Suppression of PKR promotes network excitability and enhanced cognition by interferon-γ-mediated disinhibition.

The double-stranded RNA-activated protein kinase (PKR) was originally identified as a sensor of virus infection, but its function in the brain remains unknown. Here, we report that the lack of PKR enhances learning and memory in several behavioral tasks while increasing network excitability. In addition, loss of PKR increases the late phase of long-lasting synaptic potentiation (L-LTP) in hippocampal slices. These effects are caused by an interferon-γ (IFN-γ)-mediated selective reduction in GABAergic synaptic action. Together, our results reveal that PKR finely tunes the network activity that must be maintained while storing a given episode during learning. Because PKR activity is altered in several neurological disorders, this kinase presents a promising new target for the treatment of cognitive dysfunction. As a first step in this direction, we show that a selective PKR inhibitor replicates the Pkr(-/-) phenotype in WT mice, enhancing long-term memory storage and L-LTP.

AGPAT3 deficiency impairs adipocyte differentiation and leads to a lean phenotype in mice.

Acylglycerophosphate acyltransferases (AGPATs) catalyze the de novo formation of phosphatidic acid to synthesize glycerophospholipids and triglycerides. AGPATs demonstrate unique physiological roles despite a similar biochemical function. AGPAT3 is highly expressed in the testis, kidney, and liver, with intermediate expression in adipose tissue. Loss of AGPAT3 is associated with reproductive abnormalities and visual dysfunction. However, the role of AGPAT3 in adipose tissue and whole body metabolism has not been investigated. We found that male Agpat3 knockout (KO) mice exhibited reduced body weights with decreased white and brown adipose tissue mass. Such changes were less pronounced in the female Agpat3-KO mice. Agpat3-KO mice have reduced plasma insulin growth factor 1 (IGF1) and insulin levels and diminished circulating lipid metabolites. They manifested intact glucose homeostasis and insulin sensitivity despite a lean phenotype. Agpat3-KO mice maintained an energy balance with normal food intake, energy expenditure, and physical activity, except for increased water intake. Their adaptive thermogenesis was also normal despite reduced brown adipose mass and triglyceride content. Mechanistically, Agpat3 was elevated during mouse and human adipogenesis and enriched in adipocytes. Agpat3-knockdown 3T3-L1 cells and Agpat3-deficient mouse embryonic fibroblasts (MEFs) have impaired adipogenesis in vitro. Interestingly, pioglitazone treatment rescued the adipogenic deficiency in Agpat3-deficient cells. We conclude that AGPAT3 regulates adipogenesis and adipose development. It is possible that adipogenic impairment in Agpat3-deficient cells potentially leads to reduced adipose mass. Findings from this work support the unique role of AGPAT3 in adipose tissue.NEW & NOTEWORTHY AGPAT3 deficiency results in male-specific growth retardation. It reduces adipose tissue mass but does not significantly impact glucose homeostasis or energy balance, except for influencing water intake in mice. Like AGPAT2, AGPAT3 is upregulated during adipogenesis, potentially by peroxisome proliferator-activated receptor gamma (PPARγ). Loss of AGPAT3 impairs adipocyte differentiation, which could be rescued by pioglitazone. Overall, AGPAT3 plays a significant role in regulating adipose tissue mass, partially involving its influence on adipocyte differentiation.

Association between weight-adjusted-waist index and chronic kidney disease: a cross-sectional study.

AIMS: We aimed to investigate the potential association between weight-adjusted-waist index (WWI) and chronic kidney disease (CKD). DESIGN AND METHODS: This research examined data collected from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. CKD was defined as the low estimated glomerular filtration rate (eGFR) or the existence of albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30mg/g). Low-eGFR was described as eGFR < 60 mL/min/1.73m2. The associations between WWI with CKD, albuminuria, and low-eGFR were examined using generalized additive models and weighted multivariable logistic regression models. We also analyzed the associations of other obesity indicators with CKD, albuminuria, and low-eGFR, including body mass index (BMI), waist-to-height ratio (WHtR), waist circumference(WC), height, and weight. The receiver operating characteristic (ROC) curves were used to assess and compare their diagnostic abilities. RESULTS: Males made up 48.26% of the total 40,421 individuals that were recruited. The prevalences of CKD, albuminuria, and low-eGFR were 16.71%, 10.97%, and 7.63%, respectively. WWI was found to be positively linked with CKD (OR = 1.42; 95% CI: 1.26, 1.60). A nonlinear connection between WWI and CKD was found using smooth curve fitting. Additionally, a higher prevalence of albuminuria is linked to a higher level of WWI (OR = 1.60; 95% CI: 1.40, 1.82). Different stratifications did not substantially influence the connection between WWI and CKD, albuminuria, and low-eGFR, according to subgroup analysis and interaction tests. We observed higher height was related to higher low-eGFR prevalence (OR = 1.05; 95% CI: 1.03, 1.06). ROC analysis revealed that WWI had the best discrimination and accuracy for predicting CKD and albuminuria compared to other obesity indicators (BMI, WHTR, WC, height and weight). In addition, height had the highest area under the curve (AUC) value for predicting low-eGFR. CONCLUSION: WWI is the best obesity indicator to predict CKD and albuminuria compared to other obesity indicators (BMI, WHTR, WC, height, and weight). WWI and CKD and albuminuria were found to be positively correlated. Furthermore, height had the strongest ability to predict low-eGFR. Therefore, the importance of WWI and height in assessing kidney health in US adults should be emphasized.

On the possible origin of protein homochirality, structure, and biochemical function.

Living systems have chiral molecules, e.g., native proteins that almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life's origin. The origin of homochirality and its implications are explored in computer simulations examining the stability and structural and functional properties of an artificial library of compact proteins containing 1:1 (termed demi-chiral), 3:1, and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures and fewer internal hydrogen bonds and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0-Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) 4-digit (3-digit) enzyme classification (E.C.) numbers, native active site amino acids were generated at random for 472 of 550 cases. This increases to 548 of 550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.

Sodium citrate and biochar synergistic improvement of nanoscale zero-valent iron composite for the removal of chromium (â ¥) in aqueous solutions.

Sodium citrate (SC) is a widely-used food and industrial additive with the properties of complexation and microbial degradation. In the present study, nano-zero-valent iron reaction system (SC-nZVI@BC) was successfully established by modifying nanoscale zero-valent iron (nZVI) with SC and biochar (BC), and was employed to remove Cr(Ⅵ) from aqueous solutions. The nZVI, SC-nZVI and SC-nZVI@BC were characterized and compared using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analyses (TGA), vibrating sample magnetometer (VSM), scanning electron microscope (SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The results showed that nZVI was successfully loaded on the biochar, and both the agglomeration and surface passivation problems of nanoparticles were well resolved. The dosage of SC, C:Fe, initial pH and Cr(Ⅵ) concentration demonstrated direct effects on the removal efficiency. The maximum Cr(Ⅵ) removal rate and the removal capacity within 60 min were 99.7% and 199.46 mg/g, respectively (C:Fe was 1:1, SC dosage was 1.12 mol.%, temperature was 25°C, pH = 7, and the original concentration of Cr(Ⅵ) was 20 mg/L). The reaction confirmed to follow the pseudo-second-order reaction kinetics, and the order of the reaction rate constant k was as follows: SC-nZVI@BC > nZVI@BC > SC-nZVI > nZVI. In addition, the mechanism of Cr(Ⅵ) removal by SC-nZVI@BC mainly involved adsorption, reduction and co-precipitation, and the reduction of Cr(Ⅵ) to Cr(Ⅲ) by nano Fe0 played a vital role. Findings from the present study demonstrated that the SC-nZVI@BC exhibited excellent removal efficiency toward Cr(Ⅵ) with an improved synergistic characteristic by SC and BC.

Knockdown of MCM8 inhibits development and progression of bladder cancer in vitro and in vivo.

BACKGROUND: Bladder cancer is a frequently diagnosed urinary system tumor, whose mortality remains rising. Minichromosome maintenance eight homologous recombination repair factor (MCM8), a newly discovered MCM family member, has been shown to be required for DNA replication. Unfortunately, little is known concerning the roles of MCM8 in bladder cancer. METHODS: The present study, we aimed at probing into the impacts and detailed mechanisms of MCM8 in bladder cancer progression. In this study, MCM8 expression level was detected through immunohistochemistry staining (IHC), qRT-PCR and Western blot assay. Silenced MCM8 cell models were constructed by lentivirus transfection. In vitro, the cell proliferation was evaluated by the MTT assay. The wound-healing assay and the transwell assay were utilized to assess the cell migration. Also, the cell apoptosis and the cell cycle were determined by flow cytometry. Moreover, the Human Apoptosis Antibody Array assay was performed to analyze the alterations of apoptosis-related proteins. The in vivo experiments were conducted to verify the effects of MCM8 knockdown on the tumor growth of bladder cancer. RESULTS: The results demonstrated that compared with normal adjacent tissues, MCM8 expression in bladder cancer tissues was strongly up-regulated. The up-regulation of MCM8 expression in bladder cancer may be a valuable independent prognostic indicator. Of note, MCM8 inhibition modulated the malignant phenotypes of bladder cancer cells. In terms of mechanism, it was validated that MCM8 knockdown made Akt, P-Akt, CCND1 and CDK6 levels down-regulated, as well as MAPK9 up-regulated. CONCLUSIONS: Taken together, our study demonstrated an important role of MCM8 in bladder cancer and created a rationale for the therapeutic potential of MCM8 inhibition in human bladder cancer therapy.

AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.

AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.

FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.

To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

Neddylation mediates ventricular chamber maturation through repression of Hippo signaling.

During development, ventricular chamber maturation is a crucial step in the formation of a functionally competent postnatal heart. Defects in this process can lead to left ventricular noncompaction cardiomyopathy and heart failure. However, molecular mechanisms underlying ventricular chamber development remain incompletely understood. Neddylation is a posttranslational modification that attaches ubiquitin-like protein NEDD8 to protein targets via NEDD8-specific E1-E2-E3 enzymes. Here, we report that neddylation is temporally regulated in the heart and plays a key role in cardiac development. Cardiomyocyte-specific knockout of NAE1, a subunit of the E1 neddylation activating enzyme, significantly decreased neddylated proteins in the heart. Mice lacking NAE1 developed myocardial hypoplasia, ventricular noncompaction, and heart failure at late gestation, which led to perinatal lethality. NAE1 deletion resulted in dysregulation of cell cycle-regulatory genes and blockade of cardiomyocyte proliferation in vivo and in vitro, which was accompanied by the accumulation of the Hippo kinases Mst1 and LATS1/2 and the inactivation of the YAP pathway. Furthermore, reactivation of YAP signaling in NAE1-inactivated cardiomyocytes restored cell proliferation, and YAP-deficient hearts displayed a noncompaction phenotype, supporting an important role of Hippo-YAP signaling in NAE1-depleted hearts. Mechanistically, we found that neddylation regulates Mst1 and LATS2 degradation and that Cullin 7, a NEDD8 substrate, acts as the ubiquitin ligase of Mst1 to enable YAP signaling and cardiomyocyte proliferation. Together, these findings demonstrate a role for neddylation in heart development and, more specifically, in the maturation of ventricular chambers and also identify the NEDD8 substrate Cullin 7 as a regulator of Hippo-YAP signaling.

Neddylation Regulates Class IIa and III Histone Deacetylases to Mediate Myoblast Differentiation.

As the largest tissue in the body, skeletal muscle has multiple functions in movement and energy metabolism. Skeletal myogenesis is controlled by a transcriptional cascade including a set of muscle regulatory factors (MRFs) that includes Myogenic Differentiation 1 (MYOD1), Myocyte Enhancer Factor 2 (MEF2), and Myogenin (MYOG), which direct the fusion of myogenic myoblasts into multinucleated myotubes. Neddylation is a posttranslational modification that covalently conjugates ubiquitin-like NEDD8 (neural precursor cell expressed, developmentally downregulated 8) to protein targets. Inhibition of neddylation impairs muscle differentiation; however, the underlying molecular mechanisms remain less explored. Here, we report that neddylation is temporally regulated during myoblast differentiation. Inhibition of neddylation through pharmacological blockade using MLN4924 (Pevonedistat) or genetic deletion of NEDD8 Activating Enzyme E1 Subunit 1 (NAE1), a subunit of the E1 neddylation-activating enzyme, blocks terminal myoblast differentiation partially through repressing MYOG expression. Mechanistically, we found that neddylation deficiency enhances the mRNA and protein expressions of class IIa histone deacetylases 4 and 5 (HDAC4 and 5) and prevents the downregulation and nuclear export of class III HDAC (NAD-Dependent Protein Deacetylase Sirtuin-1, SIRT1), all of which have been shown to repress MYOD1-mediated MYOG transcriptional activation. Together, our findings for the first time identify the crucial role of neddylation in mediating class IIa and III HDAC co-repressors to control myogenic program and provide new insights into the mechanisms of muscle disease and regeneration.

MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.

To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.

IRF9 Affects the TNF-Induced Phenotype of Rheumatoid-Arthritis Fibroblast-Like Synoviocytes via Regulation of the SIRT-1/NF-κB Signaling Pathway.

OBJECTIVE: To discuss how IRF9 affects the fibroblast-like synoviocytes (FLS) in TNF-induced rheumatoid arthritis (RA) via the SIRT-1/NF-κB signaling pathway. METHODS: RA-FLS were isolated and divided into control, sh-IRF9, TNF, TNF + sh-Ctrl, TNF + sh-IRF9, TNF + sh-SIRT1, and TNF + sh-IRF9 + sh-SIRT1 groups. Biological features of FLS were evaluated by MTT, wound healing, and Transwell assays, respectively. Cell apoptosis and cycle were assessed flow cytometrically. Inflammatory cytokines were determined through enzyme-linked immunosorbent assay (ELISA), while IRF9 expression and SIRT1/NF-κB signaling pathway activity were measured by Western blotting. RESULTS: TNF increased IRF9 expression as well as NF-κB signaling activity and down-regulated SIRT1 of RA-FLS. Silencing IRF9 resulted in up-regulation of SIRT1 and blocked NF-κB signaling, with significant decreases in TNF-induced cell viability, migration, and invasion, prominent enhancement in apoptosis and the proportion of cells in G0/G1 phase, but a decrease in the proportion of cells in S and G2/M phases, and reduced levels of inflammatory cytokines. However, these changes were totally abolished after silencing SIRT1, i.e., the IRF9 shRNA-induced inhibitory effect on the growth of RA-FLS was reversed. CONCLUSION: Silencing IRF9 curbs the activity of the NF-κB signaling pathway via up-regulating SIRT-1, to further suppress TNF-induced changes in the malignant features of RA-FLS, and the secretion of inflammatory cytokines, with the promoted apoptosis.

Antimalarial Peptide and Polyketide Natural Products from the Fijian Marine Cyanobacterium Moorea producens.

A new cyclic peptide, kakeromamide B (1), and previously described cytotoxic cyanobacterial natural products ulongamide A (2), lyngbyabellin A (3), 18E-lyngbyaloside C (4), and lyngbyaloside (5) were identified from an antimalarial extract of the Fijian marine cyanobacterium Moorea producens. Compounds 1 and 1 exhibited moderate activity against Plasmodium falciparum blood-stages with EC50 values of 0.89 and 0.99 µM, respectively, whereas 3 was more potent with an EC50 value of 0.15 nM, respectively. Compounds 1, 4, and 5 displayed moderate liver-stage antimalarial activity against P. berghei liver schizonts with EC50 values of 1.1, 0.71, and 0.45 µM, respectively. The threading-based computational method FINDSITEcomb2.0 predicted the binding of 1 and 2 to potentially druggable proteins of Plasmodiumfalciparum, prompting formulation of hypotheses about possible mechanisms of action. Kakeromamide B (1) was predicted to bind to several Plasmodium actin-like proteins and a sortilin protein suggesting possible interference with parasite invasion of host cells. When 1 was tested in a mammalian actin polymerization assay, it stimulated actin polymerization in a dose-dependent manner, suggesting that 1 does, in fact, interact with actin.

Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs.

Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.

Obesity-associated family with sequence similarity 13, member A (FAM13A) is dispensable for adipose development and insulin sensitivity.

BACKGROUND: Obesity and its associated morbidities represent the major and most rapidly expanding world-wide health epidemic. Recent genome-wide association studies (GWAS) reveal that single nucleotide polymorphism (SNP) variant in the Family with Sequence Similarity 13, Member A (FAM13A) gene is strongly associated with waist-hip ratio (WHR) with adjustment for body mass index (BMI) (WHRadjBMI). However, the function of FAM13A in adipose development and obesity remains largely uncharacterized. METHODS: The expression of FAM13A in adipose tissue depots were investigated using lean, genetic obese and high fat diet-induced obese (DIO) animal models and during adipocyte differentiation. Stromal vascular cells (SVCs) or 3T3-L1 cells with gain and loss of function of FAM13A were used to determine the involvement of FAM13A in regulating adipocyte differentiation. Adipose development and metabolic homeostasis in Fam13a-/- mice were characterized under normal chow and high fat diet feeding. RESULTS: Murine FAM13A expression was nutritionally regulated and dramatically reduced in epididymal and subcutaneous fat in genetic and diet-induced obesity. Its expression was enriched in mature adipocytes and significantly upregulated during murine and human adipogenesis potentially through a peroxisome proliferator-activated receptor-gamma (PPARγ)-dependent mechanism. However, Fam13a-/- mice only exhibited a tendency of higher adiposity and were not protected from DIO and insulin resistance. While Fam13a-/- SVCs maintained normal adipogenesis, overexpression of FAM13A in 3T3-L1 preadipocytes downregulated ß-catenin signaling and rendered preadipocytes more susceptible to apoptosis. Moreover, FAM13A overexpression largely blocked adipogenesis induced by a standard hormone cocktail, but adipogenesis can be partially rescued by the addition of PPARγ agonist pioglitazone at an early stage of differentiation. CONCLUSIONS: Our results suggest that FAM13A is dispensable for adipose development and insulin sensitivity. Yet the expression of FAM13A needs to be tightly controlled in adipose precursor cells for their proper survival and downstream adipogenesis. These data provide novel insights into the link between FAM13A and obesity.

Quantum Coherence Witness with Untrusted Measurement Devices.

Coherence is a fundamental resource in quantum information processing, which can be certified by a coherence witness. Due to the imperfection of measurement devices, a conventional coherence witness may lead to fallacious results. We show that the conventional witness could mistake an incoherent state as a state with coherence due to the inaccurate settings of measurement bases. In order to make the witness result reliable, we propose a measurement-device-independent coherence witness scheme without any assumptions on the measurement settings. We introduce the decoy-state method to significantly increase the capability of recognizing states with coherence. Furthermore, we experimentally demonstrate the scheme in a time-bin encoding optical system.

Repurposed FDA-approved drugs targeting genes influencing aging can extend lifespan and healthspan in rotifers.

Pharmaceutical interventions can slow aging in animals, and have advantages because their dose can be tightly regulated and the timing of the intervention can be closely controlled. They also may complement environmental interventions like caloric restriction by acting additively. A fertile source for therapies slowing aging is FDA approved drugs whose safety has been investigated. Because drugs bind to several protein targets, they cause multiple effects, many of which have not been characterized. It is possible that some of the side effects of drugs prescribed for one therapy may have benefits in retarding aging. We used computationally guided drug screening for prioritizing drug targets to produce a short list of candidate compounds for in vivo testing. We applied the virtual ligand screening approach FINDSITEcomb for screening potential anti-aging protein targets against FDA approved drugs listed in DrugBank. A short list of 31 promising compounds was screened using a multi-tiered approach with rotifers as an animal model of aging. Primary and secondary survival screens and cohort life table experiments identified four drugs capable of extending rotifer lifespan by 8-42%. Exposures to 1 µM erythromycin, 5 µM carglumic acid, 3 µM capecitabine, and 1 µM ivermectin, extended rotifer lifespan without significant effect on reproduction. Some drugs also extended healthspan, as estimated by mitochondria activity and mobility (swimming speed). Our most promising result is that rotifer lifespan was extended by 7-8.9% even when treatment was started in middle age.

FINDSITEcomb2.0: A New Approach for Virtual Ligand Screening of Proteins and Virtual Target Screening of Biomolecules.

Computational approaches for predicting protein-ligand interactions can facilitate drug lead discovery and drug target determination. We have previously developed a threading/structural-based approach, FINDSITEcomb, for the virtual ligand screening of proteins that has been extensively experimentally validated. Even when low resolution predicted protein structures are employed, FINDSITEcomb has the advantage of being faster and more accurate than traditional high-resolution structure-based docking methods. It also overcomes the limitations of traditional QSAR methods that require a known set of seed ligands that bind to the given protein target. Here, we further improve FINDSITEcomb by enhancing its template ligand selection from the PDB/DrugBank/ChEMBL libraries of known protein-ligand interactions by (1) parsing the template proteins and their corresponding binding ligands in the DrugBank and ChEMBL libraries into domains so that the ligands with falsely matched domains to the targets will not be selected as template ligands; (2) applying various thresholds to filter out falsely matched template structures in the structure comparison process and thus their corresponding ligands for template ligand selection. With a sequence identity cutoff of 30% of target to templates and modeled target structures, FINDSITEcomb2.0 is shown to significantly improve upon FINDSITEcomb on the DUD-E benchmark set by increasing the 1% enrichment factor from 16.7 to 22.1, with a p-value of 4.3 × 10-3 by the Student t-test. With an 80% sequence identity cutoff of target to templates for the DUD-E set and modeled target structures, FINDSITEcomb2.0, having a 1% ROC enrichment factor of 52.39, also outperforms state-of-the-art methods that employ machine learning such as a deep convolutional neural network, CNN, with an enrichment of 29.65. Thus, FINDSITEcomb2.0 represents a significant improvement in the state-of-the-art. The FINDSITEcomb2.0 web service is freely available for academic users at http://pwp.gatech.edu/cssb/FINDSITE-COMB-2 .

Local spatial obesity analysis and estimation using online social network sensors.

Recently, the online social networks (OSNs) have received considerable attentions as a revolutionary platform to offer users massive social interaction among users that enables users to be more involved in their own healthcare. The OSNs have also promoted increasing interests in the generation of analytical, data models in health informatics. This paper aims at developing an obesity identification, analysis, and estimation model, in which each individual user is regarded as an online social network 'sensor' that can provide valuable health information. The OSN-based obesity analytic model requires each sensor node in an OSN to provide associated features, including dietary habit, physical activity, integral/incidental emotions, and self-consciousness. Based on the detailed measurements on the correlation of obesity and proposed features, the OSN obesity analytic model is able to estimate the obesity rate in certain urban areas and the experimental results demonstrate a high success estimation rate. The measurements and estimation experimental findings created by the proposed obesity analytic model show that the online social networks could be used in analyzing the local spatial obesity problems effectively.

A knowledge-based approach for predicting gene-disease associations.

MOTIVATION: Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat deadly diseases and provide possible targets to cure these diseases. Genome-wide association studies (GWAS) have identified many individual genes associated with common diseases. To exploit the large amount of data obtained from GWAS studies and leverage our understanding of common as well as rare diseases, we have developed a knowledge-based approach to predict gene-disease associations. We first derive gene-gene mutual information by utilizing the cooccurrence of genes in known gene-disease association data. Subsequently, the mutual information is combined with known protein-protein interaction networks by a boosted tree regression method. RESULTS: The method called Know-GENE is compared with the method of random walking on the heterogeneous network using the same input data. For a set of 960 diseases, using the same training data in testing in 3-fold cross-validation, the average recall rate within the top ranked 100 genes by Know-GENE is 65.0% compared with 37.9% by the state of the art random walking on heterogeneous network. This significant improvement is mostly due to the inclusion of knowledge-based mutual information. AVAILABILITY AND IMPLEMENTATION: Predictions for genes associated with the 960 diseases are available at http://cssb2.biology.gatech.edu/knowgene CONTACT: : skolnick@gatech.edu.