Native solitary bee reproductive success depends on early season precipitation and host plant richness.

Spring-emerging bees depend upon the synchronized bloom times of angiosperms that provide pollen and nectar for offspring. The emergence of such bees and bloom times are linked to weather but can be phenologically mismatched, which could limit bee developmental success. However, it remains unclear how such phenologically asynchrony could affect spring-emerging pollinators, and especially for those that forage over a relatively short time period. We examined the relationship between weather and host plant selection on the native spring-foraging solitary bee, Osmia lignaria, across 3 years at urban and rural sites in and around Seattle, Washington, USA. We used community science weather data to test the effects of precipitation, wind, and temperature on O. lignaria oviposition and developmental success. We also collected pollen data over two distinct foraging periods, early and late spring, and used Next-Generation Sequencing to identify plant genera from pollen. Among the weather variables, precipitation during the early foraging period adversely affected larval developmental success and adult bee emergence success, but not oviposition. Using DNA metabarcoding, we observed that increases in the number of plant genera in pollen increased adult emergence in both foraging periods, but not oviposition or larval development. We also observed that foraging bees consistently visited certain genera during each foraging period, especially Acer, Salix, and Rubus. However, pollen collected by O. lignaria over different years varied in the number of total genera visited, highlighting the importance of multi-year studies to ascertain bee foraging preferences and its link to developmental success.

Bacterial and Fungal Symbionts in Pollen Provisions of a Native Solitary Bee in Urban and Rural Environments.

Among insects, symbionts such as bacteria and fungi can be linked to their physiology and immature development, and in some cases are part of a defense system against parasites and diseases. Current bacterial and fungal symbiont associations in solitary bees are understudied, especially in the Pacific Northwest region of the USA. We collected pollen provisions from the native spring-foraging solitary bee, Osmia lignaria Say, across two distinct foraging periods over 2 years at 22 sites along an urban-to-rural gradient in western Washington. We then used next-generation sequencing to identify bacterial and fungi within pollen provisions and assessed the effect of their richness and diversity on O. lignaria larval development success and adult emergence. We detected a significantly positive relationship between bacterial diversity in pollen with O. lignaria larval developmental success, and higher bacterial richness and diversity during the later foraging period. Fungal generic richness and diversity decreased with increasing plant richness. Although neither was associated with O. lignaria developmental success, we did detect Ascosphaera spp. known to be pathogenic to O. lignaria and other bee species. Neither bacterial or fungal richness or diversity was affected by site type when classified as urban or rural. This study provides new information on bacterial and fungal symbionts present in pollen provisions of a native solitary bee when foraging across urban and rural areas of the Pacific Northwest.

PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions.

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Fecal metatranscriptomics and glycomics suggest that bovine milk oligosaccharides are fully utilized by healthy adults.

Human milk oligosaccharides play a vital role in the development of the gut microbiome in the human infant. Although oligosaccharides derived from bovine milk (BMO) differ in content and profile with those derived from human milk (HMO), several oligosaccharide structures are shared between the species. BMO are commercial alternatives to HMO, but their fate in the digestive tract of healthy adult consumers is unknown. Healthy human subjects consumed two BMO doses over 11-day periods each and provided fecal samples. Metatranscriptomics of fecal samples were conducted to determine microbial and host gene expression in response to the supplement. Fecal samples were also analyzed by mass spectrometry to determine levels of undigested BMO. No changes were observed in microbial gene expression across all participants. Repeated sampling enabled subject-specific analyses: four of six participants had minor, yet statistically significant, changes in microbial gene expression. No significant change was observed in the gene expression of host cells exfoliated in stool. Levels of BMO excreted in feces after supplementation were not significantly different from baseline and were not correlated with dosage or expressed microbial enzyme levels. Collectively, these data suggest that BMO are fully fermented in the human gastrointestinal tract upstream of the distal colon. Additionally, the unaltered host transcriptome provides further evidence for the safety of BMO as a dietary supplement or food ingredient. Further research is needed to investigate potential health benefits of this completely fermentable prebiotic that naturally occurs in cow's milk.

BigTop: a three-dimensional virtual reality tool for GWAS visualization.

BACKGROUND: Genome-wide association studies (GWAS) are typically visualized using a two-dimensional Manhattan plot, displaying chromosomal location of SNPs along the x-axis and the negative log-10 of their p-value on the y-axis. This traditional plot provides a broad overview of the results, but offers little opportunity for interaction or expansion of specific regions, and is unable to show additional dimensions of the dataset. RESULTS: We created BigTop, a visualization framework in virtual reality (VR), designed to render a Manhattan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room. BigTop uses the z-axis to display minor allele frequency of each SNP, allowing for the identification of allelic variants of genes. BigTop also offers additional interactivity, allowing users to select any individual SNP and receive expanded information, including SNP name, exact values, and gene location, if applicable. BigTop is built in JavaScript using the React and A-Frame frameworks, and can be rendered using commercially available VR headsets or in a two-dimensional web browser such as Google Chrome. Data is read into BigTop in JSON format, and can be provided as either JSON or a tab-separated text file. CONCLUSIONS: Using additional dimensions and interactivity options offered through VR, we provide a new, interactive, three-dimensional representation of the traditional Manhattan plot for displaying and exploring GWAS data.

Fecal metatranscriptomics of macaques with idiopathic chronic diarrhea reveals altered mucin degradation and fucose utilization.

BACKGROUND: Idiopathic chronic diarrhea (ICD) is a common cause of morbidity and mortality among juvenile rhesus macaques. Characterized by chronic inflammation of the colon and repeated bouts of diarrhea, ICD is largely unresponsive to medical interventions, including corticosteroid, antiparasitic, and antibiotic treatments. Although ICD is accompanied by large disruptions in the composition of the commensal gut microbiome, no single pathogen has been concretely identified as responsible for the onset and continuation of the disease. RESULTS: Fecal samples were collected from 12 ICD-diagnosed macaques and 12 age- and sex-matched controls. RNA was extracted for metatranscriptomic analysis of organisms and functional annotations associated with the gut microbiome. Bacterial, fungal, archaeal, protozoan, and macaque (host) transcripts were simultaneously assessed. ICD-afflicted animals were characterized by increased expression of host-derived genes involved in inflammation and increased transcripts from bacterial pathogens such as Campylobacter and Helicobacter and the protozoan Trichomonas. Transcripts associated with known mucin-degrading organisms and mucin-degrading enzymes were elevated in the fecal microbiomes of ICD-afflicted animals. Assessment of colon sections using immunohistochemistry and of the host transcriptome suggests differential fucosylation of mucins between control and ICD-afflicted animals. Interrogation of the metatranscriptome for fucose utilization genes reveals possible mechanisms by which opportunists persist in ICD. Bacteroides sp. potentially cross-fed fucose to Haemophilus whereas Campylobacter expressed a mucosa-associated transcriptome with increased expression of adherence genes. CONCLUSIONS: The simultaneous profiling of bacterial, fungal, archaeal, protozoan, and macaque transcripts from stool samples reveals that ICD of rhesus macaques is associated with increased gene expression by pathogens, increased mucin degradation, and altered fucose utilization. The data suggest that the ICD-afflicted host produces fucosylated mucins that are leveraged by potentially pathogenic microbes as a carbon source or as adhesion sites.

SAMSA2: a standalone metatranscriptome analysis pipeline.

BACKGROUND: Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. RESULTS: SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. CONCLUSIONS: SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.

SAMSA: a comprehensive metatranscriptome analysis pipeline.

BACKGROUND: Although metatranscriptomics-the study of diverse microbial population activity based on RNA-seq data-is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, designed specifically for metatranscriptome dataset analysis, which runs in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome transcription activity levels by organism or transcript function, and is fully open source. We used this new tool to evaluate best practices for sequencing stool metatranscriptomes. RESULTS: Working with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes. Using pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100 bp) or joined paired-end reads. Each sample needs 40-50 million raw sequences, which can be expected to yield the 5-10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that overall organism transcriptional activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism transcriptional activity and specific cellular functions. CONCLUSIONS: By making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100 bp paired end format with a minimum of 40 million reads per sample.