Biology subjects

Feldmann, M.

Publications and source records attributed to Feldmann, M..

9 recordsLinked to original sources

bioRxiv2026

Breedbase 2.0 - expanding the functionality for traditional breeding and transgenics

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

bioinformatics↗

bioRxiv2025

End-to-end genomic prediction: direct prediction of images and text from genome-wide molecular markers

BackgroundMethods to predict the heritable component of phenotypes from genetic markers, collectively known as genomic prediction, have been widely applied in plant and animal breeding and in human genetics. Currently, genomic prediction is limited to numeric phenotypes. In some cases, though, plant and animal phenotypes are better understood through images and text rather than numbers. The current best practice for incorporating images and text in genomic prediction is to first extract scalar numeric phenotypes from images and text, and then to perform genomic prediction on the numeric phenotypes. While this approach is effective for some traits, it involves discarding most of the information in the image or text, including potentially useful information. Additionally, numeric phenotypes derived from images and text may not be as interpretable as the images and text themselves. ApproachWe present a novel approach for predicting images and text from SNP markers, which we refer to as end-to-end genomic prediction, and validate this approach using genotypes, text, and image phenotypes derived from a strawberry (Fragaria x ananassa) diversity panel. Our approach combines nonlinear latent space encoding with linear genomic prediction to generate accurate breeding values for a high-dimensional phenotype, for example, images or text. ResultsFor both genome-to-image and genome-to-text prediction, we found that predicting images and text and then extracting numeric traits from them was in some cases as accurate as directly predicting extracted numeric phenotypes, demonstrating for the first time that genome-to-image prediction accuracy can be comparable to conventional genomic prediction accuracy. ConclusionsBased on our proof-of-concept using the same core end-to-end method in both images and text, we believe end-to-end genomic prediction could be of use in a wide range of visual and multidimensional phenotypes in plants and animals, although further work to improve the accuracy of the embedding and genomic prediction steps is needed.

genomics↗

bioRxiv2022

Shift of lung macrophage composition is associated with COVID-19 disease severity and recovery

Though it has been 2 years since the start of the Coronavirus Disease 19 (COVID-19) pandemic, COVID-19 continues to be a worldwide health crisis. Despite the development of preventive vaccines, very little progress has been made to identify curative therapies to treat COVID-19 and other inflammatory diseases which remain a major unmet need in medicine. Our study sought to identify drivers of disease severity and death to develop tailored immunotherapy strategies to halt disease progression. Here we assembled the Mount Sinai COVID-19 Biobank which was comprised of ~600 hospitalized patients followed longitudinally during the peak of the pandemic. Moderate disease and survival were associated with a stronger antigen (Ag) presentation and effector T cell signature, while severe disease and death were associated with an altered Ag presentation signature, increased numbers of circulating inflammatory, immature myeloid cells, and extrafollicular activated B cells associated with autoantibody formation. Strikingly, we found that in severe COVID-19 patients, lung tissue resident alveolar macrophages (AM) were not only severely depleted, but also had an altered Ag presentation signature, and were replaced by inflammatory monocytes and monocyte-derived macrophages (MoM{Phi}). Notably, the size of the AM pool correlated with recovery or death, while AM loss and functionality were restored in patients that recovered. These data therefore suggest that local and systemic myeloid cell dysregulation is a driver of COVID-19 severity and that modulation of AM numbers and functionality in the lung may be a viable therapeutic strategy for the treatment of critical lung inflammatory illnesses.

immunology↗

bioRxiv2020

Social Network Analysis of the Genealogy of Strawberry: Retracing the Wild Roots of Heirloom and Modern Cultivars

The widely recounted story of the origin of cultivated strawberry (Fragaria x ananassa) oversimplifies the complex interspecific hybrid ancestry of the highly admixed populations from which heirloom and modern cultivars have emerged. To develop deeper insights into the three century long domestication history of strawberry, we reconstructed the genealogy as deeply as possible--pedigree records were assembled for 8,851 individuals, including 2,656 cultivars developed since 1775. The parents of individuals with unverified or missing pedigree records were accurately identified by applying exclusion analysis to array-genotyped single nucleotide polymorphisms. We identified 187 wild octoploid and 1,171 F. x ananassa founders in the genealogy, from the earliest hybrids to modern cultivars. The pedigree networks for cultivated strawberry are exceedingly complex labyrinths of ancestral interconnections formed by diverse hybrid ancestry, directional selection, migration, admixture, bottlenecks, overlapping generations, and recurrent hybridization with common ancestors that have unequally contributed allelic diversity to heirloom and modern cultivars. Fifteen to 333 ancestors were predicted to have transmitted 90% of the alleles found in country-, region-, and continent-specific populations. Using parent-offspring edges in the global pedigree network, we found that selection cycle lengths over the last 200 years of breeding have been extraordinarily long (16.0-16.9 years/generation) but decreased to a present-day range of 6.0-10.0 years/generation. Our analyses uncovered conspicuous differences in the ancestry and structure of North American and European populations and shed light on forces that have shaped phenotypic diversity in F. x ananassa.

plant biology↗

bioRxiv2020

Delayed maturation of the structural brain connectome in neonates with congenital heart disease

There is emerging evidence for delayed brain development in neonates with congenital heart disease (CHD). We hypothesize that the perioperative development of the structural brain connectome is a proxy to such delays. Therefore, we set out to quantify the alterations and longitudinal pre- to postoperative changes in the connectome in CHD neonates and assess risk factors for disturbed perioperative network development relative to healthy term newborns. In this prospective cohort study, 114 term neonates with CHD underwent cardiac surgery at the University Childrens Hospital Zurich. Forty-six healthy term newborns were included as controls. Pre- and postoperative structural connectomes were derived from mean fractional anisotropy values of fibre pathways traced using diffusion tractography. Graph theory parameters calculated across a range of proportional cost thresholds were compared between groups by multi-threshold permutation correction adjusting for con-founders. Network based statistic was calculated for edgewise network comparison. White matter injury (WMI) volume was quantified on 3D T1-weighted images. Random coefficient mixed models with interaction terms of (i) CHD subtype and (ii) WMI volume with postmenstrual age at MRI respectively were built to assess modifying effects on network development. Pre- and postoperatively, at the global level, efficiency, indicative of network integration, was higher in controls compared to CHD neonates. In contrast, local efficiency and transitivity, indicative of network segregation, were higher in CHD neonates compared to controls (all p<0.025 for one-sided t-tests). Preoperatively these group differences were also found across multiple widespread nodes (all p<0.025, accounting for multiple comparison), whereas postoperatively nodal differences were not evident. At the edge-level, the majority of weaker connections in CHD neonates compared to controls involved interhemispheric connections (66.7% preoperatively; 54.5% postoperatively). A trend showing a more rapid pre- to postoperative decrease in local efficiency was found in class I CHD neonates compared to controls. In CHD neonates, larger WMI volume was associated with lower strength (p=0.0026) and global efficiency (p=0.0097). The maturation of the structural connectome is delayed in neonates with CHD, with a pattern of lower structural integration and higher segregation compared to healthy controls. Trend-level evidence indicated that normalized postoperative cardiac physiology in class I CHD neonates might improve structural network topology. In contrast, the degree of WMI burden negatively impacts network strength and integration. Further research is needed to elucidate how aberrant structural network development in CHD represents neural correlates of later neurodevelopmental impairments.

neuroscience↗

bioRxiv2020

Accurate Estimation of Marker-Associated Genetic Variance and Heritability in Complex Trait Analyses

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more large-effect loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability [Formula] for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and [Formula]. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and [Formula] were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and [Formula], in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and [Formula]. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance. Author summaryThe contributions of individual genes to the phenotypic variation observed for genetically complex traits has been an ongoing and important challenge in biology, medicine, and agriculture. While many genes have statistically undetectable effects, those with large effects often warrant in-depth study and can be important predictors of complex phenotypes such as disease risk in humans or disease resistance in domesticated plants and animals. The genes identified through associations with genetic markers in complex trait analyses typically account for a fraction of the heritable variation, a genetic parameter we called marker heritability. We discovered that textbook statistical methods systematically overestimate marker heritability and thus overestimate the contributions of specific genes to the phenotypic variation observed for complex traits in natural and experimental populations. We describe the source of the upward bias, validate our findings through computer simulation, describe methods for bias-correcting estimates of marker heritability, and illustrate their application through empirical examples. The statistical methods we describe supply investigators with more accurate estimates of the contributions of specific genes or networks of interacting genes to the heritable variation observed in complex trait studies.

genetics↗

bioRxiv2019

Histone H3K27me3 demethylases regulate human Th17 cell development and effector functions by impacting on metabolism

T helper (Th) cells are CD4+ effector T cells that play an instrumental role in immunity by shaping the inflammatory cytokine environment in a variety of physiological and pathological situations. Using a combined chemico-genetic approach we identify histone H3K27 demethylases KDM6A and KDM6B as central regulators of human Th subsets. The prototypic KDM6 inhibitor GSK-J4 increases genome-wide levels of the repressive H3K27me3 chromatin mark and leads to suppression of the key transcription factor ROR{gamma}t during Th17 differentiation, whereas in mature Th17 cells an altered transcriptional program leads to a profound metabolic reprogramming with concomitant suppression of IL-17 cytokine levels and reduced proliferation. Single cell analysis reveals a specific shift from highly inflammatory cell subsets towards a resting state upon demethylase inhibition. The root cause of the observed anti-inflammatory phenotype in stimulated Th17 cells is reduced expression of key metabolic transcription factors, such as PPRC1 and c-myc. Overall, this leads to reduced mitochondrial biogenesis resulting in a metabolic switch with concomitant anti-inflammatory effects. These data are consistent with an opposing effect of GSK-J4 on Th17 T-cell differentiation pathways directly related to proliferation and effector cytokine profiles.

immunology↗

bioRxiv2019

Association between development of early cortical activity on amplitude-integrated EEG and 5-years neurodevelopmental outcome in the preterm infant

AimTo analyse the association between early aEEG and cognitive outcome at early school-age in very preterm infants. MethodsProspective cohort study including infants with gestational age (GA) <32.0 weeks, undergoing continuous aEEG recording during first 4 days of life. Semiquantitative and quantitative (maximum/minimum amplitude) measures were averaged over the recording period. Cognitive outcome was assessed with the Kaufman-Assessment Battery for Children at 5 years of age. Uni- and multivariate logistic regressions were calculated between aEEG parameters and normal cognitive outcome (IQ[≥]85). ResultsAmong 118 monitored preterm children, 89 were assessed at median(IQR) corrected age of 68.6 months (65.5-71.2) [48% female, median(IQR) GA 29.9(28.2,30.9) weeks, mean(SD) birth weight 1235(363) grams]. Mean(SD) IQ was 97.8(12.7). IQ<85 occurred in 21.3 %, cerebral palsy was found in 3.4%. Despite univariate associations of total maturity scores, cycling subscores, background pattern and minimum aEEG amplitude with normal cognitive outcome none of the associations remained significant after adjustment for confounders. Socioeconomic status was identified as independent predictor of neurodevelopmental outcome. ConclusionIn this cohort of very preterm infants, early short-term aEEG was not predictive of later cognitive outcome. Further research is needed to explore how aEEG could help to inform long-term prognosis in this population group. Key notesO_LIPreterm born infants are at high risk for neurodevelopmental impairment. C_LIO_LIEarly amplitude integrated electroencephalography characteristics are univariately associated with cognitive outcome at 5 years of age in preterm born children. C_LIO_LIHowever, socioeconomic factors and neonatal morbidity were stronger predictors of long-term neurodevelopmental outcome than early aEEG measures. C_LI

neuroscience↗

bioRxiv2018

Left planum temporale growth predicts language development in newborns with congenital heart disease

Congenital heart diseases (CHD) are the most common congenital anomalies, accounting for a third of all congenital anomaly cases. While surgical correction dramatically improved survival rates, the lag behind normal neurodevelopment appears to persist. Deficits of higher cognitive functions are particularly common, including developmental delay in communication and oral-motor apraxia. It remains unclear whether the varying degree of cognitive developmental delay is reflected in variability in brain growth patterns. To answer this question, we aimed to investigate whether the rate of regional brain growth is correlated with later life neurodevelopment. 44 newborns were included in our study, out of whom 33 were diagnosed with dextro-transposition of the great arteries (d-TGA) and 11 with other forms of severe CHD. During the first month of life, neonates underwent corrective or palliative cardiovascular bypass surgery, pre- and postoperative cerebral MRI were performed 18.7 {+/-} 7.03 days apart. MRI was performed in natural sleep on a 3.0T scanner using an 8-channel head coil, fast spin-echo T2-weighted anatomical sequences were acquired in three planes. Based on the principles of deformation based morphometry, we calculated brain growth rate maps that reflected the rate of non-linear deformation that occurs between pre- and post-operative brain images. An explorative, whole-brain, threshold-free cluster enhancement analysis revealed strong correlation between the growth rate of the left planum temporale and the posterior operculum of the left frontal lobe and language score at 12 months of age, corrected for demographic variables (p=0.018, t=5.656). No significant correlation was found between brain growth rates and motor or cognitive scores. Post hoc analysis showed that the length of hospitalization interacts with this correlation with longer hospitalization stay results in faster enlargement of the internal cerebro-spinal fluid spaces. Our study provides evidence to the early importance of left-dominant perisylvian regions in language development even before the direct postnatatal exposure to native language. In CHD patients, the perioperative period results in a critical variability of brain growth rate in this region, which is a reliable neural correlate of language development at one year of age.

neuroscience↗