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Breast cancer (BrCa) is the leading cause of cancer incidence and mortality in women worldwide. While BrCa treatment has been shown to be highly successful if detected at an early stage, there are few effective strategies to treat metastatic tumours. Hence, metastasis remains the main cause in most of BrCa deaths, highlighting the need for new approaches in this group of patients. Immunotherapy has been gaining attention as a new treatment for BrCa metastasis and the kynurenine pathway (KP) has been suggested as one of the potential targets. The KP is the major biochemical pathway in tryptophan (TRP) metabolism, catabolising TRP to nicotinamide adenine dinucleotide (NAD+). The KP has been reported to be elevated under inflammatory conditions such as cancers and that its activity suppresses immune surveillance. Dysregulation of the KP has previously been reported implicated in BrCa. This review aims to discuss and provide an update on the current mechanisms involved in KP-mediated immune suppression and cancer growth. Furthermore, we also provide a summary on 58 studies about the involvement of the KP and BrCa and five clinical trials targeting KP enzymes and their outcome.

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e.

Extracellular matrix (ECM) is essential for tissue homeostasis. Understanding the matrisome (ECM proteome) composition and mechanisms of ECM control in health and disease is crucial for discovering therapeutic agents and diagnostic tools for inflammatory, fibrotic and cancerous conditions. The challenging obstacle in the ECM analysis is the need to optimise matrisome enrichment methods for different organs, diseases, and species. Currently, there is no optimized protocol nor a publicly available matrisome database for mouse kidneys. This limits the power of murine models in renal diseases and development research. In this study, we comparatively explored the matrisome of healthy C57BL/6 mice using two matrisome extraction methods, including the Millipore Compartment Fractionation (Method-1) and the Sequential Extraction (Method-2) approaches. We examined the efficiency of these methods in matrisome profiling by LC-MS/MS, protein identification and label-free quantification using MaxQuant. As a result of the study, 113 matrisome proteins were identified, including 22 proteins that have not been previously listed in the Matrisome Database (MD). Method-2 allowed identification and quantification of all core and ECM-associated matrisome proteins detected by Method-1 and additionally revealed more core matrisome and ECM-associated proteins. By characterisation of the murine renal matrisome enhanced by our methodological insights, this study provides critically important information for biological and medical kidney research.

Precise characterization of a tissue’s extracellular matrix (ECM) protein composition (matrisome) is essential for biomedicine. However, ECM protein extraction that requires organ-specific optimization is still a major limiting factor in matrisome studies. In particular, the matrisome of mouse kidneys is still understudied, despite mouse models being crucial for renal research. Here, we comprehensively characterized the matrisome of kidneys in healthy C57BL/6 mice using two ECM extraction methods in combination with liquid chromatography tandem mass spectrometry (LC-MS/MS), protein identification, and label-free quantification (LFQ) using MaxQuant. We identified 113 matrisome proteins, including 22 proteins that have not been previously listed in the Matrisome Database. Depending on the extraction approach, the core matrisome (structural proteins) comprised 45% or 73% of kidney ECM proteins, and was dominated by glycoproteins, followed by collagens and proteoglycans. Among matrisome-associated proteins, ECM regulators had the highest LFQ intensities, followed by ECM-affiliated proteins and secreted factors. The identified kidney ECM proteins were primarily involved in cellular, developmental and metabolic processes, as well as in molecular binding and regulation of catalytic and structural molecules’ activity. We also performed in silico comparative analysis of the kidney matrisome composition in humans and mice based on publicly available data. These results contribute to the first reference database for the mouse renal matrisome.

Simple Summary The kynurenine pathway (KP) is a biochemical pathway that synthesizes the vital coenzyme, nicotinamide adenine dinucleotide (NAD+). In cancer, the KP is significantly activated, leading to tryptophan depletion and the production of downstream metabolites, which skews the immune response towards tumour tolerance. More specifically, advanced stage cancers that readily metastasize evidence the most dysregulation in KP enzymes, providing a clear link between the KP and cancer morbidity. Consequently, this provides the rationale for an attractive new drug discovery opportunity for adjuvant therapeutics targeting KP-mediated immune tolerance, which would greatly complement current pharmacological interventions. In this review, we summarize recent developments in the roles of the KP and clinical trials examining KP inhibition in liver cancer. Abstract As the second and third leading cancer-related death in men and the world, respectively, primary liver cancer remains a major concern to human health. Despite advances in diagnostic technology, patients with primary liver cancer are often diagnosed at an advanced stage. Treatment options for patients with advanced hepatocarcinoma (HCC) are limited to systemic treatment with multikinase inhibitors and immunotherapy. Furthermore, the 5-year survival rate for these late-stage HCC patients is approximately 12% worldwide. There is an unmet need to identify novel treatment options and/or sensitive blood-based biomarker(s) to detect this cancer at an early stage. Given that the liver harbours the largest proportion of immune cells in the human body, understanding the tumour–immune microenvironment has gained increasing attention as a potential target to treat cancer. The kynurenine pathway (KP) has been proposed to be one of the key mechanisms used by the tumour cells to escape immune surveillance for proliferation and metastasis. In an inflammatory environment such as cancer, the KP is elevated, suppressing local immune cell populations and enhancing tumour growth. In this review, we collectively describe the roles of the KP in cancer and provide information on the latest research into the KP in primary liver cancer.

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). Here, we employed a mixture of recombinant proteins in DDA libraries to subsequently detect cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 human recombinant proteins that had been previously implicated as possible cancer biomarkers in both our own and other studies. The rPSL was then used to identify proteins from non-depleted colorectal cancer (CRC) plasmas by SWATH-MS. Most (32/36) of the proteins in the rPSL were reliably identified in plasma samples, including 8 proteins (BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency MS in human plasmas according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and post-digestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 were identified using DDA-MS. The 20 additional proteins exclusively identified by using the rPSL approach with SWATH were mostly lower abundance (i.e.

Hypertension and estrogen deficiency in women have been identified as significant risk factors for cerebrovascular diseases. Hypertension causes excessive vascular stretch and contributes to the initiation of cellular injury in blood vessels while estrogen has been demonstrated to exert beneficial protective effects on the vascular system. Although the specific biological outcomes exerted by either excessive stretch or estrogen exposure are well established, the combined biochemical effects of both stimuli remain unclear. Therefore, this study was conducted for quantitative proteomics study on human cerebral microvascular endothelial cells (HCMECs) subjected to 20% “pathological” cyclic stretch for a period of 18 hour in the presence or absence of 17β-estradiol by isobaric Taqs for Relative and Absolute Quantification. The results showed that only some proteins responded to 17β-estradiol (e.g., thioredoxin reductase-1), stretch (e.g., 14- 3-3 protein epsilon or acidic leucine-rich nuclear phosphoprotein 32 family member B) and interestingly, some proteins returned to control pre-treatment levels when exposed to both (e.g., d-dopachrome decarboxylase, thrombospondin-1). In addition, HCMECs that exposed only to estrogen had a very similar proteomic profile (i.e., up-regulation of structural, cellular adhesion and proliferation proteins) as to those exposed to estrogen with 20% stretching for 18 hour, suggesting that estrogen ablated the detrimental effects by the stretch alone. These findings sheds light on the molecular mechanisms by which the cerebrovascular protective actions of estrogen on HCMEC exposed to pathological levels of cyclic stretch which could provide a platform for future research in therapeutic approach. DOI : http://dx.doi.org./10.17576/JSKM-2018-09

© The Author(s) 2020, The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases., Parts of this work were supported by grants to ProteoRed PRB3-ISCIII, PT17/0019/0001 Comunidad de Madrid Grant B2017/BMD-3817 (F.J.C.); Korean Ministry of Health and Welfare HI13C2098 and HI16C0257 (Y.K.P.); NIH grants P30ES017885 and U24CA210967 (G.S.O.), 5U01HL-13104204, PADOM-SPO11347 and PARYB-SPO112285 (M.P.S.); NCI CPTAC U24CA210985 and NCI EDRN U24CA115102 (D.W.C.); NIH National Institute of General Medical Sciences R01GM087221 (E.W.D./R.L.M.) and R24GM127667 (E.W.D.); NIH National Institute on Aging U19AG023122 (R.L.M.); NSF DBI-1933311 (E.W.D.); CIHR COVID-19 Rapid Research Funding (F20-01013), CIHR Foundation Grant FDN:14840 and Canada Research Chair (C.M.O.); Investissement d’Avenir Infrastructures Nationales en Biologie et Santé ANR-10-INBS-08 (Proteomics French Infrastructure ProFI (Y.V.); Wellcome Trust WT101477MA and 208391/Z/17/Z (J.A.V.); Knut and Alice Wallenberg Foundation (M.U., C.L., J.M.S., E.L.); Brazilian CAPES 88887.130697, CNPq 440613/2016-7, FAPERJ E-26/210.173/2018 (G.B.D.) and FAPERJ E-26/202.650/2018 (F.C.S.N.), Australian Commonwealth NCRIS (M.S.B.); NHMRC 1010303 (M.S.B., E.C.N.); Cancer Council NSW RG19-04 (M.S.B., S.B.A., E.C.N.); Cancer Institute NSW Fellowship 15/ECF/1-38 (S.B.A.), Sydney Vital CINSW Translational Cancer Research Centre grant (M.S.B., S.B.A., S.A.), ‘Fight on the Beaches’ (M.S.B., S.B.A., E.C.N., S.A.) funding and an International Macquarie Research Excellence Scholarship (S.A.). M.S.B. thanks the Faculty of Medicine, Stanford University for a sabbatical visiting professorship.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

To develop a new technique for improved cell surface protein detection and analysis by combining chemical labeling with mild cell lysis using model HCT 116 colorectal cancer cells. We found that Dounce homogenization by hand, rather than the typical sonication or syringe lysis method, recovered surface/membrane proteins more consistently and effectively. This was indicated by marker membrane proteins such as claudin-4 and EGFR (epidermal growth factor receptor) that span the typical 20–200 kD range. As monitored by Western blotting (WB), the Dounce lysis method combined with cell surface biotinylation showed consistent recovery of the marker proteins claudin-4 and EGFR. This lysis method was combined with a cell surface biotinylation strategy to enrich cell surface/membrane proteins using affinity bead-based purification with four-fold less cells compared to prior work. Subsequent LC/MS/MS analysis identified 49 additional surface/membrane proteins for the first time from HCT 116 cells. This combination of methodologies may fit into an advanced workflow for identifying new and elusive cell surface proteins. It can increase the protein coverage for biomarker discovery for colorectal cancer or other cancers. This new detection/analysis approach may also promote new applications in surface display systems as well as cell screening, selection, and binding processes.

Background One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high—as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I–IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Human olfactory receptors (ORs) are seven-pass transmembrane G-protein coupled receptors (GPCR) involved in smell perception and many other signaling pathways. They are primarily expressed in the olfactory epithelium and ectopically expressed in several other organs and tissues. neXtProt contains 4 PE1 (protein existence 1, evidenced at the protein level) ORs, determined on the basis of either protein interaction data (i.e., OR1D4 and OR2AG1) or convincing genetic, haplotype, or biochemical data (i.e., OR1D2 and OR2J3). Not a single OR currently qualifies for neXtProt PE1 status based on mass spectrometry (MS) evidence. Many reasons for this absence of MS-based identification have been proposed, including (i) confined or spatiotemporal or developmental expression, (ii) low copy number, (iii) OR repertoire gene silencing, and (iv) limited tissue availability. OR transmembrane domains (TMDs) inherently limit MS identification because the hydrophobic nature restricts the access of trypsin to potential cleavage sites. Equally, the extremely low frequency or lack of accessible arginine and lysine residues in TMDs renders trypsin cleavage ineffective. Here, we demonstrate an analytical approach specifically focused on the hydrophilic (trypsin-accessible) domains of ORs [i.e., with all transmembrane segments and anchored peptides excluded). We predicted the ability of OR soluble (hydrophilic) domains to yield 2 or more9 amino acids (aa) length unique mapping (unique to a protein only), non-nested (peptides with varying length at the N or C terminal but containing the same core sequence), leucine/isoleucine (I/L) switch examined (I and L have same mass and cannot be distinguished by MS) tryptic peptides. Our analysis showed that ∼58% of the human OR proteome could potentially generate tryptic peptides that satisfy current the Human Proteome Project data interpretation guidelines (version 2.1) when no missed cleavages are allowed and increases to ∼78% when one missed cleavage is allowed. The utilization of current biological data (adjuvant genomics, expression profile, transcriptomics, epigenome silencing data, etc.) and the adoption of a non-conventional proteomics approach (e.g., Confetti multiprotease digestion, CNBr cleavage of TMDs, and more-extreme chromatographic and MS methods) could aid in the detection of the remaining ORs.

While metastasis is the primary cause of colorectal cancer (CRC) mortality, the molecular mechanisms underpinning it remains elusive. Metastasis is propagated through driver oncogene/suppressor gene mutations, accompanied by passenger mutations and underlying genomic instability. To understand cancer biology, a unifying framework called the "hallmarks of cancer" (HoCs) has been developed, which organizes cell biological alterations under ten key hallmarks. Underlying these HoCs, genome instability generates mutational diversity that is amplified by inflammation. Recognizing how critical cancer cell-surface proteins influence, these HoCs have been proposed to accelerate precision medicine therapeutic development. A moderate decrease (43%↓) in HCT116 cell surface urokinase plasminogen activator receptor (uPAR) expression mitigates against many HoCs driven by these cell's KRAS and PIK3CA mutational signature. Comprehensive proteomics (whole cell lysis with two membrane protein enrichments) coupled with ingenuity pathway analysis (IPA) demonstrates that uPAR negates essential pathways across the HoC spectrum, particularly those associated with metastasis, resisting cell death, and sustaining proliferation, and parallels Cancer Hallmarks Analytics Tool analysis. Decreasing uPAR predominantly alters metastasis-related and uPAR-interactome protein expression (e.g., EGFR, caveolin, vitronectin, integrin β4). Collectively, it is demonstrated that uPAR is a lynchpin protein capable of regulating several HoC pathways in a classical CRC mutational background.

The factors that determine red blood cell (RBC) lifespan and the rate of RBC aging have not been fully elucidated. In several genetic conditions, including sickle cell disease, thalassemia, and G6PD deficiency, erythrocyte lifespan is significantly shortened. Many of these diseases are also associated with protection from severe malaria, suggesting a role for accelerated RBC senescence and clearance in malaria resistance. Here, we report a novel, N-ethyl-N-nitrosourea-induced mutation that causes a gain of function in adenosine 5'-monophosphate deaminase (AMPD3). Mice carrying the mutation exhibit rapid RBC turnover, with increased erythropoiesis, dramatically shortened RBC lifespan, and signs of increased RBC senescence/eryptosis, suggesting a key role for AMPD3 in determining RBC half-life. Mice were also found to be resistant to infection with the rodent malaria Plasmodium chabaudi. We propose that resistance to P. chabaudi is mediated by increased RBC turnover and higher rates of erythropoiesis during infection.

The evidence that any protein exists in the Human Proteome Project (HPP; protein evidence 1 or PE1) has revolved primarily (although not exclusively) around mass spectrometry (MS) (93% of PE1 proteins have MS evidence in the latest neXtProt release), with robust and stringent, well-curated metrics that have served the community well. This has led to a significant number of proteins still considered "missing" (i.e., PE2-4). Many PE2-4 proteins have MS evidence of unacceptable quality (small or not enough unitypic peptides and unacceptably high protein/peptide FDRs), transcriptomic, or antibody evidence. Here we use a Chromosome 7 PE2 example called Prestin to demonstrate that clear and robust criteria/metrics need to be developed for proteins that may not or cannot produce clear-cut MS evidence while possessing significant non-MS evidence, including disease-association data. Many of the PE2-4 proteins are inaccessible, spatiotemporally expressed in a limited way, or expressed at such a very low copy number as to be unable to be detected by current MS methodologies. We propose that the HPP community consider and lead a communal initiative to accelerate the discovery and characterization of these types of "missing" proteins.

Cytokines, chemokines and growth factors (CCGFs) in human plasma are analyzed for identification of biomarkers. However concentrations of CCGFs are very low; it is difficult to identify and quantify low abundance proteins in the presence of the high abundance proteins (HAPs) unless HAPs are removed prior to analysis. However, there is a concern that the low abundance proteins such as CCGFs may also be removed during the HAP depletion process. In this study, we have examined whether or not depletion of the HAPs enhances detection of the CCGFs by immuno-assays. Top 14 HAPs were depleted from 10 healthy volunteers’ plasma using MARS-14 immuno-depletion column and a total of 27 CCGFs were analyzed by bead-based multiplexed immuno-assay. All 27 CCGFs were detected in neat plasma (NP), 25 were detected in flow through fraction (FT) and 21 were detected in bound protein (BP) fraction. Concentrations of 22 CCGFs were significantly higher in NP compared to FT and BP. Only one CCGF had higher concentration in FT compared to NP. The remaining 2 CCGFs were not different between NP and FT. It was counter-productive for the detection of 24 CCGFs after HAP removal, primarily due to post-depletion protein precipitation and/or re-suspension of pellets.

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

In this paper, we have measured PRR, RSSI and LQI of ZigBee channels under Wi-Fi environment and have assessed channel characteristic and link quality. To confirm any relationship among RSSI, LQI values and PRR under Wi-Fi interference in overlapping and non-overlapping channels of Wi-Fi and ZigBee, the experiments were performed without Wi-Fi, with Wi-Fi and file download through Wi-Fi. Under Wi-Fi interference, We perfomed experiments to ensure channel characteristics and link quality by fixing Wi-Fi and ZigBee receiver and varying the distance between ZigBee receiver and transmitter. ZigBee transmitter sends packet of 256 bits every second to ZigBee receiver. PRR was measured from ZigBee with variance of distance between fixed Wi-Fi and ZigBee. RSSI, LQI, PRR were measured from ZigBee with fixed Wi-Fi, fixed ZigBee receiver and variance of distance of ZigBee transmitter. As a result, we confirmed decrease of PRR under Wi-Fi interference but RSSI, LQI values similar regardless of overlapped or non-overlapped channel and Wi-Fi interference. Therefore, PRR should be used for interference detection in ZigBee communication under Wi-Fi environment but RSSI and LQI are not appreciate.

Experimental samples are valuable and can represent a significant investment in time and resources. It is highly desirable at times to obtain as much information as possible from a single sample. This is especially relevant for systems biology approaches in which several ‘omics platforms are studied simultaneously. Unfortunately, each platform has a particular extraction methodology which increases sample number and sample volume requirements when multiple ‘omics are analyzed. We evaluated the integration of a yeast extraction method; specifically we explored whether fractions from a single metabolite extraction could be apportioned to multiple downstream ‘omics analytical platforms. In addition, we examined how variations to a chloroform/methanol yeast metabolite extraction regime influence metabolite recoveries. We show that protein suitable for proteomic analysis can be recovered from a metabolite extraction and that recovery of lipids, while reproducible, are not wholly quantitative. Higher quenching solution temperatures (−30 °C) can be used without significant leakage of intracellular metabolites when lower fermentation temperatures (20 °C) are employed. However, extended residence time in quenching solution, in combination with vigorous washing of quenched cell pellets, leads to extensive leakage of intracellular metabolites. Finally, there is minimal difference in metabolite amounts obtained when metabolite extractions are performed at 4 °C compared to extractions at −20 °C. The evaluated extraction method delivers material suitable for metabolomic and proteomic analyses from the same sample preparation.

The low molecular weight (LMW;10kDa)* plasma peptidome has been considered a source of useful diagnostic biomarkers and potentially therapeutic molecules, as it contains many cytokines, peptide hormones, endogenous peptide products and potentially bioactive fragments derived from the parent proteome. The small size of the peptides allows them almost unrestricted vascular and interstitial access, and hence distribution across blood-brain barriers, tumour and other vascular permeability barriers. Therefore, the peptidome may carry specific signatures or fingerprints of an individual's health, wellbeing or disease status. This occurs primarily because of the advantage the peptidome has in being readily accessible in human blood and/or other biofluids. However, the co-expression of highly abundant proteins (10kDa) and other factors present inherently in human plasma make direct analysis of the blood peptidome one of the most challenging tasks faced in contemporary analytical biochemistry. A comprehensive compendium of extraction and fractionation tools has been collected concerning the isolation and micromanipulation of peptides. However, the search for a reliable, accurate and reproducible single or combinatorial separation process for capturing and analysing the plasma peptidome remains a challenge. This review outlines current techniques used for the separation and detection of plasma peptides and suggests potential avenues for future investigation. This article is part of a Special Issue entitled: HUPO 2014.

Urokinase plasminogen activator receptor (uPAR) and the epithelial integrin αvβ6 are thought to individually play critical roles in cancer metastasis. These observations have been highlighted by the recent discovery (by proteomics) of an interaction between these two molecules, which are also both implicated in the epithelial-mesenchymal transition (EMT) that facilitates escape of cells from tissue barriers and is a common signature of cancer metastases. In this study, orthogonal in cellulo and in vitro functional proteomic approaches were used to better characterize the uPAR·αvβ6 interaction. Proximity ligation assays (PLA) confirmed the uPAR·αvβ6 interaction on OVCA429 (ovarian cancer line) and four different colon cancer cell lines including positive controls in cells with de novo β6 subunit expression. PLA studies were then validated using peptide arrays, which also identified potential physical sites of uPAR interaction with αvβ6, as well as verifying interactions with other known uPAR ligands (e.g., uPA, vitronectin) and individual integrin subunits (i.e., αv, β1, β3, and β6 alone). Our data suggest that interaction with uPAR requires expression of the complete αβ heterodimer (e.g., αvβ6), not individual subunits (i.e., αv, β1, β3, or β6). Finally, using in silico structural analyses in concert with these functional proteomics studies, we propose and demonstrate that the most likely unique sites of interaction between αvβ6 and uPAR are located in uPAR domains II and III.

Integrin αvβ6 is an epithelially-restricted heterodimeric transmembrane glycoprotein, known to interact with the urokinase plasminogen activating receptor (uPAR), playing a critical role in cancer progression. While the X-ray crystallographic structures of segments of other integrin heterodimers are known, there is no structural information for the complete αvβ6 integrin to assess its direct interaction with uPAR. We have performed structural analysis of αvβ6·uPAR interactions using model data with docking simulations to pinpoint their interface, in accord with earlier reports of the β-propeller region of integrin α-chain interacting with uPAR. Interaction of αvβ6·uPAR was demonstrated by our previous study using immunoprecipitation coupled with proteomic analysis by mass spectrometry. Recently this interaction was validated with proximity ligation assays and peptide arrays. The data suggested that two potential peptide regions from domain II and one peptide region from domain III of uPAR, interact with αvβ6 integrin. Only the peptide region from domain III is consistent with the three-dimensional interaction site proposed in this study. The molecular basis of integrin αvβ6·uPAR binding using structural data is discussed for its implications as a potential therapeutic target in cancer management.