Integrative Analysis of Cytokine and Lipidomics Datasets Following Mild Traumatic Brain Injury in Rats.

Traumatic brain injury (TBI) is a significant source of disability in the United States and around the world and may lead to long-lasting cognitive deficits and a decreased quality of life for patients across injury severities. Following the primary injury phase, TBI is characterized by complex secondary cascades that involve altered homeostasis and metabolism, faulty signaling, neuroinflammation, and lipid dysfunction. The objectives of the present study were to (1) assess potential correlations between lipidome and cytokine changes after closed-head mild TBI (mTBI), and (2) examine the reproducibility of our acute lipidomic profiles following TBI. Cortices from 54 Sprague Dawley male and female rats were analyzed by ultra-high-performance liquid chromatography mass spectrometry (LC-MS) in both positive and negative ionization modes and multiplex cytokine analysis after single (smTBI) or repetitive (rmTBI) closed-head impacts, or sham conditions. Tissue age was a variable, given that two cohorts (n = 26 and n = 28) were initially run a year-and-a-half apart, creating inter-batch variations. We annotated the lipidome datasets using an in-house data dictionary based on exact masses of precursor and fragment ions and removed features with statistically significant differences between sham control batches. Our results indicate that lipids with high-fold change between injury groups moderately correlate with the cytokines eotaxin, IP-10, and TNF-α. Additionally, we show a significant decrease in the pro-inflammatory markers IL-1ß and IP-10, TNF-α, and RANTES in the rmTBI samples relative to the sham control. We discuss the major challenges in correlating high dimensional lipidomic data with functional cytokine profiles and the implications for understanding the biological significance of two related but disparate analysis modes in the study of TBI, an inherently heterogeneous neurological disorder.

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women.

BACKGROUND: Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention. METHODS: Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches. RESULTS: We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set. CONCLUSIONS: This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women. IMPACT: Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Comparing Brain and Blood Lipidome Changes following Single and Repetitive Mild Traumatic Brain Injury in Rats.

Traumatic brain injury (TBI) is a major health concern in the United States and globally, contributing to disability and long-term neurological problems. Lipid dysregulation after TBI is underexplored, and a better understanding of lipid turnover and degradation could point to novel biomarker candidates and therapeutic targets. Here, we investigated overlapping lipidome changes in the brain and blood using a data-driven discovery approach to understand lipid alterations in the brain and serum compartments acutely following mild TBI (mTBI) and the potential efflux of brain lipids to peripheral blood. The cortices and sera from male and female Sprague-Dawley rats were analyzed via ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in both positive and negative ion modes following single and repetitive closed head impacts. The overlapping lipids in the data sets were identified with an in-house data dictionary for investigating lipid class changes. MS-based lipid profiling revealed overall increased changes in the serum compartment, while the brain lipids primarily showed decreased changes. Interestingly, there were prominent alterations in the sphingolipid class in the brain and blood compartments after single and repetitive injury, which may suggest efflux of brain sphingolipids into the blood after TBI. Genetic algorithms were used for predictive panel selection to classify injured and control samples with high sensitivity and specificity. These overlapping lipid panels primarily mapped to the glycerophospholipid metabolism pathway with Benjamini-Hochberg adjusted q-values less than 0.05. Collectively, these results detail overlapping lipidome changes following mTBI in the brain and blood compartments, increasing our understanding of TBI-related lipid dysregulation while identifying novel biomarker candidates.

Divergent Skeletal Muscle Metabolomic Signatures of Different Exercise Training Modes Independently Predict Cardiometabolic Risk Factors.

We investigated the link between enhancement of SI (by hyperinsulinemic-euglycemic clamp) and muscle metabolites after 12 weeks of aerobic (high-intensity interval training [HIIT]), resistance training (RT), or combined training (CT) exercise in 52 lean healthy individuals. Muscle RNA sequencing revealed a significant association between SI after both HIIT and RT and the branched-chain amino acid (BCAA) metabolic pathway. Concurrently with increased expression and activity of branched-chain ketoacid dehydrogenase enzyme, many muscle amino metabolites, including BCAAs, glutamate, phenylalanine, aspartate, asparagine, methionine, and γ-aminobutyric acid, increased with HIIT, supporting the substantial impact of HIIT on amino acid metabolism. Short-chain C3 and C5 acylcarnitines were reduced in muscle with all three training modes, but unlike RT, both HIIT and CT increased tricarboxylic acid metabolites and cardiolipins, supporting greater mitochondrial activity with aerobic training. Conversely, RT and CT increased more plasma membrane phospholipids than HIIT, suggesting a resistance exercise effect on cellular membrane protection against environmental damage. Sex and age contributed modestly to the exercise-induced changes in metabolites and their association with cardiometabolic parameters. Integrated transcriptomic and metabolomic analyses suggest various clusters of genes and metabolites are involved in distinct effects of HIIT, RT, and CT. These distinct metabolic signatures of different exercise modes independently link each type of exercise training to improved SI and cardiometabolic risk. ARTICLE HIGHLIGHTS: We aimed to understand the link between skeletal muscle metabolites and cardiometabolic health after exercise training. Although aerobic, resistance, and combined exercise training each enhance muscle insulin sensitivity as well as other cardiometabolic parameters, they disparately alter amino and citric acid metabolites as well as the lipidome, linking these metabolomic changes independently to the improvement of cardiometabolic risks with each exercise training mode. These findings reveal an important layer of the unique exercise mode-dependent changes in muscle metabolism, which may eventually lead to more informed exercise prescription for improving SI.

Constitutively Synergistic Multiagent Drug Formulations Targeting MERTK, FLT3, and BCL-2 for Treatment of AML.

PURPOSE: Although high-dose, multiagent chemotherapy has improved leukemia survival rates, treatment outcomes remain poor in high-risk subsets, including acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) in infants. The development of new, more effective therapies for these patients is therefore an urgent, unmet clinical need. METHODS: The dual MERTK/FLT3 inhibitor MRX-2843 and BCL-2 family protein inhibitors were screened in high-throughput against a panel of AML and MLL-rearranged precursor B-cell ALL (infant ALL) cell lines. A neural network model was built to correlate ratiometric drug synergy and target gene expression. Drugs were loaded into liposomal nanocarriers to assess primary AML cell responses. RESULTS: MRX-2843 synergized with venetoclax to reduce AML cell density in vitro. A neural network classifier based on drug exposure and target gene expression predicted drug synergy and growth inhibition in AML with high accuracy. Combination monovalent liposomal drug formulations delivered defined drug ratios intracellularly and recapitulated synergistic drug activity. The magnitude and frequency of synergistic responses were both maintained and improved following drug formulation in a genotypically diverse set of primary AML bone marrow specimens. CONCLUSIONS: We developed a nanoscale combination drug formulation that exploits ectopic expression of MERTK tyrosine kinase and dependency on BCL-2 family proteins for leukemia cell survival in pediatric AML and infant ALL cells. We demonstrate ratiometric drug delivery and synergistic cell killing in AML, a result achieved by a systematic, generalizable approach of combination drug screening and nanoscale formulation that may be extended to other drug pairs or diseases in the future.

Common fear molecules induce defensive responses in marine prey across trophic levels.

Predator-prey interactions are a key feature of ecosystems and often chemically mediated, whereby individuals detect molecules in their environment that inform whether they should attack or defend. These molecules are largely unidentified, and their discovery is important for determining their ecological role in complex trophic systems. Homarine and trigonelline are two previously identified blue crab (Callinectes sapidus) urinary metabolites that cause mud crabs (Panopeus herbstii) to seek refuge, but it was unknown whether these molecules influence other species within this oyster reef system. In the current study, homarine, trigonelline, and blue crab urine were tested on juvenile oysters (Crassostrea virginica) to ascertain if the same molecules known to alter mud crab behavior also affect juvenile oyster morphology, thus mediating interactions between a generalist predator, a mesopredator, and a basal prey species. Oyster juveniles strengthened their shells in response to blue crab urine and when exposed to homarine and trigonelline in combination, especially at higher concentrations. This study builds upon previous work to pinpoint specific molecules from a generalist predator's urine that induce defensive responses in two marine prey from different taxa and trophic levels, supporting the hypothesis that common fear molecules exist in ecological systems.

Development of constitutively synergistic nanoformulations to enhance chemosensitivity in T-cell leukemia.

Advances in multiagent chemotherapy have led to recent improvements in survival for patients with acute lymphoblastic leukemia (ALL); however, a significant fraction do not respond to frontline chemotherapy or later relapse with recurrent disease, after which long-term survival rates remain low. To develop new, effective treatment options for these patients, we conducted a series of high-throughput combination drug screens to identify chemotherapies that synergize in a lineage-specific manner with MRX-2843, a small molecule dual MERTK and FLT3 kinase inhibitor currently in clinical testing for treatment of relapsed/refractory leukemias and solid tumors. Using experimental and computational approaches, we found that MRX-2843 synergized strongly-and in a ratio-dependent manner-with vincristine to inhibit both B-ALL and T-ALL cell line expansion. Based on these findings, we developed multiagent lipid nanoparticle formulations of these drugs that not only delivered defined drug ratios intracellularly in T-ALL, but also improved anti-leukemia activity following drug encapsulation. Synergistic and additive interactions were recapitulated in primary T-ALL patient samples treated with MRX-2843 and vincristine nanoparticle formulations, suggesting their clinical relevance. Moreover, the nanoparticle formulations reduced disease burden and prolonged survival in an orthotopic murine xenograft model of early thymic precursor T-ALL (ETP-ALL), with both agents contributing to therapeutic activity in a dose-dependent manner. In contrast, nanoparticles containing MRX-2843 alone were ineffective in this model. Thus, MRX-2843 increased the sensitivity of ETP-ALL cells to vincristine in vivo. In this context, the additive particles, containing a higher dose of MRX-2843, provided more effective disease control than the synergistic particles. In contrast, particles containing an even higher, antagonistic ratio of MRX-2843 and vincristine were less effective. Thus, both the drug dose and the ratio-dependent interaction between MRX-2843 and vincristine significantly impacted therapeutic activity in vivo. Together, these findings present a systematic approach to high-throughput combination drug screening and multiagent drug delivery that maximizes the therapeutic potential of combined MRX-2843 and vincristine in T-ALL and describe a novel translational agent that could be used to enhance therapeutic responses to vincristine in patients with T-ALL. This broadly generalizable approach could also be applied to develop other constitutively synergistic combination products for the treatment of cancer and other diseases.

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

Investigation of Four Cases of Stevens-Johnson Syndrome among Participants in a Mass Drug Administration Campaign with Sulfadoxine-Pyrimethamine and Primaquine in Haiti, 2020.

In 2018, a mass drug administration (MDA) campaign for malaria elimination was piloted in Haiti. The pilot treated 36,338 people with sulfadoxine-pyrimethamine (SP) and primaquine; no severe adverse events were detected. In 2020, another MDA campaign using the same medications was implemented to mitigate an upsurge in malaria cases during the COVID-19 pandemic. Four cases of Stevens-Johnson syndrome (SJS) were identified among the 42,249 people who took the medications. Three of these individuals required hospitalization; all survived. In addition to SP ingestion, an investigation of potential causes for increased SJS cases identified that all four cases had human leukocyte antigens A*29 and/or B*44:03, another known risk factor for SJS. Additionally, three of the four case individuals had antibodies to SARS-CoV-2, and the fourth may have been exposed around the same time. These findings raise the possibility that recent SARS-CoV-2 infection may have contributed to the increased risk for SJS associated with SP exposure during the 2020 campaign.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer.

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Cryptic Chemical Variation in a Marine Red Alga as Revealed by Nontargeted Metabolomics.

Many marine algae occupy habitats that are dark, deep, or encrusted on other organisms and hence are frequently overlooked by natural product chemists. However, exploration of less-studied organisms can lead to new opportunities for drug discovery. Genetic variation at the individual, species, genus, and population levels as well as environmental influences on gene expression enable expansion of the chemical repertoire associated with a taxonomic group, enabling natural product exploration using innovative analytical methods. A nontargeted LC-MS and 1H NMR spectroscopy-based metabolomic study of 32 collections of representatives of the calcareous red algal genus Peyssonnelia from coral reef habitats in Fiji and the Solomon Islands revealed significant correlations between natural products' chemistry, phylogeny, and biomedically relevant biological activity. Hierarchical cluster analysis (HCA) of LC-MS data in conjunction with NMR profiling and MS/MS-based molecular networking revealed the presence of at least four distinct algal chemotypes within the genus Peyssonnelia. Two Fijian collections were prioritized for further analysis, leading to the isolation of three novel sulfated triterpene glycosides with a rearranged isomalabaricane carbon skeleton, guided by the metabolomic data. The discovery of peyssobaricanosides A-C (15-17) from two Fijian Peyssonnelia collections, but not from closely related specimens collected in the Solomon Islands that were otherwise chemically and phylogenetically very similar, alludes to population-level variation in secondary metabolite production. Our study reinforces the significance of exploring unusual ecological niches and showcases marine red algae as a chemically rich treasure trove.

Discovery and Biosynthesis of Ureidopeptide Natural Products Macrocyclized via Indole N-acylation in Marine Microbulbifer spp. Bacteria.

Commensal bacteria associated with marine invertebrates are underappreciated sources of chemically novel natural products. Using mass spectrometry, we had previously detected the presence of peptidic natural products in obligate marine bacteria of the genus Microbulbifer cultured from marine sponges. In this report, the isolation and structural characterization of a panel of ureidohexapeptide natural products, termed the bulbiferamides, from Microbulbifer strains is reported wherein the tryptophan side chain indole participates in a macrocyclizing peptide bond formation. Genome sequencing identifies biosynthetic gene clusters encoding production of the bulbiferamides and implicates the involvement of a thioesterase in the indolic macrocycle formation. The structural diversity and widespread presence of bulbiferamides in commensal microbiomes of marine invertebrates point toward a possible ecological role for these natural products.

Constitutively synergistic multiagent drug formulations targeting MERTK, FLT3, and BCL-2 for treatment of AML.

Although high-dose, multi-agent chemotherapy has improved leukemia survival rates in recent years, treatment outcomes remain poor in high-risk subsets, including acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) in infants. Development of new, more effective therapies for these patients is therefore an urgent, unmet clinical need. To address this challenge, we developed a nanoscale combination drug formulation that exploits ectopic expression of MERTK tyrosine kinase and dependency on BCL-2 family proteins for leukemia cell survival in pediatric AML and MLL- rearranged precursor B-cell ALL (infant ALL). In a novel, high-throughput combination drug screen, the MERTK/FLT3 inhibitor MRX-2843 synergized with venetoclax and other BCL-2 family protein inhibitors to reduce AML cell density in vitro . Neural network models based on drug exposure and target gene expression were used to identify a classifier predictive of drug synergy in AML. To maximize the therapeutic potential of these findings, we developed a combination monovalent liposomal drug formulation that maintains ratiometric drug synergy in cell-free assays and following intracellular delivery. The translational potential of these nanoscale drug formulations was confirmed in a genotypically diverse set of primary AML patient samples and both the magnitude and frequency of synergistic responses were not only maintained but were improved following drug formulation. Together, these findings demonstrate a systematic, generalizable approach to combination drug screening, formulation, and development that maximizes therapeutic potential, was effectively applied to develop a novel nanoscale combination therapy for treatment of AML, and could be extended to other drug combinations or diseases in the future.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer.

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer. Teaser: Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

Space- and Time-Resolved Metabolomics of a High-Grade Serous Ovarian Cancer Mouse Model.

The dismally low survival rate of ovarian cancer patients diagnosed with high-grade serous carcinoma (HGSC) emphasizes the lack of effective screening strategies. One major obstacle is the limited knowledge of the underlying mechanisms of HGSC pathogenesis at very early stages. Here, we present the first 10-month time-resolved serum metabolic profile of a triple mutant (TKO) HGSC mouse model, along with the spatial lipidome profile of its entire reproductive system. A high-coverage liquid chromatography mass spectrometry-based metabolomics approach was applied to longitudinally collected serum samples from both TKO (n = 15) and TKO control mice (n = 15), tracking metabolome and lipidome changes from premalignant stages to tumor initiation, early stages, and advanced stages until mouse death. Time-resolved analysis showed specific temporal trends for 17 lipid classes, amino acids, and TCA cycle metabolites, associated with HGSC progression. Spatial lipid distributions within the reproductive system were also mapped via ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry and compared with serum lipid profiles for various lipid classes. Altogether, our results show that the remodeling of lipid and fatty acid metabolism, amino acid biosynthesis, TCA cycle and ovarian steroidogenesis are critical components of HGSC onset and development. These metabolic alterations are accompanied by changes in energy metabolism, mitochondrial and peroxisomal function, redox homeostasis, and inflammatory response, collectively supporting tumorigenesis.

Lipidome Alterations following Mild Traumatic Brain Injury in the Rat.

Traumatic brain injury (TBI) poses a major health challenge, with tens of millions of new cases reported globally every year. Brain damage resulting from TBI can vary significantly due to factors including injury severity, injury mechanism and exposure to repeated injury events. Therefore, there is need for robust blood biomarkers. Serum from Sprague Dawley rats was collected at several timepoints within 24 h of mild single or repeat closed head impacts. Serum samples were analyzed via ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) in positive and negative ion modes. Known lipid species were identified through matching to in-house tandem MS databases. Lipid biomarkers have a unique potential to serve as objective molecular measures of injury response as they may be liberated to circulation more readily than larger protein markers. Machine learning and feature selection approaches were used to construct lipid panels capable of distinguishing serum from injured and uninjured rats. The best multivariate lipid panels had over 90% cross-validated sensitivity, selectivity, and accuracy. These mapped onto sphingolipid signaling, autophagy, necroptosis and glycerophospholipid metabolism pathways, with Benjamini adjusted p-values less than 0.05. The novel lipid biomarker candidates identified provide insight into the metabolic pathways altered within 24 h of mild TBI.

Stereochemical Assignment and Absolute Abundance of Nonproteinogenic Amino Acid Homoarginine in Marine Sponges.

Together with arginine, the nonproteinogenic amino acid homoarginine is a substrate for the production of vasodilator nitric oxide in the human body. In marine sponges, homoarginine has been postulated to serve as a precursor for the biosynthesis of pyrrole-imidazole alkaloid and bromotyrosine alkaloid classes of natural products. The absolute abundance of homoarginine, its abundance relative to arginine, and its stereochemical assignment in marine sponges are not known. Here, using stable isotope dilution mass spectrometry, we quantify the absolute abundances of homoarginine and arginine in marine sponges. We find that the abundance of homoarginine is highly variable and can far exceed the concentration of arginine, even in sponges where incorporation of homoarginine in natural products cannot be rationalized. The [homoarginine]/[arginine] ratio in marine sponges is greater than that in human analytes. By derivatization of sponge extracts with Marfey's reagent and comparison with authentic standards, we determine the l-isomer of homoarginine to be exclusively present in sponges. Our results shed light on the presence of the high abundance of homoarginine in marine sponge metabolomes and provide the foundation to investigate the biosynthetic routes and physiological roles of this nonproteinogenic amino acid in sponge physiology.

Enzymatic Synthesis Assisted Discovery of Proline-Rich Macrocyclic Peptides in Marine Sponges.

Proline-rich macrocyclic peptides (PRMPs) are natural products present in geographically and phylogenetically dispersed marine sponges. The large diversity and low abundance of PRMPs in sponge metabolomes precludes isolation and structure elucidation of each individual PRMP congener. Here, using standards developed via biomimetic enzymatic synthesis of PRMPs, a mass spectrometry-based workflow to sequence PRMPs was developed and validated to reveal that the diversity of PRMPs in marine sponges is much greater than that has been realized by natural product isolation-based strategies. Findings are placed in the context of diversity-oriented transamidative macrocyclization of peptide substrates in sponge holobionts.

Presence of Bromotyrosine Alkaloids in Marine Sponges Is Independent of Metabolomic and Microbiome Architectures.

Marine sponge holobionts are prolific sources of natural products. One of the most geographically widespread classes of sponge-derived natural products is the bromotyrosine alkaloids. A distinguishing feature of bromotyrosine alkaloids is that they are present in phylogenetically disparate sponges. In this study, using sponge specimens collected from Guam, the Solomon Islands, the Florida Keys, and Puerto Rico, we queried whether the presence of bromotyrosine alkaloids potentiates metabolomic and microbiome conservation among geographically distant and phylogenetically different marine sponges. A multi-omic characterization of sponge holobionts revealed vastly different metabolomic and microbiome architectures among different bromotyrosine alkaloid-harboring sponges. However, we find statistically significant correlations between the microbiomes and metabolomes, signifying that the microbiome plays an important role in shaping the overall metabolome, even in low-microbial-abundance sponges. Molecules mined from the polar metabolomes of these sponges revealed conservation of biosynthetic logic between bromotyrosine alkaloids and brominated pyrrole-imidazole alkaloids, another class of marine sponge-derived natural products. In light of prior findings postulating the sponge host itself to be the biosynthetic source of bromotyrosine alkaloids, our data now set the stage for investigating the causal relationships that dictate the microbiome-metabolome interconnectedness for marine sponges in which the microbiome may not contribute to natural product biogenesis.IMPORTANCE Our work demonstrates that phylogenetically and geographically distant sponges with very different microbiomes can harbor natural product chemical classes that are united in their core chemical structures and biosynthetic logic. Furthermore, we show that independent of geographical dispersion, natural product chemistry, and microbial abundance, overall sponge metabolomes tightly correlate with their microbiomes.

Liver-Targeting Class I Selective Histone Deacetylase Inhibitors Potently Suppress Hepatocellular Tumor Growth as Standalone Agents.

Dysfunctions in epigenetic regulation play critical roles in tumor development and progression. Histone deacetylases (HDACs) and histone acetyl transferase (HAT) are functionally opposing epigenetic regulators, which control the expression status of tumor suppressor genes. Upregulation of HDAC activities, which results in silencing of tumor suppressor genes and uncontrolled proliferation, predominates in malignant tumors. Inhibition of the deacetylase activity of HDACs is a clinically validated cancer therapy strategy. However, current HDAC inhibitors (HDACi) have elicited limited therapeutic benefit against solid tumors. Here, we disclosed a class of HDACi that are selective for sub-class I HDACs and preferentially accumulate within the normal liver tissue and orthotopically implanted liver tumors. We observed that these compounds possess exquisite on-target effects evidenced by their induction of dose-dependent histone H4 hyperacetylation without perturbation of tubulin acetylation status and G0/G1 cell cycle arrest. Representative compounds 2 and 3a are relatively non-toxic to mice and robustly suppressed tumor growths in an orthotopic model of HCC as standalone agents. Collectively, our results suggest that these compounds may have therapeutic advantage against HCC relative to the current systemic HDACi. This prospect merits further comprehensive preclinical investigations.