Biology subjects

Litichevskiy, L.

Publications and source records attributed to Litichevskiy, L..

6 recordsLinked to original sources

bioRxiv2025

Dietary manipulation of intestinal microbes prolongs survival in a mouse model of Hirschsprung disease

Enterocolitis is a common and potentially deadly manifestation of Hirschsprung disease (HSCR) but disease mechanisms remain poorly defined. Unexpectedly, we discovered that diet can dramatically affect the lifespan of a HSCR mouse model (Piebald lethal, sl/sl) where affected animals die from HAEC complications. In the sl/sl model, diet alters gut microbes and metabolites, leading to changes in colon epithelial gene expression and epithelial oxygen levels known to influence colitis severity. Our findings demonstrate unrecognized similarity between HAEC and other types of colitis and suggest dietary manipulation could be a valuable therapeutic strategy for people with HSCR. AbstractHirschsprung disease (HSCR) is a birth defect where enteric nervous system (ENS) is absent from distal bowel. Bowel lacking ENS fails to relax, causing partial obstruction. Affected children often have "Hirschsprung disease associated enterocolitis" (HAEC), which predisposes to sepsis. We discovered survival of Piebald lethal (sl/sl) mice, a well-established HSCR model with HAEC, is markedly altered by two distinct standard chow diets. A "Protective" diet increased fecal butyrate/isobutyrate and enhanced production of gut epithelial antimicrobial peptides in proximal colon. In contrast, "Detrimental" diet-fed sl/sl had abnormal appearing distal colon epithelium mitochondria, reduced epithelial mRNA involved in oxidative phosphorylation, and elevated epithelial oxygen that fostered growth of inflammation-associated Enterobacteriaceae. Accordingly, selective depletion of Enterobacteriaceae with sodium tungstate prolonged sl/sl survival. Our results provide the first strong evidence that diet modifies survival in a HSCR mouse model, without altering length of distal colon lacking ENS. HighlightsO_LITwo different standard mouse diets alter survival in the Piebald lethal (sl/sl) mouse model of Hirschsprung disease, without impacting extent of distal colon aganglionosis (the region lacking ENS). C_LIO_LIPiebald lethal mice fed the "Detrimental" diet had many changes in colon epithelial transcriptome including decreased mRNA for antimicrobial peptides and genes involved in oxidative phosphorylation. Detrimental diet fed sl/sl also had aberrant-appearing mitochondria in distal colon epithelium, with elevated epithelial oxygen that drives lethal Enterobacteriaceae overgrowth via aerobic respiration. C_LIO_LIElimination of Enterobacteriaceae with antibiotics or sodium tungstate improves survival of Piebald lethal fed the "Detrimental diet". C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/637436v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@d95251org.highwire.dtl.DTLVardef@1ab58caorg.highwire.dtl.DTLVardef@5260b0org.highwire.dtl.DTLVardef@49ce42_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Interactions between the gut microbiome, dietary restriction, and aging in genetically diverse mice

The intestinal microbiome changes with age, but the causes and consequences of microbiome aging remain unclear. Furthermore, the gut microbiome has been proposed to mediate the benefit of lifespan- extending interventions such as dietary restriction, but this hypothesis warrants further exploration. Here, by analyzing 2997 metagenomes collected longitudinally from 913 deeply phenotyped, genetically diverse mice, we provide new insights into the interplay between the microbiome, aging, dietary restriction, host genetics, and a wide range of health parameters. First, we find that microbiome uniqueness increases with age across datasets and species. Moreover, age-associated changes are better explained by cumulative exposure to stochastic events (neutral theory) than by the influence of an aging host (selection theory). Second, we unexpectedly find that the majority of microbiome features are significantly heritable and that the amount of variation explained by host genetics is as large as that of aging and dietary restriction. Third, we find that the intensity of dietary restriction parallels the extent of microbiome changes and that dietary restriction does not rejuvenate the microbiome. Lastly, we find that the microbiome is significantly associated with multiple health parameters -- including body composition, immune parameters, and frailty -- but not with lifespan. In summary, this large and multifaceted study sheds light on the factors influencing the microbiome and aspects of host physiology modulated by the microbiome.

microbiology↗

bioRxiv2023

Regulators of health and lifespan extension in genetically diverse mice on dietary restriction

Caloric restriction (CR) delays aging and extends healthy lifespan in multiple species. Alternative forms of dietary restriction (DR) such as intermittent fasting (IF) have drawn significant interest as a more sustainable regimen, but the landscape of longevity-promoting dietary interventions remains largely unexplored. Identifying the most robust, efficacious, and experimentally tractable modes of DR is key to better understanding and implementing effective longevity interventions for human healthspan. To that end, we have performed an extensive assessment of DR interventions, investigating the effects of graded levels of CR (20% and 40%) and IF (1 day and 2 days of fasting per week) on the health and survival of 960 genetically diverse female mice. All interventions extended lifespan, although only CR significantly reduced the mortality doubling time. Notably, IF did not extend lifespan in mice with high pre-intervention bodyweight. We carried out extensive phenotyping to determine the health effects of long-term DR and to better understand the mechanisms driving within-diet heterogeneity in lifespan. The top within-diet predictor of lifespan was the ability of mice to maintain bodyweight through periods of handling, an indicator of stress resilience. Additional predictors of long lifespan include specific changes in immune cells, red blood cell distribution width (RDW), and retention of adiposity in late life. We found that lifespan is heritable (h2 = 0.24), and that genetic background has a larger influence on lifespan than dietary interventions. We identified a significant association for lifespan and RDW on chromosome 18 that explained 4.3% of the diet-adjusted variation in lifespan. Diet-induced changes on metabolic traits, although beneficial, were relatively poor predictors of lifespan, arguing against the long-standing notion that DR works by counteracting the negative effects of obesity. These findings indicate that improving health and extending lifespan are not synonymous and that metabolic parameters may be inappropriate endpoints for evaluating aging interventions in preclinical models and clinical trials.

genetics↗

bioRxiv2021

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity, but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOA) and gene targets, we find that the two assays provide a partially shared, but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

bioinformatics↗

bioRxiv2017

The GCTx format and cmap{Py, R, M} packages: resources for the optimized storage and integrated traversal of dense matrices of data and annotations

MotivationComputational analysis of datasets generated by treating cells with pharmacological and genetic perturbagens has proven useful for the discovery of functional relationships. Facilitated by technological improvements, perturbational datasets have grown in recent years to include millions of experiments. While initial studies, such as our work on Connectivity Map, used gene expression readouts, recent studies from the NIH LINCS consortium have expanded to a more diverse set of molecular readouts, including proteomic and cell morphological signatures. Sharing these diverse data creates many opportunities for research and discovery, but the unprecedented size of data generated and the complex metadata associated with experiments have also created fundamental technical challenges regarding data storage and cross-assay integration.\n\nResultsWe present the GCTx file format and a suite of open-source packages for the efficient storage, serialization, and analysis of dense two-dimensional matrices. The utility of this format is not just theoretical; we have extensively used the format in the Connectivity Map to assemble and share massive data sets comprising 1.7 million experiments. We anticipate that the generalizability of the GCTx format, paired with code libraries that we provide, will stimulate wider adoption and lower barriers for integrated cross-assay analysis and algorithm development.\n\nAvailabilitySoftware packages (available in Matlab, Python, and R) are freely available at https://github.com/cmap\n\nSupplementary informationSupplementary information is available at clue.io/code.\n\nContactoana@broadinstitute.org

bioinformatics↗

bioRxiv2017

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗