State of the Interactomes: an evaluation of molecular networks for generating biological insights.

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks.

A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.

Mapping the common gene networks that underlie related diseases.

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each disease, development of new therapies and more comprehensive identification of disease genes. Accordingly, this protocol describes how to evaluate the extent to which two diseases, each characterized by a set of mapped genes, are colocalized in a reference gene interaction network. This procedure uses network propagation to measure the network 'distance' between gene sets. For colocalized diseases, the network can be further analyzed to extract common gene communities at progressive granularities. In particular, we show how to: (1) obtain input gene sets and a reference gene interaction network; (2) identify common subnetworks of genes that encompass or are in close proximity to all gene sets; (3) use multiscale community detection to identify systems and pathways represented by each common subnetwork to generate a network colocalized systems map; (4) validate identified genes and systems using a mouse variant database; and (5) visualize and further investigate select genes, interactions and systems for relevance to phenotype(s) of interest. We demonstrate the utility of this approach by identifying shared biological mechanisms underlying autism and congenital heart disease. However, this protocol is general and can be applied to any gene sets attributed to diseases or other phenotypes with suspected joint association. A typical NetColoc run takes less than an hour. Software and documentation are available at https://github.com/ucsd-ccbb/NetColoc .

Capillary zone electrophoresis separation and collection of spermatozoa for the forensic analysis of sexual assault evidence.

The processing of sexual assault kits (SAKs) relies on the genetic analysis of material extracted from swabs collected from the assault victim. A vital step in producing an identifiable DNA profile of the perpetrator is the effective separation of perpetrator (sperm) and victim (epithelial) DNA that have been isolated from the collected evidence. We report the use of capillary zone electrophoresis for the separation of intact sperm from whole and lysed epithelial cells in SAKs. The separated components are deposited into wells of a microtiter plate using a computer-controlled fraction collector, and quantitative PCR is used to verify the collection of sperm cells by targeted amplification of male DNA. We present results from simulated sexual assault samples that have been aged for up to 18 months, as well as vaginal swabs from authentic forensic kits. Components extracted from the vaginal swabs from the SAK comigrated with an aged semen sample at 6.25 ± 0.25 min. Epithelial cells migrated from 10-12 min, producing baseline resolution of the components. Sperm cells were collected in a microtiter plate for downstream analysis.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.