bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial and Temporal Inhomogeneity of Magnetic Background in Cell Culture Experiments: The Role of Type and Position of CO2-Incubator in a Laboratory
Authors: Sencha Ludmila M. , Karpova Maria A. , Dolinin Alexey A. , Sarafanov Fedor G. , Ilin Nikolay V. , Mareev Evgeny A. , Vodeneev Vladimir A. , Grinberg Marina A. , Balalaeva Irina V.
Published: November 2023

In vitro cultivation of cells in strictly controlled conditions of a biological incubator is a widely used experimental model in biological studies. The CO2-incubators allow control of temperature, atmospheric composition, and humidity; however, the recent reports point out at possible significant and incontrollable influence of incubators on magnetic background. We demonstrated that two types of CO2-incubators sufficiently alter the static magnetic field (SMF) in the inner chamber compared to natural geomagnetic field, and the effect depends on the incubator model. The values of SMF in the center of incubators’ chambers were lower than natural; nevertheless, the strongly expressed spatial inhomogeneity of SMF was registered, with both reduced, up to hypomagnetic, and increased SMF values. One of the incubators in operating mode generated oscillations in magnetic field with period of oscillations about several seconds and peak-to-peak amplitude depending on the measuring point within the chamber volume, up to 115% of the mean value. Since the magnetic background is considered to contribute in multiple biological effects, we emphasize the significant impact of CO2-incubators on magnetic background in cell culture experiments and assume that its spatial and temporal inhomogeneity may be a source for variability in cell study results.

Highlights

CO2-incubators sufficiently alter the static magnetic field in the inner chamber compared to natural geomagnetic field

Spatial inhomogeneity of the magnetic field depends on CO2-incubator type and can reach the gradient value of more than 60 μT.

In one type of the incubator, the generated oscillations in magnetic field were registered with a period of several seconds and a peak-to-peak amplitude up to 115% of the mean value.
Relationship Between Irregularities of the Nuclear Envelope and Mitochondria in HeLa cells Observed with Electron Microscopy
Authors: Brito-Pacheco D. , Karabağ C. , Brito-Loeza C. , Giannopoulos P. , Reyes-Aldasoro C.C.
Published: November 2023

This paper describes a methodology to analyse the complexity of HeLa cells as observed with electron microscopy, in particular the relationship between mitochondria and the roughness of the nuclear envelope as reflected by the invaginations of the surface. For this purpose, several segmentation mitochondria algorithms were quantitatively compared, namely: Topology, Image Processing, Topology and Image Processing, and Deep Learning, which provided the highest accuracy. The invaginations were successfully segmented with one image processing algorithm. Metrics were extracted for both structures and correlations between the mitochondria and invaginations were explored for 25 segmented cells. It was found that there was a positive correlation between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria. These results suggest that there is a relationship between the shape of a cell, its nucleus and its mitochondria; as well as a relationship between the number of mitochondria and their shapes. Whilst these results were obtained from a single cell line and a relatively small number of cells, they encourage further study as the methodology proposed can be easily applied to other cells and settings.

Code and data are freely available. HeLa images are available fromhttp://dx.doi.org/10.6019/EMPIAR-10094, code fromhttps://github.com/reyesaldasoro/MitoEM, and segmented nuclei, cells, invaginations and mitochondria fromhttps://github.com/reyesaldasoro/HeLa_Cell_Data.
Calibrating Bayesian decoders of neural spiking activity
Authors: Wei Ganchao , Tajik Mansouri Zeinab , Wang Xiaojing , Stevenson Ian H.
Published: November 2023

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. We examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables.

Significance Statement

Bayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected.
De novoDesign of Peptides that Bind Specific Conformers of α-Synuclein
Authors: Wallace Hailey M. , Yang Hyunjun , Tan Sophia , Pan Henry S. , Yang Rose , Xu Junyi , Jo Hyunil , Condello Carlo , Polizzi Nicholas F. , DeGrado William F.
Published: November 2023

Insoluble amyloids rich in cross-β fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloid in a conformationally specific manner are lacking. We introduce a novel computational method forde novodesign of peptides that tile the surface of α-synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a “target backbone” for the design of a peptide binder. Next, we interrogate structures in the PDB database with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of α-synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by α-synuclein, tau, or Aβ40. This designed peptide also markedly slowed the formation of α-synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.
Robust parametric UMAP for the analysis of single-cell data
Authors: Zhang Guangzheng , Xu Bingxian
Published: November 2023

The increasing throughput of single-cell technologies and the pace of data generation are enhancing the resolution at which we observe cell state transitions. The characterization and visualization of these transitions rely on the construction of a low dimensional embedding, which is usually done via non-parametric methods such as t-SNE or UMAP. However, existing approaches become more and more inefficient as the size of the data gets larger and larger. Here, we test the viability of using parametric methods for the fact that they can be trained with a small subset of the data and be applied to future data when needed. We observed that the recently developed parametric version of UMAP is generalizable and robust to dropout. Additionally, to certify the robustness of the model, we use the theoretical upper and lower bounds of the mapped coordinates in the UMAP space to regularize the training process.
Interpretingcis-regulatory mechanisms from genomic deep neural networks using surrogate models
Authors: Seitz Evan E , McCandlish David M , Kinney Justin B , Koo Peter K
Published: November 2023

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. Interpreting genomic DNNs in terms of biological mechanisms, however, remains difficult. Here we introduce SQUID, a genomic DNN interpretability framework based on surrogate modeling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models, i.e., simpler models that are mechanistically interpretable. Importantly, SQUID removes the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and betweencis-regulatory elements. SQUID thus advances the ability to mechanistically interpret genomic DNNs.
A Method for Calculating the Least Mutated Sequence in DNA Alignment Based on Point Mutation Sites
Authors: Shen Libing
Published: November 2023

It is hard to decide the root of a phylogenetic tree when no suitable outgroup sequence is available, especially in a high mutation rate system. Here we propose a bioinformatics method for calculating the least mutated sequence in DNA alignment based on point mutation sites. By parsimony principle, the least mutated sequence should be the ancestor (phylogenetic root) of the other sequences in an alignment result. The intuition suggests that our method would be sound only if enough sequences were collected. In this study, we used DNA mutation simulation data to test our method. The simulation results show that our method is very reliable and just a small proportion of sequences is required to find the least mutated sequence when the sampling is random. When the ancestral sequence is present in the dataset, the least mutated sequence calculated by our method is the ancestral sequence itself, i.e., the phylogenetic root of the dataset. When the ancestral sequence is not in the dataset, the least mutated sequence calculated by our method is the one closest to the phylogenetic root. Furthermore, the simulation demonstrates that our method is robust against reverse mutation and saturation mutation. The performance of our method improves with the increasing number of sequences. We developed the program based on this method which also calculates the transition/transversion ratio for each sequence in an DNA alignment. It can be used as a rough measure for estimating selection pressure and evolutionary stability for a sequence. In conclusion, our method and the corresponding programs could be a useful auxiliary tool for bioinformaticians and evolutionary biologists.
Pathogenicity Prediction of GABAAReceptor Missense Variants
Authors: Wang Ya-Juan , Vu Giang H. , Mu Ting-Wei
Published: November 2023

Variants in the genes encoding the subunits of gamma-aminobutyric acid type A (GABAA) receptors are associated with epilepsy. To date, over 1000 clinical variants have been identified in these genes. However, the majority of these variants lack functional studies and their clinical significance is uncertain although accumulating evidence indicates that proteostasis deficiency is the major disease-causing mechanism for GABAAreceptor variants. Here, we apply two state-of-the-art modeling tools, namely AlphaMissense, which uses an artificial intelligence-based approach based on AlphaFold structures, and Rhapsody, which integrates sequence evolution and known structure-based data, to predict the pathogenicity of saturating missense variants in genes that encode the major subunits of GABAAreceptors in the central nervous system, includingGABRA1,GABRB2,GABRB3, andGABRG2. Our results demonstrate that the predicted pathogenicity correlates well between AlphaMissense and Rhapsody although AlphaMissense tends to generate higher pathogenic probability. Furthermore, almost all annotated pathogenic variants in the ClinVar clinical database are successfully identified from the prediction, whereas uncertain variants from ClinVar partially due to the lack of experimental data are differentiated into different pathogenicity groups. The pathogenicity prediction of GABAAreceptor missense variants provides a resource to the community as well as guidance for future experimental and clinical investigations.
Explainable fNIRS Based Pain Decoding Under Pharmacological Conditions via Deep Transfer Learning Approach
Authors: Eken Aykut , Erdoğan Sinem Burcu , Yükselen Gülnaz , Yüce Murat
Published: November 2023

Problem Statement

Pain has a crucial function in the human body acting as an early warning signal to protect against tissue damage. However, both assessment of pain experience and its clinical diagnosis rely on highly subjective methods. Objective evaluation of the presence of pain under analgesic drug administrations becomes even more complicated.

Objectives

The aim of this study was to propose a transfer learning (TL) based deep learning (DL) methodology for accurate detection and objective classification of the neural processing of painful and non-painful stimuli that were presented under different levels of analgesia.

Method

A publicly available fNIRS dataset of 14 participants was obtained during an experimental protocol that involved painful and non-painful events. Separate fNIRS scans were taken under the same nociceptive protocol before analgesic drug (Morphine and Placebo) administration and at three different times (30,60 and 90 min) post-administration. By utilizing data from pre-drug fNIRS scans, a DL architecture for classifying painful and non-painful stimuli was constructed as a base model. Knowledge generated in pre-drug base model was transferred to 6 distinct post-drug conditions by adapting a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine R OIs for each of the pre-drug and post-drug models.

Results

Mean performance of pre-drug base model was above 95% for accuracy, sensitivity, specificity and AUC metrics. Each of the post-drug models had mean accuracy, sensitivity, specificity and AUC performance above 90%. No statistically significant difference across post-drug models were found for classification performance of any of the performance metrics. Post-placebo models had higher decoding accuracy than post-morphine models.

Conclusion

Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were altered with either analgesic or placebo intervention. Contribution of different cortical regions to classification performance varied across the post-drug models.

Importance

The proposed methodology may remove the necessity to build new DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS based BCI system designs that target other application areas.
Gene expression overlap between neuropsychiatric disorders
Authors: Panzenhagen Alana Castro , Alves-Teixeira Alexsander , Wissmann Martina Schroeder , Girardi Carolina Saibro , Santos Lucas , Silveira Alexandre Kleber , Gelain Daniel Pens , Fonseca Moreira José Cláudio
Published: November 2023

Common diseases result from a mix of genetic and environmental factors, often involving inflammation. Complex traits like diabetes and psychiatric disorders are polygenic, influenced by many genetic variants. The omnigenic model suggests all expressed genes can impact disease-related genes. This study examines blood transcriptomic variations in psychiatric and neurological disorders to understand mRNA expression profiles and address field discrepancies. Animal models are explored for similar gene expressions. This study extensively searched GEO DataSets and ArrayExpress databases, identifying gene expression profiles associated with neuropsychiatric disorders. From GEO, 10,359 samples were found, with 30 series (1,897 samples) in the qualitative synthesis, revealing 1,364 differentially expressed genes in Schizophrenia, 134 in Bipolar Disorder, 11 in Autism Spectrum Disorder, and 2,784 in Alzheimer’s Disorder. Comparisons with GWAS studies unveiled overlaps, with 81 genes for SCZ, two for BD, and 135 for ALZ. Notably, 441 genes were shared between ALZ and SCZ. Enrichment analyses indicated associations with signalling pathways. In animal models, 2,360 series were identified, with 175 in the qualitative synthesis, resulting in a meta-analysis focusing on ALZ with hippocampus tissue, revealing 14 consistently differentially expressed genes. Four overlapped with human data (ALOX5AP, P2RY13, RGS10, SH3GL1). These findings contribute to understanding shared and unique molecular signatures across neuropsychiatric disorders, bridging insights between human and animal models. The study efficiently identifies and tests consistent differentially expressed genes in psychiatric and neurological disorders, focusing on blood transcriptomes. Compared to transcriptome-wide or proteome-wide association studies, this approach analyses transcripts directly from individuals with disorders, offering real-world predictive capability. Shared genes between disorders suggest common molecular pathways, emphasizing the need for interdisciplinary approaches in understanding and treating psychiatric disorders. Limitations include sample characterization and the peripheral marker focus. Further investigations, including functional assays, are crucial for validation and extending these findings.
Waist Circumference as a potential indicator of cardiac autonomic profile in Metabolic Syndrome - A cross-sectional study
Authors: Bhasi Ankith , Puri Oshin , Pathania Monika , Saha Sarama , Dhar Meenakshi , Kulshreshta Poorvi
Published: November 2023

Introduction

- Metabolic Syndrome (MetS), a global epidemic on the rise, affects about one-third of the world’s population. MetS poses significant cardiovascular risk often assessed through the cardiac autonomic function test Heart Rate Variability (HRV). This study attempts to identify a suitable anthropometric or biochemical proxy for resource-intensive HRV to assess cardiac autonomic profile in primary and community healthcare setups.

Methodology

- This cross-section study assessed relevant anthropometric, biochemical, and HRV parameters of 174 patients recently diagnosed with MetS.

Results

- Waist circumference (WC) had a significant (p<0.05) moderate to strong (r=0.49) positive correlation with sympathetic predominant HRV parameters - LFnu and LF/HF ratio. Parasympathetically modulated HRV parameters like Average RR (r=-0.17), rMSSD (r=-0.23), and HF (r=-0.49) had a significant (p<0.05) negative correlation with WC.

Conclusion

- The study identifies WC as a suitable indicator for cardiac autonomic profile in patients with MetS and by extension of cardiovascular posed by the disease.
A blind trial of Aptamarker prediction of brain amyloid based on plasma analysis
Authors: Lecocq Soizic , Bastos Filipa , Silva Mariana , Meehan Cathal , Castro Rita , Cunha João , Silva Ana Cristina , Ruano Luis , Penner Gregory
Published: November 2023

We previously described the development of a predictive blood test for brain amyloid based on the combined use of eight aptamers (Aptamarkers) and the clinical variable, patient age with a sensitivity of 88%, specificity of 76% and an overall accuracy of 81%. This test results from an agnostic application of a defined library of 4.29 billion aptamer sequences to plasma samples derived from individuals with varying amounts of brain amyloid deposition. In this report, the same eight Aptamarkers and corresponding predictive algorithm were applied to plasma samples from 36 patients diagnosed with memory impairment in a memory clinic in Portugal. The patients were all subjected to cerebrospinal fluid (CSF) analysis with Aβ40, Aβ4 peptides, p181-tau and total tau characterization. The plasma analysis with the Aptamarkers resulted in a sensitivity of 73%, specificity of 71% and overall accuracy of 72% when considering brain amyloid alone. The predictive power when considering the diagnosis of Alzheimer’s disease based on a complete CSF analysis was a sensitivity of 68%, a specificity of 76% and an overall accuracy of 72%. These results in this diagnostic accuracy study are very similar to results obtained with model building on AIBL cohort. As such, this study demonstrates the potential efficacy of this simple qPCR assay as a basis for inclusion for subsequent CSF confirmation of brain amyloid deposition in a clinical setting.
Mental health and burnout during medical school: Longitudinal evolution and covariates
Authors: Carrard Valerie , Berney Sylvie , Bourquin Céline , Ranjbar Setareh , Castelao Enrique , Schlegel Katja , Gaume Jacques , Bart Pierre-Alexandre , Schmid Mast Marianne , Preisig Martin , Berney Alexandre
Published: November 2023

Background

Medical students’ rate of depression, suicidal ideation, anxiety, and burnout have been shown to be higher than those of the same-age general population. However, longitudinal studies spanning the whole course of medical school are scarce and present contradictory findings. This study aims to analyze the longitudinal evolution of mental health and burnout from the first to the last year of medical school using a wide range of indicators. Moreover, biopsychosocial covariates that can influence this evolution are explored.

Method

In an open cohort study design, 3066 annual questionnaires were filled in by 1595 different students from the first to the sixth year of the Lausanne Medical School (Switzerland). Depression symptoms, suicidal ideation, anxiety symptoms, stress, and burnout were measured along with biopsychosocial covariates. The longitudinal evolution of mental health and burnout and the impact of covariates were modelled with linear mixed models.

Results

Comparison to a same-aged general population sample shows that medical students reported significantly more depression symptoms and anxiety symptoms. Medical students’ mental health improved during the course of the studies in terms of depression symptoms, suicidal ideation, and stress, although suicidal ideation increased again in the last year and anxiety symptoms remained stable. Conversely, the results regarding burnout globally showed a significant worsening from beginning to end of medical school. The covariates most strongly related to better mental health and less burnout were less emotion-focused coping, more social support, and more satisfaction with health.

Conclusion

Both improvement of mental health and worsening of burnout were observed during the course of medical school. This underlines that the beginning and the end of medical school bring specific challenges with the first years’ stressors negatively impacting mental health and the last year’s difficulties negatively impacting burnout.
Heteroplasmy is rare in plant mitochondria compared to plastids despite similar mutation rates
Authors: Khachaturyan Marina , Santer Mario , Reusch Thorsten B. H. , Dagan Tal
Published: November 2023

Plant cells harbor two membrane-bound organelles containing their own genetic material – plastids and mitochondria. Although the two organelles co-exist and co-evolve within the same plant cells, they differ in genome copy number, intracellular organization, and mode of inheritance. How these attributes determine the time to fixation, or conversely, loss of neutral alleles is currently unresolved. Here we show that mitochondria and plastids share the same mutation rate yet plastid alleles remain in a heteroplasmic state significantly longer compared to mitochondrial alleles. By analysing genetic variants across populations of the marine flowering plantZostera marina and simulating organelle allele dynamics, we examine the determinants of allele segregation and fixation time. Our results suggest that bottleneck on the cell population, e.g., during branching and seeding, and stratification of the meristematic tissue, are important determinants of mitochondrial allele dynamics. Furthermore, we suggest that the prolonged plastid allele dynamics are due to a yet unknown active plastid partition mechanism. The dissimilarity between plastid and mitochondrial novel allele fixation at different levels of organization may figure into differences in adaptation processes. Our study uncovers fundamental principles of organelle population genetics that are essential for further investigations of long-term evolution and molecular dating of divergence events.
MS Binding Assays with UNC0642 as reporter ligand for the MB327 binding site of the nicotinic acetylcholine receptor
Authors: Nitsche Valentin , Höfner Georg , Kaiser Jesko , Gertzen Christoph G.W. , Seeger Thomas , Niessen Karin V. , Steinritz Dirk , Worek Franz , Gohlke Holger , Paintner Franz F. , Wanner Klaus T.
Published: November 2023

Intoxications with organophosphorus compounds (OPCs) based chemical warfare agents and insecticides may result in a detrimental overstimulation of muscarinic and nicotinic acetylcholine receptors evolving into a cholinergic crisis leading to death due to respiratory failure. In the case of the nicotinic acetylcholine receptor (nAChR), overstimulation leads to a desensitization of the receptor, which cannot be pharmacologically treated so far. Still, compounds interacting with the MB327 binding site of the nAChR like the bispyridinium salt MB327 have been found to re-establish the functional activity of the desensitized receptor. Only recently, a series of quinazoline derivatives with UNC0642 as one of the most prominent representatives has been identified to address the MB327 binding site of the nAChR as well. In the present study, MS Binding Assays utilizing UNC0642 as a reporter ligand have been established. Thus, the binding of UNC0642 towardsTorpedo-nAChR has been characterized in MS saturation and competition experiments. According to the results, UNC0642 addresses the MB327 binding site of theTorpedo-nAChR. This conclusion is further supported by the outcome ofex vivostudies performed with poisoned rat diaphragm muscles as well as byin silicostudies predicting the binding mode of the most affine analog UNC0646 in the recently proposed binding site of MB327 (MB327-PAM-1). The new MS Binding Assays based on the commercially available reporter ligand UNC0642 as one of the most affine ligands for the MB327 binding site are a potent and valuable alternative to established assays.
Vaccinia virus infection induces concurrent alterations in host chromatin architecture, accessibility, and gene expression
Authors: Venu Vrinda , Roth Cullen , Adikari Samantha H. , Small Eric M. , Starkenburg Shawn R. , Sanbonmatsu Karissa Y. , Steadman Christina R.
Published: November 2023

Genomic DNA folds into complex configurations that produce particular local and global structures thought to profoundly impact genome function. To understand the dynamic nature of this relationship, we investigated the extent of host chromatin structural and functional changes in response to a viral agent. We performed comprehensive assessments of host architecture (Hi-C), accessibility (ATAC-seq), and gene expression (RNA-seq) in a paired manner in response to attenuated vaccinia (smallpox) virus. Over time, infection significantly increased long-range intra-chromosomal interactions and decreased chromatin accessibility. Fine-scale accessibility changes were independent of broad-scale chromatin compartment exchange, which increased (up to 12% of the genome) over time, underscoring potential independent mechanisms for global and local chromatin reorganization. The majority of differentially expressed genes, including those downregulated in immune responses, had concurrent alterations in local accessibility and loop domain restructuring. Increased B compartmentalization, intra-chromosomal interactions, and decreased inter-chromosomal interactions and chromatin accessibility together indicate that infection converts the host genome into a more condensed state with nearly equal bidirectional differential gene expression. These changes in host chromatin features may have implications for developing efficacious anti-viral countermeasures. Overall, our empirical data provides evidence of orchestrated concurrent alterations in chromatin architecture, accessibility, and gene expression in response to infection, further reinforcing the notion of coordinated structure-function dynamics of the genome.
Promoter capture drives the emergence of proto-genes inEscherichia coli
Authors: uz-Zaman Md. Hassan , D’Alton Simon , Barrick Jeffrey E. , Ochman Howard
Published: November 2023

The phenomenon ofde novogene birth—the emergence of genes from non-genic sequences—has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances ofde novogene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of theEscherichia coliLong-Term Evolution Experiment (LTEE) for changes indicative of “proto-genic” phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time-span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, thereby serving as raw material for new gene emergence. Most proto-genes result either from insertion element activity or chromosomal translocations that fused pre-existing regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, persist stably, and can serve as potential substrates for new gene formation.
Exploring Pre-Exposure Prophylaxis (PrEP) modality preferences among Black cisgender women attending family planning clinics in Chicago
Authors: Johnson Amy K. , Ott Emily , Friedman Eleanor E. , Moore Amy , Alvarez Isa , Pandiani Agustina , Desmarais Catherine , Haider Sadia
Published: November 2023

Background

Despite Pre-exposure prophylaxis’s (PrEP) demonstrated effectiveness, Black cisgender women continue to be at an elevated risk for HIV acquisition and uptake of daily oral PrEP is low in this population in the US. As advancements in PrEP delivery options continue, it is important to understand women’s acceptability of these additional options, specifically Black cisgender women, in order to inform uptake and adherence among this population at increased need of HIV prevention options.

Setting

A cross-sectional survey among Black cisgender women ages 13-45 (inclusive) attending women’s health clinics in Chicago, IL, prior to the approval of CAB-LA.

Methods

Descriptive statistics were used to describe the sample and bivariate analysis was used to detect differences between categorical and outcome variables using chi-square test. Responses to open-ended questions were thematically coded to explore Black cisgender women’s attitudes and preferences between the three methods of PrEP delivery including: vaginal ring, long-acting injectable, and a combined method that would prevent both pregnancy and HIV.

Results

In total, 211 cisgender women and adolescents responded to the survey. Both injections and combination pills were popular among participants, with 64.5% and 67.3% expressing interest in these forms of PrEP, respectively. The least popular method was the vaginal ring option, with 75.4% of respondents indicating that they would not consider using this modality. Overall, responses were not statistically different between the two surveys administered (Chi square p-values for injection PrEP method 0.66, combination PrEP method 0.93, and ring PrEP method 0.66) suggesting that the popularity of each method was not dependent on clinic location or age of participants.

Conclusion

This research provides important insights into the preferences and attitudes of different PrEP modalities among Black cisgender women. As different modalities continue to be approved for use among cisgender women, more research is needed to investigate the acceptability and preferences of these different modalities in order to improve uptake and adherence among this population.
Lateral hypothalamic GABAergic neurons encode alcohol memories
Authors: Alonso-Lozares Isis , Wilbers Pelle , Asperl Lina , Teijsse Sem , van der Neut Charlotte , Schetters Dustin , van Mourik Yvar , McDonald Allison J. , Heistek Tim , Mansvelder Huibert D. , De Vries Taco J. , Marchant Nathan J.
Published: November 2023

In alcohol use disorder the alcohol memories persist during abstinence, and exposure to stimuli associated with alcohol use can lead to relapse. This highlights the importance of investigating the neural substrates underlying not only relapse, but also encoding and expression of alcohol memories. GABAergic neurons in the Lateral Hypothalamus (LH- GABA) have been shown to be critical for food-cue memories and motivation, however the extent to which this role extends to alcohol-cue memories and motivations remains unexplored. In this study, we aimed to describe how alcohol related memories are encoded and expressed in LH GABAergic neurons. Our first step was to monitor LH-GABA calcium transients during acquisition, extinction, and reinstatement of an alcohol-cue memory using fiber photometry. We trained the rats on a Pavlovian conditioning task where one conditioned stimulus (CS+) predicted alcohol (20% EtOH) and another conditioned stimulus (CS-) had no outcome. We then extinguished this association through non-reinforced presentations of the CS+ and CS-, finally in two different groups we measured relapse under non-primed and alcohol primed induced reinstatement. Our results show that initially both cues caused increased LH-GABA activity, and after learning only the alcohol-cue increased LH-GABA activity. After extinction this activity decreases, and we found no differences in LH- GABA activity during reinstatement in either group. Next, inhibited LH-GABA neurons with optogenetics to show that activity of these neurons is necessary for the formation of an alcohol-cue associations. These findings suggest that LH-GABA might be involved in attentional processes modulated by learning.
Divergent outcomes of SAK3, a T-type calcium channel enhancer, in two luminal-A type breast cancer cell lines: T-47D and MCF7
Authors: Dinesh Yashashwini Subbamanda , Subramanian Tharunika , Zinzuvadia Aarushi K , Maturana Andres D , Bhargava Anamika
Published: November 2023

Voltage-gated calcium channels have emerged as promising targets in breast cancer. Breast cancer cell lines are widely used as experimental models. In our pursuit to explore the potential of augmenting T-type voltage-gated calcium channels as a therapeutic approach for breast cancer, we made an unexpected discovery: similar breast cancer subtype cell lines exhibited varying responses to SAK3, a T-type voltage-gated calcium channel enhancer, in terms of proliferation and intracellular calcium levels. In presenting these contrasting findings here, we aim to underscore the importance of exercising caution and validating results obtained from cell lines by cross-referencing them with other cell lines or models.
Prediction mismatch responses arise as corrections of a predictive spiking code
Authors: van Driel Kjartan , Rudelt Lucas , Priesemann Viola , Mikulasch Fabian A.
Published: November 2023

Prediction mismatch responses in cortex seem to signal the difference between an internal model of the animal and sensory observations. Often these responses are interpreted as evidence for the existence of error neurons, which guide inference in models of hierarchical predictive coding. Here we show that prediction mismatch responses also arise naturally in a spiking encoding of sensory signals, where spikes predict the future signal. In this model, the predictive representation has to be corrected when a mispredicted stimulus appears, which requires additional neural activity. This adaptive correction could explain why mismatch response latency can vary with mismatch detection difficulty, as the network gathers sensory evidence before committing to a correction. Prediction mismatch responses thus might not reflect the computation of errors per se, but rather the reorganization of the neural code when new information is incorporated.
De novoSequencing and Whole Transcriptome Analysis for Biosynthesis Pathway of Diosgenin inPedalium murexL.: A Medicinal herb
Authors: Tyagi Parul , Ranjan Rajiv
Published: November 2023

Transcriptome-based investigations of candidate genes, critical pathways, and gene regulation in non- model species have been transformed by high-throughput RNA sequencing in different studies.Pedalium murexL. is one of the most important annual medicinal herbs of thePedaliaceaefamily. Because of its anti-inflammatory, antioxidant, antimicrobial, and anticancerous properties,P. murexis widely used in traditional medicine to treat gonorrhoea, leucorrhoea, urinary disorders, gastrointestinal tract disorders, cough, and asthma. Steroidal diosgenin is the major bioactive compound ofP. murex. However, transcriptional technologies have yet to be used to study the steroid diosgenin biosynthetic pathway of this herb. In this study, we performed a whole transcriptomic analysis of the root, fruit, and leaf tissues ofP. murexwith three biological replicates and obtained ∼6.77 Gb of clean raw data. A total of 148871 unigenes were assembled with an average length of N50 and 1167 bp. Putative functions could be annotated to 75198 unigenes based on a BLASTX search against the NR, Uniprot, KEGG, Pfam, GO, and COG databases. Most of the unigenes related to steroidal diosgenin backbone biosynthesis were up- regulated in the root, fruit and leaf, except for the MVD gene in the leaf. qRT-PCR further verified the differential expression analysis of selected genes. It shows the highest homology withSesamum indicum, Handroanthus impetiginosus, Erythranthe guttata, Oleaeuropaeavar. sylvestris,andDorcoceras hygrometricum. A total of 21026 unigenes of transcription factors were assembled into transcription factor families. A total of 8760 unigenes of SSR were assembled. The transcriptome data presented here will make it easier to study the functional genomics of steroidal diosgenin biosynthesis and to change the genes ofP. murexto make it more diosgenin.
Shared and diverging neural dynamics underlying false and veridical perception
Authors: Haarsma Joost , Hetenyi Dorottya , Kok Peter
Published: November 2023

We often mistake visual noise for meaningful images, which sometimes appear to be as convincing as veridical percepts. This suggests that there is considerable overlap between the mechanisms that underlie false and veridical perception. Yet, false percepts must arise at least in part from internally generated signals. Here, we apply multivariate analyses to human MEG data to study the overlap between veridical and false perception across two discrete stages of perceptual inference: discrimination of content (what did I see) and detection (did I see something?). To this end, participants performed a visual discrimination task requiring them to indicate the orientation of a noisy grating, as well as their confidence in having seen a grating. Importantly, on 50% of trials no gratings were presented (noise-only trials). On a subset of these noise-only trials, participants reported seeing a grating with high confidence, dubbed here false percepts. We found that a sensory signal reflecting the content of these false percepts was present both before and after stimulus onset. Uniquely, high confidence false, but not veridical, percepts were associated with increased pre-stimulus high alpha/low beta [11-14Hz] power, potentially reflecting enhanced reliance on top-down signalling on false percept trials. Later on, a shared neural code reflecting confidence in stimulus presence emerged for both false and veridical percepts, as revealed by cross-decoding. These findings suggest that false percepts arise through sensory-like signals reflecting both content and detection signals, similar to veridical percepts, with an increase in pre-stimulus alpha/beta power uniquely contributing to false percepts.
Macromolecule modelling for improved metabolite quantification using short echo time brain1H MRS at 3 T and 7 T: The PRaMM Model
Authors: Dell’Orco Andrea , Riemann Layla Tabea , Ellison Stephen L. R. , Aydin Semiha , Göschel Laura , Tietze Anna , Scheel Michael , Fillmer Ariane
Published: November 2023

Purpose

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain1H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints.

Methods

Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated.

Results

The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 – 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out.

Conclusion

Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.
ADEVO: Proof-of-concept of Adenovirus Directed EVOlution by random peptide display on the fiber knob
Authors: Sallard Erwan , Fischer Julian , Beaude Nissai , Affes Arsalene , Ehrke-Schulz Eric , Zhang Wenli , Westhaus Adrian , Cabanes-Creus Marti , Lisowski Leszek , Ruszics Zsolt , Ehrhardt Anja
Published: November 2023

Directed evolution of viral vectors involves the generation of randomized libraries followed by artificial selection of improved variants. Directed evolution only yielded limited results in adenovirus vector (AdV) development until now, mainly due to insufficient complexities of randomized libraries.

Clinical applications of AdVs as gene therapy or oncolytic vectors are still hampered by the predetermined tropism of natural types. To overcome this challenge, we hypothesized that the technology of randomized peptide insertions on the capsid surface can be incorporated into the AdV bioengineering toolbox for vector retargeting. Here we developed Adenovirus Directed EVOlution (ADEVO) protocols based on fiber knob peptide display.

As a proof-of-concept, HAdV-C5-derived libraries were constructed following three distinct protocols and selected on A549-DCAR cells that lack the HAdV-C5 primary receptor, with the goal of identifying variants able to infect and lyse these tumor cells more efficiently. All protocols enabled the construction of high complexity libraries with up to 9.6x10^5 unique variants, an approximate 100-fold improvement compared to previously published AdV libraries. After selection, the most enriched variants did not display enhanced infectivity but rather more efficient replication and cell lysis. This warrants investigations into potential unsuspected involvement of the fiber protein in adenovirus replication.

GRAPHICAL ABSTRACT