Biology subjects

Longo, C.

Publications and source records attributed to Longo, C..

4 recordsLinked to original sources

bioRxiv2026

A knock-in model of severe GUCA1A cone-rod dystrophy reveals retinal network dysfunction beyond phototransduction

Autosomal dominant cone-rod dystrophy caused by GUCA1A mutations is generally viewed as a disorder of phototransduction, yet the mechanisms linking photoreceptor dysfunction to progressive vision loss remain unclear. Here, using a knock-in mouse carrying the severe GCAP1 p.(E111V) variant, we show that retinal network dysfunction precedes structural degeneration. Mutant mice exhibited delayed rod photoresponses, increased light sensitivity, selective visuospatial deficits, and progressive impairment of visually evoked responses in the superior colliculus and visual cortex, demonstrating propagation of functional deficits beyond photoreceptors. Transcriptomic and ultrastructural analyses revealed early synaptic, mitochondrial and inflammatory alterations despite largely preserved retinal architecture. Acute ex vivo delivery of recombinant wild-type GCAP1 partially restored mutant rod photoresponse kinetics, indicating that these early functional deficits remain biochemically modifiable. These findings redefine severe GUCA1A-associated disease as a progressive disorder of retinal network function, identifying an early therapeutic window before structural degeneration. One-Sentence SummaryVisual function breaks down long before photoreceptors are lost, opening an early window for intervention.

neuroscience↗

bioRxiv2026

Causal Language Detection using Text-Document Features: Methodology and Insights from 10 Years of Gut Microbiome Research

Detecting causal language in scientific literature is critical for understanding how research fields frame evidence and inform interventions and policies, yet existing approaches commonly rely on manual annotation. The objective of this study was to evaluate four classifiers for detecting causal language and to apply the best-performing model to assess trends in microbiome research. Microbiome research, with its rapidly expanding observational literature, provides a relevant case study. We extracted Term Frequency-Inverse Document Frequency (TF-IDF) features from the last three sentences of available publication abstracts and trained four classifiers (L1- and L2-regularized logistic regression, Random Forest, and eXtreme Gradient Boosting) to detect causal language. A total of 475 sentences, as determined pragmatically based on annotation feasibility and observed stabilization of model performance, were manually labeled as causal or non-causal following established guidelines for systematic evaluation of causal language in observational health research. Of these, 75% of sentences were used for training and 25% for testing. L1-regularized logistic regression achieved the highest performance (accuracy 76%, F1 72%, prevalence detection accuracy 95%, sensitivity 72%, and specificity 80%) and was applied to 20,022 human gut microbiome abstracts published between 2015 and 2025 grouped into 20 thematic topics using structural topic modeling. Predicted causal language prevalence declined from 52% to 44% between 2015 and 2018, then rose to 51% by 2025, with notable variation across topics (range: 43.1-53.3%). Temporal trends differed across subfields, with increases in Metabolic disorders, Fecal microbiota transplantation, and decreases in Biomarkers and prediction, Antibiotic resistance, and In vitro fermentation. Analysis of influential words confirmed that causal meaning is primarily driven by verbs and modifiers lexically signaling change or intervention. The proposed approach for identifying causal claims in scientific abstracts enables systematic and automated, scalable assessment of how evidence is framed. Its application to the microbiome field highlighted heterogeneity in the reporting of causal relationships and informing the interpretation of microbiome findings for clinical and public health decision-making.

scientific communication and education↗

bioRxiv2025

Evaluation of Enterobacterales carrying Acinetobacter-associated blaOXA genes--United States, 2017-2022

Through their ability to hydrolyze carbapenems, Ambler class D beta-lactamases endanger patients by limiting the clinical efficacy of beta-lactam antimicrobials. Further, plasmid-mediated transmission can increase mobility of carbapenemase genes between bacteria and facilitate their spread between patients. In the United States and elsewhere, the plasmid-mediated Ambler class D carbapenemase genes blaOXA-23-like, blaOXA-24/40-like, and blaOXA-58-like are commonly associated with Acinetobacter species and have rarely been reported outside of this genus. However, multiple recent international reports indicate detection of Enterobacterales isolates carrying Acinetobacter-associated class D carbapenemase genes. This evaluation aimed to provide insight into whether Enterobacterales harboring these class D genes may be circulating undetected in the United States, thereby signaling a need for adapting testing strategies to prioritize the detection of these potentially emerging public health threats. We analyzed whole-genome sequencing data generated through multiple Centers for Disease Control and Prevention (CDC) activities, including testing conducted across the Antimicrobial Resistance Laboratory Network, to determine whether any Enterobacterales isolates sequenced from 2017-2022 harbored Acinetobacter-associated class D carbapenemase genes. Among [~]10,000 predominantly carbapenem-resistant Enterobacterales isolates, we identified only a single Enterobacterales isolate harboring an Acinetobacter-associated class D gene - a Klebsiella pneumoniae isolate harboring a blaOXA-23-like gene. Our findings suggest that blaOXA-23-like, blaOXA-24/40-like, and blaOXA-58-like genes are rare among Enterobacterales isolates sequenced through CDC public health activities in the United States and do not warrant changes to current testing priorities at this time.

microbiology↗

bioRxiv2025

Storage protein biosynthesis is affected by ionome composition in soybean (Glycine max (L.) Merrill) seeds

Soybean seeds are a significant source of protein for human and animal nutrition, primarily due to seed storage proteins (SSPs) from the albumin and globulin families, which are predominantly located in protein storage vacuoles within cotyledon cells. This study characterised the dynamics of protein and mineral nutrient accumulation in four soybean genotypes with contrasting protein content--two transgenic (tg1 and tg2) and two conventional (ct1 and ct2)--from the beginning of seed filling (R5.5) through to maturity (R8) under field conditions. Profiles of globulin SSPs (glycinin and {beta}-conglycinin), as well as the protein and elemental distribution in mature seed cotyledons were examined. Results revealed that genotypes with higher protein content showed increased S and Zn concentrations and a higher glycinin:{beta}-conglycinin ratio. Subcellular analyses further indicated co-localisation of proteins and Zn within cotyledon cells. Our findings reveal a complex association between S and Zn accumulation and SSPs biosynthesis, indicating that their availability can limit the SSP content. HIGHLIGHTSoybean seed genotypes containing higher sulphur (S) and zinc (Zn) content in the cotyledonary cells exhibit a distinct storage proteins profile by increasing the abundance of sulphur-amino acids rich globulins.

plant biology↗