Detection, characterization and possible biofragmentation of synthetic microfibers released from domestic laundering wastewater as an emerging source of marine pollution.

Synthetic microfibers are universally recognized as an emerging pollutant in all ecosystems. The present investigation focuses on the evaluation and quantification of synthetic microfiber released from domestic laundering wastewater from different regions of Bhubaneswar city of Odisha state of India. The estimated number of microfibers collected from 500 ml of sample varied from 200 to 500 in numbers with an average amount of biomass in the range of 0.4-4 g. The surface morphology of the samples was assessed by Scanning Electron Microscopic analysis which revealed that the fibers were having a length of approximately 10-30 mm and diameter of 10-20 µm. Carbonyl (CO) stretching band at 1711 cm-1 and Aldehyde (CH) Weak bond at 2917.38 cm-1 absorption were recorded from Fourier transform infrared spectroscopic analysis. As microfibers released from synthetic apparels are major source of environmental microplastic pollution their precise detection could help in controlling this problem.

Transcriptomic Profiling of Human Limbus-Derived Stromal/Mesenchymal Stem Cells-Novel Mechanistic Insights into the Pathways Involved in Corneal Wound Healing.

Limbus-derived stromal/mesenchymal stem cells (LMSCs) are vital for corneal homeostasis and wound healing. However, despite multiple pre-clinical and clinical studies reporting the potency of LMSCs in avoiding inflammation and scarring during corneal wound healing, the molecular basis for the ability of LMSCs remains unknown. This study aimed to uncover the factors and pathways involved in LMSC-mediated corneal wound healing by employing RNA-Sequencing (RNA-Seq) in human LMSCs for the first time. We characterized the cultured LMSCs at the stages of initiation (LMSC-P0) and pure population (LMSC-P3) and subjected them to RNA-Seq to identify the differentially expressed genes (DEGs) in comparison to native limbus and cornea, and scleral tissues. Of the 28,000 genes detected, 7800 DEGs were subjected to pathway-specific enrichment Gene Ontology (GO) analysis. These DEGs were involved in Wnt, TGF-ß signaling pathways, and 16 other biological processes, including apoptosis, cell motility, tissue remodeling, and stem cell maintenance, etc. Two hundred fifty-four genes were related to wound healing pathways. COL5A1 (11.81 ± 0.48) and TIMP1 (20.44 ± 0.94) genes were exclusively up-regulated in LMSC-P3. Our findings provide new insights involved in LMSC-mediated corneal wound healing.

False discovery rate: the Achilles' heel of proteogenomics.

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Suitability of Brahmi (Bacopa monnieri L.) cultivation on fly ash-amended soil for better growth and oil content.

Sustainable application of fly ash and its management in agriculture is a major challenge nowadays. A pot culture experiment was conducted to find out the most suitable level of fly ash application for soil amendments that can improve the plant growth and productivity of Brahmi (Bacopa monnieri L.). After growing seedlings of B. monnieri under different levels of fly ash for 90 days, a significant increase in plant biomass, essential oil content and tolerance index (more than 100%) was observed under 25% of fly ash amended soil in comparison to garden soil and higher fly ash treatments. Leaf chlorophyll content and photosynthetic parameters were remained unchanged under 25% of fly ash as compared to seedlings grown on garden soil. However, these parameters were significantly declined under higher concentrations of fly ash treatments. Higher levels of fly ash caused oxidative damage and the induction of some antioxidative enzymes activities in B. monnieri indicates its capability to endure oxidative stress tolerance. Overall, our study showed that 25% of fly ash can be used as soil amendment for cultivation of B. monnieri L. leading to enhance plant biomass and essential oil production.

Telomere repeat-binding factor 2 binds extensively to extra-telomeric G-quadruplexes and regulates the epigenetic status of several gene promoters.

The role of the telomere repeat-binding factor 2 (TRF2) in telomere maintenance is well-established. However, recent findings suggest that TRF2 also functions outside telomeres, but relatively little is known about this function. Herein, using genome-wide ChIP-Seq assays of TRF2-bound chromatin from HT1080 fibrosarcoma cells, we identified thousands of TRF2-binding sites within the extra-telomeric genome. In light of this observation, we asked how TRF2 occupancy is organized within the genome. Interestingly, we found that extra-telomeric TRF2 sites throughout the genome are enriched in potential G-quadruplex-forming DNA sequences. Furthermore, we validated TRF2 occupancy at several promoter G-quadruplex motifs, which did adopt quadruplex forms in solution. TRF2 binding altered expression and the epigenetic state of several target promoters, indicated by histone modifications resulting in transcriptional repression of eight of nine genes investigated here. Furthermore, TRF2 occupancy and target gene expression were also sensitive to the well-known intracellular G-quadruplex-binding ligand 360A. Together, these results reveal an extensive genome-wide association of TRF2 outside telomeres and that it regulates gene expression in a G-quadruplex-dependent fashion.

Quantitative Lipid Droplet Proteomics Reveals Mycobacterium tuberculosis Induced Alterations in Macrophage Response to Infection.

Growing evidence suggests the importance of lipid metabolism in pathogenesis of tuberculosis. Neutral lipids form the majority of lipids in a caseous granuloma, a pathology characteristic of tuberculosis. Cytosolic lipid droplets (LDs) of macrophages form the store house of these lipids and have been demonstrated to contribute to the inflammatory response to infection. The proteome of lipid droplets reflects the mechanisms of lipid metabolism active under a condition. However, infection induced changes in the proteome of these dynamic organelles remains elusive. Here, we employed quantitative proteomics to identify alterations induced upon infection with live Mycobacterium tuberculosis (Mtb) in comparison with heat killed bacilli or uninfected macrophages. We found increased abundance of proteins coupled with lipid metabolism, protein synthesis, and vesicular transport function in LDs upon infection with live Mtb. Using biochemical methods and microscopy, we validated ADP-ribosyltransferase (Arf)-like 8 (ARL8B) to be increased on the lipid droplet surface of live Mtb infected macrophages and that ARL8B is a bonafide LD protein. This study provides the first proteomic evidence that the dynamic responses to infection also encompass changes at the level of LDs. This information will be important in understanding how Mtb manipulates lipid metabolism and defense mechanisms of the host macrophage.

Adipocyte Model of Mycobacterium tuberculosis Infection Reveals Differential Availability of Iron to Bacilli in the Lipid-Rich Caseous Environment.

Mycobacterium tuberculosis, a successful human pathogen, utilizes multiple carbon sources from the host but adapts to a fatty-acid-rich environment in vivo We sought to delineate the physiologic response of M. tuberculosis to a lipid-rich environment by using differentiated adipocytes as a model system. Global transcriptome profiling based on RNA sequencing was performed for bacilli from infected adipocytes and preadipocytes. Genes involved in de novo fatty acid synthesis were downregulated, while those predicted to be involved in triglyceride biosynthesis were upregulated, in bacilli isolated from adipocytes, indicating reliance on host-derived fatty acids. Transcription factor network analysis indicated suppression of IdeR-regulated genes, suggesting decreased iron uptake by M. tuberculosis in the adipocyte model. This suppression of iron uptake coincided with higher ferritin and iron levels in adipocytes than in preadipocytes. In accord with the role of iron in mediating oxidative stress, we observed upregulation of genes involved in mitigating oxidative stress in M. tuberculosis isolated from adipocytes. We provide evidence that oleic acid, a major host-derived fatty acid, helps reduce the bacterial cytoplasm, thereby providing a safe haven for an M. tuberculosis mutant that is sensitive to iron-mediated oxidative stress. Via an independent mechanism, host ferritin is also able to rescue the growth of this mutant. Our work highlights the inherent synergy between macronutrients and micronutrients of the host environment that converge to provide resilience to the pathogen. This complex synergy afforded by the adipocyte model of infection will aid in the identification of genes required by M. tuberculosis in a caseous host environment.

Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data.

Database searching is the preferred method for protein identification from digital spectra of mass to charge ratios (m/z) detected for protein samples through mass spectrometers. The search database is one of the major influencing factors in discovering proteins present in the sample and thus in deriving biological conclusions. In most cases the choice of search database is arbitrary. Here we describe common search databases used in proteomic studies and their impact on final list of identified proteins. We also elaborate upon factors like composition and size of the search database that can influence the protein identification process. In conclusion, we suggest that choice of the database depends on the type of inferences to be derived from proteomics data. However, making additional efforts to build a compact and concise database for a targeted question should generally be rewarding in achieving confident protein identifications.

Integrated Transcriptomic-Proteomic Analysis Using a Proteogenomic Workflow Refines Rat Genome Annotation.

Proteogenomic re-annotation and mRNA splicing information can lead to the discovery of various protein forms for eukaryotic model organisms like rat. However, detection of novel proteoforms using mass spectrometry proteomics data remains a formidable challenge. We developed EuGenoSuite, an open source multiple algorithmic proteomic search tool and utilized it in our in-house integrated transcriptomic-proteomic pipeline to facilitate automated proteogenomic analysis. Using four proteogenomic pipelines (integrated transcriptomic-proteomic, Peppy, Enosi, and ProteoAnnotator) on publicly available RNA-sequence and MS proteomics data, we discovered 363 novel peptides in rat brain microglia representing novel proteoforms for 249 gene loci in the rat genome. These novel peptides aided in the discovery of novel exons, translation of annotated untranslated regions, pseudogenes, and splice variants for various loci; many of which have known disease associations, including neurological disorders like schizophrenia, amyotrophic lateral sclerosis, etc. Novel isoforms were also discovered for genes implicated in cardiovascular diseases and breast cancer for which rats are considered model organisms. Our integrative multi-omics data analysis not only enables the discovery of new proteoforms but also generates an improved reference for human disease studies in the rat model.

Probing the Missing Human Proteome: A Computational Perspective.

The missing human proteome comprises predicted protein-coding genes with no credible protein level evidence detected so far and constitutes ~18% of the human protein coding genes (neXtProt release 19/9/2014). The missing proteins may be of pharmacological interest as many of these are membrane receptors, thus requiring comprehensive characterization. In the present study, we explored various computational parameters, crucial during protein searches from tandem mass spectrometry (MS) data, for their impact on missing protein identification. Variables taken into consideration are differences in search database composition, shared peptides, semitryptic searches, post-translational modifications (PTMs), and transcriptome guided proteogenomic searches. We used a multialgorithmic approach for protein detection from publicly available mass spectra from recent studies covering diverse human tissues and cell types. Using the aforementioned approaches, we successfully detected 24 missing proteins (22-PE2, 1-PE4, and 1-PE5). Maximum of these identifications could be attributed to differences in reference proteome databases, exemplifying use of a single standard database for human protein detection from MS data. Our results suggest that search strategies with modified parameters can be rewarding alternatives for extensive profiling of missing proteins. We conclude that using complementary spectral data searches incorporating different parameters like PTMs, against a comprehensive and compact search database, might lead to discoveries of the proteins attributed so far as the missing human proteome.

ProteoStats--a library for estimating false discovery rates in proteomics pipelines.

SUMMARY: Statistical validation of peptide assignments from a large-scale shotgun proteomics experiment is a critical step, and various methods for evaluating significance based on decoy database search are in practice. False discovery rate (FDR) estimation of peptide assignments assesses global significance and corrects for multiple comparisons. Various approaches have been proposed for FDR estimation but unavailability of standard tools or libraries leads to development of many in-house scripts followed by manual steps that are error-prone and low-throughput. The ProteoStats library provides an open-source framework for developers with many FDR estimation and visualization features for several popular search algorithms. It also provides accurate q-values, which can be easily integrated in any proteomics pipeline to provide automated, accurate, high-throughput statistical validation and minimize manual errors. AVAILABILITY: https://sourceforge.net/projects/mssuite/files/ProteoStats/. CONTACT: ddash@igib.res.in or aky.compbio@gmail.com or amit.yadav@igib.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

A systematic analysis of eluted fraction of plasma post immunoaffinity depletion: implications in biomarker discovery.

Plasma is the most easily accessible source for biomarker discovery in clinical proteomics. However, identifying potential biomarkers from plasma is a challenge given the large dynamic range of proteins. The potential biomarkers in plasma are generally present at very low abundance levels and hence identification of these low abundance proteins necessitates the depletion of highly abundant proteins. Sample pre-fractionation using immuno-depletion of high abundance proteins using multi-affinity removal system (MARS) has been a popular method to deplete multiple high abundance proteins. However, depletion of these abundant proteins can result in concomitant removal of low abundant proteins. Although there are some reports suggesting the removal of non-targeted proteins, the predominant view is that number of such proteins is small. In this study, we identified proteins that are removed along with the targeted high abundant proteins. Three plasma samples were depleted using each of the three MARS (Hu-6, Hu-14 and Proteoprep 20) cartridges. The affinity bound fractions were subjected to gelC-MS using an LTQ-Orbitrap instrument. Using four database search algorithms including MassWiz (developed in house), we selected the peptides identified at <1% FDR. Peptides identified by at least two algorithms were selected for protein identification. After this rigorous bioinformatics analysis, we identified 101 proteins with high confidence. Thus, we believe that for biomarker discovery and proper quantitation of proteins, it might be better to study both bound and depleted fractions from any MARS depleted plasma sample.

MassWiz: a novel scoring algorithm with target-decoy based analysis pipeline for tandem mass spectrometry.

Mass spectrometry has made rapid advances in the recent past and has become the preferred method for proteomics. Although many open source algorithms for peptide identification exist, such as X!Tandem and OMSSA, it has majorly been a domain of proprietary software. There is a need for better, freely available, and configurable algorithms that can help in identifying the correct peptides while keeping the false positives to a minimum. We have developed MassWiz, a novel empirical scoring function that gives appropriate weights to major ions, continuity of b-y ions, intensities, and the supporting neutral losses based on the instrument type. We tested MassWiz accuracy on 486,882 spectra from a standard mixture of 18 proteins generated on 6 different instruments downloaded from the Seattle Proteome Center public repository. We compared the MassWiz algorithm with Mascot, Sequest, OMSSA, and X!Tandem at 1% FDR. MassWiz outperformed all in the largest data set (AGILENT XCT) and was second only to Mascot in the other data sets. MassWiz showed good performance in the analysis of high confidence peptides, i.e., those identified by at least three algorithms. We also analyzed a yeast data set containing 106,133 spectra downloaded from the NCBI Peptidome repository and got similar results. The results demonstrate that MassWiz is an effective algorithm for high-confidence peptide identification without compromising on the number of assignments. MassWiz is open-source, versatile, and easily configurable.

IGVBrowser--a genomic variation resource from diverse Indian populations.

The Indian Genome Variation Consortium (IGVC) project, an initiative of the Council for Scientific and Industrial Research, has been the first large-scale comprehensive study of the Indian population. One of the major aims of the project is to study and catalog the variations in nearly thousand candidate genes related to diseases and drug response for predictive marker discovery, founder identification and also to address questions related to ethnic diversity, migrations, extent and relatedness with other world population. The Phase I of the project aimed at providing a set of reference populations that would represent the entire genetic spectrum of India in terms of language, ethnicity and geography and Phase II in providing variation data on candidate genes and genome wide neutral markers on these reference set of populations. We report here development of the IGVBrowser that provides allele and genotype frequency data generated in the IGVC project. The database harbors 4229 SNPs from more than 900 candidate genes in contrasting Indian populations. Analysis shows that most of the markers are from genic regions. Further, a large fraction of genes are implicated in cardiovascular, metabolic, cancer and immune system-related diseases. Thus, the IGVC data provide a basal level variation data in Indian population to study genetic diseases and pharmacology. Additionally, it also houses data on ∼50,000 (Affy 50 K array) genome wide neutral markers in these reference populations. In IGVBrowser one can analyze and compare genomic variations in Indian population with those reported in HapMap along with annotation information from various primary data sources. Database URL: http://igvbrowser.igib.res.in.

Role of intrinsic disorder in transient interactions of hub proteins.

Hubs in the protein-protein interaction network have been classified as "party" hubs, which are highly correlated in their mRNA expression with their partners while "date" hubs show lesser correlation. In this study, we explored the role of intrinsic disorder in date and party hub interactions. The data reveals that intrinsic disorder is significantly enriched in date hub proteins when compared with party hub proteins. Intrinsic disorder has been largely implicated in transient binding interactions. The disorder to order transition, which occurs during binding interactions in disordered regions, renders the interaction highly reversible while maintaining the high specificity. The enrichment of intrinsic disorder in date hubs may facilitate transient interactions, which might be required for date hubs to interact with different partners at different times.

CoPS: Comprehensive Peptide Signature database.

UNLABELLED: We present the development of a Comprehensive database of 12 076 invariant Peptide Signatures (CoPS) derived from 52 bacterial genomes with a minimum occurrence in at least seven organisms. These peptides were observed in functionally similar proteins and are distributed over nearly 1250 different functional proteins. The database provides function, structure and occurrence in biochemical pathways of the proteins containing these signature peptides. It houses additional information on the signature peptides, such as identical match in other motif/pattern (e.g. PROSITE, BLOCKS, PRINTS and Pfam) databases and the database of interacting proteins, human proteome and mutation effect on these signature peptides. There is a wide applicability of this database in the identification of critical functional residues in proteins. The database also facilitates the identification of folding nucleus/structural determinants in proteins and functional assignment to yet unknown proteins. We demonstrate functional assignment to 2605 hypothetical proteins in bacterial genomes and 112 unknown proteins in human using this database. AVAILABILITY: The database can be freely accessed through the following URL: http://203.195.151.46/copsv2/index.html or http://203.90.127.70/copsv2/index.html

Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus.

MOTIVATION: The recent outbreak of severe acute respiratory syndrome (SARS) caused by SARS coronavirus (SARS-CoV) has necessitated an in-depth molecular understanding of the virus to identify new drug targets. The availability of complete genome sequence of several strains of SARS virus provides the possibility of identification of protein-coding genes and defining their functions. Computational approach to identify protein-coding genes and their putative functions will help in designing experimental protocols. RESULTS: In this paper, a novel analysis of SARS genome using gene prediction method GeneDecipher developed in our laboratory has been presented. Each of the 18 newly sequenced SARS-CoV genomes has been analyzed using GeneDecipher. In addition to polyprotein 1ab(1), polyprotein 1a and the four genes coding for major structural proteins spike (S), small envelope (E), membrane (M) and nucleocapsid (N), six to eight additional proteins have been predicted depending upon the strain analyzed. Their lengths range between 61 and 274 amino acids. Our method also suggests that polyprotein 1ab, polyprotein 1a, S, M and N are proteins of viral origin and others are of prokaryotic. Putative functions of all predicted protein-coding genes have been suggested using conserved peptides present in their open reading frames. AVAILABILITY: Detailed results of GeneDecipher analysis of all the 18 strains of SARS-CoV genomes are available at http://www.igib.res.in/sarsanalysis.html

Role of histidine interruption in mitigating the pathological effects of long polyglutamine stretches in SCA1: A molecular approach.

Polyglutamine expansions, leading to aggregation, have been implicated in various neurodegenerative disorders. The range of repeats observed in normal individuals in most of these diseases is 19-36, whereas mutant proteins carry 40-81 repeats. In one such disorder, spinocerebellar ataxia (SCA1), it has been reported that certain individuals with expanded polyglutamine repeats in the disease range (Q(12)HQHQ(12)HQHQ(14/15)) but with histidine interruptions were found to be phenotypically normal. To establish the role of histidine, a comparative study of conformational properties of model peptide sequences with (Q(12)HQHQ(12)HQHQ(12)) and without (Q(42)) interruptions is presented here. Q(12)HQHQ(12)HQHQ(12) displays greater solubility and lesser aggregation propensity compared to uninterrupted Q(42) as well as much shorter Q(22). The solvent and temperature-driven conformational transitions (beta structure <--> random coil --> alpha helix) displayed by these model polyQ stretches is also discussed in the present report. The study strengthens our earlier hypothesis of the importance of histidine interruptions in mitigating the pathogenicity of expanded polyglutamine tract at the SCA1 locus. The relatively lower propensity for aggregation observed in case of histidine interrupted stretches even in the disease range suggests that at a very low concentration, the protein aggregation in normal cells, is possibly not initiated at all or the disease onset is significantly delayed. Our present study also reveals that besides histidine interruption, proline interruption in polyglutamine stretches can lower their aggregation propensity.