Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements

<p>Abstract</p> <p>Background</p> <p><it>Arabidopsis thaliana </it>is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress.</p> <p>Results</p> <p>Using in house and publicly available data, we assembled a large set of gene expression measurements for <it>A. thaliana</it>. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC₅₀and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl.</p> <p>Conclusion</p> <p>Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions – in this case, predictions of genes involved in stress response in plants – and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in <it>A. thaliana </it>that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.</p

Further evidence for the involvement of EFL1 in a Shwachman-Diamond-like syndrome and expansion of the phenotypic features.

Recent evidence has implicated EFL1 in a phenotype overlapping Shwachman-Diamond syndrome (SDS), with the functional interplay between EFL1 and the previously known causative gene SBDS accounting for the similarity in clinical features. Relatively little is known about the phenotypes associated with pathogenic variants in the EFL1 gene, but the initial indication was that phenotypes may be more severe, when compared with SDS. We report a pediatric patient who presented with a metaphyseal dysplasia and was found to have biallelic variants in EFL1 on reanalysis of trio whole-exome sequencing data. The variant had not been initially reported because of the research laboratory's focus on de novo variants. Subsequent phenotyping revealed variability in her manifestations. Although her metaphyseal abnormalities were more severe than in the original reported cohort with EFL1 variants, the bone marrow abnormalities were generally mild, and there was equivocal evidence for pancreatic insufficiency. Despite the limited number of reported patients, variants in EFL1 appear to cause a broader spectrum of symptoms that overlap with those seen in SDS. Our report adds to the evidence of EFL1 being associated with an SDS-like phenotype and provides information adding to our understanding of the phenotypic variability of this disorder. Our report also highlights the value of exome data reanalysis when a diagnosis is not initially apparent

Could perturbed fetal development of the ovary contribute to the development of polycystic ovary syndrome in later life?

Polycystic ovary syndrome (PCOS) affects around 10% of young women, with adverse consequences on fertility and cardiometabolic outcomes. PCOS appears to result from a genetic predisposition interacting with developmental events during fetal or perinatal life. We hypothesised that PCOS candidate genes might be expressed in the fetal ovary when the stroma develops; mechanistically linking the genetics, fetal origins and adult ovarian phenotype of PCOS. In bovine fetal ovaries (n = 37) of 18 PCOS candidate genes only SUMO1P1 was not expressed. Three patterns of expression were observed: early gestation (FBN3, GATA4, HMGA2, TOX3, DENND1A, LHCGR and FSHB), late gestation (INSR, FSHR, and LHCGR) and throughout gestation (THADA, ERBB4, RAD50, C8H9orf3, YAP1, RAB5B, SUOX and KRR1). A splice variant of FSHB exon 3 was also detected early in the bovine ovaries, but exon 2 was not detected. Three other genes, likely to be related to the PCOS aetiology (AMH, AR and TGFB1I1), were also expressed late in gestation. Significantly within each of the three gene groups, the mRNA levels of many genes were highly correlated with each other, despite, in some instances, being expressed in different cell types. TGFβ is a well-known stimulator of stromal cell replication and collagen synthesis and TGFβ treatment of cultured fetal ovarian stromal cells inhibited the expression of INSR, AR, C8H9orf3 and RAD50 and stimulated the expression of TGFB1I1. In human ovaries (n = 15, < 150 days gestation) many of the same genes as in bovine (FBN3, GATA4, HMGA2, FSHR, DENND1A and LHCGR but not TOX3 or FSHB) were expressed and correlated with each other. With so many relationships between PCOS candidate genes during development of the fetal ovary, including TGFβ and androgen signalling, we suggest that future studies should determine if perturbations of these genes in the fetal ovary can lead to PCOS in later life

Regulation of fibrillins and modulators of TGFβ in fetal bovine and human ovaries

Fibrillins 1–3 are stromal extracellular matrix proteins that play important roles in regulating TGFβ activity, which stimulates fibroblasts to proliferate and synthesize collagen. In the developing ovary, the action of stroma is initially necessary for the formation of ovigerous cords and subsequently for the formation of follicles and the surface epithelium of the ovary. FBN3 is highly expressed only in early ovarian development and then it declines. In contrast, FBN1 and 2 are upregulated in later ovarian development. We examined the expression of FBN1–3 in bovine and human fetal ovaries. We used cell dispersion and monolayer culture, cell passaging and tissue culture. Cells were treated with growth factors, hormones or inhibitors to assess the regulation of expression of FBN1–3. When bovine fetal ovarian tissue was cultured, FBN3 expression declined significantly. Treatment with TGFβ-1 increased FBN1 and FBN2 expression in bovine fibroblasts, but did not affect FBN3 expression. Additionally, in cultures of human fetal ovarian fibroblasts (9–17 weeks gestational age), the expression of FBN1 and FBN2 increased with passage, whereas FBN3 dramatically decreased. Treatment with activin A and a TGFβ family signaling inhibitor, SB431542, differentially regulated the expression of a range of modulators of TGFβ signaling and of other growth factors in cultured human fetal ovarian fibroblasts suggesting that TGFβ signaling is differentially involved in the regulation of ovarian fibroblasts. Additionally, since the changes in FBN1–3 expression that occur in vitro are those that occur with increasing gestational age in vivo, we suggest that the fetal ovarian fibroblasts mature in vitro.Nicole A Bastian, Rosemary A Bayne, Katja Hummitzsch, Nicholas Hatzirodos, Wendy M Bonner, Monica D Hartanti, Helen F Irving-Rodgers, Richard A Anderson and Raymond J Rodger

Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license

Lack of functional alpha-lactalbumin prevents involution in Cape fur seals and identifies the protein as an apoptotic milk factor in mammary gland involution

The mammary gland undergoes a sophisticated programme of developmental changes during pregnancy/lactation. However, little is known about processes involving initiation of apoptosis at involution following weaning. We used fur seals as models to study the molecular process of involution as these animals display a unique mammary gland phenotype. Fur seals have long lactation periods whereby mothers cycle between secreting copious quantities of milk for 2 to 3 days suckling pups on land, with trips to sea alone to forage for up to 23 days during which time mammary glands remain active without initiating apoptosis/involution.<br /

Chronic kidney disease in public renal practices in Queensland, Australia, 2011–2018

Aim: To describe adults with (non-dialysis) chronic kidney disease (CKD) in nine public renal practice sites in the Australian state of Queensland. Methods: 7,060 persons were recruited to a CKD Registry in May 2011 and until start of kidney replacement therapy (KRT), death without KRT or June 2018, for a median period of 3.4 years. Results: The cohort comprised 7,060 persons, 52% males, with a median age of 68 yr; 85% had CKD stages 3A to 5, 45.4% were diabetic, 24.6% had diabetic nephropathy, and 51.7% were obese. Younger persons mostly had glomerulonephritis or genetic renal disease, while older persons mostly had diabetic nephropathy, renovascular disease and multiple diagnoses. Proportions of specific renal diagnoses varied >2-fold across sites. Over the first year, eGFR fell in 24% but was stable or improved in 76%. Over follow up, 10% started KRT, at a median age of 62 yr, most with CKD stages 4 and 5 at consent, while 18.8% died without KRT, at a median age of 80 yr. Indigenous people were younger at consent and more often had diabetes and diabetic kidney disease and had higher incidence rates of KRT. Conclusion: The spectrum of characteristics in CKD patients in renal practices is much broader than represented by the minority who ultimately start KRT. Variation in CKD by causes, age, site and Indigenous status, the prevalence of obesity, relative stability of kidney function in many persons over the short term, and differences between those who KRT and die without KRT are all important to explore

Absorbing customer knowledge: how customer involvement enables service design success

Customers are a knowledge resource outside of the firm that can be utilized for new service success by involving them in the design process. However, existing research on the impact of customer involvement (CI) is inconclusive. Knowledge about customers’ needs and on how best to serve these needs (articulated in the service concept) is best obtained from customers themselves. However, codesign runs the risk of losing control of the service concept. This research argues that of the processes of external knowledge, acquisition (via CI), customer knowledge assimilation, and concept transformation form a capability that enables the firm to exploit customer knowledge in the form of a successful new service. Data from a survey of 126 new service projects show that the impact of CI on new service success is fully mediated by customer knowledge assimilation (the deep understanding of customers’ latent needs) and concept transformation (the modification of the service concept due to customer insights). However, its impact is more nuanced. CI exhibits an “∩”-shaped relationship with transformation, indicating there is a limit to the beneficial effect of CI. Its relationship with assimilation is “U” shaped, suggesting a problem with cognitive inertia where initial learnings are ignored. Customer knowledge assimilation directly impacts success, while concept transformation only helps success in the presence of resource slack. An evolving new service design is only beneficial if the firm has the flexibility to adapt to change