Clinical and Genome-wide Analysis of Cisplatin-induced Tinnitus Implicates Novel Ototoxic Mechanisms.

Purpose Cisplatin, a commonly used chemotherapeutic, results in tinnitus, the phantom perception of sound. Our purpose was to identify the clinical and genetic determinants of tinnitus among testicular cancer survivors (TCS) following cisplatin-based chemotherapy.Experimental design TCS (n = 762) were dichotomized to cases (moderate/severe tinnitus; n = 154) and controls (none; n = 608). Logistic regression was used to evaluate associations with comorbidities and SNP dosages in genome-wide association study (GWAS) following quality control and imputation (covariates: age, noise exposure, cisplatin dose, genetic principal components). Pathway over-representation tests and functional studies in mouse auditory cells were performed.Results Cisplatin-induced tinnitus (CisIT) significantly associated with age at diagnosis (P = 0.007) and cumulative cisplatin dose (P = 0.007). CisIT prevalence was not significantly greater in 400 mg/m2-treated TCS compared with 300 (P = 0.41), but doses >400 mg/m2 (median 580, range 402-828) increased risk by 2.61-fold (P P P P P P = 0.003). GWAS suggested a variant near OTOS (rs7606353, P = 2 × 10-6) and OTOS eQTLs were significantly enriched independently of that SNP (P = 0.018). OTOS overexpression in HEI-OC1, a mouse auditory cell line, resulted in resistance to cisplatin-induced cytotoxicity. Pathway analysis implicated potassium ion transport (q = 0.007).Conclusions CisIT associated with several neuro-otological symptoms, increased use of psychotropic medication, and poorer health. OTOS, expressed in the cochlear lateral wall, was implicated as protective. Future studies should investigate otoprotective targets in supporting cochlear cells

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.R21 CA279630 - NCI NIH HHS; R21 CA260382 - NCI NIH HHS; UH2 AG064704 - NIA NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHShttps://journals.asm.org/doi/reader/10.1128/msystems.00961-22Published versio