Biology subjects

Weiss, J.

Publications and source records attributed to Weiss, J..

26 records · Page 2Linked to original sources

bioRxiv2020

Automatic classification of constitutive and non-constitutive metabolites with gcProfileMakeR

Data analysis in non-targeted metabolomics is extremely time consuming. Genetic factors and environmental cues affect the composition and quantity of present metabolites i.e. the constitutive and non-constitutive metabolites. We developed gcProfileMakeR, an R package that uses standard output files from GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR produces three outputs: a core or constitutive metabolome, a second list of compounds with high quality matches that is non-constitutive and a third set of compounds with low quality matching to MS libraries. As a proof of concept, we defined the floral scent emission of Antirrhinum majus using wild type plants, the floral identity mutants deficiens and compacta as well as RNAi lines of AmLHY. Loss of petal identity was accompanied by appearance of aldehydes typical of green leaf volatile profiles. Decreased levels of AmLHY caused a major increase in volatile complexity, and activated the synthesis of benzyl acetate, absent in WT. Furthermore, some volatiles emitted in a gated fashion in WT such as methyl 3,5-dimethoxybezoate or linalool became constitutive. Using sixteen volatiles of the constitutive profile, all genotypes were classified by Machine Learning with 0% error. gcProfileMakeR may thus help define core and pan-metabolomes. It enhances the quality of data reported in metabolomic profiles as text outputs rely on CAS numbers. This is especially important for FAIR data implementation. One sentence summarygcProfileMakeR allows the automatic annotation of the core metabolome and non-constitutive metabolites, increasing speed and accuracy of non-targeted metabolomics.

bioinformatics↗

bioRxiv2019

In vitro model for resistance in oncogene-dependent tumors at the limit of radiological detectability

In solid tumors, the response to targeted therapy is typically short-lived, as therapy-resistant mutants can quickly expand during therapy. Here we analyze the spectrum of such resistance mutations coexisting in a large population of cancer cells. We use an iterative scheme of artificial evolution to amplify and isolate different resistance mechanisms. As a proof of concept, we apply our scheme to PC-9 cells, a human non-small cell lung cancer cell line with an activating EGFR mutation. The mechanisms we find comprise the well-known gatekeeper-mutation T790M in EGFR, a mutation in NRAS, the amplification of MET-ligand HGF, as well as induction of AKT-mTOR signaling. In this model, a combination of four drugs targeting these mechanisms prevents not only the expansion of resistant cells, but also inhibits the growth of drug-tolerant cells, which can otherwise act as a reservoir for further resistance mutations. These data suggest that a finite number of drugs specifically acting on individual resistant clones may be able to control resistance in oncogenically driven lung cancer.

cancer biology↗

bioRxiv2019

Real-time monitoring of mechanical cues in the regenerative niche reveal dynamic strain magnitudes that enhance bone repair

Mechanical loads exerted on the skeleton during activities such as walking are important regulators of bone repair, but dynamic biomechanical signals are difficult to measure inside the body. The inability to measure the mechanical environment in injured tissues is a significant barrier to developing integrative regenerative and rehabilitative strategies that can accelerate recovery from fracture, segmental bone loss, and spinal fusion. Here we engineered an implantable strain sensor platform and measured strain across a bone defect in real-time throughout rehabilitation. We used the sensor to longitudinally quantify mechanical cues imparted by a load-sharing fixation plate that significantly enhanced bone regeneration in rats. We found that sensor readings correlated with the status of healing, suggesting a potential role for strain sensing as an X-ray-free healing assessment platform. This study demonstrates a promising approach to quantitatively develop and exploit mechanical rehabilitation strategies that enhance bone repair.

bioengineering↗

bioRxiv2019

Molecular and transcriptional structure of the petal and leaf circadian clock in Petunia hybrida

The plant circadian clock coordinates environmental signals with internal processes. We characterized the genomic and transcriptomic structure of the Petunia hybrida W115 clock in leaves and petals. We found three levels of evolutionary differences. First, PSEUDO-RESPONSE REGULATORS PhPRR5a, PhPRR5b, PhPRR7a, PhPRR7b, and GIGANTEA PhGI1 and PhGI2, differed in gene structure including exon number and deletions including the CCT domain of the PRR family. Second, leaves showed preferential day expression while petals tended to display night expression. Under continuous dark, most genes were delayed in leaves and petals. Importantly, photoperiod sensitivity of gene expression was tissue specific as TIMING OF CAB EXPRESSION PhNTOC1 was affected in leaves but not in petals, and PhPRR5b, PhPRR7b and the ZEITLUPE ortholog CHANEL, PhCHL, were modified in petals but not leaves. Third, we identified a strong transcriptional noise at different times of the day, and high robustness at dawn in leaves and dusk in petals, coinciding with the coordination of photosynthesis and scent emission. Our results indicate multilayered evolution of the Petunia clock including gene structure, number of genes and transcription patterns. The major transcriptional reprogramming of the clock in petals, with night expression may be involved in controlling scent emission in the dark.\n\nHighlightThe petunia leaf circadian clock shows maxima during the day while petal clock does it during the night. Reaction to dark is organ specific.

plant biology↗

bioRxiv2019

Genetic and environmental perturbations drive modular transcriptome responses

Understanding the plasticity, robustness, and modularity of transcriptome expression to genetic and environmental conditions is crucial to deciphering how organisms adapt in nature. To test how genome architecture influences transcriptome profiles, we quantified expression responses for distinct temperature-adapted genotypes of the nematode Caenorhabditis briggsae when exposed to chronic temperature stresses throughout development. We found that 56% of the 8795 differentially-expressed genes show genotype-specific changes in expression in response to temperature (genotype-by-environment interactions, GxE). Most genotype-specific responses occur under heat stress, indicating that cold versus heat stress responses involve distinct genomic architectures. The 22 co-expression modules that we identified differ in their enrichment of genes with genetic versus environmental versus interaction effects, as well as their genomic spatial distributions, functional attributes, and rates of molecular evolution at the sequence level. Genes in modules enriched for simple effects of either genotype or temperature alone tend to evolve especially rapidly, consistent with disproportionate influence of adaptation or weaker constraint on these subsets of loci. Chromosome scale heterogeneity in nucleotide polymorphism, however, rather than the scale of individual genes, predominate as the source of genetic differences among expression profiles, and natural selection regimes are largely decoupled between coding sequences and non-coding flanking sequences that contain cis-regulatory elements. These results illustrate how the form of transcriptome modularity and genome structure contribute to predictable profiles of evolutionary change.

genomics↗

bioRxiv2018

Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease

BackgroundLate-onset Alzheimers disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD represented as variation in DNA methylation (DNAm), we surveyed 420,852 DNAm sites from neurotypical controls (N=49) and late-onset AD patients (N=24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum).\n\nResultsWe identified 858 sites with robust differential methylation, collectively annotated to 772 possible genes (FDR<5%, within 10kb). These sites were overrepresented in AD genetic risk loci (p=0.00655), and nearby genes were enriched for processes related to cell-adhesion, immunity, and calcium homeostasis (FDR<5%). We analyzed corresponding RNA-seq data to prioritize 130 genes within 10kb of the differentially methylated sites, which were differentially expressed and had expression levels associated with nearby DNAm levels (p<0.05). This validated gene set includes previously reported (e.g. ANK1, DUSP22) and novel genes involved in Alzheimers disease, such as ANKRD30B.\n\nConclusionsThese results highlight DNAm changes in Alzheimers disease that have gene expression correlates, implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex disease.

genomics↗

bioRxiv2018

A novel xenograft model reveals invasive mesenchymal transition and ineffective angiogenic response during the evolution of resistance to anti-angiogenic therapy

Bevacizumab treatment of glioblastoma is limited by transient responses and acquired resistance. Because of the lengthy duration of treatment that can precede resistance in patients, in order to study changes underlying the evolution of bevacizumab resistance, we created a novel multigenerational xenograft model of acquired bevacizumab resistance. Glioblastoma xenografts were treated with bevacizumab or IgG, and the fastest growing tumor re-implanted into new mice, generating paired isogeneic responsive or resistant multigenerational xenografts. Microarray analysis revealed significant overexpression across generations of the mesenchymal subtype gene signature, paralleling results from patient bevacizumab-resistant glioblastomas (BRGs) that exhibited increasing mesenchymal gene expression correlating with increased bevacizumab treatment duration. Key mesenchymal markers, including YKL-40, CD44, SERPINE1, and TIMP1 were upregulated across generations, with altered morphology, increased invasiveness, and increased neurosphere formation confirmed in later xenograft generations. Interestingly, YKL-40 levels were elevated in serum of patients with bevacizumab-resistant vs. bevacizumab-naive glioblastomas. Finally, despite upregulation of VEGF-independent pro-angiogenic genes across xenograft generations, immunostaining revealed increased hypoxia and decreased vessel density with increasing generation of treatment, mirroring our findings in patient BRGs and suggesting tumor growth despite effective devascularization caused by VEGF blockade. Besides identifying novel targets for preventing the evolution of resistance and offering a xenograft model for testing resistance inhibitors, our work suggests YKL-40 as a blood biomarker of bevacizumab resistance worthy of further evaluation.

cancer biology↗

bioRxiv2017

The Emergence of Egalitarianism in a Model of Early Human Societies

How did egalitarianism emerge in early human societies? In contrast to dominance hierarchies in non-human primates, human simple forager bands are typically egalitarian, with male hunters often serving as the collective alpha. Here we present a thermodynamics-inspired simple population model, based on stochastic optimization of dominance relationships, in which a dominance hierarchy of individuals with exclusively self-centered characteristics (the desire to dominate, resentment at being dominated) transitions spontaneously to egalitarianism as their capacity for language develops. Language, specifically gossip, allows resentment against being dominated to promote the formation of antidominance coalitions which destabilize the alpha position for individuals, leading to a phase transition in which a coalition of the full population suddenly becomes dominant. Thus, egalitarianism emerges suddenly as the optimal power sharing arrangement in a population of selfish individuals without any inherently altruistic qualities. We speculate that egalitarianism driven by punishment for exhibiting alpha-like behavior may then set the stage for genuinely altruistic traits to propagate as predicted by game theory models. Based on model simulations, we also predict that egalitarianism is a pre-condition for adaptation of tools as weapons. Potential implications for origins of human moral belief systems are discussed.

evolutionary biology↗