Biology subjects

Chalifa-Caspi, V.

Publications and source records attributed to Chalifa-Caspi, V..

5 recordsLinked to original sources

bioRxiv2025

Mid-aged mice rapidly normalize dysglycemia but aggravate obesity-induced hypothalamic and microglial changes upon dietary obesity reversal

ObjectiveObesity-induced-dysglycemia and hypothalamic-microgliosis coincide, but whether they remain linked upon obesity reversal, and what is the effect of age, remain unclear. Here we hypothesized that rapid normalization of dysglycemia upon obesity-reversal remains linked to microgliosis resolution, but differs between young and mid-aged mice. MethodsYoung (7w) and mid-aged (1y) mice were fed normal chow (NC) or high-fat diet (HFD,8w), then switched to NC (Rev,2w). ResultsCompared to young mice, NC-fed mid-aged mice were heavier and weight-stable, gained weight with HFD comparably, and lost less weight in Rev. HFD-induced dysglycemia was less severe in mid-aged compared to young mice, but similarly normalized by obesity-reversal. However, whole-hypothalamus RNA sequencing revealed 2,419 differentially expressed genes (DEGs) in mid-aged mice, [~]4-times more than in young mice, and in both age-groups [~]80% of DEGs obesity-induced changes were aggravated in Rev. Furthermore, compared with young mice, middle-aged mice showed greater obesity-induced microglial cyto-morphological changes in the arcuate nucleus (ARC), which associated with increased p-NF{kappa}B-(p-p65) nuclear staining. Only in middle-aged mice obesity-induced microglial changes were aggravated by obesity reversal, with cell volume correlating (Rho({rho})=0.691, p=0.001) with adipose tissue crown-like-structures. ConclusionsIn conclusion, rapid dysglycemia normalization is uncoupled to the resolution of hypothalamic microgliosis, more-so in mid-age.

cell biology↗

bioRxiv2023

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human subcutaneous and visceral adipose tissues

Deconvolution algorithms rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to extract information on the cell-types composition and proportions comprising a certain tissue. Adipose tissues cellular composition exhibits enormous plasticity in response to weight changes and high variance at different anatomical locations (depots). However, adipocytes - the functionally unique cell type of adipose tissue, are not amenable to scRNA-seq, a challenge recently met by applying single-nucleus RNA-sequencing (snRNA-seq). Here we aimed to develop a deconvolution method to estimate the cellular composition of human visceral and subcutaneous adipose tissues (hVAT and hSAT, respectively) using snRNA-seq to assess the true cell-type proportions. To correlate deconvolution-estimated cell-type proportions to true (snRNA-seq -derived) proportions, we analyzed seven hVAT and 5 hSAT samples by both bulk RNA-seq and snRNA-seq. snRNA-seq uncovered 15 distinct cell types in hVAT and 13 in hSAT. Deconvolution tools - SCDC, MuSiC, and Scaden exhibited low performance in estimating cell-type proportions (median |R|= 0.12 for estimated vs. true correlations). Notably, estimation accuracy somewhat improved by decreasing the number of cell-types groups, which nevertheless remained low (|R|<0.42). We therefore developed sNuConv, a novel method that employs Scaden, a deep-learning tool, trained using snRNA-seq - based data corrected by i. snRNA-seq/bulk RNA-seq highly-correlated genes, ii. corrected estimated cell-type proportions based on individual cell-type regression models. Applying sNuConv on our bulk RNA-seq data resulted in cell-type proportion estimation accuracy with median R=0.93 (range:0.76-0.97) for hVAT, and median R=0.95 (range:0.92-0.98) for hSAT. The resulting model was depot-specific, reflecting depot-differences in gene expression patterns. Thus, we present sNuConv, a novel, AI-based, method to deduce the cellular landscape of hVAT and hSAT from bulk RNA-seq data, providing proof-of-concept for producing validated deconvolution algorithms for tissues un-amenable to single-cell RNA sequencing.

bioinformatics↗

bioRxiv2021

Positive Selection and Heat-Response Transcriptomes Reveal Adaptive Features of the Arabidopsis Desert Relative, Anastatica hierochuntica

O_LIPlant adaptation to a desert environment and its endemic heat stress is poorly understood at the molecular level. The naturally heat-tolerant Brassicaceae species Anastatica hierochuntica is an ideal extremophyte model to identify genetic adaptations that have evolved to allow plants to tolerate heat stress and thrive in deserts. C_LIO_LIWe generated an A. hierochuntica reference transcriptome and pinpointed extremophyte adaptations by comparing Arabidopsis thaliana and A. hierochuntica transcriptome responses to heat and identifying positively selected genes in A. hierochuntica. C_LIO_LIThe two species exhibit similar transcriptome adjustment in response to heat and the A. hierochuntica transcriptome does not exist in a constitutive heat "stress-ready" state. Furthermore, the A. hierochuntica global transcriptome as well as heat-responsive orthologs, display a lower basal and higher heat-induced expression than in A. thaliana. Genes positively selected in multiple extremophytes are associated with stomatal opening, nutrient acquisition, and UV-B induced DNA repair while those unique to A. hierochuntica are consistent with its photoperiod-insensitive, early-flowering phenotype. C_LIO_LIWe suggest that evolution of a flexible transcriptome confers the ability to quickly react to extreme diurnal temperature fluctuations characteristic of a desert environment while positive selection of genes involved in stress tolerance and early flowering could facilitate an opportunistic desert lifestyle. C_LI

plant biology↗

bioRxiv2019

Differential network analysis of human tissue interactomes highlights tissue-selective processes and genetic disorder genes

MotivationDifferential network analysis, designed to highlight interaction changes between conditions, is an important paradigm in network biology. However, network analysis methods have been typically designed to compare between few conditions, were rarely applied to protein interaction networks (interactomes). Moreover, large-scale benchmarks for their evaluation have been lacking.\n\nResultsHere, we assess five network analysis methods by applying them to 34 human tissues interactomes. For this, we created a manually-curated benchmark of 6,499 tissue-specific, gene ontology biological processes, and analyzed the ability of each method to expose these tissue-process associations. The four differential network analysis methods outperformed the non-differential, expression-based method (AUCs of 0.82-0.9 versus 0.69, respectively). We then created another benchmark, of 1,527 tissue-specific disease cases, and analyzed the ability of differential network analysis methods to highlight additional disease-related genes. Compared to a non-differential subnetworks surrounding a known disease-causing gene, the extremely-differential subnetwork (top 1%) was significantly enriched for additional disease-causing genes in 18.6% of the cases (p[≤]10e-3). In 5/10 tissues tested, including Muscle, nerve and heart tissues (p = 2.54E-05, 2.71E-04, 3.63E-19), such enrichments were highly significant.\n\nSummaryAltogether, our study demonstrates that differential network analysis of human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. Moreover, it offers expansive manually-curated datasets of tissue-selective processes and diseases that could serve for benchmark and for analyses in many other studies.\n\nContactestiyl@bgu.ac.il

bioinformatics↗

bioRxiv2017

NeatSeq-Flow: A Lightweight Software for Efficient Execution of High Throughput Sequencing Workflows

Nowadays, it has become almost a necessity for many biologists to execute bioinformatics workflows (WFs) as part of their research. However, most WF-management software packages require for their operation at least some programming expertise. Here we describe NeatSeq-Flow, a platform that enables users with no programming knowledge to design and execute complex high throughput sequencing WFs. This is achieved by using a compendium of pre-built modules as well as a generic module, both do not require programming expertise. Nonetheless, NeatSeq-Flow retains the flexibility to generate sophisticated WF modules using templates and only basic Python programming abilities. NeatSeq-Flow is designed to enable easy sharing of WFs and modules by conceptually separating modules, WF design, sample information and execution. Moreover, NeatSeq-Flow works hand in hand with CONDA environments for easy installation of the WFs analysis programs in one go. NeatSeq-Flow enables efficient WF execution on computer clusters by parallelizing on both samples and WF steps. NeatSeq-Flow operates by shell-script generation; thus it allows full transparency of the WF process. NeatSeq-Flow offers real-time WF execution monitoring, detailed documentation and self-sustaining WF backups for reproducibility. All of these features make NeatSeq-Flow an easy-to-use WF platform while not compromising for flexibility, reproducibility, transparency and efficiency.\n\nAvailabilityhttp://neatseq-flow.readthedocs.io/en/latest/\n\nContactsklarz@bgu.ac.il

bioinformatics↗