bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glucocorticoids modulate expression of perineuronal net component genes and parvalbumin during development of mouse cortical neurons
Authors: Yue Liang , Craig Michael T , Morris Brian J 
Published: February 2025

AbstractSevere prenatal maternal stress is a risk factor for schizophrenia in offspring. Since parvalbumin-containing GABAergic interneuron function in cortex and hippocampus is compromised in schizophrenia, and perineuronal nets (PNNs) facilitate the functioning of these cells, we tested the hypothesis that glucocorticoids, as stress mediators that can access the foetal compartment, might influence the expression of PNN component genes. In cultured mouse cortical neurons, we detected effects of hydrocortisone on many PNN component genes, via diverse mechanisms. A rapid (<4h), glucocorticoid receptor (GR)-mediated suppression ofneurocanand hyaluronan synthase (Has) 1 and 3 mRNAs was observed at 7 daysin vitro(DIV), whereas at 14DIV,brevicanandversicanexpression was reduced by hydrocortisone without GR involvement, while GR inhibition elevatedHas1andHas2mRNA levels and suppressedaggrecanmRNA levels.Tenascin Rexpression was rapidly suppressed by hydrocortisone at 7DIV but not at 14. At 21DIV, PNN component gene expression had become insensitive to hydrocortisone, althoughparvalbuminexpression was reduced after 24h but not 4h exposure. Additionally, effects on protein levels were observed that were sometimes consistent with the mRNA changes (e.g. Has3, Gad1) and sometimes unrelated to them (e.g. elevated TnR levels at 14DIV after glucocorticoid receptor antagonism). We found that hydrocortisone could directly inhibit proteasome activity, potentially explaining the ability of hydrocortisone to increase Has2 levels. As expected from these results, the overall structure of the PNN was compromised by hydrocortisone exposure, with the length of proximal dendrite covered by PNN being reduced. Overall, the data demonstrate a complex and profound, but developmental stage-dependent, regulation of PNN component gene expression by glucocorticoids. This may contribute to the action of severe prenatal or perinatal stress to increase schizophrenia risk.
Bridging the rehabilitation data gap in Uganda: learning through the implementation of the WHO Routine Health Information System - Rehabilitation module
Authors: Hasan Md Zabir , Okello Gerald , Groote Wouter De , Omaren Abraham , Adair Tim , Bachani Abdulgafoor M 
Published: February 2025

ABSTRACTBackgroundThe Health Management Information System (HMIS) of Uganda has historically lacked comprehensive, standardized, high-quality data on rehabilitation and assistive technology (AT) services. This data deficiency has hindered evidence-based decision-making for efficient resource allocation and hindered the contribution of rehabilitation and AT services to public health, limiting their integration and prioritization into health policy and strategic planning frameworks.ApproachTo address this gap, the Ministry of Health (MoH) of Uganda, in partnership with the Learning, Acting, and Building for Rehabilitation in Health Systems (ReLAB-HS) Consortium and the World Health Organization (WHO), integrated the WHO Routine Health Information System (RHIS) - Rehabilitation module into the existing HMIS. The process was facilitated by strategic engagement with national-level stakeholders, comprehensive readiness assessments of facilities, consensus building among stakeholders around a standard set of indicators, the development and pre-testing of HMIS tools, the configuration of District Health Information Software 2 (DHIS2), capacity-building activities, and the systematic deployment of the module.ResultSince adopting DHIS2 in 2011, Uganda has primarily used paper-based data collection at healthcare facilities. Rehabilitation services are mainly offered at public National and Regional Referral Hospitals, private hospitals, and clinics in urban and peri-urban settings. Rural and underserved areas face significant challenges because of limited infrastructure and a lack of human resources, which hinder the delivery of rehabilitation services, effective data capture, and evidence-informed decision-making. In September 2024, six core rehabilitation indicators were successfully integrated into the HMIS. New data collection instruments were developed and rigorously field-tested, including a standalone outpatient rehabilitation register and an integrated inpatient register. Targeted training initiatives enhanced the capacity of rehabilitation personnel and HMIS focal points, and ongoing technical support ensured sustained data quality improvements and process adherence.ConclusionThe integration process revealed the indispensable roles of stakeholder engagement, contextualized training, and continuous technical assistance. Persistent challenges included infrastructure deficits, workforce shortages, and competing health system priorities. Nonetheless, the initiative will significantly improve data availability, establishing a foundational framework for enhanced rehabilitation service delivery and strategic planning.
Putative breast cancer risk variants from populations of South Asian ancestry are under-represented in public variant classification databases
Authors: Rony Raveen , Deng Shenglong , Yang Sarah , Doig Ken , Goode David L 
Published: February 2025

AbstractThe majority of publicly available genomics data originates from populations of European ancestry. This limits understanding and detection of inherited genetic risk factors for breast cancer in other populations. To assess the extent to which deficits in knowledge of the genetics of breast cancer risk exist for populations of non-European ancestry, we compared data available on putative breast cancer risk variants in the ClinVar database for populations of different ancestry.Protein-coding insertions and deletions (indels) and single-nucleotide polymorphisms (SNPs) private to populations of Non-Finnish European (NFE), African (AFR), Admixed American (AMR), East Asian (EAS) and South Asian (SAS) ancestry from the Genome Aggregation Consortium (gnomAD v4) were identified for nine established breast cancer risk genes. The percentage of private protein-coding variants listed as ‘Unreported’ by gnomAD in ClinVar were compared between populations.The SAS population had the biggest knowledge deficit, as 43.4% of private SAS variants were not reported in ClinVar, compared to 20-30% for other populations. Proportionally fewer SAS variants were reported for all 9 genes, with the difference reaching an adjusted p < 0.05 for PALB2, ATM and BRCA2 when compared to NFE. In contrast, few genes had significantly lower ClinVar reporting rates for AFR, AMR and EAS than for NFE.ClinVar reporting deficits in the SAS population were observed for both missense and protein-truncating variants. Unreported variants were usually very rare and largely absent in other public repositories. A substantial fraction of unreported variants were protein-truncating (17.2%), or missense with high predicted pathogenicity scores, representing novel candidate breast cancer risk alleles.Our work demonstrates putative breast cancer risk variants from populations of South Asian ancestry are less likely to be reported in ClinVar. Defining and removing barriers to reporting potential risk variants for breast cancer from South Asian populations is needed to reduce this knowledge deficit.
Bacterial pathogen deploys iminosugar galactosyrin to manipulate plant glycobiology
Authors: Sanguankiattichai Nattapong , Chandrasekar Balakumaran , Sheng Yuewen , Hardenbrook Nathan , Tabak Werner W. A. , Krahn Daniel , Drapal Margit , Buscaill Pierre , Yamamoto Suzuka , Kato Atsushi , Nash Robert , Fleet George , Fraser Paul , Kaiser Markus , Zhang Peijun , Preston Gail M. , van der Hoorn Renier A. L. 
Published: February 2025

ABSTRACTThe extracellular space (apoplast) of plants is an important molecular battleground during infection by many pathogens. We previously found that a plant-secreted β-galactosidase BGAL1 acts in immunity by facilitating the release of immunogenic peptides from bacterial flagellin and thatPseudomonas syringaesuppresses this enzyme by producing a small molecule inhibitor called galactosyrin. Here, we elucidated the structure and biosynthesis of galactosyrin and uncovered its multifunctional roles during infection. Structural elucidation by cryo-EM and chemical synthesis revealed that galactosyrin is an iminosugar featuring a unique geminal diol attached to the pyrrolidine moiety that mimics galactose binding to the β-galactosidase active site. Galactosyrin biosynthesis branches off from purine biosynthesis and involves three enzymes of which the first is a reductase that is unique in iminosugar biosynthesis. Besides inhibiting BGAL1 to avoid detection, galactosyrin also changes the glycoproteome and metabolome of the apoplast. The manipulation of host glycobiology may be common to plant-associated bacteria that carry putative iminosugar biosynthesis clusters.
Linking Transcriptional and Cellular Responses of Human and Murine Pneumococcal Pneumonia
Authors: Gorki Anna-Dorothea , Lakovits Karin , Hladik Anastasiya , Mesteri Ildiko , Gawish Riem , Martins Rui , Drobits Barbara , Menche Jörg , Knapp Sylvia , Widder Stefanie 
Published: February 2025

AbstractCommunity-acquired pneumonia (CAP), often caused byStreptococcus pneumoniae, poses a significant global health challenge, especially among high-risk populations. This study investigates the temporal dynamics of pulmonary gene expression duringS. pneumoniae-induced pneumonia using a murine model to elucidate host-pathogen interactions and identify potential biomarkers of disease severity. Using bulk RNA sequencing, we analyzed lung tissues at early (1h, 8h) and acute infection (2d, 3d), as well as post-resolution (30d) time-points. At 2 days post-infection, differentially expressed genes (DEGs) revealed heightened innate immune responses, including chemokine and interferon signaling pathways. By 30 days post-infection, gene expression profiles and histological changes normalized, reflecting resolution of inflammation. Stratifying mice into recovered, sick, and moribund phenotypes during the acute infection phase highlighted significant transcriptional distinctions, including upregulated pro-inflammatory genes and Schlafen family members. Arginase 1 emerged as a predictive marker of disease severity, with elevated expression detectable as early as 8 hours post-infection. Comparative analyses revealed significant overlap between transcriptional responses in our model and those from a Gram-negativeAcinetobacter baumanniipneumonia model, implicating conserved pathways, such as IL-17 and Toll-like receptor signaling. Additionally, murine DEGs correlated with human plasma proteins associated with severe CAP, including CCL8 and CD14, suggesting translational relevance.This study underscores phase-specific transcriptional reprogramming during pneumococcal pneumonia and identifies potential biomarkers and therapeutic targets for improving outcomes in severe CAP cases.
Metabolic Response of a Chemolithoautotrophic Archaeon to Carbon Limitation
Authors: Hodgskiss Logan H. , Kerou Melina , Luo Zhen-Hao , Bayer Barbara , Maier Andreas , Weckwerth Wolfram , Naegele Thomas , Schleper Christa 
Published: February 2025

AbstractThe ubiquitously distributed ammonia-oxidizing archaea generate energy from ammonia and build cell mass from inorganic carbon sources, thereby contributing to both the global nitrogen and carbon cycles. However, little is known about the regulation of their predicted core carbon metabolism. A thermodynamic model forNitrososphaera viennensiswas developed to estimate the consumption of inorganic carbon in relation to ammonia consumed for energy and was tested experimentally by growing cells in carbon-limited and excess conditions. A combined proteomic and metabolomic approach of the experimental conditions revealed distinct metabolic adaptation depending on the amount of carbon supplied, either in a catalase or pyruvate background as a reactive oxygen species scavenger. Integration of protein and metabolite dynamics revealed a cellular strategy under carbon limitation to maintain a pool of amino acids and an upregulation of proteins necessary for translation initiation to stay primed for protein synthesis. The combination of modelling and functional genomics fills gaps in the understanding of the central metabolism and its regulation in a chemolithoautotrophic, ammonia-oxidizing archaeon even in the absence of available genetic tools.
ROCker models for reliable detection and typing of short-read sequences carryingmcr,erm,mph, andlnuantibiotic resistance genes
Authors: Conrad Roth , Gerhardt Kenji , Konstantinidis Konstantinos T. , Williams-Newkirk Amanda J. , Huang Andrew 
Published: February 2025

AbstractQuantitative monitoring of emerging antimicrobial resistance genes (ARGs) using short-read sequences remains challenging due to the high frequency of amino acid functional domains and motifs shared with related but functionally distinct (non-target) proteins. To facilitate ARG monitoring efforts using unassembled short-reads, we present novel ROCker models formcr,mph,erm, andlnuARG families as well as models for variants of special public health concern within these families includingmcr-1,mphA,ermB,lnuF, lnuB,andlnuGgenes. For this, we curated target gene sequence sets for model training and built these models using the recently updated ROCker V2 pipeline (Gerhardt et al., in review). To validate our models, we simulated reads from the whole genome of ARG-carrying isolates spanning a range of common read lengths and used them to challenge the filtering efficacy of ROCker vs. common static filtering approaches such as similarity searches using BLASTx with various e-value thresholds or hidden Markov models. ROCker models consistently showed F1 scores up to 10x higher (31% higher on average) and lower false-positive (by 30%, on average) and false-negative (by 16%, on average) rates based on 250 bp-long reads compared to alternative methods. The ROCker models and all related reference material and data are freely available throughhttp://enve-omics.ce.gatech.edu/rocker/models, further expanding the available model collection developed previously for other genes. Their application to short-read metagenomes, metatranscriptomes, and PCR amplicon data should facilitate more accurate classification and quantification of unassembled short-read sequences for these ARG families and specific genes.SignificanceAntimicrobial resistance gene families encodingermandmphgenes confer resistance to the macrolide class of antimicrobials used to treat a wide range of infections. Similarly, themcrgene family confers resistance to polymyxin E (colistin), a drug of last resort for many serious drug-resistant bacterial infections, and thelnugene family confers resistance to lincomycin, reserved for patients allergic to penicillin or where bacteria have developed resistance to other antimicrobials. Assessing the prevalence of these genes in clinical or environmental samples and monitoring their spreading to new pathogens are thus important for quantifying the associated public health risk. However, detecting these and other resistance genes in short-read sequence data is technically challenging. Our ROCker bioinformatic pipeline achieves reliable detection and typing of broad-range target gene sequences in complex data sets, and thus contributes toward solving an important problem in ongoing surveillance efforts of antimicrobial resistance.
Biofilm selection constitutively activates c-di-GMP synthesis by the bifunctional enzyme MbaA
Authors: Lorentzen Øyvind M. , Abel Sören , Johnsen Pål J. , Frøhlich Christopher 
Published: February 2025

AbstractBiofilms, in which microbes are encased within a self-produced matrix, represent the primary mode of microbial life. Yet, our understanding of the molecular mechanisms governing biofilm formation remains incomplete, limiting the development of targeted interventions against biofilm-associated infections. By selecting forVibrio choleraebiofilms, we identified mutations in the polyamine-regulated, bifunctional enzyme MbaA that alter its enzymatic activity in metabolizing the conserved second messenger cyclic diguanylate (c-di-GMP). These mutations occur in both the c-di-GMP-producing GGDEF domain and the c-di-GMP- hydrolyzing EAL domain of MbaA. Mutagenesis and enzyme kinetic analyses revealed regulatory crosstalk between these domains, where mutations in either domain enhanced production of c-di-GMP by MbaA while concurrently reducing its hydrolytic activity. In the wild- type enzyme, c-di-GMP metabolism is regulated by specific polyamines. Biofilm-evolved mutations not only shift MbaA’s enzymatic activity but also disrupt this post-translational regulation, rendering the enzyme insensitive to external polyamine signaling. Our findings illustrate how evolutionary pressure can rewire existing enzymes, driving a transition from a planktonic to a biofilm-associated lifestyle.
How to promote the technological innovation cooperation in mega construction projects at the project level?
Authors: Guo Zhenxu , Wang Qing’e , Jing Haofei , Gao Qixin 
Published: February 2025

PurposeMega construction projects (megaprojects) require technological innovation cooperation (TIC) to address complex construction demands and the interests of multiple stakeholders. Although TIC has been extensively discussed at the firm level, a significant gap remains in understanding megaprojects at the project level. This paper aims to identify TIC’s influencing factors and transmission paths and discuss stakeholders’ TIC mechanisms at the project level.Design/methodology/approachBased on case analysis, expert interviews, literature analysis and the Delphi method, this paper identifies the influencing factors of TIC in megaprojects at the project level. A structural system of these influencing factors is constructed by interpretive structural modeling (ISM), developing various mechanisms for TIC from bottom to top. The Matriced’ Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) method validates the driving forces and dependencies of the influencing factors, clarifying their roles and positions within the system. Additionally, the TIC mechanism is constructed.FindingsThe research findings identify 26 influencing factors categorized into four hierarchical levels: cooperative relationships, cooperative behavior, cooperative performance and technological innovation risks. Regarding direct factors, resource sharing affects goal congruence and communication effectiveness in megaprojects, affecting TIC’s satisfaction and trust. Most factors exist in the middle layer, and bridging the upper and lower levels depends on stakeholder collaboration. The root factors in the independent group significantly impact TIC, including policy circumstances, high technical requirements and limited site conditions. Addressing these issues influences improvements in other factors. The development of a digital resource-sharing platform, the enhancement of innovation incentives, the optimization of benefit distribution mechanisms and the improvement of risk-sharing mechanisms are essential for the effective operation of the TIC mechanism.Originality/valueThis study contributes to identifying and classifying challenges and opportunities in TIC. It explores transmission paths for enhancing TIC and presents strategies for successfully implementing and delivering megaprojects.
Human resources analytics performance measurement: a novel hybrid approach based on cumulative belief degree and PLS-SEM
Authors: Budak Muhammed Cagri , Soyer Ayberk 
Published: February 2025

PurposeHuman resources analytics (HRA) applications are of theoretical and practical interest to both researchers and practitioners. While organizations have begun to implement HRA applications, there is currently no established approach for measuring their performance. This study aims to address this gap in the literature by proposing a new approach for measuring the performance of HRA applications.Design/methodology/approachThis study proposes a hybrid approach that combines the cumulative belief degree (CBD) and partial least squares structural equation modeling (PLS-SEM) to measure organizational HRA performance.FindingsThe performance measurement approach proposed in this study has the capacity to reveal the total HRA performance level of an organization, while also providing the opportunity to measure the performance of the subdimensions that make up HRA. These subdimensions include data sufficiency, technological capability, workforce capability, application level of HRA and organizational climate. This approach has the potential to assist organizations that do not currently utilize HRA in their operations to make an informed decision regarding the implementation of HRA and enables organizations to assess their potential performance if they were to implement HRA.Practical implicationsThe proposed approach allows organizations to assess the performance of analytical applications in the human resources (HR) field. This assessment can be carried out at both the pre-implementation and post-implementation stages of HRA applications. Therefore, the approach provides a valuable contribution to organizations, enabling them to enhance their capabilities in this domain. Consequently, the study addresses a significant gap in practical research. Furthermore, in terms of the applicability of the developed HRA performance measurement model to diverse analytical domains, it paves the way for the advancement of other performance measurement studies.Originality/valueThe HRA performance assessment process encompasses multiple interrelated HRA subdimensions and performance indicators that can be measured using different scales. It is therefore essential to implement a flexible methodology that can convert diverse forms of evaluation into a unified scale and integrate them in order to effectively manage the inherent complexities and uncertainties associated with the assessment process. In this regard, the CBD approach proves particularly effective. In the CBD approach, a fuzzy set of linguistic terms is used to convert the performance indicator scores into a common scale and therefore takes into account the uncertainty inherent in the assessment process. In addition, it is also proposed to use the PLS-SEM method to combine CBDs.