bluuebunny/crossref_metadata_2025 huggingface dataset:
Episodic boundaries affect neural features of representational drift in humans
Authors: Kulkarni Nimay , Lega Bradley C.
Published: August 2023

A core feature of episodic memory is representational drift, the gradual change in aggregate oscillatory features that supports temporal association of memory items. However, models of drift overlook the role of episodic boundaries, which indicate a shift from prior to current context states. Our study focuses on the impact of task boundaries on representational drift in the parietal and temporal lobes in 99 subjects during a free recall task. Using intracranial EEG recordings, we show boundary representations reset gamma band drift in the medial parietal lobe, selectively enhancing the recall of early list (primacy) items. Conversely, the lateral temporal cortex shows increased drift for recalled items but lacked sensitivity to task boundaries. Our results suggest regional sensitivity to varied contextual features: the lateral temporal cortex uses drift to differentiate items, while the medial parietal lobe uses drift-resets to associate items with the current context. We propose drift represents relational information tailored to a region’s sensitivity to unique contextual elements. Our findings offer a mechanism to integrate models of temporal association by drift with event segmentation by episodic boundaries.
The “motive cocktail” in altruistic behaviors
Authors: Wu Xiaoyan , Ren Xiangjuan , Liu Chao , Zhang Hang
Published: August 2023

Prosocial motives such as social equality and efficiency are key to altruistic behaviors. However, predicting the range of altruistic behaviors in varying contexts and among different individuals proves challenging if we limit ourselves to one or two motives, as most previous studies have done. Here we demonstrate the numerous, interdependent motives in altruistic behaviors and the possibility to disentangle them through behavioral experimental data and computational modeling. In one laboratory experiment (N=157) and one pre-registered online replication (N=1258) across 100 different situations, we found that both third-party punishment and helping behaviors aligned best with a model of seven socioeconomic motives, referred to as a “motive cocktail”, including two compound motives. For instance, the “inequality discounting” motives imply that individuals, when confronted with costly interventions, behave as if the inequality between others barely exists. The motive cocktail model also provides a unified explanation for a range of phenomena in the literature.
Fgf signalling is required for gill slit formation in the skate,Leucoraja erinacea
Authors: Rees Jenaid M. , Palmer Michael A. , Gillis J. Andrew
Published: August 2023

The gill slits of fishes develop from an iterative series of pharyngeal endodermal pouches that contact and fuse with surface ectoderm on either side of the embryonic head. We find in the skate (Leucoraja erinacea) that all gill slits form via a stereotypical sequence of epithelial interactions:1)endodermal pouches approach overlying surface ectoderm, with2)focal degradation of ectodermal basement membranes preceding endoderm–ectoderm contact;3)endodermal pouches contact and intercalate with overlying surface ectoderm, and finally4)perforation of a gill slit occurs by epithelial remodelling, without programmed cell death, at the site of endoderm– ectoderm intercalation. Skate embryos expressFgf8andFgf3within developing pharyngeal epithelia during gill slit formation. When we inhibit Fgf signalling by treating skate embryos with the Fgf receptor inhibitor SU5402 we find that endodermal pouch formation, basement membrane degradation and endodermal–ectodermal intercalation are unaffected, but that epithelial remodelling and gill slit perforation fail to occur. These findings point to a role for Fgf signalling in epithelial remodelling during gill slit formation in the skate and, more broadly, to an ancestral role for Fgf signalling during pharyngeal pouch epithelial morphogenesis in vertebrate embryos.
KinCytE- a Kinase to Cytokine Explorer to Identify Molecular Regulators and Potential Therapeutic Opportunities
Authors: Chan Marina , Kang Yuqi , Osborne Shannon , Zager Michael , Gujral Taranjit S
Published: August 2023

Cytokines and chemokines are secreted proteins that regulate various biological processes, such as inflammation, immune response, and cell differentiation. Therefore, disruption of signaling pathways involving these proteins has been linked to a range of diseases, including cancer. However, targeting individual cytokines, chemokines, or their receptors is challenging due to their regulatory redundancy and incomplete understanding of their signaling networks. To transform these difficult-to-drug targets into a pharmacologically manageable class, we developed a web-based platform called KinCytE. This platform was designed to link the effects of kinase inhibitors, a well-established class of drugs, with cytokine and chemokine release and signaling networks. The resulting KinCytE platform enables users to investigate protein kinases that regulate specific cytokines or chemokines, generate a ranked list of FDA-approved kinase inhibitors that affect cytokine/chemokine activity, and explore and visualize cytokine signaling network thus facilitating drugging this challenging target class. KinCytE is freely accessible viahttps://atlas.fredhutch.org/kincyte.
Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha
Authors: Faucher Benjamin , Sabbatini Chiara E. , Czuppon Peter , Kraemer Moritz U.G. , Lemey Philippe , Colizza Vittoria , Blanquart Francois , Boëlle Pierre-Yves , Poletto Chiara
Published: August 2023

SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.
Current and lagged associations of meteorological variables andAedesmosquito indices with dengue incidence in the Philippines
Authors: Cruz Estrella I. , Salazar Ferdinand V. , Aguila Ariza , Ramos Jennifer , Paul Richard E.
Published: August 2023

Background

Dengue is an increasing health burden that has spread throughout the tropics and sub-tropics. There is currently no effective vaccine and control is only possible through integrated vector management. Early warning systems to alert potential dengue outbreaks are currently being explored but despite showing promise are yet to come to fruition. This study addresses the use of meteorological variables for predicting both entomological indices and dengue incidences and assesses the added value of additionally using entomological indices for predicting dengue incidences.

Methodology/Principal Findings

Entomological surveys were carried out monthly for 14 months in six sites spread across three environmentally different cities of the Philippines. Meteorological and dengue data were acquired. Non-linear generalized additive models were fitted to test associations with the meteorological variables and both entomological indices and dengue cases. Rain and the diurnal temperature range (DTR) contributed most to explaining the variation in both entomological indices and number of dengue cases. DTR and minimum temperature also explained variation in dengue cases occurring one and two months later. The number of adult mosquitoes did associate with the number of dengue cases, but contributed no additional value for predicting dengue cases.

Conclusions/Significance

The use of meteorological variables to predict future risk of dengue holds promise. The lack of added value of using entomological indices confirms several previous studies and given the onerous nature of obtaining such information, more effort should be placed on improving meteorological information at a finer scale to evaluate efficacy in early warning of dengue outbreaks.

Author summary

Dengue is a widespread mosquito-borne disease. Mosquitoes are sensitive to temperature and rainfall and hence there have been efforts to identify such variables for predicting dengue outbreaks. Several mosquito indices are measured routinely by national surveillance systems, but which vary considerably in their success of predicting dengue outbreaks. This study explored the current and lagged associations of meteorological variables with mosquito indices and dengue incidence. Associations of mosquito indices with dengue were also explored. Rain and the diurnal temperature range (DTR) contributed most to explaining the variation in both entomological indices and number of dengue cases. DTR and minimum temperature also explained variation in dengue cases occurring one and two months later. Mosquito indices did not provide any additional predictive power of dengue incidences. Given the onerous nature of measuring mosquito indices, advanced warning systems might be improved using meteorological variables measured at finer scales than that traditionally available.
The association between body mass index and metabolite response to a liquid mixed meal challenge
Authors: Hughes David A. , Li-Gao Ruifang , Bull Caroline J. , de Mutsert Renée , Rosendaal Frits R. , Mook-Kanamori Dennis O. , Willems van Dijk Ko , Timpson Nicholas J.
Published: August 2023

Background

Metabolite abundance is a dynamic trait that is not only variable in a fasting state, but also varies in response to environmental stimuli, such as food consumption. Postprandial abundance and response to a meal are emergent traits in studies of disease and which themselves may be subject to specific risk factors. We investigated body mass index (BMI) as a recognized risk factor for numerous health outcomes that may influence metabolite response to feeding. Here we use the Netherlands Epidemiology of Obesity (NEO) study to examine associations between BMI and metabolite response to a liquid meal and extend this by using Mendelian randomization (MR) to estimate potential causal effects.

Methods and findings

The NEO study conducted a liquid meal challenge and collected metabolite profiles using the Nightingale metabolomics platform in 5744 study participants. Observational and one-sample MR analysis were conducted to estimate the effect of BMI on metabolites and ratios of metabolites (n = 229) in the fasting, postprandial and response (or change in abundance) states. After an appropriate multiple testing correction, we observed 473 associations with BMI (175 fasting, 188 postprandial, 110 response) in observational analyses. In MR analyses, we observed 20 metabolite traits (5 fasting, 12 postprandial, 3 response) to be associated with BMI. In both the fasting and postprandial state, this included citrate and the ratios of linoleic acid, omega-6 fatty acid and polyunsaturated fatty acids to total fatty acids. In addition, the glucogenic amino acid alanine was inversely associated with BMI in the response state, suggesting that as alanine increased in postprandial abundance, that increase was attenuated with increasing BMI.

Conclusions

Overall, MR estimates were strongly correlated with observational effect estimates suggesting that the broad associations seen between BMI and metabolite variation in fasting, postprandial and response states have a causal underpinning. Specific effects in previously unassessed postprandial and response states were detected and these may likely mark novel life course risk exposures driven by regular nutrition.
Bidirectional association and shared risk factors between atrial fibrillation and heart failure: a Mendelian Randomization study
Authors: Lu Haibin , Gao Ziting , Wei Hongye , Wei Yajing , Qiu Ziyi , Xiao Jun , Huang Wuqing
Published: August 2023

Background

Atrial fibrillation and heart failure are closely related and share multiple risk factors. We aimed to apply the mendelian randomization (MR) analysis to explore the bidirectional causal link between atrial fibrillation and heart failure, and the independent effect of potential risk factors on the risk of both conditions.

Methods

This is a two-sample MR study using publicly available summary-level statistics of genome-wide association studies (GWAS). Bidirectional MR was performed to explore the relation between atrial fibrillation and heart failure. A total of 14 factors were selected as potential risk factors, univariable MR analyses were used to identify shared risk factors, and then the multivariable MR analyses were further used to investigate the independent effect of these factors on both conditions. Inverse-variance-weighted MR (IVW-MR) were used to obtain the effect estimates.

Results

MR analysis found evidence of causal relationship between atrial fibrillation and heart failure (odds ratio [OR], 1.24; 95% confidence interval [CI], 1.19–1.29), as well as between heart failure and atrial fibrillation (OR, 3.88; 95% CI, 1.45–10.37). Univariable MR analyses identified several shared risk factors for both conditions, including body mass index (BMI), blood pressure, smoking, coronary heart disease and myocardial infarction. After adjusting for atrial fibrillation, the observed associations between shared factors and heart failure kept stable, such as BMI, smoking, coronary heart disease and myocardial infarction. However, after adjusting for heart failure, the relationships between most risk factors and atrial fibrillation attenuated to null.

Conclusions

This two-sample MR study found a bidirectional relationship between atrial fibrillation and heart failure, and identified several shared risk factors of both conditions, which had an independent effect on the risk of heart failure while probably affected the risk of atrial fibrillation via cardiac impairment.

Funding

Start-up Fund for high-level talents of Fujian Medical University (grant no.XRCZX2021026) and Natural Science Foundation of Fujian Province (grant no. 2022J01706).
Discovery of a first-in-class small molecule ligand for WDR91 using DNA-encoded chemical library selection followed by machine learning
Authors: Ahmad Shabbir , Xu Jin , Feng Jianwen A , Hutchinson Ashley , Zeng Hong , Ghiabi Pegah , Dong Aiping , Centrella Paolo A , Clark Matthew A , Guié Marie-Aude , Guilinger John P , Keefe Anthony D , Zhang Ying , Cerruti Thomas , Cuozzo John W. , Rechenberg Moritz von , Bolotokova Albina , Li Yanjun , Loppnau Peter , Seitova Alma , Li Yen-Yen , Santhakumar Vijayaratnam , Brown Peter J. , Ackloo Suzanne , Halabelian Levon
Published: August 2023

WD40 repeat-containing protein 91 regulates endosomal phosphatidylinositol 3-phosphate levels at the critical stage of endosome maturation and plays vital roles in endosome fusion, recycling, and transport by mediating protein-protein interactions. Due to its various roles in endocytic pathways, WDR91 has recently been identified as a potential host factor responsible for viral infection. We employed DNA-Encoded Chemical Library (DEL) selection against the WDR domain of WDR91, followed by machine learning to generate a model that was then used to predict ligands from the synthetically accessible Enamine REAL database. Screening of predicted compounds enabled us to identify the hit compound1, which binds selectively to WDR91 with a KDof 6 ± 2 μM by surface plasmon resonance. The co-crystal structure confirmed the binding of1to the WDR91 side pocket, in proximity to cysteine 487. Machine learning-assisted structure activity relationship-by-catalog validated the chemotype of1and led to the discovery of covalent analogs18and19. Intact mass LC-MS and differential scanning fluorimetry confirmed the formation of a covalent adduct, and thermal stabilization, respectively. The discovery of1, 18, 19, accompanying SAR, and co-crystal structures will provide valuable insights for designing more potent and selective compounds against WDR91, thus accelerating the development of novel chemical tools to evaluate the therapeutic potential of WDR91 in disease.
Post-ischemic ubiquitination at the postsynaptic density reversibly influences the activity of ischemia-relevant kinases
Authors: Dhawka Luvna , Palfini Victoria , Hambright Emma , Blanco Ismary , Poon Carrie , Kahl Anja , Resch Ulrike , Bhawal Ruchika , Benakis Corinne , Balachandran Vaishali , Zhang Sheng , Iadecola Costantino , Hochrainer Karin
Published: August 2023

Ubiquitin modifications alter protein function and stability, thereby regulating cell homeostasis and viability, particularly under stress. Ischemic stroke induces protein ubiquitination at the ischemic periphery, wherein cells remain viable, however the identity of ubiquitinated proteins is unknown. Here, we employed a proteomics approach to identify these proteins in mice undergoing ischemic stroke. The data are available in a searchable web interface (https://hochrainerlab.shinyapps.io/StrokeUbiOmics/). We detected increased ubiquitination of 198 proteins, many of which localize to the postsynaptic density (PSD) of glutamatergic neurons. Among these were proteins essential for maintaining PSD architecture, such as PSD95, as well as NMDA and AMPA receptor subunits. The largest enzymatic group at the PSD with elevated post-ischemic ubiquitination were kinases, such as CaMKII, PKC, Cdk5, and Pyk2, whose aberrant activities are well-known to contribute to post-ischemic neuronal death. Concurrent phospho-proteomics revealed altered PSD-associated phosphorylation patterns, indicative of modified kinase activities following stroke. PSD-located CaMKII, PKC, and Cdk5 activities were decreased while Pyk2 activity was increased after stroke. Removal of ubiquitin restored kinase activities to pre-stroke levels, identifying ubiquitination as the responsible molecular mechanism for post-ischemic kinase regulation. These findings unveil a previously unrecognized role of ubiquitination in the regulation of essential kinases involved in ischemic injury.
Cortical representations of languages during natural dialogue
Authors: Yamashita Masahiro , Kubo Rieko , Nishimoto Shinji
Published: August 2023

Individuals integrate their own words, their partner’s words, and the context of dialogue into coherent meanings. Research suggests that mutual understanding between speakers and listeners is supported by a shared representation between language production and comprehension1,2. However, it remains unknown how languages are represented in the brain during dialogue, which is characterized by dynamic, adaptive behaviours such as turn-taking3,4. Here, we used functional magnetic resonance imaging (fMRI) to compare language production and comprehension maps obtained from natural dialogue in the same participants to show that separate representations exist for language production and comprehension. While production showed selectivity towards the motor system and comprehension towards the auditory system, both production and comprehension were represented in broadly distributed regions. These separate representations were similar in amodal brain regions that integrate semantic5,6and pragmatic information7,8, and provide a common ground for mutual understanding1,2, reflecting dynamic, complementary roles in interactive language use, including turn-taking3,4,9, backchannels10, and fillers11. Our findings suggest that separate and analogous linguistic representations for production and comprehension are interwoven in the same networks that underlie complementary interactions and making sense in dialogue.
Reverse engineering morphogenesis through Bayesian optimization of physics-based models
Authors: Kumar Nilay , Dowling Alexander , Zartman Jeremiah
Published: August 2023

Morphogenetic programs direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. A key challenge for systems and synthetic biology is determining optimal combinations of intra- and inter-cellular interactions to predict an organ’s shape, size, and function. Physics-based mechanistic models that define the subcellular force distribution facilitate this, but it is extremely challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the desired organ shapes observed within the experimental imaging data. This integrative framework employs Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that generate and maintain the final organ shape. We calibrated and tested the method on cross-sections ofDrosophilawing imaginal discs, a highly informative model organ system, to study mechanisms that regulate epithelial processes that range from development to cancer. As a specific test case, the parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with time series imaging data of wing discs perturbed with collagenase. Unexpectedly, the framework also identifies multiple distinct parameter sets that generate shapes similar to wild-type organ shapes. This platform enables an efficient, global sensitivity analysis to support the necessity of both actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with fixed tissue imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This framework is extensible toward reverse-engineering the morphogenesis of any organ system and can be utilized in real-time control of complex multicellular systems.
In-silicoanalysis reveals hub genes and enriched pathways in Type 2 Diabetes Mellitus
Authors: Gandham Prasad , Sharathchandra RG
Published: August 2023

The worldwide prevalence of diabetes mellitus poses a significant hazard to human health and a substantial economic burden for modern society. Multiple genes and gene-environment interactions make up the complicated genetic landscape of type 2 diabetes (T2D). The majority of genetic studies concentrated on separate genes, while some put an excessive amount of emphasis on SNPs. The objective of this work was to analyze all known T2D-associated genes in a collective manner in order to identify candidate genes and assess their possible functional role using bioinformatic tools. Interactome networks, enrichment clustering, hub genes, enriched biological pathways, and miRNAs targeting hub genes were analyzed. From the pool of 490 genes implicated in type 2 diabetes, the 25 most prominent hub genes were identified using measures of betweenness centrality and degree of each node. From the interactome analysis 8 different MCODE clusters was observed with a highest number of 63 genes in cluster one. GO analyses revealed that candidate genes were significantly enriched in glucose homeostasis, carbohydrate homeostasis, regulation of type B pancreatic cell apoptotic process, and regulation of hormone secretion biological process. Insulin resistance, PI3K-Akt signaling pathway, AMPK signaling pathway, Neuroactive ligand-receptor interaction, and Adipocytokine signaling pathway were among the notable enriched pathways. Subsequently we found 54 miRNAs targeting 25 hub genes out of which IRS1 and IGF1 hub genes were targeted by seven different miRNA. This study contributes to a thorough bioinformatics investigation of genes, functions, and pathways that are relevant to the etiology of T2DM and may be useful in the development of precision medicine-based treatments.
Thermoplasmonic modulation of cardiomyocytes activity with local temperature read-out
Authors: Guo Fanglei , Yu Wei , Deschaume Olivier , Jooken Stijn , Liu Liwang , Wei Qi , Thielemans Wim , Bartic Carmen
Published: August 2023

Cardiomyocyte beating rate modulation is required in multiplein vitroassays and it is usually done by electrical or optogenetic methods. In this work, we develop and characterize a light-based strategy to modulate the cardiomyocyte beating rate by near-infrared (NIR) controlled plasmonic stimulation. For this purpose, gold nanorods (GNRs) acting as plasmonic heaters and silica-coated quantum dots (QDs) as nanothermometers are attached to surfaces used for cardiomyocyte cultures, while the cell electrophysiological activity is visualized by monitoring calcium transients via calcium imaging. This system is capable of modulating cardiac activity with near infrared-controllable plasmonic heating while measuring local temperature changes owing to the temperature-dependent fluorescence of silica-coated QDs.

Table of Contents (TOC)
High-level prediction errors in low-level visual cortex
Authors: Richter David , Kietzmann Tim C , de Lange Floris P
Published: August 2023

Perception and behaviour are significantly moulded by expectations derived from our prior knowledge. Hierarchical predictive processing theories provide a principled account of the neural mechanisms underpinning these processes, casting perception as a hierarchical inference process. While numerous studies have shown stronger neural activity for surprising inputs, in line with this account, it is unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this upregulation of activity. Here we leveraged fMRI and visual dissimilarity metrics derived from a deep neural network to arbitrate between two hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may incorporate feedback signals and thereby inherit complex tuning properties from higher areas. Our results are in line with this second hypothesis. Prediction errors in both low- and high-level visual cortex primarily scaled with high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverges from feedforward tuning, indicating a shift induced by predictive contexts. Mechanistically, our results suggest that high-level predictions may help constrain perceptual interpretations in earlier areas thereby aiding perceptual inference. Combined, our results elucidate the feature tuning of visual prediction errors and bolster a core hypothesis of hierarchical predictive processing theories, that predictions are relayed top-down to facilitate perception.
Computational Insights into Colonic Motility: Mechanical Role of Mucus in Homeostasis and Inflammation
Authors: Erbay I.H. , Alexiadis A. , Rochev Y.
Published: August 2023

Colonic motility plays a vital role in maintaining proper digestive function. The rhythmic contractions and relaxations facilitate various types of motor functions that generate both propulsive and non-propulsive motility modes which in turn generate shear stresses on the epithelial surface. However, the interplay between colonic mucus, shear stress, and epithelium remains poorly characterized. Here, we present a colonic computational model that describes the potential roles of mucus and shear stress in both homeostasis and ulcerative colitis (UC). Our model integrates several key features, including the properties of the mucus bilayer and lumen contents, colonic pressure, and crypt characteristics to predict the time-space mosaic of shear stress. We show that the mucus thickness which could vary based on the severity of UC, may significantly reduce the amount of shear stress applied to the colonic crypts and effect colonic content velocity. Our model also reveals an important spatial shear stress variance in homeostatic colonic crypts that suggests shear stress may have a modulatory role in epithelial cell migration, differentiation, apoptosis, and immune surveillance. Together, our study uncovers the rather neglected roles of mucus and shear stress in intestinal cellular processes during homeostasis and inflammation.
Exploring the interaction of inhibitor ‘YM-1’ withPlasmodium falciparumHSP70s throughin silicomethods
Authors: Singh Bikramjit , Kaur Satinder , Nainani Chanchal , Sandhu Komalpreet Kaur , Upadhyay Vipul , Hora Rachna , Mishra Prakash Chandra
Published: August 2023

YM-1 belongs to the rhodacyanine class of the 70kDa heat shock protein (Hsp70) inhibitors. It antagonizes nucleotide exchange factor (NEF) ‘BAG3’ to block the critical ATPase cycle of HSP70 chaperones thereby disabling them. The anti-cancer potential of this small molecule derivative of MKT-077 is attributed to its improved solubility and stability over its parent. In the current study, we have used bioinformatics tools including molecular docking to study the binding strength of YM-1 forPlasmodium falciparum(Pf) HSP70s. Of the four PfHSP70 homologs, the mitochondrial counterpart was predicted to bind most efficiently with YM-1 at a previously reported inhibitor binding site. The interaction of YM-1 with mitochondrial PfHSP70 was significantly better than with any of the other Pf homologs. Further analysis of this PfHSP70-YM-1 complex helped us to predict not just the types of interactions, but also specific residues of the protein involved in inhibitor engagement. Information reported in this article is important from the standpoint of design and development of newer rhodacyanine based drugs against malaria.
EZH2 specifically regulatesISL1during embryonic urinary tract formation
Authors: Mingardo Enrico , Kalanithy Jeshurun C. , Dworschak Gabriel , Ishorst Nina , Yilmaz Öznur , Lindenberg Tobias , Hollstein Ronja , Felger Tim , Angrand Pierre-Olivier , Reutter Heiko , Odermatt Benjamin
Published: August 2023

Isl1has been described as an embryonic master control gene expressed in the pericloacal mesenchyme. Deletion ofIsl1from the genital mesenchyme in mice leads to an ectopic urethral opening and epispadias-like phenotype. Using genome wide association methods, we identifiedISL1as the key susceptibility gene for classic bladder exstrophy (CBE), comprising epispadias and exstrophy of the urinary bladder. The most significant marker (rs6874700) identified in our recent GWAS meta-analysis achieved apvalue of 1.48 × 10-24within theISL1region. In silico analysis of rs6874700 and all other genome-wide significant markers in Linkage Disequilibrium (LD) with rs6874700 (D’ = 1.0; R2> 0.90) revealed marker rs2303751 (pvalue 8.12 x 10-20) as the marker with the highest regulatory effect predicted. Here, we describe a novel 1.2 kb intragenic promoter residing between 6.2 and 7.4 kb downstream of theISL1transcription starting site, which is located in the reverse DNA strand and harbors a binding side for EZH2 at the exact region of marker rs2303751. We show, that EZH2 silencing in HEK cells reducesISL1expression. We show thatezh2-/-ko zebrafish larvae display tissues specificity of ISL1 regulation with reduced expression of Isl1 in the pronephric region of zebrafish larvae. In addition, a shorter and malformed nephric duct is observed inezh2-/-ko zebrafishTg(wt1ß:eGFP)reporter lines. Our study shows, that Ezh2 is a key regulator ofIsl1during urinary tract formation and suggests tissue specificISL1dysregulation as an underlying mechanism for CBE formation.
Brain asymmetries from mid- to late life and hemispheric brain age
Authors: Korbmacher Max , van der Meer Dennis , Beck Dani , de Lange Ann-Marie , Eikefjord Eli , Lundervold Arvid , Andreassen Ole A. , Westlye Lars T. , Maximov Ivan I.
Published: August 2023

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data inN=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conductedhemispheric brain age(HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
Deep screening of proximal and distal splicing-regulatory elements in a native sequence context
Authors: Recinos Yocelyn , Ustianenko Dmytro , Yeh Yow-Tyng , Wang Xiaojian , Jacko Martin , Yesantharao Lekha V. , Wu Qiyang , Zhang Chaolin
Published: August 2023

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is impeded by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically dead CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied toSMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identified not only known SREs, but also a novel distal intronic splicing enhancer, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.
Spatial patterns in the diet ofGypsvultures in India and their implications for conservation
Authors: Ghosh-Harihar Mousumi , Yadav Nidhi , Gurung Nehal , C.S. Darshan , B. Shashikumar , C.K. Vishnudas , Prakash Vibhu , Green Rhys E. , Ramakrishnan Uma
Published: August 2023

Dietary analyses are particularly useful for developing conservation programmes for species threatened by resource depletion, poisoning and environmental pollution.Gypsvultures in South Asia represent one such case, having undergone a population collapse caused by feeding on carcasses of cattle treated with the non-steroidal anti-inflammatory drug diclofenac. Following a ban on the veterinary use of diclofenac, populations of vultures remain low and mostly concentrated near protected areas. Understanding the role of protected areas in the recovery of these critically endangered species requires analyses of spatial variation in their diet. We used faecal metabarcoding to investigate the spatial variation in the diet ofGypsvultures across four landscapes from sites located inside and outside protected areas. We collected faecal 642 samples, of which 419 yielded adequate molecular data to identify the vulture species and 30 molecular operational taxonomic units corresponding to at least 28 diet species. The diet was dominated by large ungulates and varied across landscapes, protection status and vulture species, with the observed variation largely explained by differential intake of wild and domestic species. Domestic livestock was present in >95% of the samples from central, west and north India, but 77% of the samples had only wild species in south India. This variation was explained by livestock density within 100 km radius of the sampling site. Our results imply that protected areas may not offer a respite from possibility of diclofenac-poisoning across most parts of the country and efforts should continue unabated to remove nephrotoxic drugs from veterinary use.
Factorial state-space modelling for kinetic clustering and lineage inference
Authors: Gupta R. , Claassen M.
Published: August 2023

Single-cell RNA sequencing (scRNAseq) protocols measure the abundance of expressed transcripts for single cells. Gene expression profiles of cells (cell-states) represent the functional properties of the cell and are used to cluster cell-states that have a common functional identity (cell-type). Standard clustering methods for scRNAseq data performhardclustering based on KNN graphs. This approach implicitly assumes that variation among cell-states within a cluster does not correspond to changes in functional properties. Differentiation is a directed process of transitions between cell-types via gradual changes in cell-states over the course of the process. We propose a latent state-space Markov model that utilises cell-state transitions derived from RNA velocity to model differentiation as a sequence of latent state transitions and to performsoftkinetic clustering of cell-states that accommodates the transitional nature of cells in a differentiation process. We applied this model to the differentiation of Radial-glia cells into mature neurons and demonstrate the utility of our method in discriminating between functional and transitional cell-states.
Identification of Cholangiocarcinoma (CCA) Subtype-Specific Biomarkers
Authors: Croft Jacob , Gao Liyuan , Quintanar Odalys , Sheng Victor , Zhang Jun
Published: August 2023

Liver cancer ranks sixth globally in diagnoses and second in cancer-related deaths. Cholangiocarcinoma (CCA), a relatively rare cancer originating from bile duct epithelium, constitutes 2% of all cancers, with increasing occurrences in Westerns. Incidence is influenced by inflammation, genetics, risk factors, and regional disparities, with higher rates in the Eastern hemisphere. Diagnostic and prognostic biomarkers are pivotal for effective cancer prevention and management. Recent research explores serum proteins for non-invasive CCA diagnosis and proposes targeted receptor approaches for therapeutics.

This study aims to identify these biomarkers via bioinformatics analysis of public datasets, focusing on CCA patient transcriptomes to uncover gene biomarkers linked to age and survival. Pathway analysis reveals functions and pathways associated with these biomarkers. Additionally, the study employs the ESM-TFpredict machine learning model to predict transcription factors (TFs) using protein sequence data.

Leveraging publicly available data enhances our understanding of liver cancer’s molecular profiles and clinical relevance, particularly concerning CCA, This study integrates bioinformatics analysis, transcriptomic exploration, and machine learning to unveil a novel set of potential diagnostic and prognostic biomarkers for CCA.
Drug-target interaction prediction using a multi-modal transformer network demonstrates high generalizability to unseen proteins
Authors: Kroll Alexander , Ranjan Sahasra , Lercher Martin J.
Published: August 2023

Most drugs are small molecules, with their activities typically arising from interactions with protein targets. Accurate predictions of these interactions could greatly accelerate pharmaceutical research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two types of molecules during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform all previous models for predicting drug-target interactions, and the model demonstrates unprecedented generalization capabilities to unseen proteins. We further show that the superior performance of ProSmith is not limited to drug-target interaction predictions, but also leads to improvements in other protein-small molecule interaction prediction tasks, the prediction of Michaelis constantsKMof enzyme-substrate pairs and the identification of potential substrates for enzymes. The Python code provided can be used to easily implement and improve machine learning predictions of interactions between proteins and arbitrary drug candidates or other small molecules.
The pathogenic roles of the p.R130S prestin variant in DFNB61 hearing loss
Authors: Takahashi Satoe , Zhou Yingjie , Cheatham Mary Ann , Homma Kazuaki
Published: August 2023

DFNB61 is a recessively inherited nonsyndromic hearing loss caused by mutations inSLC26A5, the gene that encodes the voltage-driven motor protein, prestin. Prestin is abundantly expressed in the auditory outer hair cells that mediate cochlear amplification. Two DFNB61-associatedSLC26A5variants, p.W70X and p.R130S, were identified in patients who are compound heterozygous for these nonsense and missense changes (SLC26A5W70X/R130S). Our recent study showed that mice homozygous for p.R130S (Slc26a5R130S/R130S) suffer from hearing loss that is ascribed to significantly reduced motor kinetics of prestin. Given that W70X-prestin is nonfunctional, compound heterozygousSlc26a5R130S/-mice were used as a model for humanSLC26A5W70X/R130S. By examining the pathophysiological consequences of p.R130S prestin when it is the sole allele for prestin protein production, we determined that this missense change results in progressive outer hair cell loss in addition to its effects on prestin’s motor action. Thus, this study fully defines the pathogenic roles for the p.R130S prestin, which points to the presence of a limited time window for potential clinical intervention.
Relationship between reactive astrocytes, by [18F]SMBT-1 imaging, with amyloid-beta, tau, glucose metabolism, and microgliosis in mouse models of Alzheimer’s disease
Authors: Kong Yanyan , Maschio Cinzia A. , Shi Xuefeng , Yao Bolin , Xie Fang , Zuo Chuantao , Konietzko Uwe , Shi Kuangyu , Rominger Axel , Xiao Jianfei , Huang Qi , Nitsch Roger M. , Guan Yihui , Ni Ruiqing
Published: August 2023

Purpose

Reactive astrocytes play an important role in the development of Alzheimer’s disease (AD). Here, we aim to investigate the temporospatial relationship between reactive astrocytes, tau and amyloid-β, glucose metabolism, and microgliosis by using multitracer imaging in AD transgenic mouse models.

Methods

Positron emission tomography (PET) imaging with [18F]SMBT-1 (monoamine oxidase-B), [18F]florbetapir (Aβ), [18F]PM-PBB3 (tau), [18F]FDG, and [18F]DPA-714 (translocator protein) was carried out in 5- and 10-month-old APP/PS1, 11-month-old 3×Tg mice, and aged-matched wild-type mice. The brain regional referenced standard uptake value (SUVR) was computed with the cerebellum as the reference region. Immunofluorescence staining was performed in mouse brain tissue slices.

Results

[18F]SMBT-1 and [18F]florbetapir SUVRs were higher in the cortex and hippocampus of 10-month-old APP/PS1 mice than in 5-month-old APP/PS1 mice and wild-type mice. Reduced [18F]FDG SUVR was observed in the thalamus and midbrain of 5-month-old APP/PS1 mice compared to wild-type mice. No significant difference in brain regional [18F]DPA-714 SUVR was observed in 5- and 10-month-old APP/PS1 mice compared to wild- type mice. No significant difference in the SUVRs of any tracers was observed in 11-month-old 3×Tg mice compared to age-matched wild-type mice. A positive correlation between the SUVRs of [18F]SMBT-1 and [18F]DPA-714 in the cortex was observed. Immunostaining validated the distribution of MAO-B and TSPO, amyloid and tau inclusions in brain tissue from 10-month-old APP/PS1 mice and limited changes in 11-month- old 3×Tg mice.

Conclusion

The findings provide in vivo evidence for reactive astrocytes along with amyloid plaque and tau deposition preceding microgliosis in animal models of AD pathologies.
Histone mark age of human tissues and cells
Authors: de Lima Camillo Lucas Paulo , Asif Muhammad Haider , Horvath Steve , Larschan Erica , Singh Ritambhara
Published: August 2023

Background

Aging involves intricate epigenetic changes, with histone modifications playing a pivotal role in dynamically regulating gene expression. Our research comprehensively analyzes seven key histone modifications across various tissues to understand their behavior during human aging and formulate age prediction models.

Results

These histone-centric prediction models exhibit remarkable accuracy and resilience against experimental and artificial noise. They showcase comparable efficacy when compared with DNA methylation age predictors through simulation experiments. Intriguingly, our gene set enrichment analysis pinpoints vital developmental pathways crucial for age prediction. Unlike in DNA methylation age predictors, genes previously recognized in animal studies as integral to aging are amongst the most important features of our models. We also introduce a pan-histone-mark, pan-tissue age predictor that operates across multiple tissues and histone marks, reinforcing that age-related epigenetic markers are not restricted to particular histone modifications.

Conclusion

Our findings underscore the potential of histone marks in crafting robust age predictors and shed light on the intricate tapestry of epigenetic alterations in aging.