A major goal of systems biology is the development of models that accurately predict responses to perturbation. Constructing such models requires collection of dense measurements of system states, yet transformation of data into predictive constructs remains a challenge. To begin to model human immunity, we analyzed immune parameters in depth both at baseline and in response to influenza vaccination. Peripheral blood mononuclear cell transcriptomes, serum titers, cell subpopulation frequencies, and B cell responses were assessed in 63 individuals before and after vaccination and used to develop a systematic framework to dissect inter- and intra-individual variation and build predictive models of post-vaccination antibody responses. Strikingly, independent of age and pre-existing antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time-points. Most of the parameters contributing to prediction delineated temporally-stable baseline differences across individuals, raising the prospect of immune monitoring before intervention.

SUMMARY Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We also identified a large number of new PDIs for known TFs, as well as for previously uncharacterized TFs. Remarkably, we found that over three hundred proteins not previously annotated as TFs also showed sequence-specific PDIs, including RNA binding proteins, mitochondrial proteins, and protein kinases. One of such unconventional DNA-binding proteins, MAPK1, acts as a transcriptional repressor for interferon gamma-induced genes.

We present a tool that combines fast mapping, error correction, and de novo assembly (MECAT; accessible at https://github.com/xiaochuanle/MECAT) for processing single-molecule sequencing (SMS) reads. MECAT's computing efficiency is superior to that of current tools, while the results MECAT produces are comparable or improved. MECAT enables reference mapping or de novo assembly of large genomes using SMS reads on a single computer.

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

Inhibition or genetic deletion of poly(ADP-ribose) (PAR) polymerase-1 (PARP-1) is protective against toxic insults in many organ systems. The molecular mechanisms underlying PARP-1–dependent cell death involve release of mitochondrial apoptosis-inducing factor (AIF) and its translocation to the nucleus, which results in chromatinolysis. We identified macrophage migration inhibitory factor (MIF) as a PARP-1–dependent AIF-associated nuclease (PAAN). AIF was required for recruitment of MIF to the nucleus, where MIF cleaves genomic DNA into large fragments. Depletion of MIF, disruption of the AIF-MIF interaction, or mutation of glutamic acid at position 22 in the catalytic nuclease domain blocked MIF nuclease activity and inhibited chromatinolysis, cell death induced by glutamate excitotoxicity, and focal stroke. Inhibition of MIF's nuclease activity is a potential therapeutic target for diseases caused by excessive PARP-1 activation.

Summary Herpesviruses, which are major human pathogens, establish life-long persistent infections. Although the α-, β-, and γ-herpesviruses infect different tissues and cause distinct diseases, they each encode a conserved serine/threonine kinase critical for virus replication and spread. The extent of substrate conservation and the key common cell signalling pathways targeted by these kinases are unknown. Using a human protein microarray high-throughput approach we identify shared substrates of the conserved kinases from herpes simplex virus, human cytomegalovirus, Epstein-Barr virus (EBV) and Kaposi's sarcoma associated herpesvirus. DNA damage response (DDR) proteins were statistically enriched and the histone acetyltransferase TIP60, an upstream regulator of the DDR pathway, was required for efficient herpesvirus replication. During EBV replication, TIP60 activation by the BGLF4 kinase triggers EBV-induced DDR and also mediates induction of viral lytic gene expression. Identification of key cellular targets of the conserved herpesvirus kinases will facilitate the development of broadly effective anti-viral strategies.

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A high-resolution map of human phosphorylation networks was constructed by integrating experimentally determined kinase-substrate relationships with other resources, such as in vivo phosphorylation sites.