bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility and reliability of online vs in-person cognitive testing in healthy older people
Authors: Morrissey Sol , Gillings Rachel , Hornberger Michael
Published: July 2023

Background

Early evidence in using online cognitive assessments show that they potentially offer a feasible and resource efficient alternative to in-person clinical assessments in evaluating cognitive performance, yet there is currently little understanding about how these assessments relate to traditional, in-person cognitive tests.

Objectives

We assess the feasibility and reliability of NeurOn, a novel online cognitive battery, measuring processing speed, executive functioning, spatial working memory, episodic memory, attentional control, visuospatial functioning and spatial orientation.

Design

32 participants (mean age: 70.19) completed two testing sessions, unsupervised online and in-person, one-week apart. Participants were randomised in the order of testing appointments. For both sessions, participants completed questionnaires prior to a cognitive assessment. Test-retest reliability and concurrent validity of the online cognitive battery was assessed by comparing performance in repeated tasks across testing sessions as well as with traditional in-person cognitive tests.

Results

Global cognition in the NeurOn battery moderately validated against MoCA performance. The battery demonstrated moderate test-retest reliability as performance across repeated tasks did not show a significant difference. Concurrent validity was found only between the online and paper versions of the Trail Making Test -A, as well as global cognitive performance between online and in-person testing sessions.

Conclusions

The NeurOn cognitive battery provides a promising tool for measuring cognitive performance online both longitudinally and across short retesting intervals within healthy older adults. When considering cost-effectiveness, flexible administration, and improved accessibility for wider populations, online cognitive assessments have the potential to improve future screening for neurodegenerative diseases.
Structure-based network analysis predicts mutations associated with inherited retinal disease
Authors: Hauser Blake M. , Luo Yuyang , Nathan Anusha , Gaiha Gaurav D. , Vavvas Demetrios , Comander Jason , Pierce Eric A. , Place Emily M. , Bujakowska Kinga M. , Rossin Elizabeth J.
Published: July 2023

With continued advances in gene sequencing technologies comes the need to develop better tools to understand which mutations cause disease. Here we validate structure-based network analysis (SBNA)1, 2in well-studied human proteins and report results of using SBNA to identify critical amino acids that may cause retinal disease if subject to missense mutation. We computed SBNA scores for genes with high-quality structural data, starting with validating the method using 4 well-studied human disease-associated proteins. We then analyzed 47 inherited retinal disease (IRD) genes. We compared SBNA scores to phenotype data from the ClinVar database and found a significant difference between benign and pathogenic mutations with respect to network score. Finally, we applied this approach to 65 patients at Massachusetts Eye and Ear (MEE) who were diagnosed with IRD but for whom no genetic cause was found. Multivariable logistic regression models built using SBNA scores for IRD-associated genes successfully predicted pathogenicity of novel mutations, allowing us to identify likely causative disease variants in 37 patients with IRD from our clinic. In conclusion, SBNA can be meaningfully applied to human proteins and may help predict mutations causative of IRD.
Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services
Authors: Lu Yue , Wang Yaqian , Zhou Bo
Published: July 2023

Objective

We aimed to establish and authenticate a clinical prognostic nomogram for predicting long-term Major Adverse Cardiovascular Events (MACEs) among high-risk patients who have undergone Percutaneous Coronary Intervention (PCI) in county-level health service.

Methods

This prospective study included Acute Coronary Syndrome (ACS) patients treated with PCI at six county-level hospitals between September 2018 and August 2019, selected from both the original and external validation cohorts. Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques and logistic regression were used to assess potential risk factors and construct a risk predictive nomogram. Additionally, the potential non-linear relationships between continuous variables were tested using Restricted Cubic Splines (RCS). The performance of the nomogram was evaluated based on the Receiver Operating Characteristic (ROC) curve analysis, Calibration Curve, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC).

Results

The original and external validation cohorts comprised 520 and 1061 patients, respectively. The final nomogram was developed using nine clinical variables: Age, Killip functional classification III-IV, Hypertension, Hyperhomocysteinemia, Heart failure, Number of stents, Multivessel disease, Low-density Lipoprotein Cholesterol, and Left Ventricular Ejection Fraction. The AUC of the nomogram was 0.79 and 0.75 in the original and validation cohorts, respectively. The DCA and CIC validated the clinical value of the constructed prognostic nomogram.

Conclusion

Herein, we developed and validated a prognostic nomogram for predicting the probability of 3-year MACEs in ACS patients who underwent PCI at county-level hospitals. The nomogram could provide a precise risk assessment for secondary prevention in ACS patients receiving PCI.
Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T
Authors: Needleman Sarah H. , Kim Mina , McClelland Jamie R. , Naish Josephine H. , Tibiletti Marta , O’Connor James P. B. , Parker Geoff J. M.
Published: July 2023

Purpose

Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI.

Methods

Dynamic OE-MRI was performed on healthy participants using a dual-echo multi- slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with non-smokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors, were assessed.

Results

ICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and non-smokers. The scan-rescan repeatability, and the ICA pipeline repeatability, were good.

Conclusion

The developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and non-smokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies.
Exploring Leishmania linear motifs directing protein secretion, sorting and autophagy
Authors: Zeke Andras , Gibson Toby J. , Dobson Laszlo
Published: July 2023

The pathogenic tropical flagellatesLeishmaniabelong to an early-branching eukaryotic lineage (Kinetoplastida) with several unique features. Here, we explore three ancient protein targeting linear motif systems and their receptors and demonstrate how they resemble or differ from other eukaryotic organisms, including their hosts. Secretory signal peptides, endoplasmic reticulum (ER) retention motifs (KDEL motifs), and autophagy signals (motifs interacting with ATG8 family members) are essential components of cellular life. Although expected to be conserved, we observe that all three systems show a varying degree of divergence from the eukaryotic version observed in animals, plants, or fungi. We not only describe their behavior but also build predictive models that allow the prediction of localization or function for several proteins inLeishmaniaspecies for the first time. Several of these critical protein-protein interactions could serve as targets of selective antimicrobial agents against Leishmaniasis due to their divergence from the host.
Neural signatures of natural behavior in socializing macaques
Authors: Testard Camille , Tremblay Sébastien , Parodi Felipe , DiTullio Ron W. , Acevedo-Ithier Arianna , Gardiner Kristin L. , Kording Konrad , Platt Michael L.
Published: July 2023

Our understanding of the neurobiology of primate behavior largely derives from artificial tasks in highly-controlled laboratory settings, overlooking most natural behaviors primate brains evolved to produce1–3. In particular, how primates navigate the multidimensional social relationships that structure daily life4and shape survival and reproductive success5remains largely unexplored at the single neuron level. Here, we combine ethological analysis with new wireless recording technologies to uncover neural signatures of natural behavior in unrestrained, socially interacting pairs of rhesus macaques. Single neuron and population activity in prefrontal and temporal cortex unveiled robust encoding of 24 species-typical behaviors, which was strongly modulated by the presence and identity of surrounding monkeys. Male-female partners demonstrated near-perfect reciprocity in grooming, a key behavioral mechanism supporting friendships and alliances6, and neural activity maintained a running account of these social investments. When confronted with an aggressive intruder, behavioral and neural population responses reflected empathy and were buffered by the presence of a partner. By employing an ethological approach to the study of primate neurobiology, we reveal a highly-distributed neurophysiological ledger of social dynamics, a potential computational foundation supporting communal life in primate societies, including our own.
Improving fMRI-based Autism Spectrum Disorder Classification with Random Walks-informed Feature Extraction and Selection
Authors: Sotero Roberto C. , Sanchez-bornot Jose M. , Iturria-medina Yasser
Published: July 2023

Functional magnetic resonance imaging (fMRI) is a non-invasive technique measuring brain activity by detecting blood flow changes, enabling the study of cognitive processes and brain states. However, the high dimensionality of resting-state (rs) fMRI data poses challenges for machine learning applications. Feature extraction (FE) and feature selection (FS) are critical for developing efficient machine learning models. Transforming raw data into meaningful features and selecting the most relevant ones, allows models to achieve improved generalization, accuracy, and robustness. Previous studies demonstrated the effectiveness of FE and FS methods for analyzing rs-fMRI data for Autism Spectrum Disorder (ASD) classification. In this study, we apply a random walks technique for correlation-based brain networks to extract features from rs-fMRI data, specifically the number of random walkers on each brain area. We then select significant features, i.e., brain areas with a statistically significant difference in the number of random walkers between neurotypical and ASD subjects. Our random walks-based FE and FS approach reduces the number of brain areas used in the classification and converts the functional connectivity matrix into a manageable vector, enabling faster computation. We examined 16 pipelines and tested support vector machines (SVM) and logistic regression for classification, identifying the optimal pipeline to consist of no filtering, no global signal regression (GSR), and FS, achieving a 76.54% classification accuracy with SVM. Our findings suggest that random walks capture a wide range of interactions and dynamics in brain networks, providing a deeper characterization of their structure and function, ultimately enhancing classification performance.

CCS CONCEPTS

Computing methodologies→Machine learning
Prolonged activity-deprivation causes pre- and postsynaptic compensatory plasticity at neocortical excitatory synapses
Authors: Wise Derek L. , Escobedo-Lozoya Yasmin , Valakh Vera , Isaac Berith , Gao Emma Y. , Greene Samuel B. , Bhonsle Aishwarya , Lei Qian L. , Cheng Xinyu , Van Hooser Stephen D. , Nelson Sacha B.
Published: July 2023

Homeostatic plasticity stabilizes firing rates of neurons, but the pressure to restore low activity rates can significantly alter synaptic and cellular properties. Most previous studies of homeostatic readjustment to complete activity silencing in rodent forebrain have examined changes after two days of deprivation, but it is known that longer periods of deprivation can produce adverse effects. To better understand the mechanisms underlying these effects and to address how presynaptic as well as postsynaptic compartments change during homeostatic plasticity, we subjected mouse cortical slice cultures to a more severe five-day deprivation paradigm. We developed and validated a computational framework to measure the number and morphology of presynaptic and postsynaptic compartments from super resolution light microscopy images of dense cortical tissue. Using these tools, combined with electrophysiological miniature excitatory postsynaptic current measurements, and synaptic imaging at the electron microscopy level, we assessed the functional and morphological results of prolonged deprivation. Excitatory synapses were strengthened both presynaptically and postsynaptically. Surprisingly, we also observed a decrement in the density of excitatory synapses, both as measured from colocalized staining of pre- and postsynaptic proteins in tissue, and from the number of dendritic spines. Overall, our results suggest that cortical networks deprived of activity progressively move towards a smaller population of stronger synapses.

SIGNIFICANCE STATEMENT

Blocking activity in neocortical slice cultures produced coordinated pre and postsynaptic changes at excitatory synapses. Functional and structural assays suggest that deprivation results in fewer excitatory synapses, but each is strengthened both pre- and postsynaptically. This may contribute to the emergence of epileptiform activity.
Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample
Authors: Dunlop Katharine , Grosenick Logan , Downar Jonathan , Vila-Rodriguez Fidel , Gunning Faith M. , Daskalakis Zafiris J. , Blumberger Daniel M. , Liston Conor
Published: July 2023

Background

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder (MDD), but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with deeper clinical phenotyping. The goals of this work were to develop and evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, deeply phenotyped, single-site sample.

Methods

We used regularized canonical correlation analysis (RCCA) to identify data-driven brain-behavior dimensions explaining individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting state fMRI data for N=328 patients with MDD and N=461 healthy controls. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes

Results

The optimal RCCA model yielded three robust and generalizable brain-behavior dimensions explaining individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified four depression subtypes, each with distinct clinical symptom profiles, abnormal RSFC patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

Conclusions

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in MDD that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that RCCA and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
Terminal regions of a protein are a hotspot for low complexity regions (LCRs) and selection
Authors: Teekas Lokdeep , Sharma Sandhya , Vijay Nagarjun
Published: July 2023

A majority of the protein-coding genes consist of low-complexity regions (LCRs) in eukaryotes. Volatile LCRs are a novel source of adaptive variation, functional diversification, and evolutionary novelty. LCRs contribute to a wide range of neurodegenerative disorders. Conversely, these regions also play a pivotal role in critical cellular functions, such as morphogenesis, signaling, and transcriptional regulation. An interplay of selection and mutation governs the composition and length of LCRs. High %GC and mutations provide length variability because of mechanisms like replication slippage. The selection is nearly neutral for expansion/contraction within the normal range and purifying above a critical length. Because of the complex dynamics between selection and mutation, we need a better understanding of the coexistence and mechanisms of the two. Our findings indicate that site-specific positive selection and LCRs prefer the terminal regions of a gene and co-occur in most of the Tetrapoda clades. Interestingly, positively selected sites (PSS) are significantly favored in LCRs in eight of the twelve clades studied. We also observed a significant favor of PSSs in the polyQ region of MAML2 in five clades. We also found that PSSs in a gene have position-specific roles. Terminal-PSS genes are enriched for adenyl nucleotide binding, while central-PSS genes are involved in glycosaminoglycan binding. Moreover, central-PSS genes mainly participate in defense responses, but terminal-PSS genes are non-specific. LCR-containing genes have a significantly higher %GC and lower ω (dN/dS) than genes without repeats across the Tetrapoda clade. A lower ω suggests that even though LCRs provide rapid functional diversity, LCR-containing genes face intense purifying selection.
Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network
Authors: Hatano Narumi , Kamada Mayumi , Kojima Ryosuke , Okuno Yasushi
Published: July 2023

Background

In cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part.

Results

Net-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms.

Conclusions

We propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.
Epidemiology of cruciate ligament surgery in Japan: A retrospective cohort study from 2014 to 2019
Authors: Uchino Shota , Taguri Masataka
Published: July 2023

Understanding the incidence and trends of cruciate ligament (CL) surgeries in Japan is crucial for providing effective healthcare services. This study aimed to use open data available from the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) to determine the changes over time in CL surgeries and to analyze the characteristics of the Japanese population in terms of sex and age. We retrospectively identified CL surgeries of the knee joint registered from April 2014 to March 2020 using the NDB open data. Data on sex, age, and practice were extracted to determine the number of cases per 100,000 population. Trends in the annual incidence of CL surgeries were evaluated using Poisson regression analysis. In total, 112,686 CL surgeries were performed from 2014 to 2019. Arthroscopic ligament reconstruction accounted for 98% of all the cases. The number of surgeries performed had increased significantly from 16,975 in 2014 to 19,735 in 2019 (P<0.001). Overall, CL surgeries were most common in the 15–19 years age group, followed by the 20–29 years age group. However, these trends differed between males and females. The incidence of CL surgery in Japan has increased, and its characteristics vary by gender and age. These characteristics have been identified not only in younger patients but also in middle-aged and older patients. It would be valuable to further investigate the general patterns of CL surgery in Japan.
Influence of Geographical Aspect and Topography on Canopy Openness in Tropical Rainforests of Sri Lanka along an Altitudinal Gradient
Authors: Madhumali R.M.C. , Wahala W.M.P.S.B. , Sanjeewani H.K.N. , Samarasinghe D.P. , De Costa W.A.J.M.
Published: July 2023

Solar radiation energy is a limiting factor for forest growth in humid tropical environments. Radiation incident on a forest canopy varies with azimuth angle of incident radiation and aspect of the forest terrain. The objective of this work was to determine the influence of the geographical aspect and azimuth of incident radiation on the canopy structure of tropical rainforests of Sri Lanka (TRFSL). Hemispherical photography was used to capture canopy images of TRFSLs in ten permanent sampling plots across an altitudinal range from 117 m to 2132 m above mean sea level. Fraction of visible sky (Vsky) in 144 sectors of the canopy hemisphere, defined by 18 zenith angle (ZnA) × 8 azimuth angle (AzA) segments, was determined using image analysis. Canopy openness, quantified as Vskyof the overall hemisphere, increased with increasing altitude. Canopy leaf area index, decreased linearly with altitude and had a negative relationship with Vsky. Canopy openness of the top one-third (ZnA=0°–30°) of the hemisphere (Vsky(0-30)) was significantly (p<0.05) lower on the east (AzA=90°) than on the west (AzA=270°) in a majority of plots. Similarly, Vsky(0-30)was lower on the northern (AzA=0°) than the southern (AzA =180°) canopy segment. These patterns were altered in plots where nearby mountains and slope aspect of the plot influenced incident radiation. These observations suggest a hypothesis that canopies of TRFSL are structured to maximize radiation capture by allocating more leaf area, and therefore having lower canopy openness, on sides of the canopy which face directions of greater radiation receipt.
Structure and Biosynthesis of Hectoramide B, a Linear Depsipeptide from the Marine CyanobacteriumMoorena producensJHB Discovered via Co-culture withCandida albicans
Authors: Ngo Thuan-Ethan , Ecker Andrew , Guild Aurora , Remmel Ariana , Boudreau Paul D. , Alexander Kelsey L. , Naman C. Benjamin , Glukhov Evgenia , Avalon Nicole E. , Shende Vikram V. , Gerwick Lena , Gerwick William H.
Published: July 2023

The tropical marine cyanobacteriumMoorena producensJHB is a prolific source of secondary metabolites with potential biomedical utility. Previous studies of this strain led to the discovery of several novel compounds such as the hectochlorins and jamaicamides; however, bioinformatic analyses of its genome suggested that there were many more cryptic biosynthetic gene clusters yet to be characterized. To potentially stimulate the production of novel compounds from this strain, it was co-cultured withCandida albicans. From this experiment, we observed the increased production of a new compound that we characterize here as hectoramide B. Bioinformatic analysis of theM. producensJHB genome enabled the identification of a putative biosynthetic gene cluster responsible for hectoramide B biosynthesis. This work demonstrates that co-culture competition experiments can be a valuable method to facilitate the discovery of novel natural products from cyanobacteria.

Abstract Figure
Inference of host-pathogen interaction matrices from genome-wide polymorphism data
Authors: Märkle Hanna , John Sona , Metzger Lukas , Consortium STOP-HCV , Azim Ansari M , Pedergnana Vincent , Tellier Aurélien
Published: July 2023

Coevolution is defined as the evolutionary change in one antagonist (host) in response to changes in the other antagonist (pathogen). At the genetic level, these changes are determined by genotype x genotype (GxG) interactions. We build on a general theoretical model of a host-pathogen interaction to derive four indices to retrieve key features of GxG interactions. The four developed indices extract relevant information from polymorphism data of randomly sampled uninfected hosts as well as infected hosts and their respective pathogen strains. Using these indices as summary statistics in an Approximate Bayesian Computation method, we can show their power to discriminate between GxG interaction matrices. Second, we apply our ABC method to a SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains supplemented by polymorphism data of 503 individuals from the 1,000 genomes project. As our indices encompass and extend previous natural co-GWAs we recover many of the associations previously reported for this dataset and infer their underlying interaction matrix. We reveal a new candidate gene for resistance to HCV in the human genome, and two groups of significant GxG associations exhibiting gene-for-gene interactions. We suggest that the inferred types of GxG interactions result from the recent expansion, adaptation and low prevalence of the HCV virus population in Europe.

Significance statement

Why are some host individuals susceptible/resistant to infection by certain pathogen genotypes and others not? Understanding the genetic characteristics of genes driving host-pathogen interactions is crucial to predict epidemics. We develop four indices based on a mathematical model and build a Bayesian statistical method computing these indices on full genome data of infected hosts and their infecting pathogen strains and data of non-infected hosts. We can pinpoint the genes underlying host-pathogen interactions and infer their characteristics. Applying our framework to data from European humans and the Hepatitis C virus, we discover a new potential resistance gene in humans and reveal how the virus has adapted in the last 150 years to match the genetic diversity of the European human population.
Directional Sensitivity of Cortical Neurons Towards TMS Induced Electric Fields
Authors: Weise Konstantin , Worbs Torge , Kalloch Benjamin , Souza Victor H. , Jaquier Aurélien Tristan , Geit Werner Van , Thielscher Axel , Knösche Thomas R.
Published: July 2023

We derived computationally efficient average response models of different types of cortical neurons, which are subject to external electric fields from Transcranial Magnetic Stimulation. We used 24 reconstructions of pyramidal cells (PC) from layer 2/3, 245 small, nested, and large basket cells from layer 4, and 30 PC from layer 5 with different morphologies for deriving average models. With these models, it is possible to efficiently estimate the stimulation thresholds depending on the underlying electric field distribution in the brain, without having to implement and compute complex neuron compartment models. The stimulation thresholds were determined by exposing the neurons to TMS-induced electric fields with different angles, intensities, pulse waveforms, and field decays along the somato-dendritic axis. The derived average response models were verified by reference simulations using a high-resolution realistic head model containing several million neurons. Differences of only 1-2% between the average model and the average response of the reference cells were observed, while the computation time was only a fraction of a second compared to several weeks using the cells. Finally, we compared the model behavior to TMS experiments and observed high correspondence to the orientation sensitivity of motor evoked potentials. The derived models were compared to the classical cortical column cosine model and to simplified ball-and-stick neurons. It was shown that both models oversimplify the complex interplay between the electric field and the neurons and do not adequately represent the directional sensitivity of the different cell types.

The derived models are simple to apply and only require the TMS induced electric field in the brain as input variable. The models and code are available to the general public in open-source repositories for integration into TMS studies to estimate the expected stimulation thresholds for an improved dosing and treatment planning in the future.
Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots
Authors: Li Qiang , Calhoun Vince , Pham Tuan , Iraji Armin
Published: July 2023

Much of the complexity and diversity found in nature are driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics, and they can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.
Cross-regulation between proteome reallocation and metabolic flux redistribution governs bacterial growth transition kinetics
Authors: Yuan Huili , Bai Yang , Li Xuefei , Fu Xiongfei
Published: July 2023

Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. It’s still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.
Filter-aided extracellular vesicle enrichment (FAEVEr) for proteomics
Authors: Pauwels Jarne , Van de Steene Tessa , Van de Velde Jana , Muyer Freya De , Pauw Danaë De , Baeke Femke , Eyckerman Sven , Gevaert Kris
Published: July 2023

Abstract

Extracellular vesicles (EVs), membrane-delimited nanovesicles that are secreted by cells into the extracellular environment, are gaining substantial interest due to their involvement in cellular homeostasis and their contribution to disease pathology. The latter in particular has led to an exponential increase in interest in EVs as they are considered to be circulating packages containing potential biomarkers and are also a possible biological means to deliver drugs in a cell-specific manner. However, several challenges hamper straightforward proteome analysis of EVs as they are generally low abundant and reside in complex biological matrices. These matrices typically contain abundant protein concentrations that vastly exceed those of the EV proteome. Therefore, extensive EV isolation and purification protocols are imperative and many have been developed, including (density) ultracentrifugation, size-exclusion and precipitation methods. Here, we describe filter-aided extracellular vesicle enrichment (FAEVEr) as an approach based on 300 kDa MWCO filtration that allows the processing of multiple samples in parallel within a reasonable timeframe and at moderate cost. We demonstrate that FAEVEr is capable of quantitatively retaining EV particles on filters, whilst allowing extensive washing with the mild detergent TWEEN-20 to remove interfering non-EV proteins. The retained particles are directly lysed on the filter for a complete recovery of the EV protein cargo towards proteome analysis. Here, we validate and optimize FAEVEr on recombinant EV material and apply it on conditioned medium as well as on complex serum. Our results indicate that EVs isolated from MCF7 cells cultured with or without serum have a drastic different proteome because of nutrient deprivation.
TRIB1 modulates transcriptional programming in breast cancer cells to regulate cell proliferation
Authors: McMillan Hamish D. , Papachristou Evangelia K. , Hazlett Jody , Omarjee Soleilmane , Carroll Jason S. , Black Michael A. , Mace Peter D. , Dunbier Anita K.
Published: July 2023

The pseudokinase Tribbles Homolog 1 (TRIB1) is a known driver of tumorigenesis in acute myeloid leukemia and is encoded upstream of the oncogeneMYCat the 8q24 locus. We observed thatTRIB1/MYCco-amplification is associated with decreased relapse-free and overall survival in breast cancer patients, but the role ofTRIB1in this disease has not been well characterized.TRIB1knockdown in multiple breast cancer cell lines inhibited cell proliferation and suppressedMYCexpression, implicatingTRIB1in breast cancer cell proliferation. Transcriptomic and cell cycle analysis revealed cell cycle regulation as the likely mechanism through whichTRIB1influences breast cancer cell proliferation.TRIB1knockdown also resulted in significant changes in both estrogen receptor (ER) and β-catenin associated transcription. Interrogating the TRIB1 interactome in breast cancer cells by qPLEX-RIME reinforced the known association between TRIB1 and ubiquitination, while revealing a range of previously undescribed TRIB1 associated factors. Further analysis of the association between TRIB1, β-catenin and FERMT2 suggests TRIB1 may regulate β-catenin activity by controlling the levels of both β-catenin, and its co-factor FERMT2. Together, these results suggest that coregulation of β-catenin and ER-driven transcription by TRIB1, facilitates regulation ofMYCexpression and breast cancer cell proliferation.

Significance

The pseudokinaseTRIB1is frequently co-amplified in breast cancers with the potent oncogeneMYC, although the functional consequences of this event are not well understood. This study demonstratesTRIB1is a regulator of cell cycle progression andMYCexpression in breast cancer cells. It also profilesTRIB1-associated proteins in breast cancer cells, demonstrating conservation of TRIB1’s canonical interaction with COP1 and reveals associations with members of the wider ubiquitination machinery, a range of transcriptional regulators and chromatin remodelers. The data presented provide insight into the function of TRIB1 in breast cancer and the role of TRIB1 in transcriptional regulation.
SoMAS: Finding somatic mutations associated with alternative splicing in human cancers
Authors: Tan Hua , Gotea Valer , Seidel Nancy E. , Holland David O. , Fedkenheuer Kevin , Jaiswal Sushil K. , Bang-Christensen Sara , Elnitski Laura
Published: July 2023

Aberrant alternative splicing is prevalent in cancer and affects most cancer hallmarks involving proliferation, angiogenesis, and invasion. Somatic point mutations can exert their cancer-driving functions via splicing disruption. We propose “SoMAS” (SomaticMutation associated withAlternativeSplicing), an efficient computational pipeline based on principal component analysis techniques, to explore the role of somatic mutations in shaping the landscape of alternative splicing via bothcis- andtrans-acting mechanisms. Applying SoMAS to 33 cancer types consisting of 9,738 tumor samples in The Cancer Genome Atlas, we identified 908 somatically mutated genes significantly associated with altered isoform expression in three or more cancer types. These genes include many well-known oncogenes/suppressor genes, RNA binding protein and splicing factor genes with both biological and clinical significance. Many of our identified SoMAS genes were corroborated to affect gene splicing by independent cohorts and/or methodologies. With SoMAS, we for the first time demonstrate the potential network of somatic mutations associated with the overall splicing profiles of cancer transcriptomes, bridging the genetic and epigenetic regulation of human tumorigenesis in an innovative way.
Exploring G-quadruplex structure inPRCC-TFE3fusion Oncogene: Plausible use as anti cancer therapy for translocation Renal cell carcinoma (tRCC)
Authors: Neha  , Das Parimal
Published: July 2023

TheTFE3fusion gene, byproduct of Xp11.2 translocation, is the diagnostic marker for translocation renal cell carcinoma (tRCC). Absence of any clinically recognized therapy for tRCC, pressing a need to create novel and efficient therapeutic approaches. Previous studies shown that stabilization of the G-quadruplex structure in oncogenes suppresses their expression machinery. To combat the oncogenesis caused by fusion genes, our objective is to locate and stabilize the G-quadruplex structure within thePRCC-TFE3fusion gene. Using the Quadruplex- forming G Rich Sequences (QGRS) mapper and the Non-B DNA motif search tool (nBMST) online server, we found putative G-quadruplex forming sequences (PQS) in thePRCC-TFE3fusion gene. Circular dichroism demonstrating a parallel G-quadruplex in the targeted sequence. Fluorescence and UV-vis spectroscopy results suggest that pyridostatin binds to this newly discovered G-quadruplex. The PCR stop assay, as well as transcriptional or translational inhibition by PQS, revealed that stable G-quadruplex formation affects biological processes. Confocal microscopy of HEK293T cells transfected with the fusion transcript confirmed G- quadruplexes formation in cell. This investigation may shed light on G-quadruplex’s functions in fusion genes and may help in the development of therapies specifically targeted against fusion oncogenes, which would enhance the capability of current tRCC therapy approach.
Activation of goblet cell stress sensor IRE1β is controlled by the mucin chaperone AGR2
Authors: Cloots Eva , Guilbert Phaedra , Provost Mathias , Neidhardt Lisa , Van de Velde Evelien , Fayazpour Farzaneh , Sutter Delphine De , Savvides Savvas N. , Eyckerman Sven , Janssens Sophie
Published: July 2023

As secretory cells specialized in the production of mucins, intestinal goblet cells are challenged by the need for efficient protein folding. Goblet cells express Inositol-Requiring Enzyme 1β (IRE1β), a unique unfolded protein response (UPR) sensor that is part of an adaptive mechanism that regulates the demands of mucin production and secretion. However, how IRE1β activity is tuned to mucus folding load remains unknown. We identified the disulfide isomerase and mucin chaperone AGR2 as a goblet cell specific protein that crucially regulates IRE1β-, but not IRE1α-mediated signaling. AGR2 binding to IRE1β disrupts IRE1β dimerization, thereby blocking its downstream endonuclease activity. Depletion of endogenous AGR2 from goblet cells induces spontaneous IRE1β activation, suggesting that alterations in AGR2 availability in the endoplasmic reticulum sets the threshold for IRE1β activation. We found that AGR2 mutants lacking their catalytic cysteine or displaying the disease-associated mutation H117Y were no longer able to dampen IRE1β activity. Collectively, these results demonstrate that AGR2 is a central chaperone regulating the goblet cell UPR by acting as a rheostat of IRE1β endonuclease activity.
Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing
Authors: Menon Govind , Mateo Bonmati Eduardo , Reeck Svenja , Maple Robert , Wu Zhe , Ietswaart Robert , Dean Caroline , Howard Martin
Published: July 2023

Chromatin-mediated transcriptional silencing by Polycomb Repressive Complex 2 (PRC2) is critical for gene regulation in development and environmental responses. However, the mechanism and timescales controlling de novo establishment of PRC2 silencing are unclear. Here, we investigate PRC2 silencing at Arabidopsis FLOWERING LOCUS C (FLC), known to involve co-transcriptional RNA processing, histone demethylation activity, and PRC2 function; but so far not mechanistically connected. We develop and then test a computational model that describes how proximal polyadenylation/termination mediated by the RNA binding protein FCA induces H3K4me1 removal by the histone demethylase FLD. H3K4me1 removal feeds back to reduce RNA Pol II processivity and thus enhance early termination, thereby repressing productive transcription. The model predicts that this transcription-coupled repression controls the level of transcriptional antagonism to Polycomb action, Thus, the effectiveness of this repression dictates the timescale for establishment of Polycomb H3K27me3 silencing. Experimental validation of these model predictions allowed us to mechanistically connect co-transcriptional processing to setting the level of productive transcription at the locus, which then determines the rate of the ON to OFF switch to PRC2 silencing.
A CPF-like phosphatase module links transcription termination to chromatin silencing
Authors: Mateo Bonmati Eduardo , Montez Miguel , Maple Robert , Fiedler Marc , Fang Xiaofeng , Saalbach Gerhard , Passmore Lori A , Dean Caroline A
Published: July 2023

The interconnections between co-transcriptional regulation, chromatin environment and transcriptional output remain poorly understood. Here, we investigate the mechanism underlying RNA 3 processing-mediated Polycomb silencing of Arabidopsis FLOWERING LOCUS C (FLC). We show a requirement for APRF1, a homologue of yeast Swd2 and human WDR82, known to regulate RNA Pol II during transcription termination. APRF1 interacts with TOPP4 (yeast Glc7/human PP1) and LD, the latter showing structural features found in Ref2/PNUTS; all components of the yeast and human phosphatase module of the CPF 3 end processing machinery. LD has been shown to co-associate in vivo with the histone H3 K4 demethylase FLD. We show APRF1 and LD couple CPF-mediated cleavage and polyadenylation with removal of H3K4 monomethylation in the body of FLC, and this influences subsequent transcription. This work shows how transcription termination can change the local chromatin environment to modulate transcription of Arabidopsis FLC and affect flowering time.
Bioinformatic correction of bacterial morphology-based extraction bias and chimeras in microbiome sequencing data
Authors: Rauer Luise , De Tomassi Amedeo , Müller Christian L. , Hülpüsch Claudia , Traidl-Hoffmann Claudia , Reiger Matthias , Neumann Avidan U.
Published: July 2023

Introduction

Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases, originating from bacterial DNA extraction, contamination, sequence errors, and chimeras. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to bioinformatically correct extraction bias based on morphological properties.

Methods

We compared dilution series of 3 mock communities with an even or staggered composition. DNA was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks. Sequences were denoised using DADA2, and annotated by matching against mock reference genomes.

Results

Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with high input cell number. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparison of microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based bioinformatic correction of extraction bias significantly improved sample compositions when applied to different samples, even with different taxa.

Conclusions

Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that bioinformatic correction of extraction bias is feasible based on bacterial cell morphology.