Grants

NHLBI - National Heart Lung and Blood Institute

PROJECT SUMMARY/ABSTRACT Cardiac IKr is a critical repolarizing potassium current shaping the human ventricular action potential. It is conducted by heteromeric assemblies of the human ether-à-go-go-related gene (hERG1) 1a and 1b subunits. These subunits are encoded by alternate transcripts of the hERG/KCNH2 gene and differ only in their amino- terminal regions. hERG1a/1b heteromerization is vital for normal CM function, as the imbalance of subunit expression and/or function results in cellular pro-arrhythmic behaviors. hERG1a/1b assembly is mediated by the co-translational association of the encoding mRNAs in HEK293 cells, cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs), and human myocardium. Evidence suggests that interaction between the nascent proteins is not required for the co-translational complex assembly. This grant's preliminary findings indicate that this complex assembly occurs post-transcriptionally and is promoted by direct interactions between hERG1a and 1b mRNAs governed by their secondary structures. In preliminary studies, RNA binding proteins DDX3X and DDX5 were identified as part of the complex, and purified DDX3X promoted hERG1a/1b mRNAs' association in vitro. In the K99 phase, I will define the mRNA structural features promoting the co-translational association and determine the affinity and energies of the RNA/RNA interaction using in vitro systems, isothermal calorimetry (ITC), mutagenesis, hybrid protein-RNA immunoprecipitation (RIP), and live-cell imaging. I will also determine whether DDX3X and DDX5 affect hERG1a and 1b mRNAs stability, translation, and association in hiPSC-CMs using qPCR, electrophysiology, Western Blot, ribosome profiling, RIP, and single molecule fluorescent in situ hybridization (smFISH). I will use quantitative ITC and in vitro reconstitution approaches to determine the specificity, affinity, and energies of the interaction between purified DDX3X and DDX5 with hERG1a and 1b mRNAs. I will also evaluate if DDX3X and DDX5 promote the association of the mRNAs in in vitro systems. In the R00 phase, I will determine whether the stability, translation, and association of hERG1a and 1b mRNAs are impaired in arrhythmias associated with type 2 long QT syndrome (LQT2). I will use hiPSC-CM disease models to evaluate half-life, translation rate, and association of the mRNAs with qPCR, ribosome profiling, RIP, and smFISH. These experiments will contribute to understanding ion channel biogenesis and elucidate molecular mechanisms underlying LQT2 related arrhythmias. This proposal is designed to fulfill my short-term goals of expanding my skills in cardiovascular research and biophysics and transitioning into the independent phase of my career. This will ultimately allow me to obtain my long-term purpose of linking RNA and ion channel biophysics to translational cardiovascular research.

NIAID - National Institute of Allergy and Infectious Diseases

PROJECT SUMMARY Crohn's disease and ulcerative colitis, known collectively as inflammatory bowel disease (IBD), exhibit distinct and progressive patterns of inflammation in the gastrointestinal (GI) tract, sharing overlapping symptoms with other illnesses and complicating diagnosis. Although endoscopy provides visualization useful for diagnostics, it has limited reach within the GI tract and cannot provide explicit physiological measurements. Bioimpedance, a surrogate for epithelial barrier integrity, is a promising technique for quantifying dielectric tissue properties relevant to the state of inflammation in the gastrointestinal tract. However, research studying the physiological implications of bioimpedance is limited beyond the esophagus due to variations in inflammatory response and inconsistent sensor contact in the GI tract, obfuscating the real-time correlation between tissue impedance and inflammatory state. To address this, we propose to adapt a previously developed ingestible capsule platform to collect real-time measurements of pH, pressure, and bioimpedance of epithelial tissues. In prior work, we have successfully demonstrated capsule-embedded bioimpedance sensors for wireless monitoring of tissue permeability of excised colonic tissue from mice and in a cecum using a rat colitis model to evaluate the potential of bioimpedance for disease assessment. We hypothesize that a multimodal sensing approach utilizing ingestible capsule technology will enable access to barrier integrity information in local tissues throughout the GI tract, expanding the toolbox of approaches for early detection of inflammatory GI diseases. Through two distinct aims, the proposed capsule device will correlate changes in bioimpedance and intestinal permeability in the GI tract—phenomena not easily detected by traditional capsule endoscopy—and demonstrate safe GI transit using animal models. Aim 1 will focus on benchtop evaluation. Bioimpedance data will be collected from freshly excised ex vivo tissues of adult pigs with altered intestinal barrier integrity. Additionally, we will enhance measurement reliability while reducing the capsule size by integrating an array of bioimpedance and pressure sensors around the capsule to confirm tissue contact and microfabricating a pH sensor for capsule localization. The capsule will be evaluated in simulated intestinal fluids, and by ex vivo peristaltic simulators (i.e., modified organ chamber) to characterize the device under interferent GI conditions such as motion and acidic pH levels. In Aim 2, we will collect bioimpedance, contact pressure, and pH data in vivo during two animal studies in healthy adult pigs. One study will feature surgically implanted capsules fixed at stationary locations during measurement to ensure sensor contact and separate measurements by tissue type. Additionally, a small-scale pig study will be performed to understand capsule motility and ensure the safe passage of the capsule throughout GI transit while validating the reliability of the measurement. This aim will unveil measurement variability in vivo, generate datasets for statistical interpretation, and assist with determining feasibility of our technology toward future studies of permeability as an early-stage biomarker for inflammation.

NSF

Biological cells utilize ions to signal and communicate with neighboring cells and their environment. Despite the critical role of ionic signaling in biological systems, our understanding of such phenomena remains limited. Ionic kinetics, crucial for cellular functions and signaling, are only understood qualitatively due to the lack of near-real-time monitoring technologies. Studying and manipulating these ionic signals not only helps in understanding how microorganisms communicate, evolve, and develop, but also enables the probing and controlling of cellular activities. This proposal aims to develop innovative soft ionic transistors to measure and analyze the ionic characteristics of biological cells and their environments. This project seeks to bridge the gap between rigid electronic devices and soft ionic biological systems by designing soft ionic transistors that use cellular ions to trigger their gating mechanism. This advancement would enable the study and could significantly enhance the understanding of cellular functions, stress responses, and the role of ions and ionic signals in biological systems, thereby providing deeper insights into biological mechanisms. The outcomes of this project are expected to have broad societal impacts, including significant effects on drug discovery and testing, personalized medicine, aging studies, and novel therapeutic strategies such as pain management and rehabilitation. Ionic kinetics and the resultant intracellular-extracellular ion concentration gradients control cellular functions and intercellular signaling and communications. These ion concentration gradients indicate the health and functionality of cells and organs. Despite its critical role, the current understanding of cellular ionic kinetics remains largely qualitative, and the capacity to evaluate cellular functions based on their ionic activities is very limited. These limitations stem from the absence of technologies that can monitor ionic activities at the cellular level in near-real-time and continuously. This proposal aims to advance the understanding of cellular ionic kinetics by developing soft ionic transistors capable of continuously monitoring ionic activities at the cellular level. These transistors feature a novel gating mechanism triggered by ions secreted during cellular activities, allowing for near-real-time, quantitative evaluation of cellular ionic functions and addressing the current technological gap in monitoring ionic activities in biological systems. The central hypothesis of this research is that ion concentration gradients can polarize the ionic environment inside a transistor, forming electron-conductive ionic double layers at the bioenvironment-transistor interface. This hypothesis will be tested through in-vitro cell studies facilitated by soft ionic transistors. The project will involve designing, fabricating, and characterizing soft ionic transistors, followed by in-vitro cell studies to test the central hypothesis. Machine learning algorithms will be employed to develop a comprehensive model of the ionic attributes of biological systems, establishing relationships involving multiple interrelated physical, chemical, and biological variables. The anticipated outcome is a significant enhancement in the quantitative evaluation of cellular ionic activities and understanding of the correlations between ionic kinetics and cellular functions, thus bridging a critical gap in current knowledge of cellular ionics and their role in biological systems. The project outcomes are expected to have broad scientific and societal impacts. The ability to chronically study developing and growing biological systems will significantly impact various scientific fields, including drug discovery, aging research, personalized medicine, and novel therapeutic strategies such as pain management, all of which have broad societal impacts. 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.

NCI - National Cancer Institute

The spatial tumor microenvironment heterogeneity is considered as a hallmark of cancer and a main driver of disease progression and therapeutic resistance. The spatial positioning of tumor infiltrating lymphocytes (TILs) can reflect the capacity of the adaptive immune system to fight cancer, and the presence of a non-uniform or spatially heterogeneous T-cell response could limit tumor elimination and favor clonal selection. B-cells can also mount antigen specific responses, are involved in cancer rejection, and commonly form nodular aggregates within the TME known as tertiary lymphoid structures (TLS) that have been recognized as critical to the establishment of productive anti-tumor immunity and immunotherapy responses. Although spatial tumor heterogeneity has been recognized as a key feature of cancer and identified in most solid malignancies, including non-small cell lung cancer (NSCLC), little is known about its properties, and it is not being considered or used clinically. Possible reasons explaining why progress has been limited in understanding intratumor spatial heterogeneity include the paucity of suitable experimental models and the lack of standardized metrics to measure it. In this project, we hypothesize that the spatial TIL heterogeneity (STH) is a measurable biological determinant of disease progression and sensitivity/resistance to anti-cancer immunotherapy in human NSCLC. We will address this hypothesis through 2 independent aims with complementary aspects and prominent integrative potential: Aim 1 will elucidate the biological and clinical significance of the STH in NSCLC; and Aim 2 will analyze the impact of anti-cancer therapies on the spatial immune heterogeneity in NSCLC. The results from this project will expand our understanding of the biological determinants and significance of spatial immune heterogeneity in NSCLC and could open new avenues for the development of clinically relevant biomarkers or novel anti-cancer therapeutics.

NSF

After infancy most mammals stop secreting lactase (the enzyme that breaks down milk’s sugar, lactose) and lose their ability to digest milk. Some humans, however, can digest milk throughout their life because they carry genes that code for lactase persistence (LP). These genes emerged independently, and were selected for, in several populations where milk was a central food staple. This study examines the genetic, microbiome, and cultural factors that allow adult milk digestion among groups of peoples that are milk-dependent but lack LP genes. Additionally, the study examines whether genes that impact lactose tolerance co-evolved with those that permit the metabolization of high levels of milk-derived sugars. The study provides training opportunities in field, genetic, and computational methods and is relevant to human evolutionary history and public health. This study collects novel genetic, microbiome, and phenotypic data to expand current knowledge on genetic and cultural adaptations to milk consumption. Cutting-edge functional genomics approaches enable the identification of novel enhancer regions near the lactase gene and near genes involved with sugar metabolism. Genetic data integration allows for the identification of novel genetic variants influencing lactase persistence. Lactose tolerance tests are applied in tandem with a genome wide association study (GWAS) to identify novel genetic loci linked to milk-induced blood sugar level increases. Gut microbiome sequencing enables the identification of bacteria that aid in digesting lactose and quantifies the extent to which milk preparation practices shape the composition of the gut microbiome. The study creates a large genomic and phenotypic database that informs the understanding of recent human evolutionary history and gene-culture co-evolution. 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.

U.S. National Science Foundation

Biological Anthropology Program Senior Research Awards

NIA - National Institute on Aging

ABSTRACT Dementia prevalence and disease course differs by sex. Yet, despite being the leading cause of young onset dementia, very little is known about sex differences in frontotemporal dementia (FTD). Our accumulating data suggest females with FTD show patterns of “cognitive resilience” consistent with delayed symptom manifestation, despite higher neurodegenerative burden. We hypothesize that female-specific biology drives the unique clinical course of women suffering from FTD. Our overarching goal is to deeply characterize understudied female biology to unlock new insights into FTD pathophysiology, precision treatments, and novel dementia risk and resilience targets for all brains. To do so, our proposal will leverage two deeply phenotyped, longitudinally-followed local and international cohorts of genetic and sporadic forms of FTD and healthy controls (n=1095). We will be the first comprehensive evaluation of biological sex differences in FTD. Our Aims will: 1) Prospectively assay longitudinal sex hormone levels in plasma (e.g.,17𝛽-estradiol, testosterone, progesterone), 2) Identify sex-specific proteomic signatures of FTD from cerebrospinal fluid and plasma, 3) Collect a new female reproductive health history measure (e.g., age of menopause, pregnancies), and 4) Develop disease progression models that incorporate sex-specific factors to precisely prognosticate FTD trajectories in males and females. This proposal contributes to a high priority, underserved scientific area. For instance, among neuroimaging papers published since the 1990s, <0.5% consider factors specific to the female body. This knowledge gap creates treatment inequities as evidenced by the currently available therapy for Alzheimer’s disease (Lecanemab), which shows less than half the efficacy in females compared to males. Our proposal will therefore address a major health disparity, advance precision approaches to FTD before treatment inequities arise, and leverage overlooked female biology to discover new insights into dementia prevention targets relevant for all brains.

NCI - National Cancer Institute

Existing literature indicates significant differences in outcomes among patients with HPV+ OPSCC, with some groups experiencing poorer survival rates. Our analysis of two independent OPSCC cohorts from Cleveland supports these findings; patients identified as p16+ in one group had a 64% and 69% lower risk of death compared to another p16+ patient group at the University Hospitals Seidman Cancer Center and MetroHealth System, respectively. Interestingly, time to treatment initiation, defined as the number of days between pathologic diagnosis and the start of treatment, was statistically similar between these groups in both cohorts. This suggests that differences in access to medical care are not the primary contributor to the observed survival difference. While clinical outcomes in OPSCC are multifaceted and influenced by various interconnected factors, our findings highlight the potential role of biological differences in contributing to the survival gap between these patient populations. Our Cleveland-based OPSCC study cohort consists of 562 patients, including 74 individuals from this high-risk subgroup, representing the largest such cohort studied to date with HPV status based on NGS. Our proposed work, which leverages our robust OPSCC cohort, aims to provide valuable insights into the biological factors, such as HPV status, HPV genotype, and tumor-immune microenvironment landscape, which may contribute to the observed differences in clinical outcomes.

U.S. National Science Foundation

Biological Oceanography

NIGMS - National Institute of General Medical Sciences

Abstract EXPERA are a family of membrane proteins that include sigma-2 receptor and TM6SF2, both of which play crucial yet understudied roles in cholesterol metabolism and disease. Sigma-2, an enigmatic membrane receptor implicated in cancer, Alzheimer’s disease, and neuropathic pain, has attracted significant pharmacological interest due to its ability to bind a multitude of structurally diverse compounds with high affinity. Despite this therapeutic potential, the gene coding for sigma-2 remained unknown for decades until I cloned it from tissue and identified it as the ER membrane protein TMEM97. Subsequently, I solved the crystal structures of sigma-2 in complex with high-affinity ligands and discovered dozens of novel ligands through virtual docking. However, sigma-2's precise biological function remains unclear. It interacts with key proteins involved in sterol trafficking, such as Niemann-Pick type C1 (NPC1) and the low-density lipoprotein receptor (LDLR), but the regulatory mechanisms driving these interactions are poorly understood. Sigma-2’s dynamic ability to shift between different protein interaction networks in response to ligand binding or sterol stress offers promising therapeutic potential, though the molecular events behind these effects remain to be elucidated. TM6SF2, another member of the EXPERA family, is a regulator of lipid metabolism, particularly in the secretion of very-low-density lipoprotein (VLDL). Mutations in TM6SF2 are strongly associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as NAFLD, which is a leading cause of chronic liver disease. Despite its potential as a therapeutic target, TM6SF2 remains largely understudied, with no known ligands and limited structural information. Understanding TM6SF2’s role in lipid homeostasis and discovering ligands that modulate its function is critical for developing therapeutic interventions. Building on my previous success in cloning and solving the structure of the sigma-2 receptor, my research aims to address these significant knowledge gaps. We will use structural biology techniques to characterize the complexes that sigma-2 forms with key cholesterol homeostasis proteins, such as NPC1. Additionally, we will investigate sigma-2’s protein interaction network and how it shifts in response to ligands and stress conditions. These studies will improve our understanding of sigma- 2’s role in cholesterol homeostasis and inform the development of targeted therapies. Simultaneously, we will conduct structural and functional studies of TM6SF2 to discover novel ligands and clarify its role in lipid metabolism. By establishing ligand-binding assays and utilizing virtual docking, we aim to uncover new druggable mechanisms for treating MASLD and related metabolic disorders. Overall, this research will advance our molecular understanding of sigma-2 and TM6SF2, two promising therapeutic targets. By elucidating their roles in cholesterol and lipid regulation, we aim to better understand these critical physiological processes, and to leverage this knowledge to develop new strategies for treating cancer, Alzheimer’s disease, MASLD, and cardiovascular diseases, addressing significant unmet clinical needs.

NIGMS - National Institute of General Medical Sciences

PROJECT SUMMARY The subcellular localization of a biomolecule often reveals its functional significance. RNA has typically been confined to the cytoplasm and nucleus, where it is critically involved in numerous biochemical processes. Recently, glycosylated RNAs (glycoRNAs) have been discovered on the cell surface of various cell types, raising important questions about their functional relevance, mechanisms of action, and the trafficking pathways that maintain their dynamic equilibrium on the cell surface. The long-term goal of this proposal is to discover and define the functional mechanisms of cell surface RNAs in endocytosis and pathogen endocytic trafficking. Preliminary evidence suggests that cell surface RNAs are critically required in human papillomavirus (HPV) infection. This MIRA proposal is firstly focused on deciphering the functional roles of cell surface RNA in HPV endocytic trafficking. Here a combination of advanced imaging strategies, biochemical techniques, and genetic analysis will be used to dissect and determine the specific step in HPV cell entry that is regulated by cell surface RNAs. We are also exploring the mechanisms underlying this process by leveraging recent discoveries from my lab, including the identification of the cell surface RNA binding protein (RBP) DDX3X, a potential recognition site for cell surface RNA-DDX3X clustering on the HPV cell-penetrating peptide (CPP), and glycoRNA transcripts identified through RNA sequencing. Lastly, we are investigating the trafficking modes utilized by cell surface RNAs, which are critical for their cell surface presentation and functions. Collectively, this research addresses fundamental questions in cell membrane trafficking and provides valuable insights into the recently discovered cell surface RNAs. It may also introduce a new concept in which cell surface RNAs and RBPs cluster to form an endocytic receptor complex involved in pathogen cell entry. While many exciting questions lie beyond the scope of this MIRA proposal, the findings could stimulate broad interest, offer proof-of-concept frameworks, and develop techniques that enhance our understanding of the role of cell surface RNAs in endocytosis and pathogen entry mechanisms.

NIEHS - National Institute of Environmental Health Sciences

Wildfires are occurring across the US and globally with potentially harmful impacts on maternal and child health. Although research into health effects specific to wildfire smoke exposure in pregnancy is nascent, a recent meta-analysis on more than 1.7 million births showed that maternal exposure during late pregnancy was linked to reduced birth weight and preterm birth. However, the short and long-term effects of repeated wildfire smoke exposure during pregnancy on maternal and fetal health outcomes have not been investigated in depth, nor are any molecular mechanisms responsible for such effects well understood. Therefore, there is an urgent need to understand the how wildfire smoke exposure affects health and wellbeing. One hypothesized mechanism to facilitate biological communication from pollutants inhaled in the lung to distal organs and tissues is through extracellular vesicles (EVs). EVs contain a variety of biologically active molecules including microRNAs (miRNAs) which are small ~22 nucleotide-long noncoding RNA molecules. EV-miRNA might be the ideal candidates to mediate effects of wildfire smoke exposures on pregnancy because they can be produced by the respiratory system where the initial exposure occurs and then enter the circulation to affect distant tissues and organs. We hypothesize that prenatal exposure to wildfire smoke triggers a biological response that can be measured in EV-miRNA, and that these wildfire smoke-related biological signatures are negatively associated with fetal and infant growth. We will additionally investigate the interplay between smoke exposure and miRNA signatures with neighborhood characteristics, including housing, infrastructure, and other factors that may modify effects on fetal and infant growth. We will examine this hypothesis in 466 participants in MADRES—a cohort representative of the population living in Los Angeles, CA – in the following aims: Aim 1) Identify unique EV-miRNA transcriptomic signatures of wildfire smoke across pregnancy and the biological pathways associated with their predicted gene targets in a population of 466 pregnant participants with 666 maternal biospecimens. Aim 2) Evaluate the influence of wildfire-associated EV-miRNA signatures on ultrasound-measured fetal growth, infant birthweight, body composition and child growth through age 7. We will additionally evaluate effect modification by sex of child and neighborhood characteristics. Aim 3) In exploratory analyses in a subset of 96 mother/child pairs, we will test whether wildfire smoke exposures affect newborn miRNA levels in cord blood and further investigate the correlation of EV-miRNA expression profiles between mother and infant. Findings from this study may inform future screening, diagnostic, or treatment, interventions by helping us understand the biological effects of wildfire smoke.

NINDS - National Institute of Neurological Disorders and Stroke

The objective of this Task Order (TO) is to perform non-GLP toxicity and toxicokinetic studies in support of a Helping End Addiction Long-term (HEAL) Pain Therapy Development Program (PTDP) project. This Indefinite delivery, indefinite quantity (IDIQ) contract provides neuroscience product developers/innovators with a full range of industry-style, biologic drug-development services and expertise for their development of biological therapeutics for diseases of the central nervous system. These product developers/researchers are funded through other programs at the National Institutes of Health (NIH) but may lack the entire suite of capabilities necessary to advance their candidate product to a phase I clinical trial. Therefore, this contract has been put in place to augment the progress of the candidate product by providing the support required at the specific stage of its development and optimization path. The services offered through this contract are assay development, pharmacokinetics, manufacturing, formulation, fill-finish, investigational new drug (IND)-enabling toxicological studies, and support through phase I clinical trial.

NSF

This project introduces novel nanobodies, or VHHs, as cost-effective, reproducible and tunable alternatives to traditional growth factors in cell culture media. Cell culture is fundamental to modern biotechnology, supporting applications from regenerative medicine to cultured meat production, yet current methods often rely on expensive and inconsistent animal-derived growth factors. VHHs, which are small antibody fragments, can be precisely designed to activate cell growth receptors, mimicking natural proteins at a significantly lower cost. Leveraging a novel microbial production system, we can drastically improve growth factor prototyping, reduce manufacturing costs and eliminate batch-to-batch variability common with conventional growth factors. Project outcomes will lead to advances in cell culture methods with broad impacts on human health, sustainable food production, and U.S. biomanufacturing competitiveness. This project aims to engineer novel VHH-peptide fusions as next-generation growth factor alternatives for synthetic cell culture media. Our core innovation involves creating bispecific molecules that combine a receptor-binding VHH with activating peptides. Specifically in this work we will target growth factor alternatives which activate the FGF receptor (FGFR) and the epidermal growth factor receptor (EGFR). These VHHs will be produced using a two-stage bacterial expression system in E. coli, which enables soluble expression, proper disulfide bond formation, and simplified purification, significantly reducing production costs (projected from $15,000/g to $100-500/g). Our approach is systematic, beginning with the design and validation of FGF receptor-activating VHH fusions using cell-based reporter assays and surface plasmon resonance to assess binding kinetics and activation. Subsequently, we will leverage Design of Experiments methodology to optimize complete synthetic media formulations for pluripotent stem cells and bovine satellite cells, ensuring robust proliferation and maintenance of cellular identity. A multi-omics strategy, encompassing RNA-seq transcriptomics, proteomics, phosphoproteomics, and targeted metabolomics, will provide a systems-level characterization of cellular responses to the VHH-fusions, comparing them to natural growth factors across multiple temporal points to capture signaling dynamics and validate functional equivalence. The platform will then be expanded to engineer EGFR-activating VHH-fusions for mesenchymal stem cell culture. This research will generate comprehensive molecular signatures and optimized media formulations, addressing critical challenges in cost, reproducibility, and scalability of cell culture. The integrated academic-industrial collaboration with Roke Biotechnologies provides a clear commercialization pathway, enhancing U.S. biomanufacturing competitiveness. This project is being jointly supported by the Division of Molecular and Cellular Biosciences at NSF and the BioMADE Manufacturing Innovation Institute. 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.

NSF

Microbes are being used to produce a range of products. Some of those products are proteins, such as insulin or blood-clotting factors. In many cases, fungal strains are used because they have protein secretion systems. However, the secretion systems are not optimized for throughput, so strain improvement is needed. To facilitate this process, AI methods will be employed to develop a digital model that emulates most of the behaviors of the original fungal cells. This is commonly referred to as a digital twin. The digital twin will be used to identify genetic edits that will improve protein synthesis and secretion. This will be accomplished for many fungal strains and behaviors. The models will be made publicly available using open-source software. A hierarchical genome-to-phenotype model (G2PM) for fungal systems will be developed. The focus will be on Pichia strains. This model will link DNA sequence to gene expression and, ultimately, to strain-level protein secretion performance. More than 8,000 diverse fungal genomes will be curated to train genomic language models (gLMs). These are deep neural networks that learn complex probability distributions over nucleotide sequences. These pretrained fungal gLMs and their learned embeddings will then be integrated into a sequence-to-expression model that predicts high-resolution RNA-seq profiles directly from genomic sequence, across multiple fungal species and environmental contexts. In parallel, the team will generate and publicly release a comprehensive multimodal dataset of engineered Pichia strains. Each will be annotated with whole-genome sequence, transcriptome profiles, and single-cell secretion measurements. Leveraging this resource, the G2PM will combine the pretrained sequence-to-expression module with protein representations in a phenotype-prediction module to predict secretion titers for target proteins from genomic sequence alone. Together, these efforts could yield an AI framework capable of recommending genomic edits that enhance protein secretion, thereby accelerating fungal strain engineering and enabling more efficient, lower cost biomanufacturing. This project is being jointly supported by ENG/CBET/CBE, BIO/MCB/SSB, and the BioMADE Manufacturing Innovation Institute. 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.

NSF

Methane is a greenhouse gas. It is made in landfills and wastewater treatment facilities. Certain microbes can grow on methane. These microbes can also produce biodegradable polymers. However, separating these polymers from the cells that produce them and then purifying them is difficult and expensive. One promising biodegradable polymer, PHA, is the focus of this project. To reduce the cost of purification, methane-consuming bacteria that produce PHA will be genetically engineered to accomplish two objectives. One is to cause the cells to automatically break open after producing PHA. The second is to reduce the amount of protein produced to simplify purification. This could significantly improve the affordability of PHA. Educational and workforce development programs will prepare students to join the biomanufacturing industry. The overarching goal is to significantly reduce the downstream processing (DSP) costs associated with methane-derived polyhydroxyalkanoate (PHA) production through advanced genetic engineering of Type II methanotrophic bacteria. Two specific bacterial strains will be targeted for these genetic modifications: the well-established model strain Methylosinus trichosporium and the Mango Materials’ proprietary strain (str. mngo). Genetic modifications will target pathways to facilitate easier recovery of PHAs from the fermentation process. The project employs iterative Design-Build-Test-Learn (DBTL) cycles supported by proteomic analyses, techno- economic analysis (TEA), and life cycle assessment (LCA). The goal is a reduction of 5–10% in capital expenditures and up to 15% in operational expenditures. This research will yield robust genetic engineering tools and engineered strains, while also significantly advancing fundamental scientific understanding in synthetic biology, metabolic engineering, and microbial physiology, particularly in non-model organisms that have traditionally been challenging to engineer. A detailed case study and a specialized industry workshop will facilitate the dissemination of a generalizable framework for DSP-focused genetic engineering, directly benefiting the broader biomanufacturing community. This project is being jointly supported by ENG/CBET/CBE and the BioMADE Manufacturing Innovation Institute. 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.

NSF

Development, scale-up and commercialization of new fermentation processes typically requires investments in the range of $100MM - $1B. Reduced performance from seemingly small process deviations during scale-up or plant operation can significantly impact the financial viability of a biomanufacturing facility. While pilot and demonstration campaigns carry less financial risk ($1MM - $10MM), the relatively small number of batches (on the order of 10) in such a campaign represents a high technical risk, since a small number of failed batches represents a large proportion of the total product and data expected to inform commercial investment decisions. Predictable scale-up and consistently near-optimal performance is, therefore, critical for the success of the United States biomanufacturing enterprise. This project seeks to develop a machine learning model for rapid assessment of the technical and economic impact of process perturbations to inform real-time decision-making related to mitigation or termination of a batch. The team leverages recent advances in machine learning, insights from an industry partner and data collected across scales from benchtop to commercial processes to deliver the decision-making tool. The award also provides research experiences for undergraduate students and supports outreach science activities to K-12 students. The scale-up and commercialization of new fermentation processes relies on pilot and demonstration campaigns with high technical risk. Reduced performance from seemingly small process deviations during pilot operation significantly impact the data used to inform commercial investment decisions. Predictable scale-up and consistently near-optimal performance is, therefore, critical for building financially viable biomanufacturing facilities. The project develops a multiscale model to: (i) predict the impact of process perturbations on fermentation performance, (ii) quantify the economic impact of potential operator decisions on the fermentation and downstream processing, and (iii) propose the optimal response to maximize profit. More broadly, the proposed modeling framework can inform decision-making in both the design and operation of the complete process system. The approach integrates machine learning, metabolic modeling, and mechanistic process modeling to predict strain response to process perturbations and its economic impact. The resulting model is made of three hierarchical components: (1) a Machine Learning Genome Scale Metabolic Model calibrated through Bayesian design of experiments, (2) a digital twin fermentation model and (3) the decision-making model informed by techno-economic analysis. This integration bridges the gaps between biological, process, and economic modeling to allow for simultaneous prediction of performance at the strain, unit operation, and process system levels. This project is jointly supported by the NSF Division of Molecular and Cellular Biosciences and the BioMADE Manufacturing Innovation Institute. 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.

NSF

Microbial fermentation is essential for producing a wide range of products, including medicines, biofuels, and food ingredients. The microbial behavior during these complex processes must be monitored closely. This project will develop a new class of wireless, free-floating biosensors that operate directly inside fermentation tanks. These sensors will continuously track microbial health and activity. The sensors combine specially engineered microbes that emit light in response to cellular stress with embedded electronics that detect both chemical and optical signals. The resulting system provides a non-invasive way to observe changes in fermentation and improve control of biomanufacturing processes. The interdisciplinary project will train students in cutting-edge techniques across synthetic biology, semiconductor technology, and biomanufacturing. All designs and data will be shared through open-access platforms. Through partnerships with industry, the technology will also be validated in real-world production environments, supporting a stronger economy. This project develops a novel class of wireless, free-floating biosensors that integrate electronic and biological sensing elements to provide in situ, real-time monitoring of microbial dynamics within industrial fermentation environments. The sensing platform combines miniaturized, complementary metal–oxide–semiconductor-based electrochemical and optical sensor arrays with engineered whole-cell biosensors in the yeast Yarrowia lipolytica, which has been genetically modified to emit bioluminescent signals in response to intracellular stress and metabolite levels. These hybrid sensors enable continuous, spatiotemporal measurements of key fermentation parameters, including redox state, media composition, and cellular metabolic activity, without intrusive sampling or fixed probes. The NSF-supported work focuses on engineering and characterizing auto-bioluminescent Y. lipolytica strains, developing calibration frameworks for interpreting complex biological signals, and building embedded sensor nodes that operate autonomously and communicate wirelessly. These efforts advance core research areas in microbial signal transduction, bio-electronic interfacing, and systems-level bioprocess modeling, laying the groundwork for Artificial Intelligence-driven fermentation control strategies. Through collaboration with Capra Biosciences and complementary translational support from BioMADE, the platform will be further miniaturized, industrially validated, and deployed in real-world production settings. This research contributes to national bioeconomy goals by enabling tools that improve the efficiency and resilience of microbial bioproduction systems. It will provide interdisciplinary research training for undergraduate and graduate students in synthetic biology, integrated electronics, and bioprocess engineering. The project includes a partnership with Boston University’s Science, Technology, Engineering, and Mathematics Pathways program to engage students in hands-on research and offers industry-facing experience through collaboration with Capra Biosciences. All microbial reporter strains, protocols, and sensor datasets will be shared through open-access repositories to ensure broad dissemination and reuse. In addition, the team will host both virtual and in-person technical workshops, covering topics such as whole-cell biosensor design, low-power sensing technologies, and bioinstrumentation in synthetic biology, to promote broader adoption and community engagement across academic and industrial institutions. This project is being jointly supported by ENG/CBET/CBE and the BioMADE Manufacturing Innovation Institute. 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.

NSF

This project addresses a critical need in the U.S. biomanufacturing industry sector by developing flexible, modular technologies that integrate microbial conversion and chemical upgrading within a single reactor system. Such an integration is essential for producing high-performance chemicals and fuels from biobased intermediates in a cost-effective and scalable manner. The research team will design and demonstrate a novel reactor platform that supports continuous operation, efficient separation, and process intensification, which are key attributes for enabling distributed biomanufacturing. The project will also contribute to the training of students and early-career researchers in interdisciplinary areas spanning precision fermentation, biosensing, electrochemistry, and reactor engineering, thereby strengthening the future workforce in the bioeconomy. The project will develop a bio-electrocatalytic Taylor vortex reactor (BETR) system for the integrated production of 3-methylanisole (3-MA) and its upgrading into methylcyclohexane (MCH), a high-energy-density hydrocarbon. This integrated process combines microbial fermentation and electrochemical hydrogenation within a single intensified reactor platform. The team will use high-throughput microbial phenotyping, genetically encoded biosensors, and Bayesian machine learning to identify and optimize yeast strains capable of efficiently producing 3-MA from sugar-based feedstocks. A flexible microbial chassis will be engineered to enhance metabolic flux toward the desired aromatic intermediate while maintaining selectivity and robustness under aerobic fermentation conditions. In parallel, a broad library of electrocatalysts and electrolyte formulations will be screened to develop an experimental–computational framework for catalyst discovery and to establish optimal conditions for the selective hydrogenation of 3-MA to MCH. Electrocatalyst performance will be evaluated based on activity, selectivity, Faradaic efficiency, and stability under mild aqueous conditions that are compatible with upstream bioproduction. The BETR platform, leveraging Taylor–Couette flow, provides enhanced mass transfer and phase mixing that facilitate the co-location of microbial and electrochemical processes. The reactor will be engineered to support in situ product extraction to address potential toxicity issues and minimize downstream separation burdens. To guide system-level optimization and ensure economic viability, the team will use integrated machine learning models to inform technoeconomic analysis (TEA) and life cycle assessment (LCA). These tools will help identify key cost drivers, process bottlenecks, and design trade-offs early in development, supporting iterative improvements across biological, electrochemical, and reactor components. The final goal is to deliver innovative technologies and a functional, small-footprint reactor prototype that can be deployed in distributed biomanufacturing networks. Beyond the immediate application to MCH production, the approach is expected to provide generalizable insights into hybrid bio-electrocatalytic systems and enable new intensification strategies for converting sugars into high-performance products. The research outcomes will contribute foundational knowledge and technology for advancing the next generation of integrated biomanufacturing platforms. 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.

NSF

The US chocolate industry is nearly $24 billion in size. It depends on cocoa imported from a few tropical countries. Cocoa prices are vulnerable to extreme weather, disease outbreaks, political instability, and a growing global demand. The price reached an all-time high of $12,565 per metric ton in December 2024. Producing cacao in the US under controlled conditions could stabilize the supply chain. This project attempts to produce cacao from plant cells grown in low-cost reactor systems. This would guarantee a steady supply of cacao produced in the US. The project is focused on improving the production of cacao in plant cell cultures. It will also evaluate low-cost materials of construction for the reactor systems. The project will help in training students to participate in the future bioeconomy workforce. Manufacturing cacao would be expensive using current technology. An economic analysis of the technology required points to several key costs. Bioreactors are traditionally made of steel and are sterilized using high-pressure steam. Steel construction is expensive. High pressure steam is expensive, contributing to high annual operating costs. The productivity of batch cultivation of cacao is low, further contributing to high annual costs. To address these limitations, the team will pursue several strategies. High-density polyethylene (HDPE) bubble bioreactors will be custom designed. Alternate sanitization strategies will be evaluated. Process intensification strategies based on semicontinuous operation assisted by real-time in-line biomass monitoring will be developed, along with low-cost drying methods, to increase volumetric productivity and product quality. Technoeconomic analysis (TEA) and life cycle analysis (LCA) models will be developed to guide the process research and development efforts to ensure economic viability and sustainability. Results from this project directly apply to other plant cell and/or algal cell bioreactor-based processes, as well as other fermentation processes that require lower capital and operating costs, particularly microbial/fungal production of food and commodity industrial products. This project is being jointly supported by ENG/CBET/CBE and the BioMADE Manufacturing Innovation Institute. 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.

NCI - National Cancer Institute

PROJECT SUMMARY Metabolomics is the study of small molecule metabolites and has drastically improved health research, diagnostics, and patient treatment. Metabolomic approaches quantify the abundance of small molecules within biological samples using liquid chromatography coupled to mass spectrometry (LC/MS), which is capable of simultaneous detection of 1,000s of metabolites. Cancer, a fundamentally metabolic disease, has benefitted greatly from metabolomics, having enabled the discovery of tumor heterogeneity, novel biomarkers, and individualized treatments. Until recently, however, nearly all large-scale metabolomics analyses have been limited to targeted studies of a few hundred pre-discovered metabolites. In part, this is due to the data burden and uncertainty in global analysis of all detected metabolites (i.e., untargeted metabolomics). Researchers have been slow to adopt untargeted metabolomics because it relies heavily on software to evaluate the large number of signals detected. Unfortunately, current software are limited by two major factors: (1) ~90% of the detected signals do not represent unique metabolites but are false positives or redundant compound detections derived from chemical or electronic noise that current software does not address, and (2) More than 80% of measured metabolites are removed from downstream analysis due to ambiguous identifications, in large part due to software programs not providing statistical scores for metabolite identification certainty. These limitations lead to an inflated data burden, poor metabolome coverage, and misidentified metabolites, diminishing the value and impact of untargeted metabolomics to cancer research. At Panome Bio, we are the first metabolomics CRO to bring a truly untargeted metabolomics approach to biomedical researchers. Our company currently offers untargeted metabolomics in a fee-for-service model and has achieved strong initial traction, completing >100 customer projects to date. An important aspect to this platform is MassID, our Next-Generation untargeted metabolomics analysis software. While effective, our current version of MassID (MassID 1.0) is designed to focus only on the most confidently identified metabolites at the expense of removing ambiguous (but informative) data. Here, we propose to develop two software components that will improve the scoring and filtering systems used during metabolite identification: PeakDefrag and DecoID2. These will be integrated into a second-generation analysis software (MassID 2.0) that is hosted on the cloud and available through a web application, offering all researchers straightforward access to their data. The grant will be completed by benchmarking MassID 2.0 against MassID 1.0 and other untargeted software approaches in a colon cancer cohort. With these results, we plan to be the first metabolomics company to back metabolite identifications with statistical values and to further our position as the leading platform in eliminating the number of signals derived from noise. We expect Mass ID 2.0 to transform untargeted methods by increasing the number of statistically significant metabolite identifications and to advance Panome Bio as an industry leader in multi-omics discovery research, a market valued at $750M.

NHLBI - National Heart Lung and Blood Institute

Project Summary/Abstract: Sickle cell disease (SCD), a major global health burden affecting millions worldwide, is characterized by chronic anemia that drives progressive multi-organ damage through a vicious cycle of tissue hypoxia, sickling, and hemolysis. However, safe and effective therapies for anemia in SCD are limited, and recent withdrawals of SCD therapies have emphasized the importance of understanding patient-specific benefits and risks of therapies. While hemoglobin (Hb)-raising therapies such as hydroxyurea (HU), blood transfusions, and novel agents aim to alleviate anemia, their effects on hemorheology (e.g., blood viscosity, red blood cell [RBC] deformability) and tissue perfusion remain poorly understood. This knowledge gap is critical, as excessive Hb increases in SCD may increase blood viscosity and paradoxically worsen vaso-occlusion and impair tissue perfusion, particularly in HbSC disease, where higher baseline Hb levels result in higher blood viscosity. Current clinical practice lacks biomarkers to guide individualized therapy, relying instead on Hb levels—a flawed surrogate biomarker that fails to capture the interplay between anemia correction, blood flow dynamics, and tissue oxygenation. This study seeks to address existing knowledge gaps by evaluating the impact of anemia therapies on tissue perfusion and hemorheological biomarkers and identifying baseline predictors of therapeutic response in patients with SCD. The aims of this study are: 1) To determine the effects of anemia treatments (HU, transfusions) on cerebral tissue oxygenation (StO₂) and hemorheology in adults with SCD, leveraging an international cohort in Nigeria to enhance sample size and reduce treatment heterogeneity, and 2) To define how clinico-demographic factors (SCD genotype, geographic setting) influence hemorheological profiles and StO₂. Using near-infrared spectroscopy (NIRS) to measure StO₂ as a primary physiological endpoint, this study will longitudinally assess treatment effects while incorporating a comprehensive panel of hemorheological biomarkers, including whole blood viscosity, RBC deformability, and cellular adhesion, to monitor individual physiological responses. Additionally, understanding whether SCD genotype or geographic cohort influences baseline hemorheology and StO₂ will establish a foundation for appropriate comparisons and treatment strategies across diverse SCD populations and inform future international clinical trial designs. Successful completion of these aims will identify translatable biomarkers to guide personalized anemia treatment, elucidate mechanisms underlying phenotypic variability in SCD, and inform the design of a future adaptive clinical trial comparing anemia therapies. Through this career development award and guidance from my multidisciplinary mentorship team, I will acquire advanced skills in hemorheological assays, optical biomarkers (NIRS), advanced clinical trial design, Bayesian statistical analysis, and global translational research leadership and execution. This training will position me to conduct innovative and efficient trials that address the global burden of SCD, fostering my transition to independence as a physician-scientist focused on optimizing anemia management in SCD.

NIH

Accurate prognosis prediction is essential for planning rehabilitation programs and setting functional goals of veterans with spinal cord injury (SCI). A gap in our knowledge is how to identify veterans with SCI with greater potential to recover early after injury. The overall goal of this proposal is to examine the potential of physiological and blood-based biomarkers to predict neurological and functional recovery in veterans with motor complete and incomplete SCI during inpatient rehabilitation. To address this question, we propose to implement a multisite clinical trial that will involve for VA sites: Edward Hines Jr. VA Hospital (leading site), Louis Stokes Cleveland VA Medical Center, James A. Haley Veterans' Hospital, and West Roxbury VA Medical Center to assure geographic and socio-demographic variation, and successful recruitment goals. In Aim 1, we will investigate the relationship between spasticity and neurological and functional recovery in veterans with SCI. A relationship has been found between residual corticospinal connectivity and severity of spasticity following SCI. We propose to measure spasticity in muscles below the level of injury bilaterally in veterans with SCI using clinical and kinematic outcomes at admission to and discharge from inpatient rehabilitation. Neurological recovery will be assessed by the International Standards for Neurological Classification of Spinal Cord Injury exam and functional recovery will be assessed by the Functional Independence Measure exam and quality of life outcomes. In Aim 2, we propose to determine the relationship between blood-based biomarkers and neurological and functional recovery in veterans with SCI. Brain-derived neurotrophic factor (BDNF) and single-nucleotide polymorphisms in BDNF play a critical role in promoting neuronal plasticity. We propose to measure serum levels of BDNF by using enzyme-linked immunosorbent assays (ELISA) at admission to and discharge from inpatient rehabilitation. The proposed experiments will provide new knowledge that could help to identify sensitive and easy-to-use biomarkers to predict recovery in veterans with SCI and affect the standard of care in the VA health care medical system. The absence of well-accepted biomarkers to predict recovery in veterans with SCI underlines the importance of these investigations.

NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases

SUMMARY/ABSTRACT Growing evidence suggests that whole grain (WG) intake may play an important role in cardiometabolic disease prevention. However, numerous individual human studies have failed to support clear-cut conclusions on the topic. This inconsistency may be because the prevailing nutrition research relies heavily on self-reported measures of diet that are often prone to measurement error. In addition, these instruments cannot reflect the complexity of the chemical compositions of each WG, the impact of varieties, environment, cooking, and food processing on WG chemical compositions, and the interindividual variations on the absorption and metabolisms of WG phytochemicals. The molecular signatures of WG intake remain largely undefined. The major dietary cereals - wheat, rye, oat, barley, rice and corn- contain different unique phytochemicals, which can be used to reflect the intake of each individual WG. We recently found that the combination of targeted and non-targeted metabolomics approaches can effectively identify biomarkers of WG wheat and oat intake. However, the biomarkers of WG barley, rice, and corn have not been identified and these biomarkers have not been validated and associated with coronary heart disease (CHD) in an epidemiological setting. This application is aimed to test the hypothesis that bioactive WG phytochemicals, and their metabolites, are objective biomarkers of WG intake, integrate inter-individual differences, affect endogenous metabolome, and are likely more intimately associated with cardiometabolic diseases than traditional dietary assessments. This hypothesis will be tested in two aims. Aim 1 is to identify biomarkers of WG barley, rice, and corn intake following an acute exposure in a pharmacokinetic feeding study. In this aim, we will conduct a randomized crossover acute pharmacokinetic study of WG barley, rice, and corn, respectively. We will then use a targeted metabolomics approach to identify the exposure markers of WG barley, rice, and corn intake, further develop analytical methods to study their pharmacokinetics, and build our in-house WG metabolite database, and an untargeted metabolomics approach to investigate post-prandial biomarkers of WG barley, rice, and corn intake. The goals of Aim 2 are to determine 1) whether biomarkers of WG intake identified can represent habitual intake from two well phenotyped observational studies – the Men’s Lifestyle Validation Study (MLVS) and the Women’s Lifestyle Validation Study (WLVS), which have detailed and repeated measurements of diet using multiple 7-day diet records (7DDRs); and 2) whether biomarkers of WG intake are prospectively associated with risk of CHD in a cohort of female nurses. At the completion of these studies, our expectation is that we will identify WG phytochemicals and their metabolites as objective biomarkers of total and individual WGs that are commonly consumed by Americans. We also expect that the markers are robustly associated with WG intake and a lower CHD risk in free-living individuals.