Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

http://bartellab.wi.mit.edu/publication_reprints/Bartel_Cell09.pdf

MicroRNAs: Target Recognition and Regulatory Functions

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.

Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.

Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

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The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

Here we report that the major histocompatibility complex II (MHC II) antigen presentation pathway is regulated by the ALS-causative genes, FUS, TAF15, or MATR3. Of >6000 proteins detected by quantitative mass spectrometry, the subunits of the MHC II heterodimer, HLA-DR, were the top 2 downregulated proteins in HeLa knock outs (KO) of these ALS genes, but not the related gene, EWSR1. Moreover, CD74, which is the 3rd essential component of HLA-DR, was downregulated in the 3 KOs. We show that the downregulations are due to loss of CIITA, a transcription factor dedicated to expression of MHC II genes. Thus, our results reveal the 1st shared cellular pathway regulated by multiple ALS genes, and this pathway is ALS genes -> CIITA -> MHC II genes. We obtained the same results in HMC3 cells, a microglia cell line, showing that loss of the MHC II pathway extends to an ALS-relevant cell type in the central nervous system (CNS). Furthermore, the MHC II pathway is downregulated in hematopoietic progenitor cells (HPCs) bearing the ALS FUSR495X mutation. This observation may be highly significant to ALS pathogenesis as HPCs give rise to a multitude of CNS-specific and systemic immune cells, both of which have known or suspected roles in ALS. Together, our data raise the possibility that loss of the MHC II pathway in a large range of immune cells results in global failure of the immune system to protect motor neurons from damage that leads to the disease. Consequently, CIITA and the other genes in the MHC II pathway may be important new therapeutic targets for the disease.

/pdf/three-als-genes-regulate-expression-of-the-mhc-class-ii-3govhjom.pdf

Three ALS genes regulate expression of the MHC class II antigen presentation pathway

Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements.In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges.Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM . 

/pdf/dagbagm-learning-directed-acyclic-graphs-of-mixed-variables-7cmwg046.pdf

DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer

The intimate association between the endoplasmic reticulum (ER) and mitochondrial membranes at ER-Mitochondria contact sites (ERMCS) is a platform for critical cellular processes, particularly lipid synthesis. How contacts are remodeled and the impact of altered contacts on lipid metabolism remains poorly understood. We show that the p97 AAA-ATPase and its adaptor ubiquitin-X domain adaptor 8 (UBXD8) regulate ERMCS. The p97-UBXD8 complex localizes to contacts and its loss increases contacts in a manner that is dependent on p97 catalytic activity. Quantitative proteomics and lipidomics of ERMCS demonstrates alterations in proteins regulating lipid metabolism and a significant change in membrane lipid saturation upon UBXD8 deletion. Loss of p97-UBXD8 increased membrane lipid saturation via SREBP1 and the lipid desaturase SCD1. Aberrant contacts can be rescued by unsaturated fatty acids or overexpression of SCD1. We find that the SREBP1-SCD1 pathway is negatively impacted in the brains of mice with p97 mutations that cause neurodegeneration. We propose that contacts are exquisitely sensitive to alterations to membrane lipid composition and saturation. 

/pdf/the-p97-ubxd8-complex-regulates-er-mitochondria-contact-jmnh72os.pdf

The p97-UBXD8 complex regulates ER-Mitochondria contact sites by altering membrane lipid saturation and composition

Adipose thermogenesis involves specialized mitochondrial function that counteracts metabolic disease through dissipation of chemical energy as heat. However, inflammation present in obese adipose tissue can impair oxidative metabolism. Here, we show that PGC1α, a key governor of mitochondrial biogenesis and thermogenesis, is negatively regulated at the level of mRNA translation by the little-known RNA-binding protein RBM43. Rbm43 is expressed selectively in white adipose depots that have low thermogenic potential, and is induced by inflammatory cytokines. RBM43 suppresses mitochondrial and thermogenic gene expression in a PGC1α-dependent manner and its loss protects cells from cytokine-induced mitochondrial impairment. In mice, adipocyte-selective Rbm43 disruption increases PGC1α translation, resulting in mitochondrial biogenesis and adipose thermogenesis. These changes are accompanied by improvements in glucose homeostasis during diet-induced obesity that are independent of body weight. The action of RBM43 suggests a translational mechanism by which inflammatory signals associated with metabolic disease dampen mitochondrial function and thermogenesis.

https://www.biorxiv.org/content/biorxiv/early/2023/01/07/2023.01.06.522985.full.pdf

Steven P. Gygi

Papers

Three ALS genes regulate expression of the MHC class II antigen presentation pathway

DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer

The p97-UBXD8 complex regulates ER-Mitochondria contact sites by altering membrane lipid saturation and composition

RBM43 links adipose inflammation and energy expenditure through translational regulation of PGC1α

Schnelle quantitative analyse von proteinen oder proteinfunktionen in komplexen gemischen