A Computational Framework for High-Throughput Isotopic Natural Abundance Correction of Omics-Level Ultra-High Resolution FT-MS Datasets

New metabolomics applications of ultra-high resolution and accuracy mass spectrometry can provide thousands of detectable isotopologues, with the number of potentially detectable isotopologues increasing exponentially with the number of stable isotopes used in newer isotope tracing methods like stable isotope-resolved metabolomics (SIRM) experiments. This huge increase in usable data requires software capable of correcting the large number of isotopologue peaks resulting from SIRM experiments in a timely manner. We describe the design of a new algorithm and software system capable of handling these high volumes of data, while including quality control methods for maintaining data quality. We validate this new algorithm against a previous single isotope correction algorithm in a two-step cross-validation. Next, we demonstrate the algorithm and correct for the effects of natural abundance for both 13C and 15N isotopes on a set of raw isotopologue intensities of UDP-N-acetyl-D-glucosamine derived from a 13C/15N-tracing experiment. Finally, we demonstrate the algorithm on a full omics-level dataset

Untargeted Lipidomics of Non-Small Cell Lung Carcinoma Demonstrates Differentially Abundant Lipid Classes in Cancer vs. Non-Cancer Tissue

Lung cancer remains the leading cause of cancer death worldwide and non-small cell lung carcinoma (NSCLC) represents 85% of newly diagnosed lung cancers. In this study, we utilized our untargeted assignment tool Small Molecule Isotope Resolved Formula Enumerator (SMIRFE) and ultra-high-resolution Fourier transform mass spectrometry to examine lipid profile differences between paired cancerous and non-cancerous lung tissue samples from 86 patients with suspected stage I or IIA primary NSCLC. Correlation and co-occurrence analysis revealed significant lipid profile differences between cancer and non-cancer samples. Further analysis of machine-learned lipid categories for the differentially abundant molecular formulas identified a high abundance sterol, high abundance and high m/z sphingolipid, and low abundance glycerophospholipid metabolic phenotype across the NSCLC samples. At the class level, high abundances of sterol esters and cardiolipins were observed suggesting altered stearoyl-CoA desaturase 1 (SCD1) or acetyl-CoA acetyltransferase (ACAT1) activity and altered human cardiolipin synthase 1 or lysocardiolipin acyltransferase activity respectively, the latter of which is known to confer apoptotic resistance. The presence of a shared metabolic phenotype across a variety of genetically distinct NSCLC subtypes suggests that this phenotype is necessary for NSCLC development and may result from multiple distinct genetic lesions. Thus, targeting the shared affected pathways may be beneficial for a variety of genetically distinct NSCLC subtype

Evaluasi Jalur Pedestrian Bagi Tunanetra Terhadap Persyaratan Teknis Di Koridor Jalan Sam Ratulangi Kota Manado

Jalur pedestrian merupakan salah satu ruang terbuka publik perkotaan harus dapat diakses oleh semua orang termasuk tunanetra. Salah satu jalur pedestrian bagi tunanetra terdapat di koridor Jalan Sam Ratulangi Manado, tetapi pada Kenyataan tunanetra masih dipandu oleh orang yang dapat melihat dalam berjalan kaki. Jalur pedestrian bagi tunanetra ternyata belum sepenuhnya mengikuti persyaratan perancangan , dimana terdapat empat asas dalam menyediakan fasilitas dan aksesibilitas bagi tunanetra sesuai dengan Peraturan Menteri PU No.30/PRT/M/2006 yaitu keselamatan, kemudahan, kegunaan, dan kemandirian. Penelitian ini menggunakan metodologi kuantitatif rasionalistik dengan pendekatan metode deduktif. Data di analisis secara kuantitatif berdasarkan skala Likert. Populasi tunanetra di kota Manado tahun 2016 berjumlah 198 yang dipilih 67 orang sampel. Lokasi penelitian dibagi menjadi 14 segmen. Variabel dan indikator penelitian terdiri dari: (1) kriteria keselamatan (indikator permukaan pedestrian, kanstein, pagar pengaman, naik/turun penumpang, shelter, kanopi, pohon/tanaman peneduh) dan (2) kriteria kemudahan (indikator ukuran dasar, jalur pemandu/(guiding block), jalur penghubung (ramp), tempat duduk/tempat istirahat, tanda/(sign), tempat sampah). Hasil penelitian ini menyimpulkan bahwa kondisi jalur pedestrian bagi tunanetra terhadap persyaratan teknis di koridor Jalan Sam Ratulangi Kota Manado dari kriteria keselamatan belum sepenuhnya menjamin keselamatan bagi pengguna terutama bagi tunanetra. Demikian pula halnya dari aspek kemudahan, bahwa pelaksanaan beberapa elemen trotoar yang tidak sesuai persyaratan teknis menjadi hambatan bagi pengguna khususnya bagi tunanetra dalam mobilitas. Untuk itu disarankan bagi Pemerintah kota Manado agar melakukan revitalisasi dengan cara menata kembali keberadaan elemen trotoar supaya sesuai dengan pedoman persyaratan teknis yang berlaku

A Less-Biased Analysis of Metalloproteins Reveals Novel Zinc Coordination Geometries

Zinc metalloproteins are involved in many biological processes and play crucial biochemical roles across all domains of life. Local structure around the zinc ion, especially the coordination geometry (CG), is dictated by the protein sequence and is often directly related to the function of the protein. Current methodologies in characterizing zinc metalloproteins\u27 CG consider only previously reported CG models based mainly on nonbiological chemical context. Exceptions to these canonical CG models are either misclassified or discarded as outliers. Thus, we developed a less-biased method that directly handles potential exceptions without pre-assuming any CG model. Our study shows that numerous exceptions could actually be further classified and that new CG models are needed to characterize them. Also, these new CG models are cross-validated by strong correlation between independent structural and functional annotation distance metrics, which is partially lost if these new CGs models are ignored. Furthermore, these new CG models exhibit functional propensities distinct from the canonical CG models

Aberrant Coordination Geometries Discovered in Most Abundant Metalloproteins

Metalloproteins play crucial biochemical roles in our body and are essential across all domains of life. The structural environment around a metal ion, especially the coordination geometry (CG), is both sequentially and functionally relevant. Studies of the metalloprotein’s CG will greatly help alleviate the imbalance between the ample sequence data available and the insufficient knowledge on protein functions. Current methodologies in characterizing metalloproteins’ CG consider only previously reported CG (canonical CG) models based primarily on nonbiological chemical context. Exceptions to these canonical CG models can greatly hamper the ability to characterize metalloproteins both structurally and functionally

Method and System for Identification of Metabolites Using Mass Spectra

A method and system is provided for mass spectrometry for identification of a specific elemental formula for an unknown compound which includes but is not limited to a metabolite. The method includes calculating a natural abundance probability (NAP) of a given isotopologue for isotopes of non-labelling elements of an unknown compound. Molecular fragments for a subset of isotopes identified using the NAP are created and sorted into a requisite cache data structure to be subsequently searched. Peaks from raw spectrum data from mass spectrometry for an unknown compound. Sample-specific peaks of the unknown com- pound from various spectral artifacts in ultra-high resolution Fourier transform mass spectra are separated. A set of possible isotope-resolved molecular formula (IMF) are created by iteratively searching the molecular fragment caches and combining with additional isotopes and then statistically filtering the results based on NAP and mass-to-charge (m/2) matching probabilities. An unknown compound is identified and its corresponding elemental molecular formula (EMF) from statistically-significant caches of isotopologues with compatible IMFs

Advances in Gene Ontology Utilization Improve Statistical Power of Annotation Enrichment

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission or modification lest erroneous term mappings occur. To address these issues, we created the Gene Ontology Categorization Suite, or GOcats—a novel tool that organizes the Gene Ontology into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods. Specifically, we show that GOcats’ unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value = 1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats’ path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcat’s path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44

categoryCompare, an analytical tool based on feature annotations

Assessment of high-throughput—omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered “biomarkers.” The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, categoryCompare, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohn's disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohn's vs. UC comparison showed gross similarities in inflammatory response in the two diseases, with particular processes specific to each disease