Biology subjects

Fine, A.

Publications and source records attributed to Fine, A..

8 recordsLinked to original sources

bioRxiv2026

BirdCODE: Detecting bird communication at scale

Deep learning-based animal sound identification is regularly applied to large audio datasets for ecological monitoring and citizen science, but existing methods lack the fine temporal resolution required to extract insights into animal communication from these same datasets. Here we introduce Bird Communication Detector (BirdCODE), a deep learning model that detects and classifies the vocalizations of over 9000 bird species with precise temporal boundaries, a several hundredfold increase the number of species over previous bioacoustic sound event detection models. In extensive benchmarking, BirdCODE achieves state-of-the-art performance in detection and classification of bird sounds. Applying BirdCODE to 1.3M citizen-science recordings, we present four case studies of how BirdCODE-computed sound event boundaries can be used to carry out phylogenetic analyses, to describe geographic and temporal variation in acoustic communication, and to characterize cross-species interactions. Together, these demonstrate how BirdCODE can enable large-scale, data-driven studies of bird communication. Model code, weights, and predictions are publicly available.

bioinformatics↗

bioRxiv2025

An antifungal with a novel mechanism of action discovered via resistance gene-guided genome mining

Invasive fungal infections claim over two million lives annually, a problem exacerbated by rising resistance to current antifungal treatments and an increasing population of immunocompromised individuals. Despite this, antifungal drug development has stagnated, with few novel agents and fewer novel targets explored in recent decades. Here, we validate acetolactate synthase (ALS), an enzyme critical for branched-chain amino acid biosynthesis and absent in humans, as a promising target for new therapeutics. Using resistance gene-guided genome mining, we discovered a biosynthetic gene cluster in Aspergillus terreus encoding HB-35018 (1), a novel spiro-cis-decalin tetramic acid that potently inhibits ALS. Biochemical and antifungal assays demonstrate that 1 surpasses existing ALS inhibitors in efficacy against Aspergillus fumigatus and other pathogenic fungi. Structural studies via cryo-electron microscopy reveal a unique covalent binding interaction between compound 1 and ALS, distinct from known inhibitors and finally, we demonstrate that ALS is essential for the virulence in a mouse model of invasive aspergillosis. These findings position ALS as a promising target for antifungal development and demonstrate the potential of resistance gene-guided genome mining for antifungal discovery.

bioengineering↗

bioRxiv2024

Lung CD4+ resident memory T cells use airway secretory cells to stimulate and regulate neutrophilic allergic airways disease.

Neutrophilic asthma is a vexing disease, but mechanistic and therapeutic advancement will require better models of allergy-induced airway neutrophilia. Here, we find that periodic ovalbumin (OVA) inhalation in sensitized mice elicits allergic airway inflammation and pathophysiology mimicking neutrophilic asthma. OVA-experienced murine lungs harbor diverse clusters of CD4+ resident memory TRM cells, including unconventional ROR{gamma}tnegative/low TH17 cells. Acute OVA challenge instigates IL-17A secretion from these TRM cells, driving CXCL5 production from Muc5achigh airway secretory cells, leading to destructive airway neutrophilia. The TRM- and epithelial-cell signals discovered herein are also observed in adult human asthmatic airways. Epithelial antigen presentation regulates this biology by skewing TRM cells towards TH2 and TH1 fates, so that the TH1-related IFN-{gamma} suppresses IL-17A-driven, CXCL5-mediated airway neutrophilia. Concordantly, in vivo IFN-{gamma} supplementation improves disease outcomes. Thus, using our model of neutrophilic asthma we identify lung epithelial-CD4+ TRM cell crosstalk as a key rheostat of allergic airway neutrophilia. HighlightsO_LIRecurrent OVA inhalation experience predisposes mice to allergic airways neutrophilia C_LIO_LINeutrophil-prone lungs harbor CD4+ TRM cells including ROR{gamma}tnegative/low TH17 cells C_LIO_LIMuc5achigh secretory cells instruct CD4+ TRM fates and neutrophilia via MHC-II and CXCL5, respectively C_LIO_LIProphylactic or therapeutic delivery of IFN-{gamma} curbs allergic airway neutrophilia C_LI

immunology↗

bioRxiv2024

Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. The collection of root samples from the field and their subsequent cleaning and scanning in a water-filled tray ranging in size from 5 to 20 cm, followed by digital image analysis has been commonly used since the 1990s for measuring root length, volume, area, and diameter. However, one common issue has been neglected. Sometimes, the amount of roots for a sample is too much to fit into a single scanned image, so the sample is divided among several scans. There is no standard method to aggregate the root measurements across the scans of the same sample. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. Both methods rely on standardizing file naming conventions to identify scans that belong to the same sample. Image concatenation refers to combining digital images into a single larger image while maintaining the original resolution. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image for every set of images in a directory. These concatenated images (combining up to 10 scans) and the original images were processed with RhizoVision Explorer, a free and open-source software developed for estimating root traits from images, with the same settings. An R script was developed that can identify the rows of data belonging to the same sample in RhizoVision Explorer data files and apply correct statistical methods such as summation, weighted average by length, and average to the appropriate measurement types to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Overall, the new methods accomplished the goal of standardizing measurement aggregation. Most root measurements were nearly identical except median diameter, which can not be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

plant biology↗

bioRxiv2024

Functional Mapping of Movement and Speech Using Task-Based Electrophysiological Changes in Stereoelectroencephalography

IntroductionStereoelectroencephalography (sEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG are not in full agreement or definitively localizing, implanted sEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If a SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision making. ObjectiveWe present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in sEEG during behavioral tasks and test this in pediatric and adult patients. MethodssEEG was recorded in twenty patients with epilepsy, aged 6-39 (12 female, 18 of 20 patients < 21 years old), who underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded, and 2) simple picture naming or verb generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the sEEG were compared between behavior and rest. ResultsElectrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter, and generally corresponded to classically described functional anatomic organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs. ConclusionTask based electrophysiological mapping using broadband changes in the sEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.

neuroscience↗

bioRxiv2022

Stable iPSC-derived NKX2-1+ Lung Bud Tip Progenitor Organoids Give Rise to Airway and Alveolar Cell Types

Bud tip progenitors (BTPs) in the developing lung give rise to all epithelial cell types found in the airways and alveoli. The current work aimed to develop an iPSC organoid model enriched with stable NKX2-1+ BTP-like cells. Building on prior work, we optimized a directed differentiation paradigm to generate spheroids with robust NKX2-1 expression. Spheroids were expanded into organoids that possessed NKX2-1+/CPM+ BTP-like cells, which increased in number over time. Single cell RNA-sequencing analysis revealed a high degree of transcriptional similarity between induced BTPs (iBTPs) and in vivo BTPs. Using FACS, iBTPs can be purified and expanded as induced bud tip organoids (iBTO), which maintain an enriched population of bud tip progenitors. When iBTOs are directed to differentiate into airway or alveolar cell types using well-established methods, they give rise to organoids composed of organized airway or alveolar epithelium, respectively. Collectively, iBTOs are transcriptionally and functionally similar to in vivo BTPs, providing an important model to study human lung development and differentiation. SUMMARY STATEMENTiPSC-derived lung bud tip progenitors emerge in organoid culture, can be isolated and expanded, are transcriptionally similar to primary bud tip progenitors, and can differentiate into airway or alveolar organoids.

developmental biology↗

bioRxiv2021

Molecular epidemiology of peste des petits ruminants virus emergence in critically endangered Mongolian saiga antelope and other wild ungulates

Peste des petits ruminants virus (PPRV) causes disease in domestic and wild ungulates, is the target of a global eradication programme and threatens biodiversity. Understanding the epidemiology and evolution of PPRV in wildlife is important, but hampered by the paucity of wildlife-origin PPRV genomes. In this study, full PPRV genomes were generated from three Mongolian saiga antelope, one Siberian ibex and one goitered gazelle from the 2016-2017 PPRV outbreak. Phylogenetic analysis showed that for Mongolian and Chinese PPRV since 2013, the wildlife and livestock-origin genomes were closely related and interspersed. There was strong phylogenetic support for a monophyletic group of PPRV from Mongolian wildlife and livestock, belonging to clade of lineage IV PPRV from livestock and wildlife from China since 2013. Discrete diffusion analysis found strong support for PPRV spread into Mongolia from China and phylogeographic analysis indicated Xinjiang Province as the most likely origin, although genomic surveillance for PPRV is poor and lack of sampling from other regions could bias this result. Times of most recent common ancestor (TMRCA) were June 2015 (95% HPD: August 2014 - March 2016) for all Mongolian PPRV genomes and May 2016 (95% HPD: October 2015 - October 2016) for Mongolian wildlife-origin PPRV. This suggests that PPRV was circulating undetected in Mongolia for at least six months before the first reported outbreak in August 2016, and that wildlife were likely infected before livestock vaccination began in October 2016. Finally, genetic variation and positively-selected sites were identified that might be related to PPRV emergence in Mongolian wildlife. This study is the first to sequence multiple PPRV genomes from a wildlife outbreak, across several host species. Additional full PPRV genomes and associated metadata from the livestock-wildlife interface are needed to enhance the power of molecular epidemiology, support PPRV eradication and safeguard the health of the whole ungulate community. Author SummaryRecent mass mortality of critically endangered Mongolian saiga antelope due to peste des petits ruminants virus (PPRV) has dramatically highlighted the threat this viral disease represents for biodiversity. The genome of viruses such as PPRV evolve fast, so virus genetic data gathered from infected animals can be used to trace disease spread between livestock and wildlife, and to determine if the virus is adapting to infect wildlife more efficiently. Here we obtained PPRV virus genomes from Mongolian wildlife and compared them with other published PPRV genomes. Using a molecular clock, we estimated that the disease was circulating in Mongolia well before it was first reported. Genetic analyses support the hypothesis of virus spread from livestock to wildlife, with genetic changes potentially helping infection in Asian wild ungulates. However, more PPR virus genomes and epidemiology data are needed from disease outbreaks in areas shared between livestock and wildlife to confirm these results and take efficient actions to safeguard the health of the whole ungulate community.

microbiology↗

bioRxiv2019

A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease

BackgroundLate-onset Alzheimers disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimers have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes.\n\nResultsThis resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter(R) Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of three mouse models based on LOAD genetics, carrying APOE4 and TREM2*R47H alleles, demonstrated overlaps with distinct human AD modules that, in turn, are functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq shows strong correlation between gene expression changes independent of experimental platform.\n\nConclusionsTaken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.

neuroscience↗