Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

Wood-decay macrofungi play a vital role in forest ecosystems by promoting nutrient cycling and soil structure, and their evolution is closely related to their host plants. This study investigates the potential evolutionary adaptation of wood-decay macrofungi to their host plants, focusing on whether these relationships differ between gymnosperms and angiosperms. While previous research has suggested non-random associations between specific fungi and plant deadwood, direct evidence of evolutionary adaptation has been lacking. Our study, conducted in a subtropical region, utilized metabarcoding techniques to identify deadwood species and associated fungi. We found significant evidence of evolutionary adaptation when considering all sampled species collectively. However, distinct patterns emerged when comparing angiosperms and gymnosperms: a significant evolutionary adaptation was observed of wood-decay macrofungi to angiosperms, but not to gymnosperms. This variation may be due to the longer evolutionary history and more stable species interactions of gymnosperms, as indicated by a higher modularity coefficient (r = .452), suggesting greater specialization. In contrast, angiosperms, being evolutionarily younger, displayed less stable and more coevolving interactions with fungi, reflected in a lower modularity coefficient (r = .387). Our findings provide the first direct evidence of differential evolutionary adaptation dynamics of these fungi to angiosperms versus gymnosperms, enhancing our understanding of forest ecosystem carbon cycling and resource management.

[This corrects the article DOI: 10.3389/fgene.2024.1371607.].

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified \"gold standard\" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on \"gold standard\" regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.

Soil salinization poses a global threat to terrestrial ecosystems. Soil microorganisms, crucial for maintaining ecosystem services, are sensitive to changes in soil structure and properties, particularly salinity. In this study, contrasting dynamics within the rhizosphere and bulk soil were focused on exploring the effects of heightened salinity on soil microbial communities, evaluating the influences shaping their composition in saline environments. This study observed a general decrease in bacterial alpha diversity with increasing salinity, along with shifts in community structure in terms of taxa relative abundance. The size and stability of bacterial co-occurrence networks declined under salt stress, indicating functional and resilience losses. An increased proportion of heterogeneous selection in bacterial community assembly suggested salinity\'s critical role in shaping bacterial communities. Stochasticity dominated fungal community assembly, suggesting their relatively lower sensitivity to soil salinity. However, bipartite network analysis revealed that fungi played a more significant role than bacteria in intensified microbial interactions in the rhizosphere under salinity stress compared to the bulk soil. Therefore, microbial cross-domain interactions might play a key role in bacterial resilience under salt stress in the rhizosphere.

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of \"referral paths\" - sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital\'s adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

BACKGROUND: Gyrodactylus is a lineage of monogenean flatworm ectoparasites exhibiting many features that make them a suitable model to study the host-parasite coevolutionary dynamics. Previous coevolutionary studies of this lineage mainly relied on low-power datasets (a small number of samples and a single molecular marker) and (now) outdated algorithms.
METHODS: To investigate the coevolutionary relationship of gyrodactylids and their fish hosts in high resolution, we used complete mitogenomes (including two newly sequenced Gyrodactylus species), a large number of species in the single-gene dataset, and four different coevolutionary algorithms.
RESULTS: The overall coevolutionary fit between the parasites and hosts was consistently significant. Multiple indicators confirmed that gyrodactylids are generally highly host-specific parasites, but several species could parasitize either multiple (more than 5) or phylogenetically distant fish hosts. The molecular dating results indicated that gyrodactylids tend to evolve towards high host specificity. Speciation by host switch was identified as a more important speciation mode than co-speciation. Assuming that the ancestral host belonged to Cypriniformes, we inferred four major host switch events to non-Cypriniformes hosts (mostly Salmoniformes), all of which occurred deep in the evolutionary history. Despite their relative rarity, these events had strong macroevolutionary consequences for gyrodactylid diversity. For example, in our dataset, 57.28% of all studied gyrodactylids parasitized only non-Cypriniformes hosts, which implies that the evolutionary history of more than half of all included lineages could be traced back to these major host switch events. The geographical co-occurrence of fishes and gyrodactylids determined the host use by these gyrodactylids, and geography accounted for most of the phylogenetic signal in host use.
CONCLUSIONS: Our findings suggest that the coevolution of Gyrodactylus flatworms and their hosts is largely driven by geography, phylogeny, and host switches.

Plant-flower visitor interaction is one of the most important relationships regarding the co-existence of the floral and faunal communities. The implication of network approaches is an efficient way to understand the impact of community structure on ecosystem functionality. To understand the association pattern of flower visitors, we performed this study on Avicennia officinalis and Avicennia marina mangroves from the islands of Indian Sundarban over three consecutive years. We found that visiting time and sites (islands) influenced the abundance of visitors. The bipartite networks showed a significant generalized structure for both site-visitor and visiting time-visitor networks where the strength and specialization of visitor species showed a highly and moderately significant positive correlation between both networks respectively. All the site-wise visiting time-visitor networks and year-wise site-visitor networks were significantly modular in structure. For both the plants, most of the visitors showed a generalized association pattern among islands and also among visiting times. Additionally, the study of the foraging behavior of dominant visitors showed Apis dorsata and Apis mellifera as the potential visitors for these plants. Our results showed that flower visitor networks are spatiotemporally dynamic. The interactions of visitors with flowers at different times influence their contribution to the network for becoming a generalist or peripheral species in the context of their visiting time, which may subsequently change over islands. This approach will help to devise more precise plant species-specific conservation strategies by understanding the contribution of visitors through the spatiotemporal context.

Head and neck squamous cell carcinoma (HNSCC) is among the most common cancer worldwide, accounting for hundreds thousands deaths annually. Unfortunately, most patients are diagnosed in an advanced stage and only a percentage respond favorably to therapies. To help fill this gap, we hereby propose a retrospective in silico study to shed light on gene-miRNA interactions driving the development of HNSCC. Moreover, to identify topological biomarkers as a source for designing new drugs. To achieve this, gene and miRNA profiles from patients and controls are holistically reevaluated using protein-protein interaction (PPI) and bipartite miRNA-target networks. Cytoskeletal remodeling, extracellular matrix (ECM), immune system, proteolysis, and energy metabolism have emerged as major functional modules involved in the pathogenesis of HNSCC. Of note, the landscape of our findings depicts a concerted molecular action in activating genes promoting cell cycle and proliferation, and inactivating those suppressive. In this scenario, genes, including VEGFA, EMP1, PPL, KRAS, MET, TP53, MMPs and HOXs, and miRNAs, including mir-6728 and mir-99a, emerge as key players in the molecular interactions driving HNSCC tumorigenesis. Despite the heterogeneity characterizing these HNSCC subtypes, and the limitations of a study pointing to relationships that could be context dependent, the overlap with previously published studies is encouraging. Hence, it supports further investigation for key molecules, both those already and not correlated to HNSCC.

Few systematic studies have been conducted on the faunal composition and food web structure of Cassidinae of China. During 2013-2019, we systematically investigated Cassidinae beetles and their host plants in the southern Guangxi. A total of 2,255 Cassidinae individuals from 66 species, 23 genera and ten tribes were collected in southern Guangxi. Most species belonged to the tribe Hispini (23 species, 34.8%), followed by the tribe Gonophorini (13 species, 19.7%), Cassidini (eight species, 12.1%) and Aspidimorphini (six species, 9.1%). The others (16 species) belonged to the tribes Anisoderini, Botryonopini, Callispini, Oncocephalini, Notosacanthini and Leptispini. The tribe Notosacanthini was recorded from Guangxi for the first time. The genera Neownesia (Botryonopini), Gonophora (Gonophorini), Micrispa (Gonophorini), Notosacantha (Notosacanthini) and Prionispa (Oncocephalini) were firstly recorded in Guangxi. In total, we obtained 47 newly-recorded species in southern Guangxi and 33 newly-recorded species in the whole Guangxi, of which, Callispafrontalis Medvedev, 1992 was newly recorded in China. Dactylispafeae Gestro (625 individuals) and D.chinensis Weise (565 individuals) were the most common species. A total of 69 species, 53 genera and 19 families of host plants were identified for Cassidinae in southern Guangxi. Many host plant associations are new records for Cassidinae. Quantitative food web analysis indicated that Cassidinae species in southern Guangxi primarily fed on Poaceae, Convolvulaceae, Cyperaceae and Rosaceae. Generally, the plant-Cassidinae food webs were moderately complex and stable in southern Guangxi. This is the first large contribution to the knowledge of the species composition and host plant diversity of Cassidinae in southern Guangxi.