bluuebunny/crossref_metadata_2025 huggingface dataset:
The Oncogenic Role of Human Microcephalin Gene Revealed by Pan-Cancer Analysis
Authors: Wang Zerui , Li Mian , Liu Zhen , Kang Weiming , Cui Yazhou , Han Jinxiang , Du Wenbin
Published: November 2022

The human gene microcephalin (MCPH1) plays a key role in DNA damage-induced cellular responses and chromosome condensation. Recent clinical studies proposedMCPH1as a tumor suppressor gene in lung cancer, pancreatic cancer, and breast cancer, yet its roles remain poorly understood in other types of tumors. Pan-cancer analyses ofMCPH1are urgently required to help us understand the potential molecular functions ofMCPH1in other types of tumors. Here, we used several bioinformatic database and tools, including TCGA, GEO, ONCOMINE, and Human Protein Atlas to investigate the role ofMCPH1in 33 tumor types. We found that the expression ofMCPH1in tumor cases and normal cases were significantly different, and the higher expression ofMCPH1generally predicted poor overall survival for tumor patients, such as acute myeloid leukemia, liver hepatocellular carcinoma, and pancreatic adenocarcinoma. Meanwhile, lower expression of theMCPH1gene was related to poor OS prognosis for KIRC and gastric cancer. Moreover, the expression level ofMCPH1was highly associated with the immune microenvironment. Our result provides some fresh light into the oncogenic roles ofMCPH1in various human cancers and revealed thatMCPH1may be a potential diagnostic and prognostic marker in LAML, PAAD, and gastric cancer.
Multi-site assessment of reproducibility in high-content live cell imaging data
Authors: Hu Jianjiang , Serra-Picamal Xavier , Bakker Gert-Jan , Van Troys Marleen , Winograd-katz Sabina , Ege Nil , Gong Xiaowei , Didan Yuliia , Grosheva Inna , Polansky Omer , Bakkali Karima , Van Hamme Evelien , Van Erp Merijn , Vullings Manon , Weiss Felix , Clucas Jarama , Dowbaj Anna M. , Sahai Erik , Ampe Christophe , Geiger Benjamin , Friedl Peter , Bottai Matteo , Strömblad Staffan
Published: November 2022

High-content image-based cell phenotyping provides fundamental insights in a broad variety of life science areas. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, even more importantly with the advent of data sharing initiatives. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy have not been systematically investigated. Here, using high content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells and time points. Significant technical variability occurred between laboratories, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image data and meta-analysis depend on standardized procedures and batch correction applied to studies of perturbation effects.
Phase-specific signatures of wound fibroblasts and matrix patterns define cancer-associated fibroblast subtypes
Authors: Wietecha Mateusz S. , Lauenstein David , Cangkrama Michael , Seiler Sybille , Jin Juyoung , Goppelt Andreas , Claassen Manfred , Levesque Mitchell P. , Dummer Reinhard , Werner Sabine
Published: November 2022

Healing wounds and cancers present remarkable cellular and molecular parallels, but the specific roles of the healing phases are largely unknown. We developed a bioinformatics pipeline to identify genes and pathways that define distinct phases across the time course of healing. Their comparison to cancer transcriptomes revealed that a resolution-phase wound signature is associated with increased severity in skin cancer and enriches for extracellular matrix-related pathways. Comparisons of transcriptomes of early- and late-phase wound fibroblasts vs skin cancer-associated fibroblasts (CAFs) identified an “early-wound” CAF subtype, which localizes to the inner tumor stroma and expresses collagen-related genes that are controlled by the RUNX2 transcription factor. A “late-wound” CAF subtype localizes to the outer tumor stroma and expresses elastin-related genes. Matrix imaging of primary melanoma tissue microarrays validated these matrix signatures and identified collagen- vs elastin-rich niches within the tumor microenvironment, whose spatial organization predicts survival and recurrence. These results identify wound-regulated genes and matrix patterns with prognostic potential in skin cancer.
High-resolution image reconstruction with latent diffusion models from human brain activity
Authors: Takagi Yu , Nishimoto Shinji
Published: November 2022

Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic images with high semantic fidelity is still a challenging problem. Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI). More specifically, we rely on a latent diffusion model (LDM) termed Stable Diffusion. This model reduces the computational cost of DMs, while preserving their high generative performance. We also characterize the inner mechanisms of the LDM by studying how its different components (such as the latent vector of image Z, conditioning inputs C, and different elements of the denoising U-Net) relate to distinct brain functions. We show that our proposed method can reconstruct high-resolution images with high fidelity in straightforward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. We also provide a quantitative interpretation of different LDM components from a neuroscientific perspective. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs. Please check out our webpage at https://sites.google.com/view/stablediffusion-with-brain/
Ionizing Radiation induces cells with past caspase activity that contribute to the adult organ inDrosophilaand show reduced Loss of Heterozygosity
Authors: Colon Plaza Sarah , Su Tin Tin
Published: November 2022

There is increasing recognition that cells may activate apoptotic caspases but not die, instead displaying various physiologically relevant consequences. We know very little, however, of the mechanisms that underlie the life/death decision in a cell that has activated apoptotic caspases. By optimizing a published reporter for past caspase activity, we were able to visualize cells that survived caspase activation specifically after exposure to ionizing radiation inDrosophilalarval wing discs. We found that cells withX-ray-inducedpastactivecaspases (XPAC) do not arise at random but are born at specific locations within the developing wing imaginal discs ofDrosophilalarvae. We found also that the apoptotic signaling pathway is needed to induce XPAC cells. Yet, XPAC cells appear in stereotypical patterns that do not follow the pattern of IR-induced apoptosis, suggesting additional controls at play. Functional testing identified the contribution ofwingless(DrosophilaWnt1) and Ras signaling to the prevalence of XPAC cells. By following irradiated larvae into adulthood, we found that XPAC cells contribute to the adult wing. To address the relationship between XPAC and genome stability, we combined a reporter for past caspase activity withmwh, an adult marker for Loss of Heterozygosity (LOH). We found a lower incidence of LOH among XPAC compared to cells that did not activate the reporter for past caspase activity. In addition, at time points when wing disc cells are finishing DNA repair, XPAC cells show an anti-correlation with cells with unrepaired IR-induced double-stranded breaks. Our data suggest that non-lethal caspase activity safeguards the genome by facilitating DNA repair and reducing LOH after transient exposure to X-rays. These results identify a physiological role for non-lethal caspase activity during recovery from radiation damage.
Approximating carbon fixation - how important is the Calvin-Benson cycle steady-state assumption?
Authors: van Aalst Marvin , Ebenhöh Oliver , Walker Berkley J.
Published: November 2022

Plants use light energy to produce ATP and redox equivalents for metabolism. Since during the course of a day plants are exposed to constantly fluctuating light, the supply of ATP and redox equivalents is also fluctuating. Further, if the metabolism cannot use all of the supplied energy, the excess absorbed energy can damage the plant in the form of reactive oxygen species. It is thus reasonable to assume that the metabolism downstream of the energy supply is dynamic and as being capable of dampening sudden spikes in supply is advantageous, it is further reasonable to assume that the immediate downstream metabolism is flexible as well. A flexible metabolism exposed to a fluctuating input is unlikely to be in metabolic steady-state, yet a lot of mathematical models for carbon fixation assume one for the Calvin-Benson-Bassham (CBB) cycle. Here we present an analysis of the validity of this assumption by progressively simplifying an existing model of photosynthesis and carbon fixation.
HoCoRT: Host contamination removal tool
Authors: Rumbavicius Ignas , Rounge Trine B. , Rognes Torbjørn
Published: November 2022

Background

Shotgun metagenome sequencing data obtained from a host environment will usually be contaminated with sequences from the host organism. Host sequences should be removed before further analysis to avoid biases, to reduce downstream computational load, or for privacy reasons in case of a human host. The tools that we identified as designed specifically to perform host contamination sequence removal were either outdated, not maintained or complicated to use. We have therefore created HoCoRT, a fast and user friendly tool that implements several methods for optimised host sequence removal, and evaluated the speed and accuracy of these methods.

Results

HoCoRT is an open-source command-line tool for host contamination removal. It is designed to be simple to install and use, including a one-step option for genome indexing. HoCoRT uses a range of well-known mapping, classification and alignment methods for the classification of reads. The underlying classification method to be used and its parameters are selectable by the user, enabling adaptation to different circumstances. Based on our investigation of the performance of a range of methods and parameters on artificial human gut and oral microbiomes, recommendations are provided for typical data sets with short and long reads.

Conclusions

To decontaminate a human microbiome with HoCoRT, the best combination of speed and accuracy was found with BioBloom, Bowtie2 in end-to-end mode, or HISAT2 for short reads, while BioBloom performed best for long reads. For the oral microbiome, Bowtie2 was slightly more accurate than the other two, at the expense of speed. HoCoRT using Bowtie2 in end-to-end mode was found to be much faster and slightly more accurate than the dedicated DeconSeq tool. HoCoRT is distributed as a Bioconda package. Source code and documentation are available athttps://github.com/ignasrum/hocortfor Linux and macOS under the MIT licence.
SurvBoard: Standardised Benchmarking for Multi-omics Cancer Survival Models
Authors: Wissel David , Janakarajan Nikita , Grover Aayush , Toniato Enrico , Martinez Maria Rodriguez , Boeva Valentina
Published: November 2022

High-throughput "omics" data, including genomic, transcriptomic, and epigenetic data, have become increasingly produced and have contributed in recent years to the advances in cancer research. In particular, multimodal omics data get now employed in addition to clinical data to stratify patients according to their clinical outcomes. Despite some recent work on benchmarking multi-modal integration strategies for cancer survival prediction, there is still a need for the standardization of the results of model performances and for the consecutive exploration of the relative performance of statistical and deep learning models. Here, we propose a unique benchmark, SurvBoard, which standardizes several important experimental design choices to enable comparability between cancer survival models that incorporate multi-omics data. By designing several benchmarking scenarios, SurvBoard allows for the comparison of single-cancer models and models trained on pan-cancer data; SurvBoard also makes it possible to investigate the added value of using patient data with missing modalities. Additionally, in this work, we point out several potential pitfalls that might arise during the preprocessing and validation of multi-omics cancer survival models and address them in our benchmark. We compare statistical and deep learning models revealing that statistical models often outperform deep learning models, particularly in terms of model calibration. Finally, we offer a web service that enables quick model evaluation against our benchmark (https://www.survboard.science/). All code and other resources are available on GitHub: https://github.com/BoevaLab/survboard/.
Shear rheology of methyl cellulose based solutions for cell mechanical measurements at high shear rates
Authors: Büyükurgancı Beyza , Basu Santanu Kumar , Neuner Markus , Guck Jochen , Wierschem Andreas , Reichel Felix
Published: November 2022

Methyl cellulose (MC) is a widely used material in various microfluidic applications in biology. Due to its biocompatibility, it has become a popular crowding agent for microfluidic cell deformability measurements, which usually operate at high shear rates (> 10,000 s−1). However, a full rheological characterization of methyl cellulose solutions under these conditions has not yet been reported. With this study, we provide a full shear-rheological description for solutions of up to 1% MC dissolved in phosphate-buffered saline (PBS) that are commonly used in real-time deformability cytometry (RT-DC). We characterized three different MC-PBS solutions used for cell mechanical measurements in RT-DC with three different shear rheometer setups to cover a range of shear rates from 0.1 - 150,000 s−1. We report viscosities and normal stress differences in this regime. Viscosity functions can be well described using a Carreau-Yasuda model. Furthermore, we present the temperature dependency of shear viscosity and first normal stress difference of these solutions. Our results show that methyl cellulose solutions behave like power-law liquids in viscosity and first normal stress difference at shear rates between 5,000 - 150,000 s−1. We construct a general viscosity equation for each MC solution at a certain shear rate and temperature. Furthermore, we investigated how MC concentration influences the rheology of the solutions and found the entanglement concentration at around 0.64 w/w%. Our results help to better understand the viscoelastic behavior of MC solutions, which can now be considered when modelling stresses in microfluidic channels.
Identification of genomic regions of dry bean (Phaseolus vulgarisL.) associated with agronomic and physiological traits under drought stressed and well-watered conditions using genome-wide association study
Authors: Mutari Bruce , Sibiya Julia , Shayanowako Admire , Chidzanga Charity , Matova Prince M. , Gasura Edmore
Published: November 2022

Understanding the genetic basis of traits of economic importance under drought stress (DS) and well-watered (NS) conditions is important in enhancing genetic gains in dry beans (Phaseolus vulgarisL.). This research aims to: (i) identify markers associated with agronomic and physiological traits for drought tolerance and (ii) identify drought-related putative candidate genes within the mapped genomic regions. An Andean and Mesoamerican diversity panel (AMDP) comprising of 185 genotypes was screened in the field under drought stress (DS) and well-watered (NS) conditions for two successive seasons. Agronomic and physiological traits,viz., days to 50% flowering (DFW), plant height (PH), days to physiological maturity (DPM), grain yield (GYD), 100-seed weight (SW), leaf temperature (LT), leaf chlorophyll content (LCC) and stomatal conductance (SC) were phenotyped. Principal component and association analysis were conducted using filtered 9370 Diversity Arrays Technology sequencing (DArTseq) markers. The mean PH, GYD, SW, DPM, LCC and SC of the AMDP was reduced by 12.1, 29.6, 10.3, 12.6, 28.5 and 62.0%, respectively under DS. Population structure analysis revealed two sub-populations, which correspond to the Andean and Mesoamerican gene pools. Markers explained 0.08 – 0.10, 0.22 – 0.23, 0.29 – 0.32, 0.43 – 0.44, 0.65 – 0.66 and 0.69 – 0.70 of the total phenotypic variability (R2) for SC, LT, PH, GYD, SW and DFW, respectively under DS conditions. For NS,R2varied from 0.08 (LT) to 0.70 (DPM). Overall, 68 significant (p < 10−03) marker-trait associations (MTAs) and 22 putative candidate genes were identified across DS and NS conditions. Most of the identified genes had known biological functions related to regulating the response to moisture stress. The findings provide new insights into the genetic architecture of moisture stress tolerance in common bean. The findings also provide potential candidate SNPs and putative genes that can be utilized in gene discovery and marker-assisted breeding for drought tolerance after validation.
All boreal forest successional stages needed to maintain the full suite of soil biodiversity, community composition, and function following wildfire
Authors: Porter Teresita M. , Smenderovac Emily , Morris Dave , Venier Lisa
Published: November 2022

Wildfire is a natural disturbance in boreal forest systems that has been predicted to increase in frequency, intensity, and extent due to climate change. Most studies tend to assess the recovery of one component of the community at a time but here we use DNA metabarcoding to simultaneously monitor soil bacteria, fungi, and arthropods along an 85-year chronosequence following wildfire in jack pine-dominated ecosites. We describe soil successional and community assembly processes to better inform sustainable forest management practices. Soil taxa showed different recovery trajectories following wildfire. Bacteria shared a large core community across stand development stages (~ 95-97% of their unique sequences) and appeared to recover relatively quickly by crown closure. By comparison fungi and arthropods shared smaller core communities (64-77% and 68-69%, respectively) and each stage appeared to support unique biodiversity. We show the importance of maintaining a mosaic ecosystem that represents each stand development stage to maintain the full suite of biodiversity in soils following wildfire, especially for fungi and arthropods. These results will provide a useful baseline for comparison when assessing the effects of human disturbance such as harvest or for assessing the effects of more frequent wildfire events due to climate change.
Using Attention-based Deep Learning to Predict ERG:TMPRSS2 Fusion Status in Prostate Cancer from Whole Slide Images
Authors: Omar Mohamed , Xu Zhuoran , Rand Sophie B , Mohammad Mohammad , Salles Daniela C. , Schaeffer Edward M. , Robinson Brian D. , Lotan Tamara L. , Loda Massimo , Marchionni Luigi
Published: November 2022

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome including gene fusion between TMPRSS2 and members of the ETS family of transcription factors specially ERG. The expanding wealth of pathology whole slide images (WSIs) and the increasing adoption of deep learning (DL) approaches offer a unique opportunity for pathologists to streamline the detection of ERG:TMPRSS2 fusion status. Here, we used two large cohorts of digitized H&E-stained slides from radical prostatectomy specimens to train and evaluate a DL system capable of detecting the ERG fusion status and also detecting tissue regions of high diagnostic and prognostic relevance. Slides from the PCa TCGA dataset were split into training (n=318), validation (n=59), and testing sets (n=59) with the training and validation sets being used for training the model and optimizing its hyperparameters, respectively while the testing set was used for evaluating the performance. Additionally, we used an internal testing cohort consisting of 314 WSIs for independent assessment of the model’s performance. The ERG prediction model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.72 and 0.73 in the TCGA testing set and the internal testing cohort, respectively. In addition to slide-level classification, we also identified highly attended patches for the cases predicted as either ERG-positive or negative which had distinct morphological features associated with ERG status. We subsequently characterized the cellular composition of these patches using HoVer-Net model trained on the PanNuke dataset to segment and classify the nuclei into five main categories. Notably, a high ratio of neoplastic cells in the highly-attended regions was significantly associated with shorter overall and progression-free survival while high ratios of immune, stromal and stromal to neoplastic cells were all associated with longer overall and metastases-free survival. Our work highlights the utility of deploying deep learning systems on digitized histopathology slides to predict key molecular alteration in cancer together with their associated morphological features which would streamline the diagnostic process.
Strong selection is poorly aligned with genetic variation inIpomoea hederacea: implications for divergence and constraint
Authors: Henry Georgia Alexandria , Stinchcombe John R
Published: November 2022

The multivariate evolution of populations is the result of the interactions between natural selection, drift, and the underlying genetic structure of the traits involved. Covariances among traits bias responses to selection, and the multivariate axis which describes the greatest genetic variation is expected to be aligned with patterns of divergence across populations. An exception to this expectation is when selection acts on trait combinations lacking genetic variance, which limits evolutionary change. Here we used a common garden field experiment of individuals from 57 populations ofIpomoea hederaceato characterize linear and nonlinear selection on five quantitative traits in the field. We then formally compare patterns of selection to previous estimates of within population genetic covariance structure (the G-matrix) and population divergence in these traits. We found that selection is poorly aligned with previous estimates of genetic covariance structure and population divergence. In addition, the trait combinations favoured by selection were generally lacking genetic variation, possessing approximately 15- 30% as much genetic variation as the most variable combination of traits. Our results suggest that patterns of population divergence are likely the result of the interplay between adaptive responses, correlated response, and selection favoring traits lacking genetic variation.
Epigenetic and genetic differentiation betweenCoregonusspecies pairs
Authors: Venney Clare J , Mérot Claire , Normandeau Eric , Rougeux Clément , Laporte Martin , Bernatchez Louis
Published: November 2022

Phenotypic diversification is classically associated with genetic differentiation and gene expression variation. However, increasing evidence suggests that DNA methylation is involved in evolutionary processes due to its phenotypic and transcriptional effects. Methylation can increase mutagenesis and could lead to increased genetic divergence between populations experiencing different environmental conditions for many generations, though there has been minimal empirical research on epigenetically induced mutagenesis in diversification and speciation. Whitefish, freshwater members of the salmonid family, are excellent systems to study phenotypic diversification and speciation due to the repeated divergence of benthic-limnetic species pairs serving as natural replicates. Here we investigate whole genome genetic and epigenetic differentiation between sympatric benthic-limnetic species pairs in lake and European whitefish (Coregonus clupeaformisandC. lavaretus) from four lakes (N=64). We found considerable, albeit variable, genetic and epigenetic differences between species pairs. All SNP types were enriched at CpG sites supporting the mutagenic nature of DNA methylation, though C>T SNPs were most common. We also found an enrichment of overlaps between outlier SNPs with the 5% highest FSTbetween species and differentially methylated loci. This could possibly represent differentially methylated sites that have caused divergent genetic mutations between species, or divergent selection leading to both genetic and epigenetic variation at these sites. Our results support the hypothesis that DNA methylation contributes to phenotypic divergence and mutagenesis during whitefish speciation.

Significance statement

DNA methylation is an epigenetic mark known to change in response to the environment and induce genetic modifications such as point mutations, though its implications for evolution and speciation have not been thoroughly studied. We find considerable but variable genetic and epigenetic variation between whitefish benthic-limnetic species pairs, highlighting the potential for DNA methylation to contribute to mutagenesis and genetic evolution. Our study provides evidence that DNA methylation could have contributed to whitefish speciation, both through initially plastic methylation changes and by driving genetic divergence between species pairs.
A robotic system for automated genetic manipulation and analysis ofCaenorhabditis elegans
Authors: Li Zihao , Fouad Anthony D. , Bowlin Peter D. , Fan Yuying , He Siming , Chang Meng-Chuan , Du Angelica , Teng Christopher , Kassouni Alexander , Ji Hongfei , Raizen David M. , Fang-Yen Christopher
Published: November 2022

The nematodeCaenorhabditis elegansis one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research withC. elegansdepends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially those involving large numbers of animals. Here we describe the first general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferringC. eleganson standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. AutomatedC. elegansmanipulation shows reliability and throughput comparable to standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods we used the system to perform a collection of commonC. elegansprocedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerateC. elegansresearch and opens possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.

Significance Statement

The nematodeCaenorhabditis elegansis a powerful genetic model organism in life sciences due to its compact anatomy, short life cycle, and optical transparency. Current methods for worm genetics rely on laborious, time-consuming, and error-prone manual work. Here, we describe the first general-purpose automated tool capable of genetically manipulatingC. elegans. Our robotic system will accelerate a broad variety ofC. elegansresearch and opens possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.
Robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing (MGA-Seq)
Authors: Lin Da , Zou Yanyan , Wang Jinyue , Xiao Qin , Lin Fei , Zhang Ningyuan , Teng Zhaowei , Li Shiyi , Wei Yongchang , Zhou Fuling , Yin Rong , Zhang Siheng , Wu Chengchao , Zhang Jing , Hu Sheng , Dong Shuang , Li Xiaoyu , Ye Shengwei , Sun Haixiang , Cao Gang
Published: November 2022

Genomic abnormalities, including structural variation (SV), copy number variation (CNV), single-nucleotide polymorphism (SNP), homogenously staining regions (HSR) and extrachromosomal DNA (ecDNA), are strongly associated with cancer, rare diseases and infertility. A robust technology to simultaneously detect these genomic abnormalities is highly desired for clinical diagnosis and basic research. In this study, we developed a simple and cost-effective method – multiple genetic abnormality sequencing (MGA-Seq) – to simultaneously detect SNPs, CNVs, SVs, ecDNA and HSRs in a single tube. This method has been successfully applied in both cancer cell lines and clinical tumour samples and revealed that focal amplification in tumour tissue is substantially heterogeneous. Notably, we delineated the architecture of focal amplification and the ecDNA network by MGA-Seq, which facilitated the exploration of the regulation of gene expression in ecDNA. This method could be extensively applied for diagnosis and may greatly facilitate the investigation of the genomic mechanism for genetic diseases.
Engineered molecular sensors of cell surface crowding
Authors: Takatori Sho C. , Son Sungmin , Lee Daniel , Fletcher Daniel A.
Published: November 2022

Cells mediate interactions with the extracellular environment through a crowded assembly of transmembrane proteins, glycoproteins and glycolipids on their plasma membrane. The extent to which surface crowding modulates the biophysical interactions of ligands, receptors, and other macromolecules is poorly understood due to the lack of methods to quantify surface crowding on native cell membranes. In this work, we demonstrate that physical crowding on reconstituted membranes and live cell surfaces attenuates the effective binding affinity of macromolecules such as IgG antibodies in a surface crowding-dependent manner. We combine experiment and simulation to design a crowding sensor based on this principle that provides a quantitative readout of cell surface crowding. Our measurements reveal that surface crowding decreases IgG antibody binding by 2-20 fold in live cells compared to a bare membrane surface, resulting in a cell surface osmotic pressure opposing binding of 1 - 4 kPa. Our sensors show that sialic acid, a negatively charged monosaccharide, contributes disproportionately to red blood cell surface crowding via electrostatic repulsion, despite occupying only ~1% of the total cell membrane by mass. We also observe significant differences in surface crowding for different cell types and find that expression of single oncogenes can both increase and decrease crowding, suggesting that surface crowding may be an indicator of both cell type and state. Our high-throughput, single-cell measurement of cell surface osmotic pressure may be combined with functional assays to enable further biophysical dissection of the cell surfaceome.

Significance Statement

Cells interact with each other and the extracellular environment through a crowded assembly of polymers on their plasma membranes. The high density of these surface polymers can generate physical crowding that impacts cell function. However, tools to quantify the extent and effect of surface crowding on live cell membranes are lacking. In this work, we design macromolecular sensors that act as direct reporters of cell surface crowding. We combine experiments on reconstituted and live cell surfaces with molecular dynamics simulations to provide a mechanistic understanding of how cell surface crowding reduces binding of soluble molecules, and we show that crowding varies significantly with cell type and is affected by oncogene expression.
Excess mortality associated with the COVID-19 pandemic (2020-2021) in an urban community of Bangladesh
Authors: Hossain Mohammad Sorowar , Khan Jahidur Rahman , Al Mamun SM Abdullah , Islam Mohammad Tariqul , Raheem Enayetur
Published: November 2022

Measurement of COVID-19-attributed mortality is vital for public health policy decisions. Unlike high-income countries, the magnitude of COVID-19-related mortality is largely unknown in many low- and middle-income countries due to inadequate COVID-19 testing capacity and a lack of robust civil registration and vital statistics systems. COVID-19-associated excess mortality was investigated in an urban setting in Bangladesh using a cemetery-based death registration dataset. A total of 6,271 deaths (3,790 male and 2,481 female) recorded between January 2015 and December 2021 were analyzed by using the Bayesian structural time series model (BSTS). During the pre-COVID-19 period, the average monthly number of deaths was 69, whereas, during the COVID-19 period, this number significantly increased to 92. Overall, according to model-based results, during COVID-19 period, the number of deaths increased on average by 17% (95% CrI: -18%, 57%): males 29% (95 % CrI: -15%, 75%) and 2.9% for females (95% CrI: -61%, 70%). This first-of-its-kind study in Bangladesh has revealed the excess mortality due to the COVID-19 pandemic (2020-2021) in an urban community. It appears that cemetery-based death registration could help track various crises (e.g., COVID-19), especially when collecting data on the ground is challenging for resource-limited countries.
Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model
Authors: Neal Samuel R. , Fitzgerald Felicity , Chimhuya Simbarashe , Heys Michelle , Cortina-Borja Mario , Chimhini Gwendoline
Published: November 2022

Objective

To develop a clinical prediction model to diagnose neonatal sepsis in low-resource settings.

Design

Secondary analysis of data collected by the Neotree digital health system from 01/02/2019 to 31/03/2020. We used multivariable logistic regression with candidate predictors identified from expert opinion and literature review. Missing data were imputed using multivariate imputation and model performance was evaluated in the derivation cohort.

Setting

A tertiary neonatal unit at Sally Mugabe Central Hospital, Zimbabwe.

Patients

We included 2628 neonates aged <72 hours, gestation ≥ 32+0weeks and birth weight ≥ 1500 grams.

Interventions

Participants received standard care as no specific interventions were dictated by the study protocol.

Main outcome measures

Clinical early-onset neonatal sepsis (within the first 72 hours of life), defined by the treating consultant neonatologist.

Results

Clinical early-onset sepsis was diagnosed in 297 neonates (11.3%). The optimal model included eight predictors: maternal fever, offensive liquor, prolonged rupture of membranes, neonatal temperature, respiratory rate, activity, chest retractions and grunting. Receiver operating characteristic analysis gave an area under the curve of 0.736 (95% confidence interval 0.701-0.772). For a sensitivity of 95% (92-97%), corresponding specificity was 11% (10-13%), positive predictive value 12% (11-13%), negative predictive value 95% (92-97%), positive likelihood ratio 1.1 (95% CI 1.0-1.1), and negative likelihood ratio 0.4 (95% CI 0.3-0.6).

Conclusions

Our clinical prediction model achieved high sensitivity with modest specificity, suggesting it may be suited to excluding early-onset sepsis. Future work will validate and refine this model before considering it for clinical use within the Neotree.

What is already known on this topic

Various clinical prediction models exist to diagnose neonatal sepsis. However, there is a paucity of literature on models in low-resource settings, particularly sub-Saharan Africa.

What this study adds

We developed a clinical prediction model to diagnose clinical early-onset neonatal sepsis with over 2,500 neonates in a lower middle-income, low-resource neonatal unit. Our model is easy to implement, does not require laboratory tests and achieved high sensitivity with modest specificity in the derivation cohort.

How this study might affect research, practice or policy

Our model could support less experienced healthcare professionals avoid unnecessary antibiotic therapy in the absence of immediate local senior support. Before implementation, this model must be externally validated and its impact on sepsis-related neonatal morbidity and mortality must be assessed in future studies.
Quantifying the causal impact of biological risk factors on healthcare costs
Authors: Lee Jiwoo , Jukarainen Sakari , Dixon Padraig , Davies Neil M , Smith George Davey , Natarajan Pradeep , Ganna Andrea
Published: November 2022

Background

A critical step in evaluating healthcare interventions is to understand their impact on healthcare costs. However, there is a limited understanding of the causal impact that biomarkers and risk factors for disease have on healthcare-related costs. Previous studies based on observational data have major limitations including residual confounding and reverse causation. Here, we used a genetically-informed design, Mendelian Randomization (MR), to infer the causal impact of 15 routinely measured and clinically relevant risk factors on annual total healthcare costs.

Methods

We considered 373,160 participants from the FinnGen Study, which were linked to detailed healthcare costs covering inpatient, outpatient, and medication costs. Several MR approaches were used to assess the causal effects of 15 risk factors (e.g., waist circumference (WC), HDL cholesterol, vitamin D), with strong genetic bases on annual total healthcare costs, as well as stratified by service type, age, and sex. We further assessed the generalizability and robustness of our results by accounting for selection bias and by leveraging additional data from 323,774 individuals from the United Kingdom and Netherlands.

Results

Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a one standard deviation increase in the risk factors corresponded to 22.78% [95% CI: 18.75, 26.95], 13.64% [10.26, 17.12], and 13.08% [8.84, 17.48] increased annual total healthcare costs, respectively. The relative effect of WC on annual total healthcare costs was consistent across age and sex and was not attenuated when accounting for increased risk of five major diseases: back pain, chronic ischemic heart disease, type 2 diabetes, chronic obstructive pulmonary disease, and stroke. A lack of causal effects was observed for some clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D.

Conclusion

Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention should be given to WC screening, surveillance, and mitigation. On the contrary, several biomarkers relevant in clinical settings did not have a direct impact on annual total healthcare costs.
Catch-up Routine Immunization to Restore Childhood Immunization Coverage following COVID-induced Declines in Zamboanga Peninsula, Philippines
Authors: Punzalan Mary Germeyn D.
Published: November 2022

Due to numerous waves of COVID-19 surge and a more focused government initiative to jump-start the COVID-19 vaccination in 2021, routine immunization was neglected in Zamboanga Peninsula, Philippines, ending the year with a fully-immunized coverage of only 59%. To address this, an intensified catch-up immunization campaign was conducted in April-June 2022 among children 0-23 months of age who missed their routine immunizations. A program case study is utilized to describe the catch-up immunization campaign conducted. The target for the catch-up immunization campaign was to vaccinate at least 80% of infants ages 0-23 months who missed any of their routine immunizations schedule. Online microplanning workshops and consultative meetings were conducted in preparation for the campaign. A record review was conducted from data from municipal and provincial health offices and then consolidated at the regional level. The coverage for all antigens at the regional level (all cities, and municipalities combined), was 49-60%, except for inactivated polio vaccine 2 (32%). In other words, approximately half of those children under 23 months who had missed doses got vaccinated during the catch-up vaccination activities. This was a relatively fair turnout of the campaign considering that the local government units are still transitioning to non-COVID health services. To address the immunization gap, the catch-up immunization campaign will continue to be conducted wherein local government unit health workers have already allotted one day every month to conduct house-to-house vaccination activity to track and vaccinate missed children.
Elevated circulating monocytes and monocyte activation in pulmonary post-acute sequelae of SARS-CoV-2 infection
Authors: Park Juwon , Dean Logan S , Jiyarom Boonyanudh , Gangcuangco Louie Mar , Shah Parthav , Awamura Thomas , Ching Lauren L. , Nerurkar Vivek R. , Chow Dominic C. , Igno Fritzie , Shikuma Cecilia M , Devendra Gehan
Published: November 2022

Background

Monocytes and macrophages play a pivotal role in inflammation during acute SARS-CoV-2 infection. However, their contribution to the development of post-acute sequelae of SARS-CoV-2 infection (PASC) are not fully elucidated.

Methods

A cross sectional study was conducted comparing plasma cytokine and monocyte levels among three groups: participants with pulmonary PASC (PPASC) with a reduced predicted diffusing capacity for carbon monoxide [DLCOc, <80%; (PG)]; fully recovered from SARS-CoV-2 with no residual symptoms (recovered group, RG); and negative for SARS-CoV-2 (negative group, NG). The expressions of cytokines were measured in plasma of study cohort by Luminex assay. The percentages and numbers of monocyte subsets (classical, intermediate, and non-classical monocytes) and monocyte activation (defined by CD169 expression) were analyzed using flow cytometry analysis of peripheral blood mononuclear cells.

Results

Plasma IL-1Ra levels were elevated but FGF levels were reduced in PG compared to NG. Circulating monocytes and three subsets were significantly higher in PG and RG compared to NG. PG and RG exhibited higher levels of CD169+monocyte counts and higher CD169 expression was detected in intermediate and non-classical monocytes from RG and PG than that found in NG. Further correlation analysis with CD169+monocyte subsets revealed that CD169+intermediate monocytes negatively correlated with DLCOc%, and CD169+non-classical monocytes positively correlated with IL-1α, IL-1β, MIP-1α, Eotaxin, and IFNγ.

Conclusion

This study present evidence that COVID convalescents exhibit monocyte alteration beyond the acute COVID-19 infection period even in convalescents with no residual symptoms. These data provide further rational for determining the role of monocyte subsets in PPASC pathogenesis.
Oligosymptomatic long-term carriers of SARS-CoV-2 display impaired innate resistance and high Spike-specific neutralizing antibodies
Authors: Montes-Cobos Elena , Bastos Victoria C , Monteiro Clarice , de Freitas João CR , Fernandes Heiny DP , Constancio Clarice S , Rodrigues Danielle AS , Gama Andreza MDS , Vidal Vinicius M , Alves Leticia S , Zalcberg-Renault Laura , de Lira Guilherme S , Ota Victor A , Caloba Carolina , Conde Luciana , Leitão Isabela C , Tanuri Amilcar , Ferreira Orlando DC , Pereira Renata M , Vale André M , Castiñeiras Terezinha M , Kaiserlian Dominique , Echevarria-Lima Juliana , Bozza Marcelo T
Published: November 2022

The vast spectrum of clinical features of COVID-19 keeps challenging scientists and clinicians. Control of pathogen load (host resistance) and prevention of tissue damage (disease tolerance) are essential for the outcome of infectious diseases. Both low resistance and high disease tolerance might result in long-term viral persistence, but the underlying mechanisms remain unclear. Here, we studied the immune response of immunocompetent COVID-19 patients with prolonged SARS-CoV-2 infection by immunophenotyping, cytokine and serological analysis. Despite viral loads and symptoms comparable to regular mildly-symptomatic patients, long-term carriers displayed weaker systemic IFN-I responses and fewer circulating pDCs and NK cells at disease onset. Type 1 cytokines remained low, while type-3 cytokines were in turn enhanced. Interestingly, the plasma of these patients showed a higher spike-specific neutralization capacity. The identification of very early distinct immune responses in long-term carriers adds up to our understanding on essential host protective mechanisms to ensure tissue damage control despite prolonged viral infection.
Grand canonical Brownian dynamics simulations of adsorption and self-assembly of SAS-6 rings on a surface
Authors: Melo Santiago Gomez , Wörthmüller Dennis , Gönczy Pierre , Banterle Niccolo , Schwarz Ulrich S.
Published: November 2022

The protein SAS-6 forms dimers, which then self-assemble into rings that are critical for the nine-fold symmetry of the centriole organelle. It has recently been shown experimentally that the self-assembly of SAS-6 rings is strongly facilitated on a surface, shifting the reaction equilibrium by four orders of magnitude compared to the bulk. Moreover, a fraction of non-canonical symmetries (i.e., different from nine) was observed. In order to understand which aspects of the system are relevant to ensure efficient self-assembly and selection of the nine-fold symmetry, we have performed Brownian dynamics computer simulation with patchy particles and then compared our results with experimental ones. Adsorption onto the surface was simulated by a Grand Canonical Monte Carlo procedure and Random Sequential Adsorption kinetics. Furthermore, self-assembly was described by Langevin equations with hydrodynamic mobility matrices. We find that as long as the interaction energies are weak, the assembly kinetics can be described well by the coagulation-fragmentation equations in the reaction-limited approximation. By contrast, larger interaction energies lead to kinetic trapping and diffusion-limited assembly. We find that selection of nine-fold symmetry requires a small value for the angular interaction range. These predictions are confirmed by the experimentally observed reaction constant and angle fluctuations. Overall, our simulations suggest that the SAS-6 system works at the crossover between a relatively weak binding energy that avoids kinetic trapping and a small angular range that favors the nine-fold symmetry.