Global and local genomic features together modulate the spontaneous single nucleotide mutation rate.

Spontaneous mutations are evolutionary engines as they generate variants for the evolutionary downstream processes that give rise to speciation and adaptation. Single nucleotide mutations (SNM) are the most abundant type of mutations among them. Here, we perform a meta-analysis to quantify the influence of selected global genomic parameters (genome size, genomic GC content, genomic repeat fraction, number of coding genes, gene count, and strand bias in prokaryotes) and local genomic features (local GC content, repeat content, CpG content and the number of SNM at CpG islands) on spontaneous SNM rates across the tree of life (prokaryotes, unicellular eukaryotes, multicellular eukaryotes) using wild-type sequence data in two different taxon classification systems. We find that the spontaneous SNM rates in our data are correlated with many genomic features in prokaryotes and unicellular eukaryotes irrespective of their sample sizes. On the other hand, only the number of coding genes was correlated with the spontaneous SNM rates in multicellular eukaryotes primarily contributed by vertebrates data. Considering local features, we notice that local GC content and CpG content significantly were correlated with the spontaneous SNM rates in the unicellular eukaryotes, while local repeat fraction is an important feature in prokaryotes and certain specific uni- and multi-cellular eukaryotes. Such predictive features of the spontaneous SNM rates often support non-linear models as the best fit compared to the linear model. We also observe that the strand asymmetry in prokaryotes plays an important role in determining the spontaneous SNM rates but the SNM spectrum does not.

Subtype diversity and emergence of drug resistance in HIV-1 in solapur district of Maharashtra, India.

Background and Objectives: Even after four decades, HIV infection remains a global challenge and a leading cause of mortality in adults across the world. Anti-retroviral therapy (ART) that controls HIV viremia, is now available through public health facilities in India but drug resistance, which is likely to develop among these individuals remains poorly studied in India. The objectives of present study are to find out the HIV-1 virus subtypes, drug resistance mutations and HIV-1 drug resistance to NRTI, NNRTI and protease inhibitors in the Solapur district, India. Materials and Methods: In a cross sectional study, forty two ART-experienced HIV-1-infected patients with CD4+ count < 200 cells ml-1 and viral load (VL) > 3, 000 copies ml-1 were recruited. All patients belonged to Maharashtra State of India near Barshi Solapur and had been on ART treatment for over 5 years. EDTA whole blood from HIV-1-infected patients was centrifuged and the viral nucleic acid was purified from the plasma. Viral nucleic acid was amplified by PCR using protease and reverse transcriptase specific primers. The resulting amplicons were sequenced and studied for mutations. The tools from Stanford University website were used for subtyping of HIV-1 and identification of mutations conferring drug resistance. Results: In present investigation, HIV-1 subtypes were subtype C in 37 (88.09%), subtype CRF01_AE in 2 (4.76%), and subtype A in 3 patients (7.14%). Drug resistance mutations of NRTI, NNRTI and protease were observed in 15 (37.71%) of 42 patients tested. Drug resistance for NRTI was observed in 12 (28.57%) and for NNRTI in 13 (30.95%) patients. No drug resistance was observed for protease inhibitors. Conclusion: Considerable HIV-1 drug resistance exists among patients receiving ART from a rural areas of India, suggesting more studies from rural region are required to prevent development of resistance to ART.

Spatially Resolved Distribution, Sources, Exposure Levels, and Health Risks of Heavy Metals in <63 µm Size-Fractionated Road Dust from Lucknow City, North India.

In the present study, a total of 64 road dust samples were collected from five different functional areas (residential, commercial, parks, high-traffic, and industrial) in urban Lucknow to assess the accumulation, distribution, and health risk of heavy metals (HMs) (i.e., Fe, Mn, Zn, Cu, Pb, Cd, As, Cr and Ni). Acid digestion methods were used to analyze HMs, followed by inductively coupled plasma-mass spectrometry (ICPMS). The ascending frequency of HMs was Cd < As < Ni < Cr < Pb < Cu < Zn < Mn < Fe for all different functional areas. Almost all HMs exceed the limits of Indian natural soil background values (INSB) across all functional areas. The pollution assessment results reveal that the urban road dust of Lucknow is highly enriched with Zn and Pb, causing deterioration of dust quality. The spatial distribution of HMs shows that road dust found in the central and southwestern zones of the Lucknow urban area are more contaminated than in other areas. The ecological risk assessment demonstrates that Cd was the highest risk contributor, followed by Pb, Zn and Cu. The result of the health risk assessment i.e., the cumulative hazard index (HI) and the cumulative lifetime cancer risk (LCR), reveal that children (mean HIchildren = 1.26, LCRchildren = 0.000187) are more vulnerable to HM exposure than adults (HIadults = 0.14, LCRadults = 0.0000804). For carcinogenic and non-carcinogenic risk, ingestion appears to be the major pathway of HM exposure in both age groups. It is alarming that all studied four carcinogenic HMs were found in concentrations higher than 1 × 10-6 (the permissible limit for humans). This indicates slight chances of developing cancer for both age groups in all functional areas.

Inadequacy of Evolutionary Profiles Vis-a-vis Single Sequences in Predicting Transient DNA-Binding Sites in Proteins.

Sequence-based prediction of DNA-binding residues in a protein is a widely studied problem for which machine learning methods with continuously improving predictive power have been developed. Concatenated rows within a sliding window of a Position Specific Substitution Matrix (PSSM) of the protein are currently used as the primary feature set in almost all the methods of predicting DNA-binding residues. Here we report that these evolutionary profiles are powerful, only for identifying conserved binding sites and fall short for the residue positions which undergo binding to non-binding transitions in closely related proteins. We created a database of highly similar protein pairs with known protein-DNA complexes and investigated differential predictability of conserved and transient binding residues within each pair. Retraining machine learning models uniformly, we compared the predictive powers of the models trained on PSSMs against similarly trained models on sparse-encoded single sequences. We found that the transient binding site predictions from evolutionary profiles are outperformed by single-sequence based models under controlled experiments by as much as 8 percentage points. Thus, we conclude that the PSSM-based models are inadequate to predict high-specificity DNA-binding residues. These findings are of critical significance for the design of mutant- and species-specific DNA ligands and for homology based modeling of protein-DNA complexes.

Isolation and Characterization of Levoglucosan-Metabolizing Bacteria.

Bacteria were isolated from wastewater and soil containing charred wood remnants based on their ability to use levoglucosan as a sole carbon source and on their levoglucosan dehydrogenase (LGDH) activity. On the basis of their 16S rRNA gene sequences, these bacteria represented the diverse genera Microbacterium, Paenibacillus, Shinella, and Klebsiella. Genomic sequencing of the isolates verified that two isolates represented novel species, Paenibacillus athensensis MEC069T and Shinella sumterensis MEC087T, while the remaining isolates were closely related to Microbacterium lacusdiani or Klebsiella pneumoniae. The genetic sequence of LGDH, lgdA, was found in the genomes of these four isolates as well as Pseudarthrobacter phenanthrenivorans Sphe3. The identity of the P. phenanthrenivorans LGDH was experimentally verified following recombinant expression in Escherichia coli. Comparison of the putative genes surrounding lgdA in the isolate genomes indicated that several other gene products facilitate the bacterial catabolism of levoglucosan, including a putative sugar isomerase and several transport proteins. IMPORTANCE Levoglucosan is the most prevalent soluble carbohydrate remaining after high-temperature pyrolysis of lignocellulosic biomass, but it is not fermented by typical production microbes such as Escherichia coli and Saccharomyces cerevisiae. A few fungi metabolize levoglucosan via the enzyme levoglucosan kinase, while several bacteria metabolize levoglucosan via levoglucosan dehydrogenase. This study describes the isolation and characterization of four bacterial species that degrade levoglucosan. Each isolate is shown to contain several genes within an operon involved in levoglucosan degradation, furthering our understanding of bacteria that metabolize levoglucosan.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

Nonmigrant children with visceral leishmaniasis from the nonendemic area of Uttarakhand.

The emergence of visceral leishmaniasis (VL) in nonendemic areas is a matter of great concern. We conducted a study and present a brief description of six nonmigrant children with VL from the nonendemic area of Uttarakhand, diagnosed in our tertiary teaching hospital from February 2012 to June 2013. We also present here a geographic distribution of these cases to assess the impact of global warming and climate change on the spread of the disease. Patients were diagnosed as VL by clinical findings and confirmed by demonstration of Leishmania donovani bodies in the bone marrow or a positive serum rK39 test. Four cases were treated successfully with amphotericin B. One patient died during treatment and one patient was discharged on persistent request. Clinicians should suspect and investigate for VL in patients with pyrexia of unknown origin, even in nonmigrant patients from nonendemic regions, for an early diagnosis.

Differential sensitivities of the growth of Escherichia coli to acrylate under aerobic and anaerobic conditions and its effect on product formation.

The effect of acrylate on the growth of Escherichia coli was determined under aerobic and anaerobic conditions in glucose-defined medium. Growth occurred with up to 35 mM acrylate under aerobic conditions but ceased at 5 mM acrylate under anaerobic conditions. This differential sensitivity can be attributed to inhibition of pyruvate formate lyase and/or pflB gene repression, as this enzyme is necessary for anaerobic growth of E. coli. The effect of acrylate on end-product distribution was also determined by growing E. coli first aerobically, then switching to anaerobic conditions. In the absence of acrylate, E. coli generated the typical distribution of mixed-acid products, with about 12 % of pyruvate being metabolically converted to lactate. In contrast, in the presence of 5 mM acrylate, E. coli converted 83 % of pyruvate to lactate, consistent with a reduction in pyruvate formate lyase activity.

Surface-enhanced Raman scattering on periodic metal nanotips with tunable sharpness.

This paper reports on a scalable bottom-up technology for producing periodic gold nanotips with tunable sharpness as surface-enhanced Raman scattering (SERS) substrates. Inverted silicon pyramidal pits, which are templated from non-close-packed colloidal crystals prepared by a spin-coating technology, are used as structural templates to replicate arrays of polymer nanopyramids with nanoscale sharp tips. The deposition of a thin layer of gold on the polymer nanopyramids leads to the formation of SERS-active substrates with a high enhancement factor (up to 10(8)). The thickness of the deposited metal determines the sharpness of the nanotips and the resulting Raman enhancement factor. Finite-element electromagnetic modeling shows that the nanotips can significantly enhance the local electromagnetic field and the sharpness of nanotips greatly affects the SERS enhancement.