A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses

Problem vid modellering av stora cellulära kontrollnätverk

Vi har identifierat flexibelt utbyte och lagring av data i databaser, tillsammans med långvarig satsning på olika existerande och framtida modeller som nyckar till förståelse av det regler nätverk som utgör bron mellan geno- och fenotyp. Denna pilot studie av modellering av stora cellulära kontroll nätverk utgår från en intressant medicinsk frågeställning inom molekylär cellbiologi: Är framtvingad expression av Cdc6, aktivering av Cdk4/6 och Cdk2 tillräcklig för förankringsfri entré av cell cykelns S fas? Vi försöker konstruera en modell för att besvara denna fråga, på så sätt att vi kan detektera problem vid modellering av stora kontroll nätverk, diskutera implikationer och möjliga lösningar. Vår modell är baserad på 1447 reaktioner och innehåller 1343 olika molekyler. Vi använde graf teori för att studera dess topologi och gjorde följande fynd: Nätverket är skalfritt och avtar enligt en potensfunktion, som var väntat baserat på tidigare arbeten. Nätverket består av ett stort väl förenat kluster. Det kan inte bli modulariserat i form av starka komponenter eller block i en användbar form. Detta eftersom vi fann en stor komponent eller ett stort block som innehöll majoriteten av alla molekyler och mer än hundra små komponenter eller block med en eller några molekyler. Vårt nätverk stämmer inte överens med en hierarkisk nätverks modell bestående av block förenade av cut-vertices.We have identified flexible exchange and storage of data in databases, together with prolonged investment in different existing and future modelling formalisms as key issues in successful understanding of the regulatory network responsible for the connection between geno- and phenotype. This pilot study of modelling of large-scale regulatory networks starts with a medically interesting question from molecular cell biology: Is enforced expression of Cdc6, activation of Cdk4/6 and Cdk2 sufficient for anchorage-independent entry of the S phase of the cell cycle? We try to construct a model for answering this question, in such a way that we can reveal obstacles of large-scale regulatory modelling, discuss their implications and possible solutions. Our model is based on 1447 reactions and contains 1343 different molecules. We used graph theory to study its topology and made the following findings: The network is scale-free and decays as a power-law, as could be expected based on earlier works. The network consists of one huge well connected cluster. It cannot be modularised into strong components or blocks in a useful way, since we get one big component or block containing a majority of all molecules and more than a hundred tiny components or blocks with one or a few molecules. Our network does not agree with a hierarchical network model consisting of blocks linked by cut-vertices

Data from: High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer

Background: The progression of colorectal cancer (CRC) involves recurrent amplifications/mutations in the epidermal growth factor receptor (EGFR) and downstream signal transducers of the Ras pathway, KRAS and BRAF. Whether genetic events predicted to result in increased and constitutive signaling indeed lead to enhanced biological activity is often unclear and, due to technical challenges, unexplored. Here, we investigated proliferative signaling in CRC using a highly sensitive method for protein detection. The aim of the study was to determine whether multiple changes in proliferative signaling in CRC could be combined and exploited as a “complex biomarker” for diagnostic purposes. Methods: We used robotized capillary isoelectric focusing as well as conventional immunoblotting for the comprehensive analysis of epidermal growth factor receptor signaling pathways converging on extracellular regulated kinase 1/2 (ERK1/2), AKT, phospholipase Cγ1 (PLCγ1) and c-SRC in normal mucosa compared with CRC stage II and IV. Computational analyses were used to test different activity patterns for the analyzed signal transducers. Results: Signaling pathways implicated in cell proliferation were differently dysregulated in CRC and, unexpectedly, several were downregulated in disease. Thus, levels of activated ERK1 (pERK1), but not pERK2, decreased in stage II and IV while total ERK1/2 expression remained unaffected. In addition, c-SRC expression was lower in CRC compared with normal tissues and phosphorylation on the activating residue Y418 was not detected. In contrast, PLCγ1 and AKT expression levels were elevated in disease. Immunoblotting of the different signal transducers, run in parallel to capillary isoelectric focusing, showed higher variability and lower sensitivity and resolution. Computational analyses showed that, while individual signaling changes lacked predictive power, using the combination of changes in three signaling components to create a “complex biomarker” allowed with very high accuracy, the correct diagnosis of tissues as either normal or cancerous. Conclusions: We present techniques that allow rapid and sensitive determination of cancer signaling that can be used to differentiate colorectal cancer from normal tissue

Padhan BMC Cancer 2016 Supplemental Results

File contains the test statistic (T) for each possible combination of 1-3 features, including the constructed features (column 5 and 6). One combination is shown per row with the name of the feature combination in the first column and the header explaining the value in each column in the first row

Mesoscopic physical removal of material using sliding nano-diamond contacts

Wear mechanisms including fracture and plastic deformation at the nanoscale are central to understand sliding contacts. Recently, the combination of tip-induced material erosion with the sensing capability of secondary imaging modes of AFM, has enabled a slice-and-view tomographic technique named AFM tomography or Scalpel SPM. However, the elusive laws governing nanoscale wear and the large quantity of atoms involved in the tip-sample contact, require a dedicated mesoscale description to understand and model the tip-induced material removal. Here, we study nanosized sliding contacts made of diamond in the regime whereby thousands of nm3 are removed. We explore the fundamentals of high-pressure tip-induced material removal for various materials. Changes in the load force are systematically combined with AFM and SEM to increase the understanding and the process controllability. The nonlinear variation of the removal rate with the load force is interpreted as a combination of two contact regimes each dominating in a particular force range. By using the gradual transition between the two regimes, (1) the experimental rate of material eroded on each tip passage is modeled, (2) a controllable removal rate below 5 nm/scan for all the materials is demonstrated, thus opening to future development of 3D tomographic AFM.Micro and Nano Engineerin

Raw Protein RPAs Constructed features replicate corrected

File shows the relative peak area (RPA), i.e. peak area value of the measured protein after normalization to the HSP70 level analyzed in parallel in each sample (columns 2-24, 28-42). This file contains one sample per row and one protein per column with the sample name in the first column and the protein name in the first row. Column 25-27 contain the result of the mutation analysis of KRAS and BRAF. One in column 25 (MutationKRAS) indicate that KRAS is mutated in the sample, one in column 26 (MutationBRAF) indicates that BRAF is mutated, while one in column 27 (Wildtype) indicate that neither KRAS nor BRAF is mutated. Columns 28 to 42 contain the RPA values of the constructed features, i.e. features that are calculated based on the 23 different activity levels of the 7 signal transducers in column 2-24. The four replicates of each constructed feature contains the minimum, maximum, mean, and median value based on all possible ways to combine the replicates of the proteins used to construct the feature. A one in the binary variables in column 43-46 indicate the classification of each sample as normal mucosa, colorectal cancer (CRC) stage II, CRC stage IV, or metastasis. In the last column the classification is 1 = normal mucosa, 2 = colorectal cancer (CRC) stage II, 3 = CRC stage IV, or 5 = metastasis. NaN is used to indicate that no measurement was done

Raw Protein RPAs

The relative peak area (RPA), i.e. peak area value of the 23 different activity levels of the 7 signal transducers after normalization to the HSP70 level analyzed in parallel in each sample (columns 2-24). This file contains one sample per row and one protein per column with the sample name in the first column and the protein name in the first row. Columns 25-27 contain the result of the mutation analysis of KRAS and BRAF. One in column 25 (MutationKRAS) indicate that KRAS is mutated in the sample, one in column 26 (MutationBRAF) indicates that BRAF is mutated, while one in column 27 (Wildtype) indicate that neither KRAS nor BRAF is mutated. A one in the binary variables in column 28-31 indicate the classification of each sample as normal mucosa, colorectal cancer (CRC) stage II, CRC stage IV, or metastasis. NaN is used to indicate that no measurement was done