bluuebunny/crossref_metadata_2025 huggingface dataset:
68Ga-Bisphosphonates for the Imaging of Extraosseous Calcification by Positron Emission Tomography
Authors: Keeling George. P. , Baark Friedrich , Katsamenis Orestis L. , Xue Jing , Blower Philip J. , Bertazzo Sergio , de Rosales Rafael T. M.
Published: November 2022

Radiolabelled bisphosphonates (BPs) and [18F]NaF (18F-fluoride) are the two types of radiotracers available to image calcium mineralin vivo(e.g. bone), yet only [18F]NaF has been widely explored for the non-invasive molecular imaging of extraosseous calcification (EC) using the highly sensitive nuclear imaging technique positron emission tomography (PET). These two radiotracers bind calcium mineral deposits via different mechanisms, with BPs chelating to calcium ions and thus being non-selective, and [18F]NaF being selective for hydroxyapatite (HAp) which is the main component of bone mineral. Taking into account that the composition of EC has been reported to include a diverse range of non-HAp calcium minerals, we hypothesised that BPs may be more sensitive for imaging EC due to their ability to bind to both HAp and non-HAp deposits.

To test this hypothesis, we report a comparison between the68Ga-labelled BP tracer [68Ga]Ga-THP-Pam and [18F]NaF for PET imaging in a rat model of EC that develops macro- and microcalcifications in several organs. The presence of macrocalcifications was identified using preclinical computed tomography (CT) and microcalcifications were identified using μCT-based 3D X-ray histology (XRH) on isolated organsex vivo. The morphological and mineral analysis of individual calcified deposits was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The PET imaging andex vivoanalysis results demonstrated that while both radiotracers behave similarly for bone imaging, the BP-based radiotracer [68Ga]Ga-THP-Pam was able to detect EC more sensitively in several organs in which the mineral composition departs from that of HAp. We conclude that BP-based PET radiotracers such as [68Ga]Ga-THP-Pam have a particular advantage for the sensitive imaging and early detection of EC by being able to detect a wider array of relevant calcium mineralsin vivothan [18F]NaF, and should be evaluated clinically for this purpose.
Cross-protein transfer learning substantially improves disease variant prediction
Authors: Jagota Milind , Ye Chengzhong , Albors Carlos , Rastogi Ruchir , Koehl Antoine , Ioannidis Nilah , Song Yun S.
Published: November 2022

Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. We train cross-protein transfer (CPT) models using deep mutational scanning data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold2 structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.
Elevated energy costs of biomass production in mitochondrial-respiration deficientSaccharomyces cerevisiae
Authors: Grigaitis Pranas , van den Bogaard Samira L. , Teusink Bas
Published: November 2022

Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeastSaccharomyces cerevisiae, although the cross-condition differences remain poorly characterized. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model ofS. cerevisiaeto compare the optimal metabolic strategies between different conditions. We found thatS. cerevisiaewith respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants ofS. cerevisiae. Collectively, these results point to elevated costs of resolving the redox imbalance caused byde novobiosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.
The critical role of BTRC in hepatic steatosis as an ATGL E3 ligase
Authors: Qi Weiwei , Fang Zhenzhen , Luo Chuanghua , Hong Honghai , Long Yanlan , Dai Zhiyu , Liu Junxi , Zeng Yongcheng , Zhou Ti , Xia Yong , Yang Xia , Gao Guoquan
Published: November 2022

Objective

Non-alcoholic fatty liver disease (NAFLD), characterized by hepatic steatosis, is one of the most common causes of liver dysfunction. ATGL is closely related to hepatic steatosis as the speed-limited triacylglycerol lipase. Nevertheless, the expression and regulation of ATGL in NAFLD remain unclear.

Methods

Using immunohistochemistry and qRT-PCR to detect the expression of ATGL and BTRC in different models with hepatic steatosis. Co-IP evaluated the binding of ATGL and BTRC. Knockdown of BTRC employed by adenoviruses and then analyzed the ATGL expression, triglyceride levels, and lipid droplets accumulation.

Results

Our results revealed that ATGL protein level was decreased in animal and cellular models of hepatic steatosis and the liver tissues of cholangioma/hepatic carcinoma patients with hepatic steatosis, while the ATGL mRNA level had hardly changed; which means the decreased ATGL mainly degraded through the proteasome pathway. BTRC was identified as the E3 ligase for ATGL, up-regulated, and negatively correlated with ATGL level. Moreover, adenovirus-mediated knockdown of BTRC ameliorated hepatic steatosisviaup-regulating ATGL level.

Conclusions

Our study demonstrates a crucial role of elevated BTRC in hepatic steatosis through promoting ATGL proteasomal degradation as a new ATGL E3 ligase and suggests BTRC may serve as a potential therapeutic target for NAFLD.

Funding

This study was supported by The National Natural Science Foundation of China (Grants 82070888, 82070882, 82100917, 81872165, 82273116, 82203661, 81901557 and 81902693); Key Project of Nature Science Foundation of Guangdong Province, China (Grant 2019B1515120077); National Key R&D Program of China (Grant 2018YFA0800403); Guangdong Special Support Program for Young Top Scientist (Grant 201629046); Guangdong Natural Science Fund (Grant 2019A1515011810, 2021A1515010434, 2022A1515012423 and 2022A1515012513); Key Sci-Tech Research Project of Guangzhou Municipality (Grants 202201010820) Fundamental Research Funds for the Central Universities (Grant 50000-31620106); China Postdoctoral Science Foundation (Grant 2021M703679, 2020M683110).
Solving the stochastic dynamics of population growth
Authors: Marrec Loïc , Bank Claudia , Bertrand Thibault
Published: November 2022

Population growth is a fundamental process in ecology, evolution, and epidemiology. The population size dynamics during growth are often described by deterministic equations derived from kinetic models. Here, we simulate several population growth models and compare the size averaged over many stochastic realizations with the deterministic predictions. We show that these deterministic equations are generically bad predictors of the average stochastic population dynamics. Specifically, deterministic predictions overestimate the simulated population sizes, especially those of populations starting with a small number of individuals. Describing population growth as a stochastic birth process, we prove that the discrepancy between deterministic predictions and simulated data is due to unclosed-moment dynamics. In other words, the deterministic approach does not take into account the variability of birth times, which is particularly important at small population sizes. We evaluate different moment-closure approximations and show that they do not satisfactorily reduce the error between analytical predictions and simulated data. We present two novel solutions to the stochastic growth dynamics, one of which applies to any population growth model. We show that our solution exactly quantifies the dynamics of a community composed of different strains and correctly predicts the fixation probability of a strain in a serial dilution experiment. Our work sets the foundations for a more faithful modeling of community dynamics. It provides tools for a more accurate analysis of experimental results, including the inference of important growth parameters.
Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model
Authors: Pearl Owen , Shin Soyong , Godura Ashwin , Bergbreiter Sarah , Halilaj Eni
Published: November 2022

Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused RGB video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of RGB video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model significantly improved estimation of lower-extremity kinematics over the video-only approach and estimation of joint centers over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved estimation of joint kinematics and joint centers over unconstrained fusion, while unconstrained fusion was advantageous in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.
Use of the Vitiligo Extent Score Vitiligo: A Protocol for a Scoping Review
Authors: Rueda-Galvis Paola Andrea , Orozco-Jiménez Sara , Builes-Montaño Carlos E. , Arango-Salgado Andrea
Published: November 2022

Introduction

Vitiligo is a chronic skin condition with no cure. Clinical assessment and treatment evaluation relays heavily on clinometry tools and expert knowledge. The Vitiligo Extent Score has been proposed as one of the most reliable and easy-to-use clinometry tools for vitiligo.

Methods and analysis

We proposed a scoping review to identify all the available evidence on the clinical research availability around the Vitiligo Extent Score. This review will follow the approach proposed in the Joanna Briggs Institute Reviewer’s Manual Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.

Ethics and dissemination

We intend to publish the results in a specialized peer-reviewed journal and local, national, and international conference presentations. It will also be incorporated as educational material in our institution’s postgraduate program in dermatology.

Article summary

Strengths and limitations of this study

This scoping review will be the first to review the available literature on VES extensively.

The search strategy includes an extensive search of grey literature besides the primary medical databases and no language restriction, ensuring the inclusion of all the available literature.

The research questions will allow us to assess the main aspects of a measurement instrument.

This scoping review will only evaluate one of the several available measurement instruments for vitiligo.
Endoparasitoid lifestyle promotes endogenization and domestication of dsDNA viruses
Authors: Guinet Benjamin , Lepetit David , Charlat Sylvain , Buhl Peter N , Notton David G , Cruaud Astrid , Rasplus Jean-Yves , Stigenberg Julia , de Vienne Damien M. , Bastien Boussau , Varaldi Julien
Published: November 2022

The accidental endogenization of viral elements within eukaryotic genomes can occasionally provide significant evolutionary benefits, giving rise to their long-term retention, that is, to viral domestication. For instance, in some endoparasitoid wasps (whose immature stages develop inside their hosts), the membrane-fusion property of double-stranded DNA viruses has been repeatedly domesticated following ancestral endogenizations. The endogenized genes provide female wasps with a delivery tool to inject virulence factors that are essential to the developmental success of their offspring. Because all known cases of viral domestication involve endoparasitic wasps, we hypothesized that this lifestyle, relying on a close interaction between individuals, may have promoted the endogenization and domestication of viruses. By analyzing the composition of 124 Hymenoptera genomes, spread over the diversity of this clade and including free-living, ecto- and endoparasitoid species, we tested this hypothesis. Our analysis first revealed that double-stranded DNA viruses, in comparisons with other viral genomic structures (ssDNA, dsRNA, ssRNA), are more often endogenized and domesticated (that is, retained by selection) than expected from their estimated abundance in insect viral communities. Secondly, our analysis indicates that the rate at which dsDNA viruses are endogenized is higher in endoparasitoids than in ectoparasitoids or free-living hymenopterans, which also translates into more frequent events of domestication. Hence, these results are consistent with the hypothesis that the endoparasitoid lifestyle has facilitated the endogenization of dsDNA viruses, in turn increasing the opportunities of domestications that now play a central role in the biology of many endoparasitoid lineages.
Identifying genes and pathways linking astrocyte regional specificity to Alzheimer’s disease susceptibility
Authors: Zhang Ran , Knudsen Margarete , Vasques Pedro Del Cioppo , Tadych Alicja , Rodriguez-Rodriguez Patricia , Greengard Paul , Roussarie Jean-Pierre , Milosevic Ana , Troyanskaya Olga
Published: November 2022

Astrocytes have been shown to play a central role in Alzheimer’s Disease (AD). However, the genes and biological pathways underlying disease manifestation are unknown, and it is unclear whether regional molecular differences among astrocytes contribute to regional specificity of disease. Here, we began to address these challenges with integrated experimental and computational approaches. We constructed a human astrocyte-specific functional gene network using Bayesian integration of a large compendium of human functional genomics data, as well as regional astrocyte gene expression profiles we generated in the mouse. This network identifies likely region-specific astrocyte pathways that operate in healthy brains. We leveraged our findings to compile genome-wide astrocyte-associated disease-gene predictions, employing a novel network-guided differential expression analysis (NetDIFF). We also used this data to predict a list of astrocyte-expressed genes mediating region-specific human disease, using a network-guided shortest path method (NetPATH). Both the network and our results are publicly available using an interactive web interface athttp://astrocyte.princeton.edu. Our experimental and computational studies propose a strategy for disease gene and pathway prediction that may be applied to a host of human neurological disorders.
Brain-age prediction: a systematic comparison of machine learning workflows
Authors: More Shammi , Antonopoulos Georgios , Hoffstaedter Felix , Caspers Julian , Eickhoff Simon B. , Patil Kaustubh R. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: November 2022

The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-site accuracy, (2) cross-site generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-site mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-site MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-site and cross-site predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer’s and mild cognitive impairment patients. However, in the presence of age bias, the delta estimates in the diseased population varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application.

Highlights

There is an effect of both feature space and ML algorithm on prediction error.

Voxel-wise features performed better than parcel-wise features.

GPR, KRR and RVR algorithms performed well.

The within-site and cross-site delta-behavior correlations disagree.

Higher brain-age delta inference in AD depends on data used for bias correction.
KBPRNA: A novel method integrating bulk RNA-seq data and LINCS-L1000 gene signatures to predict kinase activity based on machine learning
Authors: Zhang Yuntian , Yao Lantian , Huang Yixian , Zhang Wenyang , Pang Yuxuan , Lee Tzongyi
Published: November 2022

Background

Kinases are a type of enzymes which can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates. Kinase activities could be utilized to be represented as specific biomarkers of specific cancer types. Nowadays novel algorithms have already been developed to compute kinase activities from phosphorylated proteomics data. However, phosphorylated proteomics sequencing could be costly expensive and need valuable samples. Moreover,not methods which could achieve kinase activities from bulk RNA-sequence data have been developed. Here we propose KBPRNA, a general computational framework for extracting specific kinase activities from bulk RNA-sequencing data in cancer samples. KBPRNA also achieves better performance in predicting kinase activities from bulk RNA-sequence data under cancer conditions benchmarking against other models.

Results

In this study, we used LINCS-L1000 dataset which was used to be reported as efficient gene signatures in defining bulk RNA-seq data as input dataset of KBPRNA. Also, we utilized eXtreme Gradient Boosting (XGboost) as the main algorithm to extract valuable information to predict kinase activities. This model outperforms other methods such as linear regression and random forest in predicting kinase activities from bulk RNA-seq data. KBPRNA integrated tissue samples coming from breast invasive carcinoma, hepatocellular carcinoma, lung squamous cell carcinoma, Glioblastoma multiforme and Uterine Corpus Endometrial Carcinoma. It was found that KBPRNA achieved good performance with an average R score above threshold of 0.5 in kinase activity prediction.

Conclusions

Model training and testing process showed that KBPRNA outperformed other machine learning methods in predicting kinase activities coming from various cancer types’ tissue samples. This model could be utilized to approximate basic kinase activities and link it with specific biological functions, which in further promoted the progress of cancer identification and prognosis.
Structural basis of sRNA RsmZ regulation ofPseudomonas aeruginosavirulence
Authors: Jia Xinyu , Pan Zhiling , Yuan Yang , Luo Bingnan , Luo Yongbo , Mukherjee Sunandan , Jia Guowen , Liu Liu , Ling Xiaobin , Yang Xiting , Miao Zhichao , Wei Xiawei , Bujnicki Janusz M. , Zhao Kelei , Su Zhaoming
Published: November 2022

Pseudomonas aeruginosais an opportunistic human pathogen that poses threats to hospitalized immunocompromised patients1. A non-coding small RNA (sRNA) from the repressor of secondary metabolites (Rsm) system, RsmZ, sequesters the global repressor protein RsmA to regulate downstream gene expressions that reprogram virulence repertoires associated with acute and chronicP. aeruginosainfections2,3. Molecular insights into the full-length RsmZ architecture remain elusive, leading to the lack of understanding of RsmZ binding to RsmA and subsequent modulations of gene expressions. Here we use cryo-electron microscopy (cryo-EM) to resolve structures of the full-length RsmZ in complexes with RsmA, in which five stem-loops (SLs) and one single-stranded junction carrying the GGA binding sites in RsmZ form three pairs of clamps, each binding to a RsmA homodimer. Disruptions of the base-pairings in all stems of RsmZ significantly reduced the binding affinity to RsmA by 17-fold, which resulted in enhanced RsmA downregulation of gene expressions and phenotypes associated to both acute and chronic virulence ofP. aeruginosa. Double mutations that rescued these stems of RsmZ restored the binding to RsmA by more than 5-fold, and recovered the corresponding phenotypes. Our results reveal the molecular mechanism of RsmZ regulation ofP. aeruginosavirulence and suggest RsmZ as a potential target for the development of new antimicrobial agents.
USP7/Maged1-Mediated H2A Monoubiquitination in the Paraventricular Thalamus: An Epigenetic Mechanism Involved in Cocaine Use Disorder
Authors: Cheron Julian , Beccari Leonardo , Hagué Perrine , Icick Romain , Despontin Chloé , Carusone Teresa , Defrance Mathieu , Bhogaraju Sagar , Martin-Garcia Elena , Capellan Roberto , Maldonado Rafael , Vorspan Florence , Bonnefont Jérôme , de Kerchove d’Exaerde Alban
Published: November 2022

The individual risk of developing drug addiction is highly determined by the epigenetic landscape1,2. Chromatin remodeling regulates drug-induced transcriptional and behavioral effects and the consequent development of addictive behaviors2,3. Several chromatin modifications in the ventral tegmental area and nucleus accumbens, including histone H3 methylation, H3 and H4 acetylation, have been implicated in drug addiction. Still, the contribution of other histones and their post-translational modifications (PTMs), such as monoubiquitination is unclear4–8. In the course of investigating the underlying mechanisms associated with melanoma-associated antigen D1 (Maged1)9, a scaffold protein involved in drug addiction9, we found that H2A monoubiquitination in the paraventricular thalamus (PVT) plays a major role in cocaine-adaptive behaviors and cocaine-evoked transcriptional repression. Mice undergoing chronic cocaine administration showed a significant increased monoubiquitination of H2A. Furthermore, we showed that this histone PTM is controlled, in the PVT, by Maged1, along with one of its partner, the deubiquitinase USP710. Accordingly, Maged1 specific inactivation in thalamic vGluT2 neurons, or USP7 inhibition, blocked cocaine-evoked H2A monoubiquitination and abolished cocaine locomotor sensitization. Finally, we identified genetic variations ofMAGED1andUSP7associated with modified transition to cocaine addiction and cocaine-induced aggressive behavior in human subjects. These findings identified a new epigenetic modification in a non-canonical reward pathway of the brain and a potent marker of epigenetic risk factor for drug addiction in human.
Direct conversion of human fibroblasts to pancreatic epithelial cells through transient progenitor states is controlled by temporal activation of defined factors
Authors: Fei Liangru , Zhang Kaiyang , Poddar Nikita , Hautaniemi Sampsa , Sahu Biswajyoti
Published: November 2022

Cell fate can be reprogrammed by ectopic expression of lineage-specific transcription factors (TF). For example, few specialized cell types like neurons, hepatocytes and cardiomyocytes have been generated from fibroblasts by defined factors (Wanget al, 2021). However, the exact cell state transitions and their control mechanisms during cell fate conversion are still poorly understood. Moreover, the defined TFs for generating vast majority of the human cell types are still elusive. Here, we report a novel protocol for reprogramming human fibroblasts to pancreatic exocrine cells with phenotypic and functional characteristics of ductal epithelial cells using a minimal set of six TFs. We mapped the molecular determinants of lineage dynamics at single-cell resolution using a novel factor-indexing method based on single-nuclei multiome sequencing (FI-snMultiome-seq) that enables dissecting the role of each individual TF and pool of TFs in cell fate conversion. We show that transdifferentiation – although being considered a direct cell fate conversion method – occurs through transient progenitor states orchestrated by stepwise activation of distinct TFs. Specifically, transition from mesenchymal fibroblast identity to epithelial pancreatic exocrine fate involves two deterministic steps: first, an endodermal progenitor state defined by activation of HHEX concurrently with FOXA2 and SOX17, and second, temporal GATA4 activation essential for maintenance of pancreatic cell fate program. Collectively, our data provide a high-resolution temporal map of the epigenome and transcriptome remodeling events that facilitate cell fate conversion, suggesting that direct transdifferentiation process occurs through transient dedifferentiation to progenitor cell states controlled by defined TFs.
MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal Transport
Authors: Gossi Federico , Pati Pushpak , Martinelli Adriano , Rapsomaniki Maria Anna
Published: November 2022

Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. However, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality of the data and the multiple sources of acquisition noise. In this work, we propose MatchCLOT, a novel method for single-cell data integration based on ideas borrowed from contrastive learning, optimal transport, and transductive learning. In particular, we use contrastive learning to learn a common representation between two modalities and apply entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance in the modality matching task from the NeurIPS 2021 multimodal single-cell data integration challenge, improving the previous best competition score by 28.9%. Our code can be accessed athttps://github.com/AI4SCR/MatchCLOT.
A combination of Metformin and Epigallocatechin Gallate Potentiates Glioma Chemotherapyin vivo
Authors: Kuduvalli Shreyas S , Precilla S Daisy , Vaithy Anandraj , Purushothaman Mugilarasi , Muralidharan Arumugam Ramachandran , Kumar B Agiesh , Mezger Markus , Antony Justin S , Subramani Madhu , Dubashi Biswajit , Biswas Indrani , Guruprasad K P , Anitha T.S
Published: November 2022

Glioma is the most devastating high-grade tumor of the central nervous system, with dismal prognosis. Existing treatment modality does not provide substantial benefit to patients and demands novel strategies. One of the first-line treatments for glioma, temozolomide, provides marginal benefit to glioma patients. Repurposing of existing non-cancer drugs to treat oncology patients is gaining momentum in recent years. In this study, we investigated the therapeutic benefits of combining three repurposed drugs, namely, metformin (anti-diabetic) and epigallocatechin gallate (green tea-derived antioxidant) together with temozolomide in a glioma-induced xenograft rat model. Our triple-drug combination therapy significantly inhibited tumor growthin vivoand increased the survival rate (50%) of rats when compared with individual or dual treatments. Molecular and cellular analyses revealed that our triple-drug cocktail treatment inhibited glioma tumor growth in rat model through ROS-mediated inactivation of PI3K/AKT/mTOR pathway, arrest of the cell cycle at G1 phase and induction of molecular mechanisms of caspases-dependent apoptosis. In addition, the docking analysis and quantum mechanics studies performed here hypothesize that the effect of triple-drug combination could have been attributed by their difference in molecular interactions, that maybe due to varying electrostatic potential. Thus, repurposing metformin and epigallocatechin gallate and concurrent administration with temozolomide would serve as a prospective therapy in glioma patients.
Aneuploid embryos as a proposal for improving Artificial Intelligence performance
Authors: Güell Penas Enric , Esquerrà Parés Marina , Vives Perelló Andreu , Mladenova Koleva Mikaela
Published: November 2022

RESEARCH QUESTION

Could we improve the performance of Machine Learning algorithms by using aneuploid embryos instead of non-implanted embryos as the contrary reference to Live-Birth embryos?

DESIGN

A single-center retrospective analysis of 343 embryos through 3 ML algorithms, based on manually annotated morphokinetics from Day 1 to Day 3. Two datasets were built including the same Live-Birth embryos (117). Dataset A included 123 non-implanted embryos, while Dataset B included 103 aneuploid embryos. V-Fold Cross-Validation was performed for each dataset and algorithm and the Area Under the Curve (AUC) was registered.

RESULTS

AUC for Dataset A did not reach 0.6 for any of the algorithms; while AUC values for “Dataset B” surpassed 0.7. According to this, different morphokinetic patterns were detected by Machine Learning algorithms.

CONCLUSIONS

Algorithms’ minor performance with non-implanted embryos may be due to an increased Label Noise effect, suggesting that including aneuploid embryos could be more appropriate when building predictive algorithms for embryo viability. Machine Learning algorithms results were improved when aneuploid embryos were taken into consideration.
Screening of small molecule chemical library identifies leads that reduce cholesterol accumulation in human NPC1 fibroblasts
Authors: Pipalia Nina H. , Huang Amy Y. , Ralph Harold , Maxfield Frederick R.
Published: November 2022

Although considerable progress has been made in finding a therapy for Niemann-Pick type C1 (NPC1) disease, there still is no effective approved treatment. Previously, we reported results from a small molecule screen that was conducted on Chinese Hamster Ovary (CHO) cells (CT60) that lacked NPC1 expression. The hit compounds that were effective in reducing cholesterol accumulation in CT60 cells were not effective on human cells bearing disease-causing mutations in NPC1. Hence, we conducted a new small molecule screen on patient-derived NPC1 skin fibroblasts using a combinatorial library of ~48,000 compounds. We have identified several compounds that corrected the phenotype in human heterozygous mutant NPC1 skin fibroblasts (GM03123) bearing the inactivating I1061T mutation in one allele and another set of inactivating mutations in the other. Among the hit compounds were several histone deacetylase inhibitors, which we have explored further and reported previously. With the determination of the structure of NPC1 protein and the development of computational docking algorithms, it seems worthwhile to show the structures of 128 compounds that reproducibly reduced cholesterol storage in mutant NPC1 fibroblasts but with no known mechanism of action. These compounds were not toxic and specifically effective on mutant cells but not on NPC1−/−cells, indicating on-target effect directly or indirectly.
The effect of background noise and its removal on the analysis of single-cell expression data
Authors: Janssen Philipp , Kliesmete Zane , Vieth Beate , Adiconis Xian , Simmons Sean , Marshall Jamie , McCabe Cristin , Heyn Holger , Levin Joshua Z. , Enard Wolfgang , Hellmann Ines
Published: November 2022

BACKGROUND

In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Here, we characterize this background noise exemplified by three single-cell RNA-seq (scRNA-seq) and two single-nucleus RNA-seq (snRNA-seq) replicates of mouse kidney cells. For each experiment, kidney cells from two mouse subspecies were pooled, allowing to identify cross-genotype contaminating molecules and estimate the levels of background noise.

RESULTS

We find that background noise is highly variable across replicates and individual cells, making up on average 3-35% of the total counts (UMIs) per cell and show that this has a considerable impact on the specificity and detectability of marker genes. In search of the source of background noise, we find that expression profiles of cell-free droplets are very similar to expression profiles of cross-genotype contamination and hence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure.

CONCLUSION

Our findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.
Female-Specific Pituitary Hypersensitivity to Gonadotropin-Releasing Hormone in a Mouse Model of Chronic Temporal Lobe Epilepsy
Authors: Cutia Cathryn A. , Leverton Leanna K. , Weis Karen E. , Raetzman Lori T. , Christian-Hinman Catherine A.
Published: November 2022

Gonadotropin hormone release from the anterior pituitary is critical to regulating reproductive endocrine function. Clinical evidence has documented that people with epilepsy display altered levels of gonadotropin hormones, both acutely following seizures and chronically. Despite this relationship, pituitary function remains a largely understudied avenue in preclinical epilepsy research. Recently, we showed that females in the intrahippocampal kainic acid (IHKA) mouse model of temporal lobe epilepsy were found to display changes in pituitary expression of gonadotropin hormone and GnRH receptor genes. Circulating gonadotropin hormone levels, however, have yet to be measured in an animal model of epilepsy. Here, we evaluated the circulating levels of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), GnRH receptor (Gnrhr) gene expression, and sensitivity to exogenous GnRH in IHKA males and females. Although no changes in overall dynamics of pulsatile patterns of LH release were found in IHKA mice of either sex, estrus vs. diestrus changes in basal and mean LH levels were larger in IHKA females with prolonged, disrupted estrous cycles. In addition, IHKA females displayed increased pituitary sensitivity to GnRH and higherGnrhrexpression. The hypersensitivity to GnRH was observed on diestrus, but not estrus. Chronic seizure severity was not found to be correlated with LH parameters, and FSH levels were unchanged in IHKA mice. These results indicate that although there are changes in pituitary gene expression and sensitivity to GnRH in IHKA females, there may also be compensatory mechanisms that aid in maintaining gonadotropin release in the state of chronic epilepsy in this model.
The Cul5 E3 Ligase Complex Is a Key Negative Feedback Regulator of TCR/IL2 Signaling and Anti-Tumor Activity in CD8+T Cells
Authors: Liao Xiaofeng , Li Wenxue , Li Ao , Rajendran Barani Kumar , Ren Jingjing , Zhou Hongyue , Calderwood David , Turk Benjamin , Tang Wenwen , Liu Yansheng , Wu Dianqing
Published: November 2022

CD8+T cells play an important role in tumor immune surveillance and control. Better understanding of the regulation of their anti-tumor actions and improving their cytotoxic function and persistence will help advancing cancer immunotherapies. Here, we report the development of a step-wise CRISPR knockout (KO) screening strategy under the selection of TGF-β, a clinically relevant immunosuppressive pressure. The screen identifies Cul5 as a negative-feedback regulator of the core signaling pathways, differentiation, and persistence of CD8+T cell. Cul5 KO in mouse CD8+T cells significantly improves their tumor control abilityin vitroandin vivowith significant proteomic alterations that generally enhance TCR and cytokine signaling, effector function, stemness, and survival of CD8+T cell. Mechanistically, Cul5, whose protein content and active, neddylated form increase upon TCR-stimulation, interacts with SOCS-box-containing Pcmtd2 and negatively regulates TCR and IL2/STAT5 signaling by decreasing TCR and IL2 signaling molecules. Moreover, Cul5 KO in human CD8+T cells phenocopies that in mouse CD8+T cells. Furthermore, KO of CTLA4 that is markedly upregulated by Cul5 KO in mouse and human CD8+cells further enhances anti-tumor effect of Cul5 KO, and a neddylation inhibitor enhances CD8 effector activities largely dependently of Cul5. These results together not only reveal a previously unknown negative-feedback regulatory mechanism for CD8+T cells, but also have strong translational implications in cancer immunotherapy.
Initiation of fibronectin fibrillogenesis is an enzyme-dependent process
Authors: Melamed Shay , Zaffryar-Eilot Shelly , Nadjar-Boger Elisabeth , Aviram Rohtem , Zhao Huaning , Yaseen-Badarne Wesal , Kalev-Altman Rotem , Sela-Donenfeld Dalit , Lewinson Oded , Astrof Sophie , Hasson Peleg , Wolfenson Haguy
Published: November 2022

Fibronectin fibrillogenesis and mechanosensing both depend on integrin-mediated force transmission to the extracellular-matrix. However, force transmission is in itself dependent on fibrillogenesis, and fibronectin fibrils are found in soft embryos where high forces cannot be applied, suggesting that force cannot be the sole initiator of fibrillogenesis. Here we identify a nucleation step prior to force transmission, driven by fibronectin oxidation mediated by lysyl-oxidase enzyme family members. This oxidation induces fibronectin clustering that promotes early adhesion, alters cellular response to soft matrices, and enhances force transmission to the matrix. In contrast, absence of fibronectin oxidation abrogates fibrillogenesis, perturbs cell-matrix adhesion, and compromises mechanosensation. Moreover, fibronectin oxidation promotes cancer cells colony formation in soft agar as well as collective and single-cell migration. These results reveal a force-independent enzyme-dependent mechanism that initiates fibronectin fibrillogenesis, establishing a critical step in cell adhesion and mechanosensing.
Consumption of the non-nutritive sweetener stevia for 12 weeks does not alter the composition of the gut microbiota
Authors: Singh Gurdeep , McBain Andrew J , McLaughlin John T , Stamataki Nikoleta S
Published: November 2022

The use of non-nutritive sweeteners (NNS) as an alternative to caloric sugars has increased in recent years. Stevia is a NNS that has demonstrated beneficial effects on appetite and energy intake. However, the impact on the gut microbiome is not well understood. Therefore, we investigated how regular consumption of stevia, for up to 12 weeks, impacts upon the human gut microbiome. Healthy subjects with a normal body mass index participated in the study; the stevia group (n= 14) were asked to consume five drops of stevia, twice daily, compared against control participants (n= 13). Faecal samples collected before and after treatment were analysed by 16S sequencing. Stevia did not cause significant changes in the beta or alpha diversity when compared to the control groups. When relative abundances of taxa were investigated, no clear differences could be detected. Conversely, random forest analysis could correctly associate the gut microbiome with control and stevia groups with an average of 75% accuracy, suggesting that there are intrinsic patterns that could discriminate between control and stevia use. However, large-scale changes in the gut microbiome were not apparent in this study and therefore, our data suggest that stevia does not significantly impact the gut microbiome.
CHRRT: boosting coordinate hit-and-run with rounding by thinning
Authors: Jadebeck Johann F. , Wiechert Wolfgang , Nöh Katharina
Published: November 2022

Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling convex polytopes uniformly, a highly relevant use-case in systems biology, we here demonstrate that thinning generally boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. We benchmark CHRR with thinning (CHRRT) with simplices and constrained-based metabolic networks with up to thousands of dimensions. With appropriate thinning, CHRRT offers a substantial increase in computational efficiency compared to unthinned CHRR, in our examples of up to three orders of magnitude, as measured by the effective sample size per time (ESS/t). Our experiments reveal that the performance gain of CHRRT by optimal thinning grows substantially with polytope (effective model) dimension. Based on our experiments, we provide practically useful advice for tuning thinning to efficient and effective use of compute resources. Besides allocating computational resources optimally to permit sampling convex polytopes uniformly to convergence in a fraction of time, exploiting thinning unlocks investigating hitherto intractable models under limited computational budgets. CHRRT thereby paves the way to keep pace with progressing model sizes within the existing constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available athttps://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.
A cell circuit approach to dissect fibroblast-macrophage interactions in the tumor microenvironment
Authors: Mayer Shimrit , Milo Tomer , Isaacson Achinoam , Halperin Coral , Miyara Shoval , Stein Yaniv , Pevsner-Fischer Meirav , Tzahor Eldad , Alon Uri , Scherz-Shouval Ruth
Published: November 2022

The tumor microenvironment (TME) is composed of various nonmalignant cell types that interact with each other and with cancer cells, impacting all aspects of cancer biology. The TME is complex and heterogeneous, and thus simplifying systems and concepts are needed. Here we provide a tractable experimental system and powerful mathematical circuit concepts to identify the main molecular interactions that govern the composition of the TME. We focus on two major components of the TME - cancer associated fibroblasts (CAFs) and tumor associated macrophages (TAMs), define their interactions and verify our predictions in mouse and human breast cancer. We measure the population dynamics starting from many initial conditions of co-cultures of macrophages and organ-derived fibroblasts from mammary, lung, and fat, and explore the effects of cancer-conditioned medium on the circuits. We define the circuits and their inferred parameters from the data using a mathematical approach, and quantitatively compare the cell circuits in each condition. We find that while the homeostatic steady-states are similar between the organs, the cancer-conditioned medium profoundly changes the circuit. Fibroblasts in all contexts depend on autocrine secretion of growth factors whereas macrophages are more dependent on external cues, including paracrine growth factors secreted from fibroblasts and cancer cells. Transcriptional profiling reveals the molecular underpinnings of the cell circuit interactions and the primacy of the fibroblast autocrine loop. The same fibroblast growth factors are shared by the co-cultures and mouse and human breast cancer. The cell circuit approach thus provides a quantitative account of cell interactions in the cancer microenvironment.