Circadian genes are expressed in some peripheral tissues, but the expression status of the female reproductive tract and the conceptus over the preimplantation period is unknown. Oocytes, uterine, oviducal tissues and preimplantation conceptuses from days 1-4 of mouse pregnancy were analysed for transcript presence by reverse transcription polymerase chain reaction. Transcripts encoded by the seven known mammalian canonical circadian genes (Per1-3, Cry1-2, Bmal1 and Clock), plus the mammalian genetic homologue of the Drosophila canonical gene Timeless, were detected in the uteri and oviducts taken from mice on days 1-4 of pregnancy and in unfertilized oocytes. After fertilization, transcripts for Per1, Cry1, Bmal1, Clock and Tim have been detected unambiguously. Transcript levels for each of these five genes fall at the two-cell stage, but are restored rapidly for Per1, Cry1 and Bmal1, presumptively by zygotic gene expression. In contrast, transcripts for Clock and Tim recover more slowly. It is concluded that circadian genes are expressed, and may therefore have a role, during the early development of the mammal.

Slow-growing broilers offer differentiation in the chicken meat market for consumers who have distinct preferences based on perceived higher welfare indices and willingness to pay a higher price for the product. Although breeding for slow-growing broilers is relatively advanced in Europe and the United States, it is limited in Australia. Crossbreeding is one of the approaches taken to developing slow-growing broiler strains. Thus, the aim of this study was to compare performance, immune response, leg health, carcass characteristics, and meat quality of a novel crossbred slow-growing broiler breed ( SGB ) with the conventional, fast-growing Cobb 500 broiler ( CB ) to assess their suitability as an alternative for chicken meat production in Australia. A total of 236 one-day-old broiler chicks (116 SGB and 120 fast-growing CB) were reared on standard commercial diet in an intensive production system. Birds and feed were weighed on a weekly basis and feed intake and feed conversion ratio calculated. At 21 d of age, a 2% suspension of sheep red blood cells was injected subcutaneously into 8 broilers of each breed to compare their antibody response. Birds from both breeds were grown to a final live weight of 2.0–2.2 kg, before a latency-to-lie ( LTL ) test, carcass analysis and apparent metabolizable energy ( AME ) assay were performed. The SGB reached the target weight at 55 d of age compared with 32 d in CB. However, SGB stood for longer during LTL, had higher thigh, drumstick, and wing yields (as a percentage of carcass weight) as well as darker and redder meat in comparison with the CB. The CB had better feed conversion efficiency, higher antibody ( IgM ) production, higher AME, heavier breast yield, and lower meat drip loss than the SGB. Although fast-growing CB outperformed the SGB for traditional performance parameters, the crossbred in this study was comparable with other slow-growing broiler breeds and strains across different countries and is thus a suitable candidate for a slow-growing alternative in Australia.

e12563 Background: Large-scale genetic sequencing of breast cancer has enabled modern approaches to precision medicine, with the discovery of a handful of variants now known to be associated with breast cancer. However, it is critical to identify additional gene variants in breast cancer that are associated with clinically relevant features of cancers, such as staging and molecular subtype. Methods: We took an unsupervised machine learning approach that clustered the somatic whole exome sequences (WES) of 1533 breast cancers. We performed k-modes clustering on the binarized mutational state of the top 250 most frequently mutated genes. Following two rounds of clustering, 11 distinct “barcodes” for each genetic cluster’s mutation profile became apparent. We systematically tested each genetically defined cluster for associations with molecular subtypes of breast cancer. We performed non-parametric significance testing by randomly permuting cluster assignments to generate an empirical null distribution of the effect of clustering on proportions of the clinical factor of interest. Results: As an example of our set of results, two clusters showed roughly three-fold enrichment of triple-negative breast cancer (TNBC) patients, compared to the whole-group proportion. We calculated SHAP values to provide model explainability and identify the genes that placed a cancer into a particular cluster; TP53 and TTN were the strongest drivers in relation to TNBC. Genetic clusters were also found to associate with T-, N-, and M-stages. Conclusions: Our approach, which uses unsupervised machine learning on WES to create genetic groups of cancers, considers the joint mutational state – present or absent – of multiple genes for their clinical relevance. This reveals many additional variants that may have been previously overlooked or of uncertain significance.