Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

The leaf microbiome is influenced by both biotic and abiotic factors. Currently, we know little about the relative importance of these factors in determining microbiota composition and dynamics. To explore this issue, we collected weekly leaf samples over a 98-day growing season from multiple cultivars of common bean, soybean, and canola planted at three locations in Ontario, Canada, and performed Illumina-based microbiome analysis. We find that the leaf microbiota at the beginning of the season is very strongly influenced by the soil microbiota but, as the season progresses, it differentiates, becomes significantly less diverse, and transitions to having a greater proportion of leaf-specific taxa that are shared among all samples. A phylogenetic investigation of communities by reconstruction of unobserved states imputation of microbiome function inferred from the taxonomic data found significant differences between the soil and leaf microbiome, with a significant enrichment of motility gene categories in the former and metabolic gene categories in the latter. A network co-occurrence analysis identified two highly connected clusters as well as subclusters of putative pathogens and growth-promoting bacteria. These data reveal some of the complex ecological dynamics that occur in microbial communities over the course of a growing season and highlight the importance of community succession.

The cells of multicellular organisms receive extracellular signals using surface receptors. The extracellular domains (ECDs) of cell surface receptors function as interaction platforms, and as regulatory modules of receptor activation. Understanding how interactions between ECDs produce signal-competent receptor complexes is challenging because of their low biochemical tractability. In plants, the discovery of ECD interactions is complicated by the massive expansion of receptor families, which creates tremendous potential for changeover in receptor interactions. The largest of these families in Arabidopsis thaliana consists of 225 evolutionarily related leucine-rich repeat receptor kinases (LRR-RKs), which function in the sensing of microorganisms, cell expansion, stomata development and stem-cell maintenance. Although the principles that govern LRR-RK signalling activation are emerging, the systems-level organization of this family of proteins is unknown. Here, to address this, we investigated 40,000 potential ECD interactions using a sensitized high-throughput interaction assay, and produced an LRR-based cell surface interaction network (CSI) that consists of 567 interactions. To demonstrate the power of CSI for detecting biologically relevant interactions, we predicted and validated the functions of uncharacterized LRR-RKs in plant growth and immunity. In addition, we show that CSI operates as a unified regulatory network in which the LRR-RKs most crucial for its overall structure are required to prevent the aberrant signalling of receptors that are several network-steps away. Thus, plants have evolved LRR-RK networks to process extracellular signals into carefully balanced responses.

Sarcomas are cancers of the bone and soft tissue often defined by gene fusions. Ewing sarcoma involves fusions between , a gene encoding an RNA binding protein, and E26 transformation-specific (ETS) transcription factors. We explored how and when fusions arise by studying the whole genomes of Ewing sarcomas. In 52 of 124 (42%) of tumors, the fusion gene arises by a sudden burst of complex, loop-like rearrangements, a process called chromoplexy, rather than by simple reciprocal translocations. These loops always contained the disease-defining fusion at the center, but they disrupted multiple additional genes. The loops occurred preferentially in early replicating and transcriptionally active genomic regions. Similar loops forming canonical fusions were found in three other sarcoma types. Chromoplexy-generated fusions appear to be associated with an aggressive form of Ewing sarcoma. These loops arise early, giving rise to both primary and relapse Ewing sarcoma tumors, which can continue to evolve in parallel.

In many repeat diseases, like Huntington’s disease (HD), ongoing repeat expansions in affected tissues contribute to disease onset, progression and severity. Inducing contractions of expanded repeats by exogenous agents is not yet possible. Traditional approaches would target proteins driving repeat mutations. Here we report a compound N aphthyridine- A zaquinolone (NA) that specifically binds slipped-CAG DNA intermediates of expansion mutations, a previously unsuspected target. NA efficiently induces repeat contractions in HD patient cells as well as en masse contractions in medium spiny neurons of HD mouse striatum. Contractions are specific for the expanded allele, independent of DNA replication, require transcription across the coding CTG strand, and arise by blocking repair of CAG slip-outs. NA-induced contractions depend upon active expansions driven by MutSβ. NA injections in HD mouse striatum reduce mutant HTT protein aggregates, a biomarker of HD pathogenesis and severity. Repeat structure-specific DNA ligands are a novel avenue to contract expanded repeats.

Leiomyosarcomas (LMS) are genetically heterogeneous tumors differentiating along smooth muscle lines. Currently, LMS treatment is not informed by molecular subtyping and is associated with highly variable survival. While disease site continues to dictate clinical management, the contribution of genetic factors to LMS subtype, origins, and timing are unknown. Here we analyze 70 genomes and 130 transcriptomes of LMS, including multiple tumor regions and paired metastases. Molecular profiling highlight the very early origins of LMS. We uncover three specific subtypes of LMS that likely develop from distinct lineages of smooth muscle cells. Of these, dedifferentiated LMS with high immune infiltration and tumors primarily of gynecological origin harbor genomic dystrophin deletions and/or loss of dystrophin expression, acquire the highest burden of genomic mutation, and are associated with worse survival. Homologous recombination defects lead to genome-wide mutational signatures, and a corresponding sensitivity to PARP trappers and other DNA damage response inhibitors, suggesting a promising therapeutic strategy for LMS. Finally, by phylogenetic reconstruction, we present evidence that clones seeding lethal metastases arise decades prior to LMS diagnosis.

Over 90% of cystic fibrosis (CF) patients die due to chronic lung infections leading to respiratory failure. The decline in CF lung function is greatly accelerated by intermittent and progressively severe acute pulmonary exacerbations (PEs). Despite their clinical impact, surprisingly few microbiological signals associated with PEs have been identified. Here we introduce an unsupervised, systems-oriented approach to identify key members of the microbiota. We used two CF sputum microbiome data sets that were longitudinally collected through periods spanning baseline health and PEs. Key taxa were defined based on three strategies: overall relative abundance, prevalence, and co-occurrence network interconnectedness. We measured the association between changes in the abundance of the key taxa and changes in patient clinical status over time via change-point detection, and found that taxa with the highest level of network interconnectedness tracked changes in patient health significantly better than taxa with the highest abundance or prevalence. We also cross-sectionally stratified all samples into the clinical states and identified key taxa associated with each state. We found that network interconnectedness most strongly delineated the taxa among clinical states, and that anaerobic bacteria were over-represented during PEs. Many of these anaerobes are oropharyngeal bacteria that have been previously isolated from the respiratory tract, and/or have been studied for their role in CF. The observed shift in community structure, and the association of anaerobic taxa and PEs lends further support to the growing consensus that anoxic conditions and the subsequent growth of anaerobic microbes are important predictors of PEs.

A dependable long-term hydrologic prediction is essential to planning, designing and 12 management activities of water resources. A three-stage indirect multi-step-ahead prediction model,