Biology subjects

Goldberg, T.

Publications and source records attributed to Goldberg, T..

5 recordsLinked to original sources

bioRxiv2025

Super-Resolved Spatial Transcriptomics Reveals Early Changes in RNA Localization in the 5xFAD Hippocampus

Cell-type-specific changes in gene expression and RNA localization are hallmarks of Alzheimers disease (AD) and other neurodegenerative disorders, yet spatial dysregulation in early disease stages remains poorly defined. Here, we applied Expansion Sequencing (ExSeq) to map the spatial distribution of 101 genes at super-resolution in the hippocampus of 4-week-old 5xFAD and wild-type (WT) mice, prior to overt pathology. We uncovered early alterations in RNA spatial organization and gene expression, including 23 genes showing altered localization without changes in abundance in the 5xFAD hippocampus. Using spatial expression analysis and single-cell neighborhood analysis, we identified cell-type- and region-specific molecular programs associated with synaptic function, neuroinflammation, and metabolic stress that differed between 5xFAD and WT mice. Spatial RNA velocity further revealed state differences influenced by local cell to cell interactions. Together, these results suggest that RNA positioning and transcriptional programs are perturbed at early disease stages. Finally, we provide the full super-resolution ExSeq dataset as an open resource for spatial and cell-type-specific analyses in early Alzheimers disease research. HighlightsO_LISuper-resolved transcriptomic profiling of the hippocampus at early disease stages C_LIO_LIIdentification of 23 genes with altered spatial localization without changes in abundance C_LIO_LIEarly alterations in single-cell neighborhood organization in the 5xFAD hippocampus C_LIO_LISpatial RNA velocity reveals cell-type-specific cell state differences shaped by cell-cell proximity C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/678295v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1b46cf2org.highwire.dtl.DTLVardef@5cc55eorg.highwire.dtl.DTLVardef@a1ade4org.highwire.dtl.DTLVardef@9f833e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Altered hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer's disease

Alzheimers disease (AD) is a devastating neurodegenerative disease that affects millions of older adults in the US and worldwide. Resting-state functional magnetic resonance imaging (rs-fMRI) has become a widely used neuroimaging tool to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of intrinsic neural signals using rs-fMRI, is thought to quantify the duration that neural information is stored in a local cortical circuit. The heterogeneity of the timescales is considered to be a basis of the functional hierarchy in the brain. In addition, INT captures an aspect of circuit dynamics relevant to excitation/inhibition (E/I) balance, which is thought to be broadly relevant for cognitive functions. Here we examined its relevance to AD. We used rs-fMRI data of 904 individuals from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. The subjects were divided into 4 groups based on their baseline and end-visit clinical status, which were cognitively normal (CN), stable MCI, Converter, and AD groups. Linear mixed effect model and pairwise comparison were implemented to investigate the large-scale hierarchical organization and local differences. We observed high similarities between AD and Converter groups. Specifically, among the eight identified ROIs with distinct INT alterations in AD, three ROIs (inferior temporal, caudate, pallidum areas) exhibit stable and significant alteration in AD converter. In addition, distinct INT related pathological changes in stable MCI and AD/Converter were found. For AD and Converter groups, neural information is stored for a longer time in lower hierarchical order areas, while higher levels of hierarchy seem to be preferentially impaired in stable MCI leading to a less pronounced hierarchical gradient effect. These results inform that the INT holds great potential as an additional measure for AD prediction, a stable biomarker for clinical diagnosis and an important therapeutic target in AD.

neuroscience↗

bioRxiv2021

Nitrogen uptake and macronutrients distribution in mango trees (cv. Keitt) under three N fertigation treatments

We assessed the effects of N concentration in the irrigation water on nutrient uptake and distribution in leaves and fruit of mango cv. Keitt grown in a lysimeter for four years. We applied three treatments: N1 - no N fertilization (less than 2 mg/L in the tap water); N2 - 10 mg/L N; N3 - 20 mg/L N. Deficient N conditions (N1) generated low vegetative yield, high fruit:leaf ratio, high photosynthetic activity, high leaf P and K concentrations, and high sugar content along with low acidity in the fruit. Excess N concentration (N3) induced vegetative growth, and reduced fruit yield and gas-exchange characteristics. The calculated annual nitrogen uptake heavily depended on the nitrogen supply (N1-26 g/tree; N2-196 g/tree; N3- 185 g/tree). Fruits were the major N sink being 0.82, 0.26 and 0.05 from the total annual N supplied. The N quantities accumulated in N1 fruits during the reproductive season (May-August), were above the N quantities supplied via fertigation, suggesting that N reserve in the vegetative tissues supplied the fruits high N demand. The finding shows the importance of adequate nitrogen supply to mango trees and the dangers of excessive fertilization.

physiology↗

bioRxiv2021

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗

bioRxiv2019

Spectrum of protein localization in proteomes captures evolutionary relation between species

The native subcellular localization or cellular compartment of a protein is the one in which it acts most often; it is one aspect of protein function. Do ten eukaryotic model organisms differ in their location spectrum, i.e. the fraction of its proteome in each of its seven major compartments? As experimental annotations of locations remain biased and incomplete, we need prediction methods to answer this question. To gauge the bias of prediction methods, we merged all available experimental annotations for the human proteome. In doing so, we found important values in both Swiss-Prot and the Human Protein Atlas (HPA). After systematic bias corrections, the complete but faulty prediction methods appeared to be more appropriate to compare location spectra between species than the incomplete more accurate experimental data. This work compared the location spectra for ten eukaryotes: Homo sapiens, Gorilla gorilla, Pan troglodytes, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Anopheles gambiae, Caenorhabitis elegans, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Overall, the predicted location spectra were similar. However, the detailed differences were significant enough to plot trees and 2D (PCA) maps relating the ten organisms using a simple Euclidean distance in seven states, corresponding to the seven studied localization classes. The relations based on the simple predicted location spectra captured aspects of cross-species comparisons usually revealed only by much more detailed evolutionary comparisons.

bioinformatics↗