Biology subjects

Korf, I.

Publications and source records attributed to Korf, I..

9 recordsLinked to original sources

bioRxiv2025

Female cortical cellular mosaicism underlies shared MeCP2 and PCB impacted gene pathways

Etiologies of neurodevelopmental disorders involve genes and environment however their interactions are understudied. Rett Syndrome (RTT) is an X-linked, dominant neurodevelopmental disorder caused by mutations in MECP2, encoding the epigenetic regulator methyl CpG binding protein. Epigenetic features of MECP2 expression due to X-linked cellular mosaicism and the variability in severity and timing of progression in RTT suggest interaction with environmental neurotoxicants such as lipophilic polychlorinated biphenyls (PCBs). To understand shared mechanisms, we exposed WT and Mecp2e1-/+ female mice to a human-relevant PCB mixture and dose, then performed single-nucleus 5 RNA-seq from cortex. We identified significant overlap in dysregulated genes and 71 shared pathways between the effects of PCB exposure and MeCP2 mutation, and co-mitigation of their transcriptional impacts. PCBs influenced the non-cell- autonomous transcriptional effects of MeCP2 mutations in wild-type-expressing neurons within the mosaic mutant female cortex in both mouse and human, suggesting that the interactions predominantly involve homeostatic gene networks.

systems biology↗

bioRxiv2024

Placental-brain axis in females detected within broadly impacted metabolic gene networks protects against prenatal PCB exposure

BackgroundNeurodevelopmental disorders have a strong male bias that is poorly understood. Placenta is a rich source of molecular information about environmental interactions with genetics (including biological sex), that affect the developing brain. We investigated placental-brain transcriptional responses in an established mouse model of prenatal exposure to a human-relevant mixture of polychlorinated biphenyls (PCBs). ResultsTo understand sex, tissue, and dosage effects in embryonic (E18) brain and placenta by RNAseq, we used weighted gene correlation network analysis (WGCNA) to create correlated gene networks that could be compared across sex or tissue. WGCNA revealed that expression within most correlated gene networks was significantly and strongly associated with PCB exposures, but frequently in opposite directions between male-female and placenta-brain comparisons. In both WGCNA and differentially expressed gene analyses, male brain showed more PCB-induced transcriptional changes than male placenta, but the reverse pattern was seen in females. Furthermore, non-monotonic dose responses to PCBs were observed in most gene networks but were most prominent in male brain. The transcriptomic effects of low dose PCB exposure were significantly reversed by dietary folic acid supplementation across both sexes, but these effects were strongest in female placenta. PCB-dysregulated and folic acid-reversed gene networks were commonly enriched in functions in metabolic pathways involved in energy usage and translation, with female-specific protective effects enriched in PPAR, thermogenesis, glycerolipids, and O-glycan biosynthesis, as opposed to toxicant responses in male brain. ConclusionsThe female protective effect in prenatal PCB exposures appears to be mediated by dose-dependent sex differences in transcriptional modulation of metabolism in placenta. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/603326v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@14d4936org.highwire.dtl.DTLVardef@101b87eorg.highwire.dtl.DTLVardef@1f787e6org.highwire.dtl.DTLVardef@1fb1b62_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression

Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time, and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were prominent and dynamic across disease progression of Mecp2e1 mutant females, indicating wild-type-expressing cells normalizing transcriptional homeostasis. These results improve understanding of RTT progression and treatment.

genetics↗

bioRxiv2024

Do organisms need an impact factor? Citations of key biological resources including model organisms reveal usage patterns and impact.

Research resources like transgenic animals and antibodies are the workhorses of biomedicine, enabling investigators to relatively easily study specific disease conditions. As key biological resources, transgenic animals and antibodies are often validated, maintained, and distributed from university based stock centers. As these centers heavily rely largely on grant funding, it is critical that they are cited by investigators so that usage can be tracked. However, unlike systems for tracking the impact of papers, the conventions and systems for tracking key resource usage and impact lag behind. Previous studies have shown that about 50% of the resources are not findable, making the studies they are supporting irreproducible, but also makes tracking resources difficult. The RRID project is filling this gap by working with journals and resource providers to improve citation practices and to track the usage of these key resources. Here, we reviewed 10 years of citation practices for five university based stock centers, characterizing each reference into two broad categories: findable (authors could use the RRID, stock number, or full name) and not findable (authors could use a nickname or a common name that is not unique to the resource). The data revealed that when stock centers asked their communities to cite resources by RRID, in addition to helping stock centers more easily track resource usage by increasing the number of RRID papers, authors shifted from citing resources predominantly by nickname ([~]50% of the time) to citing them by one of the findable categories ([~]85%) in a matter of several years. In the case of one stock center, the MMRRC, the improvement in findability is also associated with improvements in the adherence to NIH rigor criteria, as determined by a significant increase in the Rigor and Transparency Index for studies using MMRRC mice. From this data, it was not possible to determine whether outreach to authors or changes to stock center websites drove better citation practices, but findability of research resources and rigor adherence was improved.

bioinformatics↗

bioRxiv2022

SpecDB: A Relational Database for Archiving Biomolecular NMR Spectra Data

NMR is a valuable experimental tool in the structural biologists toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Protein NMR research groups produce large amounts of data, and there is renewed interest in organizing this data to train new, sophisticated machine learning architectures to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectra Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/480212v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11a8109org.highwire.dtl.DTLVardef@10da6a6org.highwire.dtl.DTLVardef@137ffb5org.highwire.dtl.DTLVardef@174c5b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

A comparative analysis of chromatin accessibility in cattle, pig, and mouse tissues

BackgroundAlthough considerable progress has been made towards annotating the noncoding portion of the human and mouse genomes, regulatory elements in other species, such as livestock, remain poorly characterized. This lack of functional annotation poses a substantial roadblock to agricultural research and diminishes the value of these species as model organisms. As active regulatory elements are typically characterized by chromatin accessibility, we implemented the Assay for Transposase Accessible Chromatin (ATAC-seq) to annotate and characterize regulatory elements in pigs and cattle, given a set of eight adult tissues. ResultsOverall, 306,304 and 273,594 active regulatory elements were identified in pig and cattle, respectively. 71,478 porcine and 47,454 bovine regulatory elements were highly tissue-specific and were correspondingly enriched for binding motifs of known tissue-specific transcription factors. However, in every tissue the most prevalent accessible motif corresponded to the insulator CTCF, suggesting pervasive involvement in 3-D chromatin organization. Taking advantage of a similar dataset in mouse, open chromatin in pig, cattle, and mice were compared, revealing that the conservation of regulatory elements, in terms of sequence identity and accessibility, was consistent with evolutionary distance; whereas pig and cattle shared about 20% of accessible sites, mice and ungulates only had about 10% of accessible sites in common. Furthermore, conservation of accessibility was more prevalent at promoters than at intergenic regions. ConclusionsThe lack of conserved accessibility at distal elements is consistent with rapid evolution of enhancers, and further emphasizes the need to annotate regulatory elements in individual species, rather than inferring elements based on homology. This atlas of chromatin accessibility in cattle and pig constitutes a substantial step towards annotating livestock genomes and dissecting the regulatory link between genome and phenome.

genomics↗

bioRxiv2019

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

BackgroundShotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX.\n\nResultsUsing metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene.\n\nConclusionsShotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

bioinformatics↗

bioRxiv2019

Laocoön: a tool for high-throughput automated cell counting

STRUCTURED ABSTRACTO_ST_ABSMotivationC_ST_ABSThere are current programs and plugins that automatically count the number of cells in a given image. However, many of these processes are not entirely automatic, as they require user input to specify a region of interest and are also frequently inaccurate. ResultsThis project presents laocoon, a Python package specifically designed to automatically and efficiently count the number of fluorescently-labelled cells in images. This package not only allows for reliable cell counting, but returns the proportion of cells in each cell cycle relative to all the cells in the DAPI channel, which is currently used for research purposes, but could ultimately be utilized for clinical purposes. Availability and ImplementationThis package, its corresponding execution instructions, and further information about the underlying algorithms, are currently available in the GitHub repository https://github.com/edukait/laocoon under the MIT license and can be run on the command terminal of any operating system. Alternatively, laocoon is available in the Python Package Index (PyPi), so the user can use the pip command to immediately download the package. Contactkaitlin.y.lim@gmail.com

bioinformatics↗

bioRxiv2017

SAMSA2: A standalone metatranscriptome analysis pipeline

BackgroundComplex microbial communities are an area of rapid growth in biology. Metatranscriptomics allows one to investigate the gene activity in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and the sequences must be compared with many references.\n\nResultsHere we present SAMSA2, an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution.\n\nConclusionsUsing publicly available example data, we demonstrate that SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large paired-end RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the specifics of any experiment.

bioinformatics↗