Establishing Informative Prior for Gene Expression Variance from Public Databases

Identifying differential expressed genes across various conditions or genotypes is the most typical approach to studying the regulation of gene expression. An estimate of gene-specific variance is often needed for the assessment of statistical significance in most differential expression (DE) detection methods, including linear models (e.g., for transformed and normalized microarray data) and generalized linear models (e.g., for count data in RNAseq). Due to a common limit in sample size, the variance estimate is often unstable in small experiments. Shrinkage estimates using empirical Bayes methods have proven useful in improving the variance estimate, hence improving the detection of DE. The most widely used empirical Bayes methods borrow information across genes within the same experiments. In these methods, genes are considered exchangeable or exchangeable conditioning on expression level. We propose, with the increasing accumulation of expression data, borrowing information from historical data on the same gene can provide better estimate of gene-specific variance, thus further improve DE detection. Specifically, we show that the variation of gene expression is truly gene-specific and reproducible between different experiments. We present a new method to establish informative gene-specific prior on the variance of expression using existing public data, and illustrate how to shrink the variance estimate and detect DE. We demonstrate improvement in DE detection under our strategy compared to leading DE detection methods.     

Fast- or Slow-inactivated State Preference of Na+ Channel Inhibitors: A Simulation and Experimental Study

Sodium channels are one of the most intensively studied drug targets. Sodium channel inhibitors (e.g., local anesthetics, anticonvulsants, antiarrhythmics and analgesics) exert their effect by stabilizing an inactivated conformation of the channels. Besides the fast-inactivated conformation, sodium channels have several distinct slow-inactivated conformational states. Stabilization of a slow-inactivated state has been proposed to be advantageous for certain therapeutic applications. Special voltage protocols are used to evoke slow inactivation of sodium channels. It is assumed that efficacy of a drug in these protocols indicates slow-inactivated state preference. We tested this assumption in simulations using four prototypical drug inhibitory mechanisms (fast or slow-inactivated state preference, with either fast or slow binding kinetics) and a kinetic model for sodium channels. Unexpectedly, we found that efficacy in these protocols (e.g., a shift of the “steady-state slow inactivation curve”), was not a reliable indicator of slow-inactivated state preference. Slowly associating fast-inactivated state-preferring drugs were indistinguishable from slow-inactivated state-preferring drugs. On the other hand, fast- and slow-inactivated state-preferring drugs tended to preferentially affect onset and recovery, respectively. The robustness of these observations was verified: i) by performing a Monte Carlo study on the effects of randomly modifying model parameters, ii) by testing the same drugs in a fundamentally different model and iii) by an analysis of the effect of systematically changing drug-specific parameters. In patch clamp electrophysiology experiments we tested five sodium channel inhibitor drugs on native sodium channels of cultured hippocampal neurons. For lidocaine, phenytoin and carbamazepine our data indicate a preference for the fast-inactivated state, while the results for fluoxetine and desipramine are inconclusive. We suggest that conclusions based on voltage protocols that are used to detect slow-inactivated state preference are unreliable and should be re-evaluated.     

Ultrasensitive isolation,identification and quantification of DNA–protein adducts by ELISA-based RADAR assay

Enzymes that form transient DNA–protein covalent complexes are targets for several potent classes of drugs used to treat infectious disease and cancer, making it important to establish robust and rapid procedures for analysis of these complexes. We report a method for isolation of DNA–protein adducts and their identification and quantification, using techniques compatible with high-throughput screening. This method is based on the RADAR assay for DNA adducts that we previously developed (Kiianitsa and Maizels (2013) A rapid and sensitive assay for DNA–protein covalent complexes in living cells. Nucleic Acids Res., 41:e104), but incorporates three key new steps of broad applicability. (i) Silica-assisted ethanol/isopropanol precipitation ensures reproducible and efficient recovery of DNA and DNA–protein adducts at low centrifugal forces, enabling cell culture and DNA precipitation to be carried out in a single microtiter plate. (ii) Rigorous purification of DNA–protein adducts by a procedure that eliminates free proteins and free nucleic acids, generating samples suitable for detection of novel protein adducts (e.g. by mass spectroscopy). (iii) Identification and quantification of DNA–protein adducts by direct ELISA assay. The ELISA-based RADAR assay can detect Top1–DNA and Top2a–DNA adducts in human cells, and gyrase–DNA adducts in Escherichia coli. This approach will be useful for discovery and characterization of new drugs to treat infectious disease and cancer, and for development of companion diagnostics assays for individualized medicine.     

Disease signatures are robust across tissues and experiments

Meta‐analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta‐analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large‐scale analysis of disease‐associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta‐analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi‐tissue systems models of disease pathology using public data.     

Gene expression profiles of human small airway epithelial cells treated with low doses of 14- and 16-membered macrolides

Although long-term treatment with low doses of 14-membered macrolides is widely applied in management of patients with chronic inflammatory diseases, e.g., diffuse panbronchiolitis, chronic bronchitis, or chronic lung damage in newborns, the physiological mechanisms underlying the action of macrolides in these conditions are unclear. To clarify the pathological basis of these diseases and also to aid in the design of novel drugs to treat them, we chose to investigate the molecular target(s) of macrolides. Our experiments involved long-term culture of human small airway epithelial cells (hSAEC) in media containing 14-membered macrolides erythromycin (EM) or clarithromycin (CAM), or a 16-membered macrolide, josamycin (JM), which lacks clinical anti-inflammatory effects. We then analyzed gene expression profiles in the treated cells using a cDNA microarray consisting of 18,432 genes. We identified nine genes whose expression was significantly altered during 22 days of culture with EM, and seven that were altered by CAM in that time. Four of those genes revealed similar behavior in cells treated with either of the 14-membered macrolides, but not JM. The products of these four genes may be candidates for mediating the ability of 14-membered macrolides to suppress chronic inflammation.     

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.     

Quantifying compartment‐associated variations of protein abundance in proteomics data

Quantitative mass spectrometry enables to monitor the abundance of thousands of proteins across biological conditions. Currently, most data analysis approaches rely on the assumption that the majority of the observed proteins remain unchanged across compared samples. Thus, gross morphological differences between cell states, deriving from, e.g., differences in size or number of organelles, are often not taken into account. Here, we analyzed multiple published datasets and frequently observed that proteins associated with a particular cellular compartment collectively increase or decrease in their abundance between conditions tested. We show that such effects, arising from underlying morphological differences, can skew the outcome of differential expression analysis. We propose a method to detect and normalize morphological effects underlying proteomics data. We demonstrate the applicability of our method to different datasets and biological questions including the analysis of sub‐cellular proteomes in the context of Caenorhabditis elegans aging. Our method provides a complementary perspective to classical differential expression analysis and enables to uncouple overall abundance changes from stoichiometric variations within defined group of proteins.     

Active and passive vaccination against hantavirus pulmonary syndrome with Andes virus M genome segment-based DNA vaccine

Hantavirus pulmonary syndrome (HPS) is a rapidly progressing human disease with one of the highest case fatality rates (30 to 50%) of any acute viral disease known. There are no vaccines, effective antiviral drugs, or immunologics to prevent or treat HPS. In an attempt to develop HPS medical countermeasures, we constructed an expression plasmid, pWRG/AND-M, that contains the full-length M genome segment of Andes virus (ANDV), a South American hantavirus. Transfection experiments in cell culture indicated that both the G1 and G2 glycoproteins are expressed from pWRG/AND-M. Rhesus macaques vaccinated by gene gun with pWRG/AND-M developed remarkably high levels of neutralizing antibodies that not only neutralized ANDV but also cross-neutralized other HPS-associated hantaviruses, including Sin Nombre virus. To determine if the antibodies elicited in the monkeys could confer protection, we performed a series of passive-transfer experiments using a recently described lethal HPS animal model (i.e., adult Syrian hamsters develop HPS and die within 10 to 15 days after challenge with ANDV). When injected into hamsters 1 day before challenge, sera from the vaccinated monkeys either provided sterile protection or delayed the onset of HPS and death. When injected on day 4 or 5 after challenge, the monkey sera protected 100% of the hamsters from lethal disease. These data provide a proof of concept for a gene-based HPS vaccine and also demonstrate the potential value of a postexposure immunoprophylactic to treat individuals after exposure, or potential exposure, to these highly lethal hantaviruses.     

An estrogen receptor (ER)‐related signature in predicting prognosis of ER‐positive breast cancer following endocrine treatment

Quite a few estrogen receptor (ER)‐positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER‐related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER‐related gene signature that can predict the prognosis of ER‐positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER‐related signature was developed to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER‐related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER‐related prognostic signature is a promising method for predicting the prognosis of ER‐positive breast cancer patients receiving endocrine therapy.     

PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes

Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns.     

piRNAs initiate an epigenetic memory of nonself RNA in the C. elegans germline

Organisms employ a fascinating array of strategies to silence invasive nucleic acids such as transposons and viruses. Although evidence exists for several pathways that detect foreign sequences, including pathways that sense copy number, unpaired DNA, or aberrant RNA (e.g., dsRNA), in many cases, the mechanisms used to distinguish "self" from "nonself" nucleic acids remain mysterious. Here, we describe an RNA-induced epigenetic silencing pathway that permanently silences single-copy transgenes. We show that the Piwi Argonaute PRG-1 and its genomically encoded piRNA cofactors initiate permanent silencing, and maintenance depends on chromatin factors and the WAGO Argonaute pathway. Our findings support a model in which PRG-1 scans for foreign sequences and two other Argonaute pathways serve as epigenetic memories of "self" and "nonself" RNAs. These findings suggest how organisms can utilize RNAi-related mechanisms to detect foreign sequences not by any molecular signature, but by comparing the foreign sequence to a memory of previous gene expression.     

Markovian domain fingerprinting: statistical segmentation of protein sequences.

MOTIVATION: Characterization of a protein family by its distinct sequence domains is crucial for functional annotation and correct classification of newly discovered proteins. Conventional Multiple Sequence Alignment (MSA) based methods find difficulties when faced with heterogeneous groups of proteins. However, even many families of proteins that do share a common domain contain instances of several other domains, without any common underlying linear ordering. Ignoring this modularity may lead to poor or even false classification results. An automated method that can analyze a group of proteins into the sequence domains it contains is therefore highly desirable. RESULTS: We apply a novel method to the problem of protein domain detection. The method takes as input an unaligned group of protein sequences. It segments them and clusters the segments into groups sharing the same underlying statistics. A Variable Memory Markov (VMM) model is built using a Prediction Suffix Tree (PST) data structure for each group of segments. Refinement is achieved by letting the PSTs compete over the segments, and a deterministic annealing framework infers the number of underlying PST models while avoiding many inferior solutions. We show that regions of similar statistics correlate well with protein sequence domains, by matching a unique signature to each domain. This is done in a fully automated manner, and does not require or attempt an MSA. Several representative cases are analyzed. We identify a protein fusion event, refine an HMM superfamily classification into the underlying families the HMM cannot separate, and detect all 12 instances of a short domain in a group of 396 sequences. CONTACT: jill@cs.huji.ac.il; tishby@cs.huji.ac.il.     

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.     

Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive tumor with a poor prognosis. Currently, only sorafenib is approved by the FDA for advanced HCC treatment; therefore, there is an urgent need to discover candidate therapeutic drugs for HCC. We hypothesized that if a drug signature could reverse, at least in part, the gene expression signature of HCC, it might have the potential to inhibit HCC-related pathways and thereby treat HCC. To test this hypothesis, we first built an integrative platform, the "Encyclopedia of Hepatocellular Carcinoma genes Online 2", dubbed EHCO2, to systematically collect, organize and compare the publicly available data from HCC studies. The resulting collection includes a total of 4,020 genes. To systematically query the Connectivity Map (CMap), which includes 6,100 drug-mediated expression profiles, we further designed various gene signature selection and enrichment methods, including a randomization technique, majority vote, and clique analysis. Subsequently, 28 out of 50 prioritized drugs, including tanespimycin, trichostatin A, thioguanosine, and several anti-psychotic drugs with anti-tumor activities, were validated via MTT cell viability assays and clonogenic assays in HCC cell lines. To accelerate their future clinical use, possibly through drug-repurposing, we selected two well-established drugs to test in mice, chlorpromazine and trifluoperazine. Both drugs inhibited orthotopic liver tumor growth. In conclusion, we successfully discovered and validated existing drugs for potential HCC therapeutic use with the pipeline of Connectivity Map analysis and lab verification, thereby suggesting the usefulness of this procedure to accelerate drug repurposing for HCC treatment.     

A systems biology approach to identify effective cocktail drugs

Background

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

Results

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

Conclusions

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

     

Profiling of hepatic gene expression in rats treated with fibric acid analogs

Peroxisome proliferator-activated receptors (PPARs) are a group of nuclear receptors whose ligands include fatty acids, eicosanoids and the fibrate class of drugs. In humans, fibrates are used to treat dyslipidemias. In rodents, fibrates cause peroxisome proliferation, a change that might explain the observed hepatomegaly. In this study, rats were treated with multiple dose levels of six fibric acid analogs (including fenofibrate) for up to two weeks. Pathological analysis identified hepatocellular hypertrophy as the only sign of hepatotoxicity, and only one compound at the highest dose caused any significant increase in serum ALT or AST activity. RNA profiling revealed that the expression of 1288 genes was related to dose or length of treatment and correlated with hepatocellular hypertrophy. This gene list included expression changes that were consistent with increased mitochondrial and peroxisomal beta-oxidation, increased fatty acid transport, increased hepatic uptake of LDL-cholesterol, decreased hepatic uptake of glucose, decreased gluconeogenesis and decreased glycolysis. These changes are likely linked to many of the clinical benefits of fibrate drugs, including decreased serum triglycerides, decreased serum LDL-cholesterol and increased serum HDL-cholesterol. In light of the fact that all six compounds stimulated similar or identical changes in the expression of this set of 1288 genes, these results indicate that hepatomegaly is due to PPARalpha activation, although signaling through other receptors (e.g. PPARgamma, RXR) or through non-receptor pathways cannot be excluded.     

Metformin rescues cell surface major histocompatibility complex class I (MHC-I) deficiency caused by oncogenic transformation

Active avoidance by tumor cells from attack and elimination by immune cells is an emerging cancer hallmark that is achieved primarily through decreasing the levels of major histocompatibility complex class I (MHC-I) at the cancer cells’ surface. Deficiencies in MHC-I antigen-restricted immunosurveillance may be intertwined with an altered, Warburg-like cancer cell-intrinsic metabolism, another emerging hallmark of cancer that involves a switch from mitochondrial respiration to glycolysis to efficiently support large-scale biosynthetic programs that are required for active cell proliferation. We recently envisioned that intervention strategies aimed at reversing the bioenergetic signature of cancer cells (e.g., the antidiabetic biguanide metformin) should correct oncogene (e.g., HER2)-driven MHC-I defects, thus preventing immune escape of oncogene transformants. First, we explored how metformin treatment impacted mitochondrial biogenesis in cultured breast cancer cells overexpressing the membrane tyrosine kinase receptor HER2, the best-characterized downregulator of MHC-I. Metformin exposure was found to dose-dependently increase the expression levels of cytochrome c oxidase I and mitochondrial succinate dehydrogenase, which are encoded by mitochondrial and nuclear DNA, respectively. Second, we explored whether metformin-enhanced mitochondrial biogenesis might significantly alter the MHC-I status in breast carcinoma cells. MHC-I expression, as assessed by flow cytometry using an anti-HLA-ABC monoclonal antibody, was fully restored (up to ~25-fold upregulation) in MHC-I-negative HER2 gene-amplified carcinoma cells. These findings may help delineate a previously unrecognized mechanism through which metformin (and metformin-like drugs) may enable a cancer patient’s own immune system to mount an efficient anti-metastasis response that can prevent or delay disease recurrence. Restored antigenicity and immunogenicity of tumor cells may represent a previously unrecognized primary mode of action underlying the cancer-preventive effects of metformin.     

A device to study the effects of stretch gradients on cell behavior

Mechanical forces are key regulators of cell function with varying loads capable of modulating behaviors such as alignment, migration, phenotype modulation, and others. Historically, cell-stretching experiments have employed mechanically simple environments (e.g., uniform uniaxial or equibiaxial stretches). However, stretch distributions in vivo can be highly non-uniform, particularly in cases of disease or subsequent to interventional treatments. Herein, we present a cell-stretching device capable of subjecting cells to controllable gradients in biaxial stretch via radial deformation of circular elastomeric membranes. By including either a defect or a rigid fixation at the center of the membrane, various gradients are generated. Capabilities of the device were quantified by tracking marked positions of the membrane while applying various loads, and experimental feasibility was assessed by conducting preliminary experiments with 3T3 fibroblasts and 10T1/2 cells subjected to 24 h of cyclic stretch. Quantitative real-time PCR was used to measure changes in mRNA expression of a profile of genes representing the major smooth muscle phenotypes. Genes associated with the contractile state were both upregulated (e.g., calponin) and downregulated (e.g., α-2-actin), and genes associated with the synthetic state were likewise both upregulated (e.g., SKI-like oncogene) and downregulated (e.g., collagen III). In addition, cells aligned with an orientation perpendicular to the maximal stretch direction. We have developed an in vitro cell culture device that can produce non-uniform stretch environments similar to in vivo mechanics. Cells stretched with this device showed alignment and altered mRNA expression indicative of phenotype modulation. Understanding these processes as they relate to in vivo pathologies could enable a more accurately targeted treatment to heal or inhibit disease, either through implantable device design or pharmaceutical approaches.