Extracellular vesicles (EVs) are of growing interest due to their potential diagnostic, disease surveillance, and therapeutic applications. While several studies have evaluated EV isolation methods in various biofluids, there are few if any data on these techniques when applied to stool. The latter is an ideal biospecimen for studying EVs and colorectal cancer (CRC) because the release of tumour markers by luminal exfoliation into stool occurs earlier than vascular invasion. Since EV release is a conserved mechanism, bacteria in stool contribute to the overall EV population. In this study, we assessed five EV separation methods (ultracentrifugation [UC], precipitation [EQ‐O, EQ‐TC], size exclusion chromatography [SEC], and ultrafiltration [UF]) for total recovery, reproducibility, purity, RNA composition, and protein expression in stool supernatant. CD63, TSG101, and ompA proteins were present in EV fractions from all methods except UC. Human (18s) and bacterial (16s) rRNA was detected in stool EV preparations. Enzymatic treatment prior to extraction is necessary to avoid non‐vesicular RNA contamination. Ultrafiltration had the highest recovery, RNA, and protein yield. After assessing purity further, SEC was the isolation method of choice. These findings serve as the groundwork for future studies that use high throughput omics technologies to investigate the potential of stool‐derived EVs as a source for novel biomarkers for early CRC detection.

Regular screening for colorectal cancer (CRC) is critical for early detection and long-term survival. Despite the current screening options available and advancements in therapies there will be around 53,000 CRC related deaths this year. There is great interest in non-invasive alternatives such as plasma cell-free RNA (cfRNA) for diagnostic, prognostic, and predictive applications. In the current study, our aim was to identify and validate potential cfRNA candidates to improve early CRC diagnosis. In phase 1 (n = 49; 25 controls, 24 cancers), discovery total RNA sequencing was performed. Select exons underwent validation in phase 2 (n = 73; 35 controls, 29 cancers, 9 adenomas) using targeted capture sequencing (n = 10,371 probes). In phase 3 (n = 57; 30 controls, 27 cancers), RT-qPCR was performed on previously identified candidates (n = 99). There were 895 exons that were differentially expressed (325 upregulated, 570 downregulated) among cancers versus controls. In phases 2 and 3, fewer markers were validated than expected in independent sets of patients, most of which were from previously published literature (FGA, FGB, GPR107, CDH3, and RP23AP7). In summary, we optimized laboratory processes and data analysis strategies which can serve as methodological framework for future plasma RNA studies beyond just the scope of CRC detection. Additionally, further exploration is needed in order to determine if the few cfRNA candidates identified in this study have clinical utility for early CRC detection. Over time, advancements in technologies, data analysis, and RNA preservation methods at time of collection may improve the biological and technical reproducibility of cfRNA biomarkers and enhance the feasibility of RNA-based liquid biopsies.

Colorectal cancer (CRC) is the 2^nd most fatal cancer in the United States, but when detected early it is highly curable. Stool-derived extracellular vesicles (EVs) are a novel biomarker source that could augment the sensitivity for detection of CRC precursors. However, standardization of isolation methods for stool-derived EVs remains underexplored. We previously reported that size-exclusion chromatography (SEC) followed by ultrafiltration (UF-100kDa) was suitable for human stool supernatant EV isolation. In this study, we first assess alternative EV concentration methods (ultrafiltration [UF]; 10 kDa, 30 kDa, 50 kDa, 100 kDa and speed vacuum [SV]). Second, we investigate the host/bacterial EV proteomes by mass spectrometry. We report no difference in recovery, RNA and soluble protein contamination among concentration methods. Proteomic analysis reveals a diverse bacterial proteome, while human-derived proteins are more abundant. Specifically, pancreatic enzymes are among the most abundant proteins, further exploration revealed that zymogen granules are likely co-isolated in stool EV preparations. To enable discovery of EV-based molecular signatures of CRC precursors with high sensitivity, immunocapture strategies will likely be needed. Notably, we identified 10 surface proteins that may serve as candidates for the purification of colon-derived EVs. This work serves as framework for the future discovery and validation of EV-based biomarkers for CRC.

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies (GWAS) have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a GWAS (Pitchers et al. 2019) in three genes: dachsous (ds) , extra-macrochaete (emc) and neuralized (neur), using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the ds shape change vector, we observe genetic differentiation of variants in ds and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the emc shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of ds , suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix ( G ), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

This study evaluated the implementation of reproductive technologies and their effects on pregnancy rates (PRs) and calving distribution. Producers (n = 11) were enrolled in Level 1 or 2 and subsequently transitioned to Level 2 or 3. Level 1: females were exposed to natural service (NS) only versus estrous synchronization (7d-CIDR) before NS (SynNS). Level 2: SynNS versus fixed-time artificial insemination (SynAI; 7d-CO-Synch + CIDR) with conventional semen. Level 3: conventional versus sex-skewed semen (FTAI-con and FTAI-sexed, respectively). Artificial insemination occurred 60–66 h post CIDR removal (SynAI; FTAI-con; FTAI-sexed) and bulls were introduced on d 0 (NS and SynNS) or 10–14 d post artificial insemination (SynAI; FTAI-con; FTAI-sexed). Breeding season PRs did not differ between treatments (p > 0.50). In Level 2, SynNS had greater 21 d PRs compared to SynAI (p < 0.01). For Level 3, FTAI-con had greater 21 d PRs compared to FTAI-sexed (p < 0.01). In Level 1, the proportion of cows that calved by day 14 was greater for SynNS compared to NS (p < 0.01). In Level 2, SynAI had a greater proportion calved by day 7 (p = 0.01); however, SynNS had a greater proportion calved by day 21 and 42 (p < 0.01). In Level 3, FTAI-con had a greater proportion calved by day 14 and 21 (p < 0.01) compared to FTAI-sexed. In conclusion, reproductive technologies altered the calving distribution with more calves born earlier.