Variable responses of small and large human hepatocytes to hypoxia and hypoxia/reoxygenation (H-R)

Hypoxia and hypoxia-reoxygenation (H-R) regulate human hepatocyte cell death by mediating the accumulation of reactive oxygen species (ROS). Hepatocytes within the liver are organised into peri-portal (PP) and peri-venous (PV) subpopulations. PP and PV hepatocytes differ in size and function. We investigated whether PP and PV human hepatocytes exhibit differential susceptibility to hypoxic stress. Isolated hepatocytes were used in an in vitro model of hypoxia and H-R. ROS production and cell death were assessed using flow cytometry. PV, and not PP hepatocytes, accumulate intracellular ROS in a mitochondrial dependent manner during hypoxia and H-R. This increased ROS regulates hepatocyte apoptosis and necrosis via a mitochondrial pathway. These findings have implications on the understanding of liver injury and application of potential therapeutic strategies.     

Cyclic AMP- and (Rp)-cAMPS-induced Conformational Changes in a Complex of the Catalytic and Regulatory (RIα) Subunits of Cyclic AMP-dependent Protein Kinase

We took a discovery approach to explore the actions of cAMP and two of its analogs, one a cAMP mimic ((S_p)-adenosine cyclic 3′:5′-monophosphorothioate ((S_p)-cAMPS)) and the other a diastereoisomeric antagonist ((R_p)-cAMPS), on a model system of the type Iα cyclic AMP-dependent protein kinase holoenzyme, RIα(91–244)·C-subunit, by using fluorescence spectroscopy and amide H/²H exchange mass spectrometry. Specifically, for the fluorescence experiments, fluorescein maleimide was conjugated to three cysteine single residue substitution mutants, R92C, T104C, and R239C, of RIα(91–244), and the effects of cAMP, (S_p)-cAMPS, and (R_p)-cAMPS on the kinetics of R-C binding and the time-resolved anisotropy of the reporter group at each conjugation site were measured. For the amide exchange experiments, ESI-TOF mass spectrometry with pepsin proteolytic fragmentation was used to assess the effects of (R_p)-cAMPS on amide exchange of the RIα(91–244)·C-subunit complex. We found that cAMP and its mimic perturbed at least parts of the C-subunit interaction Sites 2 and 3 but probably not Site 1 via reduced interactions of the linker region and αC of RIα(91–244). Surprisingly, (R_p)-cAMPS not only increased the affinity of RIα(91–244) toward the C-subunit by 5-fold but also produced long range effects that propagated through both the C- and R-subunits to produce limited unfolding and/or enhanced conformational flexibility. This combination of effects is consistent with (R_p)-cAMPS acting by enhancing the internal entropy of the R·C complex. Finally, the (R_p)-cAMPS-induced increase in affinity of RIα(91–244) toward the C-subunit indicates that (R_p)-cAMPS is better described as an inverse agonist because it decreases the fractional dissociation of the cyclic AMP-dependent protein kinase holoenzyme and in turn its basal activity.Cyclic AMP-dependent protein kinase (PKA)¹ plays a crucial role in a plethora of cellular functions. All isoforms of PKA are composed of two catalytic (C) subunits and homodimeric regulatory (R) subunits (1–3). As the name implies, cAMP is a major PKA regulator (4). Much progress has been made in the last decade in delineating the molecular basis of action of cAMP. An important tactic in this endeavor has been through the comparison of the effects of cAMP with those of two phosphorothioate cAMP analogs: (S_p)-cAMPS (a cAMP mimic) and (R_p)-cAMPS (an antagonist and a diastereoisomer of (S_p)-cAMPS). Although the importance of geometry of the sulfur substitution is critical in determining the pharmacological properties of the two phosphorothioate cAMP analogs, the molecular basis for this behavior is not fully understood. To date, these comparisons have only been made using either wild-type or truncated mutants of the type Iα regulatory subunit (RIα) that are free in solution, not complexed to the C-subunit. X-ray spectroscopic examination of ligand-bound RIα(92–379) complexes reveals few differences between ligand-bound complexes, but the (R_p)-cAMPS complex is structurally “looser” with higher thermal factors than complexes formed with either cAMP or (S_p)-cAMPS (5). This is consistent with the observation that both cAMP and (S_p)-cAMPS, but not (R_p)-cAMPS, raise the urea concentration required for wild-type RIα unfolding (6). Further insight into the structural basis of cAMP action stems from NMR spectroscopic comparison of the effects of (R_p)-cAMPS, cAMP, and (S_p)-cAMPS on chemical shifts and ¹⁵N relaxation of the RIα(119–244) mutant (7). In addition to producing fewer significant chemical shift changes than either cAMP or (S_p)-cAMPS, (R_p)-cAMPS binding is associated with enhanced millisecond to microsecond time scale backbone motions of a β-turn (β2,3 loop) and around the phosphate-binding cassette (PBC) (7).Further insight into the molecular basis of actions of cAMP and its analogs should come from the analysis of ligand-bound R·C complexes. Unfortunately, the large size of even the heterodimeric R·C complex (∼95 kDa) and the difficulty of preparing (R_p)-cAMPS·R·C-subunit crystals currently preclude the use of both NMR spectroscopy and x-ray crystallography. Consequently, we took two alternative lower resolution approaches to this issue. One approach involves the use of site-directed labeling combined with fluorescence spectroscopy to examine both the effects of cAMP and its analogs on R-C subunit binding kinetics and on the conformational dynamics of RIα(91–244). RIα(91–244) includes the “A” cyclic nucleotide binding (CNB) domain, the pseudosubstrate, and linker domains and represents the minimal segments necessary for high affinity C-subunit binding (Fig. 1) (8). The other approach involves an examination of the effects of cAMP and its analogs on solvent exposure/conformational flexibility of RIα(91–244)·C-subunit complex using H/²H amide exchange measured with a combination of mass spectrometry (ESI-Q-TOF) and proteolytic fragmentation. In the first approach, fluorescein maleimide (FM) was conjugated to three cysteine substitution mutants with the substitution sites located near or within the pseudosubstrate sequence, the linker domain, or αC (R92C, T104C, and R239C, respectively) of RIα(91–244) (Fig. 1). The time-resolved fluorescence anisotropy results suggest that cAMP and (S_p)-cAMPS reduce the interaction of the RIα linker domain and αC with the two peripheral R-C interaction sites on the C-subunit (so-called Sites 2 and 3) without affecting the interaction of the pseudosubstrate sequence with the active site cleft (so-called Site 1). Because of limitations of the amide H/²H exchange experiments, only the effects of (R_p)-cAMPS on H/²H amide exchange in RIα(91–244)·C-subunit complex could be investigated. The results showed that (R_p)-cAMPS induces a relatively widespread increase in amide exchange, indicating limited unfolding and/or enhanced conformational flexibility that is propagated almost globally through the C-subunit and, at least, part of RIα. These conformational changes were accompanied by a 5-fold increase in the affinity of RIα(91–244) toward C-subunit, suggesting that, at least, some of the (R_p)-cAMPS effects are mediated by an increase in internal entropy. Finally, the (R_p)-cAMPS-induced increase in R-C affinity indicates that (R_p)-cAMPS is better described as an inverse agonist because the basal activity of the PKA holoenzyme should be decreased by (R_p)-cAMPS.Open in a separate windowFig. 1.Overview of PKA structure and cAMP analogs. A, domain organization of RIα showing the domain boundaries of RIα(91–244) where the pseudosubstrate in green is connected to CNB-A domain in blue by a linker segment. B, structure of RIα(91–244) in the C-subunit-bound conformation (Protein Data Bank code 1U7E (23)) showing the pseudosubstrate in green, linker in yellow, and helical subdomain comprising helices αN, αA, αB, and αC in blue and β-subdomain in tan. The PBC is in red. C, structure of the C·RIα(91–244) holoenzyme showing the C-subunit in tan and RIα(91–244) in blue. Sites for introduction of cysteines by site-directed mutagenesis are represented by red circles. The cAMP binding site (PBC) is in red. D, structure of cAMP showing the 2′-OH group and 3′–5′ phosphodiester bond. The exocyclic oxygens upon replacement with sulfur atoms to generate the (S_p)-cAMPS and (R_p)-cAMPS diastereomers are highlighted.     

Dynamic patterns of circulating seasonal and pandemic A(H1N1)pdm09 influenza viruses from 2007-2010 in and around Delhi, India

Influenza surveillance was carried out in a subset of patients with influenza-like illness (ILI) presenting at an Employee Health Clinic (EHS) at All India Institute of Medical Sciences (AIIMS), New Delhi (urban) and pediatric out patients department of civil hospital at Ballabhgarh (peri-urban), under the Comprehensive Rural Health Services Project (CRHSP) of AIIMS, in Delhi region from January 2007 to December 2010. Of the 3264 samples tested, 541 (17%) were positive for influenza viruses, of which 221 (41%) were pandemic Influenza A(H1N1)pdm09, 168 (31%) were seasonal influenza A, and 152 (28%) were influenza B. While the Influenza viruses were detected year-round, their types/subtypes varied remarkably. While there was an equal distribution of seasonal A(H1N1) and influenza B in 2007, predominance of influenza B was observed in 2008. At the beginning of 2009, circulation of influenza A(H3N2) viruses was observed, followed later by emergence of Influenza A(H1N1)pdm09 with co-circulation of influenza B viruses. Influenza B was dominant subtype in early 2010, with second wave of Influenza A(H1N1)pdm09 in August-September, 2010. With the exception of pandemic H1N1 emergence in 2009, the peaks of influenza activity coincided primarily with monsoon season, followed by minor peak in winter at both urban and rural sites. Age group analysis of influenza positivity revealed that the percent positivity of Influenza A(H1N1)pdm09 influenza virus was highest in >5–18 years age groups (OR 2.5; CI = 1.2–5.0; p = 0.009) when compared to seasonal influenza. Phylogenetic analysis of Influenza A(H1N1)pdm09 from urban and rural sites did not reveal any major divergence from other Indian strains or viruses circulating worldwide. Continued surveillance globally will help define regional differences in influenza seasonality, as well as, to determine optimal periods to implement influenza vaccination programs among priority populations.     

Discovery of 3-morpholino-imidazole[1,5-a]pyrazine BTK inhibitors for rheumatoid arthritis

8-Amino-imidazo[1,5-a]pyrazine-based Bruton’s tyrosine kinase (BTK) inhibitors, such as 6, exhibited potent inhibition of BTK but required improvements in both kinase and hERG selectivity (Liu et al., 2016; Gao et al., 2017). In an effort to maintain the inhibitory activity of these analogs and improve their selectivity profiles, we carried out SAR exploration of groups at the 3-position of pyrazine compound 6. This effort led to the discovery of the morpholine group as an optimized pharmacophore. Compounds 13, 23 and 38 displayed excellent BTK potencies, kinase and hERG selectivities, and pharmacokinetic profiles.     

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes.     

Host plant induced variation in gut bacteria of Helicoverpa armigera

Helicoverpa are important polyphagous agricultural insect pests and they have a worldwide distribution. In this study, we report the bacterial community structure in the midgut of fifth instar larvae of Helicoverpa armigera, a species prevalent in the India, China, South Asia, South East Asia, Southern & Eastern Africa and Australia. Using culturable techniques, we isolated and identified members of Bacillus firmus, Bacillus niabense, Paenibacillus jamilae, Cellulomonas variformis, Acinetobacter schindleri, Micrococcus yunnanesis, Enterobacter sp., and Enterococcus cassiliflavus in insect samples collected from host plants grown in different parts of India. Besides these the presence of Sphingomonas, Ralstonia, Delftia, Paracoccus and Bacteriodetes was determined by culture independent molecular analysis. We found that Enterobacter and Enterococcus were universally present in all our Helicoverpa samples collected from different crops and in different parts of India. The bacterial diversity varied greatly among insects that were from different host plants than those from the same host plant of different locations. This result suggested that the type of host plant greatly influences the midgut bacterial diversity of H. armigera, more than the location of the host plant. On further analyzing the leaf from which the larva was collected, it was found that the H. armigera midgut bacterial community was similar to that of the leaf phyllosphere. This finding indicates that the bacterial flora of the larval midgut is influenced by the leaf surface bacterial community of the crop on which it feeds. Additionally, we found that laboratory made media or the artificial diet is a poor bacterial source for these insects compared to a natural diet of crop plant.