In recent years, a substantial number of tissue microbiome studies have been published, mainly due to the recent improvements in the minimization of microbial contamination during whole transcriptome analysis. Another reason for this trend is due to the capability of next-generation sequencing (NGS) to detect microbiome composition even in low biomass samples. Several recent studies demonstrate a significant role for the tissue microbiome in the development and progression of cancer and other diseases. For example, the increase of the abundance of Proteobacteria in tumor tissues of the breast has been revealed by gene expression analysis. The link between human papillomavirus infection and cervical cancer has been known for some time, but the relationship between the microbiome and breast cancer (BC) is more novel. There are also recent attempts to investigate the possible link between the brain microbiome and the cognitive dysfunction caused by neurological diseases. Such studies pointing to the role of the brain microbiome in Huntington’s disease (HD) and Alzheimer’s disease (AD) suggest that microbial colonization is a risk factor. In this review, we aim to summarize the studies that associate the tissue microbiome, rather than gut microbiome, with cancer and other diseases using whole-transcriptome analysis, along with 16S rRNA analysis. After providing several case studies for each relationship, we will discuss the potential role of transcriptome analysis on the broader portrayal of the pathophysiology of the breast, brain, and vaginal microbiome.

The ancient Gulf of Latmos is an iconic example of a dynamic landscape and humankind's historical relationship with it. Using extensive new primary data and original models for calibrating radiocarbon dates in transitional lagoon environments, we demonstrate that Lake Bafa (or Bafa Gölü, in Turkish) formed at a much earlier date than previously thought. In questioning the logical process by which previous dates were achieved, we re‐examine the relationship between sedimentological data, archaeology and written history. We reassert the need to establish independently dated environmental data sets as the foundation of regional studies as distinct from archaeological and historical interpretive processes. We conclude that Lake Bafa slowly transitioned to become an isolated lagoon sometime between the end of the second millennium B.C. and end of the first millennium B.C.; becoming a fully closed brackish lake during the second millennium A.D. This marks a major shift in our understanding of the nature of human occupation and activity here during the last four millennia but also in the way we date ancient lagoons and integrate historical and environmental data in general.

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Motivation: Understanding the host response to SARS-CoV-2 infection is crucial for deciding on the correct treatment of this epidemic disease. Although several recent studies reported the comparative transcriptome analyses of the three coronaviridae (CoV) members; namely SARS-CoV, MERS-CoV, and SARS-CoV-2, there is yet to exist a web-tool to compare increasing number of host transcriptome response datasets against the pre-processed CoV member datasets. Therefore, we developed a web application called CompCorona, which allows users to compare their own transcriptome data of infected host cells with our pre-built datasets of the three epidemic CoVs, as well as perform functional enrichment and principal component analyses (PCA). Results: Comparative analyses of the transcriptome profiles of the three CoVs revealed that numerous differentially regulated genes directly or indirectly related to several diseases (e.g., hypertension, male fertility, ALS, and epithelial dysfunction) are altered in response to CoV infections. Transcriptome similarities and differences between the host PBMC and lung tissue infected by SARS-CoV-2 are presented. Most of our findings are congruent with the clinical cases recorded in the literature. Hence, we anticipate that our results will significantly contribute to ongoing studies investigating the pre-and/or post-implications of SARS-CoV-2 infection. In addition, we implemented a user-friendly public website, CompCorona for biomedical researchers to compare users own CoV-infected host transcriptome data against the built-in CoV datasets and visualize their results via interactive PCA, UpSet and Pathway plots. Availability: CompCorona is freely available on the web at http://compcorona.mu.edu.tr Contact: tugbasuzek@mu.edu.tr