Biology subjects

Kafri, T.

Publications and source records attributed to Kafri, T..

8 recordsLinked to original sources

bioRxiv2025

MITScan: Score-Based Genome-Wide Association Analysis of the Microbiome and Host Transcriptome

Microbiome research explores how microbial communities interact with the human body to influence health and well-being, offering insights for advancing personalized medicine and targeted treatments. Microbiome profiles can be measured, for example, by 16S rRNA sequencing, where sequencing reads are clustered into genus-or species-level operational taxonomic units (OTUs), yielding count-based abundance data. In addition, these data are often characterized by sparsity with extra zeros. Emerging evidence suggests that imbalances in microbial composition, potentially regulated by host gene expression, have been associated with various diseases. Investigating the associations between the microbiome and host transcriptome can help uncover the mechanisms underlying human health and disease. Current existing approaches for evaluating their associations largely rely on Pearson or Spearman correlations, ignoring the fact that microbiome data are sparse count data, leading to potentially biased results. To overcome this issue, we present MITScan, a genome-wide MI crobiome-host T ranscriptome Score-based association an alysis, employing zero-inflated negative binomial models to accommodate extra zeros commonly observed in microbiome data. To address the large number of paired association tests arising from two high-dimensional omics datasets, we utilize score tests and matrix calculations for computational efficiency. We further apply an empirical permutation method with genomic control to effectively control the family-wise error rate in studies with small sample sizes. With real datasets, we demonstrate that MITScan achieves computational gains of three orders of magnitude compared to commonly used Wald or t tests, while identifying similar significant OTU-gene pairs. The MITScan R package is accessible on GitHub at https://github.com/yajing-hao/MITScan.

genomics↗

bioRxiv2025

Unbiased genetics identifies a glutamatergic signaling network as a mediator of daily sleep patterns.

Sleep is a fundamental, conserved behavior important for survival. In many species, sleep behavior is controlled by poorly understood interactions between a system of circadian rhythms (CR), which promote sleep at ecologically appropriate times, and a homeostatic sleep drive that accumulates with time awake. The CR is a cellular phenomenon, driven by molecular oscillations of "clock genes" in nearly all cells. Emerging evidence indicates neuronal synapses are a key locus for the accumulation and resolution of sleep need, supporting a cellular basis of sleep need. Indeed, efforts to understand the genetic basis of sleep need identified Homer1a, a regulator of synapse homeostasis. To develop further insight into the genetic basis of sleep regulation, we measured daily sleep patterns in genetically diverse strains of mice from the Collaborative Cross. Strains with 1) highly consolidated light-phase sleep, or 2) fragmented, arrhythmic sleep, were identified for genetic analysis using quantitative trait loci (QTL) mapping. Excitingly, in F2 hybrids, 19 of 32 metrics of sleep and circadian behavior mapped to a narrow QTL containing GRM5, a postsynaptic glutamate receptor and binding partner of Homer1a, and GCPII, an astrocytic enzyme that regulates NAAG, a peptide agonist for the presynaptic/astrocytic glutamate receptor GRM3. Collectively, these genes form a coordinated glutamatergic signaling network across the tri-partite synapse. Pharmacology targeting GRM5, GCPII, and GRM3 strongly modulated sleep, functionally validating them as sleep-regulating genes. Our findings support a model in which synapses act as a cellular site for integration of circadian and sleep-need signals to regulate daily sleep patterns. Significance StatementSleep is a fundamental pillar of human health. Restorative sleep is supported by circadian rhythms (CR). Sleep and CR disruptions are prevalent in neuropsychiatric conditions and are increasingly understood as causal drivers of altered brain function and behavior. Therefore, sleep and CR are attractive therapeutic targets to improve health. However, the basic biological mechanisms that couple our need to sleep with the CR are not understood. Genetic discovery using diverse strains of mice identifies a glutamatergic signaling network coordinated across the synapse as an underlying genetic basis for daily sleep patterns. This work builds upon a growing body showing that excitatory synapses are a cellular site for the resolution of sleep need and interaction with the CR.

neuroscience↗

bioRxiv2025

PETScan: Score-Based Genome-Wide Association Analysis of RNA-Seq and ATAC-Seq Data

High-dimensional sequencing data, such as RNA-Seq for gene expression and ATAC-Seq for chromatin accessibility, are widely used in studying systems biology. Accessible chromatin allows transcription factors and regulatory elements to bind to DNA, thereby regulating transcription through the activation or repression of target genes. The association analysis of RNA-Seq and ATAC-Seq data provides insights into gene regulatory mechanisms. Most existing analytic tools exclusively focus on cis-associations, despite regulatory elements being able to physically interact with distant target genes. Furthermore, conventional approaches often utilize Pearson or Spearman correlations, which ignore the count-based nature of RNA-Seq data. To address these limitations, we introduce PETScan, a computationally efficient genome-wide PEak-Transcript Score-based association analysis, utilizing negative binomial models to better accommodate RNA-Seq data. We leverage score tests and matrix calculations for improved computational efficiency, and combine an empirical permutation method with genomic control to ensure valid p-value calculations in studies with limited sample sizes. In real-world datasets, PETScan achieved three orders of magnitude faster than Wald tests, while identifying similar significant gene-peak pairs. The PETScan R package is available on GitHub at https://github.com/yajing-hao/PETScan.

genomics↗

bioRxiv2025

Muscle Cathepsin B treatment improves behavioral and neurogenic deficits in a mouse model of Alzheimer's Disease

Muscle secretes factors during exercise that enhance cognition. Myokine Cathepsin B (Ctsb) is linked to memory function, but its role in neurodegenerative disease is unclear. Here we show that AAV-vector-mediated Ctsb overexpression in skeletal muscle in an Alzheimers Disease (AD) mouse model (APP/PS1), improves motor coordination, memory function and adult hippocampal neurogenesis, while plaque pathology and neuroinflammation remain unchanged. Additionally, in AD mice, Ctsb treatment modifies hippocampal, muscle and plasma proteomic profiles to resemble that of wildtype controls. Conversely, in wildtype mice, Ctsb expression causes memory deficits and results in protein profiles across tissues that are comparable to AD control mice. In AD mice, Ctsb treatment increases the abundance of hippocampal proteins involved in mRNA metabolism and protein synthesis, including those relevant to adult hippocampal neurogenesis and memory function. Furthermore, Ctsb treatment enhances plasma metabolic and mitochondrial processes, and reduces inflammatory responses. In muscle, Ctsb expression elevates protein translation in AD mice, whereas in wildtype mice mitochondrial proteins decrease. Overall, the biological changes in the treatment groups are consistent with effects on memory function. Thus, skeletal muscle Ctsb application has potential as an AD therapeutic intervention.

neuroscience↗

bioRxiv2024

Analysis of hepatic lentiviral vector transduction; implications on preclinical studies and clinical gene therapy protocols.

Lentiviral vector-transduced T-cells were approved by the FDA as gene therapy anti-cancer medications. Little is known about the host genetic variation effects on the safety and efficacy of the lentiviral vector gene delivery system. To narrow this knowledge-gap, we characterized hepatic gene delivery by lentiviral vectors across the Collaborative Cross (CC) mouse genetic reference population. For 24 weeks, we periodically measured hepatic luciferase expression from lentiviral vectors in 41 CC mouse strains. Hepatic and splenic vector copy numbers were determined. We report that CC mouse strains showed highly diverse outcomes following lentiviral gene delivery. For the first time, moderate correlation between mouse strain-specific sleeping patterns and transduction efficiency was observed. We associated two quantitative trait loci (QTLs) with intra-strain variations in transduction phenotypes, which mechanistically relates to the phenomenon of metastable epialleles. An additional QTL was associated with the kinetics of hepatic transgene expression. Genes comprised in the above QTLs are potential targets to personalize gene therapy protocols. Importantly, we identified two mouse strains that open new directions in characterizing continuous viral vector silencing and HIV latency. Our findings suggest that wide-range patient-specific outcomes of viral vector-based gene therapy should be expected. Thus, novel escalating dose-based clinical protocols should be considered.

genetics↗

bioRxiv2024

The Updated Mouse Universal Genotyping Array Bioinformatic Pipeline Improves Genetic QC in Laboratory Mice

The MiniMUGA genotyping array is a popular tool for genetic QC of laboratory mice and genotyping of samples from most types of experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve arrays performance and the associated reports usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA in 2020. Here we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for inbred strains and increase the number of constructs that can reliably be detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have made changes to the layout of the report, to simplify the interpretation and completeness of the analysis and added a table summarizing the ideogram. We believe that these changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research.

genetics↗

bioRxiv2022

Inadvertent Transfer of Murine VL30 Retrotransposons to CAR-T Cells.

For more than a decade genetically engineered autologous T-cells have been successfully employed as immunotherapy drugs for patients with incurable blood cancers. The active component in some of these game-changing medicines are autologous T-cells that express viral vector-delivered chimeric antigen receptors (CARs), which specifically target proteins that are preferentially expressed on cancer cells. Some of these therapeutic CAR expressing T-cells (CAR-Ts) are engineered via transduction with {gamma}-retroviral vectors ({gamma}-RVVs) produced in a stable producer cell line that was derived from murine PG13 packaging cells (ATCC CRL-10686). Earlier studies reported on the co-packaging of murine virus-like 30S RNA (VL30) genomes with {gamma}-retroviral vectors generated in murine stable packaging cells. In an earlier study VL30 mRNA was found to enhance the metastatic potential of human melanoma cells. These findings raise biosafety concerns regarding the possibility that therapeutic CAR-Ts have been inadvertently contaminated with potentially oncogenic VL30 retrotransposons. In this study, we demonstrated the presence of infectious VL30 particles in PG13 cells conditioned media and observed the ability of these particles to deliver transcriptionally active VL30 genomes to human cells. Notably, VL30 genomes packaged by HIV-1-based vector particles transduced naive human cells in culture. Furthermore, we detected transfer and expression of VL30 genomes in clinical-grade CAR-Ts generated by transduction with PG13 cells-derived {gamma}-retroviral vectors. Our findings raise biosafety concerns regarding the use of murine packaging cell lines in ongoing clinical applications.

microbiology↗

bioRxiv2020

Content and performance of the MiniMUGA genotyping array, a new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well annotated genome, wealth of genetic resources and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost effective, informative and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole genome sequencing. Here we describe the content and performance of a new Mouse Universal Genotyping Array (MUGA). MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: 1) chromosomal sex determination, 2) discrimination between substrains from multiple commercial vendors, 3) diagnostic SNPs for popular laboratory strains, 4) detection of constructs used in genetically engineered mice, and 5) an easy to interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA we genotyped 6,899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived and recombinant inbred lines. Here we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, the extension of the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. There is preliminary but striking evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the main stock for a significant action of the genotyped inbred samples. We conclude that MiniMUGA is a valuable platform for genetic QC and important new tool to the increase rigor and reproducibility of mouse research.

genetics↗