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Human subjects were experimentally infected with Haemophilus ducreyi for up to 2 weeks. Bacterial suspensions were delivered into the epidermis and dermis through puncture wounds made by an allergy-testing device. Subjects developed papular lesions that evolved into pustules resembling natural disease. Some papular lesions resolved spontaneously, indicating that host responses may clear infection. Bacteria were shed intermittently from lesions, suggesting that H. ducreyi may be transmissible before ulceration. Host responses to infection consisted primarily of cutaneous infiltrate of polymorphonuclear leukocytes, Langerhans cells, macrophages, and CD4 T cells of alpha beta lineage. Expression of HLA-DR by keratinocytes was associated with the presence of interferon-gamma mRNA in the skin. There was little evidence for humoral or peripheral blood mononuclear cell responses to bacterial antigens. The cutaneous infiltrate of CD4 cells and macrophages provides a mechanism that facilitates transmission of human immunodeficiency virus by H. ducreyi. [ABSTRACT FROM AUTHOR]

Abstract Calorie restriction (CR) extends life span in mammals and delays the onset of age-related diseases, including cancer and diabetes. Drugs that target the same genes and pathways as CR may have enormous therapeutic potential. Recently, genome-scale data on the responses of human cell lines to over 1,000 drug treatments have become available. Here we integrate these data with gene expression signatures of CR in mouse liver to generate a prioritized list of candidate CR mimetics. We identify 14 drugs that reproduce the effects of CR at the transcriptional level. [ABSTRACT FROM AUTHOR]

Haemophilus ducreyi expresses fine tangled pili, which are composed predominantly of a major subunit (FtpA). Confocal microscopy showed that an FtpA-specific monoclonal antibody bound to bacteria in biopsy samples obtained from infected human volunteers. To test the role of pili in pathogenesis, an isogenic mutant (35000HP-SMS1) was constructed by insertionally inactivating ftpA. 35000HP-SMS1 did not express FtpA and was nonpiliated but was otherwise identical to its parent, 35000HP. Seven healthy adults were challenged on the upper arm with the isogenic isolates in a double-blinded, escalating dose-response study. Sites inoculated with the mutant produced papules and pustules at rates similar to the rates observed at sites inoculated with the parent. The recovery rate of H. ducreyi from cultures and the histopathology of biopsy samples obtained from pustules inoculated with 35000HP or 35000HP-SMS1 were similar. Although pili are expressed in vivo, FtpA is not required for pustule formation in the human challenge model. [ABSTRACT FROM AUTHOR]

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan.

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA., (© 2024. Springer Nature America, Inc.)

Streptococcus pneumoniae (pneumococcus) remains a serious cause of pulmonary and systemic infections globally, and host-directed therapies are lacking. The aim of this study was to test the therapeutic efficacy of asapiprant, an inhibitor of prostaglandin D2 signaling, against pneumococcal infection. Treatment of young mice with asapiprant after pulmonary infection with invasive pneumococci significantly reduced systemic spread, disease severity, and host death. Protection was specific against bacterial dissemination from the lung to the blood but had no effect on pulmonary bacterial burden. Asapiprant-treated mice had enhanced antimicrobial activity in circulating neutrophils, elevated levels of reactive oxygen species (ROS) in lung macrophages/monocytes, and improved pulmonary barrier integrity indicated by significantly reduced diffusion of fluorescein isothiocyanate (FITC)-dextran from lungs into the circulation. These findings suggest that asapiprant protects the host against pneumococcal dissemination by enhancing the antimicrobial activity of immune cells and maintaining epithelial/endothelial barrier integrity in the lungs., Competing Interests: F.A., K.K., J.R., Y.W., K.W., and K.F. are employees of BIOAGE Labs. All other authors have no competing interests.

NLRP3 is an intracellular sensor protein that detects a broad range of danger signals and environmental insults. Its activation results in a protective pro-inflammatory response designed to impair pathogens and repair tissue damage via the formation of the NLRP3 inflammasome. Assembly of the NLRP3 inflammasome leads to caspase 1-dependent secretory release of the pro-inflammatory cytokines IL-1β and IL-18 as well as to gasdermin d-mediated pyroptotic cell death. Herein, we describe the discovery of a novel indazole series of high affinity, reversible inhibitors of NLRP3 activation through screening of DNA-encoded libraries and the potent lead compound 3 (BAL-0028, IC50 = 25 nM) that was identified directly from the screen. SPR studies showed that compound 3 binds tightly (K D range 104-123 nM) to the NACHT domain of NLRP3. A CADD analysis of the interaction of compound 3 with the NLRP3 NACHT domain proposes a binding site that is distinct from those of ADP and MCC950 and includes specific site interactions. We anticipate that compound 3 (BAL-0028) and other members of this novel indazole class of neutral inhibitors will demonstrate significantly different physical, biochemical, and biological properties compared to NLRP3 inhibitors previously identified., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: George Hartman, Paul Humphries, Robert Hughes, Andrew Ho, Rusty Montgomery Aditi Deshpande, Maitriyee Mahanta, Sarah Tronnes, Samantha Cowdin, Paul Rubin, Kristen Fortney and Kevin Wilhelmsen are either current or former employees of BioAge Labs. Xu He, Fangchao Liu, Lifang Zhang, Chuan Liu, Dengfeng Dou, and Jin Li are either current or former employees of HitGen, Inc. Aleksander Spasic is a current employee of WuXi AppTec. Rebecca Coll and Matthias Geyer are current Advisors to BioAge Labs., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

FOXO3 is a ubiquitous transcription factor expressed in response to cellular stress caused by nutrient deprivation, inflammatory cytokines, reactive oxygen species, radiation, hypoxia, and other factors. We showed previously that the association of inherited FOXO3 variants with longevity was the result of partial protection against mortality risk posed by aging-related life-long stressors, particularly cardiometabolic disease. We then referred to the longevity-associated genotypes as conferring "mortality resilience." Serum proteins whose levels change with aging and are associated with mortality risk may be considered as "stress proteins." They may serve as indirect measures of life-long stress. Our aims were to (1) identify stress proteins that increase with aging and are associated with an increased risk of mortality, and (2) to determine if FOXO3 longevity/resilience genotype dampens the expected increase in mortality risk they pose. A total of 4500 serum protein aptamers were quantified using the Somalogic SomaScan proteomics platform in the current study of 975 men aged 71-83 years. Stress proteins associated with mortality were identified. We then used age-adjusted multivariable Cox models to investigate the interaction of stress protein with FOXO3 longevity-associated rs12212067 genotypes. For all the analyses, the p values were corrected for multiple comparisons by false discovery rate. This led to the identification of 44 stress proteins influencing the association of FOXO3 genotype with reduced mortality. Biological pathways were identified for these proteins. Our results suggest that the FOXO3 resilience genotype functions by reducing mortality in pathways related to innate immunity, bone morphogenetic protein signaling, leukocyte migration, and growth factor response., (© 2023. The Author(s).)

Coronavirus disease 2019 (COVID-19) is especially severe in aged populations 1 . Vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are highly effective, but vaccine efficacy is partly compromised by the emergence of SARS-CoV-2 variants with enhanced transmissibility 2 . The emergence of these variants emphasizes the need for further development of anti-SARS-CoV-2 therapies, especially for aged populations. Here we describe the isolation of highly virulent mouse-adapted viruses and use them to test a new therapeutic drug in infected aged animals. Many of the alterations observed in SARS-CoV-2 during mouse adaptation (positions 417, 484, 493, 498 and 501 of the spike protein) also arise in humans in variants of concern 2 . Their appearance during mouse adaptation indicates that immune pressure is not required for selection. For murine SARS, for which severity is also age dependent, elevated levels of an eicosanoid (prostaglandin D 2 (PGD 2 )) and a phospholipase (phospholipase A2 group 2D (PLA 2 G2D)) contributed to poor outcomes in aged mice 3,4 . mRNA expression of PLA 2 G2D and prostaglandin D 2 receptor (PTGDR), and production of PGD 2 also increase with ageing and after SARS-CoV-2 infection in dendritic cells derived from human peripheral blood mononuclear cells. Using our mouse-adapted SARS-CoV-2, we show that middle-aged mice lacking expression of PTGDR or PLA 2 G2D are protected from severe disease. Furthermore, treatment with a PTGDR antagonist, asapiprant, protected aged mice from lethal infection. PTGDR antagonism is one of the first interventions in SARS-CoV-2-infected animals that specifically protects aged animals, suggesting that the PLA 2 G2D-PGD 2 /PTGDR pathway is a useful target for therapeutic interventions., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Coronavirus disease 2019 (COVID-19) is especially severe in aged populations 1 . Resolution of the COVID-19 pandemic has been advanced by the recent development of SARS-CoV-2 vaccines, but vaccine efficacy is partly compromised by the recent emergence of SARS-CoV-2 variants with enhanced transmissibility 2 . The emergence of these variants emphasizes the need for further development of anti-SARS-CoV-2 therapies, especially in aged populations. Here, we describe the isolation of a new set of highly virulent mouse-adapted viruses and use them to test a novel therapeutic drug useful in infections of aged animals. Initially, we show that many of the mutations observed in SARS-CoV-2 during mouse adaptation (at positions 417, 484, 501 of the spike protein) also arise in humans in variants of concern (VOC) 2 . Their appearance during mouse adaptation indicates that immune pressure is not required for their selection. Similar to the human infection, aged mice infected with mouse-adapted SARS-CoV-2 develop more severe disease than young mice. In murine SARS, in which severity is also age-dependent, we showed that elevated levels of an eicosanoid, prostaglandin D2 (PGD 2 ) and of a phospholipase, PLA 2 G2D, contributed to poor outcomes in aged mice 3,4 . Using our virulent mouse-adapted SARS-CoV-2, we show that infection of middle-aged mice lacking expression of DP1, a PGD 2 receptor, or PLA 2 G2D are protected from severe disease. Further, treatment with a DP1 antagonist, asapiprant, protected aged mice from a lethal infection. DP1 antagonism is one of the first interventions in SARS-CoV-2-infected animals that specifically protects aged animals, and demonstrates that the PLA 2 G2D-PGD 2 /DP1 pathway is a useful target for therapeutic interventions., Competing Interests: COMPETING FINANCIAL INTERESTS Kevin Wilhelmsen, Klaus Klumpp, Fred Aswad, Justin Rebo and Kristen Fortney are employees of BIOAGE Labs. All of the other authors have no competing financial or nonfinancial interests.

Background: Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals., Methods: We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS., Results: We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms., Conclusions: These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Glucuronic acid is a metabolite of glucose that is involved in the detoxification of xenobiotic compounds and the structure/remodeling of the extracellular matrix. We report for the first time that circulating glucuronic acid is a robust biomarker of mortality that is conserved across species. We find that glucuronic acid levels are significant predictors of all-cause mortality in three population-based cohorts from different countries with 4-20 years of follow-up (HR=1.44, p=2.9×10 -6 in the discovery cohort; HR=1.13, p=0.032 and HR=1.25, p=0.017, respectively in the replication cohorts), as well as in a longitudinal study of genetically heterogenous mice (HR=1.29, p=0.018). Additionally, we find that glucuronic acid levels increase with age and predict future healthspan-related outcomes. Together, these results demonstrate glucuronic acid as a robust biomarker of longevity and healthspan.

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70-88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: K.Fortney and E.K.M. are affiliated with BioAge Labs, Inc.; J.N.H. serves on the Scientific Advisory Board of Camp4 Therapeutics.

Computational drug repositioning requires assessment of the functional similarities among compounds. Here, we report a new method for measuring compound functional similarity based on gene expression data. This approach takes advantage of deep neural networks to learn an embedding that substantially denoises expression data, making replicates of the same compound more similar. Our method uses unlabeled data in the sense that it only requires compounds to be labeled by identity rather than detailed pharmacological information, which is often unavailable and costly to obtain. Similarity in the learned embedding space accurately predicted pharmacological similarities despite the lack of any such labels during training and achieved substantially improved performance in comparison with previous similarity measures applied to gene expression measurements. Our method could identify drugs with shared therapeutic and biological targets even when the compounds were structurally dissimilar, thereby revealing previously unreported functional relationships between compounds. Thus, our approach provides an improved engine for drug repurposing based on expression data, which we have made available through the online tool DeepCodex ( http://deepcodex.org ).

Biomarkers of all-cause mortality are of tremendous clinical and research interest. Because of the long potential duration of prospective human lifespan studies, such biomarkers can play a key role in quantifying human aging and quickly evaluating any potential therapies. Decades of research into mortality biomarkers have resulted in numerous associations documented across hundreds of publications. Here, we present MortalityPredictors.org , a manually-curated, publicly accessible database, housing published, statistically-significant relationships between biomarkers and all-cause mortality in population-based or generally healthy samples. To gather the information for this database, we searched PubMed for appropriate research papers and then manually curated relevant data from each paper. We manually curated 1,576 biomarker associations, involving 471 distinct biomarkers. Biomarkers ranged in type from hematologic (red blood cell distribution width) to molecular (DNA methylation changes) to physical (grip strength). Via the web interface, the resulting data can be easily browsed, searched, and downloaded for further analysis. MortalityPredictors.org provides comprehensive results on published biomarkers of human all-cause mortality that can be used to compare biomarkers, facilitate meta-analysis, assist with the experimental design of aging studies, and serve as a central resource for analysis. We hope that it will facilitate future research into human mortality and aging.

People want to live long, healthy lives. Previous surveys suggest very limited interest in much longer lifespans, but we show that stipulating good health changes responses to favor longer lives by an order of magnitude. Advances in aging research hold out hope for greatly slowed aging with associated good health. Understanding the public's desires correctly is important to avoid misallocation of resources for research.

We developed a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from large studies of age-related disease in order to narrow the search for SNPs associated with longevity. To gain support for our approach, we first show there is an overlap between loci involved in disease and loci associated with extreme longevity. These results indicate that several disease variants may be depleted in centenarians versus the general population. Next, we used iGWAS to harness information from 14 meta-analyses of disease and trait GWAS to identify longevity loci in two studies of long-lived humans. In a standard GWAS analysis, only one locus in these studies is significant (APOE/TOMM40) when controlling the false discovery rate (FDR) at 10%. With iGWAS, we identify eight genetic loci to associate significantly with exceptional human longevity at FDR < 10%. We followed up the eight lead SNPs in independent cohorts, and found replication evidence of four loci and suggestive evidence for one more with exceptional longevity. The loci that replicated (FDR < 5%) included APOE/TOMM40 (associated with Alzheimer's disease), CDKN2B/ANRIL (implicated in the regulation of cellular senescence), ABO (tags the O blood group), and SH2B3/ATXN2 (a signaling gene that extends lifespan in Drosophila and a gene involved in neurological disease). Our results implicate new loci in longevity and reveal a genetic overlap between longevity and age-related diseases and traits, including coronary artery disease and Alzheimer's disease. iGWAS provides a new analytical strategy for uncovering SNPs that influence extreme longevity, and can be applied more broadly to boost power in other studies of complex phenotypes.

We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.

Many attempts have been made to evaluate the safety and potency of human induced pluripotent stem cells (iPSCs) for clinical applications using transcriptome data, but results so far have been ambiguous or even contradictory. Here, we characterized stem cells at the pathway level, rather than at the gene level as has been the focus of previous work. We meta-analyzed publically-available gene expression data sets and evaluated signaling and metabolic pathway activation profiles for 20 human embryonic stem cell (ESC) lines, 12 human iPSC lines, five embryonic body lines, and six fibroblast cell lines. We demonstrated the close resemblance of iPSCs with ESCs at the pathway level, and provided examples of how pathway activity can be applied to identify iPSC line abnormalities or to predict in vitro differentiation potential. Our results indicate that pathway activation profiling is a promising strategy for evaluating the safety and potency of iPSC lines in translational medicine applications.

Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.

Supercentenarians (110 years or older) are the world's oldest people. Seventy four are alive worldwide, with twenty two in the United States. We performed whole-genome sequencing on 17 supercentenarians to explore the genetic basis underlying extreme human longevity. We found no significant evidence of enrichment for a single rare protein-altering variant or for a gene harboring different rare protein altering variants in supercentenarian compared to control genomes. We followed up on the gene most enriched for rare protein-altering variants in our cohort of supercentenarians, TSHZ3, by sequencing it in a second cohort of 99 long-lived individuals but did not find a significant enrichment. The genome of one supercentenarian had a pathogenic mutation in DSC2, known to predispose to arrhythmogenic right ventricular cardiomyopathy, which is recommended to be reported to this individual as an incidental finding according to a recent position statement by the American College of Medical Genetics and Genomics. Even with this pathogenic mutation, the proband lived to over 110 years. The entire list of rare protein-altering variants and DNA sequence of all 17 supercentenarian genomes is available as a resource to assist the discovery of the genetic basis of extreme longevity in future studies.

Bivalve molluscs are newly discovered models of successful aging. Here, we test the hypothesis that extremely long-lived bivalves are not uniquely resistant to oxidative stressors (eg, tert-butyl hydroperoxide, as demonstrated in previous studies) but exhibit a multistress resistance phenotype. We contrasted resistance (in terms of organismal mortality) to genotoxic stresses (including topoisomerase inhibitors, agents that cross-link DNA or impair genomic integrity through DNA alkylation or methylation) and to mitochondrial oxidative stressors in three bivalve mollusc species with dramatically differing life spans: Arctica islandica (ocean quahog), Mercenaria mercenaria (northern quahog), and the Atlantic bay scallop, Argopecten irradians irradians (maximum species life spans: >500, >100, and ~2 years, respectively). With all stressors, the short-lived A i irradians were significantly less resistant than the two longer lived species. Arctica islandica were consistently more resistant than M mercenaria to mortality induced by oxidative stressors as well as DNA methylating agent nitrogen mustard and the DNA alkylating agent methyl methanesulfonate. The same trend was not observed for genotoxic agents that act through cross-linking DNA. In contrast, M mercenaria tended to be more resistant to epirubicin and genotoxic stressors, which cause DNA damage by inhibiting topoisomerases. To our knowledge, this is the first study comparing resistance to genotoxic stressors in bivalve mollusc species with disparate longevities. In line with previous studies of comparative stress resistance and longevity, our data extends, at least in part, the evidence for the hypothesis that an association exists between longevity and a general resistance to multiplex stressors, not solely oxidative stress. This work also provides justification for further investigation into the interspecies differences in stress response signatures induced by a diverse array of stressors in short-lived and long-lived bivalves, including pharmacological agents that elicit endoplasmic reticulum stress and cellular stress caused by activation of innate immunity.

Drug modes of action are complex and still poorly understood. The set of known drug targets is widely acknowledged to be biased and incomplete, and so gives only limited insight into the system-wide effects of drugs. But a high-throughput assay unique to yeast-barcode-based chemogenomic screens-can measure the individual drug response of every yeast deletion mutant in parallel. NetwoRx (http://ophid.utoronto.ca/networx) is the first resource to store data from these extremely valuable yeast chemogenomics experiments. In total, NetwoRx stores data on 5924 genes and 466 drugs. In addition, we applied data-mining approaches to identify yeast pathways, functions and phenotypes that are targeted by particular drugs, compute measures of drug-drug similarity and construct drug-phenotype networks. These data are all available to search or download through NetwoRx; users can search by drug name, gene name or gene set identifier. We also set up automated analysis routines in NetwoRx; users can query new gene sets against the entire collection of drug profiles and retrieve the drugs that target them. We demonstrate with use case examples how NetwoRx can be applied to target specific phenotypes, repurpose drugs using mode of action analysis, investigate bipartite networks and predict new drugs that affect yeast aging.

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity., (Copyright © 2012 Elsevier Inc. All rights reserved.)

When we learn something new, our brain may store the information in synapses or in reverberating loops of electrical activity, but current theories of motor learning focus almost entirely on the synapses. Here we show that loops could also play a role and would bring advantages: loop-based algorithms can learn complex control tasks faster, with exponentially fewer neurons, and avoid the problem of weight transport. They do all this at a cost: in the presence of long feedback delays, loop algorithms cannot control very fast movements, but in this case, loop and synaptic mechanisms can complement each other-mixed systems quickly learn to make accurate but not very fast motions and then gradually speed up. Loop algorithms explain aspects of consolidation, the role of attention, and the relapses that are sometimes seen after a task has apparently been learned, and they make further predictions.

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.

A central goal of biogerontology is to identify robust gene-expression biomarkers of aging. Here we develop a method where the biomarkers are networks of genes selected based on age-dependent activity and a graph-theoretic property called modularity. Tested on Caenorhabditis elegans, our algorithm yields better biomarkers than previous methods - they are more conserved across studies and better predictors of age. We apply these modular biomarkers to assign novel aging-related functions to poorly characterized longevity genes.

In eukaryotic genomes, nucleosomes function to compact DNA and to regulate access to it both by simple physical occlusion and by providing the substrate for numerous covalent epigenetic tags. While competition with other DNA-binding factors and action of chromatin remodeling enzymes significantly affect nucleosome formation in vivo, nucleosome positions in vitro are determined by steric exclusion and sequence alone. We have developed a biophysical model, DNABEND, for the sequence dependence of DNA bending energies, and validated it against a collection of in vitro free energies of nucleosome formation and a set of in vitro nucleosome positions mapped at high resolution. We have also made a first ab initio prediction of nucleosomal DNA geometries, and checked its accuracy against the nucleosome crystal structure. We have used DNABEND to design both strong and weak histone- binding sequences, and measured the corresponding free energies of nucleosome formation. We find that DNABEND can successfully predict in vitro nucleosome positions and free energies, providing a physical explanation for the intrinsic sequence dependence of histone-DNA interactions.