CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species.

Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture.

Using biological constraints to improve prediction in precision oncology.

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential.

Donor-derived acute myeloid leukemia in solid organ transplantation.

We report the transmission of acute myeloid leukemia (AML) undetected at donation from a deceased organ donor to two kidneys and one liver recipients. We reviewed the medical records, and performed molecular analyses and whole exome sequencing (WES) to ascertain AML donor origin and its molecular evolution. The liver recipient was diagnosed 11 months after transplantation and died from complications 2 months later. The two kidney recipients (R1 and R2) were diagnosed 19 and 20 months after transplantation and both received treatment for leukemia. R1 died of complications 11 months after diagnosis, while R2 went into complete remission for 44 months, before relapsing. R2 died 10 months later of complications from allogenic bone marrow transplantation. Microsatellite analysis demonstrated donor chimerism in circulating cells from both kidney recipients. Targeted molecular analyses and medical records revealed NPM1 mutation present in the donor and recipients, while FLT3 was mutated only in R1. These findings were confirmed by WES, which revealed additional founder and clonal mutations, and HLA genomic loss in R2. In conclusion, we report the first in-depth genomic analysis of AML transmission following solid organ transplantation, revealing distinct clonal evolution, and providing a potential molecular explanation for tumor escape.

RNA-sequencing highlights differential regulated pathways involved in cell cycle and inflammation in orbitofacial neurofibromas.

Although most commonly benign, neurofibromas (NFs) can have devastating functional and cosmetic effects in addition to the possibility of malignant transformation. Orbitofacial NFs, in particular, may cause progressive, disfiguring tumors of the lid, brow, temple, face, and orbit, and clinical evidence suggests that they may have increased local aggressiveness compared to NFs developing at other sites. The purpose of this study was to identify biological differences between orbitofacial NFs and those occurring at other anatomic sites. We performed RNA-sequencing in orbitofacial (n = 10) and non-orbitofacial (n = 9) NFs. Differential gene expression analysis demonstrated that a variety of gene sets including genes involved in cell proliferation, interferon, and immune-related pathways were enriched in orbitofacial NF. Comparisons with publicly available databases of various Schwann cell tumors and malignant peripheral nerve sheath tumor (MPNST) revealed a significant overlap of differentially expressed genes between orbitofacial versus non-orbitofacial NF and plexiform NF versus MPNST. In summary, we identified gene expression differences between orbitofacial NF and NFs occurring at other locations. Further investigation may be warranted, given that orbitofacial NF are notoriously difficult to treat and associated with disproportionate morbidity.

recount3: summaries and queries for large-scale RNA-seq expression and splicing.

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio .

Transcriptional landscape of PTEN loss in primary prostate cancer.

BACKGROUND: PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa. METHODS: Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas. RESULTS: The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression. CONCLUSION: We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

covid19census: U.S. and Italy COVID-19 metrics and other epidemiological data.

Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the 'covid19census' package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes. Database URL: https://github.com/c1au6i0/covid19census.

Influenza Vaccination and COVID-19 Mortality in the USA: An Ecological Study.

The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.

Influenza Vaccination and COVID19 Mortality in the USA.

COVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them. In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 together with population health data for the United States at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the number of deaths from COVID-19 as the outcome variable. We adjusted for a wide array of potential confounding variables using both county-level generalized propensity scores for influenza vaccination rates, as well as direct adjustment. Our results suggest that influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19. This finding is robust to using different analysis periods, different thresholds for inclusion of counties, and a variety of methodologies for confounding adjustment. In conclusion, our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality in the elderly population. The significant public health implications of this possibility point to an urgent need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level, to investigate both the epidemiology and any underlying biological mechanism.

Differential gene methylation and expression of HOX transcription factor family in orbitofacial neurofibroma.

Although most commonly benign, neurofibromas (NFs) can have devastating functional and cosmetic effects in addition to the possibility of malignant transformation. In orbitofacial neurofibromatosis type 1, NFs may cause progressive, disfiguring tumors of the lid, brow, temple, face and orbit. The purpose of this study was to identify biological differences between orbitofacial NFs and those occurring at other anatomic sites. We used Illumina Methylation EPIC BeadChip to study DNA methylation differences between orbitofacial NFs (N = 20) and NFs at other sites (N = 4). Global methylation differences were detected between the two groups and the top differentially methylated genes were part of the HOX (Homebox) family of transcription factors (HOXC8, HOXC4, HOXC6, HOXA6 and HOXD4), which were hypomethylated in orbitofacial NFs compared to the non-orbital NFs. Conversely, LTF (lactoferrin) was relatively hypermethylated in orbitofacial NF compared to non-orbitofacial NF. HOXC8 protein levels were higher in orbitofacial plexiform NFs (p = 0.04). We found no significant differences in the expression of HOXC4, HOXA6, or HOXD4 between the two groups. HOXC8 mRNA levels were also higher in orbitofacial NFs and HOXC8 overexpression in a non-neoplastic human Schwann cell line resulted in increased growth. In summary, we identified gene methylation and expression differences between orbitofacial NF and NFs occurring at other locations. Further investigation may be warranted, given that the HOX family of genes play an important role during development, are dysregulated in a variety of cancers, and may provide novel insights into therapeutic approaches.

Correction to: Differential gene methylation and expression of HOX transcription factor family in orbitofacial neurofibroma.

An amendment to this paper has been published and can be accessed via the original article.

Recounting the FANTOM CAGE-Associated Transcriptome.

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Indole-3-acetic acid production via the indole-3-pyruvate pathway by plant growth promoter Rhizobium tropici CIAT 899 is strongly inhibited by ammonium.

Like many rhizobia, Rhizobium tropici produces indole-3-acetic acid (IAA), an important signal molecule required for root hair infection in rhizobia-legume symbioses. However, the IAA biosynthesis pathway and its regulation by R. tropici are still poorly understood. In this study, IAA synthesis and the effects of mineral N in IAA production by R. tropici CIAT 899 were verified by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). Furthermore, expression of genes related to IAA biosynthesis and metabolism were evaluated by RT-qPCR. Results indicated that IAA production by CIAT 899 was 12 times lower in the presence of [Formula: see text] . Moreover, it was found that indole-3-pyruvate (IPyA) is the major IAA biosynthesis intermediate. Genes y4wE, lao and iorA were identified by analysis of R. tropici genome in silico and were upregulated by tryptophan, indicating a possible role of these genes in IAA biosynthesis by CIAT 899. In conclusion, we show that IPyA is the major pathway for IAA biosynthesis in CIAT 899 and that its production is strongly inhibited by [Formula: see text] . Although present results arose from in vitro experiments, they provide new insight into the role of nitrogen in early events related to legume nodulation.