. Data may be shared by COPSAC through a collaboration agreement. Data access requests should be directed to Morten A. Rasmussen (morten.arendt@dbac.dk).
Differences in maternal diet fiber content influence patterns of gene expression and chromatin accessibility in fetuses and piglets
Authors: Chalabi Smahane , Loonen Linda , Boekhorst Jos , Li Houcheng , Fang Lingzhao , Harrison Peter W. , Lakhal Wassim , Lluch Jerome , Sokolov Alexey , Djebali Sarah , Rau Andrea , Giuffra Elisabetta , Wells Jerry
Published: August 2024

This study investigates the impact of maternal gestation diets with varying fiber contents on gene expression and chromatin accessibility in fetuses and piglets fed a low fiber diet post weaning. High-fiber maternal diets, enriched with sugar beet pulp or pea internal fiber, were compared to a low-fiber maternal diet to evaluate their effects on liver and muscle tissues. The findings demonstrate that maternal high-fiber diets significantly alter the chromatin accessibility, predicted transcription factor activity and transcriptional landscape in both fetuses and piglets. A gene set enrichment analysis revealed over-expression of gene ontology terms related to metabolic processes and under-expression of those linked to immune responses in piglets from sows given the high-fiber diets during gestation. This suggests better metabolic health and immune tolerance of the fetus and offspring, in line with the documented epigenetic effects of short chain fatty acids on immune and metabolic pathways. A deconvolution analysis of the bulk RNA-seq data was performed using cell-type specific markers from a single cell transcriptome atlas of adult pigs. These results confirmed that the transcriptomic and chromatin accessibility data do not reflect different cell type compositions between maternal diet groups but rather phenotypic changes triggered by the critical role of maternal nutrition in shaping the epigenetic and transcriptional environment of fetus and offspring. Our findings have implications for improving animal health and productivity as well as broader implications for human health, suggesting that optimizing maternal diet with high-fiber content could enhance metabolic health and immune function in the formative years after birth and potentially to adulthood.
Proteochemometric modeling strengthens the role of Q299 for GABA transporter subtype selectivity
Authors: Kickinger Stefanie , Seiler Anna , Digles Daniela , Ecker Gerhard F.
Published: August 2024

Proteochemometric modeling (PCM) combines ligand information as well as target information in order to predict an output variable of interest (e.g. activity of a compound). The big advantage of PCM compared to conventional Quantitative Structure-Activity Relationship (QSAR) modeling is, that by creating a single model one can not only predict the affinity of a diverse set of compounds to a diverse set of targets, but also extrapolate the specific ligand-protein interactions that might be relevant for activity. In this study, we compiled a dataset of 323 compounds and their bioactivity data regarding the inhibition of the four GABA-transporter (GAT1/BGT1/GAT2/GAT3) subtypes, which are potential new drug targets for treating epilepsy. Proteochemometric modeling using partial least squares and random forest provided models which performed equally well than conventional QSAR models for each individual transporter. However, by analyzing the importance of the protein descriptors used in the PCM models, we identified the amino acid Leu300/Q299/L294/L314/ in GAT1/BGT1/GAT2/GAT3 to be relevant for binding and subtype selectivity.
Weed Instance Segmentation from UAV ortho-mosaic Images based on Deep Learning
Authors: Lu Chenghao , Yu Kang
Published: August 2024

Weeds significantly impact agricultural production, and traditional weed control methods often harm soil health and the environment. This study aims to develop deep learning-based segmentation models in identifying weeds in potato fields captured by Unmanned Aerial Vehicle (UAV)orthophotos and to explore the effects of weeds on potato yield. UAVs were used to collect RGB data from potato fields, flying at an altitude of 10m, with Real-ESRGAN Super-Resolution (SR) enhancing image resolution. We applied the Segment Anything Model (SAM) to do semi-automatic annotation, followed by training the YOLOv8 and MASK-RCNN models for segmentation. Also we used ANOVA and linear regression to analyze the effects of weeds and nitrogen fertilizer on yield. Results showed that the detection accuracy mAP50 scores for YOLOv8 and Mask R-CNN were 0.902 and 0.920, respectively, with the Real-ESRGAN-enhanced model achieving 0.909. When multiple weed types were present, accuracy decreased to 0.86. The linear regression model, incorporating plant and weed coverage areas, explained 41.2% of yield variation (R2= 0.412, p-value = 0.01). Both YOLOv8 and Mask R-CNN achieved high accuracy, with YOLOv8 converging faster. Real-ESRGAN reconstruction slightly improved accuracy. Different nitrogen fertilizer treatments had no significant effect on yield, while weed control treatments significantly impacted yield, showing the importance of precise weed mapping.. This study provides insights into weed segmentation and contributes to environmentally friendly precision weed control.

Graphic Abstract
Shared and Unique Neural Codes for Biological Motion Perception in Humans and Macaque Monkeys
Authors: Cheng Yuhui , Xin Yumeng , Lu Xiqian , Yang Tianshu , Ma Xiaohan , Yuan Xiangyong , Liu Ning , Jiang Yi
Published: August 2024

Throughout evolution, living organisms have honed the ability to swiftly recognize biological motion (BM) across species. However, how the brain processes within- and cross-species BM, and the evolutionary progression of these processes, remain unclear. To investigate these questions, we examined brain activity in the lateral temporal areas of humans and monkeys as they passively observed upright and inverted human and macaque BM stimuli. In humans, the middle temporal area (hMT+) responded to both human and macaque BM stimuli, while the right posterior superior temporal sulcus (pSTS) exhibited selective responses to human BM stimuli. This selectivity was evidenced by an increased feedforward connection from hMT+ to pSTS during the processing of human BM stimuli. In monkeys, the MT region processed BM stimuli from both species, but no subregion in the STS anterior to MT was specific to conspecific BM stimuli. A comparison of these findings suggests that upstream brain regions (i.e., MT) may retain homologous functions across species, while downstream brain regions (i.e., STS) may have undergone differentiation and specialization throughout evolution. These results provide insights into the commonalities and differences in the specialized visual pathway engaged in processing within- and cross-species BMs, as well as their functional divergence during evolution.
Exploring tropical freshwater soundscapes in Southeast Asia: insights into auditory sensory adaptation of wild Siamese fighting fishBetta splendens
Authors: RAMOS Andreia , GONÇALVES David , VASCONCELOS Raquel O.
Published: August 2024

While soundscapes shape the structure and function of auditory systems over evolutionary timescales, there is limited information regarding the adaptation of wild fish populations to their natural acoustic environments. This is particularly relevant for freshwater ecosystems, which are extremely diverse and face escalating pressures from human activities and associated noise pollution. The Siamese fighting fishBetta splendensis one of the most important cultured species in the global ornamental fish market and is increasingly recognized as a model organism for genetics and behavioural studies. This air-breathing species (Anabantoidei), characterized by the presence of a suprabranchial labyrinth organ, is native to Southeast Asia and inhabits low flow freshwater ecosystems that are increasingly threatened due to habitat destruction and pollution.

We characterized the underwater soundscape, along with various ecological parameters, across five marshland habitats ofB. splendens, from lentic waterbodies to small canals near a lake in Chiang Rai province (Thailand). All habitats exhibited common traits of low dissolved oxygen and dense herbaceous vegetation. Soundscapes were relatively quiet with Sound Pressure Level (SPL) around 102-105 dB re 1 µPa and most spectral energy below 1000 Hz. Sound recordings captured diverse biological sounds, including potential fish vocalizations, but primarily insect sounds. Based on Auditory Evoked Potential (AEP) recordings, we identified sex-specific differences and a best hearing range within 100-400 Hz. This low-frequency tuning highlights the potential susceptibility ofB. splendensto anthropogenic noise activities.

This study provides first characterization of the auditory sensitivity and natural soundscape ofB. splendens,establishing an important ground for future hearing research in this species. The information provided on the auditory sensory adaptation ofB. splendensemphasizes the importance of preserving quiet soundscapes from lentic freshwater ecosystems.
KnowVolution of an efficient polyamidase through molecular dynamics simulations of incrementally docked oligomeric substrates
Authors: Puetz Hendrik , Illig Alexander-Maurice , Vorobii Mariia , Janknecht Christoph , Contreras Francisca , Flemig Fabian , Schwaneberg Ulrich
Published: August 2024

Management of synthetic polymer waste is one of the most pressing challenges for society today. Enzymatic recycling of polycondensates like polyamides (PA), however, remains limited due to a lack of efficient polyamidases. This study reports the directed evolution of the polyamidase NylCp2-TS. Key positions involved in enzyme-substrate interactions and PA 6 hydrolysis are identified through random mutagenesis and molecular dynamics (MD) simulations. The final variant, NylC-HP (NylCp2-TSF134W/D304M/R330A), exhibits a 6.9-fold increased specific activity (520 ± 19 µmol6-AHAeq. h−1mgenzyme−1) and enhanced thermal stability (Tm = 90 °C, ΔTm = 4.2 °C), making NylC-HP the fastest polyamidase for PA 6 and PA 6,6 hydrolysis. Despite the improved reaction rate, the degree of depolymerization remains below 1 %. To understand the molecular basis of achieved improvements and factors limiting the degree of depolymerization, intra- and intermolecular interactions of various enzyme-substrate complexes are analyzed by incremental docking of PA 6 tetramers and MD simulations. After optimizing the activity and stability of NylC-HP, the findings suggest that widening the substrate binding pocket is likely necessary to improve substrate accessibility to target more buried attack sites on the polymer surface, and thereby enhancing the degree of depolymerization.
Integrating sequence composition information into microbial diversity analyses with k-mer frequency counting
Authors: Bokulich Nicholas A.
Published: August 2024

K-mer frequency information in biological sequences is used for a wide range of applications, including taxonomy classification, sequence similarity estimation, and supervised learning. However, in spite of its widespread utility, k-mer counting has been largely neglected for diversity estimation. This work examines the application of k-mer counting for alpha and beta diversity as well as supervised classification from microbiome marker-gene sequencing datasets (16S rRNA gene and full-length fungal ITS sequences). Results demonstrate a close correspondence with phylogenetically aware diversity metrics, and advantages for using k-mer-based metrics for measuring microbial biodiversity in microbiome sequencing surveys. K-mer counting appears to be a suitable and efficient strategy for feature processing prior to diversity estimation as well as supervised learning in microbiome surveys. This allows incorporation of subsequence-level information into diversity estimation without the computational cost of pairwise sequence alignment. K-mer counting is proposed as a complementary approach for feature processing prior to diversity estimation and supervised learning analyses, enabling large-scale reference-free profiling of microbiomes in biogeography, ecology, and biomedical data. A method for k-mer counting from marker-gene sequence data is implemented in the QIIME 2 plugin q2-kmerizer (https://github.com/bokulich-lab/q2-kmerizer).

Importance

K-mers are all of the subsequences of lengthkthat comprise a sequence. Comparing the frequency of k-mers in DNA sequences yields valuable information about the composition of these sequences and their similarity. This work demonstrates that k-mer frequencies from marker-gene sequence surveys can be used to inform diversity estimates and machine learning predictions that incorporate sequence composition information. Alpha and beta diversity estimates based on k-mer frequencies closely correspond to phylogenetically aware diversity metrics, suggesting that k-mer-based diversity estimates are useful proxy measurements especially when reliable phylogenies are not available, as is often the case for some DNA sequence targets such as for internal transcribed spacer sequences.
Using Dynamic Bayesian Optimization to Induce Desired Effects in the Presence of Motor Learning: a Simulation Study
Authors: Kim GilHwan , Chishty Haider A. , Sergi Fabrizio
Published: August 2024

Human-in-the-loop (HIL) optimization is a control paradigm used for tuning the control parameters of human-interacting devices while accounting for variability among individuals. A limitation of state-of-the-art HIL optimization algorithms such as Bayesian Optimization (BO) is that they assume that the relationship between control parameters and user response does not change over time. BO can be modified to account for the dynamics of the user response by implementing time into the kernel function, a method known as Dynamic Bayesian Optimization (DBO). However, it is unknown if DBO outperforms BO when the human response is characterized by models of human motor learning.

In this work, we simulated runs of HIL optimization using BO and DBO towards establishing if DBO is a suitable paradigm for HIL optimization in the presence of motor learning. Simulations were conducted assuming either purely time-dependent participant responses, or assuming that responses would arise from state-space models of motor learning capable of describing both adaptation and use-dependent learning behavior.

Statistical comparisons indicated that DBO was never inferior to BO, and, after a certain number of iterations, generally outperformed BO in convergence to optimal inputs and outputs. The number of iterations beyond which DBO was superior to BO occurred earlier when the input-output relationship of the simulated responses was more dynamic. Our results suggest that DBO may improve the performance of HIL optimization over BO when a sufficient number of iterations can be evaluated to accurately distinguish between unstructured variability (noise) and learning.
A Biologically Inspired Attention Model for Neural Signal Analysis
Authors: Skatchkovsky Nicolas , Glazman Natalia , Sadeh Sadra , Iacaruso Florencia
Published: August 2024

Understanding how the brain represents sensory information and triggers behavioural responses is a fundamental goal in neuroscience. Recent advances in neuronal recording techniques aim to progress towards this milestone, yet the resulting high dimensional responses are challenging to interpret and link to relevant variables. In this work, we introduce SPARKS, a model capable of generating low dimensional latent representations of high dimensional neural recordings. SPARKS adapts theself-attentionmechanism of large language models to extract information from the timing of single spikes and the sequence in which neurons fire using Hebbian learning. Trained with a criterion inspired by predictive coding to enforce temporal coherence, our model produces interpretable embeddings that are robust across animals and sessions. Behavioural recordings can be used to inform the latent representations of the neural data, and we demonstrate state-of-the-art predictive capabilities across diverse electrophysiology and calcium imaging datasets from the motor, visual and entorhinal cortices. We also show how SPARKS can be applied to large neuronal networks by revealing the temporal evolution of visual information encoding across the hierarchy of the visual cortex. Overall, by integrating biological mechanisms into a machine learning model, we provide a powerful tool to study large-scale network dynamics. SPARKS’ capacity to generalize across animals and behavioural states suggests it is capable of estimating the internal latent generative model of the world in animals, paving the way towards a foundation model for neuroscience.
Sequence-encoded Spatiotemporal Dependence of Viscoelasticity of Protein Condensates Using Computational Microrheology
Authors: Sundaravadivelu Devarajan Dinesh , Mittal Jeetain
Published: August 2024

Many biomolecular condensates act as viscoelastic complex fluids with distinct cellular functions. Deciphering the viscoelastic behavior of biomolecular condensates can provide insights into their spatiotemporal organization and physiological roles within cells. Though there is significant interest in defining the role of condensate dynamics and rheology in physiological functions, the quantification of their time-dependent viscoelastic properties is limited and mostly done through experimental rheological methods. Here, we demonstrate that a computational passive probe microrheology technique, coupled with continuum mechanics, can accurately characterize the linear viscoelasticity of condensates formed by intrinsically disordered proteins (IDPs). Using a transferable coarse-grained protein model, we first provide a physical basis for choosing optimal values that define the attributes of the probe particle, namely its size and interaction strength with the residues in an IDP chain. We show that the technique captures the sequence-dependent viscoelasticity of heteropolymeric IDPs that differ either in sequence charge patterning or sequence hydrophobicity. We also illustrate the technique's potential in quantifying the spatial dependence of viscoelasticity in heterogeneous IDP condensates. The computational microrheology technique has important implications for investigating the time dependent rheology of complex biomolecular architectures, resulting in the sequence-rheology-function relationship for condensates.
Exacerbation of influenza virus induced lung injury by alveolar macrophages and its suppression by pyroptosis blockade in a human lung alveolus chip
Authors: Man Yuncheng , Zhai Yunhao , Jiang Amanda , Bai Haiqing , Gulati Aakanksha , Plebani Roberto , Mannix Robert J. , Merry Gwenn E. , Goyal Girija , Belgur Chaitra , Hall Sean R. R. , Ingber Donald E.
Published: August 2024

Alveolar macrophages (AMs) are the major sentinel immune cells in human alveoli and play a central role in eliciting host inflammatory responses upon distal lung viral infection. Here, we incorporated peripheral human monocyte-derived macrophages within a microfluidic human Lung Alveolus Chip that recreates the human alveolar-capillary interface under an air-liquid interface along with vascular flow to study how residential AMs contribute to the human pulmonary response to viral infection. When Lung Alveolus Chips that were cultured with macrophages were infected with influenza H3N2, there was a major reduction in viral titers compared to chips without macrophages; however, there was significantly greater inflammation and tissue injury. Pro-inflammatory cytokine levels, recruitment of immune cells circulating through the vascular channel, and expression of genes involved in myelocyte activation were all increased, and this was accompanied by reduced epithelial and endothelial cell viability and compromise of the alveolar tissue barrier. These effects were partially mediated through activation of pyroptosis in macrophages and release of pro-inflammatory mediators, such as interleukin (IL)-1β, and blocking pyroptosisviacaspase-1 inhibition suppressed lung inflammation and injury on-chip. These findings demonstrate how integrating tissue resident immune cells within human Lung Alveolus Chip can identify potential new therapeutic targets and uncover cell and molecular mechanisms that contribute to the development of viral pneumonia and acute respiratory distress syndrome (ARDS).
Low concentrations of the food contaminant Deoxynivalenol trigger apoptosis and impair GnRH-induced LH secretion in pituitary gonadotrope-like cells
Authors: Cai Guodong , Yang Lingchen , Marien-Bourgeois Francis , Boerboom Derek , Zamberlam Gustavo , Alassane-Kpembi Imourana
Published: August 2024

TheFusariummycotoxin deoxynivalenol (DON) is one of the most frequently occurring food contaminants. Nearly all individuals are exposed to DON, due to it widespread presence in grains and grain-based products. Chronic DON poisoning is associated with growth retardation, immunotoxicity as well as impaired reproduction and fetal development. At the molecular level, DON alters intracellular signaling by activating mitogen-activated protein kinases (MAPKs) that modulate cell growth, differentiation, and apoptosis. Of note, these MAPKs are also critical mediators of gonadotrophin-releasing hormone (GnRH)-induced synthesis and secretion of follicle-stimulating hormone (FSH) and luteinizing hormone (LH) by pituitary gonadotrope cells. So far, no research has explored the potential endocrine-disrupting effects of DON on pituitary gonadotropins production. Herein, we show the first evidence that DON affects LH production by the immortalized gonadotrope-like cell line LβT2 in a concentration-dependent manner. Taken together, our experiments demonstrated that low concentrations of DON affect GnRH-induced signaling through a mechanism that, at least in part, involves apoptosis and inhibition of GnRH-induced phosphorylation of ERK-MAPK. Consequently, DON also affects the GnRH-induced expression ofCgaandLhb, two critical genes for LH synthesis and secretion by gonadotrope cells in mammals. This research broadens our knowledge of the toxicity of DON and brings a new depth to the potential neuroendocrine implications for reproduction.

Graphical abstract
Improving the accuracy of cerebral blood flow measurement by phase contrast MRI
Authors: Yang Xiuli
Published: August 2024

Cerebral blood flow (CBF) is a critical hemodynamic marker for natural aging and pathological conditions. It can be assessed non-invasively by the phase-contrast (PC) magnetic resonance imaging (MRI) technique. Although the technical principle of PC MRI is straightforward, related experimental settings, e.g., spatial resolution, partial volume effect, slice positioning angle, and signal-to-noise ratio (SNR), require deliberate considerations to ensure measurement accuracy. In this study, we performed simulations to systematically investigate the dependence of measurement accuracy of PC MRI on the spatial resolution, extent of partial volume effect, slice imperfection angle, and SNR. It has been found that at least 6 voxels along the vessel diameter are required to ensure accurate CBF measurements. Partial volume effect acts to underestimate CBF measurements. The tolerance against slice imperfection angle is ≤15 degree for the commonly seen artery in mice under isoflurane anesthesia. A normalized SNR of 25.47 dB is required to ensure the accuracy of CBF measurement. Our study will promote the utilization of CBF as a pathophysiological marker for future studies by delineating the factors affecting measurement accuracy in PC MRI.
ComBat-met: Adjusting Batch Effects in DNA Methylation Data
Authors: Wang Junmin
Published: August 2024

Integration of genomics data is routinely hindered by unwanted technical variations known as batch effects. Despite wide availability, existing batch correction methods often fall short in capturing the unique characteristics of DNA methylation data. We present ComBat-met, a beta regression framework to adjust batch effects in DNA methylation studies. Our method fits beta regression models to the data, calculates batch-free distributions, and maps the quantiles of the estimated distributions to their batch-free counterparts. Compared to traditional methods, ComBat-met followed by differential methylation analysis shows improved statistical power without compromising false positive rates based on simulated data. Additionally, we demonstrate the ability of ComBat-met to remove cross-batch variations and recover biological signals using data from The Cancer Genome Atlas.
Targeting Interferon-Driven Inflammatory Memory Prevents Epigenetic Evolution of Cancer Immunotherapy Resistance
Authors: Qiu Jingya , Ye Darwin , Chen Xinyi E. , Dangle Nathan , Yoshor Benjamin , Zhang Thomas , Shao Yue , Nallamala Vamshidhar C. , Wang Shangshang , Ren Diqiu , Xu Yuanming , Chen Jie , Zhang Nancy R. , Shi Junwei , Greenberg Roger A. , Minn Andy J.
Published: August 2024

Acquired resistance is a growing obstacle to durable responses after cancer immune checkpoint blockade (ICB). The mechanisms by which heterogeneous tumors evolve under immunotherapy pressure and strategies targeting key populations to prevent relapse are poorly understood. We show that chronic interferon (IFN) enables a subpopulation of cancer cells to acquire inflammatory memory and express memory ISGs, a subset of IFN-stimulated genes enriched for immune evasion properties, leading to subclonal epigenetic evolution of ICB-resistant states. Inflammatory memory is epigenetically encoded through chronic virus mimicry – feedforward MDA5 signaling likely activated by endogenous retroelements. While JAK inhibitors can improve ICB response, combining them with TBK1 inhibitors collapses this feedforward mechanism, erasing inflammatory memory and preventing differentiation into resistance states. Across human cancers, small subpopulations of memory ISG-expressing cells are prevalent and coupled to T cell exhaustion, suggesting inflammatory memory may be a common mechanism of acquired resistance targetable by JAK plus TBK1 inhibition.
E-cigarette market share by nicotine claims
Authors: Ma Shaoying , Brinkman Marielle C. , Berman Micah , Wagener Theodore , Shang Ce
Published: August 2024

Background

Nicotine forms (salt vs. freebase) and isomers (synthetic vs. tobacco-derived) are key characteristics of e-cigarettes that manufacturers manipulate, and “tobacco-free” claims may have served to attract new consumers and increase their intention to use.

Method

This study presents a snapshot of nicotine marketing claims for e-cigarettes using Nielsen ScanTrack data from brick-and-mortar stores. Market share was calculated as the ratio of unit sales of each nicotine claim category to the total unit sales of e-cigarettes during the four weeks ending 1-20-2024.

Results

We summarized the market share for the following six nicotine form/isomer category: 1) nicotine (77%), 2) nicotine salt (10%), 3) synthetic nicotine (2%), 4) zero tobacco or tobacco-free (2%), 5) zero nicotine or nicotine-free (0.03%), and 6) no claim or CBD/hemp/cannabis (9%).

Conclusion

The market share of products that explicitly carried nicotine salt claims (10%) or synthetic nicotine or tobacco-free claims (2% each) was notable. This study informs regulatory authorities on the recent trend of nicotine claims marketed by the e-cigarette industry, which may be contributing to the use of these products or addiction to nicotine among young people and non-users.
PhyClone: Accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing
Authors: Hurtado Emilia , Bouchard-Côté Alexandre , Roth Andrew
Published: August 2024

Motivation

Cancer is driven by somatic mutations that result in the expansion of genomically distinct sub-populations of cells called clones. Identifying the clonal composition of tumours and understanding the evolutionary relationships between clones is a crucial task in cancer genomics. Bulk DNA sequencing is commonly used for studying the clonal composition of tumours, but it is challenging to infer the genetic relationship between different clones due to the mixture of different cell populations.

Results

In this work, we introduce a new probabilistic model called PhyClone that can infer clonal phylogenies from bulk sequencing data. We demonstrate the performance of PhyClone on simulated and real-world datasets and show that it outperforms previous methods in terms of accuracy and scalability.

Availability and implementation

Source code is available on Github at:https://github.com/Roth-Lab/PhyCloneunder the GPL v3.0 license.

Contact

aroth@bccrc.ca
Protein overabundance is driven by growth robustness
Authors: Choi H. James , Lo Teresa W. , Cutler Kevin J. , Huang Dean , Will W. Ryan , Wiggins Paul A.
Published: August 2024

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacteriumAcinetobacter baylyiby characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.
GENOMICON-Seq: A comprehensive tool for the simulation of mutations in amplicon and whole exome sequencing
Authors: Stosic Milan S. , Costanzi Jean-Marc , Ambur Ole Herman , Rounge Trine B.
Published: August 2024

GENOMICON-Seq is a comprehensive genomic sequencing simulation tool that enables the assessment of laboratory and bioinformatics parameters influencing the detection of mutations. The tool generates genomes with mutations, mimicking processes such as low-frequency mutations, APOBEC3 activity in viruses, somatic mutations and single base substitution (SBS) mutational signals. GENOMICON-Seq adds amplicon and whole exome sequencing biases and errors. It outputs sequencing reads compatible with mutation detection tools and a report on mutation origin (generated mutations and PCR errors), nucleotide context, and position. GENOMICON-Seq aids in the evaluation of bioinformatics tools and experimental designs, reducing the need for costly real-world sequencing experiments.