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Mycobacterium tuberculosis encounters diverse microenvironments, including oxidative assault (ROS and RNS), when it attempts to establish itself within its human host. Therefore, redox sensory and regulation processes are assumed significant importance, as these are essential processes for M. tuberculosis to survive under these hostile conditions. M. tuberculosis contains thioredoxin system to maintain redox homeostasis, which establish a balance between the thiol/dithiol couple.Still very less is known about it. In the present study, we attempted to capture the targets of all the M. tuberculosis thioredoxin proteins (viz., TrxB and TrxC) and a thioredoxin-like protein, NrdH, under aerobic and hypoxic conditions by performing thioredoxin trapping chromatography followed by mass spectrometry. We found that TrxC captured the maximum number of targets in both the physiological conditions and most of the targets of TrxB and NrdH showing overlap with targets of TrxC, indicating that TrxC acts as main thioredoxin. Further the PANTHER classification system provides involvement of targets in various metabolic processes and Gene Ontology analysis suggests that glutamine biosynthetic process and Fe–S cluster biosynthesis are the most enriched processes in the target list of TrxC and TrxB respectively. Also, we suggest that the thioredoxin system might play an important role under hypoxia by targeting those proteins which are responsible to sense and maintain hypoxic conditions. Furthermore, our studies establish a link between TrxB and iron-sulfur cluster biogenesis in M. tuberculosis. Ultimately, these findings open a new direction to target the thioredoxin system for screening new anti-mycobacterial drug targets.

Abstract Although skin is the primary affected organ in Leprosy, the role of the skin microbiome in its pathogenesis is not well understood. Recent reports have shown that skin of leprosy patients (LP) harbours perturbed microbiota which grants inflammation and disease progression. Herein, we present the results of nested Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE) which was initially performed for investigating the diversity of bacterial communities from lesional skin (LS) and non-lesional skin (NLS) sites of LP (n = 11). Further, we performed comprehensive analysis of 16S rRNA profiles corresponding to skin samples from participants (n = 90) located in two geographical locations i.e. Hyderabad and Miraj in India. The genus Staphylococcus was observed to be one of the representative bacteria characterizing healthy controls (HC; n = 30), which in contrast was underrepresented in skin microbiota of LP. Taxa affiliated to phyla Firmicutes and Proteobacteria were found to be signatures of HC and LS, respectively. Observed diversity level changes, shifts in core microbiota, and community network structure support the evident dysbiosis in normal skin microbiota due to leprosy. Insights obtained indicate the need for exploring skin microbiota modulation as a potential therapeutic option for leprosy.

The ability of chaperonins to buffer mutations that affect protein folding pathways suggests that their abundance should be evolutionarily advantageous. Here, we investigate the effect of chaperonin overproduction on cellular fitness in Escherichia coli. We demonstrate that chaperonin abundance confers 1) an ability to tolerate higher temperatures, 2) improved cellular fitness, and 3) enhanced folding of metabolic enzymes, which is expected to lead to enhanced energy harvesting potential.

Metabolic adaptation of Mycobacterium tuberculosis (M. tuberculosis) to microbicidal intracellular environment of host macrophages is fundamental to its pathogenicity. However, an in-depth understanding of metabolic adjustments through key reaction pathways and networks is limited. To understand how such changes occur, we measured the cellular metabolome of M. tuberculosis subjected to four microbicidal stresses using liquid chromatography-mass spectrometric multiple reactions monitoring (LC-MRM/MS). Overall, 87 metabolites were identified. The metabolites best describing the separation between stresses were identified through multivariate analysis. The coupling of the metabolite measurements with existing genome-scale metabolic model, and using constraint-based simulation led to several new concepts and unreported observations in M. tuberculosis; such as (i) the high levels of released ammonia as an adaptive response to acidic stress was due to increased flux through L-asparaginase rather than urease activity; (ii) nutrient starvation-induced anaplerotic pathway for generation of TCA intermediates from phosphoenolpyruvate using phosphoenolpyruvate kinase; (iii) quenching of protons through GABA shunt pathway or sugar alcohols as possible mechanisms of early adaptation to acidic and oxidative stresses; and (iv) usage of alternate cofactors by the same enzyme as a possible mechanism of rewiring metabolic pathways to overcome stresses. Besides providing new leads and important nodes that can be used for designing intervention strategies, the study advocates the strength of applying flux balance analyses coupled with metabolomics to get a global picture of complex metabolic adjustments.

Abstract Background A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved. Results We propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how “distant” they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions. Conclusions Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving.

Anthrax bacteria: a step in the pathway to biofilms An enzyme that adds phosphate groups to other proteins, PrkC, mediates molecular signaling events that allow anthrax bacteria to form biofilms. Bacillus anthracis is widely used as a model to explore the formation of biofilms that allows many bacterial infections to resist immune defenses. An international research team led by Yogendra Singh and Andaleeb Sajid at the CSIR-Institute of Genomics and Integrative Biology in Delhi, India, studied the bacterial protein kinase PrkC. The researchers found that PrkC phosphorylates a “chaperone” protein that assist the assembly and disassembly of other protein-based structures. This signaling protein and the chaperone help in biofilm formation. Establishing this link in the signaling chain leading to biofilms will guide future research to combat the role of biofilms in disease.

The aim of the present article is to give a historical survey of some important research works related to queues in machining system since 2010. Queues of failed machines in machine repairing problem occur due to the failure of machines at random in the manufacturing industries, where different jobs are performed on machining stations. Machines are subject to failure what may result in significant loss of production, revenue, or goodwill. In addition to the references on queues in machining system, which is also called `Machine Repair Problem' (MRP) or `Machine Interference Problem' (MIP), a meticulous list of books and survey papers is also prepared so as to provide a detailed catalog for understanding the research in queueing domain. We have classified the relevant literature according to a year of publishing, methodological, and modeling aspects. The author(s) hope that this survey paper could be of help to learners contemplating research on queueing domain.

Background: Video-assisted thoracoscopic surgical decortication (VATSD) is widely applicable in fibrinopurulent Stage II empyema. While, more chronic thick walled Stage III empyema (organizing stage) needs conversion to open thoracotomy, and existing reports reveal a lacuna in the realm of late stage empyema patient's management through VATS utilization, particularly Stage III empyema. We prospectively evaluated the application of VATSD regardless of the stage of pleural empyema for the effective management of late stage empyema in comparison to open decortications (ODs) to minimize the adverse effects of the disease. Methods: All patients with pyogenic pleural empyema (Stage II and Stage III) in King Khalid University Hospital (KKUH) (admitted from January 2009 to December 2013) who did not respond to chest tube/pigtail drainage and/or antibiotic therapy were treated with VATSD and/or open thoracotomy. Prospective evaluation was carried out, and the effect of this technique on perioperative outcomes was appraised to evaluate our technical learning with the passage of time and experience with VATS for late stage empyema management. Results: Out of total 63 patients, 26 had Stage II empyema and 37 had Stage III empyema. VATSD was employed on all empyema patients admitted in the KKUH. VATSD was successful in all patients with Stage II empyema. Twenty-five patients (67.6%) with Stage III empyema completed VATSD successfully. However, only 12 cases (32.4%) required conversions to open (thoracotomy) drainage (OD). The median hospital stay for Stage III VATSD required 9.65 ± 4.1 days. Whereas, patients who underwent open thoracotomy took longer time (21.82 ± 16.35 days). Similarly, Stage III VATSD and Stage III open surgery cases showed significance difference among chest tube duration (7.84 ± 3.33 days for VATS and 15.92 ± 8.2 days for open thoracotomy). Significantly, lower postoperative complications were detected in patients treated with VATSD in terms of atelectasis, prolonged air leak, wound infection, etc. Conlcusion: VATSD facilitates the management of fibrinopurulent, organized pyogenic pleural empyema with less postoperative discomfort, reduced hospitalization, and have fewer postoperative complications. VATSD can be an effective, safe first option for patients with Stage II pleural empyema, and feasible in most patients with Stage III pleural empyema.

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.

Chaperonins are a subclass of molecular chaperones that assist cellular proteins to fold and assemble into their native shape. Much work has been done on Type I chaperonins, which has elucidated their elegant mechanism. Some debate remains about the details in these mechanisms, but nonetheless the roles of these in helping protein folding have been understood in great depth. In this review we discuss the known functions of atypical Type I chaperonins, highlighting evolutionary aspects that might lead chaperonins to perform alternate functions.

A bacterium that grows and expresses plant growth promotion traits at 4°C was isolated from the rhizospheric soil of Amaranth, cultivated at a high altitude location in the North Western Indian Himalayas. The isolate was Gram negative and the cells appeared as rods (2.91 x 0.71 μm in size). It grew at temperatures ranging from 4 to 30°C, with a growth optimum at 28°C. It exhibited tolerance to a wide pH range (5-10; optimum 8.0) and salt concentrations up to 6% (wt/vol). Although it was sensitive to Rifampicin (R 20 μg mi-1), Gentamicin (G 3 μg mi-1), and Streptomycin (S 5 μg mi-1), it showed resistance to higher concentrations of Ampicillin (A 500 μg mi-1), Penicillin (P 300 μg mi-1), Polymixin B sulphate (Pb 100 μg mi-1) and Chloramphenicol (C 200 μg mi-1). The 16S rRNA sequence analysis revealed maximum identity with Pseudomonas lurida. The bacterium produced indole Acetic Acid (IAA) and solubilizes phosphate at 4, 15 and 28°C. It also retained its ability to produce rhamnolipids and siderophores at 15°C. Seed bacterization with the isolate enhanced the germination, shoot and root lengths of thirty-day-old wheat seedlings by 19.2, 30.0 & 22.9% respectively, as compared to the un-inoculated controls.

High-throughput experiments such as microarrays and deep sequencing provide large scale information on the pattern of gene expression, which undergoes extensive remodeling as the cell dynamically responds to varying environmental cues or has its function disrupted under pathological conditions. An important initial step in the systematic analysis and interpretation of genome-scale expression alteration involves identification of a set of perturbed transcriptional regulators whose differential activity can provide a proximate hypothesis to account for these transcriptomic changes. In the present work, we propose an unbiased and logically natural approach to transcription factor enrichment. It involves overlaying a list of experimentally determined differentially expressed genes on a background regulatory network coming from e.g. literature curation or computational motif scanning, and identifying that subset of regulators whose aggregated target set best discriminates between the altered and the unaffected genes. In other words, our methodology entails testing of all possible regulatory subnetworks, rather than just the target sets of individual regulators as is followed in most standard approaches. We have proposed an iterative search method to efficiently find such a combination, and benchmarked it on E. coli microarray and regulatory network data available in the public domain. Comparative analysis carried out on artificially generated differential expression profiles, as well as empirical factor overexpression data for M. tuberculosis, shows that our methodology provides marked improvement in accuracy of regulatory inference relative to the standard method that involves evaluating factor enrichment in an individual manner.

Cluster of differentiation 14 (CD14) gene is an important component of the human innate immune system and its role in tuberculosis (TB) has been sparsely documented. The enhanced plasma CD14 levels in TB patients as compared to healthy controls are associated with CD14 gene promoter (C-159T) polymorphism. In the past few years, the relationship between CD14 -159 C>T (rs2569190) polymorphism and risk of TB has been reported in various ethnic populations; however, those studies have yielded contradictory results. In this study systemic assessment was done for the published studies based on the association between CD14 -159 C>T polymorphism and TB risk retrieved from PubMed (Medline) and EMBASE search. A total number of 1389 TB cases and 1421 controls were included in this study and meta-analysis was performed to elucidate the association between CD14 -159 C>T polymorphism and its susceptibility towards TB. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for allele contrast, homozygous comparison, heterozygous comparison, dominant and recessive genetic model. It was found that T allele carrier was significantly associated with increased TB risk (T vs. C: p-value = 0.023; OR = 1.305, 95% CI = 1.038 to 1.640). Similarly, homozygous mutant TT genotype also revealed 1.6 fold increased risk of TB (TT vs. CC; p-value = 0.040; OR = 1.652, 95% CI = 1.023 to 2.667). Additionally, dominant genetic model demonstrated increased risk of developing TB (TT vs. CC+CT: p-value = 0.006; OR = 1.585, 95% CI = 1.142 to 2.201). The study demonstrates that CD14 gene (-159 C>T) polymorphism contributes increased susceptibility for TB. Moreover, this meta-analysis also suggests for future larger studies with stratified case control population and biological characterization for validation studies.

About 90% of the people infected with Mycobacterium tuberculosis carry latent bacteria that are believed to get activated upon immune suppression. One of the fundamental challenges in the control of tuberculosis is therefore to understand molecular mechanisms involved in the onset of latency and/or reactivation. We have attempted to address this problem at the systems level by a combination of predicted functional protein:protein interactions, integration of functional interactions with large scale gene expression studies, predicted transcription regulatory network and finally simulations with a boolean model of the network. Initially a prediction for genome-wide protein functional linkages was obtained based on genome-context methods using a Support Vector Machine. This set of protein functional linkages along with gene expression data of the available models of latency was employed to identify proteins involved in mediating switch signals during dormancy. We show that genes that are up and down regulated during dormancy are not only coordinately regulated under dormancy-like conditions but also under a variety of other experimental conditions. Their synchronized regulation indicates that they form a tightly regulated gene cluster and might form a latency-regulon. Conservation of these genes across bacterial species suggests a unique evolutionary history that might be associated with M. tuberculosis dormancy. Finally, simulations with a boolean model based on the regulatory network with logical relationships derived from gene expression data reveals a bistable switch suggesting alternating latent and actively growing states. Our analysis based on the interaction network therefore reveals a potential model of M. tuberculosis latency.

Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.

Using high-resolution angle-resolved photoemission spectroscopy and ab initio calculation, we have studied the bulk and surface electronic structure of metallic rutile 5d transition metal oxide IrO2 that harbors both edge and corner sharing Ir-O octahedrons. We observe strong modulation of the band structure by spin-orbit coupling (SOC). The measured band structure is well reproduced by our ab initio calculation without band renormalization, suggesting the absence of the SOC-enhanced correlation effect in IrO2. In accordance with the calculation, we visualize two types of Dirac nodal lines (DNLs) protected by mirror symmetry and nonsymmorphic crystal symmetry, respectively. SOC gaps the first type of DNLs, which may contribute largely to the strong spin Hall effect. The second type of DNLs at the edges of Brillouin zone, however, remain intact against SOC. Our results not only provide important insights into the exotic transport properties of IrO2, but also shed light on the understanding of the role of SOC in the iridate family.

Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth's environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat). AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.

The recent discovery of the extreme magnetoresistance (XMR) in the nonmagnetic rare-earth monopnictides LaX (X = P, As, Sb, Bi,), a recently proposed new topological semimetal family, has inspired intensive research effort in the exploration of the correlation between the XMR and their electronic structures. In this work, using angle-resolved photoemission spectroscopy to investigate the three-dimensional band structure of LaBi, we unraveled its topologically nontrivial nature with the observation of multiple topological surface Dirac fermions, as supported by our ab initio calculations. Furthermore, we observed substantial imbalance between the volume of electron and hole pockets, which rules out the electron-hole compensation as the primary cause of the XMR in LaBi.

Topological insulators represent unusual topological quantum states, typically with gapped bulk band structure but gapless surface Dirac fermions protected by time-reversal symmetry. Recently, a distinct kind of topological insulator resulting from nonsymmorphic crystalline symmetry was proposed in the KHgX (X=As, Sb, Bi) compounds. Unlike regular topological crystalline insulators, the nonsymmorphic glide-reflection symmetry in KHgX guarantees the appearance of an exotic surface fermion with hourglass shape dispersion (where two pairs of branches switch their partners) residing on its (010) side surface, contrasting to the usual two-dimensional Dirac fermion form. Here, by using high-resolution angle-resolved photoemission spectroscopy, we systematically investigate the electronic structures of KHgSb on both (001) and (010) surfaces and reveal the unique in-gap surface states on the (010) surface with delicate dispersion consistent with the "hourglass Fermion" recently proposed. Our experiment strongly supports that KHgSb is a nonsymmorphic topological crystalline insulator with hourglass fermions, which serves as an important step to the discovery of unique topological quantum materials and exotic fermions protected by nonsymmorphic crystalline symmetry.

In this study, we simulated the performance of solar cells using appropriate optical and electrical parameters for MoX2 compounds. We used the solar cell capacitance simulator software to simulate MoS2-based solar cells with two structures (ZnO/WS2/MoS2 or ZnO/WSe2/MoS2). We investigated the impact of varying the thickness of the MoS2 absorbing layer, doping, temperature elevation, and exploring the effect of the buffer layer on the electrical characteristics of the solar cell, including parameters like open-circuit voltage (Voc), short-circuit current (Jsc), and solar cell efficiency (η). This analysis aimed to provide valuable insights into optimizing the design and performance of MoS2-based solar cells, contributing to advancements in thin-film solar cell technology. [ABSTRACT FROM AUTHOR]

Topological quantum materials represent a new class of matter with both exotic physical phenomena and novel application potentials. Many Heusler compounds, which exhibit rich emergent properties such as unusual magnetism, superconductivity and heavy fermion behaviour, have been predicted to host non-trivial topological electronic structures. The coexistence of topological order and other unusual properties makes Heusler materials ideal platform to search for new topological quantum phases (such as quantum anomalous Hall insulator and topological superconductor). By carrying out angle-resolved photoemission spectroscopy and ab initio calculations on rare-earth half-Heusler compounds LnPtBi (Ln=Lu, Y), we directly observe the unusual topological surface states on these materials, establishing them as first members with non-trivial topological electronic structure in this class of materials. Moreover, as LnPtBi compounds are non-centrosymmetric superconductors, our discovery further highlights them as promising candidates of topological superconductors.