Notes

Yeasts from the attine ant-fungus mutualism: Selection, useful functions, along with putative biotechnological software.
We found population-specific as well as shared signals of selection, with folate metabolism and the related ultraviolet response and skin pigmentation standing out as a shared pathway, perhaps as a response to the high levels of ultraviolet irradiation, and in addition strong signals in genes such as IFNA, MRC1, immunoglobulins and T-cell receptors which contribute to defend against pathogens.

Signals of positive selection were detected in Ethiopian populations revealing novel adaptations in East Africa, and abundant targets for functional follow-up.
Signals of positive selection were detected in Ethiopian populations revealing novel adaptations in East Africa, and abundant targets for functional follow-up.
In population ecology, the concept of reproductive potential denotes the most vital indicator of chances to produce and sustain a healthy descendant until his/her reproductive maturity under the best conditions. This concept links quality of life and longevity of an individual with disease susceptibilities encoded by his/her genome. Female reproductive potential has been investigated deeply, widely, and comprehensively in the past, but the male one has not received an equal amount of attention. Therefore, here we focused on the human Y chromosome and found candidate single-nucleotide polymorphism (SNP) markers of male reproductive potential.

Examining in silico (i.e., using our earlier created Web-service SNP_TATA_Z-tester) all 1206 unannotated SNPs within 70 bp proximal promoters of all 63 Y-linked genes, we found 261 possible male-reproductive-potential SNP markers that can significantly alter the binding affinity of TATA-binding protein (TBP) for these promoters. Among them, there are candidate SNP mar5) as if male self-domestication could have happened, with its experimentally known disruptive natural selection. Because there is still not enough scientific evidence that this could have happened, we discuss the human diseases associated with candidate SNP markers of male reproductive potential that may correspond to domestication-related disorders in pets.

Overall, our findings seem to support a self-domestication syndrome with disruptive natural selection by male reproductive potential preventing Y-linked underexpression of a protein.
Overall, our findings seem to support a self-domestication syndrome with disruptive natural selection by male reproductive potential preventing Y-linked underexpression of a protein.
Fusarium oxysporum f. sp. niveum (FON) causes Fusarium wilt in watermelon. Several disease-resistant watermelon varieties have been developed to combat Fusarium wilt. However, the key metabolites that mount defense responses in these watermelon varieties are unknown. Herein, we analyzed hormones, melatonin, phenolic acids, and amino acid profiles in the leaf tissue of FON zero (0)-resistant (PI-296341, Calhoun Grey, and Charleston Grey) and -susceptible (Sugar Baby) watermelon varieties before and after infection.

We found that jasmonic acid-isoleucine (JA-Ile) and methyl jasmonate (MeJA) were selectively accumulated in one or more studied resistant varieties upon infection. However, indole-3-acetic acid (IAA) was only observed in the FON 0 inoculated plants of all varieties on the 16th day of post-inoculation. The melatonin content of PI-296341 decreased upon infection. see more Conversely, melatonin was only detected in the FON 0 inoculated plants of Sugar Baby and Charleston Grey varieties. On the 16th day of post-inoculation, the lysine content in resistant varieties was significantly reduced, whereas it was found to be elevated in the susceptible variety.

Taken together, Me-JA, JA-Ile, melatonin, and lysine may have crucial roles in developing defense responses against the FON 0 pathogen, and IAA can be a biomarker of FON 0 infection in watermelon plants.
Taken together, Me-JA, JA-Ile, melatonin, and lysine may have crucial roles in developing defense responses against the FON 0 pathogen, and IAA can be a biomarker of FON 0 infection in watermelon plants.
Elucidating the candidate genes and key metabolites responsible for pulp and peel coloration is essential for breeding pitaya fruit with new and improved appeal and high nutritional value. Here, we used transcriptome (RNA-Seq) and metabolome analysis (UPLC-MS/MS) to identify structural and regulatory genes and key metabolites associated with peel and pulp colors in three pitaya fruit types belonging to two different Hylocereus species.

Our combined transcriptome and metabolome analyses suggest that the main strategy for obtaining red color is to increase tyrosine content for downstream steps in the betalain pathway. The upregulation of CYP76ADs is proposed as the color-breaking step leading to red or colorless pulp under the regulation by WRKY44 transcription factor. Supported by the differential accumulation of anthocyanin metabolites in red pulped pitaya fruit, our results showed the regulation of anthocyanin biosynthesis pathway in addition to betalain biosynthesis. However, no color-breaking step for hese findings will greatly complement the existing knowledge on the biosynthesis of natural pigments for their applications in food and health industry.
Together, our results propose several candidate genes and metabolites controlling a single horticultural attribute i.e. color formation for further functional characterization. link2 This study presents useful genomic resources and information for breeding pitaya fruit with commercially attractive peel and pulp colors. These findings will greatly complement the existing knowledge on the biosynthesis of natural pigments for their applications in food and health industry.
Microorganisms are not only indispensable to ecosystem functioning, they are also keystones for emerging technologies. In the last 15 years, the number of studies on environmental microbial communities has increased exponentially due to advances in sequencing technologies, but the large amount of data generated remains difficult to analyze and interpret. Recently, metabarcoding analysis has shifted from clustering reads using Operational Taxonomical Units (OTUs) to Amplicon Sequence Variants (ASVs). Differences between these methods can seriously affect the biological interpretation of metabarcoding data, especially in ecosystems with high microbial diversity, as the methods are benchmarked based on low diversity datasets.

In this work we have thoroughly examined the differences in community diversity, structure, and complexity between the OTU and ASV methods. We have examined culture-based mock and simulated datasets as well as soil- and plant-associated bacterial and fungal environmental communities. Foepth sequencing of the samples, choice of the most appropriate filtering strategy for the specific research goal, and use of family level for data clustering.
Investigation of metabarcoding data should be done with care. Correct biological interpretation depends on several factors, including in-depth sequencing of the samples, choice of the most appropriate filtering strategy for the specific research goal, and use of family level for data clustering.
Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes.

Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http//github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker.

We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.
We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.
The catheter-related bladder discomfort (CRBD) of male patients is a common clinical problem, albeit lacking effective solutions. The present study aimed to investigate whether intravesical dexmedetomidine instillation alleviates the postoperative urinary discomfort in male patients with catheter under general anesthesia.

This single-blinded, prospective, randomized study included a total of 167 male patients American Society of Anesthesiologists (ASA) physical status I-II scheduled for surgery under general anesthesia were allocated to two groups 84 in the dexmedetomidine group and 83 in the control group. Dexmedetomidine group patients received intravesical instillation of the drug 0.5 μg/kg and normal saline 20 mL, while the control group received intravesical instillation of 20 mL normal saline. The catheter was clamped for 30 min after intravesical instillation for all patients. CRBD scores and urethra pain numerical rating scale (NRS) scores were measured at admittance to post-anesthesia care unit (1800016429 ), date of registration 1st June 2018.
Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.

We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). link3 This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.

NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.
NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.
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