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Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH‐MS is a specific variant of data‐independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH‐MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH‐MS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH‐MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH‐MS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATH‐MS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed. Graphical Abstract SWATH‐MS combines deep proteome coverage with quantitative consistency and accuracy and is often the method of choice for personalized medicine, drug screens or systems biology. This tutorial provides guidelines on how to set up SWATH‐MS experiments, perform the mass spectrometric measurements and analyse the data.

To the Editor: Over the past two decades, the scale and complexity of genomics technologies and data have advanced from sequencing genomes of a few organisms to generating metagenomes, genome variation, gene expression, metabolites, and phenotype data for thousands of organisms and their communities. A major challenge in this data-rich age of biology is integrating heterogeneous and distributed data into predictive models of biological function, ranging from a single gene to entire organisms and their ecologies. Here we present the DOE Systems Biology Knowledgebase (KBase, http://kbase.us), an open-source software and data platform that enables data sharing, integration, and analysis of microbes, plants, and their communities. Once a Narrative has been shared or made public, other users can copy the Narrative and rerun it on their own data, or modify it to suit their scientific needs. [...]public Narratives serve as resources for the user community by capturing valuable data sets, associated computational analyses, and scientific context describing the rationale behind a scientific study in a form that is immediately reproducible and reusable.

Key Points When applying high-throughput molecular methods to the study of neurodevelopmental disorders, major challenges include the spatial and temporal heterogeneity of the brain, a lack of appropriate tissue available for studies and poorly defined phenotypes. Transcriptomics assays are currently the most widely used functional genomic assays in neurobiology owing to their ability to efficiently capture tissue-specific spatial and temporal heterogeneity in a high-throughput manner. Principles from transcriptomic studies will aid in evaluating additional molecular and cellular levels of regulation. We review the principles of network analysis and describe how gene networks provide a framework to organize, integrate and analyse large-scale genomic data sets in neurobiology. We review representative differential expression and gene network studies in neurodevelopmental disorders and neurodegenerative diseases and identify some next steps in data generation and integration that are necessary for progress in the field. We provide guidelines for designing, analysing and evaluating high-throughput transcriptomic studies in the brain in order to improve study quality and reproducibility. The study of the genetic basis of neurodevelopmental disorders and neurodegenerative diseases has progressed through recent large-scale association studies as well as the application of a range of high-throughput molecular methods. In this Review, the authors examine systems biology approaches and demonstrate how gene networks provide an organizing framework to integrate the analysis of large-scale genetic and molecular profiling data sets to characterize the genetic basis of phenotypes that affect the central nervous system. Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re‐wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis. Protein and genetic interaction maps have typically been generated under a single condition, providing a static view of the interactome. Recent studies employing differential analysis, however, have revealed that widespread re‐wiring of the interactome underlies key biological responses.

Nanoparticles are often engineered as a scaffolding system to combine targeting, imaging and/or therapeutic moieties into a unitary agent. However, mostly overlooked, the nanomaterial itself interacts with biological systems exclusive of application-specific particle functionalization. This nanoparticle biointerface has been found to elicit specific biological effects, which we term ‘ancillary effects’. In this Review, we describe the current state of knowledge of nanobiology gleaned from existing studies of ancillary effects with the objectives to describe the potential of nanoparticles to modulate biological effects independently of any engineered function; evaluate how these effects might be relevant for nanomedicine design and functional considerations, particularly how they might be useful to inform clinical decision-making; identify potential clinical harm that arises from adverse nanoparticle interactions with biology; and, finally, highlight the current lack of knowledge in this area as both a barrier and an incentive to the further development of nanomedicine.Nanoparticles used for biomedical applications might elicit unexpected adverse or beneficial biological effects unrelated to the function for which they were designed. In this Review, the authors describe some of these ‘yin and yang’ ancillary effects, and discuss their implications for nanomedicine development.

The methylotrophic yeast Ogataea polymorpha shows potential in methanol biotransformation that is a promising approach for green biomanufacturing and carbon neutrality.The development of synthetic biology tools has facilitated the metabolic rewiring and production of various chemicals in O. polymorpha.Systems biology could help deepen our understanding of global metabolic systems and support the engineering of synthetic metabolic capabilities in O. polymorpha. Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry. Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.

The platinum derivative cis -diamminedichloroplatinum(II), best known as cisplatin, is currently employed for the clinical management of patients affected by testicular, ovarian, head and neck, colorectal, bladder and lung cancers. For a long time, the antineoplastic effects of cisplatin have been fully ascribed to its ability to generate unrepairable DNA lesions, hence inducing either a permanent proliferative arrest known as cellular senescence or the mitochondrial pathway of apoptosis. Accumulating evidence now suggests that the cytostatic and cytotoxic activity of cisplatin involves both a nuclear and a cytoplasmic component. Despite the unresolved issues regarding its mechanism of action, the administration of cisplatin is generally associated with high rates of clinical responses. However, in the vast majority of cases, malignant cells exposed to cisplatin activate a multipronged adaptive response that renders them less susceptible to the antiproliferative and cytotoxic effects of the drug, and eventually resume proliferation. Thus, a large fraction of cisplatin-treated patients is destined to experience therapeutic failure and tumor recurrence. Throughout the last four decades great efforts have been devoted to the characterization of the molecular mechanisms whereby neoplastic cells progressively lose their sensitivity to cisplatin. The advent of high-content and high-throughput screening technologies has accelerated the discovery of cell-intrinsic and cell-extrinsic pathways that may be targeted to prevent or reverse cisplatin resistance in cancer patients. Still, the multifactorial and redundant nature of this phenomenon poses a significant barrier against the identification of effective chemosensitization strategies. Here, we discuss recent systems biology studies aimed at deconvoluting the complex circuitries that underpin cisplatin resistance, and how their findings might drive the development of rational approaches to tackle this clinically relevant problem.

Metagenomics has enabled researchers to compile inventories of viruses, bacteria and archaea that inhabit specific niches. Here, the authors discuss the tools that are needed for us to progress to an integrated understanding of microbial ecosystems biology. Systems-biology approaches, which are driven by genome sequencing and high-throughput functional genomics data, are revolutionizing single-cell-organism biology. With the advent of various high-throughput techniques that aim to characterize complete microbial ecosystems (metagenomics, meta-transcriptomics and meta-metabolomics), we propose that the time is ripe to consider molecular systems biology at the ecosystem level (eco-systems biology). Here, we discuss the necessary data types that are required to unite molecular microbiology and ecology to develop an understanding of community function and discuss the potential shortcomings of these approaches.