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At least 109 records · Page 6

The evolution of transcriptional regulation in eukaryotes

Gene expression is central to the genotype-phenotype relationship in all organisms, and it is an important component of the genetic basis for evolutionary change in diverse aspects of phenotype. However, the evolution of transcriptional regulation remains understudied and poorly understood. Here we review the evolutionary dynamics of promoter, or cis-regulatory, sequences and the evolutionary mechanisms that shape them. Existing evidence indicates that populations harbor extensive genetic variation in promoter sequences, that a substantial fraction of this variation has consequences for both biochemical and organismal phenotype, and that some of this functional variation is sorted by selection. As with protein-coding sequences, rates and patterns of promoter sequence evolution differ considerably among loci and among clades for reasons that are not well understood. Studying the evolution of transcriptional regulation poses empirical and conceptual challenges beyond those typically encountered in analyses of coding sequence evolution: promoter organization is much less regular than that of coding sequences, and sequences required for the transcription of each locus reside at multiple other loci in the genome. Because of the strong context-dependence of transcriptional regulation, sequence inspection alone provides limited information about promoter function. Understanding the functional consequences of sequence differences among promoters generally requires biochemical and in vivo functional assays. Despite these challenges, important insights have already been gained into the evolution of transcriptional regulation, and the pace of discovery is accelerating.

Review, Academic↗

Data for FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Genomics↗

MAGNETIC ACTIVITY ANALYSIS FOR A SAMPLE OF G-TYPE MAIN SEQUENCE KEPLER TARGETS

The variation of a stellar light curve owing to rotational modulation by magnetic features (starspots and faculae) on the star’s surface can be used to investigate the magnetic properties of the host star. In this paper, we use the periodicity and magnitude of the light-curve variation as two proxies to study the stellar magnetic properties for a large sample of G-type main sequence Kepler targets, for which the rotation periods were recently determined. By analyzing the correlation between the two magnetic proxies, it is found that: (1) the two proxies are positively correlated for most of the stars in our sample, and the percentages of negative, zero, and positive correlations are 4.27%, 6.81%, and 88.91%, respectively; (2) negative correlation stars cannot have a large magnitude of light-curve variation; and (3) with the increase of rotation period, the relative number of positive correlation stars decreases and the negative correlation one increases. These results indicate that stars with shorter rotation period tend to have positive correlation between the two proxies, and a good portion of the positive correlation stars have a larger magnitude of light-curve variation (and hence more intense magnetic activities) than negative correlation stars.

79 ASTRONOMY AND ASTROPHYSICS↗

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

Abstract A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β -lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

59 BASIC BIOLOGICAL SCIENCES↗

Enrichment of root-associated Streptomyces strains in response to drought is driven by diverse functional traits and does not predict beneficial effects on plant growth

The genus Streptomyces has consistently been found enriched in drought-stressed plant root microbiomes, yet the ecological basis and functional variation underlying this enrichment at the strain and isolate level remain unclear. Using two 16S rRNA sequencing methods with different levels of taxonomic resolution, we confirmed drought-associated enrichment (DE) of Streptomyces in field-grown sorghum roots and identified five closely related but distinct amplicon sequence variants (ASVs) belonging to the genus with variable drought enrichment patterns. From a culture collection of sorghum root endophytes, we selected 12 Streptomyces isolates representing these ASVs for phenotypic and genomic characterization. Whole-genome sequencing revealed substantial variation in gene content, even among closely related isolates, and exometabolomic profiling showed distinct metabolic responses to media supplemented with drought- versus well-watered root tissue. Traits linked to drought survival, including osmotic stress tolerance, siderophore production, and carbon utilization, varied widely among isolates and were not phylogenetically conserved. Using a broader panel of 48 Streptomyces, we demonstrate that DE scores, determined through mono-association experiments in gnotobiotic sorghum systems, showed high variability and lacked correlation with plant growth promotion. Pangenome-wide association identified orthogroups involved in osmolyte transport (e.g., proP) and membrane biosynthesis (e.g., fabG) as positively associated with DE, though most associations lacked phylogenetic signal. Collectively, these results demonstrate that Streptomyces DE is not a conserved genus-level trait but is instead strain-specific and functionally heterogeneous. Furthermore, DE in the root microbiome was shown not to predict beneficial effects on plant growth. This work underscores the need to resolve functional traits at the strain level and highlights the complexity of microbe-host-environment interactions under abiotic stress.

Fonseca-Garcia, Citlali↗

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗

A direct time series comparison between the La Jolla and Belfast radiocarbon records

For many years it has been widely assumed that the variations in the level of atmospheric carbon-14 were due to statistical fluctuations arising from experimental error. This is understandable since the signal/noise ratio is very low and the time sequences representing the variations are strongly stochastic. Interlaboratory comparisons show that baseline variations in the absolute value of the carbon-14 concentration do exist. However, assuming linearity, the delta 14C values are independent of these. The importance of assessing the quantitive reality of the delta 14C values is based upon their expression of the interplanetary cosmic ray source function, because in the range of 100 to 1000 year periods, there appears to be no evidence that the Earth's magnetic field is the source modulating function. Therefore the modulation is either due to changes in the solar atmosphere propagated out into the solar wind, or extra-heliospheric pressure effects, but these appear to be unlikely for the periods noted here. The recent availability of the new high quality Belfast time sequence of delta 14C now permits a simple mutual assessment of the several sequences which are available. Since the La Jolla record has been a standard for many years , these two were chosen for a simple comparison. Although differences exist, the close agreement between these two sequences, one carried out on White mountain Bristlecone pines, and the other done using Irish peat bog wood, is striking. This correlation between the two strongly reinforces the statistical view that the delta 14C record is that of real interplanetary modulation of the cosmic ray source leading to the generation of atmospheric 14C.

Sonett, C. P.↗

Optimizing Cell-based Antimicrobials through Pooled Genomic Libraries

DNA synthesis and assembly technologies ushered in through synthetic biology have great promise for biomanufacturing, bioremediation, and the development of living therapeutics. Unfortunately, predicting sequence to function relationships, including for biosynthetic pathways expressed in a new host organism, is difficult and often requires many iterative cycles of design, construction, and testing. We are working to develop data-driven approaches to identify the genetic determinants of growth defects and productivity for the expression of a cell-based antimicrobial. We assayed the growth, pigment production, and antimicrobial activity of a collection of over 10,000 genetic mutants of the violacein biosynthetic pathway and sequenced the genetic variation of these mutants. Through this project, we have developed an innovative codebase to automate the determination of pigmentation and antimicrobial clearing diameter for tens of thousands of genetic mutants cultivated on agar dishes. Further, we have written DNA sequence analysis code to demultiplex & provide consensus sequences from high-throughput PacBio long-read circular consensus sequencing (CCS) datasets. From this foundation, we plan to map DNA sequence to function to predict an optimal genetic design to maximize antimicrobial activity while minimizing deleterious growth effects. The workflows and algorithms developed through this project can be broadly applied to other engineered functions in microbes, uncovering sequence to function relationships for complex phenotypes where function impacts fitness.

59 BASIC BIOLOGICAL SCIENCES↗

Sexual reproduction, sporophyte development and molecular variation in the model moss Physcomitrella patens : introducing the ecotype Reute

Summary Rich ecotype collections are used for several plant models to unravel the molecular causes of phenotypic differences, and to investigate the effects of environmental adaption and acclimation. For the model moss Physcomitrella patens collections of accessions are available, and have been used for phylogenetic and taxonomic studies, for example, but few have been investigated further for phenotypic differences. Here, we focus on the Reute accession and provide expression profiling and comparative developmental data for several stages of sporophyte development, as well as information on genetic variation via genomic sequencing. We analysed cross‐technology and cross‐laboratory data to define a confident set of 15 mature sporophyte‐specific genes. We find that the standard laboratory strain Gransden produces fewer sporophytes than Reute or Villersexel, although gametangia develop with the same time course and do not show evident morphological differences. Reute exhibits less genetic variation relative to Gransden than Villersexel, yet we found variation between Gransden and Reute in the expression profiles of several genes, as well as variation hot spots and genes that appear to evolve under positive Darwinian selection. We analyzed expression differences between the ecotypes for selected candidate genes in the GRAS transcription factor family, the chalcone synthase family and in genes involved in cell wall modification that are potentially related to phenotypic differences. We confirm that Reute is a P. patens ecotype, and suggest its use for reverse‐genetics studies that involve progression through the life cycle and multiple generations.

Hiss, Manuel↗

Peptoid-Directed Formation of Five-Fold Twinned Au Nanostars through Particle Attachment and Facet Stabilization

While bio-inspired synthesis offers great potential for controlling nucleation and growth of inorganic particles, precisely tuning biomolecule-particle interactions is a long-standing challenge. Herein, we used variations in peptoid sequence to manipulate peptoid-Au interaction, leading to synthesis of concave five-fold twinned, five-pointed Au nanostars via a process of repeated particle attachment and facet stabilization. Ex situ and liquid-phase TEM observations show that a balance between particle attachment biased to occur near the star points, preferential growth along the [100] direction, and stabilization of (111) facets is critical to forming star-shaped particles. Molecular simulations predict that interaction strengths between peptoids and distinct Au facets differ significantly and thus can alter attachment kinetics and surface energies to form the stars. This work provides new insights into how sequence-defined ligands affect particle growth to regulate crystal morphology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

On the distribution of stacking faults at dissociated medium-angle grain boundaries: Crystallographic geometry and metastability

Grain boundaries in FCC metals with low stacking-fault energy can form in dissociated configurations of stacking faults. Perhaps the most studied example is 9R stacking at boundaries near Σ3{112}, where the distribution of stacking faults is related to the emission of Shockley partial dislocations. Here, we combine atomic-resolution electron microscopy, atomistic simulations, and dislocation theory to demonstrate that boundaries vicinal to Σ33a support the stabilization of dissociated 9R stacking within a narrow range of inclinations. This boundary is interesting since its misorientation (20.05°) lies in the medium-angle regime, just past the upper misorientation limit for low-angle boundaries, motivating questions for how best to describe it in terms of dislocations. Our HAADF-STEM observations of thin film bicrystals, supported by atomistic modeling, reveal that this inclination dependence arises from specific geometric constraints on the arrangement of Shockley partial dislocations at the interface. Quantification of stacking-fault distributions across multiple boundaries indicates that the density and spacing of faults closely follow the ideal 9R motif, with subtle variations reflecting the complex energy landscape of these boundaries. Through energetic analysis, we establish the presence of competing metastable states enabled by variations in stacking sequences, emphasizing the significant role of crystallographic geometry. We generalize our analysis as a function of misorientation, showing how 9R at the Σ33a boundary is related to previous observations and calculations of HCP at a 29.7° boundary. This study provides a crystallographically grounded framework connecting dislocation structures, stacking-fault distributions, and metastability at grain boundaries in FCC metals.

Atomistic modeling↗

Genome resources for three modern cotton lines guide future breeding efforts

Cotton ( Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (‘UGA230’, ‘UA48’ and ‘CSX8308’) and updating the ‘TM-1’ cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from ‘Pima’ cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

59 BASIC BIOLOGICAL SCIENCES↗

Molecular and biochemical characterization of rice developed through conventional integration of nDart1-0 transposon gene

Mutations, the genetic variations in genomic sequences, play an important role in molecular biology and biotechnology. During DNA replication or meiosis, one of the mutations is transposons or jumping genes. An indigenous transposon nDart1-0 was successfully introduced into local indica cultivar Basmati-370 from transposon-tagged line viz., GR-7895 (japonica genotype) through conventional breeding technique, successive backcrossing. Plants from segregating populationsshowed variegated phenotypes were tagged as BM-37 mutants. Blast analysis of the sequence data revealed that the GTP-binding protein, located on the BAC clone OJ1781_H11 of chromosome 5, contained an insertion of DNA transposon nDart1-0. The nDart1-0 has “A” at position 254 bp, whereas nDart1 homologs have “G”, which efficiently distinguishes nDart1-0 from its homologs. The histological analysis revealed that the chloroplast of mesophyll cells in BM-37 was disrupted with reduction in size of starch granules and higher number of osmophillic plastoglobuli, which resulted in decreased chlorophyll contents and carotenoids, gas exchange parameters (Pn, g, E, Ci), and reduced expression level of genes associated with chlorophyll biosynthesis, photosynthesis and chloroplast development. Along with the rise of GTP protein, the salicylic acid (SA) and gibberellic acid (GA) and antioxidant contents(SOD) and MDA levels significantly enhanced, while, the cytokinins (CK), ascorbate peroxidase (APX), catalase (CAT), total flavanoid contents (TFC) and total phenolic contents (TPC) significantly reduced in BM-37 mutant plants as compared with WT plants. These results support the notion that GTP-binding proteins influence the process underlying chloroplast formation. Therefore, it is anticipated that to combat biotic or abiotic stress conditions, the nDart1-0 tagged mutant (BM-37) of Basmati-370 would be beneficial.

60 APPLIED LIFE SCIENCES↗

Unpinning the skyrmion lattice in MnSi: Effect of substitutional disorder

By employing magnetization and small angle neutron scattering measurements, we have investigated the behavior of the skyrmion lattice (SKL) and the helical order in MnSi 0.992 Ga 0.008 . Our results indicate that the order of the SKL is sensitive to the orientation of an applied magnetic field with respect to the crystal lattice and to variations in the sequence of small temperature and applied magnetic field changes. The disorder caused by the substitution of the heavier element Ga for Si is sufficient to reduce the pinning of the SKL to the underlying crystalline lattice, reducing the propensity for the SKL to be aligned with the crystal lattice. This tendency is most evident when the applied field is not well oriented with respect to the high symmetry axes of the crystal resulting in disorder in the long range SKL while maintaining sharp short range (radial) order. We have also investigated the effect of substituting heavier elements into MnSi on the reorientation process of the helical domains with field cycling in MnSi 0.992 Ga 0.008 and Mn 0.985 Ir 0.015 Si A comparison of the reorientation process in these materials with field reduction indicates that the substitution of heavier elements on either Mn or Si sites creates a higher energy barrier for the reorientation of the helical order and for the formation of domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

A gamma ray monitor for the OSO-7 spacecraft

A 3 in. x 3 in. NaI(Tl) gamma ray (0.3 to 10 MeV) spectrometer with a CsI(Na) charged particle and anti-Compton shield has been developed for the Orbiting Solar Observatory (OSO-7) which was launched September 30, 1971. The instrument, designed for a rotating wheel compartment, utilizes a 377 channel quadratic PHA with accumulation times of 3, 1, or 0.5 minutes. Quick look and calibration data obtained via a direct data link to a minicomputer allows near real time monitoring and control of the experiment. Various commands changing the operating mode can be executed. The functions which can be commanded include: rotation of the quadrants in which data is collected by 90 deg; gain adjustment of the central detector over a 6:1 range; manual or automatic sequencing of calibrations; variations of accumulation times by telemetering selected channels; and selection of reference directions. A small X-ray detector covering the range 7.5 to 120 keV is also included.

Higbie, P. R.↗

Multispectral remote sensing as stratigraphic and structural tool, Wind River Basin and Big Horn Basin areas, Wyoming

The use of Landsat TM, Airborne Imaging Spectrometer, and airborne Thermal IR Multispectral Scanner data in the geological evaluation of two sites in central Wyoming is described and illustrated with diagrams, maps, photographs, sample images, and tables of numerical data. The value of the remotely sensed information on the areal variation of attitude, sequence, thickness, and lithology of exposed strata is demonstrated; details of the data analysis are given; and the specialized software packages employed are briefly characterized.

Lang, Harold R.↗