Diogo M. Camacho

Wisdom of crowds for robust gene network inference

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

Antibiotic-induced bacterial cell death exhibits physiological and biochemical hallmarks of apoptosis

Programmed cell death is a gene-directed process involved in the development and homeostasis of multicellular organisms. The most common mode of programmed cell death is apoptosis, which is characterized by a stereotypical set of biochemical and morphological hallmarks. Here we report that Escherichia coli also exhibit characteristic markers of apoptosis-including phosphatidylserine exposure, chromosome condensation, and DNA fragmentation-when faced with cell death-triggering stress, namely bactericidal antibiotic treatment. Notably, we also provide proteomic and genetic evidence for the ability of multifunctional RecA to bind peptide sequences that serve as substrates for eukaryotic caspases, and regulation of this phenotype by the protease, ClpXP, under conditions of cell death. Our findings illustrate that prokaryotic organisms possess mechanisms to dismantle and mark dying cells in response to diverse noxious stimuli and suggest that elaborate, multilayered proteolytic regulation of these features may have evolved in eukaryotes to harness and exploit their deadly potential.

Functional characterization of bacterial sRNAs using a network biology approach

Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

Comparison of Reverse‐Engineering Methods Using an in Silico Network

Abstract:  The reverse engineering of biochemical networks is a central problem in systems biology. In recent years several methods have been developed for this purpose, using techniques from a variety of fields. A systematic comparison of the different methods is complicated by their widely varying data requirements, making benchmarking difficult. Also, because of the lack of detailed knowledge about most real networks, it is not easy to use experimental data for this purpose. This paper contains a comparison of four reverse‐engineering methods using data from a simulated network. The network is sufficiently realistic and complex to include many of the challenges that data from real networks pose. Our results indicate that the two methods based on genetic perturbations of the network outperform the other methods, including dynamic Bayesian networks and a partial correlation method.

Central role for RNase YbeY in Hfq-dependent and Hfq-independent small-RNA regulation in bacteria.

BackgroundConceptual parallels exist between bacterial and eukaryotic small-RNA (sRNA) pathways, yet relatively little is known about which protein may recognize and recruit bacterial sRNAs to interact with targets. In eukaryotes, Argonaute (AGO) proteins discharge such functions. The highly conserved bacterial YbeY RNase has structural similarities to the MID domain of AGOs. A limited study had indicated that in Sinorhizobium meliloti the YbeY ortholog regulates the accumulation of sRNAs as well as the target mRNAs, raising the possibility that YbeY may play a previously unrecognized role in bacterial sRNA regulation.ResultsWe have applied a multipronged approach of loss-of-function studies, genome-wide mRNA and sRNA expression profiling, pathway analysis, target prediction, literature mining and network analysis to unravel YbeY-dependent molecular responses of E. coli exposed to hydroxyurea (HU). Loss of ybeY function, which results in a marked resistance to HU, had global affects on sRNA-mediated gene expression. Of 54 detectable E. coli sRNAs in our microarray analysis, 30 sRNAs showed a differential expression upon HU stress, of which 28 sRNAs displayed a YbeY-dependent change in expression. These included 12 Hfq-dependent and 16 Hfq-independent sRNAs. We successfully identified at least 57 experimentally inferred sRNA-mRNA relationships. Further applying a ‘context likelihood of relatedness’ algorithm, we reverse engineered the YbeY-dependent Hfq-dependent sRNA-mRNA network as well as YbeY-dependent Hfq-independent sRNA-mRNA network.ConclusionYbeY extensively modulates Hfq-dependent and independent sRNA-mRNA interactions. YbeY-dependent sRNAs have central roles in modulating cellular response to HU stress.

Systems biology strikes gold.

Integrating synthetic biology and systems biology efforts can advance our understanding of biomolecular systems. This is illustrated in this issue by Cantone et al. (2009), who construct a synthetic gene network in yeast and use it to assess and benchmark systems biology approaches for reverse engineering endogenous gene networks.

Next-Generation Machine Learning for Biological Networks

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.

Complex human gut microbiome cultured in anaerobic human intestine chips

The diverse bacterial populations that comprise the commensal microbiota of the human intestine play a central role in health and disease, yet no method is available to sustain these complex microbial communities in direct contact with living human intestinal cells and their overlying mucus layer in vitro. Here we describe a human Organ-on-a-Chip (Organ Chip) microfluidic platform that permits control and real-time assessment of physiologically-relevant oxygen gradients, and which enables co-culture of living human intestinal epithelium with stable communities of aerobic and anaerobic human gut microbiota. When compared to aerobic co-culture conditions, establishment of a transluminal hypoxia gradient sustained higher microbial diversity with over 200 unique operational taxonomic units (OTUs) from 11 different genera, and an abundance of obligate anaerobic bacteria with ratios of Firmicutes and Bacteroidetes similar to those observed in human feces, in addition to increasing intestinal barrier function. The ability to culture human intestinal epithelium overlaid by complex human gut microbial communities within microfluidic Intestine Chips may enable investigations of host-microbiome interactions that were not possible previously, and serve as a discovery tool for development of new microbiome-related therapeutics, probiotics, and nutraceuticals.

Directed differentiation of human induced pluripotent stem cells into mature kidney podocytes and establishment of a Glomerulus Chip

Protocols have been established to direct the differentiation of human induced pluripotent stem (iPS) cells into nephron progenitor cells and organoids containing many types of kidney cells, but it has been difficult to direct the differentiation of iPS cells to form specific types of mature human kidney cells with high yield. Here, we describe a detailed protocol for the directed differentiation of human iPS cells into mature, post-mitotic kidney glomerular podocytes with high (>90%) efficiency within 26 d and under chemically defined conditions, without genetic manipulations or subpopulation selection. We also describe how these iPS cell–derived podocytes may be induced to form within a microfluidic organ-on-a-chip (Organ Chip) culture device to build a human kidney Glomerulus Chip that mimics the structure and function of the kidney glomerular capillary wall in vitro within 35 d (starting with undifferentiated iPS cells). The podocyte differentiation protocol requires skills for culturing iPS cells, and the development of a Glomerulus Chip requires some experience with building and operating microfluidic cell culture systems. This method could be useful for applications in nephrotoxicity screening, therapeutic development, and regenerative medicine, as well as mechanistic study of kidney development and disease.Ingber and colleagues describe how to generate kidney glomerular podocytes from human pluripotent stem cells and how to engineer a human kidney Glomerulus Chip.