Biology subjects

Chavez, E.

Publications and source records attributed to Chavez, E..

8 recordsLinked to original sources

bioRxiv2025

MuFIX - Enabling Combinations of Concurrent Optogenetics and Lock-in Amplification Fiber Photometry via Removal of Optogenetic Stimulation Crosstalk

Simultaneous fiber photometry and optogenetics is a powerful emerging technique for precisely studying the interactions of neuronal brain networks. However, spectral overlap between photometry and optogenetic components has severely limited the application of an all-optical approach. Due to spectral overlap, light from optogenetic stimulation saturates the photosensor and occludes photometry fluorescence, which is especially problematic in physically smaller model organism brains like mice. Here, we demonstrate the Multi-Frequency Interpolation X- talk removal algorithm (MuFIX, or {micro}FIX) for recovering crosstalk-contaminated photometry responses recorded with lock-in amplification. {micro}FIX exploits multi-frequency lock-in amplification by modeling the remaining uncontaminated data to interpolate across crosstalk- affected segments (R2 [~] 1.0); we found that this approach accurately recovers the original photometry response after demodulation (Pearsons r [~] 1.0). When applied to crosstalk- contaminated data, {micro}FIX recovered a photometry response closely resembling the dynamics of non-crosstalk photometry recorded simultaneously. Upon further verification using simulated and empirical data, we demonstrated that {micro}FIX reproduces any signal that underwent simulated crosstalk contamination (r [~] 1.0). We believe adopting {micro}FIX will enable experimental designs using simultaneous fiber photometry and optogenetics that were previously not feasible due to crosstalk.

neuroscience↗

bioRxiv2023

Complete substitution with modified nucleotides suppresses the early interferon response and increases the potency of self-amplifying RNA

Self-amplifying RNA (saRNA) will revolutionize vaccines and in situ therapeutics by enabling protein expression for longer duration at lower doses. However, a major barrier to saRNA efficacy is the potent early interferon response triggered upon cellular entry, resulting in saRNA degradation and translational inhibition. Substitution of mRNA with modified nucleotides (modNTPs), such as N1-methylpseudouridine (N1m{Psi}), reduce the interferon response and enhance expression levels. Multiple attempts to use modNTPs in saRNA have been unsuccessful, leading to the conclusion that modNTPs are incompatible with saRNA, thus hindering further development. Here, contrary to the common dogma in the field, we identify multiple modNTPs that when incorporated into saRNA at 100% substitution confer immune evasion and enhance expression potency. Transfection efficiency enhances by roughly an order of magnitude in difficult to transfect cell types compared to unmodified saRNA, and interferon production reduces by >8 fold compared to unmodified saRNA in human peripheral blood mononuclear cells (PBMCs). Furthermore, we demonstrate expression of viral antigens in vitro and observe significant protection against lethal challenge with a mouse-adapted SARS-CoV-2 strain in vivo. A modified saRNA vaccine, at 100-fold lower dose than a modified mRNA vaccine, results in a statistically improved performance to unmodified saRNA and statistically equivalent performance to modified mRNA. This discovery considerably broadens the potential scope of self-amplifying RNA, enabling entry into previously impossible cell types, as well as the potential to apply saRNA technology to non-vaccine modalities such as cell therapy and protein replacement.

bioengineering↗

bioRxiv2022

Intermittent hypoxia therapy engages multiple longevity pathways to double lifespan in C.elegans

Genetic activation of the hypoxia response robustly extends lifespan in C. elegans, while environmental hypoxia shows more limited benefit. Here we describe an intermittent hypoxia therapy (IHT) able to double the lifespan of wildtype worms. The lifespan extension observed in IHT does not require HIF-1 but is partially blocked by loss of DAF-16/FOXO. RNAseq analysis shows that IHT triggers a transcriptional state distinct from continuous hypoxia and affects down-stream genes of multiple longevity pathways. We performed a temperature sensitive forward genetic screen to isolate mutants with delayed nuclear localization of DAF-16 in response to IHT and suppression of IHT longevity. One of these mutations mapped to the enzyme Inositol Polyphosphate MultiKinase (IPMK-1). ipmk-1 mutants, like daf-16 mutants, partially suppress the benefits of IHT, while other effectors of phosphatidyl inositol signaling pathways (PLC{beta}4, IPPK, Go/i) more robustly suppress IHT longevity. One-Sentence SummaryIntermittent hypoxia therapy is frequency dependent, HIF independent, and requires FOXO, PLC{beta}, Go/i, IPMK, and IPPK.

physiology↗

bioRxiv2021

Macrophages govern antiviral responses in human lung tissues protected from SARS-CoV-2 infection

The majority of SARS-CoV-2 infections among healthy individuals result in asymptomatic to mild disease. However, the immunological mechanisms defining effective lung tissue protection from SARS-CoV-2 infection remain elusive. Unlike mice solely engrafted with human fetal lung xenograft (fLX), mice co-engrafted with fLX and a myeloid-enhanced human immune system (HNFL mice) are protected against SARS-CoV-2 infection, severe inflammation, and histopathology. Effective control of viral infection in HNFL mice associated with significant macrophage infiltration, and the induction of a potent macrophage-mediated interferon response. The pronounced upregulation of the USP18-ISG15 axis (a negative regulator of IFN responses), by macrophages was unique to HNFL mice and represented a prominent correlate of reduced inflammation and histopathology. Altogether, our work shed light on unique cellular and molecular correlates of lung tissue protection during SARS-CoV-2 infection, and underscores macrophage IFN responses as prime targets for developing immunotherapies against coronavirus respiratory diseases. HIGHLIGHTSO_LIMice engrafted with human fetal lung xenografts (fLX-mice) are highly susceptible to SARS-CoV-2. C_LIO_LICo-engraftment with a human myeloid-enriched immune system protected fLX-mice against infection. C_LIO_LITissue protection was defined by a potent and well-balanced antiviral response mediated by infiltrating macrophages. C_LIO_LIProtective IFN response was dominated by the upregulation of the USP18-ISG15 axis. C_LI

microbiology↗

bioRxiv2019

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

BackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous - i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity. ResultsOur workflow utilizes PROXIMAL - a tool that uses reactant-product transformation patterns from the KEGG database - to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB). ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

systems biology↗

bioRxiv2019

Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via \"mapping\" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

bioinformatics↗

bioRxiv2018

Resource: Scalable whole genome sequencing of 40,000 single cells identifies stochastic aneuploidies, genome replication states and clonal repertoires

Essential features of cancer tissue cellular heterogeneity such as negatively selected genome topologies, sub-clonal mutation patterns and genome replication states can only effectively be studied by sequencing single-cell genomes at scale and high fidelity. Using an amplification-free single-cell genome sequencing approach implemented on commodity hardware (DLP+) coupled with a cloud-based computational platform, we define a resource of 40,000 single-cell genomes characterized by their genome states, across a wide range of tissue types and conditions. We show that shallow sequencing across thousands of genomes permits reconstruction of clonal genomes to single nucleotide resolution through aggregation analysis of cells sharing higher order genome structure. From large-scale population analysis over thousands of cells, we identify rare cells exhibiting mitotic mis-segregation of whole chromosomes. We observe that tissue derived scWGS libraries exhibit lower rates of whole chromosome anueploidy than cell lines, and loss of p53 results in a shift in event type, but not overall prevalence in breast epithelium. Finally, we demonstrate that the replication states of genomes can be identified, allowing the number and proportion of replicating cells, as well as the chromosomal pattern of replication to be unambiguously identified in single-cell genome sequencing experiments. The combined annotated resource and approach provide a re-implementable large scale platform for studying lineages and tissue heterogeneity.

genomics↗

bioRxiv2017

The dental calculus metabolome in modern and historic samples

IntroductionDental calculus is a mineralized microbial dental plaque biofilm that forms throughout life by precipitation of salivary calcium salts. Successive cycles of dental plaque growth and calcification make it an unusually well-preserved, long-term record of host-microbial interaction in the archaeological record. Recent studies have confirmed the survival of authentic ancient DNA and proteins within historic and prehistoric dental calculus, making it a promising substrate for investigating oral microbiome evolution via direct measurement and comparison of modern and ancient specimens.\n\nObjectiveWe present the first comprehensive characterization of the human dental calculus metabolome using a multi-platform approach.\n\nMethodsUltra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) quantified 285 metabolites in modern and historic (200 years old) dental calculus, including metabolites of drug and dietary origin. A subset of historic samples was additionally analyzed by high-resolution gas chromatography-MS (GC-MS) and UPLC- MS/MS for further characterization of polar metabolites and lipids, respectively. Metabolite profiles of modern and historic calculus were compared to identify patterns of persistence and loss.\n\nResultsDipeptides, free amino acids, free nucleotides, and carbohydrates substantially decrease in abundance and ubiquity in archaeological samples, with some exceptions. Lipids generally persist, and saturated and mono-unsaturated medium and long chain fatty acids appear to be well-preserved, while metabolic derivatives related to oxidation and chemical degradation are found at higher levels in archaeological dental calculus than fresh samples.\n\nConclusionsThe results of this study indicate that certain metabolite classes have higher potential for recovery over long time scales and may serve as appropriate targets for oral microbiome evolutionary studies.

microbiology↗