bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficiency and novelty of using environmental swabs for dry surface biofilm recovery.
Authors: Watson Fergus , Wilks Sandra , Chewins John , Keevil Bill
Published: November 2023

Studies on the epidemiology of dry surface biofilms within healthcare has shown an almost universal distribution across frequently touched items.  Despite a growing body of evidence for dry surface biofilms in hospitals little attention has been paid to the recovery capacity of techniques used to detect these microbial communities. Biofilms are inherently difficult to remove from surfaces due to adhesive substances within their matrix and may act as sources of infection but to what extent is largely unknown. In this study we evaluate the recovery efficiencies of commonly used environmental swabs against dry surface biofilms containing 7.24-Log10 Acinetobacter baumannii/cm2, using a drip flow reactor and desiccation cycle. Biofilm presence was visually confirmed using episcopic differential interference contrast microscopy combined with epifluorescence and quantified using sonicated viable plate counts. The swab materials used comprised of foam, viscose and cotton, all of which were pre-moistened using a buffer solution. The surfaces were vigorously swabbed by each material type and the resultant microbe populations for both swabs and remaining dry surface biofilms were quantified. Our results found foam tipped swabs to be superior, detecting on average 30% of the original dry surface biofilm contamination; followed by viscose (6%) and cotton (3%). However, no distinct difference was revealed in the concentration of microbes remaining on the surface after swabbing for each swab type suggesting there is variation in the capacity for each swab to release biofilm associated microorganisms. We conclude whilst environmental swabs do possess the ability to detect biofilms on dry surfaces; the reduced efficiencies are likely to cause an underestimation of the microbes present and should be considered during clinical application.
Arthrobacter zhaoxinii sp. nov. and Arthrobacter jinronghuae sp. nov., isolated from Marmota himalayana
Authors: Zhang Gui , Cheng Yanpeng , Li Weiguang , Chen Yulu , Yang Jing , Jin Dong , Lu Shan , Xu Jianguo
Published: November 2023

Four yellow-coloured strains (zg-Y815T/zg-Y108 and zg-Y859T/zg-Y826) were isolated from the intestinal contents of Marmota himalayana and assigned to the '

Arthrobacter citreus

group'. The four strains grew optimally on brain heart infusion agar with 5 % defibrinated sheep blood plate at 30 °C, pH 7.0 and with 0.5 % NaCl (w/v). Comparative analysis of their 16S rRNA genes indicated that the two strain pairs belong to the genus

Arthrobacter

, showing the highest similarity to

Arthrobacter yangruifuii

785T (99.52 %), which was further confirmed by the 16S rRNA gene and genome-based phylogenetic analysis. The comparative genomic analysis [digital DNA–DNA hybridization, (dDDH) and average nucleotide identity (ANI)] proved that the four strains are two different species (zg-Y815T/zg-Y108, 71.7 %/96.8 %; zg-Y859T/zg-Y826, 87.3 %/98.5 %) and differ from other known species within the genus

Arthrobacter

(zg-Y815T, 19.6–32.3 %/77.2–88.0 %; zg-Y859T, 19.5–29.3 %/77.4–86.3 %). Strain pairs zg-Y815T/zg-Y108 and zg-Y859T/zg-Y826 had the same major cellular fatty acids (iso-C16 : 0 and anteiso-C15 : 0), with MK-8(H2) as their dominant respiratory quinone (70.6 and 61.7 %, respectively). The leading polar lipids were diphosphatidylglycerol, phosphatidylglycerol, and phosphatidylinositol. The detected amino acids and cell-wall sugars of the two new species were identical (amino acids: alanine, glutamic acid, and lysine; sugars: rhamnose, galactose, mannose, glucose, and ribose). According to the phylogenetic, phenotypic, and chemotaxonomic analyses, we concluded that the four new strains represented two different novel species in the genus

Arthrobacter

, for which the names Arthrobacter zhaoxinii sp. nov. (zg-Y815T= GDMCC 1.3494T = JCM 35821T) and Arthrobacter jinronghuae sp. nov. (zg-Y859T = GDMCC 1.3493T = JCM 35822T) are proposed.
Dietary reconstructions of Magdalenian canids from SW-Germany do not indicate that the area was a centre of early European wolf domestication
Authors: Bons Paul D. , Bauer Catherine C. , Papkalla Lydia J.
Published: November 2023

In their paper‘A refined proposal for the origin of dogs: the case study of Gnirshöhle, a Magdalenian cave site’, Baumann and colleagues[1]claim that their data‘support the hypothesis that the Hegau Jura was a potential center of early European wolf domestication’, and that‘such a scenario becomes plausible considering a close proximity of canids and humans thereby introducing a controlled, or at least a restrictive diet’. The study focusses on fossil remains of‘large canids’from the Gnirshöhle cave site in SW Germany. Morphometric data on only one specimen, GN-999, as well as collagen δ15N and δ13C isotopic data and mitochondrial DNA analyses on the Gnirshöhle specimens and a comparative sample were used to conclude that the Gnirshöhle specimens shed light on the‘origin of dogs’as purported by the title of the paper. Here we argue that the paper is fundamentally flawed and excluded available relevant data.
Terminally differentiated effector memory T cells associate with cognitive and AD-related biomarkers in an aging-based community cohort
Authors: Winford Edric , Lutshumba Jenny , Martin Barbara J. , Wilcock Donna M. , Jicha Gregory A. , Nikolajczyk Barbara S. , Stowe Ann M , Bachstetter Adam D.
Published: November 2023

Background and Purpose

The immune response changes during aging and the progression of Alzheimer’s disease (AD) and related dementia (ADRD). Terminally differentiated effector memory T cells (called TEMRA) are important during aging and AD due to their cytotoxic phenotype and association with cognitive decline. However, it is not clear if the changes seen in TEMRAsare specific to AD-related cognitive decline specifically or are more generally correlated with cognitive decline. This study aimed to examine whether TEMRAsare associated with cognition and plasma biomarkers of AD, neurodegeneration, and neuroinflammation in a community-based cohort of older adults.

Methods

Study participants from a University of Kentucky Alzheimer’s Disease Research Center (UK-ADRC) community-based cohort of aging and dementia were used to test our hypothesis. There were 84 participants, 44 women and 40 men. Participants underwent physical examination, neurological examination, medical history, cognitive testing, and blood collection to determine plasma biomarker levels (Aβ42/Aβ40 ratio, total tau, Neurofilament Light chain (Nf-L), Glial Fibrillary Acidic Protein (GFAP)) and to isolate peripheral blood mononuclear cells (PBMCs). Flow cytometry was used to analyze PBMCs from study participants for effector and memory T cell populations, including CD4+and CD8+central memory T cells (TCM), Naïve T cells, effector memory T cells (TEM), and effector memory CD45RA+T cells (TEMRA) immune cell markers.

Results

CD8+TEMRAswere positively correlated with Nf-L and GFAP. We found no significant difference in CD8+TEMRAsbased on cognitive scores and no associations between CD8+TEMRAsand AD-related biomarkers. CD4+TEMRAswere associated with cognitive impairment on the MMSE. Gender was not associated with TEMRAs, but it did show an association with other T cell populations.

Conclusion

These findings suggest that the accumulation of CD8+TEMRAsmay be a response to neuronal injury (Nf-L) and neuroinflammation (GFAP) during aging or the progression of AD and ADRD. As our findings in a community-based cohort were not clinically- defined AD participants but included all ADRDs, this suggests that TEMRAsmay be associated with changes in systemic immune T cell subsets associated with the onset of pathology.
Generating the sigmoid form in neuroscience: A threshold-crossing model
Authors: Auty Andrew R
Published: November 2023

The conventional interpretation1of a logistic curve in biology is that it indicates the presence of an infection-like mechanism bounded by a saturation limit e.g. saturation occurs when the whole population has been infected. However, a logistic form may be generated by the fluctuating approach of biological systems to, and then exceeding, an effect threshold.

The commonly observed trends in frequency and degree of cognitive loss in neurodegenerative disease (NDD) are consistent with a threshold-crossing model. Whereas direct evidence of real life NDD being driven by an infection-like mechanism remains elusive2,3.

Variation in susceptibility to NDD may be explained by variation in the rate of biological ageing (e.g. neurodegeneration rate, or, metabolic interference), height of threshold and initial reserve. Each is potentially modifiable and clinically informative.
T-cell receptor sequences correlate with and predict gene expression levels in T cells
Authors: Wang Hao , Ji Zhicheng
Published: November 2023

T cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities. We analyzed fifteen single-cell immune profiling datasets to systematically investigate the association between T-cell receptor (TCR) sequences and the gene expression profiles of T cells. Our findings reveal that T cells sharing identical or similar TCR sequences tend to have highly similar gene expression profiles. Additionally, we developed a foundational model that leverages TCR information to predict gene expression levels in T cells.
Convergent evolution in toxin detection and resistance provides evidence for conserved bacterial-fungal interactions
Authors: Dolan Stephen K. , Duong Ashley T. , Whiteley Marvin
Published: November 2023

Microbes rarely exist in isolation, and instead form complex polymicrobial communities. As a result, microbes have developed intricate offensive and defensive strategies that enhance their fitness in these complex communities. Thus, identifying and understanding the molecular mechanisms controlling polymicrobial interactions is critical for understanding the function of microbial communities. In this study, we show that the Gram-negative opportunistic human pathogenPseudomonas aeruginosa, which frequently causes infection alongside a plethora of other microbes including fungi, encodes a genetic network which can detect, and defend against gliotoxin, a potent, disulfide-containing antimicrobial produced by the ubiquitous filamentous fungusAspergillus fumigatus. We show that gliotoxin exposure disruptsP. aeruginosazinc homeostasis, leading to transcriptional activation of a gene encoding a previously uncharacterized dithiol oxidase (DnoP), which detoxifies gliotoxin and structurally related toxins. While the enzymatic activity of DnoP is identical to that used byA. fumigatusto protect itself against gliotoxin, DnoP shares little homology to theA. fumigatusgliotoxin resistance protein. Thus, DnoP and its transcriptional induction by low zinc represent an example of both convergent evolution of toxin defense and environmental cue sensing across kingdoms. Collectively, these data support disulfide-containing natural products as mediators of inter-kingdom interactions and provide evidence thatP. aeruginosahas evolved to survive exposure to these molecules in the natural environment.

Significance statement

Bacteria and fungi frequently exist as complex, polymicrobial communities during infection. Reconstructing ecological structure in the laboratory is challenging and, consequently, the precise molecular mechanisms which underpin microbial interactions remain elusive. Using a pre-clinical model that mimics the cystic fibrosis lung, we discovered that the bacteriumPseudomonas aeruginosadetects and defends against a disulfide-containing toxin produced by the fungusAspergillus fumigatus. In an example of both convergent evolution of toxin defense and environmental cue sensing across kingdoms, we discovered that these organisms use the same cue to produce/sense this toxin, and the same enzymatic mechanism to protect against toxicity. This discovery of convergent evolution provides strong evidence forP. aeruginosaexposure to microbially-produced disulfide-containing toxins in natural environments.
Nanoporous PEGDA ink for High-Resolution Additive Manufacturing of Scaffolds for Organ-on-a-Chip
Authors: Karamzadeh Vahid , Shen Molly L. , Shafique Houda , Lussier Felix , Juncker David
Published: November 2023

Polydimethylsiloxane (PDMS), commonly used in organ-on-a-chip (OoC) systems, faces limitations in replicating complex geometries, hindering its effectiveness in creating 3D OoC models. In contrast, poly(ethylene glycol)diacrylate (PEGDA-250), favored for its fabrication ease and resistance to small molecule absorption, is increasingly used for 3D printing microfluidic devices. However, applications in cell culture have been limited due to poor cell adhesion. Here, we introduce a nanoporous PEGDA ink (P-PEGDA) designed to enhance cell adhesion. P-PEGDA is formulated with a porogen, photopolymerized, followed by the porogen removal. Utilizing P-PEGDA, complex microstructures and membranes as thin as 27 µm were 3D-printed. Porogen concentrations from 10-30% were tested yielding constructs with increasing porosity and oxygen permeability surpassing PDMS, without compromising printing resolution. Tests across four cell lines showed >80% cell viability, with a notable 77-fold increase in MDA-MB-231 cell coverage on the porous scaffolds. Finally, we introduce an OoC model comprising a gyroid scaffold with a central opening filled with a cancer spheroid. This setup, after a 14-day co-culture, demonstrated significant endothelial sprouting and integration within the spheroid. The P-PEGDA formulation is suitable for high-resolution 3D printing of constructs for 3D cell culture and OoC owing to its printability, gas permeability, biocompatibility, and cell adhesion.
Rapid estimates of leaf litter chemistry using reflectance spectroscopy
Authors: Kothari Shan , Hobbie Sarah E. , Cavender-Bares Jeannine
Published: November 2023

Measuring the chemical traits of leaf litter is important for understanding plants’ roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validationR2= 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validationR2= 0.491-0.946; %RMSE = 7.00-19.5). Notably intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.
High-throughput and scalable single cell proteomics identifies over 5000 proteins per cell
Authors: Ye Zilu , Sabatier Pierre , van der Hoeven Leander , Phlairaharn Teeradon , Hartlmayr David , Izaguirre Fabiana , Seth Anjali , Joshi Hiren J. , Bekker-Jensen Dorte B. , Bache Nicolai , Olsen Jesper V.
Published: November 2023

The emergence of mass spectrometry (MS)-based single-cell proteomics (SCP) promise to revolutionize the study of cellular biology and biomedicine by providing an unparalleled view of the proteome in individual cells. Despite its groundbreaking potential, SCP is nascent and faces challenges including limited sequence depth, throughput, and reproducibility, which have constrained its broader utility. This study introduces key methodological advances, which considerably improve the sensitivity, coverage and dependability of protein identification from single cells. We developed an almost lossless SCP workflow encompassing sample preparation to MS analysis, doubling the number of identified proteins from roughly 2000 to over 5000 in individual HeLa cells. A comprehensive evaluation of analytical software tools, alongside strict false discovery rate (FDR) controls solidified the reliability of our results. These enhancements also facilitated the direct detection of post-translational modifications (PTMs) in single cells, negating the need for enrichment and thereby simplifying the analytical process. Although throughput in MS remains a challenge, our study demonstrates the feasibility of processing up to 80 label-free SCP samples per day. Moreover, an optimized tissue dissociation buffer enabled effective single cell disaggregation of drug-treated cancer cell spheroids, refining the overall proteomic analysis. Our workflow sets a new benchmark in SCP for sensitivity and throughput, with broad applications ranging from the study of cellular development to disease progression and the identification of cell type-specific markers and therapeutic targets.
Establishing syndromic surveillance of gastrointestinal infections in emergency departments using routine emergency department data and validating it against laboratory-based surveillance, Germany, January 2019 – June 2023
Authors: Baum Jonathan Hans Josef , Dörre Achim , Boender Tamara Sonia , Heldt Katharina , Wilking Hendrik , Drynda Susanne , Erdmann Bernadett , Grashey Rupert , Grupp Caroline , Habbinga Kirsten , Hamelmann Eckard , Heining Amrei , Höger-Schmidt Heike , Kill Clemens , Reichert Friedrich , Riße Joachim , Schilling Tobias ,   AKTIN Research Group, Schranz Madlen
Published: November 2023

2.

Structured Abstract

Background

Gastrointestinal infections in Germany account for 24.5 million outpatient visits annually. Surveillance of gastrointestinal infections in emergency departments strengthens timely outbreak detection and disease trend monitoring.

Aim

We developed a syndrome definition for automated syndromic surveillance of gastrointestinal infections in emergency departments, and validated it against statutory laboratory-based surveillance.

Methods

To develop a syndrome definition, we selected presenting complaints (Canadian Emergency Department Information System) and diagnoses (ICD-10). We validated the definition through time series and cross-correlation analysis, comparing trends between syndromic and laboratory-based surveillance. We analysed German emergency department registry (AKTIN) data and included emergency departments that continuously transferred (01/2019-06/2023) data. As reference we combined statutory norovirus-gastroenteritis, rotavirus-gastroenteritis, campylobacteriosis and salmonellosis notifications.

Results

Our syndrome definition combined presenting complaints (diarrhoea, vomiting and nausea) and diagnoses (Intestinal infectious diseases). Accordingly, in 7 emergency departments withn= 864,353 visits, 2.1% (n= 18,158) were gastrointestinal infection cases. Of those, 57% (n= 10,424) were female, with 23% 0–19 years (n= 4,108) and 23% 20–29 years (n= 4,116) old. We visually observed similar gastrointestinal infection trends in both surveillance systems. The cross-correlation was 0.73 (95%-confidence interval 0.61–0.85;p<0.001) at lag −1, indicating a 1-week relative reporting delay of laboratory-based surveillance.

Conclusion

The coherent trends and significant cross-correlation validated our syndrome definition, which adequately captures gastrointestinal infection cases in emergency departments. Our novel automated surveillance complements laboratory-based surveillance, while offering advantages regarding timeliness and reduced workload. Therefore, it will be implemented in national routine surveillance.
Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting
Authors: Mustaq Mashiat , Ahmed Naeem , Mahbub Sazan , Li Clara , Miyaoka Yuichiro , TCW Julia , Andrews Shea , Bayzid Md. Shamsuzzoha ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: November 2023

Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current diagnostic methods, which are reliant on invasive and costly procedures, underscore the need for scalable and innovative approaches. To date, considerable effort has been dedicated to developing machine learning (ML)-based approaches using different combinations of medical, demographic, cognitive, and clinical data, achieving varying levels of accuracy. However, they often lack the scalability required for large-scale screening and fail to identify underlying risk factors for AD progression. Polygenic risk scores (PRS) have shown promise in predicting disease risk from genetic data. Here, we aim to leverage ML techniques to develop a multi-PRS model that captures both genetic and non-genetic risk factors to diagnose and predict the progression of AD in different stages in older adults. We developed an automatic feature selection pipeline that identifies the relevant traits that predict AD. Leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Religious Orders Study and Memory and Aging Project (ROSMAP), and the IEU OpenG-WAS Project, our study presents the first known end-to-end ML-based multi-PRS model for AD. This approach provides an automatic mechanism for harnessing genetic data for AD diagnosis and prognosis for comprehending the role of various traits in AD development and progression. Our method produced AUROC scores of 77% on ADNI and 72% on ROSMAP for predicting the diagnosis of AD, substantially surpassing the performance of the uni-variate PRS models. Our models also showed promise in predicting transitions between various cognitive stages. Moreover, the features identified by our automated feature selection pipeline are closely aligned with the widely recognized, potentially modifiable risk factors for AD.
Mechanism-based classification of SARS-CoV-2 Variants by Molecular Dynamics Resembles Phylogenetic Tree
Authors: Arns Thais , Fouquier d’Hérouël Aymeric , May Patrick , Tkatchenko Alexandre , Skupin Alexander
Published: November 2023

The COVID-19 pandemics has demonstrated the vulnerability of our societies to viral infectious disease. The mitigation of COVID-19 was complicated by the emergence of Variants of Concern (VOCs) with varying properties including increased transmissibility and immune evasion. Traditional population sequencing proved to be slow and not conducive for timely action. To tackle this challenge, we introduce the Persistence Score (PS) that assesses the pandemic potential of VOCs based on molecular dynamics of the interactions between the SARS-CoV-2 Receptor Binding Domain (RBD) and the ACE2 residues. Our mechanism-based classification approach successfully grouped VOCs into clinically relevant subgroups with higher sensitivity than classical affinity estimations and allows for risk assessment of hypothetical new VOCs. The PS-based interaction analysis across VOCs resembled the phylogenetic tree of SARS-Cov-2 demonstrating its predictive relevance for pandemic preparedness. Thus, PS allows for early detection of a variant’s pandemic potential, and an early risk evaluation for data-driven policymaking.
Towards faster identification of MRSA and MSSA: analysis of calorimetric curve parameters from large hospital bacterial collections
Authors: Zunk-Parras Sara , Trampuz Andrej , Arias-Sánchez Flor I.
Published: November 2023

There is a need to develop faster yet precise diagnostic tools for the identification of infectious agents and their levels of antimicrobial susceptibility. One such tool is calorimetry, as previous studies show that bacteria display unique signatures of calorimetric/metabolic activity that such signatures can be used for pathogen identification under controlled laboratory conditions. However, the level of variation of these unique signatures remains largely unexplored. Here, we use real-time isothermal microcalorimetry to disentangle the inter- and intra-species metabolic differences betweenS. aureusandS. epidermidis. We worked with a large collection of bacterial isolates obtained from patients with prosthetic joint infections as well as reference laboratory strains. We analyzed their calorimetric profiles and decomposed the curve parameters derived from them. As a result, we identified six calorimetric parameters that are useful for identification at the intra-species level, with a particular focus on MRSA. Furthermore, we found important differences between the thermograms of ATCC laboratory strains when compared against model thermograms generated from all our clinical isolates. These results indicate that accounting for metabolic variability can impact diagnosis precision. Finally, we discuss our findings and suggest ways to optimize calorimetric diagnostics and treatment approaches.
Tau P301L mutation promotes core 4R tauopathy fibril fold through near-surface water structuring and conformational rearrangement
Authors: Vigers Michael P. , Lobo Samuel , Najafi Saeed , Dubose Austin , Tsay Karen , Ganguly Pritam , Longhini Andrew P. , Jin Yingying , Buratto Steven K. , Kosik Kenneth S. , Shell M. Scott , Shea Joan-Emma , Han Songi
Published: November 2023

Tau forms toxic fibrillar aggregates in a family of neurodegenerative diseases known as tauopathies. The faithful replication of tauopathy-specific fibril structures is a critical gap for developing diagnostic and therapeutic tools. This study debuts a strategy of identifying a critical segment of tau that forms a folding motif that is characteristic of a family of tauopathies and isolating it as a standalone peptide that form seeding-competent fibrils. The 19-residue jR2R3 peptide (295-313) spanning the R2/R3 splice junction of tau, in the presence of P301L, forms seeding-competent amyloid fibrils. This tau fragment contains the hydrophobic VQIVYK hexapeptide that is part of the core of every pathological tau fibril structure solved to-date and an intramolecular counter-strand that stabilizes the strand-loop-strand (SLS) motif observed in 4R tauopathy fibrils. This study shows that P301L exhibits a duality of effects: it lowers the barrier for the peptide to adopt aggregation-prone conformations and enhances the local structuring of water around the mutation site that facilitates site-specific dewetting and in-register stacking of tau to form cross β-sheets. We solve a 3 Å cryo-EM structure of jR2R3-P301L fibrils with a pseudo 21screw symmetry in which each half of the fibril’s cross-section contains two jR2R3-P301L peptides. One chain adopts a SLS fold found in 4R tauopathies that is stabilized by a second chain wrapping around the SLS fold, reminiscent of the 3-fold and 4-fold structures observed in 4R tauopathies. These jR2R3-P301L fibrils are able to template full length tau in a prion-like fashion.

Significance Statement

This study presents a first step towards designing a tauopathy specific aggregation pathway by engineering a minimal tau prion building block, jR2R3, that can template and propagate distinct disease folds. We present the discovery that P301L—among the widest used mutations in cell and animal models of Alzheimer’s Disease—destabilizes an aggregation-prohibiting internal hairpin and enhances the local surface water structure that serves as an entropic hotspot to exert a hyper-localized effect in jR2R3. Our study suggests that P301L may be a more suitable mutation to include in modeling 4R tauopathies than for modelling Alzheimer’s Disease, and that mutations are powerful tools for the purpose of designing of tau prion models as therapeutic tools.
Genome-wide association study and regional heritability mapping of protein efficiency and performance traits in Swiss Large White pigs
Authors: Ewaoluwagbemiga Esther Oluwada , Lloret-Villas Audald , Nosková Adéla , Pausch Hubert , Kasper Claudia
Published: November 2023

Background

The improvement of protein efficiency (PE) is a key factor for a sustainable pig production as nitrogen excretion contributes substantially to environmental pollution. Protein efficiency has been shown to be clearly heritable and genetically correlated with some performance traits, such as feed conversion ratio (FCR) and average daily feed intake (ADFI). This study aimed to identify genomic regions associated with these traits through genome-wide association studies (GWAS) and regional heritability mapping (RHM) using whole-genome sequence variants from low-pass sequencing of more than 1,000 Swiss Large White pigs.

Results

The genomic-based heritability estimates using ∼15 million variants were moderate to high, ranging from 0.33 to 0.47. Using GWAS, no significant variants were found at the genome-wide thresholds for PE and FCR, but 45 variants were identified for ADFI on chromosome 1and one for ADG on chromosome 14. No region was found to be significantly associated with PE and FCR using RHM. We identified five suggestive regions on chromosome 1 for ADFI and one on chromosome 14 for ADG. Combining both analyses, we were able to highlight putative candidate genes for PE, which includedPHYKPL, COL23A1, PPFIBP2, GVIN1, SYT9, RBMXL2, ZNF215and olfactory receptor genes.

Conclusions

Combining GWAS and RHM allowed us to suggest genomic regions that potentially influence PE and production traits. The apparent difficulty in detecting significant regions for these traits probably reflects the relatively small sample size, differences in genetic architecture across experimental conditions, and the possibility that polymorphisms explaining large parts of the trait variation may not segregate in the population. Nevertheless, we identified plausible functional candidate genes in the highlighted regions, including those involved in nutrient sensing, the urea cycle, and metabolic pathways, in particular IGF1-insulin), and have previously been reported in association with nitrogen metabolism in cattle, muscle and adipose tissue metabolism and feed intake in pigs. We also highlighted a range of noncoding RNAs, and their targets and gene regulation in general in this context should be investigated in the future.
Exploring the Potential of Large Language Models in Molecular Tasks: An Insightful Evaluation with GPT‐4
Authors: Zhang Jinlu , Fang Yin , Zhang Ningyu , Shao Xin , Chen Huajun , Fan Xiaohui
Published: November 2023

In the rapidly changing realm of artificial intelligence, large language models (LLMs) such as GPT-4 are increasingly being explored for their potential to aid and enhance the field of molecular research. This study explores the performance of GPT-4 and GPT-3.5 in molecular research, particularly in generating and optimizing molecular structures. The results highlight GPT-4’s strengths in certain areas of molecular optimization, while also revealing challenges in accurately generating complex molecules. The findings underscore the necessity for integrating these models with domain-specific tools to enhance their application in scientific research, particularly in molecular studies. The study offers insights into the potential of LLMs for advancing molecular research, paving the way for future developments in this rapidly evolving field.
A library of reporters of the global regulators of gene expression ofEscherichia coli
Authors: Dash Suchintak , Jagadeesan Rahul , Baptista Ines S.C. , Chauhan Vatsala , Kandavalli Vinodh , Oliveira Samuel M.D. , Ribeiro Andre S.
Published: November 2023

The topology of the transcription factor network (TFN) ofE. coliis far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs for this purpose. Each consists of a single-copy plasmid coding for GFP fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, as well as under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs ofE. coli. Moreover, the library can be invaluable in bioindustrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.

Abstract Figure
ChemXTree:A Tree-enhanced Classification Approach to Small-molecule Drug Discovery
Authors: Xu Yuzhi , Liu Xinxin , Ge Jiankai , Xia Wei , Ju Cheng-Wei , Zhang Haiping , Zhang John Z.H.
Published: November 2023

The rapid advancement of machine learning, particularly deep learning, has propelled significant strides in drug discovery, offering novel methodologies for molecular property prediction. However, despite these advancements, existing approaches often face challenges in effectively extracting and selecting relevant features from molecular data, which is crucial for accurate predictions. Our work introduces ChemXTree, a novel graph-based model that integrates tree-based algorithms to address these challenges. By incorporating a Gate Modulation Feature Unit (GMFU) for refined feature selection and a differentiable decision tree in the output layer. Extensive evaluations on benchmark datasets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree’s superior performance, particularly in feature optimization. Permutation experiments and ablation studies further validate the effectiveness of GMFU, positioning ChemXTree as a significant advancement in molecular informatics, capable of rivaling state-of-the-art models.
Population coding of distinct categories of behavior in the frontal eye field
Authors: Cain Matan , Joshua Mati
Published: November 2023

Brain regions frequently contribute to the control of diverse behaviors. Within these regions, individual neurons often respond to various motor tasks, suggesting that the ability to control multiple behaviors is rooted in the organization of neuronal populations. To study this organization, we examined how the frontal eye field (FEF) encodes distinct behaviors by recording the activity of 1200 neurons during various eye movement tasks in two female fascicularis monkeys. We focused on three behaviors: smooth pursuit, pursuit suppression, and saccades. We found that single neurons tended to respond in all three tasks, thus challenging the notion that the FEF is organized into task-specific clusters. We then identified the low-dimensional subspaces that contained most of the population activity during each behavior and quantified the extent of overlap between these spaces across behaviors. Population activity during pursuit and pursuit suppression exhibited a substantial overlap, with highly correlated directional tuning at the single-neuron level, as reflected by similar outcomes for the population decoders. These distinct behaviors combined with similar encoding suggest that the suppression of movement occurs mostly downstream from the FEF. By contrast, pursuit and saccades mostly occupied orthogonal subspaces, prompting an independent linear readout of saccades and pursuit. Thus overall, these results indicate that distinct behaviors can exhibit either separate or overlapping population codes within a specific brain region, hence emphasizing the importance of the system-level organization of behavior.

Significance statement

How do brain areas control multiple behaviors? We investigated the organization of the FEF in monkeys during three different types of eye movements: smooth pursuit, pursuit suppression, and saccades. We found that individual neurons tended to respond to all three tasks, thus challenging a task-specific FEF cluster organization. Further, the low-dimensional subspaces that contained most of the population activity during pursuit and pursuit suppression overlapped substantially, implying that movement suppression occurs downstream from the FEF. In contrast, pursuit and saccades occupied orthogonal subspaces, prompting independent linear readouts. These results underscore the importance of adopting a system-level perspective to comprehend how diverse behaviors are encoded in the brain.
In vivodetection of DNA secondary structures using Permanganate/S1 Footprinting with Direct Adapter Ligation and Sequencing (PDAL-Seq)
Authors: Lahnsteiner Angelika , Craig Sarah J. C. , Kamali Kaivan , Weissensteiner Bernadette , McGrath Barbara , Risch Angela , Makova Kateryna D.
Published: November 2023

DNA secondary structures are essential elements of the genomic landscape, playing a critical role in regulating various cellular processes. These structures refer to G-quadruplexes, cruciforms, Z-DNA or H-DNA structures, amongst others (collectively called ‘non-B DN’), which DNA molecules can adopt beyond the B conformation. DNA secondary structures have significant biological roles, and their landscape is dynamic and can rearrange due to various factors, including changes in cellular conditions, temperature, and DNA-binding proteins. Understanding this dynamic nature is crucial for unraveling their functions in cellular processes. Detecting DNA secondary structures remains a challenge. Conventional methods, such as gel electrophoresis and chemical probing, have limitations in terms of sensitivity and specificity. Emerging techniques, including next-generation sequencing and single-molecule approaches, offer promise but face challenges since these techniques are mostly limited to only one type of secondary structure. Here we describe an updated version of a technique permanganate/S1 nuclease footprinting, which uses potassium permanganate to trap single-stranded DNA regions as found in non-B structures, in combination with S1 nuclease digest and adapter ligation to detect genome-wide non-B formation. To overcome technical hurdles, we combined this method with direct adapter ligation and sequencing (PDAL-Seq). Furthermore, we established a user-friendly pipeline available on Galaxy to standardize PDAL-Seq data analysis. This optimized method allows the analysis of many types of DNA secondary structures that form in a living cell and will advance our knowledge of their roles in health and disease.
Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection
Authors: Liu Hongwei , Zhang Wei , Zhang Yihao , Adegboro Abraham Ayodeji , Dai Luohuan , Pan Zhouyang , Li Wang , Peng Kang , Fasoranti Deborah Oluwatosin , Wanggou Siyi , Li Xuejun
Published: November 2023

With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patient’s outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.
Analysis ofE. coligrowth dynamics during Lambda(vir) phage infection reveals phage decay
Authors: Gitai Benjamin M. , Sheehan Joseph P.
Published: November 2023

Biological interactions like those between a pathogen and a host are intrinsically dynamic. The Luria-Delbruck experiment is a classic quantitative analysis demonstrating that the mutation rates of bacterial hosts are independent of the presence of their bacteriophage pathogens. However, this experiment was performed as an endpoint analysis. To determine if new insights could be gained from measuring the dynamics of bacterial-host interactions, we used a high-throughput spectrophotometer to repeat this classic experiment. We examined interactions betweenEscherichia colibacteria and the lytic lambda(vir) phage to confirm the fundamental finding of the Luria-Delbruck experiment. Specifically, we demonstrated that the variance in the timing required for phage-resistant mutants to emerge increased as the size of the starting bacterial population decreased. In addition to confirming the Luria-Delbruck results, our analysis of the dynamics of bacterial growth in the presence of phage revealed unexpected findings, including the significant decay in lambda(vir) potency and the substantial impact of dead bacterial debris on culture density measurements. Phage are typically thought to be extremely stable, such that our discovery that their potency rapidly decays in experimental settings may have implications for real-world applications where phage potency is important, such as phage therapy pharmacology. Our results thus establish a more rapid method of analyzing bacterial resistant mutant fluctuations and illustrate how dynamical measurements can provide new insights into even extremely well-characterized experimental systems.
Topological Learning Approach to Characterizing Biological Membranes
Authors: Arango Andres S. , Park Hyun , Tajkhorshid Emad
Published: November 2023

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here we present a persistent homology-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information of lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of DPPC membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by a persistent homology filtration, in which interactions spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed asimplicial complexthat captures enduring keytopological featuresof the configuration landscape, using homology, yieldingpersistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g. sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, calledMembTDA, can be accessed athttps://github.com/hyunp2/Memb-TDA.
Morphometrics and phylogenomics of coca (Erythroxylumspp.) illuminate its reticulate evolution, with implications for taxonomy
Authors: Przelomska Natalia A. S. , Diaz Rudy A. , Ávila Fabio Andrés , Ballen Gustavo A. , Cortés-B Rocío , Kistler Logan , Chitwood Daniel H. , Charitonidou Martha , Renner Susanne S. , Pérez-Escobar Oscar A. , Antonelli Alexandre
Published: November 2023

South American coca (Erythroxylum cocaandE. novogranatense) has been a key-stone crop for many Andean and Amazonian communities for at least 8,000 years. However, over the last half century, global demand for cocaine has placed this plant in the centre of armed conflict, deforestation, and explosive growth of illegal economies. While national and international agencies progress from a ‘war on drugs’ policy model towards locally appropriate, data-informed strategies to tackle coca plantations, monitoring their expansion and composition remains essential. The principal means to identify coca plants is leaf morphology, yet the extent to which it is reflected in taxonomy is uncertain. Here, we analyse the consistency of the current naming system of coca and its four closest wild relatives (the ‘coca clade’), using morphometrics, phylogenomics, and population genomics. We include the name-bearing type specimens of coca’s closest wild relativesE. gracilipesandE. cataractarum. Morphometrics of 342 digitized herbarium specimens show that leaf shape and size fail to reliably discriminate between species and varieties. However, the rounder and more obovate leaves of certain coca varieties could be associated with domestication syndrome of this crop. Our phylogenomic data indicate gene flow involving monophyletic clades ofE. gracilipesand theE. cocaclade. These results further clarify the evolution of coca and support a taxonomic framework whereinE. gracilipesis retained as a single species. Our findings have implications for the development of cost-effective genotyping methods to effectively discriminate varieties of cultural significance from high-yielding cultivars fuelling the lucrative cocaine market.
BCG vaccination impacts the epigenetic landscape of progenitor cells in human bone marrow
Authors: Sun Sarah J. , Aguirre-Gamboa Raúl , de Bree L. Charlotte J. , Sanz Joaquin , Dumaine Anne , Joosten Leo A.B. , Divangahi Maziar , Netea Mihai G. , Barreiro Luis B.
Published: November 2023

While the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo administration. We show that BCG vaccination significantly alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occur primarily on the most uncommitted stem cells and are reflective of a persistent myeloid bias. In contrast, BCG-induced changes in chromatin accessibility are most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF)/SP and EGR transcription factors (TFs). These TFs are also activated in the most uncommitted stem cells, indicating that activated TFs, which drive persistent changes in HSC gene expression, likely also drive chromatin dynamics appearing within downstream progenitor cells. This perspective contests the prevailing notion that epigenetic modifications linked to innate immune memory transfer directly from stem cells to their differentiated derivatives. Finally, we show that alterations in gene expression and chromatin accessibility in HSPCs due to BCG vaccination were highly correlated (r>0.8) with the IL-1β secretion capacity of paired PBMCs upon secondary immune challenge. Overall, our findings shed light on BCG vaccination’s profound and lasting effects on HSPCs and its influence on innate immune responses.