The Staphylococcus aureus small non-coding RNA IsrR regulates TCA cycle activity and virulence.

Staphylococcus aureus has evolved mechanisms to cope with low iron (Fe) availability in host tissues. S. aureus uses the ferric uptake transcriptional regulator (Fur) to sense titers of cytosolic Fe. Upon Fe depletion, apo-Fur relieves transcriptional repression of genes utilized for Fe uptake. We demonstrate that an S. aureus Δfur mutant has decreased expression of acnA, which codes for the Fe-dependent enzyme aconitase. Decreased acnA expression prevented the Δfur mutant from growing with amino acids as sole carbon and energy sources. Suppressor analysis determined that a mutation in isrR, which produces a regulatory RNA, permitted growth by decreasing isrR transcription. The decreased AcnA activity of the Δfur mutant was partially relieved by an ΔisrR mutation. Directed mutation of bases predicted to facilitate the interaction between the acnA transcript and IsrR, decreased the ability of IsrR to control acnA expression in vivo and IsrR bound to the acnA transcript in vitro. IsrR also bound to the transcripts coding the alternate TCA cycle proteins sdhC, mqo, citZ, and citM. Whole cell metal analyses suggest that IsrR promotes Fe uptake and increases intracellular Fe not ligated by macromolecules. Lastly, we determined that Fur and IsrR promote infection using murine skin and acute pneumonia models.

Inhibition of E. coli RecQ Helicase Activity by Structurally Distinct DNA Lesions: Structure-Function Relationships.

DNA helicase unwinding activity can be inhibited by small molecules and by covalently bound DNA lesions. Little is known about the relationships between the structural features of DNA lesions and their impact on unwinding rates and processivities. Employing E.coli RecQ helicase as a model system, and various conformationally defined DNA lesions, the unwinding rate constants kobs = kU + kD, and processivities P = (kU/(kU + kD) were determined (kU, unwinding rate constant; kD, helicase-DNA dissociation rate constant). The highest kobs values were observed in the case of intercalated benzo[a]pyrene (BP)-derived adenine adducts, while kobs values of guanine adducts with minor groove or base-displaced intercalated adduct conformations were ~10-20 times smaller. Full unwinding was observed in each case with the processivity P = 1.0 (100% unwinding). The kobs values of the non-bulky lesions T(6-4)T, CPD cyclobutane thymine dimers, and a guanine oxidation product, spiroiminodihydantoin (Sp), are up to 20 times greater than some of the bulky adduct values; their unwinding efficiencies are strongly inhibited with processivities P = 0.11 (CPD), 0.062 (T(6-4)T), and 0.63 (Sp). These latter observations can be accounted for by correlated decreases in unwinding rate constants and enhancements in the helicase DNA complex dissociation rate constants.

Adenovirus Encoding Tumor Necrosis Factor Alpha and Interleukin 2 Induces a Tertiary Lymphoid Structure Signature in Immune Checkpoint Inhibitor Refractory Head and Neck Cancer.

Immune checkpoint inhibitors (ICI) have provided significant improvement in clinical outcomes for some patients with solid tumors. However, for patients with head and neck cancer, the response rate to ICI monotherapy remains low, leading to the exploration of combinatorial treatment strategies. In this preclinical study, we use an oncolytic adenovirus (Ad5/3) encoding hTNFα and hIL-2 and non-replicate adenoviruses (Ad5) encoding mTNFα and mIL-2 with ICI to achieve superior tumor growth control and improved survival outcomes. The in vitro effect of Ad5/3-E2F-D24-hTNFa-IRES-hIL-2 was characterized through analyses of virus replication, transgene expression and lytic activity using head and neck cancer patient derived cell lines. Mouse models of ICI naïve and refractory oral cavity squamous cell carcinoma were established to evaluate the local and systemic anti-tumor immune response upon ICI treatment with or without the non-replicative adenovirus encoding mTNFα and mIL-2. We delineated the mechanism of action by measuring the metabolic activity and effector function of CD3+ tumor infiltrating lymphocytes (TIL) and transcriptomic profile of the CD45+ tumor immune compartment. Ad5/3-E2F-D24-hTNFa-IRES-hIL-2 demonstrated robust replicative capability in vitro across all head and neck cell lines screened through potent lytic activity, E1a and transgene expression. In vivo, in both ICI naïve and refractory models, we observed improvement to tumor growth control and long-term survival when combining anti-PD-1 or anti-PD-L1 with the non-replicative adenovirus encoding mTNFα and mIL-2 compared to monotherapies. This observation was verified by striking CD3+ TIL derived mGranzyme b and interferon gamma production complemented by increased T cell bioenergetics. Notably, interrogation of the tumor immune transcriptome revealed the upregulation of a gene signature distinctive of tertiary lymphoid structure formation upon treatment of murine anti-PD-L1 refractory tumors with non-replicative adenovirus encoding mTNFα and mIL-2. In addition, we detected an increase in anti-tumor antibody production and expansion of the memory T cell compartment in the secondary lymphoid organs. In summary, a non-replicative adenovirus encoding mTNFα and mIL-2 potentiates ICI therapy, demonstrated by improved tumor growth control and survival in head and neck tumor-bearing mice. Moreover, the data reveals a potential approach for inducing tertiary lymphoid structure formation. Altogether our results support the clinical potential of combining this adenovirotherapy with anti-PD-1 or anti-PD-L1.

Potent Activation of Indoleamine 2,3-Dioxygenase by Polysulfides.

Indoleamine 2,3-dioxygenase (IDO1) is a heme enzyme that catalyzes the oxygenation of the indole ring of tryptophan to afford N-formylkynurenine. This activity significantly suppresses the immune response, mediating inflammation and autoimmune reactions. These consequential effects are regulated through redox changes in the heme cofactor of IDO1, which autoxidizes to the inactive ferric state during turnover. This change in redox status increases the lability of the heme cofactor leading to further suppression of activity. The cell can thus regulate IDO1 activity through the supply of heme and reducing agents. We show here that polysulfides bind to inactive ferric IDO1 and reduce it to the oxygen-binding ferrous state, thus activating IDO1 to maximal turnover even at low, physiologically significant concentrations. The on-rate for hydrogen disulfide binding to ferric IDO1 was found to be >106 M-1 s-1 at pH 7 using stopped-flow spectrometry. Fe K-edge XANES and EPR spectroscopy indicated initial formation of a low-spin ferric sulfur-bound species followed by reduction to the ferrous state. The µM affinity of polysulfides for IDO1 implicates these polysulfides as important signaling factors in immune regulation through the kynurenine pathway. Tryptophan significantly enhanced the relatively lower-affinity binding of hydrogen sulfide to IDO1, inspiring the use of the small molecule 3-mercaptoindole (3MI), which selectively binds to and activates ferric IDO1. 3MI sustains turnover by catalytically transferring reducing equivalents from glutathione to IDO1, representing a novel strategy of upregulating innate immunosuppression for treatment of autoimmune disorders. Reactive sulfur species are thus likely unrecognized immune-mediators with potential as therapeutic agents through these interactions with IDO1.

Authenticity and credibility aware detection of adverse drug events from social media.

OBJECTIVES: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media. METHODS: Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content. RESULTS: We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F1 - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F1 = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets. CONCLUSIONS: Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.

Authenticity and credibility aware detection of adverse drug events from social media.

OBJECTIVES: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media. METHODS: Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content. RESULTS: We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F1 - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F1 = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets. CONCLUSIONS: Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.

Detecting signals of detrimental prescribing cascades from social media.

MOTIVATION: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths. Identifying detrimental PCs would enable warnings and contraindications to be disseminated and assist the detection of unknown drug AEs. Nonetheless, the detection is difficult and has been limited to case reports or case assessment using administrative health claims data. Social media is a promising source for detecting signals of detrimental PCs due to the public availability of many discussions regarding treatments and drug AEs. OBJECTIVE: In this paper, we investigate the feasibility of detecting detrimental PCs from social media. METHODS: The detection, however, is challenging due to the data uncertainty and data rarity in social media. We propose a framework to mine sequences of drugs and AEs that signal detrimental PCs, taking into account the data uncertainty and data rarity. RESULTS: We conduct experiments on two real-world datasets collected from Twitter and Patient health forum. Our framework achieves encouraging results in the validation against known detrimental PCs (F1=78% for Twitter and 68% for Patient) and the detection of unknown potential detrimental PCs (Precision@50=72% and NDCG@50=95% for Twitter, Precision@50=86% and NDCG@50=98% for Patient). In addition, the framework is efficient and scalable to large datasets. CONCLUSION: Our study demonstrates the feasibility of generating hypotheses of detrimental PCs from social media to reduce pharmacists' guesswork.

Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method.

BACKGROUND: The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. RESULTS: In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have proposed several possible chemical modifications to improve the inhibitor affinity towards multiple targets in the Hedgehog Signaling Pathway. CONCLUSIONS: Our model with the feature selection strategy presented here is efficient, robust, and flexible, and can be easily extended to model large-scale multiple cell line/QSAR data. The data and scripts for collaborative QSAR modeling are available in the Additional file 1.

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study.

BACKGROUND: Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. RESULTS: An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. CONCLUSIONS: The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.