Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure

Mechanisms of increased Trichodesmium fitness under iron and phosphorus co-limitation in the present and future ocean

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 7 (2016): 12081, doi:10.1038/ncomms12081.Nitrogen fixation by cyanobacteria supplies critical bioavailable nitrogen to marine ecosystems worldwide; however, field and lab data have demonstrated it to be limited by iron, phosphorus and/or CO2. To address unknown future interactions among these factors, we grew the nitrogen-fixing cyanobacterium Trichodesmium for 1 year under Fe/P co-limitation following 7 years of both low and high CO2 selection. Fe/P co-limited cell lines demonstrated a complex cellular response including increased growth rates, broad proteome restructuring and cell size reductions relative to steady-state growth limited by either Fe or P alone. Fe/P co-limitation increased abundance of a protein containing a conserved domain previously implicated in cell size regulation, suggesting a similar role in Trichodesmium. Increased CO2 further induced nutrient-limited proteome shifts in widespread core metabolisms. Our results thus suggest that N2-fixing microbes may be significantly impacted by interactions between elevated CO2 and nutrient limitation, with broad implications for global biogeochemical cycles in the future ocean.Grant support was provided by U.S. National Science Foundation OCE 1260490 to D.A.H., E.A.W. and F.-X.F., and OCE OA 1220484 and G.B. Moore Foundation 3782 and 3934 to M.A.S

Primate proteomic composition of seminal plasma and prostate-specific transglutaminase activity in relation to sexual selection.

Humans (Homo sapiens), chimpanzees (Pan troglodytes), and gorillas (Gorilla gorilla) have diverse mating systems with varying levels of sperm competition. Several seminal plasma genes have been claimed to evolve under positive selection, while others are altered or lost. This study aims to identify biologically relevant differences among seminal plasma proteomes of primates in relation to mating systems and previous genomic studies. Seminal plasma from three individuals of each species were run in triplicate in shotgun liquid chromatography – tandem mass spectrometry (LC-MS/MS) and confirmed with Western blots. Over 7,000 peptides were identified across all individuals; 168 proteins were identified with high confidence, 70 seminal plasma proteins were identified for human, 64 proteins for chimpanzee, and 34 proteins for gorilla. The gorilla seminal plasma proteome has higher variation among individuals and many proteins involved in semen coagulation and liquefaction have been lost. Chimpanzees have approximately 7-fold higher prostate specific transglutaminase (TGM4) expression than humans. TGM4 was not detected in gorillas, supporting pseudogenization of this gene. The structural semenogelin proteins, SEMG1 and SEMG2, were detected in high abundance in only one of three gorilla individuals, and in all three human and chimpanzee individuals. Chimpanzees have significantly higher expression of SEMG1 (~2.5-fold) compared to human; whereas, they only produce a small amount of SEMG2; ~6.5 –fold less than humans. Chimpanzees have roughly 34-fold higher expression of a serine protease inhibitor, SERPINA3 (Serpin Family A Member 3), than humans. SERPINA3 paralogs, SERPINA1 and SERPINA5, also have increased expression (~2.5 –fold) compared to human, and only SERPINA1 was detected in gorilla. SERPINAs may delay protease dissolution of the copulatory plug in chimpanzees. Recombinant human TGM4 and the reconstructed ancestral TGM4 sequence of our last common ancestor (LCA) with chimpanzees (the human-chimpanzee ancestor) proteins were produced and incubated with casein and monodansylcaverdine to determine enzymatic activity. The human-chimpanzee ancestor TGM4 had higher activity compared to human TGM4. Considering the importance of TGM4 in semen coagulation and copulatory plug formation in chimpanzee, the increased activity of the human-chimpanzee ancestor TGM4 may be indicative of elevated female promiscuity of our LCA, perhaps similar to a chimp-like mating system

A power law global error model for the identification of differentially expressed genes in microarray data

BACKGROUND: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG. RESULTS: In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm. CONCLUSIONS: The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples

Assigning Significance in Label-Free Quantitative Proteomics to Include Single-Peptide-Hit Proteins with Low Replicates

When sample replicates are limited in a label-free proteomics experiment, selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change. This paper aims to address this issue. An important component of the approach employed here is to utilize the rule of Minimum number of Permuted Significant Pairings (MPSP) to reduce false positives. The MPSP rule generates permuted sample pairings from limited analytical replicates and simply requires that a differentially regulated protein can be selected only when it is found significant in designated number of permuted sample pairings. Both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold were initially used to select differentially regulated proteins. But both methods were found not stringent enough to control the false discovery rate to 5% in this study. On the other hand, the combination of the MPSP rule with either of these two methods significantly reduces false positives with little effect on the sensitivity to select differentially regulated proteins including those with a single-peptide hit or with a <2-fold change

동물 모델 및 환자 혈청의 통합분석을 통한 자폐 스펙트럼 장애 바이오마커 발굴 연구

학위논문(석사) -- 서울대학교대학원 : 융합과학기술대학원 분자의학 및 바이오제약학과, 2023. 2. Eugene C. Yi.Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders (NDD) characterized by three core symptoms: (1) impaired social communication, (2) reciprocal interaction, and (3) the presence of repetitive behaviors and restricted interest. Although various studies have been conducted to discover pathophysiological mechanisms of ASD, the cause of disease is poorly understood, hence no reliable diagnostic biomarkers are available. Since the prevalence of ASD is rapidly increasing, there is an urgent need to identify ASD-related molecular biomarkers. In this study, we conducted brain tissue and serum proteomic analysis of the Contactin-associated protein-like 2 (Cntnap2) KO mouse, a well-recognized genetic mouse model of ASD. We used a Data Dependent Acquisition (DDA) mass spectrometry to identify differentially expressed proteins in brain tissue samples followed by the analysis of serum samples to identify potential ASD biomarkers. Potential biomarkers were further verified by Data Independent Acquisition (DIA) mass spectrometry with ASD patients sera and identified three proteins TNC, TLN1 and SERPINC1 in patients sera.자폐 스펙트럼 장애(ASD)는 (1) 사회적 의사소통 장애, (2) 상호 작용, (3) 반복적인 행동의 존재 및 제한된 관심의 세 가지 핵심 증상을 특징으로 하는 신경 발달 장애다. ASD의 병태생리학적 기전을 밝히기 위한 다양한 연구가 진행되고 있지만, 질병의 원인에 대한 이해가 부족하여 신뢰할 수 있는 진단 바이오마커가 없는 실정이다. ASD의 유병률이 급격히 증가함에 따라 ASD 관련 분자 바이오마커를 확인하는 것이 시급하다. 이 연구에서는 ASD의 잘 알려진 유전 마우스 모델 인 Contactin 관련 단백질 유사 2 (Cntnap2) KO 마우스의 뇌 조직 및 혈청 단백질 분석을 수행했다. DDA(Data Dependent Acquisition) 질량 분석법을 사용하여 뇌 조직 샘플에서 차별적으로 발현된 단백질을 식별한 다음 혈액으로 분비된 단백질을 식별하기 위해 혈청 샘플을 분석했다. 선별된 단백질은 ASD 환자의 혈청을 사용하여 DIA(Data Independent Acquisition) 질량 분석법으로 추가 검증되었다. 우리는 환자의 혈청에서 3개의 단백질 TNC, TLN1 및 SERPINC1을 확인하였으며 이들을 잠재적 ASD 바이오마커 후보로 제안한다.Introduction 1 Method with materials 5 Results 17 Discussion 30 Reference 33석

AMDA: an R package for the automated microarray data analysis

BACKGROUND: Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. RESULTS: To address these problems we have developed an automated microarray data analysis (AMDA) software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. CONCLUSION: AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopoli

Nuclear proteome of virus-infected and healthy potato leaves

Abstract Background Infection of plants by viruses interferes with expression and subcellular localization of plant proteins. Potyviruses comprise the largest and most economically damaging group of plant-infecting RNA viruses. In virus-infected cells, at least two potyviral proteins localize to nucleus but reasons remain partly unknown. Results In this study, we examined changes in the nuclear proteome of leaf cells from a diploid potato line (Solanum tuberosum L.) after infection with potato virus A (PVA; genus Potyvirus; Potyviridae) and compared the data with that acquired for healthy leaves. Gel-free liquid chromatography–coupled to tandem mass spectrometry was used to identify 807 nuclear proteins in the potato line v2–108; of these proteins, 370 were detected in at least two samples of healthy leaves. A total of 313 proteins were common in at least two samples of healthy and PVA-infected leaves; of these proteins, 8 showed differential accumulation. Sixteen proteins were detected exclusively in the samples from PVA-infected leaves, whereas other 16 proteins were unique to healthy leaves. The protein Dnajc14 was only detected in healthy leaves, whereas different ribosomal proteins, ribosome-biogenesis proteins, and RNA splicing–related proteins were over-represented in the nuclei of PVA-infected leaves. Two virus-encoded proteins were identified in the samples of PVA-infected leaves. Conclusions Our results show that PVA infection alters especially ribosomes and splicing-related proteins in the nucleus of potato leaves. The data increase our understanding of potyvirus infection and the role of nucleus in infection. To our knowledge, this is the first study of the nuclear proteome of potato leaves and one of the few studies of changes occurring in nuclear proteomes in response to plant virus infection

Nuclear proteome of virus-infected and healthy potato leaves

BackgroundInfection of plants by viruses interferes with expression and subcellular localization of plant proteins. Potyviruses comprise the largest and most economically damaging group of plant-infecting RNA viruses. In virus-infected cells, at least two potyviral proteins localize to nucleus but reasons remain partly unknown.ResultsIn this study, we examined changes in the nuclear proteome of leaf cells from a diploid potato line (Solanum tuberosum L.) after infection with potato virus A (PVA; genus Potyvirus; Potyviridae) and compared the data with that acquired for healthy leaves. Gel-free liquid chromatography-coupled to tandem mass spectrometry was used to identify 807 nuclear proteins in the potato line v2-108; of these proteins, 370 were detected in at least two samples of healthy leaves. A total of 313 proteins were common in at least two samples of healthy and PVA-infected leaves; of these proteins, 8 showed differential accumulation. Sixteen proteins were detected exclusively in the samples from PVA-infected leaves, whereas other 16 proteins were unique to healthy leaves. The protein Dnajc14 was only detected in healthy leaves, whereas different ribosomal proteins, ribosome-biogenesis proteins, and RNA splicing-related proteins were over-represented in the nuclei of PVA-infected leaves. Two virus-encoded proteins were identified in the samples of PVA-infected leaves.ConclusionsOur results show that PVA infection alters especially ribosomes and splicing-related proteins in the nucleus of potato leaves. The data increase our understanding of potyvirus infection and the role of nucleus in infection. To our knowledge, this is the first study of the nuclear proteome of potato leaves and one of the few studies of changes occurring in nuclear proteomes in response to plant virus infection.Peer reviewe