Hao Chen - US grants

This SBIR Phase II Grant Application has as its goal the discovery, development and commercialization of new, small molecule drugs which have their origin as natural products elaborated by marine microorganisms. Initial disease targets for drug development include: i) new antibiotics which are broadly effective against microbial pathogens and are effective in controlling pathogens resistant to currently utilized antibiotic drugs, and ii) new drugs able to modulate inflammatory process and suppress subsequent allergic responses. Marine microorganisms represent a vast, unexplored, and promising source material for the discovery of novel drugs. Work completed under Phase I of this Application was successful in establishing routine methods and manipulations to circumvent difficulties traditionally associated with drug discovery efforts employing marine microorganisms. These inherent difficulties include the isolation and growth of marine microorganisms, the stimulation of bioactive material production, and the removal or suppression of interfering media components during bioassay. Phase I work also resulted in the discovery of more than 20 highly active materials. Work proposed under Phase II represents the application and expansion of these routine methods and manipulations to a substantial bioactive materials discovery and development effort. The screening program will employ: i) a collection of taxonomically heterogeneous marine microbial isolates collected from widely diverse geographic regions and ecological niches, and ii) a battery of antibiotic assays, and targeted receptor binding based pharmacological assays. The active materials discovered in the screening component are advanced through assay guided fractination for compound isolation and prufication followed by a chemistry intensive effort focused on determining the structure of the active chemical moity. The most active drug materials will be advanced to pre-clinical development. PROPOSED COMMERCIAL APPLICATION Successful development and application of the proposed technology should revitalize the flow of novel, small molecule, natural product drug candidates into preclinical safety and efficacy testing programs. This Phase II Grant program is specially focused on developing new drugs to treat important disease categories comprising; I infectious diseases including those infections resulting from antibiotic resistant pathogens and ii) new treatment for inflammation and allergy.

DESCRIPTION (adapted from applicant's abstract): A comprehensive activity profile-database of cocaine and prescription drugs is a powerful knowledge base that can be employed to develop combination drug therapies for cocaine abuse and addiction. This NIH SBIR Phase I application is aimed at producing a comprehensive and robust receptor binding/pharmacological mode of action profile of cocaine. The profile will employ a comprehensive panel of pharmacological target assays with a bias towards receptors located in the central nervous system (CNS) as well as related enzymes. Specifically, this application seeks support to compile an extensive receptor binding/mode of action profile database of numerous known CNS active chemicals that will include certain marketed prescription drugs and over-the-counter "(OTC) remedies". At the completion of Phase I and Phase II activities we will provide an organized and interactive comprehensive data set in the form of an easily used and queried database containing receptor binding profiles of cocaine, and the profiles of prescription and OTC drugs. To supplement this interactive database, an OLAP (online analytical processing) Cube report that is a multi-dimensional interactive report using SPSS Base 9.0 will also be produced. With the OLAP Cube report and database, researchers and clinicians will be able to rapidly examine and establish models to assist in the selection and design of drug-combination therapies to treat cocaine addiction. The applicant proposes to develop an interactive database of receptor binding data for cocaine, as well as for a panel of compounds that could potentially interact with cocaine, for the purpose of treating cocaine addiction. The panel of compounds to be tested will include marketed pharmaceutical and over the counter drugs for which the safety and efficacy profile is already established. The Phase I studies include gathering of the relevant data for a number of compounds and compiling the database. Both activities will be expanded in Phase II. PROPOSED COMMERCIAL APPLICATION: NOT AVAILABLE

Cocaine abuse and addiction has reached epidemic levels and has emerged as a national crisis. There is an urgent need to develop effective therapeutics to address this crisis. This proposal is focused on further defining the complex pharmacological elements that underlie cocaine abuse and addiction with an emphasis on expediting identification of potential therapeutics. Initially, we will compile and assemble an extensive full-rank dataset of the activity profiles of prescription and OTC drugs screened in key receptor, transport, enzyme and functional assays. This dataset will be compared to an identical activity profile of cocaine. With an internally consistent set of in vitro activity profiles and ancillary safety, drug-drug interactions, ADME pharmaco-dynamic and - kinetics information within the database, our aim is to identify combinations of existing medications and regiments potentially useful in treating cocaine abuse and addiction. We will employ this initial database as a computational "training-set" and use the results for in silico screening to illuminate new chemical entities that may be useful treating this disease. The complete dataset will be assembled into a Cocaine Profile Database which will be deployed as a technology platform for drug discovery and development related to addiction. PROPOSED COMMERCIAL APPLICATIONS: Cocaine abuse and addiction are life threatening to the abuse, disruptive to the fabric of our society and consume an estimated $50 billion each year in the U.S. in treatment costs, lost wages and other economic impacts. Successful completion of this Phase II Grant will lead to an identification of combinations of existing medications that may potentially result in therapeutics to treatment cocaine abuse and addiction. We will construct a cocaine-centered pharmacological database that will provide an information-rich, drug discovery platform useful in developing new therapeutics. We envision that this platform will become the basis for NovaScreen to establish collaborative research and development relationships with other corporations and institutions.

DESCRIPTION (provided by applicant): Current treatments for alcohol abuse and dependency are marginally effective. Application of naltrexone, an opiate antagonist, appears to have only a moderate effect in reducing drinking and is not sufficiently effective in treating some sub-populations of problematic alcohol consumers or in preventing relapses. Recent attempts of using multiple drug combinations, where each individual drug addresses a different target set or neurotransmitter system, appear to improve efficacy in curtailing alcohol consumption. Concerns of multiple drug combination regimens, however, are the combined drug side effects, toxicology and pharmacokinetic issues, adding to the stress on the already stressed liver. Developing and evaluating new and more potent medications for alcoholism is still a high priority. Single agents that address multiple CNS targets may be especially attractive. The proposed Phase I research is focused on finding novel small organic molecules with modulating activities at specific membrane receptors in multiple CNS target classes. The successful execution of the proposed research will produce a suite of novel molecules that selectively modulate activity at different opiate receptor subtypes and concurrently at a specific serotonergic receptor subtype. The compounds may be used as research tools to aid in the understanding alcohol addiction and abuse, and later become the basis for therapeutics to treat alcoholism. The success of our approach will rely on the innovative integration of in silico and in vitro screening methods and large readily accessible chemical libraries. The ensuing Phase II research will focus on improving the pharmacological properties of the active compounds in order to enhance the potential for finding new and safe medications for treating alcoholism.

This project is developing a method to formally verify SPIDS that monitor other components of a system, including network protocols, routers, and applications. In each case, the creative steps are to (1) develop the specification that determines when an alert is to be delivered, (2) develop a security requirement against which the SPIDS is verified, and (3) carry out the verification. Beyond verifying each of these SPIDS in isolation, this proposal addresses their composition driven by the verification with respect to a global security requirement for an entire system. A running example of the system to be considered consists of a host running Linux, a wireless network running auto-configuration protocols and the OLSR routing protocol. Although not containing the functionality of a real network, this example includes most of the layers in the network protocol stack for a wireless environment.

As the Internet becomes an essential part of our everyday computing and communication infrastructure, it has also grown to be a complex distributed system that is hard to characterize. There have been numerous studies on network topology, IP-reachability, and routing dynamics to analyze end-to-end packet forwarding performance. However, there is very little systematic investigation into the influence of other packet transformations that happen along the path, e.g., firewalls, packet filtering, and quality-of-service mapping. Among these, firewalls are ubiquitous as they become indispensable security defense mechanisms used in business and enterprise networks. Just as router mis-configurations can lead to unpredictable routing problems, misconfigured firewalls may fail to enforce the intended security policies, or may incur high packet processing delay. Unfortunately, firewall configuration for a large, complex enterprise network is a demanding and error-prone task, even for experienced administrators. Firewalls can be distributed in many parts of the network or across layers (IP-layer filtering versus application-layer solutions) to cooperatively achieve a global, network-wide policy. As distributed firewall rules are concatenated, it becomes extremely difficult to predict the resulting end-to-end behavior and whether it meets the higher-level security policy.

Intellectual merit: In this project, the principal investigators (PIs) propose to develop a unified framework for policy-checking, optimization, and auto-reconfiguration of distributed firewalls. This research will provide novel analysis, design techniques, and tools to better protect our critical information infrastructures from attacks. The PIs will explore providing consistent and efficient security protection for an enterprise that may have geographically distributed business networks served by different local Internet Service Providers. They adopt an inter-disciplinary technical approach that leverages multi-way communications among the three PIs with expertise in networking, security, and programming languages and compilers areas to design an integrated solution. In particular, the PIs propose a systematic treatment of the problem by casting it as a static program analysis question, exploiting well-established and rigorous techniques from the area of programming languages and compilers. The PIs will pursue the following closely related tasks:

Policy Validation for Security: The PIs first classify all possible policy anomalies (including both inconsistency and inefficiency) in firewall configurations. They will model firewalls as finite-state transition systems and apply symbolic model checking techniques on these finite-state representations to detect both intra-firewall and inter-firewall policy anomalies. The policy validation method consists of two phases. First, they perform control-flow analysis and identify all possible flow paths. Second, they perform data-flow analysis and check for anomalies on every path. Identifying most intra-firewall and inter-firewall anomalies can be accomplished in one traversal. The processing results of each path are further used to identify inter-path misconfigurations.

Policy Optimization for Performance: In a typical firewall setting, a packet is compared against a list of rules sequentially until the packet matches a rule. Firewalls with complex rule sets can cause significant delays on network traffic and therefore becomes a bottleneck (especially in high-speed networks) and an attractive target for DoS attacks. Therefore, it is important to optimize packet filtering to provide network Quality of Service (QoS) requirement. In addition, the total number of rules configured and the order of rules also play major roles in the load and efficiency of a firewall. The PIs approach this problem by representing filtering rules as binary decision diagrams (BDDs) and generating "optimal filter rule sets" from the internal BDD representation. They also apply dataflow analysis to hoist same or similar rules from different paths to a common location to reduce traffic. They will leverage the underlying network topology, routing, and traffic distribution information in the optimization step to improve the efficiency of firewall checking, which enhances packet-forwarding performance. The key advantage of this approach is the ability to pro-actively prevent vulnerabilities in firewalls since static analysis can be applied before the actual deployment of firewalls.

Broader Impacts: The proposed research efforts will help system and network administrators to configure networked systems more securely and efficiently. The educational component, which is directed at both undergraduate and graduate students, complements the research activities. Research results will be incorporated into new and existing courses. The PIs will actively participate in UC Davis' minority outreach programs to recruit students from underrepresented groups into science and engineering. In addition, firewall configuration tools developed in the project will be distributed for teaching

DESCRIPTION (provided by applicant): Cystic Fibrosis (CF) is a lethal genetic disease characterized in part by one or more mutations in the cystic fibrosis transmembrane conductance regulator (CFTR). Mutations of the CFTR protein, typically a deletion of a phenylalanine at position 508 (deltaF508), disable the chloride ion transport activity of CFTR, leading to CF disease symptoms, especially in the lung epithelium. Relying on pharmacological techniques, this Phase I proposal characterizes membrane receptors and ion channels on selected epithelial cell lines that also express wild type or mutant (defective) forms of CFTR. The study will provide a composite of radioligand binding profiles (establishing the presence of various targets) and cellular response profiles of three different cell lines, CFPAC-1, PANC-1 and Calu-3. The cellular response will be measured as changes in intracellular Ca++ or cAMP concentration and in Cl- efflux when the cells are stimulated by receptor/ion channel agonists. The proposed body of research seeks to establish a system comprised of a set of targets, for instance a set (n is equal to or more than 2) of membrane receptors, which on agonist stimulation will mediate a potentially synergistic effect of enhancing Cl- secretion by the cells. This effect may occur either through stimulation of CFTR itself or, as a more novel approach, via stimulation of other Cl- transport mechanisms present in the cells. As a potential means to establish alternative targets and alternative therapeutic approaches for restoring Cl- efflux, this Phase I research should provide a significant contribution to cystic fibrosis medication development; as well as relevant information for related diseases in respiratory, urinary, reproductive, digestive and/or cardiovascular systems.

[unreadable] DESCRIPTION (provided by applicant): Cocaine addiction and dependency have reached pandemic levels and have become a continuing national healthcare concern. At present, no effective and safe medication is available to treat cocaine dependency and to ameliorate the underlying chemical addiction. During previous SBIR Phase I and Phase II supported work, we discovered several promising novel chemical entities (NCEs) with significant potential as cocaine addiction therapeutics. This SBIR continuing Phase II application focuses on extending the pharmacological characterization and preclinical development of these NCEs. The continuing Phase II research will consist of an assessment of in vivo efficacy and safety profiles of these compounds in rodent animal models and characterization of the selected drug candidates for their ADME properties. Medicinal chemistry for development of backup candidates will also be part of the effort. We propose to advance the development of these NCEs towards clinical trials for the treatment of cocaine addiction and related disorders. [unreadable] These NCEs are the most selective chemical inhibitors of dopamine reuptake that have been described. [unreadable] [unreadable]

[unreadable] DESCRIPTION (provided by applicant): NovaScreen Biosciences Corp. has successfully completed a Phase I SBIR project (#1 R43 AA014542-01, entitled "Novel Molecules to Treat Alcoholism") and is submitting this Phase II SBIR application in response to RFA-AA-04-002, "Medications Development to Treat Alcoholism." The specific objective of this Phase II application is to discover and to advance at least one novel, optimized, new chemical entity (NCE) up to the stage of preclinical development. That NCE will be designed to be concurrently active at multiple (two) molecular targets, each a validated drug target for treating alcoholism. Compounds active at multiple targets may have greater therapeutic efficacy than agents acting at single targets, and display fewer side effect liabilities than cocktails of two or more different drugs each active at single targets. Our Phase I SBIR research has produced a promising lead compound and an array of structure-activity relationship (SAR/QSAR) models of small molecules that concurrently modulate the serotonin 5HT3 receptor and the mu-opioid receptor. Additionally, we also produced SAR/QSAR models for compounds that display dual activity at 5HT3 and at other opioid receptor subtypes (i.e., 5HT3-delta opioid and 5HT3-kappa opioid). Our emphasis in this Phase II builds on Phase I results and employs these SAR/QSAR models to continue optimization and development of the identified lead compound to produce a new generation of therapeutic candidates for the treatment of alcohol dependency. [unreadable] [unreadable]

Cellular networks are part of our critical information infrastructure.
However, the upcoming broadband cellular data networks and mobile
devices have unique vulnerabilities that have attracted little
research attention. The PI proposes a five-year plan for
understanding the inherent vulnerabilities and developing technologies
for improving the security of cellular data networks and mobile
devices. Particularly, the PI will focus on the following unique
vulnerabilities in cellular data systems: (1) Scarce battery power in
mobile devices; (2) Expensive wireless bandwidth; (3) Scarce wireless
resources shared among benign and malicious mobile devices. To
address these vulnerabilities, the PI proposes a comprehensive
solution consisting of three components: (1) On mobile devices,
designing and enforcing access control policies on power consumption;
(2) At mobile switching centers (MSC), designing and deploying
firewalls configurable by individual mobile devices but verifiable by
the MSC; (3) At base stations, detecting malicious mobile devices that
abuse shared resources.

This project will have direct, visible impact on improving the
security of broadband cellular data networks. The discovered
vulnerabilities and proposed defense mechanisms will help cellular
providers secure their networks proactively before the adversaries
deliver devastating attacks. Experiences gained in this project will
help guide the security design of future cellular data networks. The
novel analysis, simulation, and evaluation techniques developed in
this project will be valuable to teachers and researchers in
in security, networking, and programming languages.

The proposed education mission is to train students to become capable
security researchers or practitioners through classroom teaching,
real-life problem solving, and research advising.

Privacy is an important concern in the digital age, yet the adoption
of privacy preserving technologies has lagged behind that of other
security technologies. This is, to a great extent, due to the lack of
practical, usable privacy preserving technologies. The wide adoption
of confidentiality and integrity technologies was triggered by a few
practical software tools, such as SSL and SSH. By contrast, practical
privacy preserving technologies still face considerable challenges.

The goal of this project is to develop two practical privacy
preserving technologies, which serve as a case study on unique
requirements and pitfalls of privacy preserving software.

Privacy preserving computation: Generic techniques are close to
practical for some functions of interest, but a tantalizing gap still
remains for many applications. The practicality gap widens further if
malicious adversarial behavior must be tolerated. This project focus
on new efficient methods to protect against malicious faults,
retro-fitting at the algorithmic level to gain new efficiencies at the
cryptographic protocol level, and new efficient methods to facilitate
the ongoing storage and processing of privacy-sensitive data.

Off-the-record communication: This project will design protocols for
off-the-record communication between group members. Each member can
verify the identity of the sender and ensure the confidentiality of
the message, but no subset of group members may conspire to implicate
the sender to any third party.

Broader impacts of this project are to inform the general public that
privacy is not a lost cause in the digital age, and to create public
appreciation and support for applied cryptography research.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

The Analytical and Surface Chemistry Program supports Professor Hao Chen of Ohio University in studies targeting improved insight into the mechanisms of ion/ion reactions important for characterizing a wide range of molecules, including proteins. The approach entails structural analysis of neutral and ionic products of gaseous ion/ion reactions at atmospheric pressure. Ionic products are directly analyzed by mass spectrometry; neutral products are re-ionized using on-line extractive electrospray ionization (EESI) prior to mass analysis. Additionally, "soft landing" is used to collect both neutral and ionic reaction products for further structural elucidation. Derived insights may potentially impact disciplines such as proteomics and synthetic organic chemistry.

The work provides research opportunities for both undergraduate and graduate students, including members of underrepresented and disadvantaged groups. Dr. Chen is also actively involved in educational outreach activities, including workshops/seminars for pre-college students to learn about modern instrumental analysis and to develop enthusiasm for scientific research.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We have developed a method to map protein solvent acessible surfaces using hydrogen peroxide and UV light to generate hydroxyl radicals. Although the early efforts made use of continuous light source, we recently moved beyond that and developed an ultrafast reaction method to probe protein solvent-accessible surfaces by using a pulsed laser and a quencher. This approach is capable of following reaction times on the order of 100 nanoseconds and slower--faster than any protein known unfolding event. Specifically, the method circumvents problems that reaction with OH (protein oxidation) could cause protein unfolding and oxidation of sites that are not accessible in the native protein, giving misleading results. We avoid unwanted oxidation by using a 248-nm KrF excimer laser to cleave hydrogen peroxide at low concentrations (15 mM, 0.04%), affording hydroxyl radicals that modify the protein in less than a microsecond. In the presence of a scavenger, the radical lifetimes decrease to 1 microsecond, yet the reaction timescales are sufficient to provide significant oxidation of the protein. These times are arguably faster than super-secondary protein structure can unfold as a result of the modification. The radical formation step takes place in a nanoliter flow cell so that only one laser pulse irradiates each bolus of sample. The oxidation sites are located using standard analytical proteomics, requiring less than a nanomole of protein. A new direction in this research is the development of new reagents. One is the sulfate radical anion, generated in the photolysis of persulfate.

DESCRIPTION (provided by applicant): Taste has profound effects on smoking behavior. Most importantly, smokers of flavored cigarette have higher odds for relapse. Gustatory and other visceral sensory information are processed and integrated in the insular cortex. Damaging of insular cortex in stroke patients resulted in immediate quitting of smoking, without relapse, and without persistence of the urge to smoke. Therefore, the first aim of this proposal is to establish an adolescent animal model of i.v. nicotine self-administration using licking as the operant behavior and a novel taste as the conditioned stimulus. In the second aim, we will test the hypothesis that gustatory, audio and visual cues activate common subregions of the insular cortex during reinstatement of nicotine seeking behavior. This proposal is suited for the CEBRA program not only because it aims to establish an innovative drug SA model that incorporates contingent gustatory cue, but, more importantly, because it tests a highly novel and significant hypothesis, which there is scant precedent or preliminary data. If confirmed, this would have a substantial impact on current thinking of the brain circuitry involved in relapse to drug abuse. PUBLIC HEALTH RELEVANCE: Relapse is a major problem in treating nicotine addiction. An animal model of voluntary nicotine consumption with a taste cue will be established to explore the role of insular cortex in relapse to smoking.

DESCRIPTION (provided by applicant): Cigarette smoking is a pre-eminent public health problem, recognized as early as 1965. The rapid acquisition and addiction to cigarettes during adolescence is at the root of this continuing epidemic. A cardinal feature of smoking is the high level of individual variation in susceptibility to acquiring the habit;44% of this vulnerability to initiate smoking is attributed to genetics. Identifying differences in gene expression within phenotype- specific neurons that control and respond to the reinforcing properties of nicotine, across 5 adolescent inbred strains of rats and 10 F1 isogenic crosses, will provide novel and fundamental insight into this variation in susceptibility. We will exploit sophisticated, but well established rat models with a high level of genetic and phenotypic variation, cutting- edge expression approaches (laser-capture microdissection, array and RNA-seq analysis), and innovative bioinformatic resources developed by our groups (primarily Chilibot and GeneNetwork). A two-year grant is perfectly suited to our design. SA 1 will identify, analyze and confirm Signature Genes characteristic of phenotype-specific neurons (e.g., ventral tegmental dopamine neurons projecting to accumbal shell), combining retrograde tracing, immunocytochemistry, laser capture microdissection and analysis of gene expression arrays in Lewis and Fisher344 rats. In SA 2, clusters of associated transcripts, identified statistically from transcriptomes of heterogenous tissue sections (e.g., ventral tegmentum), will be associated with phenotype-specific neurons, using the Signature Gene sets. Those transcripts that covary with acquisition of nicotine self-administration across all inbred rat strains and F1 crosses are Candidate Genes. These are highly likely to participate in determining strain-dependent differences in the function of these specific neurons, which in turn contribute to variation in the acquisition of nicotine SA across strains. SA 3 will predict nicotine acquisition behavior from the expression of Candidate Genes in adolescent HXB recombinant inbred rats. This project will broadly impact the field by developing foundational models and predictions with general applicability, regarding genetic and expression differences that contribute to differences in the likelihood of initiating and acquiring cigarette smoking. PUBLIC HEALTH RELEVANCE: Habitual cigarette smoking is a major public health problem that is resistant to treatment. Individuals usually become smokers during adolescence, and the likelihood of developing this habit varies from individual to individual. Genetic factors play a large role in determining who will become a chronic smoker during adolescence. This research will identify these genetic factors, providing the foundation for new smoking prevention strategies.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)

Objective: The objective of this program is to develop a new framework for studying distributed inference with dependent observations. A broad range of issues will be addressed under this program, ranging from distributed detection, distributed estimation, robust inference, to asymptotic performance in a large system setting.

Intellectual merit: The intellectual merit is in the unifying nature of the proposed framework. Existing results in distributed inference with dependent observations are rather fragmented. The proposed framework unifies these isolated studies and provides intuition behind many of the observations reported in existing literature. Such intuition, as well as the framework itself, can be applied and adapted to various distributed inference in complex systems. The transformative nature of the proposed research lies in its potential to connect existing isolated studies, and to identify and resolve distributed inference problems that were never before addressed, thereby providing clear design principles for distributed inference systems under realistic dependent data models.

Broader impacts: The broader impacts are multifaceted. The study sheds light on the fundamental cause of difficulty in dealing with dependent observations in distributed systems; it provides useful insights that may lead to research advances in areas beyond that of distributed inference. The research results are to be disseminated to both the research community through publications and tutorial presentations and to graduate students through curriculum development. Encouraging and facilitating graduate student participation in professional meetings and conferences and recruiting undergraduate students in research projects which will help instill enthusiasm and foster their interest in scientific activities.

Authentication is a quintessential problem in computer security. However, most commonly used authentication mechanisms suffer from a variety of shortcomings. Passwords, the most common authentication mechanism, are vulnerable to replay attacks. Physical authentication tokens overcome some of these problems; however, they face deployment and compatibility obstacles.

Recently, advanced mobile devices have become widely available. These devices provide new hardware capabilities, such as MIMO (multiple-input and multiple-output) radio, a variety of sensors, and hardware authentication modules.

We investigate how to take advantage of hardware capabilities in advanced mobile devices to design better authentication mechanisms. We are focusing on three types of hardware capabilities. First, certain mobile devices authenticate to their networks via built-in hardware modules. We investigate how to leverage such existing authenticating infrastructure for other authentication tasks, such as authenticating users to websites. Second, mobile devices have MIMO radio transceivers. We investigate how to use these transceivers to pair nearby mobile devices without requiring the user to enter shared secrets into the devices. Finally, many advanced mobile devices have sensors, such as accelerometers. We investigate how to design gesture-based user authentication using accelerometers.

The impact of this project will be highly visible. Most current authentication mechanisms have various security, usability, and deployment problems. Our new mechanisms overcome many of these problems. Since these mechanisms are based on hardware capabilities that are increasingly common in mobile devices, they can be deployed to billions of mobile users to make their authentication tasks easier and securer.

This CAREER award by the Chemical Measurement and Imaging (CMI) program of the Division of Chemistry supports work by Professor Hao Chen at Ohio University for the development of microsecond time-resolved mass spectrometry based on ionization of a flying liquid jet generated from ultrafast mixing of reactant solutions by an ambient ionization method. The proposed method is expected to improve sampling resolution from current millisecond sampling to micro-second time resolution. The sucessful development of the described technology will facilitate the investigation of kinetics and mechanisms of fast biochemical reactions, which may lead to a better understanding of protein folding/unfolding, enzymatic catalysis and protein/DNA oxidation processes.

The work provides research opportunities for both undergraduate and graduate students, including members of underrepresented and disadvantaged groups. Dr. Chen is also actively involved in educational outreach activities, including ChemShows/Workshops/Seminars for pre-college students to learn about modern instrumental analysis and to develop enthusiasm for scientific research.

With this award from the Major Research Instrumentation Program (MRI) and support from the Chemistry Research Instrumentation Program (CRIF), Professor Stephen Bergmeier from Ohio University and colleagues Hao Chen, Michael Held II, Justin Holub and Marcia Kieliszewski will acquire a high resolution orbitrap liquid chromatograph tandem mass spectrometer (LC-MS/MS). In general, mass spectrometry (MS) is one of the key analytical methods used to identify and characterize small quantities of chemical species embedded in complex matrices. In a typical experiment, the components flow into a mass spectrometer where they are ionized into the parent ion and its fragment ions and their masses are measured. This highly sensitive technique allows detection and determination of the structure of molecules in a complex mixture. An instrument with a liquid chromatograph provides additional structural identification power by separating mixtures of compounds before they reach the mass spectrometer. In an orbitrap instrument there an outer section that acts as an electrode and a coaxial inner spindle-like electrode that traps ions in an orbital motion around the inner electrode. An image of the current is produced and converted to a mass spectrum using mathematical (Fourier transform) methods. This spectrometer will also be used in curricular activities involving students and researchers from the southeastern Ohio region and it will be used also by industrial partners.

The proposal is aimed at enhancing research and education at all levels, especially in areas such as (a) the study of proteins involving elucidation of glycoprotein structures, probing protein redox chemistry, detection of miniature protein ligands and analyses of protein damage from oxidative stress; (b) characterizing synthesized drug candidates, natural products and their metabolites; and (c) establishing chemometric approaches for the identification of biomarkers and validation of chemometric models and algorithms.

An Award is made to Ohio University to develop an on-chip electrochemical cross-linking mass spectrometry which will be used to identify protein-protein interactions (PPIs) in vivo. The study of PPIs is very important to understand protein biological functions as the biological functions of almost all proteins are provided by specific, non-covalent interactions with other molecules. The identification of interacting partners and interaction sites of protein complexes or aggregates will shed light on the elucidation of biological process mechanisms. This instrumentation project will provide opportunities for students to be trained in interdisciplinary research involving analytical chemistry, biochemistry, and instrumentation. The proposed instrument for biological research will first be used for the study of UV-induced apoptotic signaling pathways. Following instrument construction and software training, many other biological research groups in structural and molecular biology at Ohio University will have access to the developed instrument. The instrument will also serve as a prototype instrument for commercialization, enabling dissemination to the biological research community. It is expected that the proposed research will benefit the research communities of structural biology, molecular biology, and drug discovery.

Cross-linking mass spectrometry (MS) has become a useful technique for mapping protein-protein interactions and elucidating protein networks in living cells. However, several experimental obstacles limit the usefulness of cross-linking MS, including difficulty in the identification of cross-linked peptides in the complex mixture, the complexity of the fragmentation patterns of cross-linked peptide ions and the inability to quantify cross-links. This project proposes a new approach using electrochemistry (EC)-assisted cross-linking MS for probing protein-protein interactions in vivo, based on an electrochemical cross-linking reagent, diselenide [succinimidyl propionate] (SSP). Following online digestion, the cross-linked protein complexes will undergo capillary electrolysis (CE) separation, online EC reduction and online MS detection. The EC reduction provides a novel way to quickly identify and quantify cross-links based on the reduction current (also on MS signal), significantly reducing time required for data analysis by more than one order of magnitude. The online EC reduction takes seconds, which is much faster than hours required in chemical reduction. Furthermore, EC reduction yields easily identifiable linear peptides, facilitating MS/MS identification of cross-link structures for pinpointing out the interaction sites in the protein complexes.

Technological advances allow for the collection of massive data in the study of complex phenomena over time and/or space in various fields. Many of these data involve sequences of high dimensional or non-Euclidean measurements, where change-point analysis is a crucial early step in understanding the data: Segmentation or offline change-point analysis divides data into homogeneous temporal or spatial segments, making subsequent analysis easier; its online counterpart detects changes in sequentially observed data, allowing for real-time anomaly detection. Traditional change-point analyses primarily focus on univariate measurements. There is some literature on multivariate data, but very little on object data. This project considers both offline and online change-point analysis for multivariate and object data, for instance, for temporal analysis of multiple sensor systems, images, and social networks.

The proposed methods and corresponding theory build on previous work of the PI, which adapts nonparametric graph-based two-sample tests to the segmentation problem. The PI has shown that the graph-based approach scales flexibly to high dimensional and object data, and allows for a universal analytic permutation p-value approximation that is decoupled from application-specific modeling. Despite this recent development, many challenges remain. This project identifies these challenges, formulates them into approachable frameworks, and develops appropriate methods and theoretical treatments. In particular, this project will (1) study more sensitive distance-based tests for testing equality of distributions in high dimensional or in non-Euclidean spaces, which will be adapted to the change-point testing and estimation problem, resulting in a more sensitive and accurate detection of general changes; (2) address methodological and theoretical issues in extending the nonparametric graph-based framework on the offline case to the online scenario; and (3) extend graph-based segmentation and online detection to a circular block permutation framework, enabling them to work for multivariate and object data with weak local dependence.

? DESCRIPTION (provided by applicant): Fragile X syndrome (FXS) represents the most common form of inherited cause of intellectual disability and the most frequent monogenic cause of autism spectrum disorder. As a result of a trinucleotide (CGG) expansion, the FMR1 gene is silenced and makes little or no FMR1 protein (FMRP). FMRP is an RNA-binding protein that negatively regulates protein synthesis. The loss of FMRP causes neurodevelopmental deficits and alterations in cerebral metabolism. ACT01 is a novel precision small molecule candidate drug inhibiting only the activity of dopamine transporter. In behavioral studies, administration of ACT01 restored phenotype behaviors of FMR1-null animals back to their wild type littermates. The outcomes of these experiments for the first time substantiate the possible links of abnormal behavior phenotypes, to reduced extracellular dopamine or dopamine deficiency, to excessive dopamine transporter activity, and to abnormal metabolism. These outcomes further suggest that ACT01 is a viable therapeutic candidate for FXS. DRI Biosciences intends to develop ACT01 toward FXS clinical use. The aim of the phase I proposal examines the effect of dopamine reuptake inhibition on 1) mediations of insulin secretion, 2) membrane protein presentation, and 3) the activities of the insulin signal transduction pathways. The entire body of research (Phase I/II) examines the effects (efficacy) of ACT01 on FXS pathophysiology and biochemistry, which will ultimately generate not only a useful medication but also a more relevant, translatable, and objective efficacy biomarker as an adjunct to the behavior assessments in human clinic.

With support from the Chemical Measurement and Imaging (CMI) program in the Division of Chemistry and the Molecular Biophysics Program from the Division of Molecular and Cellular Biosciences, Dr. Hao Chen and his group at Ohio University are developing powerful new analytical tools for the fundamental study of protein chemistry, with long-term aims of probing protein functions in living cells and organisms. The study seeks new methods for rapid elucidation of protein structures and insights into the mechanisms of electrochemical reactions involving proteins. The work provides research opportunities for both undergraduate and graduate students, including members of underrepresented groups. Dr. Chen is also actively involved in educational outreach activities, including ChemShows/Workshops/Seminars for pre-college students to learn about modern instrumental analysis and to develop enthusiasm for scientific research.

In this project, Dr. Chen's group is combining mass spectrometry (MS) and electrochemistry (EC) techniques for the study of protein redox reactions. The aim is a powerful tool for identification of the protein electrochemical reaction products and intermediates. One specific target is to combine fast and selective cleavage of disulfide bond linkages in a protein (via electroreduction) with MS analysis to enable protein sequencing and disulfide bond mapping. The work is also probing biomolecular electron transfer pathways for increased understanding of cellular oxidative stress and similar phenomena.

Machine learning techniques, particularly deep neural networks, are increasingly integrated into safety and security-critical applications such as autonomous driving, precision health care, intrusion detection, malware detection, and spam filtering. A number of studies have shown that these models can be vulnerable to adversarial evasion attacks where the attacker makes small, carefully crafted changes to normal examples in order to trick the model into making incorrect decisions. This project's goal is to develop formal understandings of and defenses against these vulnerabilities through characterizing the relationship between adversarial and non-adversarial examples, developing mechanisms that exploit this relationship to support better detection of adversarial examples, and metrics and methods to demonstrate the robustness of machine learning models against them. Together, the theories, algorithms, and metrics developed will improve the robustness of machine learning systems, allowing them to be deployed more securely in mission-critical applications. The team will also make their datasets and source code publicly available and use them in their own courses and research with both graduate and undergraduate students, with particular efforts to include students from underrepresented groups in Science, Technology, Engineering and Math. The work will also support high school outreach programs and summer camps to attract younger students to study machine learning, security, and computer science.

The project is organized around three main thrusts that combine to provide a holistic approach to modeling and defending against evasion attacks. The first thrust aims to characterize both normal and adversarial examples via systematic measurement studies. This includes considering different types of regions around specific examples (e.g., metric ball, manifold, and transformation-induced regions) and then characterizing the examples' vulnerability based on a number of algorithms for combining classifications of other examples in the nearby regions. The second thrust focuses on designing robust defenses against adversarial examples by using representative data points in a region, aggregating multiple data points, and using a diverse set of classifiers to reduce the vulnerability induced by using single data points or algorithms. The third thrust involves defining metrics for modeling robustness along with theories and algorithms that leverage those metrics to analyze model robustness. These include lower bounds of adversarial perturbation in metric balls, robustness metrics based on computational costs, analyses of the representativeness of new datasets relative to training data, and methods for leveraging human estimation of adversarialness.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Summary Cigarette smoking causes 480,000 deaths annually, making nicotine about 10 times more lethal than opioids. In addition, smoking-related illnesses in the United States cost more than $300 billion each year. Both genetic factors and social environment have a strong in?uence on smoking behavior. At least 26 human genome-wide association studies (GWAS) on smoking have been conducted to date. Only 11 loci, each accounting for 0.2?0.99% of the variances of a few self-reported phenotypes have been replicated. We have developed a model of nicotine self-administration in adolescent rats that captures the role of social learning in promoting nicotine intake. This operant licking model delivers intravenous nicotine with a contingent oral ?avor (i.e., taste and odor) cue. We found social learning facilitated the extinction of conditioned nicotine aversion and promoted nicotine intake. In the prior funding period, we have almost ?nished phenotyping 1,600 adolescent heterogenous stock male and female rats using this model. We also measured several social, novelty-seeking and anxiety-like behaviors in these rats. Our regression analysis showed that social and emotional-like behaviors explain approximately 30% of the variance in nicotine intake. We also sequenced the transcriptome of 440 samples from naïve rats. Our genetic analysis has identi?ed many quantitative trait loci (QTL) for both behavior and gene expression phenotypes. Human GWAS has shown that increasing sample size exponentially increases the number of signi?cant associations. Similarly, we have completed a GWAS of body weight and related traits using almost 3,200 rats. By examining what we would have found with only 1,600 rats, we show the increase in QTL from 1,600 to 3,200 is exponential rather than linear. Therefore, in this renewal application, we are proposing to extend our study by phenotyping an additional 1,600 rats, which will bring our ?nal sample size to 3,200. We anticipate the combined study will identify genes involved in different aspects of nicotine addiction, such as the rewarding and aversive effects of nicotine, progression of nicotine intake, and relapse, among many others. We plan to maintain the experimental design from the last funding period, because it worked well, and to assure that the full cohort of 3,200 rat is as homogeneous as possible. However, we will add a new social interaction test, where we will analyze the social behaviors of two freely moving rats using Yorodent, an arti?cial intelligence-based analysis method developed in our lab. In Aim 1, we will phenotype adolescent heterogeneous rats. Breeders will be obtained from Core B (HS Breeding Core), which we will use to generate 400 adolescent rats per year in years 1-4. These rats will ?rst be phenotyped for their social, novelty-seeking and anxiety-like behaviors. They then will be implanted with a jugular catheter. Nicotine IVSA will start on postnatal day 38. In Aim 2, We will analyze the relationships between behavioral traits using regression and genetic correlations. We will also perform a phenome-wide associations study to identify pleiotropic effects of genetic variants identi?ed in this project. In Aim 3, we will obtain brain tissues that are anatomically precise from naïve rats to expand our transcriptome database. These data will provide mechanistic insights for behavior associations obtained from Projects 1?3 and be used by Project 4 for network analysis.

Abstract and Project Summary Fragile X Syndrome (FXS) is a leading inherit cause of intellectual disability. The goal of this project is to develop effective medications to improve conditions of FXS. The proposed research is based on the hypothesis that a combined administration of ACT01 and DHA to the Fmr1 KO mice, a mouse model of FXS, will rescue brain development. The hypothesis is based on experimental observations that dopaminergic deficit and adrenergic excess are involved in the delayed Fmr1 KO brain development. ACT01 is a dopamine reuptake specific blocker capable of reversing DA deficit; DHA is an n-3 fatty acid reportedly capable of attenuating excessive norepinephrine actions. DA and DHA are required for brain functions. ACT01 and DHA combination should restore catecholaminergic balance and rescue brain development. The research will characterize the effects of ACT01, DHA, and their combination, on behaviors and blood and cerebral biochemistries. The behavior research examines the effects of each agent and their combination behaviors associated with different brain regions. The blood biochemistry characterizes the effects of ACT01, DHA, and their combination on catecholaminergic regulated lipid and glucose metabolic balances. Studying these metabolic activities as biomarkers is a mean to assess these agents' targeted (catecholaminergic activities) engagements. The cerebral biochemistry focuses on examining expression levels of neuronal and astrocytic proteins (e.g., glucose transporters) down stream from respective mTOR pathways of different cells. Catecholamines take part in regulating the balanced expressions of these proteins during brain maturation process. Combination of ACT01 and DHA should help to restored DA-NE balance. Restored catecholaminergic homeostasis should rescue behavior, normalize metabolic activities, and restore the neuronal-astrocytic balance. Completion of these studies will provide a coherent body of proof-of-concept experimental evidence to support our goal of providing effective therapeutic for FXS.

Project Summary/Abstract. The goal of this project is to test the hypothesis that perfluoroalkylated substances (PFAS) negatively impact formation of the placenta, and consequently, pregnancy outcomes. This work gains added significance in light of the increasing public health concerns towards these persistent compounds. The proposed experiments will also fill gaps in our understanding regarding the effects of these chemicals during human placental development, about which little is known. Pregnant mothers are exposed to a variety of chemicals, including PFAS. The latter exposures are widespread and high levels are linked with adverse effects on thyroid function, cholesterol metabolism, and birth outcomes. The placenta, a temporary embryonic/fetal organ that forms during pregnancy, facilitates gas, nutrients, and waste exchange with the mother. Deficiencies in placental development and function underlie numerous pregnancy complications, such as preeclampsia and intrauterine growth restriction. Despite its importance much remains unknown about the placenta, especially its role as a toxicological target. Here I propose studying PFAS effects on the organ's population of progenitor cells, termed cytotrophoblasts (CTBs), which establish the architecture of the maternal-fetal interface during pregnancy. To do so I will use an in vitro model of this process. Primary CTBs will be isolated and exposed to PFOA, PFNA, or GenX. The toxicological effects of these PFAS will be elucidated in two ways. First, using the CTB model, relevant effective concentrations of PFOA, PFNA, or GenX will be determined and a mass spectrometry-based approach will be used to determine their global effects at the level of the proteome (Aim 1). Second, honing in on levels relevant to public health exposures, the functional relevance of PFAS protein targets that could play hierarchical roles in placental development will be investigated by mimicking the observed chemical effects, e.g., up or down regulation (Aim 2). Thus, the results of these experiments will advance our knowledge about the human health effects of the compounds during a critical developmental window. Completing this study will advance the applicant's training in important new directions that are enabled by the expertise of his primary mentor, Dr. Susan Fisher: human placental biology and mass spectrometry- based proteomics analyses. Dr. Hao Chen will receive valuable input from his mentorship team, composed of experts in prenatal environmental exposures, bioinformatics, and reproductive biology. In collaboration with his mentors, Dr. Chen will develop critical skills that are required for a successful transition to an independent academic career in environmental health. This will be accomplished through a focused development plan consisting of didactic courses and close collaboration with his mentors. At the conclusion of this proposal, Dr. Chen will have led the first investigation of PFAS effects on CTBs and their function, providing insight into the impact of these chemicals towards developmental and reproductive health.

Abstract: Mass spectrometry (MS) is powerful in protein discovery and identification. Nevertheless, accurate MS quantitation of peptides and proteins has challenges due to the fact that the MS signal fluctuates and the ion signal intensity does not correlate well with the amount of sample. Typically, popular MS quantitation relies on using isotope-labeling methods which have associated drawbacks including the need for expensive and time-consuming synthesis of isotope-labeled peptide, limitation in multiplexing analysis, and non-identical ionization efficiencies/elution times for heavy and light isotope-labeled peptides during LC/MS run. Herein we propose a conceptually new approach of using electrochemistry (EC)-assisted mass spectrometry (MS) for absolute quantitation for both peptides and proteins, without using any standards or isotope-labeled peptides. It could also allow direct quantitation of modified peptides such as phosphopeptides and simultaneous quantitation of multiple proteins in a mixture. In our approach, a target peptide, if containing an electroactive residue (e.g., tyrosine, cysteine, or tryptophan), is first introduced to an electrochemical cell for electrochemical oxidation and followed by MS detection. According to Faraday's Law, the total electric charge (Q), which is responsible for peptide oxidation in coulombs, is proportional to quantity of the oxidized peptide: Q = nzF, where n is the moles of the oxidized peptide, z is the number of electrons transferred per molecule during the redox reaction, and F is the Faraday constant (9.65×104 C/mol). Q can be directly measured from the integration of Faradaic current over time. The moles of the oxidized peptide can be calculated as n = Q/zF. Meanwhile, the peptide shows reduced intensity in the acquired MS spectra upon oxidation, and the relative MS intensity change upon oxidation, ?i, can reflect the oxidation yield. Thus, the amount of target peptide converted, in combination with the oxidation yield, can be used to calculate the total amount of target peptide. Such a strategy is proposed to quantify peptides including those carrying post- translational modifications (e.g., phosphopeptides, glycopeptides) and proteins (e.g., G-protein coupled receptor GPCRs and circadian clock proteins) in this proposal. This method is expected to have significance not only in GPCR-related disease studies but also help understand the circadian regulation of the gene expression of cyanobacteria. It would lead to a paradigm shift in quantitative proteomics and prosperous biological applications.

Abstract The current opioid epidemic is fueled by the steady rise of prescription painkillers, such as OxyContin, which is a controlled-release tablet of oxycodone. Although both clinical and animal studies have found that the rate of onset of drug action influences the development of addiction, the exceptionally strong abuse liability of oxycodone was manifested even when it was consumed in the controlled-release from. The heritability of opioid addiction has been estimated to be approximately 0.5 in humans. However, few human genetics studies have been conducted due to the difficulty in assembling the necessary large study population. In this proposal, we aim to conduct a genetic mapping study to identify genetic factors influencing oxycodone-motivated behaviors and vulnerability to stress, a major risk factor of opioid use disorder. To follow the clinical use pattern, we developed an operant oral oxycodone self-administration model, where rats voluntarily consume oral oxycodone to obtain doses that are well above clinical prescriptions. The WMI and WLI inbred strains of rats we propose to use in this study were selectively bred from the stress-vulnerable Wistar Kyoto rat. The WMI is an established animal model of depression and vulnerability to stress, while the WLI serves as its isogenic control. Our preliminary data showed higher levels of oxycodone intake and oxycodone seeking in the WMI compared to the WLI strains. We also found that females have higher oxycodone intake than males. There were also strain and sex differences in basal plasma corticosterone (CORT) and steady-state hippocampal glucocorticoid receptor (Nr3c1) expression. We therefore hypothesized that genetically-determined stress response to oxycodone withdrawal drives the strain differences in oxycodone self-administration and reinstatement of oxycodone seeking. In Aim 1, we will use a reduced complexity mapping strategy to identify the causal genetic factors for oxycodone and stress response phenotypes. This mapping strategy is supported by the high heritability, large effect size of strain on phenotypes, and existing whole genome sequencing data for the WMI and WLI strains ( ~100x coverage per strain, with ~4,400 high confidence polymorphisms between strains). In Aim 2, we will identify candidate genes using a systems genetics approach. The low number of segregating variants between WLI and WMI greatly facilitates this goal. In Aim 3, we will confirm causal genes using an established knockin CAG-LSL-Cas9 rat model on the WMI/WLI genetic background.

The steady rise in prescription opioids such as oxycodone has led to widespread abuse and deaths in the US. importance of drug pharmacokinetics in determining abuse potential, we have designed an oral operant rat self-administration (SA) procedure to model the pattern of drug intake of most human users/abusers of oxycodone, who initiate using oral tablets. Although genetic variants play important roles in susceptibility to opioid addiction, very limited data are available regarding specific genes and sequence variants that predispose to opioid addiction, and under what conditions. Given the We propose to use an innovative hybrid rat diversity panel (HRDP), which consists of 91 diverse rat genomes, to identify genetic variants influencing operant oxycodone intake in rats. The HRDP is unique in that it: 1) contains a high level of genetic diversity similar to that of human populations; 2) provides a way to control oxycodone exposure and to systematically study gene-by-environment and gene-by-drug interactions; and 3) integrates multi-omics addictome data: from genetics to epigenomics to brain connectomes to treatments. We have three aims: Aim 1: We will analyze whole genome sequencing data to define virtually all sequence variants that underlie heritable variations. De novo assemblies will be conducted using linked-reads data for selected high impact strains. Hi-C data (Dovetail Genomics) will be generated to further improve the quality of these assemblies. We will also generate RNA-seq data for key brain regions to obtain mechanistic insights into oxycodone intake. Aim 2: Using the HRDP (both sexes), we will phenotype oral oxycodone SA with a unique behavioral model. Rats will also be tested for sensitivity to pain, social behaviors, and anxiety-like traits - all signs of oxycodone withdrawal. Critically, we estimated the heritability (h2) of oxycodone intake in the range of 0.3 ? 0.4. When using n=6/sex, the effective h2 is ~0.8 ?sufficient for high precision mapping. Aim 3: We will use systems genetics methods to map and integrate behavioral phenotypes with sequence and transcriptome data. Both forward (QTL) and reverse (PheWAS) genetic methods will be used. We use new linear mixed models to map and test candidate genes with key cofactors using the GeneNetwork2 platform. Finally, we evaluate the translational relevance of candidate genes and biomarkers by comparison to GWAS cohorts and longitudinal reports of addiction in humans. Technical and conceptual advances that underlie this application are: new genomic methods combined with highly diverse rat populations allow us to quickly define novel gene variants that modulate key phases of opiate addiction. It is highly likely that a subset of variants and molecular networks we define will provide key components of a predictive framework linking sequence differences to human opioid addiction and potential treatments. This project uses new systems genetics approaches, open source genomic data and software, and a new type of hybrid rodent mapping panel to precisely define causal linkages between DNA variation and voluntary oxycodone intake.

The rat is the most commonly used model organism for behavioral studies of addiction. We propose to establish an innovative hybrid rat diversity panel (HRDP) in this work. The HRDP is unique in that it integrates: 1) a high level of genetic diversity similar to that of admixed human populations; 2) a way to control drug exposures and to systematically study gene-by-environment and gene-by-drug interactions; and 3) a way to integrate addictome data across scale: from genetics, genomics, and other molecular data together with key addiction related risks. This work will be a step toward developing experimental resources for precision medicine. The HRDP consists of 91 highly diverse genomes of rats that are all open access and can be used by any investigators to study facet of addiction and in different environments or under different treatments. We will use the HRDP to identify sequence variants that control motivational effects of nicotine with a menthol cue. Approximately 25% of smokers prefer mentholated cigarettes. Clinical studies have shown that menthol facilitates initiation, enhances dependence and makes quitting more difficult. Given the large sample size needed in human studies to identify key sequence variants associated with drug addiction, we argue that animal models provide an efficient means to define and test genetic and molecular mechanisms that contribute to the addiction-enhancing effects of menthol. We developed a rat model of nicotine i.v. self-administration (IVSA) with an oral menthol cue. We found that 1) menthol facilitates the acquisition of nicotine IVSA, 2) rats that receive the menthol cue for nicotine show a strong extinction burst, a model for drug craving, and 3) these rats also demonstrate a strong cue-induced reinstatement, a model of relapse. We also showed that the cooling sensation of menthol functions as a conditioned cue for nicotine reward, and that oral menthol treatment increases brain nicotine accumulation. Critically, in the context of this U01 mechanism, we estimate that heritability of these traits are greater than 0.6. We have three aims: In Aim 1 we conduct whole genome sequencing of the HRDP. We will define all sequence variants that underlie heritable variation using innovative linked-read libraries and de novo assemblies. In Aim 2 we phenotype nicotine IVSA with a menthol cue in adolescent HRDP animals of both sexes with deep replication. We will phenotype nicotine IVSA with a visual cue as a control. Effects of oral menthol on brain nicotine level will be measured. In Aim 3 we use systems genetics methods to map and integrated behavioral phenotypes. Both forward (QTL) and reverse (PheWAS) genetic methods will be used. We will use new linear mixed models to map and test candidate genes with key cofactors (i.e., different cues). Finally, we evaluate the translational relevance of candidate genes and biomarkers by comparison to GWAS cohorts and longitudinal reports of addiction in humans. This U01 will define high impact variants and molecular networks, and will provide a predictive and expandable experimental framework to link sequence differences to critical aspects of human nicotine addiction.