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Alpine bumble bees are the most important pollinators in temperate mountain ecosystems. Although they are used to encounter small-scale successions of very different climates in the mountains, many species respond sensitively to climatic changes, reflected in spatial range shifts and declining populations worldwide. Cuticular hydrocarbons (CHCs) mediate climate adaptation in some insects. However, whether they predict the elevational niche of bumble bees or their responses to climatic changes remains poorly understood. Here, we used three different approaches to study the role of bumble bees’ CHCs in the context of climate adaptation: using a 1,300 m elevational gradient, we first investigated whether the overall composition of CHCs, and two potentially climate-associated chemical traits (proportion of saturated components, mean chain length) on the cuticle of six bumble bee species were linked to the species’ elevational niches. We then analyzed intraspecific variation in CHCs of Bombus pascuorum along the elevational gradient and tested whether these traits respond to temperature. Finally, we used a field translocation experiment to test whether CHCs of Bombus lucorum workers change, when translocated from the foothill of a cool and wet mountain region to (a) higher elevations, and (b) a warm and dry region. Overall, the six species showed distinctive, species-specific CHC profiles. We found inter- and intraspecific variation in the composition of CHCs and in chemical traits along the elevational gradient, but no link to the elevational distribution of species and individuals. According to our expectations, bumble bees translocated to a warm and dry region tended to express longer CHC chains than bumble bees translocated to cool and wet foothills, which could reflect an acclimatization to regional climate. However, chain lengths did not further decrease systematically along the elevational gradient, suggesting that other factors than temperature also shape chain lengths in CHC profiles. We conclude that in alpine bumble bees, CHC profiles and traits respond at best secondarily to the climate conditions tested in this study. While the functional role of species-specific CHC profiles in bumble bees remains elusive, limited plasticity in this trait could restrict species’ ability to adapt to climatic changes.
Xiphophorus fish exhibit a clear phenotypic polymorphism in puberty onset and reproductive strategies of males. In X. nigrensis and X. multilineatus, puberty onset is genetically determined and linked to a melanocortin 4 receptor (Mc4r) polymorphism of wild-type and mutant alleles on the sex chromosomes. We hypothesized that Mc4r mutant alleles act on wild-type alleles by a dominant negative effect through receptor dimerization, leading to differential intracellular signaling and effector gene activation. Depending on signaling strength, the onset of puberty either occurs early or is delayed. Here, we show by Förster Resonance Energy Transfer (FRET) that wild-type Xiphophorus Mc4r monomers can form homodimers, but also heterodimers with mutant receptors resulting in compromised signaling which explains the reduced Mc4r signaling in large males. Thus, hetero- vs. homo- dimerization seems to be the key molecular mechanism for the polymorphism in puberty onset and body size in male fish.
Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.
Highlights
• The integrated stress response leads to a general ATF4-dependent activation of NRF2
• ATF4 causes a CHAC1-dependent GSH depletion, resulting in NRF2 stabilization
• An elevation of NRF2 transcript levels fosters this effect
• NRF2 supports the ISR/ATF4 pathway by improving cystine and antioxidant supply
Summary
The redox regulator NRF2 becomes activated upon oxidative and electrophilic stress and orchestrates a response program associated with redox regulation, metabolism, tumor therapy resistance, and immune suppression. Here, we describe an unrecognized link between the integrated stress response (ISR) and NRF2 mediated by the ISR effector ATF4. The ISR is commonly activated after starvation or ER stress and plays a central role in tissue homeostasis and cancer plasticity. ATF4 increases NRF2 transcription and induces the glutathione-degrading enzyme CHAC1, which we now show to be critically important for maintaining NRF2 activation. In-depth analyses reveal that NRF2 supports ATF4-induced cells by increasing cystine uptake via the glutamate-cystine antiporter xCT. In addition, NRF2 upregulates genes mediating thioredoxin usage and regeneration, thus balancing the glutathione decrease. In conclusion, we demonstrate that the NRF2 response serves as second layer of the ISR, an observation highly relevant for the understanding of cellular resilience in health and disease.
Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C\(_1\)) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus.
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R
2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R
2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
Aim
Global warming is assumed to restructure mountain insect communities in space and time. Theory and observations along climate gradients predict that insect abundance and richness, especially of small‐bodied species, will increase with increasing temperature. However, the specific responses of single species to rising temperatures, such as spatial range shifts, also alter communities, calling for intensive monitoring of real‐world communities over time.
Location
German Alps and pre‐alpine forests in south‐east Germany.
Methods
We empirically examined the temporal and spatial change in wild bee communities and its drivers along two largely well‐protected elevational gradients (alpine grassland vs. pre‐alpine forest), each sampled twice within the last decade.
Results
We detected clear abundance‐based upward shifts in bee communities, particularly in cold‐adapted bumble bee species, demonstrating the speed with which mobile organisms can respond to climatic changes. Mean annual temperature was identified as the main driver of species richness in both regions. Accordingly, and in large overlap with expectations under climate warming, we detected an increase in bee richness and abundance, and an increase in small‐bodied species in low‐ and mid‐elevations along the grassland gradient. Community responses in the pre‐alpine forest gradient were only partly consistent with community responses in alpine grasslands.
Main Conclusion
In well‐protected temperate mountain regions, small‐bodied bees may initially profit from warming temperatures, by getting more abundant and diverse. Less severe warming, and differences in habitat openness along the forested gradient, however, might moderate species responses. Our study further highlights the utility of standardized abundance data for revealing rapid changes in bee communities over only one decade.
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.
Cancer is one of the leading causes of death worldwide. The underlying tumorigenesis is driven by the accumulation of alterations in the genome, eventually disabling tumor suppressors and activating proto-oncogenes.
The MYC family of proto-oncogenes shows a strong deregulation in the majority of tumor entities. However, the exact mechanisms that contribute to MYC-driven oncogenesis remain largely unknown. Over the past decades, the influence of the MYC protein on transcription became increasingly apparent and was thoroughly investigated. Additionally, in recent years several publications provided evidence for so far unreported functions of MYC that are independent of a mere regulation of target genes. These findings suggest an additional role of MYC in the maintenance of genomic stability and this role is strengthened by key findings presented in this thesis.
In the first part, I present data revealing a pathway that allows MYC to couple transcription elongation and DNA double-strand break repair, preventing genomic instability of MYC-driven tumor cells. This pathway is driven by a rapid transfer of the PAF1 complex from MYC onto RNAPII, a process that is mediated by HUWE1. The transfer controls MYC-dependent transcription elongation and, simultaneously, the remodeling of chromatin structure by ubiquitylation of histone H2B. These regions of open chromatin favor not only elongation but also DNA double-strand break repair.
In the second part, I analyze the ability of MYC proteins to form multimeric structures in response to perturbation of transcription and replication. The process of multimerization is also referred to as phase transition. The observed multimeric structures are located proximal to stalled replication forks and recruit factors of the DNA-damage response and transcription termination machinery. Further, I identified the HUWE1-dependent ubiquitylation of MYC as an essential step in this phase transition. Cells lacking the ability to form multimers display genomic instability and ultimately undergo apoptosis in response to replication stress.
Both mechanisms present MYC as a stress resilience factor under conditions that are characterized by a high level of transcriptional and replicational stress. This increased resilience ensures oncogenic proliferation.
Therefore, targeting MYC’s ability to limit genomic instability by uncoupling transcription elongation and DNA repair or disrupting its ability to multimerize presents a therapeutic window in MYC-dependent tumors.
Since the advent of high-throughput sequencing technologies in the mid-2010s, RNA se-
quencing (RNA-seq) has been established as the method of choice for studying gene
expression. In comparison to microarray-based methods, which have mainly been used to
study gene expression before the rise of RNA-seq, RNA-seq is able to profile the entire
transcriptome of an organism without the need to predefine genes of interest. Today,
a wide variety of RNA-seq methods and protocols exist, including dual RNA sequenc-
ing (dual RNA-seq) and multi RNA sequencing (multi RNA-seq). Dual RNA-seq and
multi RNA-seq simultaneously investigate the transcriptomes of two or more species, re-
spectively. Therefore, the total RNA of all interacting species is sequenced together and
only separated in silico. Compared to conventional RNA-seq, which can only investi-
gate one species at a time, dual RNA-seq and multi RNA-seq analyses can connect the
transcriptome changes of the species being investigated and thus give a clearer picture of
the interspecies interactions. Dual RNA-seq and multi RNA-seq have been applied to a
variety of host-pathogen, mutualistic and commensal interaction systems.
We applied dual RNA-seq to a host-pathogen system of human mast cells and Staphylo-
coccus aureus (S. aureus). S. aureus, a commensal gram-positive bacterium, can become
an opportunistic pathogen and infect skin lesions of atopic dermatitis (AD) patients.
Among the first immune cells S. aureus encounters are mast cells, which have previously
been shown to be able to kill the bacteria by discharging antimicrobial products and re-
leasing extracellular traps made of protein and deoxyribonucleic acid (DNA). However,
S. aureus is known to evade the host’s immune response by internalizing within mast
cells. Our dual RNA-seq analysis of different infection settings revealed that mast cells
and S. aureus need physical contact to influence each other’s gene expression. We could
show that S. aureus cells internalizing within mast cells undergo profound transcriptome
changes to adjust their metabolism to survive in the intracellular niche. On the host side,
we found out that infected mast cells elicit a type-I interferon (IFN-I) response in an
autocrine manner and in a paracrine manner to non-infected bystander-cells. Our study
provides the first evidence that mast cells are capable to produce IFN-I upon infection
with a bacterial pathogen.