Tuberculosis incidence rates during 8 years of follow-up of an antiretroviral treatment cohort in South Africa: comparison with rates in the community

Background

Although antiretroviral therapy (ART) is known to be associated with time-dependent reductions in tuberculosis (TB) incidence, the long-term impact of ART on incidence remains imprecisely defined due to limited duration of follow-up and incomplete CD4 cell count recovery in existing studies. We determined TB incidence in a South African ART cohort with up to 8 years of follow-up and stratified rates according to CD4 cell count recovery. We compared these rates with those of HIV-uninfected individuals living in the same community.

Methodology/Principal Findings

Prospectively collected clinical data on patients receiving ART in a community-based cohort in Cape Town were analysed. 1544 patients with a median follow-up of 5.0 years (IQR 2.4–5.8) were included in the analysis. 484 episodes of incident TB (73.6% culture-confirmed) were diagnosed in 424 patients during 6506 person-years (PYs) of follow-up. The TB incidence rate during the first year of ART was 12.4 (95% CI 10.8–14.4) cases/100PYs and decreased to 4.92 (95% CI 3.64–8.62) cases/100PYs between 5 and 8 years of ART. During person-time accrued within CD4 cell strata 0–100, 101–200, 201–300, 301–400, 401–500, 501–700 and ≥700 cells/µL, TB incidence rates (95% CI) were 25.5 (21.6–30.3), 11.2 (9.4–13.5), 7.9 (6.4–9.7), 5.0 (3.9–6.6), 5.1 (3.8–6.8), 4.1 (3.1–5.4) and 2.7 (1.7–4.5) cases/100PYs, respectively. Overall, 75% (95% CI 70.9–78.8) of TB episodes were recurrent cases. Updated CD4 cell count and viral load measurements were independently associated with long-term TB risk. TB rates during person-time accrued in the highest CD4 cell count stratum (>700 cells/µL) were 4.4-fold higher that the rate in HIV uninfected individuals living in the same community (2.7 versus 0.62 cases/100PYs; 95%CI 0.58–0.65).

Conclusions/Significance

TB rates during long-term ART remained substantially greater than rates in the local HIV uninfected populations regardless of duration of ART or attainment of CD4 cell counts exceeding 700 cells/µL.     

Effects of biotic disturbances on forest carbon cycling in the United States and Canada

Forest insects and pathogens are major disturbance agents that have affected millions of hectares in North America in recent decades, implying significant impacts to the carbon (C) cycle. Here, we review and synthesize published studies of the effects of biotic disturbances on forest C cycling in the United States and Canada. Primary productivity in stands was reduced, sometimes considerably, immediately following insect or pathogen attack. After repeated growth reductions caused by some insects or pathogens or a single infestation by some bark beetle species, tree mortality occurred, altering productivity and decomposition. In the years following disturbance, primary productivity in some cases increased rapidly as a result of enhanced growth by surviving vegetation, and in other cases increased slowly because of lower forest regrowth. In the decades following tree mortality, decomposition increased as a result of the large amount of dead organic matter. Net ecosystem productivity decreased immediately following attack, with some studies reporting a switch to a C source to the atmosphere, and increased afterward as the forest regrew and dead organic matter decomposed. Large variability in C cycle responses arose from several factors, including type of insect or pathogen, time since disturbance, number of trees affected, and capacity of remaining vegetation to increase growth rates following outbreak. We identified significant knowledge gaps, including limited understanding of carbon cycle impacts among different biotic disturbance types (particularly pathogens), their impacts at landscape and regional scales, and limited capacity to predict disturbance events and their consequences for carbon cycling. We conclude that biotic disturbances can have major impacts on forest C stocks and fluxes and can be large enough to affect regional C cycling. However, additional research is needed to reduce the uncertainties associated with quantifying biotic disturbance effects on the North American C budget.     

Designing allosteric modulators for active conformational state of m-glutamate G-protein coupled receptors

G-protein coupled receptors (GPCRs) are found to be attractive drug targets for the treatment of various neuronal diseases. Allosteric modulators have their role in enhancing or suppressing the effect of glutamate on mGluRs. Structure of mGluR1 was generated with the help of Modeller software by considering human B2-adrenergic GPCR protein as template. Structure of various already known drug molecules were used for similarity search in the ZINC database and a large number of similar molecules were obtained, than filtering of these molecules were done by applying drug features. Molecules were screened by Molegro Virtual Docking program and numbers of novel molecules were generated by using LigBuilder software. Finally 16 novel drug candidates were selected, which were showing better results than the seed molecule and previously known modulators. These results will help in designing and synthesis of better drugs against diseases like Epilepsy and Parkinson's.     

Metabolic pathway analysis and molecular docking analysis for identification of putative drug targets in Toxoplasma gondii: novel approach

Toxoplasma gondii is an obligate intracellular apicomplexan parasite that can infect a wide range of warm-blooded animals including humans. In humans and other intermediate hosts, toxoplasma develops into chronic infection that cannot be eliminated by host's immune response or by currently used drugs. In most cases, chronic infections are largely asymptomatic unless the host becomes immune compromised. Thus, toxoplasma is a global health problem and the situation has become more precarious due to the advent of HIV infections and poor toleration of drugs used to treat toxoplasma infection, having severe side effects and also resistance have been developed to the current generation of drugs. The emergence of these drug resistant varieties of T. gondii has led to a search for novel drug targets. We have performed a comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen T. gondii. The enzymes in the unique pathways of T. gondii, which do not show similarity to any protein from the host, represent attractive potential drug targets. We have listed out 11 such potential drug targets which are playing some important work in more than one pathway. Out of these, one important target is Glutamate dehydrogenase enzyme; it plays crucial part in oxidation reduction, metabolic process and amino acid metabolic process. As this is also present in the targets of tropical diseases of TDR (Tropical disease related Drug) target database and no PDB and MODBASE 3D structural model is available, homology models for Glutamate dehydrogenase enzyme were generated using MODELLER9v6. The model was further explored for the molecular dynamics simulation study with GROMACS, virtual screening and docking studies with suitable inhibitors against the NCI diversity subset molecules from ZINC database, by using AutoDock-Vina. The best ten docking solutions were selected (ZINC01690699, ZINC17465979, ZINC17465983, ZINC18141294_03, ZINC05462670, ZINC01572309, ZINC18055497_01, ZINC18141294, ZINC05462674 and ZINC13152284_01). Further the Complexes were analyzed through LIGPLOT. On the basis of Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds, specifically ZINC01690699 (as it has minimum energy score and one of the highest number of interactions with the active site residue), could be promising inhibitors for T. gondii using Glutamate dehydrogenase as Drug target.     

Single-molecule pull-down for studying protein interactions

This protocol describes a single-molecule pull-down (SiMPull) assay for analyzing physiological protein complexes. The assay combines the conventional pull-down assay with single-molecule total internal reflection fluorescence (TIRF) microscopy and allows the probing of single macromolecular complexes directly from cell or tissue extracts. In this method, antibodies against the protein of interest are immobilized on a passivated microscope slide. When cell extracts are applied, the surface-tethered antibody captures the protein together with its physiological interaction partners. After washing away the unbound components, single-molecule fluorescence microscopy is used to probe the pulled-down proteins. Captured proteins are visualized through genetically encoded fluorescent protein tags or through antibody labeling. Compared with western blot analysis, this ultrasensitive assay requires considerably less time and reagents and provides quantitative data. Furthermore, SiMPull can distinguish between multiple association states of the same protein. SiMPull is generally applicable to proteins from a variety of cellular contexts and to endogenous proteins. Starting with the cell extracts and passivated slides, the assay requires 1.5-2.5 h for data acquisition and analysis.     

The implications of stochastic synthesis for the conjugation of functional groups to nanoparticles

Stochastic synthesis of a ligand coupled to a nanoparticle results in a distribution of populations with different numbers of ligands per nanoparticle. This distribution was resolved and quantified using HPLC and is in excellent agreement with the ligand/nanoparticle average measured by 1H NMR, gel permeation chromatography (GPC), and potentiometric titration, and yet significantly more disperse than commonly held perceptions of monodispersity. Two statistical models were employed to confirm that the observed heterogeneity is consistent with theoretical expectations.     

Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.     

Genetic diversity and population structure of <Emphasis Type="Italic">Litsea glutinosa</Emphasis> (Lour.) in Central India

Litsea glutinosa (Lour.), one of the most dwindling forest species in central India, is represented by highly fragmented populations that have been drastically reduced for the last 40 years, promulgating government ban on its extraction. For the first time with the help of ISSR markers, we investigated genetic variation and population structure of L. glutinosa in central Indian states. A total of 84 genotypes from 10 populations covering the entire potential pockets of the species in central India were collected. The percentage of polymorphic loci ranged from 44.79% (Rewa) to 94.79% (Marvahi) with a mean value of 70.10%. The sampled populations harbored high level of genetic diversity (mean h?=?0.294 and I?=?0.424) that was partitioned more within populations (73%) than between populations (27%). Bayesian structure analysis revealed the existence of four admixed genetic pools in L. glutinosa. The unsustainable extraction rather than genetic factor seems to be responsible for population fragmentation and dwindling status of this species. The dioecious nature of the species advocates an in-situ conservation to be the most suited approach for which Chhindwara, Jagdalpur, Balaghat and Jabalpur populations are appropriate.     

Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations

Solar‐induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite‐observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory‐2 (OCO‐2) provides the first opportunity to examine the SIF–GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO‐2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO‐2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R² = 0.72, p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R² = 0.57–0.79, p < 0.0001) except evergreen broadleaf forests (R² = 0.16, p < 0.05) at the daily timescale. A higher slope was found for C₄ grasslands and croplands than for C₃ ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome‐specific SIF–GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO‐2 SIF generally had a better performance for predicting GPP than satellite‐derived vegetation indices and a light use efficiency model. The universal SIF–GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO‐2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies.     

Gene Specific Impedimetric Bacterial DNA Sensor for Rheumatic Heart Disease

An impedimetric mga gene specific DNA sensor was developed by immobilization of single stranded DNA probe onto the screen printed modified gold-dendrimer nanohybrid composite electrode for early and rapid detection of S. pyogenes in human throat swab samples causing rheumatic heart disease. Electrochemical impedance response was measured after hybridization with bacterial single stranded genomic DNA (ssG-DNA) with probe. The sensor was found highly specific to S. pyogenes and can detect as low as 0.01 ng ssDNA in 6 µL sample only in 30 min. The nanohybrid sensor was also tested with non-specific pathogens and characterized by FTIR. An early detection of the pathogen S. pyogenes in human can save damage of mitral and aortic heart valves (rheumatic heart disease) by proper medical care.